diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..dfdb8b7 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.sh text eol=lf diff --git a/.github/agents/skill-reviewer/check-skill.py b/.github/agents/skill-reviewer/check-skill.py old mode 100755 new mode 100644 diff --git a/.github/workflows/diagnostic-regression.yml b/.github/workflows/diagnostic-regression.yml new file mode 100644 index 0000000..7354317 --- /dev/null +++ b/.github/workflows/diagnostic-regression.yml @@ -0,0 +1,149 @@ +name: Diagnostic Regression Suite + +# The Diagnostic Regression Suite is the deterministic half of the kit's +# testing. Diagnostic scripts are tools, not agents, so the same database state +# must return the same answer every time. It is fast, free and runs on every PR. +# +# Cross-Model Skill Evaluations are deliberately NOT here — they cost AI +# credits. See skill-evaluations.yml, which is dispatch/schedule only. + +on: + push: + branches: [main] + pull_request: + branches: [main] + workflow_dispatch: + +permissions: + contents: read + +env: + MONGOSH_VERSION: "2.3.8" + MONGOSH_SHA256: "23edb768189663aaa9732a2340a25b5fc05a314940538809a7840be7f2ce221f" + DOCUMENTDB_LOCAL_IMAGE: "ghcr.io/microsoft/documentdb/documentdb-local@sha256:0fcf634531c1917ad0855ff9f4354aca0a5c5d8e435f08250568deb37eeb0ad5" + +jobs: + # ------------------------------------------------------------------------- + # Everything that needs no database. Runs in ~30s with no Docker at all, so + # a broken eval config or a wrong cost metric is caught before the expensive + # job even starts. + # ------------------------------------------------------------------------- + static: + name: Infrastructure-free contracts + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install test deps + run: pip install -r testing/requirements.txt + + - name: Token accounting, eval configs, router + working-directory: testing + # These scenarios override the container fixture with a no-op, so they + # run with no Docker, no credentials and no network. + run: python -m pytest -ra scenarios/token-accounting scenarios/evals-config scenarios/portable-cli + + windows: + name: Windows portable CLI contracts + runs-on: windows-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install test deps + run: pip install -r testing/requirements.txt + + - name: Validate Python and PowerShell launchers + working-directory: testing + run: python -m pytest -ra scenarios/portable-cli + + - name: Execute a PowerShell launcher + shell: pwsh + run: .\scripts\perf-advisor.ps1 --help + + # ------------------------------------------------------------------------- + # The full scenario suite against a live container. + # ------------------------------------------------------------------------- + scenarios: + name: Diagnostic scenario contracts + runs-on: ubuntu-latest + needs: static + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Start DocumentDB Local + run: | + # Throwaway credential for this ephemeral CI container: generated per + # run, never hardcoded. Masked so it cannot leak into the log. + DOCDB_PASSWORD="Ci$(openssl rand -hex 12)" + echo "::add-mask::$DOCDB_PASSWORD" + echo "DOCDB_PASSWORD=$DOCDB_PASSWORD" >> "$GITHUB_ENV" + docker run -d --name documentdb-local \ + -p 10260:10260 -p 9712:9712 \ + -e USERNAME=docdbadmin -e PASSWORD="$DOCDB_PASSWORD" \ + "$DOCUMENTDB_LOCAL_IMAGE" + + - name: Install mongosh into the container + # NOT optional. The documentdb-local image ships psql but NO mongosh + # (verified against the pristine image), while every diagnostic script + # drives the database with `docker exec mongosh ...`. + # Without this step every scenario fails with a confusing "executable + # file not found", which looks like a broken test rather than a missing + # dependency. + run: | + MV="${MONGOSH_VERSION}" + curl -fsSL "https://downloads.mongodb.com/compass/mongosh-${MV}-linux-x64.tgz" \ + -o /tmp/mongosh.tgz + echo "${MONGOSH_SHA256} /tmp/mongosh.tgz" | sha256sum -c - + tar xzf /tmp/mongosh.tgz -C /tmp + docker cp "/tmp/mongosh-${MV}-linux-x64/bin/mongosh" \ + documentdb-local:/usr/local/bin/mongosh + docker cp "/tmp/mongosh-${MV}-linux-x64/bin/mongosh_crypt_v1.so" \ + documentdb-local:/usr/local/lib/ || true + INSTALLED_VERSION="$(docker exec documentdb-local mongosh --version | tr -d '\r')" + if [ "$INSTALLED_VERSION" != "$MV" ]; then + echo "::error::Expected mongosh $MV, installed $INSTALLED_VERSION" + exit 1 + fi + echo "Installed compatible mongosh $INSTALLED_VERSION" + rm -rf /tmp/mongosh.tgz "/tmp/mongosh-${MV}-linux-x64" + + - name: Wait for the gateway + run: | + for i in $(seq 1 90); do + if docker exec documentdb-local \ + mongosh "localhost:10260/admin" -u docdbadmin -p "$DOCDB_PASSWORD" \ + --authenticationMechanism SCRAM-SHA-256 --tls \ + --tlsAllowInvalidCertificates --quiet \ + --eval "db.runCommand({ping:1}).ok" 2>/dev/null | grep -q 1; then + echo "Ready after ${i}s"; exit 0 + fi + sleep 2 + done + echo "Gateway did not become ready in time" >&2 + docker logs documentdb-local || true + exit 1 + + - name: Install test deps + run: pip install -r testing/requirements.txt + + - name: Run the scenario suite + working-directory: testing + env: + DOCDB_PASSWORD: ${{ env.DOCDB_PASSWORD }} + run: python -m pytest -ra + + - name: Container logs on failure + if: failure() + run: docker logs documentdb-local || true diff --git a/.github/workflows/skill-efficacy-benchmark.yml b/.github/workflows/skill-efficacy-benchmark.yml new file mode 100644 index 0000000..2021049 --- /dev/null +++ b/.github/workflows/skill-efficacy-benchmark.yml @@ -0,0 +1,170 @@ +name: MSBench Skill-Efficacy Benchmark + +# The MSBench Skill-Efficacy Benchmark is the publication layer: a hermetic, +# externally-citable pass@k for the DocumentDB agent kit, run on Microsoft's +# MSBench platform. +# +# Split by what each job needs: +# +# validate — no Docker, no MSBench, no network. Runs on every PR. +# build — Docker only. Builds the images and proves the ORACLE scores 1 +# and an EMPTY submission scores 0. Manual/scheduled. +# publish — needs internal MSBench access. Manual only. +# +# The paid/gated jobs never trigger on push or pull_request. + +on: + pull_request: + branches: [main] + paths: + - "benchmarks/**" + - "testing/scenarios/benchmark-*/**" + push: + branches: [main] + paths: + - "benchmarks/**" + workflow_dispatch: + inputs: + run_oracle: + description: "Build the images and run the oracle end-to-end" + required: false + default: true + type: boolean + pass_at_k: + description: "Attempts per instance when submitting to MSBench" + required: false + default: "5" + type: string + +permissions: + contents: read + +jobs: + # ------------------------------------------------------------------------- + validate: + name: Benchmark structure (free) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install test deps + run: pip install -r testing/requirements.txt + + - name: Validate registration, task layout and cost metrics + working-directory: testing + # No container, no credentials, no network — these override the + # container fixture with a no-op. + run: python -m pytest -ra scenarios/benchmark-config scenarios/benchmark-metrics + + - name: Lint shell entrypoints + run: | + sudo apt-get update -qq && sudo apt-get install -y -qq shellcheck + # Lint EVERY shell script under benchmarks/, not a hand-maintained + # list. An explicit list silently stops covering new scripts, which is + # how a real bug (an agent prompt that was never passed, SC2034) sat + # unlinted in the route-efficiency harness. + # + # SC1091: sourced files live inside the container image, not the repo. + mapfile -t scripts < <(find benchmarks -name '*.sh' -type f | sort) + printf 'linting %d script(s)\n' "${#scripts[@]}" + printf ' %s\n' "${scripts[@]}" + shellcheck -e SC1091 "${scripts[@]}" + + # ------------------------------------------------------------------------- + oracle: + name: Build images + oracle must pass, empty must fail + runs-on: ubuntu-latest + needs: validate + if: ${{ github.event_name == 'workflow_dispatch' && inputs.run_oracle }} + steps: + - uses: actions/checkout@v4 + + - name: Stage inputs and build benchmark images + working-directory: benchmarks/documentdb-sdk-skills + # build.sh stages the ignored .skills/ and .wheels/ build inputs before + # invoking Docker. Calling either Dockerfile directly fails in a clean + # checkout because those generated directories do not exist. + run: bash build.sh + + - name: Oracle must score 1 + # The positive control. If this fails, the grader is impossible to + # satisfy and every measured score is meaningless. + working-directory: benchmarks/documentdb-sdk-skills + # The image intentionally does not bake /solution or /tests. The + # control runner mounts them read-only and asserts the reward. + run: bash verify-controls.sh --only oracle + + - name: Empty submission must score 0 + # The negative control. A grader that never fails is worthless, and this + # is the cheapest possible proof that it can. + working-directory: benchmarks/documentdb-sdk-skills + run: bash verify-controls.sh --only empty + + # ------------------------------------------------------------------------- + publish: + name: Submit to MSBench (internal access required) + runs-on: ubuntu-latest + needs: oracle + if: ${{ github.event_name == 'workflow_dispatch' }} + environment: msbench + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Azure login + uses: azure/login@7184910d9eb2b1c5e48f7073824a90609bb9b6d6 # v2.3.1 + with: + client-id: ${{ secrets.AZURE_CLIENT_ID }} + tenant-id: ${{ secrets.AZURE_TENANT_ID }} + subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }} + + - name: Install msbench-cli + env: + FEED: https://pkgs.dev.azure.com/devdiv/_packaging/MicrosoftSweBench/pypi/simple/ + run: | + pip install keyring artifacts-keyring + TOKEN=$(az account get-access-token \ + --resource 499b84ac-1321-427f-aa17-267ca6975798 \ + --query accessToken -o tsv) + echo "::add-mask::$TOKEN" + pip install msbench-cli --index-url="https://user:${TOKEN}@${FEED#https://}" + msbench-cli version + + - name: Run both arms + # BOTH arms, always. A treatment score without its control is not a + # result — it cannot be interpreted, let alone published. + run: | + K="${{ inputs.pass_at_k }}" + for arm in documentdb-sdk-skills documentdb-sdk-skills-noskills; do + echo "::group::$arm" + msbench-cli run \ + --benchmark "$arm" \ + --dataset "benchmarks/documentdb-sdk-skills/msbench-registration/$arm/dataset.jsonl" \ + --pass_at_k "$K" \ + --runner benchmarks/documentdb-sdk-skills/shared/ces/runner.sh + echo "::endgroup::" + done + + - name: Summarise + if: always() + run: | + { + echo "## MSBench Skill-Efficacy Benchmark" + echo + echo "Report the **delta** between \`documentdb-sdk-skills\` (kit" + echo "installed, never mentioned) and \`documentdb-sdk-skills-noskills\`" + echo "(control). An absolute pass@k on either arm alone is not a result." + echo + echo "Per-instance token cost and observed model provenance are in" + echo "\`custom_metrics.json\`; export them with" + echo '`msbench-cli report --run_id --output report.json`.' + echo "The report generator refuses comparisons with missing or different" + echo "model identifiers between treatment and control." + } >> "$GITHUB_STEP_SUMMARY" diff --git a/.github/workflows/skill-evaluations.yml b/.github/workflows/skill-evaluations.yml new file mode 100644 index 0000000..c148f5f --- /dev/null +++ b/.github/workflows/skill-evaluations.yml @@ -0,0 +1,157 @@ +name: Cross-Model Skill Evaluations + +# Cross-Model Skill Evaluations measure the text skills against real models: +# does the kit change what an agent does, and what does it cost? +# +# NEVER runs on push or pull_request. Every execution spends AI credits, and the +# full matrix is 3 models x 2 arms x 5 runs = 30 trials PER STIMULUS. It is +# manual-dispatch plus a monthly schedule, and the schedule defaults to the +# cheapest useful configuration. +# +# Requires COPILOT_SDK_AUTH_TOKEN. Without it the job stops early with an +# explanation rather than failing obscurely halfway through the matrix. + +on: + workflow_dispatch: + inputs: + variant: + description: "Single matrix cell to run (blank = the whole matrix)" + required: false + type: string + runs: + description: "Runs per cell (>=3 for a usable spread)" + required: false + default: "5" + type: string + dry_run: + description: "Resolve and print the plan only — costs nothing" + required: false + default: false + type: boolean + schedule: + # 05:00 UTC on the 1st. Monthly, not nightly: the signal changes when the + # skills or the models change, and neither happens daily. + - cron: "0 5 1 * *" + +permissions: + contents: read + +jobs: + # ------------------------------------------------------------------------- + # Free, always runs: catches a broken matrix or a bad skill path before any + # credits are spent. `vally --dry-run` does NOT validate skill paths, so the + # pytest guard is the part that actually protects the treatment arm. + # ------------------------------------------------------------------------- + validate: + name: Validate the experiment (free) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-node@v4 + with: + node-version: "20" + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install + run: | + pip install -r testing/requirements.txt + npm ci --prefix evals + + - name: Static config guards + working-directory: testing + run: python -m pytest -ra scenarios/evals-config + + - name: Lint the eval spec + working-directory: evals + run: npm run lint + + - name: Resolve the experiment matrix + working-directory: evals + run: npm run experiment:plan + + # ------------------------------------------------------------------------- + # The paid job. + # ------------------------------------------------------------------------- + experiment: + name: Cross-model matrix (costs credits) + runs-on: ubuntu-latest + needs: validate + if: ${{ github.event_name == 'schedule' || inputs.dry_run != true }} + env: + # Vally phones home by default; both opt-outs are set explicitly. + VALLY_TELEMETRY_OPTOUT: "1" + DO_NOT_TRACK: "1" + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-node@v4 + with: + node-version: "20" + + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Check for credentials + env: + TOKEN: ${{ secrets.COPILOT_SDK_AUTH_TOKEN }} + run: | + if [ -z "$TOKEN" ]; then + echo "::error::COPILOT_SDK_AUTH_TOKEN is not configured. Cross-Model Skill Evaluations "\ + "cannot run real models without it. Add the secret, or use "\ + "dry_run to validate the matrix for free." + exit 1 + fi + + - name: Install + run: npm ci --prefix evals + + - name: Run the matrix + working-directory: evals + env: + COPILOT_SDK_AUTH_TOKEN: ${{ secrets.COPILOT_SDK_AUTH_TOKEN }} + run: | + ARGS=() + if [ -n "${{ inputs.variant }}" ]; then + # Quoted: variant selectors contain commas and '=' (e.g. + # 'model=claude-opus-5,skills=kit'). + ARGS+=(--variant "${{ inputs.variant }}") + fi + npx vally experiment run documentdb-skills.experiment.yaml \ + --output-dir vally-experiment-results \ + --compare "${ARGS[@]}" + + - name: Cost report + if: always() + # Vally reports its own token usage; this cross-checks it against the + # Copilot CLI session store and converts to cost-to-outcome metrics. + # Advisory only — a missing session store must not fail the matrix. + run: | + python3 evals/harness/token_usage.py sessions --limit 20 \ + || echo "No local session store on this runner (expected in CI)." + + - name: Upload results + if: always() + uses: actions/upload-artifact@v4 + with: + name: vally-experiment-results + path: evals/vally-experiment-results/ + retention-days: 30 + + - name: Summarise + if: always() + run: | + { + echo "## Cross-Model Skill Evaluation results" + echo + echo "Matrix: 3 models x {kit, control}. **Read the deltas, not the" + echo "absolute scores** — an absolute pass-rate is uninterpretable" + echo "without its control arm." + echo + REPORT=$(find evals/vally-experiment-results -name '*.md' | head -1) + if [ -n "$REPORT" ]; then cat "$REPORT"; else echo "_No report produced._"; fi + } >> "$GITHUB_STEP_SUMMARY" diff --git a/.gitignore b/.gitignore index ea1f005..51ec632 100644 --- a/.gitignore +++ b/.gitignore @@ -15,3 +15,6 @@ __pycache__/ # Generated benchmark output (regenerate via scenarios/contoso/scaling-benchmark/) scenarios/contoso/scaling-benchmark/results.tsv + +# Local token-payload study — not checked in. +token-tests/ diff --git a/CHANGELOG.md b/CHANGELOG.md index a28f0b4..8f0d5ef 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,19 @@ # Changelog +## 2026-09-15 — Portable diagnostic launchers for Windows + +Added native Python entry points and PowerShell wrappers for all six read-only +diagnostics. Windows users now need only Python 3.10+ and Docker Desktop; Git +Bash and WSL are not required. The portable launcher copies the selected Bash +diagnostic into the Linux DocumentDB container and runs the established logic +there through a direct-command transport, preserving the existing flags and +JSON contracts. + +The Diagnostic Regression Suite now invokes the portable Python entry points +on Linux as well, includes Bash-versus-portable JSON-shape parity checks, +covers the TOAST split advisor's JSON shape, and runs infrastructure-free +launcher contracts on `windows-latest`. + ## 2026-05-27 — Mark project as Public Preview Add a Public Preview status badge to `README.md` and a top-of-document diff --git a/README.md b/README.md index bd1f2e1..cfd66ef 100644 --- a/README.md +++ b/README.md @@ -29,13 +29,14 @@ All are **read-only** (they never modify data) and **cross-layer** (MongoDB API PostgreSQL engine). Each takes `--db `; add `--json` for a compact machine-readable result (what the router consumes). -| Script | Answers | `--json` | +| Diagnostic | Answers | `--json` | |--------|---------|:--:| -| `document-bloat-advisor.sh` | Which collections have large text TOASTed and detoasted on every scan; which field to split out. | ✅ | -| `index-redundancy-finder.sh` | Redundant (prefix/duplicate/reverse) or unused indexes safe to drop. | ✅ | -| `db-config-advisor.sh` | Working set vs cache, TOAST share, cache-hit ratios — evidence-based config review. | ✅ | -| `perf-advisor.sh` | Overall health: collection-scan audit, query timing, PG I/O / locks / config. | ✅ | -| `data-integrity-check.sh` | Orphaned foreign-key references and mixed-type fields (hard structural integrity). | ✅ | +| `document-bloat-advisor` | Which collections have large text TOASTed and detoasted on every scan; which field to split out. | ✅ | +| `toast-split-advisor` | Which large fields can move to a side collection and the projected hot-document size. | ✅ | +| `index-redundancy-finder` | Redundant (prefix/duplicate/reverse) or unused indexes safe to drop. | ✅ | +| `db-config-advisor` | Working set vs cache, TOAST share, cache-hit ratios — evidence-based config review. | ✅ | +| `perf-advisor` | Overall health: collection-scan audit, query timing, PG I/O / locks / config. | ✅ | +| `data-integrity-check` | Orphaned foreign-key references and mixed-type fields (hard structural integrity). | ✅ | Common flags: `--container NAME`, `--password PASS`, `--port`, `--pg-port`; env vars `DB_USER` / `DB_PASSWORD` / `PORT` / `PG_PORT` are also honored. **No password @@ -62,12 +63,28 @@ bash scripts/index-redundancy-finder.sh --db ecommerce bash knowledge-base/kb-route.sh --db contoso "why are my aggregations slow even though I have indexes" ``` +The diagnostics also have host-portable Python and PowerShell entry points. +They require Python 3.10+ and Docker Desktop, but do not require Bash or WSL: + +```powershell +python scripts\document-bloat-advisor.py --db contoso +.\scripts\index-redundancy-finder.ps1 --db ecommerce --json +``` + +The Python launcher executes the same read-only diagnostic logic inside the +Linux DocumentDB container, so Linux, macOS, and Windows share one behavior +contract. + Demo datasets are seeders under [`scenarios/`](scenarios/) (they plant the -problems the tools find). The regression suite in [`testing/`](testing/README.md) -guards the scripts. +problems the tools find). The kit is guarded by **two test loops** — deterministic +script tests in [`testing/`](testing/README.md) and cross-model skill evals in +[`evals/`](evals/README.md), plus an MSBench benchmark in +[`benchmarks/`](benchmarks/documentdb-sdk-skills/README.md) — all described in +[`docs/TESTING.md`](docs/TESTING.md). - **Router:** [`knowledge-base/README.md`](knowledge-base/README.md) · **Demo datasets:** [`scenarios/`](scenarios/) -- **Regression tests:** [`testing/README.md`](testing/README.md) · **Token study:** [`token-tests/RESULTS.md`](token-tests/RESULTS.md) +- **Testing:** [`docs/TESTING.md`](docs/TESTING.md) · **Route cost study:** [`benchmarks/documentdb-route-efficiency/`](benchmarks/documentdb-route-efficiency/README.md) +- **DocumentDB compatibility quick-start:** [`compat/`](compat/QUICKSTART.md) — run a MongoDB-compatible Node.js e-commerce project end-to-end on DocumentDB. ## Repo Structure @@ -82,9 +99,11 @@ skills/ scripts/ # diagnostic toolbox — read-only analyzers + seeders knowledge-base/ # NL → script router (kb.json + kb_route.py) + demo scenarios/contoso/ # ready-to-run TOAST demo dataset (+ optional scaling-benchmark/) -testing/ # fixture-first regression suite for the scripts (pytest) -token-tests/ # measured token savings of scripts vs text-skill workflows -docs/ # SKILLS.md (catalog) + DIAGNOSTICS.md (toolbox guide) +compat/ # MongoDB-compatible ecommerce project on DocumentDB +testing/ # Diagnostic Regression Suite — deterministic diagnostics (pytest) +evals/ # Cross-Model Skill Evaluations — routing, quality, and cost (Vally) +benchmarks/ # MSBench Skill-Efficacy Benchmark + route-cost study +docs/ # Skills, diagnostics, testing, and installation guidance ``` ## Installation diff --git a/benchmarks/documentdb-route-efficiency/.gitignore b/benchmarks/documentdb-route-efficiency/.gitignore new file mode 100644 index 0000000..4bee5fe --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/.gitignore @@ -0,0 +1,3 @@ +# Per-run agent output: trajectories, logs, session dumps. Only the curated +# artifacts in results/ are committed. +results/raw/ diff --git a/benchmarks/documentdb-route-efficiency/README.md b/benchmarks/documentdb-route-efficiency/README.md new file mode 100644 index 0000000..84635fa --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/README.md @@ -0,0 +1,128 @@ +# Route efficiency: text skills vs diagnostic scripts + +## The two arms + +The intervention is **which route the kit exposes**, and nothing else. + +| | Control — `route-text` | Treatment — `route-script` | +|---|---|---| +| `~/.copilot/skills/` | the kit's **text skills** | *(empty)* | +| `scripts/`, `knowledge-base/` | **absent** | **present** | +| How it answers | reads a `SKILL.md`, runs its own `mongosh` / `psql`, interprets raw output | routes with `kb-route.sh`, runs one read-only script, reads the compact verdict | +| Database | identical fixture | identical fixture | +| Question asked | identical | identical | + +Both arms are told the same thing and neither is told which route it has. The +arm is expressed purely by what exists on disk — `shared/arms/install-arm.sh`. + +### Why not "agent chooses"? + +A third design — install both and see which it picks — measures something +different and more realistic, but it cannot answer *this* question: it would +confound "how expensive is each route" with "how good is the agent at choosing". +Worth doing later as a separate task; it is not this one. + +--- + +## Preventing leakage + +Leakage would flatter whichever arm runs second, so every shared surface is +reset. This is the part most easily got wrong. + +| Leak | Why it matters here | Control | +|---|---|---| +| **Conversation history** | a second run could answer from the first run's reasoning | one fresh agent session per run; no multi-turn reuse across runs | +| **Prompt cache** | a warm cache makes the same payload cost far less | arm order is **randomised per iteration**, so warmth cannot systematically favour one arm | +| **Database state** | `$indexStats` / `idx_scan` counters accumulate; `index-redundancy-finder`'s *unused* findings depend on them | **a fresh database per run**, uniquely named, seeded from the same deterministic fixture | +| **Planner statistics** | stale stats flip query plans (this caused a real 2-in-7 flake in Diagnostic Regression Suite) | `ANALYZE` after every seed | +| **Filesystem** | a leftover script or `SKILL.md` silently converts one arm into the other | the arm installer **deletes the other route** and asserts it is gone | +| **Answer file** | a stale `/output/finding.json` would be graded as this run's answer | removed before each run | + +The database point is the one that would most easily go unnoticed: run the +script arm first and it warms the index counters, so the text arm's *correct* +answer changes. Same fixture, fresh database, every time. + +--- + +## What is measured + +### 1. Parity — graded first, and it gates everything + +Both arms must reach the **same finding**. A route that is cheap because it +answers *worse* is not a saving, and reporting its token count as one would be +actively misleading. + +Each arm writes `/output/finding.json`: + +```json +{ + "redundant_indexes": ["tenant_id_1"], + "reason": "prefix of tenant_id_1_status_1" +} +``` + +The verifier compares that against `expected-findings.yaml` — **structurally, no +judge**. The finding is a fact about the database, so it is checkable exactly. + +### 2. Cost — reported only for runs that achieved parity + +`tokens_fresh_input`, `tokens_output`, `ai_credits`, `agent_turns`, harvested +from the Copilot CLI session store by the same +[`harvest_metrics.py`](../documentdb-sdk-skills/shared/verifier/harvest_metrics.py) +the SDK-skills benchmark uses, so the two benchmarks cannot report cost +differently. + +**The headline is cost conditional on parity.** Comparing tokens across runs +where one arm got the wrong answer compares nothing. + +--- + +## Cross-model, and why cost is in USD + +**Tokens are not comparable across models.** Measured against published rates, +output differs 2.5x between Gemini 3.1 Pro ($12/MTok) and GPT-5.6 Sol ($30/MTok), +and input 2.5x ($2 vs $5). A cross-model table in raw tokens would rank +tokenisers and verbosity, not cost. `shared/verifier/pricing.py` converts each +run at its own model's published rate, itemising fresh input, cache reads +(~90% cheaper) and output. + +Cross-checked against a second, independent source: Copilot's `total_nano_aiu` +is token-based per-model billing at 1 credit = $0.01, and the two agree within +~3% (gemini 1.000, opus-5 0.995, opus-4.8 0.985, sol 0.968). A test fails if +they ever diverge, which would mean either the rate table has gone stale or +billing changed. + +## Running it: subagents, no extra credential + +The cross-model matrix was thought to need `COPILOT_SDK_AUTH_TOKEN`. It does +not. The Copilot CLI's own subagents supply the three properties this +experiment needs: + +| Requirement | How a subagent satisfies it | +|---|---| +| clean context per run | each subagent has its own context window — structural, not cleanup | +| model selection | pinned per subagent | +| per-run attribution | usage lands in `assistant_usage_events` with a distinct non-null `agent_id`, exactly one model each | + +Verified with a probe pinned to `gemini-3.1-pro-preview`: one row, +`agent_id=toolu_01MYbS4…`, 6,867 in / 3 out, $0.01377 — matching the 1.377 +credits Copilot recorded. The orchestrating parent's rows carry +`agent_id=NULL` and are excluded. + +```bash +python3 shared/verifier/attribute.py snapshot # -> +# ... launch subagents, one per (model, arm) ... +python3 shared/verifier/attribute.py collect --since --out results/raw/runs.json +python3 shared/verifier/pricing.py results/raw/runs.json +``` + +**What this harness is not:** a subagent is not the MSBench or Vally executor — +different scaffolding, system prompt and tool surface. Absolute numbers are not +comparable to an MSBench run. They are comparable *within* this harness, which +is all a cross-model or cross-arm comparison needs. + +## Status + +⬜ **Not yet run end-to-end.** Attribution, pricing, arms, fixture and the +parity grader are built and validated; the subagent path removes the credential +blocker. See [`results/`](results/). diff --git a/benchmarks/documentdb-route-efficiency/results/README.md b/benchmarks/documentdb-route-efficiency/results/README.md new file mode 100644 index 0000000..68ef4f0 --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/results/README.md @@ -0,0 +1,34 @@ +# Results — route efficiency + +Same contract as [`../../documentdb-sdk-skills/results/`](../../documentdb-sdk-skills/results/): +curated artifacts committed, raw runs gitignored, provenance mandatory. + +## Naming + +``` +-route-efficiency.json the aggregated comparison +-route-efficiency.md the rendered report +raw/ per-run output — GITIGNORED +``` + +Unlike the SDK-skills benchmark there is no `-treatment` / `-control` file +split: both arms are aggregated into one artifact by `summarize.py`, which +cannot produce a report from a single arm. + +## Generating + +```bash +export DB_PASSWORD='' +export AGENT_CMD='copilot --allow-all-tools -p "$PROMPT"' + +bash ../run-comparison.sh --iterations 5 +python3 ../summarize.py raw --out "$(date -u +%F)-route-efficiency.md" +python3 ../summarize.py raw --json > "$(date -u +%F)-route-efficiency.json" +``` + +## What is here now + +**Nothing.** No agent run has been executed — that needs model credentials. The +harness, arms, fixture and parity grader are built and validated; see +[`../README.md`](../README.md). + diff --git a/benchmarks/documentdb-route-efficiency/run-comparison.sh b/benchmarks/documentdb-route-efficiency/run-comparison.sh new file mode 100755 index 0000000..bd56dec --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/run-comparison.sh @@ -0,0 +1,199 @@ +#!/usr/bin/env bash +# run-comparison.sh — measure text-route vs script-route, with a clean context +# for every single run. +# +# for each iteration: +# for each arm (ORDER RANDOMISED): +# fresh database -> install exactly one route -> fresh agent session +# -> grade parity -> harvest tokens +# +# WHY EACH RESET IS HERE +# ---------------------- +# Every one of these is a leak that would silently favour whichever arm ran +# second, producing a confident and wrong comparison. +# +# fresh database $indexStats / idx_scan counters accumulate, and the +# redundancy finding depends on them. Running the script +# arm first warms those counters and CHANGES THE CORRECT +# ANSWER for the text arm. Each run gets its own database +# name, seeded from the same deterministic fixture. +# ANALYZE after seed stale planner statistics flip query plans; this caused a +# real 2-in-7 flake in the deterministic suite. +# randomised order prompt-cache warmth is not controllable, so it is +# randomised instead — it cannot then systematically +# favour one arm across iterations. +# fresh agent session no conversation history carries between runs, or the +# second run answers from the first run's reasoning. +# arm mutual exclusion install-arm.sh deletes the other route and ASSERTS it +# is gone. A leftover file turns one arm into the other. +# clear /output a stale finding.json would be graded as this run's answer. +# +# THE AGENT COMMAND +# ----------------- +# Set AGENT_CMD to whatever drives your agent non-interactively. The prompt is +# supplied BOTH on stdin and in $PROMPT, so use whichever your agent expects; +# the answer must land at $OUTPUT_DIR/finding.json. +# +# There is deliberately NO default: a silent fallback would emit plausible +# numbers from no agent at all, which is the one outcome worse than no data. +# +# Usage: +# export DB_PASSWORD=... +# export AGENT_CMD='copilot --allow-all-tools -p "$PROMPT"' # prompt as arg +# # ... or, for an agent that reads stdin: +# export AGENT_CMD='my-agent --stdin' +# bash run-comparison.sh --iterations 5 + +set -uo pipefail + +HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +KIT="$(cd "$HERE/../.." && pwd)" +TASK="$HERE/tasks/index-redundancy-diagnosis" +CONTAINER="${DOCDB_CONTAINER:-documentdb-local}" +ITERATIONS=3 +OUT_DIR="$HERE/results/raw" +SEED="${RANDOM_SEED:-20260817}" + +while [ $# -gt 0 ]; do + case "$1" in + --iterations) ITERATIONS="$2"; shift 2;; + --output-dir) OUT_DIR="$2"; shift 2;; + *) echo "unknown option: $1" >&2; exit 2;; + esac +done + +if [ -z "${AGENT_CMD:-}" ]; then + cat >&2 <<'MSG' +AGENT_CMD is not set. + +This harness will not invent a fallback. Without a real agent it could only +produce numbers that look like measurements and are not, which is worse than +producing nothing. + + export AGENT_CMD='copilot --allow-all-tools -p "$PROMPT"' + +The prompt is supplied both on stdin and in $PROMPT; the answer must be +written to $OUTPUT_DIR/finding.json. +MSG + exit 2 +fi + +[ -n "${DB_PASSWORD:-}" ] || { echo "DB_PASSWORD is not set" >&2; exit 2; } +mkdir -p "$OUT_DIR" + +MONGO_FLAGS=(-u "${DB_USER:-docdbadmin}" -p "$DB_PASSWORD" + --authenticationMechanism SCRAM-SHA-256 --tls + --tlsAllowInvalidCertificates --quiet) + +seed_fresh_db() { # $1 = db name + local db="$1" + docker cp "$TASK/fixture.js" "$CONTAINER:/tmp/fx-$db.js" >/dev/null + docker exec "$CONTAINER" mongosh "localhost:${DOCDB_PORT:-10260}/$db" \ + "${MONGO_FLAGS[@]}" --file "/tmp/fx-$db.js" >/dev/null 2>&1 || return 1 + # Settle planner statistics before anything queries the new database. + docker exec "$CONTAINER" psql -h localhost -p "${DOCDB_PG_PORT:-9712}" \ + -U "${PG_USER:-documentdb}" -d "${PG_DB:-postgres}" -q -c "ANALYZE" \ + >/dev/null 2>&1 || true +} + +drop_db() { + docker exec "$CONTAINER" mongosh "localhost:${DOCDB_PORT:-10260}/$1" \ + "${MONGO_FLAGS[@]}" --eval 'db.dropDatabase()' >/dev/null 2>&1 || true +} + +# Deterministic arm ordering per iteration, from a fixed seed: reproducible, +# but not the same order every time. +arm_order() { # $1 = iteration + python3 -c " +import random +r = random.Random($SEED + $1) +arms = ['route-text', 'route-script'] +r.shuffle(arms) +print(' '.join(arms))" +} + +echo "iterations=$ITERATIONS seed=$SEED output=$OUT_DIR" + +for i in $(seq 1 "$ITERATIONS"); do + for arm in $(arm_order "$i"); do + run_id="iter${i}-${arm}" + db="route_eval_${i}_${arm//-/_}" + run_out="$OUT_DIR/$run_id" + rm -rf "$run_out"; mkdir -p "$run_out" + + echo + echo "── $run_id (db=$db) ─────────────────────────────────" + + # 1. fresh database, identical fixture + drop_db "$db" + if ! seed_fresh_db "$db"; then + echo " seed FAILED; skipping" >&2 + continue + fi + + # 2. exactly one route on disk, asserted + if ! KIT_SRC="$KIT" bash "$HERE/shared/arms/install-arm.sh" "$arm" \ + > "$run_out/arm.log" 2>&1; then + echo " arm install FAILED — see $run_out/arm.log" >&2 + cat "$run_out/arm.log" >&2 + continue + fi + + # 3. fresh agent session, identical prompt. + # + # The prompt is supplied BOTH on stdin and as $PROMPT, so AGENT_CMD can + # use whichever its agent expects. + # + # This previously assigned a lowercase `prompt` while the command + # referenced `$PROMPT`, which was never set — so every agent received an + # EMPTY task, and `|| true` swallowed it. Every run would have failed + # parity and the conclusion would have been "neither route works". + # Caught by static analysis (SC2034: `prompt` assigned but never used). + PROMPT="$(sed "s/__DATABASE__/$db/g" "$TASK/instruction.md")" + export PROMPT + if [ -z "$PROMPT" ]; then + echo " FATAL: prompt is empty (missing $TASK/instruction.md?)" >&2 + continue + fi + OUTPUT_DIR="$run_out" \ + DOCUMENTDB_DATABASE="$db" \ + timeout "${AGENT_TIMEOUT:-900}" \ + bash -c "$AGENT_CMD" <<<"$PROMPT" \ + > "$run_out/agent.log" 2>&1 || true + + # 4. parity FIRST — cost is meaningless without it + python3 "$HERE/shared/verifier/check_parity.py" \ + --finding "$run_out/finding.json" \ + --expected "$TASK/expected-findings.yaml" \ + --output-dir "$run_out" > "$run_out/parity.log" 2>&1 + parity=$? + + # 5. cost + python3 "$KIT/benchmarks/documentdb-sdk-skills/shared/verifier/harvest_metrics.py" \ + --output-dir "$run_out" >/dev/null 2>&1 || true + + python3 - "$run_out" "$arm" "$i" "$db" "$parity" <<'PYEOF' +import json, sys, pathlib +run_out, arm, iteration, db, parity_rc = sys.argv[1:6] +d = pathlib.Path(run_out) +metrics = {} +for name in ("custom_metrics.json", "parity.json"): + p = d / name + if p.is_file(): + try: metrics.update(json.loads(p.read_text())) + except Exception: pass +metrics.update({"arm": arm, "iteration": int(iteration), "database": db, + "parity": parity_rc == "0"}) +(d / "run.json").write_text(json.dumps(metrics, indent=2, sort_keys=True) + "\n") +print(" parity=%s tokens_available=%s fresh_input=%s" % ( + metrics.get("parity"), metrics.get("tokens_available"), + metrics.get("tokens_fresh_input"))) +PYEOF + + drop_db "$db" + done +done + +echo +echo "raw runs in $OUT_DIR — aggregate with:" +echo " python3 $HERE/summarize.py $OUT_DIR" diff --git a/benchmarks/documentdb-route-efficiency/shared/arms/install-arm.sh b/benchmarks/documentdb-route-efficiency/shared/arms/install-arm.sh new file mode 100755 index 0000000..cab2913 --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/shared/arms/install-arm.sh @@ -0,0 +1,86 @@ +#!/usr/bin/env bash +# install-arm.sh — put exactly ONE route on disk, and prove the other is gone. +# +# route-text the kit's text skills; scripts/ and knowledge-base/ REMOVED +# route-script scripts/ + knowledge-base/; skills/ REMOVED +# +# The arm is expressed purely by what exists on disk. The agent is never told +# which arm it is in, and the task prompt is identical either way — so any +# difference in cost or correctness comes from the route, not from instruction. +# +# WHY THIS SCRIPT ASSERTS RATHER THAN JUST COPYING +# ------------------------------------------------ +# A leftover file silently converts one arm into the other, and the run would +# still complete and still report a number. That is the worst failure mode +# available here: it produces a confident comparison of an arm against itself. +# So after installing, this verifies the other route is actually absent and +# fails loudly if it is not. + +set -uo pipefail + +ARM="${1:-}" +KIT_SRC="${KIT_SRC:-/opt/documentdb-agent-kit}" +SKILLS_DEST="${SKILLS_DEST:-$HOME/.copilot/skills}" +TOOLS_DEST="${TOOLS_DEST:-/opt/documentdb-tools}" + +case "$ARM" in + route-text|route-script) ;; + *) echo "usage: install-arm.sh {route-text|route-script}" >&2; exit 2;; +esac + +echo "[arm] installing $ARM" + +# Always start from nothing, so a previous run cannot contribute. +rm -rf "$SKILLS_DEST" "$TOOLS_DEST" 2>/dev/null || true +mkdir -p "$SKILLS_DEST" + +case "$ARM" in +route-text) + if [ ! -d "$KIT_SRC/skills" ]; then + echo "[arm] FATAL: $KIT_SRC/skills missing — the text arm would be empty," >&2 + echo "[arm] which silently makes it a no-kit control." >&2 + exit 1 + fi + cp -r "$KIT_SRC/skills/." "$SKILLS_DEST"/ + echo "[arm] installed $(find "$SKILLS_DEST" -name SKILL.md | wc -l) skills" + ;; +route-script) + if [ ! -d "$KIT_SRC/scripts" ] || [ ! -d "$KIT_SRC/knowledge-base" ]; then + echo "[arm] FATAL: $KIT_SRC/{scripts,knowledge-base} missing — the script" >&2 + echo "[arm] arm would have no route and would silently fall back" >&2 + echo "[arm] to reasoning, i.e. become the text arm." >&2 + exit 1 + fi + mkdir -p "$TOOLS_DEST" + cp -r "$KIT_SRC/scripts" "$KIT_SRC/knowledge-base" "$TOOLS_DEST"/ + chmod +x "$TOOLS_DEST"/scripts/*.sh "$TOOLS_DEST"/knowledge-base/*.sh 2>/dev/null || true + echo "[arm] installed $(find "$TOOLS_DEST/scripts" -maxdepth 1 -name '*.sh' 2>/dev/null | wc -l) scripts + router" + ;; +esac + +# --------------------------------------------------------------------------- +# Mutual exclusion. An arm contaminated with the other route is not an arm. +# --------------------------------------------------------------------------- +fail=0 +if [ "$ARM" = "route-text" ]; then + if [ -d "$TOOLS_DEST" ]; then + echo "[arm] FATAL: $TOOLS_DEST exists in the TEXT arm" >&2; fail=1 + fi + if [ ! -s "$(find "$SKILLS_DEST" -name SKILL.md | head -1)" ] 2>/dev/null; then + echo "[arm] FATAL: no SKILL.md installed in the TEXT arm" >&2; fail=1 + fi +else + if find "$SKILLS_DEST" -name SKILL.md 2>/dev/null | grep -q .; then + echo "[arm] FATAL: SKILL.md files present in the SCRIPT arm" >&2; fail=1 + fi + if [ ! -x "$TOOLS_DEST/knowledge-base/kb-route.sh" ]; then + echo "[arm] FATAL: router not executable in the SCRIPT arm" >&2; fail=1 + fi +fi + +if [ "$fail" -ne 0 ]; then + echo "[arm] the arms are not mutually exclusive; refusing to run." >&2 + exit 1 +fi + +echo "[arm] $ARM verified: the other route is absent" diff --git a/benchmarks/documentdb-route-efficiency/shared/verifier/attribute.py b/benchmarks/documentdb-route-efficiency/shared/verifier/attribute.py new file mode 100644 index 0000000..e0e83bc --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/shared/verifier/attribute.py @@ -0,0 +1,155 @@ +#!/usr/bin/env python3 +"""Attribute token usage to individual subagent runs. + +WHY THIS EXISTS +--------------- +The cross-model matrix was blocked on `COPILOT_SDK_AUTH_TOKEN`, which Vally and +MSBench both need. It turns out the Copilot CLI's own subagents provide the same +three properties, with no extra credential: + + per-run isolation each subagent runs in its own context window, so there is + no conversation carry-over between runs — the "clean + context" requirement, satisfied structurally rather than + by cleanup + model selection a subagent can be pinned to a specific model + attribution its usage lands in assistant_usage_events with a distinct + non-null `agent_id` (the spawning tool-call id), and + exactly one model per agent_id + +Verified empirically: a probe subagent pinned to gemini-3.1-pro-preview produced +one row, agent_id=toolu_01MYbS4…, model=gemini-3.1-pro-preview, 6867 in / 3 out. +The orchestrating parent's own rows carry agent_id=NULL and are excluded. + +USAGE +----- + python3 attribute.py snapshot # before launching subagents + ... run subagents ... + python3 attribute.py collect --since --out runs.json + +WHAT THIS HARNESS IS NOT +------------------------ +A subagent is not the MSBench or Vally executor: different scaffolding, system +prompt and tool surface. Absolute numbers from here are NOT comparable to an +MSBench run. They are comparable **within** this harness, which is all a +cross-model or cross-arm comparison needs, because everything except the varied +factor is held constant. +""" +from __future__ import annotations + +import argparse +import json +import shutil +import sqlite3 +import tempfile +from pathlib import Path + +DEFAULT_STORE = Path.home() / ".copilot" / "session-store.db" +NANO_AIU_PER_CREDIT = 1_000_000_000 + + +def _open(db: Path): + """Snapshot the store before reading; it is being written to live.""" + tmp = Path(tempfile.mkdtemp(prefix="attrib-")) + snap = tmp / db.name + shutil.copy2(db, snap) + for suffix in ("-wal", "-shm"): + side = db.with_name(db.name + suffix) + if side.exists(): + shutil.copy2(side, snap.with_name(snap.name + suffix)) + conn = sqlite3.connect(f"file:{snap}?mode=ro", uri=True) + conn.row_factory = sqlite3.Row + return conn, tmp + + +def snapshot(db: Path) -> int: + conn, tmp = _open(db) + try: + row = conn.execute("SELECT COALESCE(MAX(id), 0) AS m " + "FROM assistant_usage_events").fetchone() + return int(row["m"]) + finally: + conn.close() + shutil.rmtree(tmp, ignore_errors=True) + + +def collect(db: Path, since: int) -> list[dict]: + """One record per subagent run created after `since`. + + `agent_id IS NOT NULL` is what separates subagent spend from the + orchestrator's own. Including the parent would swamp the measurement — its + context is far larger than any subagent's. + """ + conn, tmp = _open(db) + try: + rows = conn.execute( + """ + SELECT agent_id, + MIN(model) AS model, + COUNT(*) AS requests, + COUNT(DISTINCT model) AS n_models, + COALESCE(SUM(input_tokens), 0) AS input_tokens, + COALESCE(SUM(output_tokens), 0) AS output_tokens, + COALESCE(SUM(cache_read_tokens), 0) AS cache_read_tokens, + COALESCE(SUM(reasoning_tokens), 0) AS reasoning_tokens, + COALESCE(SUM(total_nano_aiu), 0) AS nano_aiu, + COALESCE(SUM(duration_ms), 0) AS duration_ms, + MIN(id) AS first_id + FROM assistant_usage_events + WHERE id > ? AND agent_id IS NOT NULL + GROUP BY agent_id + ORDER BY first_id + """, + (since,), + ).fetchall() + finally: + conn.close() + shutil.rmtree(tmp, ignore_errors=True) + + out = [] + for r in rows: + d = dict(r) + inp, cache = d["input_tokens"], d["cache_read_tokens"] + # cache_read is a SUBSET of input; fresh input is what is billed at the + # uncached rate. Quoting raw input overstates cost by ~an order of + # magnitude once a payload is cached. + d["fresh_input_tokens"] = max(inp - cache, 0) + d["cache_read_share_pct"] = round(100.0 * cache / inp, 2) if inp else 0.0 + d["ai_credits"] = round(d.pop("nano_aiu") / NANO_AIU_PER_CREDIT, 4) + d["wall_secs"] = round(d.pop("duration_ms") / 1000.0, 1) + # A subagent should use exactly one model. More than one means the run + # was not what we think it was, so it is flagged rather than averaged. + d["single_model"] = d.pop("n_models") == 1 + out.append(d) + return out + + +def main(argv=None) -> int: + p = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + p.add_argument("--store", type=Path, default=DEFAULT_STORE) + sub = p.add_subparsers(dest="cmd", required=True) + sub.add_parser("snapshot") + c = sub.add_parser("collect") + c.add_argument("--since", type=int, required=True) + c.add_argument("--out", type=Path) + args = p.parse_args(argv) + + if not args.store.is_file(): + raise SystemExit(f"session store not found: {args.store}") + + if args.cmd == "snapshot": + print(snapshot(args.store)) + return 0 + + runs = collect(args.store, args.since) + text = json.dumps(runs, indent=2, sort_keys=True) + if args.out: + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(text + "\n") + print(f"wrote {args.out} ({len(runs)} subagent run(s))") + else: + print(text) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/documentdb-route-efficiency/shared/verifier/check_parity.py b/benchmarks/documentdb-route-efficiency/shared/verifier/check_parity.py new file mode 100644 index 0000000..a884c10 --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/shared/verifier/check_parity.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +"""Parity grader — did this run reach the correct finding? + +Graded FIRST, and it gates the cost comparison. A route that is cheaper because +it answers worse has saved nothing, and reporting its token count as a saving +would be actively misleading. + +Parity is checked STRUCTURALLY. The correct answer here is a fact about the +database — which indexes are redundant — so it is exactly checkable, and an LLM +judge would add cost and variance for no gain. + +Three ways to fail, deliberately: + + MISSED a redundant index was not reported + FALSE POSITIVE a healthy index was reported as redundant + UNJUSTIFIED the right names, but no stated mechanism + +The false-positive gate matters as much as the missed-finding one: without it an +arm could "win" parity by naming every index in the database, which is not +correctness. + +Usage: + python3 check_parity.py --finding /output/finding.json \ + --expected /tests/expected-findings.yaml --output-dir /output +""" +from __future__ import annotations + +import argparse +import json +import re +import sys +from pathlib import Path + + +def _load_yaml(path: Path) -> dict: + try: + import yaml + except ImportError: # pragma: no cover - the image installs pyyaml + raise SystemExit("pyyaml is required") + return yaml.safe_load(path.read_text()) + + +def _normalise(names) -> set[str]: + """Index names, lowercased and stripped. + + Agents legitimately write `accounts.tenant_id_1` or `{tenant_id: 1}`; the + finding is the same one either way, and failing parity over formatting + would measure prose, not correctness. + """ + out = set() + for raw in names or []: + n = str(raw).strip().lower() + n = n.split(".")[-1] # accounts.tenant_id_1 -> tenant_id_1 + n = n.strip("`'\" ") + if n: + out.add(n) + return out + + +def grade(finding: dict, expected: dict) -> dict: + reported = _normalise(finding.get("redundant_indexes")) + want = _normalise(expected["redundant_indexes"]) + forbidden = _normalise(expected.get("must_not_report")) + + missed = sorted(want - reported) + false_positives = sorted(reported & forbidden) + # Anything reported that is neither expected nor explicitly forbidden is + # still wrong — it is an index that is not redundant. + unexpected = sorted(reported - want - forbidden) + + # Justification: the reasoning must name the mechanism, so a lucky guess at + # the names does not pass as understanding. + reason_text = " ".join( + str(v) for v in ( + [finding.get("reason", "")] + + list(finding.get("reasons", {}).values() if isinstance( + finding.get("reasons"), dict) else []) + ) + ).lower() + unjustified = [] + for index, keywords in (expected.get("reason_keywords") or {}).items(): + if _normalise([index]) & reported: + if not any(re.search(re.escape(k.lower()), reason_text) for k in keywords): + unjustified.append(index) + + passed = not (missed or false_positives or unexpected or unjustified) + return { + "parity": passed, + "reported": sorted(reported), + "expected": sorted(want), + "missed": missed, + "false_positives": false_positives, + "unexpected": unexpected, + "unjustified": sorted(unjustified), + } + + +def main(argv=None) -> int: + p = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + p.add_argument("--finding", type=Path, required=True) + p.add_argument("--expected", type=Path, required=True) + p.add_argument("--output-dir", type=Path, default=Path("/output")) + args = p.parse_args(argv) + + expected = _load_yaml(args.expected) + + if not args.finding.is_file(): + result = { + "parity": False, + "error": f"no finding written to {args.finding}", + } + else: + try: + finding = json.loads(args.finding.read_text()) + except (json.JSONDecodeError, UnicodeDecodeError) as exc: + result = {"parity": False, "error": f"finding is not valid JSON: {exc}"} + else: + result = grade(finding, expected) + + args.output_dir.mkdir(parents=True, exist_ok=True) + (args.output_dir / "parity.json").write_text(json.dumps(result, indent=2, + sort_keys=True) + "\n") + + print(json.dumps(result, indent=2, sort_keys=True)) + if result["parity"]: + print("[parity] PASS", file=sys.stderr) + return 0 + print("[parity] FAIL — cost from this run must NOT be compared", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/documentdb-route-efficiency/shared/verifier/pricing.py b/benchmarks/documentdb-route-efficiency/shared/verifier/pricing.py new file mode 100644 index 0000000..cfac1e3 --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/shared/verifier/pricing.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python3 +"""Convert token usage to USD, so models can be compared on cost. + +WHY TOKENS ARE NOT COMPARABLE ACROSS MODELS +------------------------------------------- +One token of Gemini 3.1 Pro is not one token of GPT-5.6 Sol. Measured against +the published rates below, output tokens differ by **2.5x** ($12 vs $30 per +MTok) and input by **2.5x** ($2 vs $5). A cross-model table in raw tokens would +rank the models by their tokenisers and their verbosity, not by what they cost. + +RELATIONSHIP TO COPILOT AI CREDITS (validated, and it surprised me) +------------------------------------------------------------------- +`total_nano_aiu` turns out to BE token-based per-model pricing at +1 credit = $0.01. Pricing this machine's own history with the published rates +below and dividing by (credits x $0.01) gives a median ratio of: + + gemini-3.1-pro-preview 1.000 claude-opus-5 0.995 + claude-opus-4.8 0.985 gpt-5.6-sol 0.968 + +i.e. the two agree within ~3%. + +An earlier reading of this data concluded the opposite — that credits were a +premium-request metric only weakly related to tokens (correlations 0.15-0.46). +That was a measurement error worth recording: it correlated RAW token counts +with cost, but cached tokens are billed ~10x cheaper, so total tokens and cost +are not proportional by construction. Once fresh and cached input are priced +separately, the relationship is essentially exact. + +So why keep this module rather than just report credits? + + * it is an INDEPENDENT check on Copilot's billing — two methods agreeing + within 3% is far stronger evidence than either alone, and a future + divergence is a signal worth catching + * it itemises fresh input vs cache reads vs output, which is what explains + WHERE a route's cost goes; a single credit figure cannot + * it prices against each provider's published rates, so a number can be + quoted to someone who does not use Copilot billing + * it still works when credits are absent or zero + +CACHE PRICING IS THE REASON THE FRESH/CACHED SPLIT MATTERS +----------------------------------------------------------- +All three providers discount cache reads by ~90% ($0.50/MTok against $2-$5). +Billing every input token at the full rate would overstate a skill payload's +cost by roughly 10x, because it is sent once and then read from cache. +""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +# --------------------------------------------------------------------------- +# Published rates, USD per 1M tokens. Retrieved 2026-08-17 from each provider's +# own documentation — not from a third-party aggregator. +# +# Re-check before publishing: these change, and a stale table silently produces +# wrong dollar figures with no other symptom. +# --------------------------------------------------------------------------- +PRICING_RETRIEVED = "2026-08-17" + +PRICING = { + "claude-opus-5": { + "input": 5.00, "cached_input": 0.50, "output": 25.00, + "cache_write_5m": 6.25, + "source": "https://platform.claude.com/docs/en/about-claude/pricing", + }, + "claude-opus-4.8": { + "input": 5.00, "cached_input": 0.50, "output": 25.00, + "cache_write_5m": 6.25, + "source": "https://platform.claude.com/docs/en/about-claude/pricing", + }, + "gpt-5.6-sol": { + "input": 5.00, "cached_input": 0.50, "output": 30.00, + "cache_write_5m": 6.25, + # Prompts over 272K input tokens bill at 2x input and 1.5x output for + # the WHOLE request, not just the overflow. + "long_context_threshold": 272_000, + "long_context_input_mult": 2.0, + "long_context_output_mult": 1.5, + "source": "https://developers.openai.com/api/docs/models/gpt-5.6-sol", + }, + "gemini-3.1-pro-preview": { + "input": 2.00, "cached_input": 0.50, "output": 12.00, + "cache_write_5m": 2.00, + # Over 200K context the whole request moves to the higher tier. + "long_context_threshold": 200_000, + "long_context_input_mult": 2.0, # $2 -> $4 + "long_context_output_mult": 1.5, # $12 -> $18 + "source": "https://ai.google.dev/gemini-api/docs/pricing", + }, +} + +# `output_tokens` is treated as the billable output and `reasoning_tokens` is +# NOT added on top. In this machine's history reasoning <= output in 99.6% of +# rows (16 of 4,269 exceptions), which is what you would expect if reasoning is +# already counted inside output. Adding them would double-bill thinking tokens. +# Flip with --add-reasoning if a provider is shown to report them separately. +ADD_REASONING_BY_DEFAULT = False + + +class UnknownModel(KeyError): + """Raised rather than guessing a price. + + A default rate would produce a plausible dollar figure for a model we have + no pricing for, and nothing downstream could tell it was invented. + """ + + +def cost_usd(usage: dict, model: str, add_reasoning: bool = ADD_REASONING_BY_DEFAULT) -> dict: + """Itemised USD cost for one run. + + `usage` needs: fresh_input_tokens (or input_tokens + cache_read_tokens), + output_tokens, and optionally reasoning_tokens. + """ + if model not in PRICING: + raise UnknownModel( + f"no published pricing for {model!r}. Add it to PRICING with a " + f"source URL rather than falling back to a default rate." + ) + p = PRICING[model] + + cache_read = int(usage.get("cache_read_tokens", 0) or 0) + if "fresh_input_tokens" in usage: + fresh = int(usage["fresh_input_tokens"]) + else: + fresh = max(int(usage.get("input_tokens", 0) or 0) - cache_read, 0) + + output = int(usage.get("output_tokens", 0) or 0) + if add_reasoning: + output += int(usage.get("reasoning_tokens", 0) or 0) + + in_rate, out_rate = p["input"], p["output"] + total_input = fresh + cache_read + long_context = False + threshold = p.get("long_context_threshold") + if threshold and total_input > threshold: + long_context = True + in_rate *= p.get("long_context_input_mult", 1.0) + out_rate *= p.get("long_context_output_mult", 1.0) + + fresh_cost = fresh / 1e6 * in_rate + cache_cost = cache_read / 1e6 * p["cached_input"] + out_cost = output / 1e6 * out_rate + + return { + "model": model, + "usd_total": round(fresh_cost + cache_cost + out_cost, 6), + "usd_fresh_input": round(fresh_cost, 6), + "usd_cached_input": round(cache_cost, 6), + "usd_output": round(out_cost, 6), + "billed_fresh_input_tokens": fresh, + "billed_cache_read_tokens": cache_read, + "billed_output_tokens": output, + "long_context_tier": long_context, + "rates_usd_per_mtok": { + "input": in_rate, "cached_input": p["cached_input"], "output": out_rate, + }, + "pricing_retrieved": PRICING_RETRIEVED, + "pricing_source": p["source"], + } + + +def price_runs(runs: list[dict], add_reasoning: bool = ADD_REASONING_BY_DEFAULT) -> list[dict]: + out = [] + for r in runs: + priced = dict(r) + try: + priced["cost"] = cost_usd(r, r.get("model", ""), add_reasoning) + except UnknownModel as exc: + priced["cost"] = None + priced["cost_error"] = str(exc) + out.append(priced) + return out + + +def main(argv=None) -> int: + p = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + p.add_argument("runs", type=Path, nargs="?", + help="JSON array of runs from attribute.py (default: show the rate table)") + p.add_argument("--add-reasoning", action="store_true", + help="bill reasoning_tokens on top of output_tokens") + p.add_argument("--out", type=Path) + args = p.parse_args(argv) + + if not args.runs: + print(f"Published rates (USD per 1M tokens), retrieved {PRICING_RETRIEVED}\n") + print(f"{'model':26} {'input':>8} {'cached':>8} {'output':>8}") + for name, v in PRICING.items(): + print(f"{name:26} {v['input']:>8.2f} {v['cached_input']:>8.2f} {v['output']:>8.2f}") + print("\nSources:") + for name, v in PRICING.items(): + print(f" {name:26} {v['source']}") + return 0 + + runs = json.loads(args.runs.read_text()) + priced = price_runs(runs, args.add_reasoning) + text = json.dumps(priced, indent=2, sort_keys=True) + if args.out: + args.out.write_text(text + "\n") + print(f"wrote {args.out}") + else: + print(text) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/documentdb-route-efficiency/summarize.py b/benchmarks/documentdb-route-efficiency/summarize.py new file mode 100644 index 0000000..e58371e --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/summarize.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python3 +"""Aggregate route-efficiency runs into the comparison table. + +THE RULE THIS ENFORCES +---------------------- +Cost is compared **only among runs that reached parity**. A route that is +cheaper because it answered wrong has saved nothing, and averaging its tokens in +would manufacture a saving out of a failure. Runs that missed parity are counted +and reported — they are the more interesting result — but excluded from the cost +means. + +Usage: + python3 summarize.py results/raw [--json] [--out results/-route-efficiency.md] +""" +from __future__ import annotations + +import argparse +import json +import statistics +from pathlib import Path + +import sys as _sys +_sys.path.insert(0, str(Path(__file__).parent / "shared" / "verifier")) +import pricing # noqa: E402 + +ARMS = ["route-text", "route-script"] + +# USD is the headline. One token of Gemini 3.1 Pro is not one token of GPT-5.6 +# Sol — output differs 2.5x and input 2.5x — so a cross-model table in raw +# tokens ranks tokenisers and verbosity rather than cost. +COST_KEYS = [ + ("usd_total", "USD", "${:,.4f}"), + ("tokens_fresh_input", "Fresh input tokens", "{:,.0f}"), + ("tokens_output", "Output tokens", "{:,.0f}"), + ("ai_credits", "AI credits", "{:,.2f}"), + ("agent_turns", "Turns", "{:,.1f}"), + ("cache_read_share_pct", "Cache share %", "{:.1f}"), +] + + +def load_runs(root: Path) -> list[dict]: + runs = [] + for path in sorted(root.glob("*/run.json")): + try: + runs.append(json.loads(path.read_text())) + except (json.JSONDecodeError, UnicodeDecodeError): + continue + return runs + + +def summarise(runs: list[dict]) -> dict: + out = {} + for arm in ARMS: + mine = [r for r in runs if r.get("arm") == arm] + parity_ok = [r for r in mine if r.get("parity")] + # Cost means come from parity-passing runs ONLY. + costed = [r for r in parity_ok if r.get("tokens_available") + or r.get("input_tokens")] + + # Price each run against its OWN model's published rates before + # averaging; averaging tokens across models and pricing afterwards + # would charge one model's rate for another's usage. + for r in costed: + if "usd_total" not in r and r.get("model"): + try: + r["usd_total"] = pricing.cost_usd(r, r["model"])["usd_total"] + except pricing.UnknownModel: + r["usd_total"] = None + + cost = {} + for key, _, _ in COST_KEYS: + vals = [float(r[key]) for r in costed if r.get(key) is not None] + cost[key] = statistics.mean(vals) if vals else None + + out[arm] = { + "runs": len(mine), + "parity_passed": len(parity_ok), + "parity_rate": (len(parity_ok) / len(mine)) if mine else None, + "costed_runs": len(costed), + "missing_token_data": len(parity_ok) - len(costed), + "cost": cost, + } + return out + + +def render(summary: dict, runs: list[dict]) -> str: + L, A = [], None + A = L.append + A("# Route efficiency — text skills vs diagnostic scripts") + A("") + A("") + + A("## Parity — graded first") + A("") + A("| Arm | Runs | Reached correct finding | Rate |") + A("|---|---|---|---|") + for arm in ARMS: + s = summary[arm] + rate = "—" if s["parity_rate"] is None else f"{s['parity_rate']:.0%}" + A(f"| `{arm}` | {s['runs']} | {s['parity_passed']} | {rate} |") + A("") + + t, sc = summary["route-text"], summary["route-script"] + if t["parity_rate"] is not None and sc["parity_rate"] is not None: + if t["parity_rate"] < sc["parity_rate"]: + A("> The text route reached the correct finding **less often**. If " + "that holds up, the headline is about correctness, not tokens — " + "and a cheaper route that is also more reliable is a stronger " + "claim than a token saving.") + A("") + elif t["parity_rate"] > sc["parity_rate"]: + A("> ⚠️ The **script** route reached the correct finding less often. " + "Any token saving it shows is therefore suspect: check whether the " + "script answers a narrower question than the one asked.") + A("") + + A("## Cost (parity-passing runs only)") + A("") + A("| Metric | text route | script route | Saving |") + A("|---|---|---|---|") + for key, label, fmt in COST_KEYS: + tv, sv = t["cost"].get(key), sc["cost"].get(key) + if tv is None or sv is None: + saving = "—" + elif key == "cache_read_share_pct": + saving = "n/a" + elif tv: + saving = f"{(tv - sv) / tv:+.0%}" + else: + saving = "—" + A(f"| {label} | {'—' if tv is None else fmt.format(tv)} | " + f"{'—' if sv is None else fmt.format(sv)} | {saving} |") + A("") + + warnings = [] + for arm in ARMS: + s = summary[arm] + if s["runs"] and s["runs"] < 3: + warnings.append(f"`{arm}` has only {s['runs']} run(s) — indicative, " + f"not measured.") + if s["missing_token_data"]: + warnings.append( + f"`{arm}`: {s['missing_token_data']} parity-passing run(s) had " + f"no token data and are excluded from the cost means rather " + f"than counted as zero.") + if s["parity_passed"] == 0 and s["runs"]: + warnings.append(f"`{arm}` never reached parity, so it has no " + f"comparable cost at all.") + if warnings: + A("## Caveats") + A("") + for w in warnings: + A(f"- ⚠️ {w}") + A("") + + A("---") + A("") + A("**How to read the saving.** It is a comparison of end-to-end cost " + "between two routes that both reached the correct finding. It is not a " + "context-size ratio: caching, turn count, and output tokens all move it, " + "and none of them are visible in a payload measurement.") + return "\n".join(L) + + +def main(argv=None) -> int: + p = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + p.add_argument("raw_dir", type=Path) + p.add_argument("--json", action="store_true") + p.add_argument("--out", type=Path) + args = p.parse_args(argv) + + runs = load_runs(args.raw_dir) + if not runs: + raise SystemExit(f"no run.json files under {args.raw_dir}") + summary = summarise(runs) + + text = (json.dumps({"summary": summary, "n_runs": len(runs)}, indent=2) + if args.json else render(summary, runs)) + if args.out: + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(text + "\n") + print(f"wrote {args.out}") + else: + print(text) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/expected-findings.yaml b/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/expected-findings.yaml new file mode 100644 index 0000000..34d2c99 --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/expected-findings.yaml @@ -0,0 +1,30 @@ +# expected-findings.yaml — the parity oracle. +# +# The correct answer is a FACT about the database, not an opinion, so parity is +# graded structurally. No LLM judge is involved or needed. +# +# Both arms must arrive here. A route that is cheaper because it answers WORSE +# has not saved anything, so cost is only compared among runs that reached +# parity. + +question: >- + Which indexes in this database are redundant and safe to drop, and why? + +# Exact set. Reported as a set, so ordering does not matter. +redundant_indexes: + - tenant_id_1 # prefix of tenant_id_1_status_1 + - email_1 # duplicate of email_unique + +# Naming any of these is a FALSE POSITIVE and fails parity. Without this, an arm +# could "win" by listing every index — over-reporting is not correctness. +must_not_report: + - tenant_id_1_status_1 + - email_unique + - customer_id_1 + - _id_ + +# The reasoning must identify the mechanism, not merely land on the right names +# by luck. Each redundant index requires at least one of its keywords. +reason_keywords: + tenant_id_1: [prefix, covered, subset, leading] + email_1: [duplicate, identical, same key, unique] diff --git a/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/fixture.js b/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/fixture.js new file mode 100644 index 0000000..0c2ad0a --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/fixture.js @@ -0,0 +1,50 @@ +// fixture.js — index-redundancy diagnosis +// +// Plants a KNOWN, unambiguous set of redundant indexes so the correct answer is +// a fact, not a judgement — which is what lets parity be graded structurally +// rather than by an LLM. +// +// Deterministic by construction: no Math.random() anywhere. Every run of both +// arms sees byte-identical data, so a cost difference cannot be a data +// difference. +// +// Planted redundancies (and nothing else): +// accounts.tenant_id_1 prefix of tenant_id_1_status_1 -> REDUNDANT +// accounts.email_1 duplicate of email_unique -> REDUNDANT +// Deliberately NOT redundant, as controls against over-reporting: +// accounts.tenant_id_1_status_1 the covering compound index +// accounts.email_unique the unique constraint +// orders.customer_id_1 sole index on another collection + +db.accounts.drop(); +db.orders.drop(); + +var bulk = []; +for (var i = 1; i <= 2000; i++) { + bulk.push({ + account_id: i, + email: "user" + i + "@example.test", + tenant_id: (i % 25) + 1, + status: ["active", "inactive", "pending"][i % 3] + }); + if (bulk.length === 1000) { db.accounts.insertMany(bulk); bulk = []; } +} +if (bulk.length) db.accounts.insertMany(bulk); + +db.accounts.createIndex({ tenant_id: 1 }); // redundant +db.accounts.createIndex({ tenant_id: 1, status: 1 }); // keep +db.accounts.createIndex({ email: 1 }); // redundant +db.accounts.createIndex({ email: 1 }, { unique: true, name: "email_unique" }); + +bulk = []; +for (var i = 1; i <= 1000; i++) { + bulk.push({ order_id: i, customer_id: "C" + (i % 200), amount: (i % 90) + 10 }); +} +db.orders.insertMany(bulk); +db.orders.createIndex({ customer_id: 1 }); // keep + +print("FIXTURE_READY index-redundancy-diagnosis"); +db.getCollectionNames().sort().forEach(function (c) { + print(" " + c + ": " + db[c].countDocuments() + " docs, " + + db[c].getIndexes().length + " indexes"); +}); diff --git a/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/instruction.md b/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/instruction.md new file mode 100644 index 0000000..9115135 --- /dev/null +++ b/benchmarks/documentdb-route-efficiency/tasks/index-redundancy-diagnosis/instruction.md @@ -0,0 +1,20 @@ +Review the indexes in the Azure DocumentDB database `__DATABASE__` and identify +which ones are redundant and safe to drop. + +The database is running locally in the `documentdb-local` container. Connection +settings are in the environment (`DOCUMENTDB_HOST`, `DOCUMENTDB_PORT`, +`DOCUMENTDB_USER`, `DOCUMENTDB_PASSWORD`, `DOCUMENTDB_DATABASE`). + +Write your answer to `$OUTPUT_DIR/finding.json`: + +```json +{ + "redundant_indexes": ["", "..."], + "reason": "why each one is redundant" +} +``` + +Use the index names as the database reports them. List only indexes that are +genuinely redundant — naming a healthy index is as wrong as missing a redundant +one. State the mechanism for each: an index is not redundant merely because it +is unused today. diff --git a/benchmarks/documentdb-sdk-skills/.gitignore b/benchmarks/documentdb-sdk-skills/.gitignore new file mode 100644 index 0000000..be8e991 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/.gitignore @@ -0,0 +1,11 @@ +# Build inputs staged by build.sh — never committed. +# .wheels/ is ~50MB of binary artifacts; .skills/ is a copy of ../../skills/. +# Both are regenerated by `bash build.sh`. +.wheels/ +.skills/ + +# Raw MSBench output — large per-instance dumps, trajectories and logs. +# Only the CURATED artifacts in results/ are committed; see results/README.md. +msbench-runs/ +*-raw.json +extracted-logs/ diff --git a/benchmarks/documentdb-sdk-skills/README.md b/benchmarks/documentdb-sdk-skills/README.md new file mode 100644 index 0000000..44215ed --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/README.md @@ -0,0 +1,325 @@ +# documentdb-sdk-skills + +A **skill-efficacy benchmark**: does a coding agent obey Azure DocumentDB best +practices when it merely *has* the [documentdb-agent-kit](../../README.md) +skills installed? + +> **Status: built and verified locally; not yet submitted to MSBench.** Both +> images build offline, the oracle scores 1, and both an empty and a +> working-but-naive submission score 0. See +> [Verification status](#verification-status). + +## The question + +Not "is the kit good?" — unfalsifiable. This one: + +> Given the same task, the same model and the same environment, does an agent +> **with** the DocumentDB skills installed produce a measurably better service +> than one **without** — and what did each cost? + +## Two arms, always + +| Benchmark | Skills installed | Role | +|---|---|---| +| `documentdb-sdk-skills` | yes, discoverable, **never mentioned** | treatment | +| `documentdb-sdk-skills-noskills` | none | control | + +The control arm is a **separately registered benchmark**, not a runtime flag. +That mirrors the platform's existing `skillsbench` / `skillsbenchnoskills` +convention. + +**Only the delta is publishable.** An absolute pass@k on either arm alone +cannot be interpreted — you cannot tell a good kit from an easy task. + +## The organic-discovery protocol + +`shared/ces/runner.sh` copies the skills into the agent's personal skills +directory as normally discoverable skills, then drives the **default** agent +with **no hint** to use them. `instruction.md` is deliberately silent about +indexing, ESR, connection pooling, schema versioning and type discriminators — +a static test (`testing/scenarios/benchmark-config/`) fails the build if any of +those words appear in it. + +Hinting would turn "does an installed kit get applied?" into "can the model +follow an instruction?" — a much easier question with a much less useful answer. + +The instruction *does* state the scale requirement ("millions of orders", the +customer lookup is the hot path). That is a requirement, not a hint: without it, +choosing not to index would be a defensible engineering decision rather than a +miss. + +## What gets evaluated + +| # | Category | Module | Strength | +|---|---|---|---| +| 1 | **Engine behaviour** | `check_engine.py` | ★★★ strongest | +| 2 | **Live behaviour** | `check_behavior.py` | ★★★ | +| 3 | Data shape & indexing | `check_documentdb.py` | ★★ structural | +| 4 | API conformance | `check_api.py` | ★★ | +| 5 | Security hygiene | `check_skills.py` | ★★ | +| 6 | Client configuration | `check_source.py` | ★ static — used only where no runtime signal exists | + +### One advantage this benchmark has over its Cosmos ancestor + +The Cosmos benchmark notes that client-side properties "a single-node local +emulator **cannot** prove behaviorally" are checked with static source regex +instead — ~520 lines of it. Given their platform that is the right call; a +static check beats no coverage. It is simply easier to satisfy by accident than +a runtime observation is. + +DocumentDB lets us avoid the trade-off. It is MongoDB-compatible on the surface +and **PostgreSQL underneath**, and the verifier talks to *both*. So instead of +grepping for `create_index(` and hoping: + +- `explain()` through the Mongo API → was the query planned as an index scan, + and how many documents did it read per document returned? +- `pg_stat_user_indexes` → did the index actually get **used**, or is it dead + weight costing write throughput? + +An agent can fake the source. It cannot fake the engine's own statistics. +Our `check_source.py` is ~130 lines, not 520, because almost everything moved +to a stronger rung. + +### Model provenance + +The current MSBench command surface used here does not configure a model in the +benchmark invocation. To prevent an undocumented platform model change from +looking like a skill regression or improvement, each instance reads the actual +model identifier from the Copilot CLI session store. The verifier losslessly +encodes the model set, API endpoint, reasoning effort, agent identity, and +observable Copilot CLI version into numeric `custom_metrics.json` fields. + +`report.py` refuses to publish a treatment/control comparison if any instance +lacks model provenance, if instances within an arm used inconsistent model +sets, or if the two arms used different model identifiers. If MSBench adds a +supported explicit model option, the workflow should pin it as well as retaining +the observed-value check. + +### The metric: scan amplification + +`totalDocsExamined / nReturned`. + +Raw "documents examined" is the **wrong** metric here — measured on DocumentDB +Local, the planner picks an index scan even for very unselective predicates: + +| Query | Plan | Examined | Returned | +|---|---|---|---| +| `amount > 490` (selective) | IXSCAN | 760 | 760 | +| `amount > 10` (unselective) | IXSCAN | 19,960 | 19,960 | + +Both are healthy: they read only rows they return. Amplification captures that +independently of selectivity — 2000× before an index, 1× after. + +### Grading at realistic volume + +The contract seeds only 4 rows through the API, but the engine checks run +against **20,000** filler documents loaded by the verifier +(`bulk_filler` in `conftest.py`), followed by `ANALYZE`. + +This is necessary, not a workaround: on a 4-document collection a sequential +scan genuinely *is* cheaper, PostgreSQL is right to choose it, and the oracle +itself would fail. The task tells the agent the service runs against millions +of orders, so the verifier must grade at a size where that statement has +consequences. + +## Cost metrics + +MSBench's reward is binary — it says whether a submission complied, not what it +cost. The verifier writes `$OUTPUT_DIR/custom_metrics.json` (MSBench's +first-class per-instance hook, surfaced by +`msbench-cli report --output report.json`) with token usage harvested from the +Copilot CLI's own session store: + +| Metric | Meaning | +|---|---| +| `tokens_fresh_input` | input **actually billed** at the uncached rate | +| `tokens_input` / `tokens_cache_read` | raw counts, for transparency | +| `cache_read_share_pct` | how much of the payload was amortised by caching | +| `ai_credits` | the money number | +| `agent_turns` / `agent_requests` | round trips | +| `credits_to_green` | cost of a **passing** run (absent when it failed) | +| `checks__passed/_total` | which rung failed, per category | +| `tokens_available` | `0` when harvesting failed — so a missing value is never read as free | + +**Do not quote `tokens_input` as the cost of a skill.** `cache_read_tokens` is a +*subset* of `input_tokens`, and the skill payload is cached after first use — on +a real session, 91% of input was cache reads. `tokens_fresh_input` is the honest +figure. + +`credits_to_green` is emitted **only** for a run that passed; otherwise an agent +that gave up early would look like the cheapest one. + +## Layout + +``` +documentdb-sdk-skills/ +├── orders.toml # harbor-format-curation config +├── msbench-registration/ +│ ├── documentdb-sdk-skills/ # treatment: registry.json + loaders + dataset +│ └── documentdb-sdk-skills-noskills/ # control +├── shared/ +│ ├── base/Dockerfile # DocumentDB Local + mongosh + verifier deps +│ ├── base/start-documentdb.sh +│ ├── ces/runner.sh # installs skills, un-hinted +│ ├── contracts/orders.json # the scenario contract +│ └── verifier/ +│ ├── conftest.py # contract loading, both DB clients, filler +│ ├── check_api.py +│ ├── check_behavior.py +│ ├── check_documentdb.py +│ ├── check_engine.py # ← the differentiator +│ ├── check_source.py +│ ├── check_skills.py +│ ├── harvest_metrics.py # cost metrics -> custom_metrics.json +│ └── runner.sh # reward.txt entrypoint +└── tasks/orders-api-python/ + ├── task.toml instruction.md + ├── environment/{Dockerfile,reference/} + ├── solution/solve.sh # oracle + └── tests/{test.sh,checks.py} +``` + +`registry.json` is **required** by the live platform — the Cosmos benchmark's +registration predates it and would be rejected today. + +## Reward model + +`tests/test.sh` writes `0` or `1` to `/logs/verifier/reward.txt`. A run scores +`1` only when **every** mandatory check passes. A CTRF report +(`/logs/verifier/ctrf.json`) records per-check results so a failure is +diagnosable without re-running. + +### No verification is skipped, deliberately + +The platform's own `skillsbench` config carries several +`skip = "oracle", reason = "Non-deterministic: …"` entries. Under a **binary** +reward one flaky check corrupts the entire signal, so `[tasks.skip-verification]` +here is empty and a test enforces that: a check that cannot be made +deterministic must be **removed**, not skipped. + +## Reproducing the results + +```bash +bash build.sh # build both images (offline, from vendored wheels) +bash verify-controls.sh # oracle=1, empty=0, naive=0 — asserts all three +``` + +Full stage-by-stage guide, from a Docker-only build through to a published +`pass@k`, in [`docs/REPORT.md` §4](docs/REPORT.md). + +## Building and running locally + +```bash +cd benchmarks/documentdb-sdk-skills +docker build -f shared/base/Dockerfile -t documentdb-orders-base:latest . +cd tasks/orders-api-python +docker build -f environment/Dockerfile -t documentdb-orders-api-python:latest . + +# Positive control: the oracle must score 1 +docker run --rm documentdb-orders-api-python:latest \ + bash -c '/solution/solve.sh && /tests/test.sh; cat /logs/verifier/reward.txt' + +# Negative control: an empty /app must score 0 +docker run --rm documentdb-orders-api-python:latest \ + bash -c '/tests/test.sh; cat /logs/verifier/reward.txt' +``` + +`--backend local` lets MSBench run harbor-native benchmarks entirely on your +machine, so the whole benchmark can be iterated **without** pushing to the +internal registry: + +```bash +pip install "msbench-cli[harbor]" +msbench-cli run --benchmark documentdb-sdk-skills \ + --dataset msbench-registration/documentdb-sdk-skills/dataset.jsonl \ + --backend local +``` + +## Results + +`results/` is **generated output**, not source. It may be absent — a fresh +clone has none, and pruning stale artifacts is housekeeping. The benchmark's +configuration is valid either way; the tests only assert things about results +that actually exist. + +When results *are* committed, two rules apply (enforced by +`testing/scenarios/benchmark-config/`): + +| Rule | Why | +|---|---| +| **Arms come in pairs** — a `*-treatment.json` needs its `*-control.json` | a treatment score with no control is not a result: 80% resolved could mean an excellent kit or an easy task | +| **Provenance is mandatory** — `run_id`, `date`, `benchmark`, `image_tag`, `dataset_version`, `kit_commit`, `model`, `pass_at_k` | without `kit_commit` you cannot say which skills the agent had; without `model` you cannot compare like-for-like | + +Naming: `-[-].{json,md}` — a `.json` for tooling, a `.md` for +humans. Raw MSBench dumps stay gitignored; only curated artifacts are committed. + +Regenerate the grader-validation artifact any time: + +```bash +bash verify-controls.sh --output "results/$(date -u +%F)-controls-validation.json" +``` + +**No MSBench run has been executed yet**, so there is no effectiveness data. +[`docs/REPORT.md`](docs/REPORT.md) records what has been measured (grader +validation), what has not, and how to generate the real report with +[`report.py`](report.py) once both arms have run. + +## Verification status + +Verified by running, not by inspection: + +| Claim | Status | +|---|---| +| Registration matches the live platform schema | ✅ read from the central repo; asserted by tests | +| Task follows the Harbor layout | ✅ asserted by tests | +| Instruction contains no hints | ✅ asserted (mutation-verified) | +| Cost metrics agree with the Cross-Model Skill Evaluations module | ✅ asserted (mutation-verified) | +| Verifier parses as Python 3.10 (the image's interpreter) | ✅ asserted in CI | +| Images build | ✅ base + task, offline from vendored wheels | +| **Oracle scores 1** | ✅ **REWARD=1, 31 checks passed** | +| **Empty submission scores 0** | ✅ **REWARD=0** | +| **A working-but-naive app scores 0** | ✅ **REWARD=0, 10 checks failed** | +| Cost metrics emitted | ✅ `custom_metrics.json` with per-category counts | +| Submitted to MSBench | ⬜ not yet | + +### The discrimination that matters + +An empty `/app` failing is a weak result — it proves only that the harness +notices missing files. The real test is a submission that **works**: correct +HTTP behaviour, correct persistence, no DocumentDB best practices. + +That app passes **every** API and behavioural check and still scores 0, failing +exactly the ten skill-specific ones: + +``` +check_documentdb type discriminator, schemaVersion, queryable timestamp, + secondary index exists, queried field is indexed +check_engine query is not a full scan, scan amplification is low +check_source connection options set explicitly, TLS not hardcoded off, + client is not constructed per request +``` + +That is the benchmark working as intended: it measures best practices, not +basic competence. + +### Three real bugs the controls found + +None of these would have been caught by inspection, and all three would have +produced a *wrong published number*: + +| Bug | Consequence | Now guarded by | +|---|---|---| +| `check_source.py` regex had catastrophic backtracking (nested quantifiers over lines) | verifier **hung for minutes** on a 6 KB file; replaced with `ast` (~1000× faster) | a CI test banning quantified-group-followed-by-quantifier | +| Credential check matched an f-string **template** (`mongodb://{quote_plus(user)}:{quote_plus(password)}@…`) | **failed the reference implementation** — a false positive would fail every correct submission | a CI test asserting env-driven URIs pass and real secrets still fail | +| Singleton check only looked inside route handlers | the naive app hid its per-request client in a helper and **passed** | a CI test with the exact helper pattern | + +The third was found by the naive-submission control, which is precisely what +negative controls are for. + +## What is *not* here + +- **The skills themselves.** They live in [`skills/`](../../skills/). This + benchmark measures obedience to them; it does not own them. +- **The fast development loops.** `testing/` (deterministic) and `evals/` + (cross-model) are for iteration; see [`docs/TESTING.md`](../../docs/TESTING.md). + This is the slow, hermetic publication layer. diff --git a/benchmarks/documentdb-sdk-skills/build.sh b/benchmarks/documentdb-sdk-skills/build.sh new file mode 100755 index 0000000..cb6e28a --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/build.sh @@ -0,0 +1,77 @@ +#!/usr/bin/env bash +# build.sh — stage build inputs, then build the benchmark images. +# +# Two things have to be staged before `docker build` can run, and both are the +# same pattern the Cosmos benchmark uses for its skills: +# +# 1. THE SKILLS (`.skills/`) +# The treatment arm needs the agent kit's skills inside the image. They live +# in the sibling `skills/` tree, outside this build context, so they are +# copied in here rather than widening the context to the whole repo. +# +# 2. THE PYTHON WHEELS (`.wheels/`) +# The image installs its verifier dependencies from a local wheel directory, +# with no network access at build time. That is deliberate: +# +# * The benchmark instruction promises the agent no internet during +# grading. An image that needs PyPI to build does not honour that. +# * A pinned wheel set makes the image byte-reproducible; resolving from +# PyPI months later may not be. +# * Practically: Docker's VM on some hosts cannot complete a TLS handshake +# with files.pythonhosted.org even when the host can (an MTU/NAT issue), +# so downloading on the host and copying in is the only reliable path. +# +# Usage: +# bash build.sh # base + task images +# bash build.sh --base-only + +set -euo pipefail + +HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO="$(cd "$HERE/../.." && pwd)" +BASE_TAG="${BASE_TAG:-documentdb-orders-base:latest}" +TASK_TAG="${TASK_TAG:-documentdb-orders-api-python:latest}" + +cd "$HERE" + +# --------------------------------------------------------------------------- +echo "==> Staging skills from $REPO/skills" +# --------------------------------------------------------------------------- +rm -rf .skills +mkdir -p .skills +cp -r "$REPO/skills/." .skills/ +echo " staged $(find .skills -name SKILL.md | wc -l) skills" + +# --------------------------------------------------------------------------- +echo "==> Vendoring Python wheels" +# --------------------------------------------------------------------------- +bash shared/base/vendor-wheels.sh .wheels + +# --------------------------------------------------------------------------- +echo "==> Building base image: $BASE_TAG" +# --------------------------------------------------------------------------- +docker build -f shared/base/Dockerfile -t "$BASE_TAG" . + +if [ "${1:-}" = "--base-only" ]; then + echo "==> base image built; stopping (--base-only)" + exit 0 +fi + +# --------------------------------------------------------------------------- +echo "==> Building task image: $TASK_TAG" +# --------------------------------------------------------------------------- +# The task image reuses the base's vendored wheels for the reference app's +# dependencies, so it also builds with no network. +cp -r .wheels tasks/orders-api-python/.wheels +trap 'rm -rf "$HERE/tasks/orders-api-python/.wheels"' EXIT +docker build -f tasks/orders-api-python/environment/Dockerfile \ + -t "$TASK_TAG" tasks/orders-api-python + +echo +echo "Built:" +echo " $BASE_TAG" +echo " $TASK_TAG" +echo +echo "Verify both controls:" +echo " docker run --rm $TASK_TAG bash -c '/solution/solve.sh && /tests/test.sh; cat /logs/verifier/reward.txt' # must print 1" +echo " docker run --rm $TASK_TAG bash -c '/tests/test.sh; cat /logs/verifier/reward.txt' # must print 0" diff --git a/benchmarks/documentdb-sdk-skills/controls/README.md b/benchmarks/documentdb-sdk-skills/controls/README.md new file mode 100644 index 0000000..a07ff6b --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/controls/README.md @@ -0,0 +1,45 @@ +# Control submissions + +Reference submissions used to validate that the **grader discriminates**. They +are not part of the benchmark an agent sees; they exist so the numbers in +[`../docs/REPORT.md`](../docs/REPORT.md) can be reproduced. + +| Control | Expected reward | What it proves | +|---|---|---| +| *(empty `/app`)* | **0** | the harness notices missing deliverables | +| [`naive-python/`](naive-python/) | **0** | **the grader separates best practice from basic competence** | +| [`../tasks/orders-api-python/environment/reference/`](../tasks/orders-api-python/environment/reference/) (the oracle) | **1** | the grader can be satisfied at all | + +Run all three with [`../verify-controls.sh`](../verify-controls.sh). + +## Why `naive-python` is the important one + +An empty `/app` scoring 0 proves very little — only that missing files are +noticed. A grader that *only* rejects empty submissions would be useless. + +`naive-python` is **fully functional**. It implements every endpoint correctly, +persists to DocumentDB, rejects duplicates with 409, and returns the right rows +for a filtered query. It passes **every** API and behavioural check. + +What it does *not* do is follow any DocumentDB best practice: + +- no secondary index at all, so the hot-path query is a collection scan +- no type discriminator, no `schemaVersion`, no timestamp +- a **new `MongoClient` per request**, hidden inside a `coll()` helper, so every + call builds a fresh connection pool +- no explicit pool or timeout settings +- `tlsAllowInvalidCertificates=True` hardcoded rather than env-driven + +Measured result: **20/30, reward 0** — 12/12 on competence, 3/13 on best +practice. That gap is the benchmark's entire reason to exist, and this fixture +is what demonstrates it. + +## It has already earned its keep + +The per-request client in `coll()` was **not** caught by the first version of +the singleton check, which only inspected route-decorated functions. This +fixture is what exposed that blind spot; the check now examines any function +and the regression is pinned in `testing/scenarios/benchmark-config/`. + +That is the argument for keeping a *working-but-wrong* control rather than only +an empty one: it probes the grader where the grader is actually weak. diff --git a/benchmarks/documentdb-sdk-skills/controls/naive-python/app.py b/benchmarks/documentdb-sdk-skills/controls/naive-python/app.py new file mode 100644 index 0000000..953606d --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/controls/naive-python/app.py @@ -0,0 +1,48 @@ +# NAIVE but FUNCTIONAL: correct HTTP + persistence, no DocumentDB best practices. +import os +from urllib.parse import quote_plus +from fastapi import FastAPI, HTTPException, Query +from pydantic import BaseModel +from pymongo import MongoClient + +app = FastAPI() + +def _uri(): + u = quote_plus(os.environ["DOCUMENTDB_USER"]); p = quote_plus(os.environ["DOCUMENTDB_PASSWORD"]) + h = os.environ.get("DOCUMENTDB_HOST","localhost"); port = os.environ.get("DOCUMENTDB_PORT","10260") + return f"mongodb://{u}:{p}@{h}:{port}/?authMechanism=SCRAM-SHA-256&directConnection=true" + +def coll(): + c = MongoClient(_uri(), tls=True, tlsAllowInvalidCertificates=True) + return c[os.environ.get("DOCUMENTDB_DATABASE","ordersdb")][os.environ.get("DOCUMENTDB_ORDERS_COLLECTION","orders")] + +class OrderIn(BaseModel): + id: str + customer_id: str + status: str + amount: float + items: list[str] = [] + +def _out(d): + d = dict(d); d["id"] = d.get("id", d.get("_id")); d.pop("_id", None); return d + +@app.get("/health") +def health(): return {"status":"ok"} + +@app.post("/orders", status_code=201) +def create(o: OrderIn): + c = coll() + if c.find_one({"_id": o.id}): raise HTTPException(409,"exists") + c.insert_one({"_id":o.id,"id":o.id,"customer_id":o.customer_id,"status":o.status,"amount":o.amount,"items":o.items}) + return _out(c.find_one({"_id": o.id})) + +@app.get("/orders/{oid}") +def get(oid: str): + d = coll().find_one({"_id": oid}) + if not d: raise HTTPException(404,"not found") + return _out(d) + +@app.get("/orders") +def lst(customer_id: str | None = Query(default=None)): + q = {"customer_id": customer_id} if customer_id else {} + return [_out(d) for d in coll().find(q)] diff --git a/benchmarks/documentdb-sdk-skills/controls/naive-python/build.sh b/benchmarks/documentdb-sdk-skills/controls/naive-python/build.sh new file mode 100755 index 0000000..b32247a --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/controls/naive-python/build.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash +set -euo pipefail +pip3 install --no-cache-dir --no-index --find-links=/opt/wheels -r /app/requirements.txt diff --git a/benchmarks/documentdb-sdk-skills/controls/naive-python/requirements.txt b/benchmarks/documentdb-sdk-skills/controls/naive-python/requirements.txt new file mode 100644 index 0000000..9bf8315 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/controls/naive-python/requirements.txt @@ -0,0 +1,4 @@ +fastapi==0.115.4 +uvicorn[standard]==0.32.0 +pymongo==4.10.1 +pydantic==2.9.2 diff --git a/benchmarks/documentdb-sdk-skills/controls/naive-python/run.sh b/benchmarks/documentdb-sdk-skills/controls/naive-python/run.sh new file mode 100755 index 0000000..51cc753 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/controls/naive-python/run.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash +set -euo pipefail +cd /app +exec uvicorn app:app --host 0.0.0.0 --port "${APP_PORT:-9080}" diff --git a/benchmarks/documentdb-sdk-skills/docs/REPORT.md b/benchmarks/documentdb-sdk-skills/docs/REPORT.md new file mode 100644 index 0000000..388ce4f --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/docs/REPORT.md @@ -0,0 +1,611 @@ +# MSBench effectiveness report + +**Status as of 2026-08-13 (`5659218`): NO MSBENCH RUN HAS BEEN EXECUTED.** + +There is therefore **no effectiveness data yet** — no skills-on vs skills-off +comparison, and no agent token usage. This document records what *has* been +measured, states plainly what has not, and shows how to produce the real report +once a run exists. + +> Any number in a GTM deck must come from the generated report described in +> [§4](#4-how-to-produce-the-real-report), not from this page. + +--- + +## 0. What "treatment" and "control" mean here + +Borrowed from experimental design: you change **one** thing and hold everything +else fixed, so any difference in the result is attributable to that one thing. + +**The treatment is: the DocumentDB agent kit's skills are installed and +discoverable.** That is the entire intervention. + +| | Control arm | Treatment arm | +|---|---|---| +| Benchmark | `documentdb-sdk-skills-noskills` | `documentdb-sdk-skills` | +| `~/.copilot/skills/` | **empty** | **17 SKILL.md files** copied in | +| Task, prompt, API contract | identical | identical | +| Model, agent, container, verifier | identical | identical | +| Is the agent told to use the skills? | n/a | **no — never mentioned** | + +Implemented in [`shared/ces/runner.sh`](../shared/ces/runner.sh): `SKILLS_ARM=kit` +copies the skills into the agent's personal skills directory; `SKILLS_ARM=control` +installs nothing and deletes the directory defensively, so a stale image layer +cannot leak skills into the control. + +### What that means for the TOKEN comparison specifically + +The token delta answers: **what does having the kit installed cost?** + +- **Control:** there are no skill files, so there is nothing for the agent to + discover or read. Its input tokens are the task and its own work. +- **Treatment:** the skills are on disk. When the agent judges one relevant it + *reads* the `SKILL.md`, and those bytes become input tokens. + +So `tokens_fresh_input(treatment) − tokens_fresh_input(control)` is, quite +literally, the price of the kit being available and used. + +Two things stop that number from being read naively: + +1. **Skills are cached.** A payload is sent once and then served from cache + (~92% of input was cache reads in practice), so raw `tokens_input` badly + overstates the marginal cost. The report leads with *fresh* (uncached) input. +2. **More tokens per attempt is not the same as more expensive.** "Skills add + context, therefore skills cost more" is trivially true and uninteresting. The + question is whether that extra context buys **fewer attempts and more + successes** — which is why the headline cost figure is *credits per passing + result*, not credits per run. + +The hypothesis being tested is: treatment spends **more per attempt**, needs +**fewer turns**, passes **more often**, and therefore costs **less per success**. +If the run shows otherwise, that is a finding we need, not a result to bury. + +### Why not just run it once and look at the number? + +Because an absolute score is uninterpretable. If the treatment arm resolves 80% +of attempts, that could mean the kit is excellent — or that the task is easy +enough for any competent agent. Only the control tells you which. +[`report.py`](../report.py) refuses to produce a report from a single arm for +this reason. + +--- + +## 1. What has been measured + +The benchmark's **grader** has been validated end-to-end on a local build. This +proves the instrument works. It says nothing about whether the kit helps — no +agent has attempted the task. + +Measured on 2026-08-17 at kit commit `3707cb1`. Regenerate at any time — the +script asserts each expected reward and exits non-zero on a deviation: + +```bash +bash verify-controls.sh --output "results/$(date -u +%F)-controls-validation.json" +``` + +| Submission | Reward | Checks | +|---|---|---| +| Oracle (reference implementation) | **1** | 31 / 31 | +| Empty `/app` | **0** | fails at deliverables | +| **Working-but-naive app** | **0** | 20 / 30 — 10 failed | + +*(The naive run grades 30 rather than 31 because the ESR ordering check is +skipped when no compound index exists at all — a skipped check is not counted as +a pass or a failure.)* + +### Why the naive control is the meaningful one + +An empty `/app` failing only proves the harness notices missing files. The naive +app is **fully functional**: correct HTTP behaviour, correct persistence, +duplicate rejection, right result sets. It passes every API and behavioural +check and still scores 0, failing exactly the best-practice checks: + +Measured per-category result for that submission: + +| Check category | Naive | Oracle | What it covers | +|---|---|---|---| +| `api` | **6/6** | 6/6 | endpoints, status codes, payloads | +| `behavior` | **6/6** | 6/6 | real persistence, round-trip, no duplicates | +| `documentdb` | **0/5** | 6/6 | discriminator, schemaVersion, timestamp, indexing | +| `engine` | **2/4** | 4/4 | query plan, scan amplification | +| `source` | **1/4** | 4/4 | singleton client, pool/timeouts, TLS | +| `skills` | **4/4** | 4/4 | no hardcoded creds, env-driven config | + +That is the discrimination the benchmark exists to provide: **basic competence +is perfect (12/12) while best practices collapse (3/13)**. It separates the two +cleanly, which is exactly what a skills benchmark must do. + +This is also a useful prior for what the **control arm** may look like. It is a +prior, not a prediction — a real agent is not this app, and may do better or +worse. + +### Cost pipeline — proven, but with no agent data + +Token harvesting was verified inside the task container by mounting a real +Copilot CLI session store. `custom_metrics.json` was written with +`tokens_available: 1` and populated fields: + +```json +{ + "tokens_fresh_input": 148301071, + "tokens_cache_read": 1618077206, + "cache_read_share_pct": 91.6, + "ai_credits": 194081.25, + "credits_to_green": 194081.25, + "checks_engine_passed": 4, "checks_engine_total": 4, + "reward": 1, "tokens_available": 1 +} +``` + +⚠️ **Those numbers are a plumbing demonstration, not a result.** They are the +usage of an interactive development session that was mounted in to exercise the +code path. The oracle is a `cp` of a reference implementation, not an agent, so +it consumes no tokens of its own. Real figures require an agent run. + +One number *is* generalisable and worth carrying into the analysis: +**91.6% of input tokens were cache reads.** Skill payloads are sent once and +then served from cache, which is why the report leads with fresh (uncached) +input rather than raw `tokens_input`. + +Reproduce the pipeline (substitute your own session store; the numbers will be +your session's, not these): + +```bash +cd benchmarks/documentdb-sdk-skills + +# The oracle consumes no tokens of its own, so a real store is mounted in to +# exercise the harvest path end-to-end inside the container. +cp ~/.copilot/session-store.db /tmp/demo-store.db + +docker run --rm \ + -v "$PWD/tasks/orders-api-python/tests:/tests:ro" \ + -v "$PWD/tasks/orders-api-python/solution:/solution:ro" \ + -v /tmp/demo-store.db:/root/.copilot/session-store.db:ro \ + documentdb-orders-api-python:latest \ + bash -c '/solution/solve.sh >/dev/null 2>&1 && /tests/test.sh >/dev/null 2>&1; \ + cat /output/custom_metrics.json' +``` + +Expect `"tokens_available": 1` with populated token fields. Without the mount +you get `"tokens_available": 0` and no token keys at all — which is the correct +behaviour: a missing measurement must never be reported as zero cost. + +The same harvester also runs standalone, against any session store: + +```bash +python3 shared/verifier/harvest_metrics.py \ + --store ~/.copilot/session-store.db --output-dir /tmp/out +cat /tmp/out/custom_metrics.json +``` + +--- + +## 2. What has NOT been measured + +| Question | Status | +|---|---| +| Does the kit raise the pass rate? | ⬜ **unknown** — no agent run | +| What does a task cost with vs without the kit? | ⬜ **unknown** | +| Which check categories does the kit move? | ⬜ **unknown** | +| `pass@k` reliability across attempts | ⬜ **unknown** | +| Does it hold across models? | ⬜ out of scope here (that is Cross-Model Skill Evaluations) | + +Blocking work, in order: + +1. Push both task images to the internal ACR (`harbor-format-curation`). +2. PR `msbench-registration/` into the central repo under + `benchmarks/skillsbench/`. +3. Run **both** arms with `--pass_at_k 5`. + +--- + +## 3. The report this will produce + +Generated by [`report.py`](../report.py). Shape shown here with **illustrative +placeholder values** so reviewers can agree the format before the run: + +``` +| | Control | Treatment | Delta | +|---|---|---|---| +| Instances | 5 | 5 | | +| Resolved | 1 | 4 | +3 | +| **Pass rate** | 20% | 80% | +60% | + +| Metric | Control | Treatment | Delta | +|--------------------|---------|-----------|----------| +| Fresh input tokens | 403,718 | 488,730 | +85,012 | +| AI credits | 832.40 | 965.70 | +133.3 | +| Turns | 9.8 | 6.6 | -3.2 | + +| Derived | Control | Treatment | +| **Credits per passing result** | 4,162.00 | 1,207.12 | + +| Check category | Control | Treatment | Delta | +| `api` | 100% | 100% | +0% | +| `behavior` | 100% | 100% | +0% | +| `documentdb` | 33% | 83% | +50% | +| `engine` | 20% | 80% | +60% | +| `source` | 60% | 90% | +30% | +``` + +**These are placeholders, not findings.** They illustrate the hypothesis the +run will test: the kit costs *more per attempt* but needs *fewer attempts*, so +cost per passing result falls, and the movement concentrates in the +`documentdb` / `engine` / `source` categories while `api` / `behavior` stay flat +in both arms. + +If the real run contradicts that — for instance if `api`/`behavior` also move, +suggesting the control arm is simply worse at the basic task — the benchmark +design needs revisiting, not the write-up. + +--- + +## 3b. What "quality" means here — and what the judge is + +**There is no LLM judge in this benchmark.** The verifier makes zero model +calls. The judge is **30 deterministic pytest checks**, and the quality +comparison between arms is the per-category pass rate +(`checks__passed / _total`). + +| Module | Checks | What it judges | +|---|---|---| +| `check_api` | 6 | endpoints, status codes, payload shapes | +| `check_behavior` | 6 | real persistence, round-trip, no duplicates | +| `check_documentdb` | 6 | discriminator, schemaVersion, timestamp, indexing, ESR | +| `check_engine` | 4 | query plan, scan amplification, index actually used | +| `check_source` | 4 | singleton client, pool/timeouts, TLS | +| `check_skills` | 4 | no hardcoded credentials, env-driven config | + +That is a real quality signal and a reproducible one. But it measures a +specific thing, and the claim must match it. + +### What this can and cannot support + +✅ **Can support:** *"agents with the kit installed follow Azure DocumentDB +best practices more often"* — that is precisely what the rubric encodes, and +the per-category table shows where. + +❌ **Cannot support (without qualification):** *"the kit produces better +solutions."* Three blind spots: + +1. **Ties are invisible.** Two submissions both scoring 31/31 may differ a lot + in readability, structure or error handling. The rubric cannot separate them. +2. **Unanticipated merit scores zero.** The rubric rewards only what it names. + An agent that solves the problem in a smarter way we did not encode gets no + credit for it. +3. **The rubric is our opinion, frozen into code.** If a rule is wrong or + incomplete, the benchmark measures the wrong thing — and does so + confidently, with a reassuring number attached. + +### Where a judge would belong + +Not here. The verifier runs offline in a task container with no model access, +and the reward is binary, so a judge would fit badly. Qualitative assessment +belongs in **Cross-Model Skill Evaluations**, which drives real models and has native support for +`panel` / prompt graders. + +**Status: built** — [`evals/documentdb-quality/quality-eval.yaml`](../../../evals/documentdb-quality/quality-eval.yaml) +scores guidance quality with a blinded three-vendor judge panel against an +anchored rubric, treatment vs control, with correctness as a hard gate. Not yet +executed against real models. + +So the division of labour across the kit is now: + +| Claim | Evidence | Where | +|---|---|---| +| "the scripts are reproducible" | byte-identical output | Diagnostic Regression Suite | +| "the right skill fires" | `skill-invocation`, binary | Cross-Model Skill Evaluations, `eval.yaml` | +| **"the guidance is better"** | **blinded judge panel** | **Cross-Model Skill Evaluations, `quality-eval.yaml`** | +| "the produced code follows best practice" | 30 deterministic checks | MSBench Skill-Efficacy Benchmark, this benchmark | + +This benchmark still cannot speak to guidance quality — that is not its job, and +the quality eval now covers it. + +--- + +## 4. Reproducing every number + +Each stage below is independent — you can stop after any of them. Stages 1–2 +need only Docker; stage 3 onward needs internal MSBench access. + +### TL;DR — everything that needs no internal access + +Copy-paste. Reproduces every number in [§1](#1-what-has-been-measured) from a +clean checkout in about 25 minutes. **Verified by running it end-to-end from a +fresh clone** — 50 config tests, both images built offline, all three controls +behaving as documented. + +```bash +git clone https://github.com/Azure/documentdb-agent-kit.git +cd documentdb-agent-kit + +# 1. config guards — no Docker, no credentials, ~1s +python3 -m venv testing-venv && testing-venv/bin/pip install -q -r testing/requirements.txt +(cd testing && ../testing-venv/bin/python -m pytest -q \ + scenarios/benchmark-config scenarios/benchmark-metrics) + +# 2. build both images offline (wheels resolved on the host) +cd benchmarks/documentdb-sdk-skills +bash build.sh + +# 3. the grader validation: oracle=1, empty=0, naive=0 +bash verify-controls.sh +``` + +Everything below is the same thing, stage by stage, with the internal-access +stages included. + +### Prerequisites + +```bash +# Docker, plus the MSBench CLI for stages 3+ +export PATH="$HOME/pgmongo/msbench-tools/venv/bin:$PATH" +msbench-cli version # -> 0.3.51 +az login # runs submit under your entitlement +``` + +> `pip install msbench` fetches an **unrelated** public package. The real tool +> is `msbench-cli` from the internal feed — see `msbench-tools/README.md`. + +--- + +### Stage 1 — Build the images (Docker only, ~10 min) + +```bash +cd benchmarks/documentdb-sdk-skills +bash build.sh +``` + +`build.sh` stages two things before `docker build`, both deliberately: + +- **`.skills/`** — the kit's skills, copied from the sibling `skills/` tree so + the build context stays narrow. +- **`.wheels/`** — every Python dependency, resolved **on the host** and + installed with `--no-index`. The task promises the agent no internet during + grading, so the image must not need PyPI to build. (It also cannot: on this + network `files.pythonhosted.org` is unreachable from Docker's VM even though + the host can reach it.) + +--- + +### Stage 2 — Reproduce the grader validation (Docker only, ~15 min) + +This regenerates every number in [§1](#1-what-has-been-measured): + +```bash +bash verify-controls.sh + +# or record the outcome as a committed artifact +bash verify-controls.sh --output results/$(date -u +%F)-controls-validation.json +``` + +Expected output — and the script exits non-zero if any control deviates: + +``` +control: oracle REWARD=1 CHECKS=31/31 +control: empty REWARD=0 +control: naive REWARD=0 CHECKS=20/30 + api 6/6 behavior 6/6 documentdb 0/5 + engine 2/4 source 1/4 skills 4/4 +``` + +Run one at a time with `--only naive`. The naive control is the meaningful one; +see [`controls/README.md`](../controls/README.md) for why. + +Each run seeds 20,000 filler documents, which is why it is not fast. That +volume is required, not incidental: on a 4-row collection a sequential scan is +genuinely cheaper and the engine checks would fail the oracle itself. + +--- + +### Stage 3 — Run the benchmark locally through MSBench (no ACR push) + +The task is registered `task_style: harbor-native`, so `--backend local` runs +the whole thing on your machine — no image push, no central-repo registration. +**Do this before publishing anything.** + +```bash +pip install "msbench-cli[harbor]" + +msbench-cli run \ + --benchmark documentdb-sdk-skills \ + --dataset msbench-registration/documentdb-sdk-skills/dataset.jsonl \ + --backend local \ + --runner shared/ces/runner.sh +``` + +The arm is selected by the runner's `SKILLS_ARM` variable (`kit` or `control`). +It **fails loudly** if `SKILLS_ARM=kit` but the skills are absent — a treatment +arm whose skills silently fail to load is just a second control arm, and would +produce a confident, wrong "the kit makes no difference". + +--- + +### Stage 4 — Publish the images and register (internal access) + +```bash +# Build and push to the internal registry +harbor-format-curation import orders.toml +harbor-format-curation build --profile staging orders.toml +harbor-format-curation update-database --profile staging orders.toml +``` + +Then PR both registration folders into the central benchmarks repo: + +``` +msbench-registration/documentdb-sdk-skills/ -> benchmarks/skillsbench/documentdb-sdk-skills/ +msbench-registration/documentdb-sdk-skills-noskills/ -> benchmarks/skillsbench/documentdb-sdk-skills-noskills/ +``` + +Each folder carries `registry.json`, `benchmark_loaders.toml` and +`dataset.jsonl`. **`registry.json` is required** by the live platform; the +Cosmos benchmark's registration predates that requirement and would be rejected +today. + +--- + +### Stage 5 — Run both arms + +```bash +for arm in documentdb-sdk-skills documentdb-sdk-skills-noskills; do + msbench-cli run \ + --benchmark "$arm" \ + --dataset "msbench-registration/$arm/dataset.jsonl" \ + --pass_at_k 5 \ + --runner shared/ces/runner.sh +done +``` + +**Both arms, always.** A treatment score with no control is not a result — 80% +resolved could mean an excellent kit or an easy task, and nothing in the number +distinguishes them. + +`--pass_at_k 5` uses the unbiased estimator `1 − C(n−c,k)/C(n,k)`. Agent runs +are non-deterministic; a single attempt is an anecdote. + +--- + +### Stage 6 — Generate the effectiveness report + +Committed results let a number quoted later be traced to the run that produced +it. Write them into `results/` (generated output — the directory may not exist +until you create it): + +```bash +DATE=$(date -u +%F) + +msbench-cli report --run_id \ + --output "results/$DATE-effectiveness-treatment.json" +msbench-cli report --run_id \ + --output "results/$DATE-effectiveness-control.json" + +python3 report.py \ + --treatment "results/$DATE-effectiveness-treatment.json" \ + --control "results/$DATE-effectiveness-control.json" \ + --out "results/$DATE-effectiveness.md" +``` + +Two rules, both enforced by tests in `testing/scenarios/benchmark-config/`: +**arms must be committed in pairs** (a treatment score with no control is not a +result), and **every result carries provenance** — `run_id`, `image_tag`, +`dataset_version`, `kit_commit`, `model`, `pass_at_k`. Raw MSBench dumps stay +gitignored; only the curated artifact is checked in. The full contract is in +the [benchmark README](../README.md#results). + +`report.py` **refuses to run on a single arm**. Add `--json` for machine-readable +output. + +The per-instance token metrics travel inside those exports: the verifier writes +`$OUTPUT_DIR/custom_metrics.json` and MSBench surfaces it as +`custom_metrics_values`. + +--- + +### Stage 7 — Guidance quality (Cross-Model Skill Evaluations, not MSBench) + +MSBench grades produced code against a fixed rubric. It cannot say whether the +*advice* was good — that lives in Cross-Model Skill Evaluations: + +```bash +cd evals && npm ci +npm run experiment:quality:plan # free: resolve the matrix +npm run experiment:quality # blinded 3-vendor judge panel, treatment vs control +``` + +--- + +### Continuous checks (no infrastructure, every PR) + +```bash +cd testing && pytest scenarios/benchmark-config scenarios/benchmark-metrics +``` + +Validates the registration files, the Harbor layout, that `instruction.md` +leaks no hints, that the verifier parses as Python 3.10 (the image's +interpreter), and that the cost metrics agree with the Cross-Model Skill +Evaluation cost module — all with no +Docker, credentials or network. + +--- + +### What each stage proves + +| Stage | Needs | Establishes | +|---|---|---| +| 1–2 | Docker | the grader can pass, can fail, and **discriminates** | +| 3 | Docker + `msbench-cli[harbor]` | the Harbor/MSBench wiring works | +| 4–5 | internal access | a citable `pass@k` for both arms | +| 6 | — | the effectiveness + cost report | +| 7 | Copilot SDK auth | whether the *guidance* is better | +| CI | nothing | the configuration has not rotted | + +Stages 1–2 are worth running on every meaningful change to the verifier. They +are the only ones that answer "is this instrument still trustworthy", and they +cost nothing but time. + +--- + +## 5. Reading the numbers honestly + +Four traps, each guarded by a test in +`testing/scenarios/benchmark-metrics/`: + +**Never quote `tokens_input` as the cost of the kit.** `cache_read_tokens` is a +*subset* of it, and ~92% of input is cache reads. Use `tokens_fresh_input`. + +**A pass rate without a control arm means nothing.** `report.py` refuses to run +on one arm for this reason. 80% could be an excellent kit or an easy task. + +**Cost per *passing result*, not per run.** Failed attempts are part of the +price of a success, so credits are divided by passes. A treatment arm that costs +more per attempt but passes far more often is cheaper where it counts. + +**`tokens_available: 0` means "not measured", never "free".** Instances missing +token data are excluded from cost means and reported separately, so a +harvesting failure cannot quietly drag an average down. + +Also: agent runs are non-deterministic. `report.py` flags any arm with fewer +than 3 instances as indicative rather than measured, and `--pass_at_k 5` exists +so a single lucky or unlucky attempt does not become the headline. + +--- + +## 6. Where each number comes from in the code + +| Number | Written by | Read by | +|---|---|---| +| `reward` (0/1) | `shared/verifier/runner.sh` → `/logs/verifier/reward.txt` | MSBench (Harbor contract) | +| per-check results | `pytest --ctrf` → `/logs/verifier/ctrf.json` | `harvest_metrics.py` | +| `checks__*` | `harvest_metrics.py: read_ctrf()` | `report.py` | +| token counts, `ai_credits` | `harvest_metrics.py: read_usage()`, from the Copilot CLI session store | `report.py` | +| observed model, endpoint, reasoning effort, agent and CLI version | `harvest_metrics.py`, losslessly encoded as numeric custom metrics | `report.py`, which rejects missing/mismatched model provenance | +| `custom_metrics.json` | `harvest_metrics.py: main()` → `$OUTPUT_DIR` | `msbench-cli report` | +| `resolved`, `pass_at_k_status` | MSBench | `report.py: summarise()` | +| the tables above | `report.py: render()` | you | + +Inspect any layer directly: + +```bash +# the metric artifact, from a local run +docker run --rm -v "$PWD/tests:/tests:ro" -v "$PWD/solution:/solution:ro" \ + documentdb-orders-api-python:latest \ + bash -c '/solution/solve.sh >/dev/null && /tests/test.sh >/dev/null 2>&1; \ + cat /output/custom_metrics.json' + +# the same code, against your own Copilot session store +python3 benchmarks/documentdb-sdk-skills/shared/verifier/harvest_metrics.py \ + --store ~/.copilot/session-store.db --output-dir /tmp/out && cat /tmp/out/custom_metrics.json + +# the Cross-Model Skill Evaluations equivalent (same cost definitions, pinned equal by test) +python3 evals/harness/token_usage.py sessions --limit 10 + +# the guards on all of the above +cd testing && pytest scenarios/benchmark-metrics scenarios/benchmark-config +``` + +The two cost implementations (`harvest_metrics.py` for MSBench Skill-Efficacy Benchmark, +`evals/harness/token_usage.py` for Cross-Model Skill Evaluations) cannot share an import — the +harvester must run inside a minimal task container — so +`test_harvester_agrees_with_the_skill_evaluation_cost_module` pins them to identical +output instead. If they ever diverge, that test fails rather than the two +publishing different costs for the same run. diff --git a/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/benchmark_loaders.toml b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/benchmark_loaders.toml new file mode 100644 index 0000000..62c5f52 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/benchmark_loaders.toml @@ -0,0 +1,8 @@ +# Place this file at: +# /benchmarks/skillsbench/documentdb-sdk-skills-noskills/benchmark_loaders.toml +# +# Tells generate_database.py how to load this benchmark. + +[benchmark] +name = "documentdb-sdk-skills-noskills" +path = "dataset.jsonl" diff --git a/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/dataset.jsonl b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/dataset.jsonl new file mode 100644 index 0000000..5618a49 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/dataset.jsonl @@ -0,0 +1 @@ +{"benchmark": "documentdb-sdk-skills-noskills", "instance_id": "orders-api-python", "image_tag": "documentdb-sdk-skills-noskills.eval.x86_64.orders-api-python:msbench-0.0.1", "problem_statement": "Build an Orders API in Python backed by Azure DocumentDB (MongoDB-compatible). See /instruction.md inside the container for the API contract, build/run conventions and connection details.", "sdk": "python", "task_style": "harbor-native", "dataset_version": "0.0.1"} diff --git a/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/registry.json b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/registry.json new file mode 100644 index 0000000..d889e3e --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills-noskills/registry.json @@ -0,0 +1,22 @@ +{ + "id": "documentdb-sdk-skills-noskills", + "name": "DocumentDB SDK Skills (No Skills)", + "owner": "Azure DocumentDB", + "creator": "Azure DocumentDB Agent Kit", + "contactEmail": "msbench@microsoft.com", + "description": "Control arm for documentdb-sdk-skills: the identical task, environment and verifier, with NO DocumentDB skills installed. Establishes the baseline an unassisted agent achieves so the treatment arm's score can be interpreted.", + "source": "1P", + "format": "Harbor", + "quality": "Trusted", + "category": "Feature Addition", + "license": "MIT", + "languages": [ + "Python" + ], + "platforms": [ + "Linux" + ], + "site": "https://github.com/Azure/documentdb-agent-kit", + "notes": "Control arm. Identical to documentdb-sdk-skills except that no skills are installed into the agent's skills directory. Mirrors the platform's existing skillsbench / skillsbenchnoskills convention.", + "externalInstances": 1 +} diff --git a/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/benchmark_loaders.toml b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/benchmark_loaders.toml new file mode 100644 index 0000000..f5bb1a5 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/benchmark_loaders.toml @@ -0,0 +1,8 @@ +# Place this file at: +# /benchmarks/skillsbench/documentdb-sdk-skills/benchmark_loaders.toml +# +# Tells generate_database.py how to load this benchmark. + +[benchmark] +name = "documentdb-sdk-skills" +path = "dataset.jsonl" diff --git a/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/dataset.jsonl b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/dataset.jsonl new file mode 100644 index 0000000..461ee75 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/dataset.jsonl @@ -0,0 +1 @@ +{"benchmark": "documentdb-sdk-skills", "instance_id": "orders-api-python", "image_tag": "documentdb-sdk-skills.eval.x86_64.orders-api-python:msbench-0.0.1", "problem_statement": "Build an Orders API in Python backed by Azure DocumentDB (MongoDB-compatible). See /instruction.md inside the container for the API contract, build/run conventions and connection details.", "sdk": "python", "task_style": "harbor-native", "dataset_version": "0.0.1"} diff --git a/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/registry.json b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/registry.json new file mode 100644 index 0000000..ffec849 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/msbench-registration/documentdb-sdk-skills/registry.json @@ -0,0 +1,22 @@ +{ + "id": "documentdb-sdk-skills", + "name": "DocumentDB SDK Skills", + "owner": "Azure DocumentDB", + "creator": "Azure DocumentDB Agent Kit", + "contactEmail": "msbench@microsoft.com", + "description": "Measures whether a coding agent applies Azure DocumentDB best practices when building a service backed by DocumentDB. The agent has the documentdb-agent-kit skills installed and discoverable, and the task statement is deliberately silent about indexing, data modelling and connection management. Graded behaviourally: the verifier drives the agent's HTTP API, independently reads the database, and inspects the PostgreSQL engine underneath for query plans and index usage.", + "source": "1P", + "format": "Harbor", + "quality": "Trusted", + "category": "Feature Addition", + "license": "MIT", + "languages": [ + "Python" + ], + "platforms": [ + "Linux" + ], + "site": "https://github.com/Azure/documentdb-agent-kit", + "notes": "Treatment arm: the DocumentDB agent kit skills are installed and discoverable, but never mentioned in the prompt. Compare against documentdb-sdk-skills-noskills, which is the identical task with no skills installed. Only the DELTA between the two is meaningful; an absolute pass rate on either arm alone is not interpretable.", + "externalInstances": 1 +} diff --git a/benchmarks/documentdb-sdk-skills/orders.toml b/benchmarks/documentdb-sdk-skills/orders.toml new file mode 100644 index 0000000..abd5948 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/orders.toml @@ -0,0 +1,63 @@ +# orders.toml — harbor-format-curation config for documentdb-sdk-skills. +# +# Build: +# harbor-format-curation import orders.toml +# harbor-format-curation build --profile local orders.toml +# harbor-format-curation update-database --profile local orders.toml +# +# The registries below mirror the platform's existing skillsbench benchmark. + +[maintainer] +team = "Azure DocumentDB" +name = "Azure DocumentDB Agent Kit" +email = "msbench@microsoft.com" + +[import] +from = "path-local" +path = "tasks" +staging-folder = "${TEMP}/harborcuration/documentdb-sdk-skills" + +[docker.local] +registry = "localhost:5000" +provider = "Docker localhost registry" +benchmark = "documentdb-sdk-skills" +type = "eval" +architecture = "x86_64" +version = "0.0.1" + +[docker.staging] +registry = "msbenchstaging.azurecr.io" +provider = "Azure container registry" +benchmark = "documentdb-sdk-skills" +type = "eval" +architecture = "x86_64" +version = "0.0.1" + +[docker.prod] +registry = "codeexecservice.azurecr.io" +provider = "Azure container registry" +benchmark = "documentdb-sdk-skills" +type = "eval" +architecture = "x86_64" +version = "0.0.1" + +# ============================================================================ +# Task Exclusions — completely skip these tasks during build +# ============================================================================ +[tasks.exclude] +# (none yet — a single task ships today) + +# ============================================================================ +# Verification Skips — build the task but skip a specific verification phase. +# Valid phases: "environment", "agent", "oracle". +# +# DELIBERATELY EMPTY, and it should stay that way. +# +# The platform's own skillsbench config carries several +# `skip = "oracle", reason = "Non-deterministic: ..."` entries. That is the +# failure mode this benchmark is built to avoid: under a BINARY reward, a +# single flaky check corrupts the whole signal, so a check that cannot be made +# deterministic must be REMOVED rather than skipped. Every check here is +# required to clear the determinism bar in testing/scenarios/determinism/. +# ============================================================================ +[tasks.skip-verification] diff --git a/benchmarks/documentdb-sdk-skills/report.py b/benchmarks/documentdb-sdk-skills/report.py new file mode 100644 index 0000000..bb881b2 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/report.py @@ -0,0 +1,379 @@ +#!/usr/bin/env python3 +"""Build the skills-effectiveness report from MSBench run output. + +WHAT THIS PRODUCES +------------------ +The headline table for the GTM story: does having the DocumentDB agent kit +installed change what an agent builds, and what does it cost? + + msbench-cli report --run_id --output treatment.json + msbench-cli report --run_id --output control.json + python3 report.py --treatment treatment.json --control control.json + +WHY BOTH ARMS ARE REQUIRED +-------------------------- +This script REFUSES to emit a report from one arm. An absolute pass rate is +uninterpretable: "80% resolved" could mean the kit is excellent or that the task +is easy, and there is no way to tell them apart without a control. The only +publishable figure is the delta between `documentdb-sdk-skills` (kit installed, +never mentioned in the prompt) and `documentdb-sdk-skills-noskills` (identical +task, no skills). + +INPUT SCHEMA +------------ +`msbench-cli report --output .json` emits `{"schema": {"id": +"msbench.report", "version": "2.x"}}` containing, among others: + + resolved {benchmark: {instance: bool | "error"}} + resolved_rate overall resolution rate + custom_metrics {benchmark: {instance: {"values": {...}}}} + pass_at_k_status {complete, expected_attempts, ...} + +The per-instance `values` are what `shared/verifier/harvest_metrics.py` wrote +into `custom_metrics.json` — token counts, AI credits, and per-category check +tallies. + +COST FIGURES: READ `tokens_fresh_input`, NOT `tokens_input` +----------------------------------------------------------- +`cache_read_tokens` is a SUBSET of `input_tokens`, and a skill payload is sent +once then served from cache (measured: ~91% of input was cache reads). Quoting +raw input as "the cost of the skills" overstates it by roughly an order of +magnitude, so this report leads with fresh (uncached) input and shows the cache +share next to it. +""" +from __future__ import annotations + +import argparse +import json +import statistics +import sys +from pathlib import Path + +TREATMENT_DEFAULT = "documentdb-sdk-skills" +CONTROL_DEFAULT = "documentdb-sdk-skills-noskills" + +# Metrics summarised per arm: (key, label, formatter) +COST_METRICS = [ + ("tokens_fresh_input", "Fresh input tokens", "{:,.0f}"), + ("tokens_output", "Output tokens", "{:,.0f}"), + ("ai_credits", "AI credits", "{:,.2f}"), + ("agent_turns", "Turns", "{:,.1f}"), + ("cache_read_share_pct", "Cache share %", "{:.1f}"), +] + +CHECK_CATEGORIES = ["api", "behavior", "documentdb", "engine", "source", "skills"] +TEXT_CHUNK_BYTES = 6 + + +def load(path: Path) -> dict: + if not path.is_file(): + raise SystemExit(f"report not found: {path}") + data = json.loads(path.read_text()) + schema = (data.get("schema") or {}).get("id") + if schema and schema != "msbench.report": + print(f"warning: unexpected schema {schema!r} in {path}", file=sys.stderr) + return data + + +def _instances(report: dict, benchmark: str) -> dict: + resolved = report.get("resolved") or {} + if benchmark in resolved: + return resolved[benchmark] or {} + # Fall back to the only benchmark present, so a single-benchmark report + # still works when the caller did not pin a name. + keys = [k for k, v in resolved.items() if isinstance(v, dict)] + if len(keys) == 1: + return resolved[keys[0]] or {} + raise SystemExit( + f"benchmark {benchmark!r} not in report (found: {sorted(resolved)}). " + f"Pass --treatment-benchmark / --control-benchmark." + ) + + +def _metrics(report: dict, benchmark: str) -> dict: + cm = report.get("custom_metrics") or {} + if benchmark in cm: + return cm[benchmark] + keys = list(cm) + return cm[keys[0]] if len(keys) == 1 else {} + + +def decode_text_metrics(values: dict, prefix: str) -> str | None: + """Reconstruct a string encoded as exact numeric custom metrics.""" + if not values.get(f"{prefix}_available"): + return None + length = int(values.get(f"{prefix}_utf8_len", 0)) + count = int(values.get(f"{prefix}_chunk_count", 0)) + data = bytearray() + for index in range(count): + key = f"{prefix}_chunk_{index:03d}" + if key not in values: + return None + remaining = length - len(data) + width = min(TEXT_CHUNK_BYTES, remaining) + data.extend(int(values[key]).to_bytes(width, "big")) + try: + return bytes(data).decode("utf-8") + except UnicodeDecodeError: + return None + + +def summarise(report: dict, benchmark: str) -> dict: + """Reduce one arm to pass rate + mean cost, per instance and overall.""" + instances = _instances(report, benchmark) + metrics = _metrics(report, benchmark) + + # `resolved` values may be bool or the string "error"; only True counts as a + # pass. Treating "error" as anything else would silently inflate the score. + passed = [k for k, v in instances.items() if v is True] + attempted = list(instances) + + collected: dict[str, list[float]] = {k: [] for k, _, _ in COST_METRICS} + checks: dict[str, list[float]] = {} + missing_tokens = 0 + provenance = { + "model_ids": set(), + "api_endpoints": set(), + "reasoning_efforts": set(), + "agent_identity": set(), + "copilot_cli_version": set(), + } + missing_model_data = 0 + + for inst in attempted: + values = ((metrics.get(inst) or {}).get("values")) or {} + if not values.get("tokens_available"): + missing_tokens += 1 + for key, _, _ in COST_METRICS: + if key in values: + collected[key].append(float(values[key])) + for cat in CHECK_CATEGORIES: + p, t = values.get(f"checks_{cat}_passed"), values.get(f"checks_{cat}_total") + if p is not None and t: + checks.setdefault(cat, []).append(float(p) / float(t)) + for prefix in provenance: + value = decode_text_metrics(values, prefix) + if value: + provenance[prefix].add(value) + if not decode_text_metrics(values, "model_ids"): + missing_model_data += 1 + + return { + "benchmark": benchmark, + "instances": len(attempted), + "passed": len(passed), + "pass_rate": (len(passed) / len(attempted)) if attempted else None, + "cost": {k: (statistics.mean(v) if v else None) for k, v in collected.items()}, + "cost_n": {k: len(v) for k, v in collected.items()}, + "check_rates": {c: statistics.mean(v) for c, v in checks.items()}, + "missing_token_data": missing_tokens, + "missing_model_data": missing_model_data, + "provenance": {key: sorted(values) for key, values in provenance.items()}, + "pass_at_k": report.get("pass_at_k_status") or {}, + } + + +def require_comparable_model_provenance(treatment: dict, control: dict) -> str: + """Fail publication unless both arms observed one identical model set.""" + for arm in (treatment, control): + if arm["missing_model_data"]: + raise SystemExit( + f"{arm['benchmark']} is missing observed model provenance for " + f"{arm['missing_model_data']} instance(s)" + ) + identities = arm["provenance"]["model_ids"] + if len(identities) != 1: + raise SystemExit( + f"{arm['benchmark']} used inconsistent model identities: {identities}" + ) + treatment_model = treatment["provenance"]["model_ids"][0] + control_model = control["provenance"]["model_ids"][0] + if treatment_model != control_model: + raise SystemExit( + "treatment and control used different models: " + f"{treatment_model!r} vs {control_model!r}" + ) + return treatment_model + + +def _pct(x): + return "—" if x is None else f"{x:.0%}" + + +def _delta(t, c, fmt="{:+.1f}"): + if t is None or c is None: + return "—" + return fmt.format(t - c) + + +def render(t: dict, c: dict) -> str: + L = [] + A = L.append + A("# DocumentDB agent kit — MSBench effectiveness report") + A("") + A("**The intervention being measured is: the DocumentDB agent kit's skills " + "are installed and discoverable.** Everything else — task, prompt, model, " + "agent, container, verifier — is identical between the two arms, so any " + "difference is attributable to the kit.") + A("") + models = t.get("provenance", {}).get("model_ids") or [] + if len(models) == 1: + shown_models = "`, `".join(models[0].splitlines()) + A( + f"- **Observed model identifier(s):** `{shown_models}` " + "(from the Copilot session store)" + ) + endpoints = t.get("provenance", {}).get("api_endpoints") or [] + if len(endpoints) == 1: + shown_endpoints = "`, `".join(endpoints[0].splitlines()) + A(f"- **Observed API endpoint(s):** `{shown_endpoints}`") + efforts = t.get("provenance", {}).get("reasoning_efforts") or [] + if len(efforts) == 1: + shown_efforts = "`, `".join(efforts[0].splitlines()) + A(f"- **Observed reasoning effort(s):** `{shown_efforts}`") + agent_ids = t.get("provenance", {}).get("agent_identity") or [] + if len(agent_ids) == 1: + A(f"- **Agent:** `{agent_ids[0]}`") + cli_versions = t.get("provenance", {}).get("copilot_cli_version") or [] + if len(cli_versions) == 1: + A(f"- **Observed Copilot CLI version:** `{cli_versions[0]}`") + A(f"- **Treatment** (`{t['benchmark']}`): skills installed in the agent's " + f"skills directory, and **never mentioned in the prompt** — this measures " + f"whether an agent that merely *has* the kit applies it.") + A(f"- **Control** (`{c['benchmark']}`): no skills installed. Nothing for the " + f"agent to discover or read.") + A("") + pak = t.get("pass_at_k") or {} + if pak: + A(f"- **pass@k:** complete={pak.get('complete')} " + f"expected_attempts={pak.get('expected_attempts')}") + A("") + + A("## Headline") + A("") + A("| | Control | Treatment | Delta |") + A("|---|---|---|---|") + A(f"| Instances | {c['instances']} | {t['instances']} | |") + A(f"| Resolved | {c['passed']} | {t['passed']} | " + f"{t['passed'] - c['passed']:+d} |") + A(f"| **Pass rate** | {_pct(c['pass_rate'])} | {_pct(t['pass_rate'])} | " + f"{_delta(t['pass_rate'], c['pass_rate'], '{:+.0%}')} |") + A("") + + A("## Cost") + A("") + A("What the kit costs. The control has no skill files to read, so the input " + "delta is literally the price of the kit being available and used. Note " + "that *more tokens per attempt is not the same as more expensive* — see " + "credits per passing result below.") + A("") + A("| Metric | Control | Treatment | Delta |") + A("|---|---|---|---|") + for key, label, fmt in COST_METRICS: + tv, cv = t["cost"].get(key), c["cost"].get(key) + A(f"| {label} | {'—' if cv is None else fmt.format(cv)} | " + f"{'—' if tv is None else fmt.format(tv)} | {_delta(tv, cv, '{:+,.1f}')} |") + A("") + + # Cost per passing result: the ROI figure. Failed attempts are part of the + # price of a success, so total credits are divided by PASSES, not runs. + for arm in (c, t): + credits, n = arm["cost"].get("ai_credits"), arm["instances"] + arm["_cpp"] = (credits * n / arm["passed"]) if (credits and arm["passed"]) else None + c_cpp = "—" if c["_cpp"] is None else f"{c['_cpp']:,.2f}" + t_cpp = "—" if t["_cpp"] is None else f"{t['_cpp']:,.2f}" + A("| Derived | Control | Treatment |") + A("|---|---|---|") + A(f"| **Credits per passing result** | {c_cpp} | {t_cpp} |") + A("") + A("*Credits per passing result* is the figure that matters: total spend " + "divided by successes, so failed attempts are charged to the successes " + "they paid for. An arm that costs more per attempt but passes far more " + "often is cheaper where it counts.") + A("") + + if t["check_rates"] or c["check_rates"]: + A("## Where the difference shows up") + A("") + A("Per-category check pass rates. The kit should move the " + "`documentdb`, `engine` and `source` rows — those encode the " + "best-practice rules. `api` and `behavior` measure basic competence " + "and should already be high in both arms.") + A("") + A("| Check category | Control | Treatment | Delta |") + A("|---|---|---|---|") + for cat in CHECK_CATEGORIES: + tv, cv = t["check_rates"].get(cat), c["check_rates"].get(cat) + if tv is None and cv is None: + continue + A(f"| `{cat}` | {_pct(cv)} | {_pct(tv)} | " + f"{_delta(tv, cv, '{:+.0%}')} |") + A("") + + warnings = [] + for arm in (t, c): + if arm["missing_token_data"]: + warnings.append( + f"{arm['missing_token_data']}/{arm['instances']} instances in " + f"`{arm['benchmark']}` had no token data " + f"(`tokens_available=0`) — cost means exclude them rather than " + f"counting them as zero.") + if arm["instances"] < 3: + warnings.append( + f"`{arm['benchmark']}` has only {arm['instances']} instance(s); " + f"agent runs are non-deterministic, so treat this as indicative " + f"rather than a measurement.") + if arm["missing_model_data"]: + warnings.append( + f"{arm['missing_model_data']}/{arm['instances']} instances in " + f"`{arm['benchmark']}` had no observed model provenance." + ) + if warnings: + A("## Caveats") + A("") + for w in warnings: + A(f"- ⚠️ {w}") + A("") + + A("---") + A("") + A("**Reading note.** Cost is reported as *fresh* (uncached) input tokens. " + "`cache_read_tokens` is a subset of `tokens_input`, and the skill payload " + "is cached after first use, so raw input overstates the cost of the kit " + "by roughly an order of magnitude.") + return "\n".join(L) + + +def main(argv=None) -> int: + p = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + p.add_argument("--treatment", type=Path, required=True, + help="msbench-cli report JSON for the skills arm") + p.add_argument("--control", type=Path, required=True, + help="msbench-cli report JSON for the no-skills arm") + p.add_argument("--treatment-benchmark", default=TREATMENT_DEFAULT) + p.add_argument("--control-benchmark", default=CONTROL_DEFAULT) + p.add_argument("--out", type=Path) + p.add_argument("--json", action="store_true", + help="emit the summary as JSON instead of markdown") + args = p.parse_args(argv) + + t = summarise(load(args.treatment), args.treatment_benchmark) + c = summarise(load(args.control), args.control_benchmark) + require_comparable_model_provenance(t, c) + + if args.json: + out = json.dumps({"treatment": t, "control": c}, indent=2, default=str) + else: + out = render(t, c) + + if args.out: + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(out + "\n") + print(f"wrote {args.out}", file=sys.stderr) + else: + print(out) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/documentdb-sdk-skills/shared/base/Dockerfile b/benchmarks/documentdb-sdk-skills/shared/base/Dockerfile new file mode 100644 index 0000000..ac4692f --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/base/Dockerfile @@ -0,0 +1,163 @@ +# documentdb-orders-base +# ---------------------- +# Shared base image for every orders- task. Carries: +# - DocumentDB Local (MongoDB-compatible gateway + PostgreSQL engine) +# - mongosh (the image does NOT ship it — see below) +# - Python 3 + verifier dependencies (pytest, pymongo, psycopg, requests) +# - The shared /verifier/ grader library and /verifier/contracts/ +# - /logs/verifier/ ready for the reward file +# +# Per-SDK task Dockerfiles do `FROM documentdb-orders-base` and add only the +# SDK runtime + the per-task /reference and /tests payload. +# +# WHY THIS IS EASIER THAN THE COSMOS EQUIVALENT +# --------------------------------------------- +# The Cosmos benchmark's hardest problem is its base image: a Mariner-based +# emulator that runs as PID 1, needs its ENTRYPOINT overridden, and must be +# coaxed into running as an unprivileged user. DocumentDB Local is a plain +# Ubuntu container we already run daily in the kit's test suite, and its base +# layer is PUBLIC — so anyone can reproduce this image even though the MSBench +# harness itself is Microsoft-internal. + +# Pinned from ghcr.io/microsoft/documentdb/documentdb-local:latest on +# 2026-09-22. Update deliberately and rerun the benchmark controls when the +# base changes; a mutable tag would silently change the published environment. +FROM ghcr.io/microsoft/documentdb/documentdb-local@sha256:0fcf634531c1917ad0855ff9f4354aca0a5c5d8e435f08250568deb37eeb0ad5 + +USER root +WORKDIR / + +# The upstream image starts the gateway as its entrypoint. Harbor needs to +# drive tests/test.sh as the verifier entrypoint and the agent needs a shell, +# so reset it and let start-documentdb control the lifecycle. +ENTRYPOINT [] +CMD ["/bin/bash"] + +ARG MONGOSH_VERSION=2.3.8 +ARG MONGOSH_SHA256=23edb768189663aaa9732a2340a25b5fc05a314940538809a7840be7f2ce221f + +RUN set -eux; \ + apt-get update; \ + apt-get install -y --no-install-recommends \ + python3 python3-pip python3-venv ca-certificates curl jq procps \ + tar gzip unzip zip; \ + rm -rf /var/lib/apt/lists/* +# NOTE: `which` is deliberately absent from that list. It is not a package on +# this base (it ships inside debianutils) and asking for it fails the whole +# apt transaction with "Unable to locate package which". The binary is already +# present at /usr/bin/which. + +# --------------------------------------------------------------------------- +# mongosh. NOT OPTIONAL, and not present upstream. +# +# The documentdb-local image ships `psql` but NO `mongosh` (verified against +# the pristine image). Every seeder, the verifier and the reference app's +# tooling talk to the database through it. Without this the whole benchmark +# fails with "executable file not found", which reads like a broken task +# rather than a missing dependency. +# +# Download directly from MongoDB's official distribution endpoint. The binary +# is not stored in this repository. Both version and archive digest are pinned +# so a changed upstream file cannot silently alter the benchmark image. +# --------------------------------------------------------------------------- +RUN set -eux; \ + curl -fsSL "https://downloads.mongodb.com/compass/mongosh-${MONGOSH_VERSION}-linux-x64.tgz" \ + -o /tmp/mongosh.tgz; \ + echo "${MONGOSH_SHA256} /tmp/mongosh.tgz" | sha256sum -c -; \ + tar xzf /tmp/mongosh.tgz -C /tmp; \ + cp "/tmp/mongosh-${MONGOSH_VERSION}-linux-x64/bin/mongosh" /usr/local/bin/mongosh; \ + cp "/tmp/mongosh-${MONGOSH_VERSION}-linux-x64/bin/mongosh_crypt_v1.so" /usr/local/lib/ || true; \ + rm -rf /tmp/mongosh.tgz "/tmp/mongosh-${MONGOSH_VERSION}-linux-x64"; \ + test "$(mongosh --version | tr -d '\r')" = "${MONGOSH_VERSION}" + +# --------------------------------------------------------------------------- +# Verifier dependencies, installed from VENDORED WHEELS with no network. +# +# `.wheels/` is populated on the host by shared/base/vendor-wheels.sh before +# `docker build` runs (see build.sh). Two reasons this is right, not a hack: +# * files.pythonhosted.org is not reachable from Docker's VM on this network, +# though the host can reach it; +# * the benchmark promises the agent NO INTERNET during grading, and a pinned +# wheel set makes the image reproducible when a fresh PyPI resolution months +# later might not be. +# +# --no-index makes the guarantee enforceable: if a wheel is missing the build +# FAILS rather than silently reaching out to the network. +# +# No --break-system-packages: this base is Ubuntu 22.04 with pip 22.x, which +# does not have that flag (it arrived in pip 23 for PEP 668). Jammy is not +# externally-managed, so a plain install is correct here. +# --------------------------------------------------------------------------- +COPY .wheels /opt/wheels +# psycopg is what makes check_engine.py possible: it reads the PostgreSQL +# engine underneath the Mongo API, a second observation plane the Cosmos +# benchmark does not have. +RUN python3 -m pip install --no-cache-dir --no-index --find-links=/opt/wheels \ + pytest==8.3.3 \ + pytest-json-ctrf==0.3.5 \ + requests==2.32.3 \ + pymongo==4.10.1 \ + "psycopg[binary]==3.2.3" \ + pyyaml==6.0.2 + +# Isolated venv for the verifier so the agent's /app/build.sh cannot shadow a +# verifier dependency by pip-installing a different pymongo into system +# site-packages. runner.sh prefers /opt/verifier-venv/bin/python. +RUN python3 -m venv /opt/verifier-venv && \ + /opt/verifier-venv/bin/pip install --no-cache-dir --no-index --find-links=/opt/wheels \ + pytest==8.3.3 \ + pytest-json-ctrf==0.3.5 \ + requests==2.32.3 \ + pymongo==4.10.1 \ + "psycopg[binary]==3.2.3" \ + pyyaml==6.0.2 + +# Shared grader library + scenario contracts. Owned by the image, not by the +# agent — /app cannot modify these. +COPY shared/verifier/ /verifier/ +COPY shared/contracts/ /verifier/contracts/ +COPY shared/base/start-documentdb.sh /usr/local/bin/start-documentdb +RUN chmod +x /usr/local/bin/start-documentdb /verifier/runner.sh + +# Remote-backend (CES) contract files. The runtime expects these at root. +COPY shared/ces/ /tmp/ces/ +RUN set -eux; \ + for f in ces_activate.sh entry.sh save.sh restore.sh parse.py \ + glob_files.py cut_metadata.py secret_files.txt \ + nonsecret_metadata_keys.txt; do \ + if [ -f "/tmp/ces/$f" ]; then cp "/tmp/ces/$f" "/$f"; fi; \ + done; \ + if [ -f /tmp/ces/activate_python.sh ]; then cp /tmp/ces/activate_python.sh /opt/activate_python.sh; fi; \ + if [ -f /tmp/ces/safe_save.sh ]; then cp /tmp/ces/safe_save.sh /opt/safe_save.sh; fi; \ + rm -rf /tmp/ces; \ + chmod +x /ces_activate.sh /entry.sh /save.sh /restore.sh \ + /opt/activate_python.sh /opt/safe_save.sh 2>/dev/null || true + +# --------------------------------------------------------------------------- +# The agent kit's skills, for the TREATMENT arm. +# +# shared/ces/runner.sh copies these into the agent's personal skills directory +# when SKILLS_ARM=kit, and installs nothing when SKILLS_ARM=control. They are +# staged into the build context by build.sh rather than widening the context to +# the whole repository. +# --------------------------------------------------------------------------- +COPY .skills /opt/documentdb-agent-kit/skills + +RUN mkdir -p /logs/verifier /app /reference /output /drop /agent /testbed && \ + chmod -R 1777 /logs /output /drop /agent /testbed /app /reference 2>/dev/null || true + +# Connection settings. The agent is expected to read these from the +# environment — check_skills.py fails any app that hardcodes them. +ENV SCENARIO="orders" \ + DOCUMENTDB_HOST="localhost" \ + DOCUMENTDB_PORT="10260" \ + DOCUMENTDB_USER="docdbadmin" \ + DOCUMENTDB_DATABASE="ordersdb" \ + DOCUMENTDB_ORDERS_COLLECTION="orders" \ + PG_PORT="9712" \ + PG_USER="documentdb" \ + PG_DB="postgres" \ + APP_PORT="9080" \ + APP_WORKDIR="/app" \ + REFERENCE_DIR="/reference" \ + VERIFIER_LOG_DIR="/logs/verifier" diff --git a/benchmarks/documentdb-sdk-skills/shared/base/start-documentdb.sh b/benchmarks/documentdb-sdk-skills/shared/base/start-documentdb.sh new file mode 100755 index 0000000..01a90a6 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/base/start-documentdb.sh @@ -0,0 +1,89 @@ +#!/usr/bin/env bash +# start-documentdb — bring up DocumentDB Local inside the task container and +# wait until the Mongo-compatible gateway actually accepts authenticated +# connections. +# +# Called by the verifier runner (and available to the agent) so the database +# lifecycle is controlled by the test harness rather than by the image's +# entrypoint, which Harbor overrides. +# +# The password is generated per container and exported, never baked into the +# image — check_skills.py fails any app that hardcodes a credential, so the +# benchmark must not model bad practice itself. + +set -uo pipefail + +PORT="${DOCUMENTDB_PORT:-10260}" +USER_="${DOCUMENTDB_USER:-docdbadmin}" +LOG="${VERIFIER_LOG_DIR:-/logs/verifier}/documentdb.log" +mkdir -p "$(dirname "$LOG")" + +if [ -z "${DOCUMENTDB_PASSWORD:-}" ]; then + DOCUMENTDB_PASSWORD="Bench$(head -c 12 /dev/urandom | od -An -tx1 | tr -d ' \n')" + export DOCUMENTDB_PASSWORD +fi + +# Idempotent: a second call must not start a second gateway. +if pgrep -f "documentdb" >/dev/null 2>&1 && \ + mongosh "localhost:${PORT}/admin" -u "$USER_" -p "$DOCUMENTDB_PASSWORD" \ + --authenticationMechanism SCRAM-SHA-256 --tls --tlsAllowInvalidCertificates \ + --quiet --eval "db.runCommand({ping:1}).ok" 2>/dev/null | grep -q 1; then + echo "[start-documentdb] already running" + exit 0 +fi + +echo "[start-documentdb] starting (log: $LOG)" +# The upstream image's ENTRYPOINT is +# /bin/bash -c '/home/documentdb/gateway/scripts/emulator_entrypoint.sh "$@"' +# and the image runs as the unprivileged `documentdb` user (uid 1000). +# +# We reset ENTRYPOINT and switched to root in the Dockerfile so Harbor can drive +# tests/test.sh and the verifier can write /logs. But PostgreSQL REFUSES to run +# as root ("initdb: cannot be run as root"), so the database must be started +# back under `documentdb` while everything else stays root. This is the same +# split the Cosmos benchmark makes for its emulator user. +ENTRY="" +for candidate in \ + /home/documentdb/gateway/scripts/emulator_entrypoint.sh \ + /usr/local/bin/entrypoint.sh \ + /entrypoint.sh; do + [ -x "$candidate" ] && { ENTRY="$candidate"; break; } +done + +if [ -z "$ENTRY" ]; then + echo "[start-documentdb] ERROR: no entrypoint script found in the image." >&2 + echo "[start-documentdb] Looked for: /home/documentdb/gateway/scripts/emulator_entrypoint.sh" >&2 + echo "[start-documentdb] /usr/local/bin/entrypoint.sh /entrypoint.sh" >&2 + exit 1 +fi + +DB_RUN_USER="${DOCUMENTDB_RUN_USER:-documentdb}" +if id "$DB_RUN_USER" >/dev/null 2>&1 && [ "$(id -u)" = "0" ]; then + # The data directory must belong to the user that will own the server + # process, or initdb refuses to touch it. + chown -R "$DB_RUN_USER":"$DB_RUN_USER" /home/documentdb 2>/dev/null || true + su -s /bin/bash "$DB_RUN_USER" -c \ + "USERNAME='$USER_' PASSWORD='$DOCUMENTDB_PASSWORD' nohup '$ENTRY'" \ + >"$LOG" 2>&1 & +else + USERNAME="$USER_" PASSWORD="$DOCUMENTDB_PASSWORD" \ + nohup "$ENTRY" >"$LOG" 2>&1 & +fi + +echo "[start-documentdb] waiting for the gateway to accept connections..." +for i in $(seq 1 120); do + if mongosh "localhost:${PORT}/admin" -u "$USER_" -p "$DOCUMENTDB_PASSWORD" \ + --authenticationMechanism SCRAM-SHA-256 --tls --tlsAllowInvalidCertificates \ + --quiet --eval "db.runCommand({ping:1}).ok" 2>/dev/null | grep -q 1; then + echo "[start-documentdb] ready after ${i}s" + exit 0 + fi + sleep 1 +done + +echo "[start-documentdb] ERROR: gateway did not become ready in 120s." >&2 +echo "[start-documentdb] NOTE: DocumentDB reports a wrong password as" >&2 +echo "[start-documentdb] 'MongoServerError: Invalid key' — an AUTH failure," >&2 +echo "[start-documentdb] not a malformed document." >&2 +tail -40 "$LOG" >&2 || true +exit 1 diff --git a/benchmarks/documentdb-sdk-skills/shared/base/vendor-wheels.sh b/benchmarks/documentdb-sdk-skills/shared/base/vendor-wheels.sh new file mode 100755 index 0000000..5251766 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/base/vendor-wheels.sh @@ -0,0 +1,81 @@ +#!/usr/bin/env bash +# vendor-wheels.sh — download the verifier + reference-app wheels on the HOST +# so the image can install them with no network access. +# +# WHY +# --- +# `files.pythonhosted.org` is not reachable from inside Docker's VM on this +# network (blocked by IT policy), although the host itself can reach it. So the +# wheels are resolved once, here, and copied into the build context. +# +# This is not a workaround bolted on: it is how the image SHOULD be built. +# The benchmark promises the agent no internet during grading, and a pinned +# wheel set makes the image reproducible months later when a fresh PyPI +# resolution might not be. +# +# If the public index is blocked on the host too, set PIP_INDEX_URL to an +# approved internal mirror before running, e.g. an Azure Artifacts feed that +# proxies PyPI: +# +# export PIP_INDEX_URL="https://pkgs.dev.azure.com//_packaging//pypi/simple/" +# +# Usage: bash vendor-wheels.sh [dest-dir] + +set -euo pipefail + +DEST="${1:-.wheels}" + +# Pinned, and shared by the base image (verifier) and the task image +# (reference app). One list keeps them from drifting apart. +PACKAGES=( + "pytest==8.3.3" + "pytest-json-ctrf==0.3.5" + "requests==2.32.3" + "pymongo==4.10.1" + "psycopg[binary]==3.2.3" + "pyyaml==6.0.2" + "fastapi==0.115.4" + "uvicorn[standard]==0.32.0" + "pydantic==2.9.2" + # Backports that only apply to the TARGET interpreter (3.10), listed + # explicitly because pip evaluates `python_version` environment markers + # against the interpreter RUNNING the download, not --python-version. On a + # 3.12 host these silently resolve to "not needed" and the image build then + # fails with "No matching distribution found for tomli". + "tomli==2.0.2" + "exceptiongroup==1.2.2" +) + +mkdir -p "$DEST" + +# Already vendored? Skip the download — this script runs on every build. +if [ -n "$(ls -A "$DEST" 2>/dev/null || true)" ] && [ -z "${VENDOR_FORCE:-}" ]; then + echo " $DEST already populated ($(find "$DEST" -maxdepth 1 -type f | wc -l) files); set VENDOR_FORCE=1 to refresh" + exit 0 +fi + +echo " resolving ${#PACKAGES[@]} pinned packages into $DEST" + +# Target the image's interpreter, not the host's: the container is Ubuntu 22.04 +# (CPython 3.10, manylinux x86_64). Without these constraints pip would happily +# fetch wheels for the host's Python and they would not import in the image. +PYVER="${VENDOR_PYTHON_VERSION:-310}" + +if ! python3 -m pip download \ + --dest "$DEST" \ + --only-binary=:all: \ + --python-version "$PYVER" \ + --implementation cp \ + --abi "cp${PYVER}" \ + --platform manylinux2014_x86_64 \ + "${PACKAGES[@]}" 2>&1 | tail -5; then + echo + echo "vendor-wheels: download FAILED." >&2 + echo " If files.pythonhosted.org is blocked on this host as well, point pip" >&2 + echo " at an approved internal mirror and re-run:" >&2 + echo " export PIP_INDEX_URL=" >&2 + rm -rf "$DEST" + exit 1 +fi + +echo " vendored $(find "$DEST" -maxdepth 1 -type f | wc -l) wheels" diff --git a/benchmarks/documentdb-sdk-skills/shared/ces/runner.sh b/benchmarks/documentdb-sdk-skills/shared/ces/runner.sh new file mode 100755 index 0000000..b58a507 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/ces/runner.sh @@ -0,0 +1,53 @@ +#!/usr/bin/env bash +# runner.sh — agent runner for documentdb-sdk-skills. +# +# THE ORGANIC-DISCOVERY PROTOCOL +# ------------------------------ +# This installs the DocumentDB agent kit's skills into the agent's personal +# skills directory as NORMALLY DISCOVERABLE skills, and then drives the default +# agent with NO HINT to use them. +# +# That is the entire point of the benchmark. Telling the agent "use the +# DocumentDB skills" would measure whether a model can follow an instruction — +# an easy question with an uninteresting answer. Not telling it measures +# whether an agent that merely HAS the kit installed actually applies it, which +# is the question a user's experience actually depends on. +# +# ARM SELECTION +# ------------- +# SKILLS_ARM=kit -> install the skills (benchmark: documentdb-sdk-skills) +# SKILLS_ARM=control -> install nothing (benchmark: documentdb-sdk-skills-noskills) +# +# The control arm is a separately registered benchmark, mirroring the +# platform's existing skillsbench / skillsbenchnoskills convention. Only the +# DELTA between the two arms is meaningful. + +set -uo pipefail + +SKILLS_ARM="${SKILLS_ARM:-kit}" +SKILLS_SRC="${SKILLS_SRC:-/opt/documentdb-agent-kit/skills}" +SKILLS_DEST="${SKILLS_DEST:-$HOME/.copilot/skills}" + +echo "[runner] arm=${SKILLS_ARM}" + +if [ "$SKILLS_ARM" = "kit" ]; then + if [ ! -d "$SKILLS_SRC" ]; then + # Fail loudly. A treatment arm whose skills are missing is silently just + # a second control arm, and would produce a confident, wrong conclusion + # that the kit makes no difference. + echo "[runner] FATAL: SKILLS_ARM=kit but $SKILLS_SRC does not exist." >&2 + exit 1 + fi + mkdir -p "$SKILLS_DEST" + cp -r "$SKILLS_SRC"/. "$SKILLS_DEST"/ + echo "[runner] installed $(find "$SKILLS_DEST" -name SKILL.md | wc -l) skills into $SKILLS_DEST" +else + echo "[runner] control arm: no skills installed" + # Defensive: make sure a stale layer cannot leak skills into the control. + rm -rf "$SKILLS_DEST" 2>/dev/null || true +fi + +# The prompt is the task statement, verbatim and unaugmented. Nothing here +# mentions DocumentDB best practices, indexes, ESR, connection pooling or the +# skills themselves. +exec "$@" diff --git a/benchmarks/documentdb-sdk-skills/shared/contracts/orders.json b/benchmarks/documentdb-sdk-skills/shared/contracts/orders.json new file mode 100644 index 0000000..9ee1686 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/contracts/orders.json @@ -0,0 +1,49 @@ +{ + "scenario": "orders", + "database_env": "DOCUMENTDB_DATABASE", + "database_default": "ordersdb", + "health_path": "/health", + "roots": [ + { + "name": "order", + "collection_env": "DOCUMENTDB_ORDERS_COLLECTION", + "collection_default": "orders", + "create": { "path": "/orders", "duplicate_status": 409 }, + "get": { "path": "/orders/{id}" }, + "list": { + "path": "/orders", + "filter_param": "customer_id", + "filter_field": "customer_id" + }, + "compare_fields": ["customer_id", "status", "amount", "items"], + "ordered_array_fields": ["items"], + "string_array_fields": ["items"], + "string_fields": ["customer_id", "status"], + "numeric_fields": ["amount"], + "modeling": { + "type_discriminator": true, + "schema_version": true, + "timestamp_field": "created_at" + }, + "indexing": { + "require_secondary_index": true, + "required_query_fields": ["customer_id"], + "esr_compound": { + "equality": ["customer_id"], + "sort": ["created_at"] + } + }, + "engine": { + "max_scan_amplification": 5.0, + "forbid_full_scan_for": ["customer_id"] + }, + "seed": [ + { "id": "o-alpha", "customer_id": "CUST-001", "status": "shipped", "amount": 120.5, "items": ["widget", "gasket"] }, + { "id": "o-bravo", "customer_id": "CUST-001", "status": "pending", "amount": 42.0, "items": ["bolt"] }, + { "id": "o-charlie", "customer_id": "CUST-002", "status": "shipped", "amount": 999.99, "items": ["frame", "wheel", "chain"] }, + { "id": "o-delta", "customer_id": "CUST-003", "status": "cancelled", "amount": 7.25, "items": [] } + ] + } + ], + "children": [] +} diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/check_api.py b/benchmarks/documentdb-sdk-skills/shared/verifier/check_api.py new file mode 100644 index 0000000..5d5b005 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/check_api.py @@ -0,0 +1,116 @@ +"""API conformance — does the service expose the contracted HTTP surface? + +Weakest of the behavioural checks and deliberately first: if the API is wrong, +every later failure is a consequence rather than an independent finding, and +the per-check logs should make that obvious. + +SDK-agnostic: no SDK token appears in a test name, so these run for every SDK. +""" +from __future__ import annotations + +import pytest + +from conftest import CONTRACT, ROOTS, fmt_path, root_ids + + +def test_health_endpoint_responds(api): + path = CONTRACT.get("health_path", "/health") + resp = api.get(path) + assert resp.status_code == 200, ( + f"GET {path} returned {resp.status_code}, expected 200. The service must " + f"expose a health endpoint so the harness can tell 'not started yet' " + f"apart from 'started but broken'." + ) + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestCreate: + def test_create_accepts_contract_rows(self, root, seed_roots): + results = seed_roots[root["name"]] + bad = [r for r in results if r["status"] not in (200, 201)] + assert not bad, ( + f"POST {root['create']['path']} rejected valid rows: " + + ", ".join(f"{r['row']['id']} -> {r['status']}" for r in bad) + ) + + def test_duplicate_create_is_rejected(self, root, api, seed_roots): + """A second create of the same id must not silently succeed. + + Accepting it would let an implementation overwrite or duplicate rows, + which the behavioural check then catches as data corruption — better to + name the cause here. + """ + create = root.get("create") + expected = create.get("duplicate_status", 409) + row = root["seed"][0] + resp = api.post(create["path"], json=row) + assert resp.status_code == expected, ( + f"Re-POSTing existing {root['name']} {row['id']!r} returned " + f"{resp.status_code}, expected {expected}." + ) + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestRead: + def test_get_by_id_returns_the_row(self, root, api, seed_roots): + get = root.get("get") + if not get: + pytest.skip(f"{root['name']} has no GET-by-id endpoint") + for row in root["seed"]: + resp = api.get(fmt_path(get["path"], id=row["id"])) + assert resp.status_code == 200, ( + f"GET {fmt_path(get['path'], id=row['id'])} -> {resp.status_code}" + ) + body = resp.json() + for field in root.get("compare_fields", []): + assert body.get(field) == row.get(field), ( + f"{root['name']} {row['id']}: API returned {field}=" + f"{body.get(field)!r}, expected {row.get(field)!r}" + ) + + def test_get_unknown_id_is_404(self, root, api): + get = root.get("get") + if not get: + pytest.skip(f"{root['name']} has no GET-by-id endpoint") + resp = api.get(fmt_path(get["path"], id="definitely-not-a-real-id")) + assert resp.status_code == 404, ( + f"GET of an unknown {root['name']} returned {resp.status_code}, " + f"expected 404." + ) + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestList: + def test_filtered_list_returns_exactly_the_matching_rows( + self, root, api, seed_roots + ): + """The filter is the query the whole benchmark is built around. + + Returning too many rows means the filter is ignored; too few means the + query or the index is wrong. Both are graded here as a set comparison + so ordering is not accidentally required. + """ + lst = root.get("list") + if not lst or not lst.get("filter_param"): + pytest.skip(f"{root['name']} has no filtered list endpoint") + + field = lst["filter_field"] + values = sorted({row[field] for row in root["seed"]}) + for value in values: + resp = api.get(lst["path"], params={lst["filter_param"]: value}) + assert resp.status_code == 200, ( + f"GET {lst['path']}?{lst['filter_param']}={value} -> " + f"{resp.status_code}" + ) + body = resp.json() + rows = body if isinstance(body, list) else body.get("items", body.get("data")) + assert isinstance(rows, list), ( + f"Filtered list must return a JSON array (or an object with " + f"'items'/'data'); got {type(body).__name__}" + ) + got = {r.get("id") for r in rows} + expected = {r["id"] for r in root["seed"] if r[field] == value} + assert got == expected, ( + f"{lst['path']}?{lst['filter_param']}={value} returned {sorted(got)}, " + f"expected {sorted(expected)}" + ) diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/check_behavior.py b/benchmarks/documentdb-sdk-skills/shared/verifier/check_behavior.py new file mode 100644 index 0000000..5dfbaa3 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/check_behavior.py @@ -0,0 +1,131 @@ +"""Behavioural checks — the concrete, hard-to-game core of the grader. + +For every declared root entity we: + + 1. Seed deterministic rows through the agent's public HTTP API. + 2. Independently read the persisted documents straight from DocumentDB + with the VERIFIER'S OWN client. + 3. Assert the API, the persisted documents and the contract all agree. + +This catches what a source-code regex cannot: an app that answers HTTP but +stores nothing (in-memory dict, SQLite), a filter that returns the wrong rows, +or a "create" that silently overwrites duplicates. + +Why this is the primary category: source checks tell you the agent *wrote* +something that looks right. Only this tells you the system *behaves* right. +""" +from __future__ import annotations + +import pytest + +from conftest import ROOTS, collection_name, fmt_path, root_ids + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestPersistenceIsReal: + """Every row POSTed through the API must exist as a real document.""" + + def test_every_seeded_row_is_persisted(self, root, seed_roots, root_persisted): + docs = root_persisted[root["name"]] + missing = [r["id"] for r in root["seed"] if docs.get(r["id"]) is None] + assert not missing, ( + f"These {root['name']} rows were accepted by the API but are NOT in " + f"DocumentDB: {missing}. The service must persist through the " + f"MongoDB driver — an in-memory or SQLite store that never writes to " + f"the database fails this gate." + ) + + def test_persisted_fields_match_input(self, root, seed_roots, root_persisted): + docs = root_persisted[root["name"]] + for row in root["seed"]: + doc = docs[row["id"]] + assert doc is not None, f"{row['id']} not persisted" + for field in root.get("compare_fields", []): + assert doc.get(field) == row.get(field), ( + f"{root['name']} {row['id']}: {field!r} stored as " + f"{doc.get(field)!r}, expected {row.get(field)!r} " + f"(arrays must round-trip in order)." + ) + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestRoundTripIntegrity: + """GET must return what is actually stored, not a cached copy that has + drifted from the persisted document.""" + + def test_api_read_matches_database(self, root, seed_roots, root_persisted, api): + get = root.get("get") + if not get: + pytest.skip(f"{root['name']} has no GET-by-id endpoint") + docs = root_persisted[root["name"]] + for row in root["seed"]: + doc = docs[row["id"]] + resp = api.get(fmt_path(get["path"], id=row["id"])) + assert resp.status_code == 200 + body = resp.json() + for field in root.get("compare_fields", []): + assert body.get(field) == doc.get(field), ( + f"{root['name']} {row['id']}: API says {field}=" + f"{body.get(field)!r} but the database holds {doc.get(field)!r}. " + f"The read path is not reading from the database." + ) + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestNoDuplication: + def test_duplicate_create_did_not_create_a_second_document( + self, root, db, seed_roots, api + ): + """The duplicate POST in check_api must not have written a second row. + + Returning 409 while still inserting is a real bug this catches, and one + an API-only grader would miss entirely. + """ + create = root.get("create") + if not create: + pytest.skip("no create endpoint") + row = root["seed"][0] + api.post(create["path"], json=row) # attempt another duplicate + coll = db[collection_name(root)] + count = coll.count_documents( + {"$or": [{"_id": row["id"]}, {"id": row["id"]}]} + ) + assert count == 1, ( + f"{root['name']} {row['id']!r} exists {count} times after duplicate " + f"POSTs; expected exactly 1." + ) + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestDataShape: + """Types must survive the round trip. + + A numeric amount stored as a string is the single most common DocumentDB + modelling error, it breaks range queries and index use silently, and the + kit's data-modeling skill warns about it explicitly. + """ + + def test_numeric_fields_are_stored_as_numbers(self, root, root_persisted): + for field in root.get("numeric_fields", []): + for row in root["seed"]: + doc = root_persisted[root["name"]][row["id"]] + assert doc is not None + value = doc.get(field) + assert isinstance(value, (int, float)) and not isinstance(value, bool), ( + f"{root['name']} {row['id']}: {field!r} stored as " + f"{type(value).__name__} ({value!r}), expected a number. " + f"Numbers stored as strings break range queries and index use." + ) + + def test_string_arrays_stay_arrays(self, root, root_persisted): + for field in root.get("string_array_fields", []): + for row in root["seed"]: + doc = root_persisted[root["name"]][row["id"]] + value = doc.get(field) + assert isinstance(value, list), ( + f"{root['name']} {row['id']}: {field!r} stored as " + f"{type(value).__name__}, expected an array." + ) + assert all(isinstance(v, str) for v in value), ( + f"{root['name']} {row['id']}: {field!r} must contain strings" + ) diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/check_documentdb.py b/benchmarks/documentdb-sdk-skills/shared/verifier/check_documentdb.py new file mode 100644 index 0000000..ca22693 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/check_documentdb.py @@ -0,0 +1,153 @@ +"""DocumentDB-level checks — modelling and indexing, read via pymongo. + +These grade the decisions the kit's `documentdb-data-modeling` and +`documentdb-indexing` skills teach, observed in the actual collection rather +than in the source code: + + * a type discriminator and schemaVersion on documents (schema evolution) + * ISO-8601 timestamps stored in a queryable form + * at least one secondary index covering the query the API exposes + * compound indexes ordered Equality -> Sort (ESR) + +Index checks are structural, not source-based, so they hold regardless of which +driver or language the agent used. +""" +from __future__ import annotations + +import re + +import pytest + +from conftest import ROOTS, collection_name, root_ids + +ISO8601 = re.compile( + r"^\d{4}-\d{2}-\d{2}[T ]\d{2}:\d{2}:\d{2}(\.\d+)?(Z|[+-]\d{2}:?\d{2})?$" +) + + +def _indexes(db, root): + return list(db[collection_name(root)].list_indexes()) + + +def _secondary(indexes): + return [ix for ix in indexes if ix.get("name") != "_id_"] + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestModelling: + def test_documents_carry_a_type_discriminator(self, root, root_persisted): + """Mixed-type collections are normal in DocumentDB; without a + discriminator you cannot tell the shapes apart later.""" + if not root.get("modeling", {}).get("type_discriminator"): + pytest.skip("not required by the contract") + candidates = ("type", "_type", "docType", "doc_type", "entityType") + for row in root["seed"]: + doc = root_persisted[root["name"]][row["id"]] + assert doc is not None, f"{row['id']} not persisted" + assert any(doc.get(c) for c in candidates), ( + f"{root['name']} {row['id']} has no type discriminator " + f"(looked for {candidates}). Documents should record what they " + f"are so a collection can hold more than one shape safely." + ) + + def test_documents_carry_a_schema_version(self, root, root_persisted): + if not root.get("modeling", {}).get("schema_version"): + pytest.skip("not required by the contract") + candidates = ("schemaVersion", "schema_version", "_schemaVersion", "v") + for row in root["seed"]: + doc = root_persisted[root["name"]][row["id"]] + assert any(doc.get(c) is not None for c in candidates), ( + f"{root['name']} {row['id']} has no schema version " + f"(looked for {candidates}). Without one, migrating a live " + f"collection later means guessing which documents are old." + ) + + def test_timestamp_is_queryable(self, root, root_persisted): + """Accept a BSON date or an ISO-8601 string; reject a bare epoch int or + a locale-formatted string, both of which sort incorrectly.""" + field = root.get("modeling", {}).get("timestamp_field") + if not field: + pytest.skip("no timestamp field in the contract") + import datetime as _dt + for row in root["seed"]: + doc = root_persisted[root["name"]][row["id"]] + value = doc.get(field) + assert value is not None, ( + f"{root['name']} {row['id']} has no {field!r}." + ) + ok = isinstance(value, _dt.datetime) or ( + isinstance(value, str) and ISO8601.match(value) + ) + assert ok, ( + f"{root['name']} {row['id']}: {field!r} is {value!r}. Store a " + f"BSON date or an ISO-8601 string — other formats do not sort " + f"or range-query correctly." + ) + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestIndexing: + def test_a_secondary_index_exists(self, root, db): + if not root.get("indexing", {}).get("require_secondary_index"): + pytest.skip("not required by the contract") + secondary = _secondary(_indexes(db, root)) + assert secondary, ( + f"{collection_name(root)} has only the default _id index. The API " + f"exposes a filtered query, so it will collection-scan on every " + f"call as the collection grows." + ) + + def test_queried_fields_are_indexed(self, root, db): + """Every field the API filters on must lead some index. + + Leading position matters: a field buried in the middle of a compound + index cannot be used for an equality lookup on its own. + """ + required = root.get("indexing", {}).get("required_query_fields", []) + if not required: + pytest.skip("no required query fields") + leading = set() + for ix in _secondary(_indexes(db, root)): + keys = list(ix.get("key", {}).keys()) + if keys: + leading.add(keys[0]) + missing = [f for f in required if f not in leading] + assert not missing, ( + f"{collection_name(root)}: no index LEADS with {missing}. Existing " + f"secondary indexes lead with {sorted(leading) or 'nothing'}. A " + f"field that does not lead an index cannot serve an equality lookup." + ) + + def test_compound_index_follows_esr(self, root, db): + """Equality keys must precede Sort keys in a compound index. + + This is the single most valuable rule in the indexing skill, and it is + checkable structurally — no source parsing, no judgement. + """ + esr = root.get("indexing", {}).get("esr_compound") + if not esr: + pytest.skip("no ESR expectation in the contract") + equality, sort = esr["equality"], esr["sort"] + + compound = [ix for ix in _secondary(_indexes(db, root)) + if len(ix.get("key", {})) > 1] + if not compound: + pytest.skip( + "no compound index present; the single-field index requirement " + "is graded separately" + ) + + offenders = [] + for ix in compound: + keys = list(ix["key"].keys()) + for s in sort: + if s not in keys: + continue + s_pos = keys.index(s) + for e in equality: + if e in keys and keys.index(e) > s_pos: + offenders.append((ix.get("name"), keys)) + assert not offenders, ( + f"Compound index does not follow ESR (Equality before Sort): " + f"{offenders}. Expected equality {equality} to precede sort {sort}." + ) diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/check_engine.py b/benchmarks/documentdb-sdk-skills/shared/verifier/check_engine.py new file mode 100644 index 0000000..6d8df44 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/check_engine.py @@ -0,0 +1,193 @@ +"""Engine-level checks — the second observation plane. + +WHY THIS FILE IS THE DIFFERENTIATOR +----------------------------------- +The Cosmos benchmark notes that client-side properties "a single-node local +emulator CANNOT prove behaviorally" have to be checked with static source-code +regex instead. That is the right call given their platform — a static check is +better than no coverage — but it is inherently easier to satisfy accidentally +than a runtime observation. + +DocumentDB lets us avoid the trade-off. It is MongoDB-compatible on the +surface and PostgreSQL underneath, and the verifier can talk to BOTH. So +instead of grepping for `create_index(` and hoping, we ask the engine what +actually happened: + + * `explain()` through the Mongo API -> was the query planned as an index + scan, and how many documents did it read per document returned? + * `pg_stat_user_indexes` -> did the index actually get USED, or + is it dead weight that only costs + write throughput? + +An agent can fake the source. It cannot fake the engine's own statistics. + +THE METRIC: SCAN AMPLIFICATION +------------------------------ +`totalDocsExamined / nReturned`. Raw "documents examined" is the wrong metric +on this engine: measured on DocumentDB Local, the planner picks an index scan +even for very unselective predicates, so `examined` mostly reflects how many +rows legitimately match rather than whether the index helped. + + amount > 490 (selective) IXSCAN examined=760 returned=760 + amount > 10 (unselective) IXSCAN examined=19960 returned=19960 + +Both are healthy — they read only rows they return. Amplification captures +that independently of selectivity. + +DELIBERATELY NOT GRADED +----------------------- +Index-backed SORT and covered queries. The DocumentDB gateway pins the +experimental GUCs that enable them (`enableIndexOrderbyPushdown`, +`enableNewCompositeIndexOpClass`, `defaultUseCompositeOpClass`) off per +session, and `ALTER SYSTEM` plus an index rebuild does not change the +Mongo-API plan. Grading advice the harness cannot demonstrate would be worse +than not grading it. +""" +from __future__ import annotations + +import pytest + +from conftest import ROOTS, collection_name, database_name, root_ids + + +def _plan_stages(node, limit=30): + stages, guard = [], 0 + while node and guard < limit: + guard += 1 + stage = node.get("stage") + if stage: + stages.append(stage) + node = node.get("inputStage") or ( + node.get("inputStages")[0] if node.get("inputStages") else None + ) + return stages + + +def _explain(db, root, filt): + coll = collection_name(root) + plan = db.command({ + "explain": {"find": coll, "filter": filt}, + "verbosity": "executionStats", + }) + stats = plan.get("executionStats", {}) or {} + examined = int(stats.get("totalDocsExamined", 0) or 0) + returned = int(stats.get("nReturned", 0) or 0) + return { + "stages": _plan_stages((plan.get("queryPlanner") or {}).get("winningPlan")), + "examined": examined, + "returned": returned, + "amplification": (examined / returned) if returned else float("inf"), + } + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestQueryPlan: + """Prove the index is USED, not merely created.""" + + def test_filtered_query_is_not_a_full_collection_scan( + self, root, db, seed_roots, bulk_filler + ): + fields = root.get("engine", {}).get("forbid_full_scan_for", []) + if not fields: + pytest.skip("no engine expectations in the contract") + + for field in fields: + value = root["seed"][0][field] + plan = _explain(db, root, {field: value}) + assert plan["returned"] > 0, ( + f"The probe {{{field}: {value!r}}} matched no documents, so it " + f"cannot grade anything. Seeding likely failed." + ) + assert "COLLSCAN" not in plan["stages"], ( + f"Query on {field!r} was planned as a COLLSCAN " + f"(stages={plan['stages']}). An index exists only if it is " + f"actually usable by the query the API issues." + ) + + def test_scan_amplification_is_low(self, root, db, seed_roots, bulk_filler): + """The headline engine metric: documents read per document returned.""" + engine = root.get("engine", {}) + cap = engine.get("max_scan_amplification") + fields = engine.get("forbid_full_scan_for", []) + if cap is None or not fields: + pytest.skip("no amplification expectation in the contract") + + for field in fields: + value = root["seed"][0][field] + plan = _explain(db, root, {field: value}) + assert plan["amplification"] <= cap, ( + f"Query on {field!r} read {plan['amplification']:.1f} documents " + f"per document returned (examined={plan['examined']}, " + f"returned={plan['returned']}), above the {cap}x limit. The " + f"query is scanning rows it then throws away." + ) + + +@pytest.mark.parametrize("root", ROOTS, ids=root_ids) +class TestEngineStatistics: + """Read PostgreSQL's own counters. This is the plane a Mongo-API-only + grader — and the Cosmos benchmark — cannot see.""" + + def test_a_secondary_index_actually_served_a_read( + self, root, pg, db, seed_roots, bulk_filler + ): + """An index that is never scanned is pure write tax. + + `pg_stat_user_indexes.idx_scan` is the engine's own count of how many + times each index was used, so this cannot be satisfied by declaring an + index the queries never touch. + """ + # Issue the query the API exposes so the counters have something to show. + fields = root.get("engine", {}).get("forbid_full_scan_for", []) + for field in fields: + list(db[collection_name(root)].find({field: root["seed"][0][field]})) + + with pg.cursor() as cur: + cur.execute( + """ + SELECT indexrelname, idx_scan + FROM pg_stat_user_indexes + WHERE schemaname LIKE %s + ORDER BY idx_scan DESC + """, + ("documentdb_data%",), + ) + rows = cur.fetchall() + + assert rows, ( + "No DocumentDB index statistics found in pg_stat_user_indexes. The " + "collection may live in an unexpected schema, or nothing was written." + ) + used = [(name, scans) for name, scans in rows if (scans or 0) > 0] + assert used, ( + "Not one index recorded a single scan " + f"({[(n, s) for n, s in rows][:8]}). Every read is going through a " + "sequential scan, so any index that exists is costing writes and " + "buying nothing." + ) + + def test_collection_is_not_scan_dominated( + self, root, pg, db, seed_roots, bulk_filler + ): + """Sequential scans are not always wrong — on a tiny table they are + cheaper. This only fails when there is measurable index usage nowhere + while sequential scans dominate, i.e. the access pattern is wrong in a + way that will not improve with scale. + """ + with pg.cursor() as cur: + cur.execute( + """ + SELECT COALESCE(SUM(seq_scan), 0), COALESCE(SUM(idx_scan), 0) + FROM pg_stat_user_tables + WHERE schemaname LIKE %s + """, + ("documentdb_data%",), + ) + seq, idx = cur.fetchone() + + if (seq or 0) + (idx or 0) == 0: + pytest.skip("no table statistics recorded yet") + assert (idx or 0) > 0, ( + f"Every access was a sequential scan (seq_scan={seq}, idx_scan={idx}). " + f"The schema or the indexes do not match the queries the API issues." + ) diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/check_skills.py b/benchmarks/documentdb-sdk-skills/shared/verifier/check_skills.py new file mode 100644 index 0000000..f8e2270 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/check_skills.py @@ -0,0 +1,91 @@ +r"""Skills-compliance checks — hygiene rules that must hold in any submission. + +These are the non-negotiables from the kit's `documentdb-security` skill. Unlike +check_source.py, credential checks run against the RAW source (comments +included): a password committed in a comment is still a leaked password. +""" +from __future__ import annotations + +import re + +import pytest + +# The base image generates a password at container start and exports it. Any +# literal that looks like a credential in source is therefore a real finding. +_CREDENTIAL_PATTERNS = [ + # mongodb://user:password@host — a password embedded in a URI LITERAL. + # + # The password class excludes { } $ % + on purpose: those characters mean + # the value is being INTERPOLATED, e.g. + # f"mongodb://{quote_plus(user)}:{quote_plus(password)}@{host}:{port}/" + # which is the CORRECT pattern (credentials from the environment), not a + # leak. Without this exclusion the check fails every well-written + # submission — it did exactly that to the reference implementation. + (r"mongodb(?:\+srv)?://[^\s\"']*:[^\s\"'@/{}$%+]{3,}@", + "a connection URI with an embedded password"), + # password = "literal" (not os.environ / getenv / a format placeholder) + (r"(?i)\b(?:password|passwd|pwd)\s*=\s*[\"'][^\"'{}$<>%+]{4,}[\"']", + "a hardcoded password literal"), +] + +_ENV_READ = re.compile( + r"os\.environ|os\.getenv|getenv\(|dotenv|Environment\.GetEnvironmentVariable|" + r"process\.env|System\.getenv" +) + + +class TestNoHardcodedCredentials: + def test_no_credential_literals_in_source(self, source_text_raw): + """Deliberately scans RAW source. + + A credential in a comment is still a credential. This is the one place + where comment-stripping would weaken rather than strengthen the check. + """ + findings = [] + for pattern, label in _CREDENTIAL_PATTERNS: + for match in re.finditer(pattern, source_text_raw): + snippet = match.group(0) + # Redact before reporting: the failure message ends up in logs. + findings.append(f"{label}: {snippet[:24]}…") + assert not findings, ( + "Hardcoded credentials found in the source:\n " + + "\n ".join(findings[:5]) + + "\nRead the password from the environment " + "(DOCUMENTDB_PASSWORD) instead — the container generates a fresh " + "one per run, so a literal cannot even be correct." + ) + + +class TestConfigurationComesFromTheEnvironment: + def test_connection_settings_are_read_from_env(self, source_text): + assert _ENV_READ.search(source_text), ( + "No environment-variable reads found. The host, port, credentials " + "and database name are supplied via the environment " + "(DOCUMENTDB_HOST / _PORT / _USER / _PASSWORD / _DATABASE); an app " + "that does not read them cannot be deployed anywhere else." + ) + + def test_database_name_is_not_hardcoded_over_the_env(self, source_text): + """A default is fine; ignoring the env var is not.""" + if not re.search(r"DOCUMENTDB_DATABASE", source_text): + pytest.fail( + "The database name is never read from DOCUMENTDB_DATABASE. " + "Hardcoding it means the same build cannot target another " + "database." + ) + + +class TestNoForbiddenAntiPatterns: + def test_no_insecure_auth_bypass(self, sdk, source_text): + """`authMechanism` must not be downgraded to none/PLAIN. + + Azure DocumentDB uses SCRAM-SHA-256 or Entra. Disabling auth to make a + local run work is the kind of shortcut that survives into production. + """ + if sdk != "python": + pytest.skip(f"does not apply to {sdk}") + bad = re.search(r"authMechanism\s*=\s*[\"'](?:PLAIN|NONE|none)[\"']", source_text) + assert not bad, ( + "authMechanism is set to an insecure value. Azure DocumentDB uses " + "SCRAM-SHA-256 (or Microsoft Entra)." + ) diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/check_source.py b/benchmarks/documentdb-sdk-skills/shared/verifier/check_source.py new file mode 100644 index 0000000..03016b6 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/check_source.py @@ -0,0 +1,179 @@ +r"""Source-code checks — the STATIC, deliberately WEAKER half of the grader. + +IMPORTANT: these are regex scans over the agent's source with comments +stripped (see `_strip_comments` in conftest.py). String *literals* are NOT +stripped, so every pattern here requires code adjacency (`foo\s*=`, +`Foo\s*\(`) rather than a bare keyword a log line could satisfy by accident. + +WHY SO FEW CHECKS LIVE HERE +--------------------------- +The Cosmos benchmark has ~520 lines of source checks because a single-node +emulator cannot prove client configuration behaviourally, so most of its +rubric has nowhere else to go. + +We are not in that position. DocumentDB lets the verifier read the +PostgreSQL engine underneath, so index usage, scan behaviour and connection +reuse are graded BEHAVIOURALLY in check_engine.py. This file is therefore +small on purpose: only properties that genuinely leave no runtime trace stay +here. Prefer adding a check to check_engine.py or check_behavior.py whenever +the behaviour is observable. + +The rules asserted here come from the kit's `documentdb-driver`, +`documentdb-connection` and `documentdb-data-modeling` skills. +""" +from __future__ import annotations + +import ast +import re + +import pytest + + +def _need(sdk: str, *want: str): + if sdk not in want: + pytest.skip(f"does not apply to {sdk} (only: {', '.join(want)})") + + +ROUTE_DECORATORS = {"get", "post", "put", "delete", "patch", "route"} +# Decorators that make a per-call construction safe because the result is +# memoised, or that mark application start-up. +CACHING_DECORATORS = {"lru_cache", "cache", "cached_property", + "on_event", "lifespan", "before_first_request"} + + +def _client_calls_inside_functions(source_files) -> list[str]: + """Find MongoClient constructions that are NOT module-level singletons. + + Uses the AST rather than a regex. The regex version of this check had + catastrophic backtracking — nested quantifiers over lines — and hung the + verifier for minutes on a 6 KB file. Parsing is exact and ~1000x faster. + + WHY "ANY FUNCTION" AND NOT "ANY ROUTE HANDLER": + an earlier version only looked inside route-decorated functions, and a + deliberately naive submission slipped through by hiding the construction in + a helper: + + def coll(): + c = MongoClient(...) # a NEW pool on every request + return c[db][collection] + + @app.get("/orders") + def list_orders(): return list(coll().find(...)) + + That is the exact anti-pattern the check exists to catch. A correct + singleton is built once at module import (or behind an explicit cache / + start-up hook), so anything else is reported. + """ + offenders = [] + for path in source_files: + try: + tree = ast.parse(path.read_text(encoding="utf-8", errors="ignore")) + except (SyntaxError, OSError): + continue + + # Map every node to its enclosing function, if any. + for node in ast.walk(tree): + if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)): + continue + + exempt = False + for dec in node.decorator_list: + call = dec.func if isinstance(dec, ast.Call) else dec + name = getattr(call, "attr", None) or getattr(call, "id", None) + if name in CACHING_DECORATORS: + exempt = True + break + if exempt: + continue + + for inner in ast.walk(node): + if (isinstance(inner, ast.Call) + and isinstance(inner.func, ast.Name) + and inner.func.id == "MongoClient"): + offenders.append( + f"{path.name}:{inner.lineno} in {node.name}()") + return offenders + + +class TestClientLifecycle: + """Singleton client. + + A MongoClient owns a connection pool. Constructing one per request + exhausts server connections under load and defeats pooling entirely — + the single most common driver mistake, and what `documentdb-driver` and + `documentdb-connection` both open with. + + check_engine.py checks the runtime consequence; this names the cause. + """ + + def test_client_is_not_constructed_per_request(self, sdk, source_files): + _need(sdk, "python") + offenders = _client_calls_inside_functions(source_files) + assert not offenders, ( + f"A MongoClient is constructed inside a function " + f"({', '.join(offenders[:5])}), so a new connection pool is created " + f"on every call. Build ONE client at application start-up and reuse " + f"it — per-request construction exhausts server connections and " + f"defeats pooling entirely. (A memoised factory, e.g. @lru_cache, " + f"is also accepted.)" + ) + + def test_a_client_is_constructed_somewhere(self, sdk, source_text): + _need(sdk, "python") + assert re.search(r"MongoClient\s*\(", source_text), ( + "No MongoClient construction found in the source. The service must " + "talk to DocumentDB through the MongoDB driver." + ) + + +class TestConnectionConfiguration: + """Explicit pool and timeout settings. + + Defaults are not wrong, but an unset serverSelectionTimeoutMS means a + database blip surfaces as a 30-second hang rather than a fast error. The + `documentdb-connection` skill is entirely about making these explicit. + """ + + def test_connection_options_are_set_explicitly(self, sdk, source_text): + _need(sdk, "python") + options = ( + r"maxPoolSize", r"minPoolSize", r"serverSelectionTimeoutMS", + r"connectTimeoutMS", r"socketTimeoutMS", r"retryWrites", + ) + found = [o for o in options if re.search(o + r"\s*=", source_text)] + assert found, ( + "MongoClient is constructed with no explicit connection options. " + f"Set at least one of {', '.join(options)} — relying on defaults " + "means a database blip becomes a long hang instead of a fast error." + ) + + +class TestTlsHygiene: + """TLS must not be silently disabled. + + Azure DocumentDB requires TLS. Turning off certificate validation to make + a local container work, then shipping it, is a real and common failure — + and exactly what the `documentdb-security` skill warns about. + """ + + def test_certificate_validation_is_not_hardcoded_off(self, sdk, source_text): + _need(sdk, "python") + offenders = re.findall( + r"tlsAllowInvalidCertificates\s*=\s*True|" + r"tlsInsecure\s*=\s*True", + source_text, + ) + if offenders: + # Allowed only when driven by an environment variable, which is how + # a local-vs-cloud difference should be expressed. + env_driven = re.search( + r"tlsAllowInvalidCertificates\s*=\s*[^,\n)]*(?:os\.environ|getenv|" + r"os\.getenv|settings\.|config\.)", + source_text, + ) + assert env_driven, ( + "TLS certificate validation is disabled with a hardcoded True " + f"({offenders[:3]}). Azure DocumentDB requires TLS; if a local " + "container needs a relaxed setting, drive it from an environment " + "variable so production cannot inherit it." + ) diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/conftest.py b/benchmarks/documentdb-sdk-skills/shared/verifier/conftest.py new file mode 100644 index 0000000..79899b6 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/conftest.py @@ -0,0 +1,446 @@ +"""Shared pytest fixtures for the documentdb-sdk-skills verifier. + +The verifier is **contract-driven**, mirroring the Cosmos benchmark's design. +Each task image bakes a scenario contract (shared/contracts/.json, +copied to /verifier/contracts/) and selects it with the SCENARIO env var. The +contract declares, per scenario: + + * the DocumentDB database + collection env vars and their defaults, + * the "root" entities reachable via create/get/list, their deterministic seed + data, field shapes, modelling / indexing expectations, and + * the engine-level expectations (scan amplification, forbidden full scans). + +Everything is read from environment variables the base image and the per-task +test.sh set up: + + SCENARIO -- orders (selects the contract) + SDK -- python | dotnet | java | nodejs | go + APP_PORT -- port the agent's app should listen on + APP_WORKDIR -- where the agent put its source (/app) + DOCUMENTDB_HOST/PORT/USER -- Mongo-API connection + DOCUMENTDB_PASSWORD -- generated per container, never baked in + DOCUMENTDB_DATABASE -- scenario database + DOCUMENTDB_*_COLLECTION -- per-entity collection names + PG_PORT / PG_USER / PG_DB -- the PostgreSQL engine underneath + VERIFIER_LOG_DIR -- where per-check logs land + +The verifier owns no test data: seed data lives in the contract and is inserted +through the agent's own API, so an app that answers HTTP without persisting +fails the behavioural gate. + +TWO OBSERVATION PLANES +---------------------- +Unlike the Cosmos equivalent, this verifier can observe the system two ways: +the Mongo API (`pymongo`) and the PostgreSQL engine underneath (`psycopg`). +Client-side properties Cosmos can only regex out of source code — index usage, +connection reuse — are checked BEHAVIOURALLY here. See check_engine.py. +""" +from __future__ import annotations + +import json +import re +import os +import time +from pathlib import Path + +import pytest +import requests + +SDK_ALIASES = { + "py": "python", "python": "python", + "dotnet": "dotnet", ".net": "dotnet", "csharp": "dotnet", + "java": "java", + "node": "nodejs", "nodejs": "nodejs", "javascript": "nodejs", "js": "nodejs", + "go": "go", "golang": "go", +} + +CONTRACTS_DIR = Path(os.environ.get("CONTRACTS_DIR", "/verifier/contracts")) + + +# --------------------------------------------------------------------- +# Contract loading (module level so check_*.py can parametrize on it) +# --------------------------------------------------------------------- +def load_contract() -> dict: + """Load the scenario contract selected by SCENARIO. + + Fails loudly rather than silently grading nothing: a mis-wired task image + that grades an empty contract would report success for any submission, + which is far worse than an error. + """ + scenario = os.environ.get("SCENARIO", "orders") + path = CONTRACTS_DIR / f"{scenario}.json" + if not path.is_file(): + available = ( + sorted(p.name for p in CONTRACTS_DIR.glob("*.json")) + if CONTRACTS_DIR.is_dir() else "contracts dir missing" + ) + raise RuntimeError( + f"Scenario contract not found: {path}. SCENARIO={scenario!r}. " + f"Available: {available}" + ) + return json.loads(path.read_text()) + + +CONTRACT = load_contract() +ROOTS = CONTRACT.get("roots", []) + + +def root_ids(root: dict) -> str: + return root["name"] + + +def fmt_path(template: str, **kw) -> str: + return template.format(**kw) + + +def collection_name(root: dict) -> str: + return os.environ.get( + root.get("collection_env", ""), root.get("collection_default", root["name"]) + ) + + +def database_name() -> str: + return os.environ.get( + CONTRACT.get("database_env", "DOCUMENTDB_DATABASE"), + CONTRACT.get("database_default", "ordersdb"), + ) + + +# --------------------------------------------------------------------- +# environment +# --------------------------------------------------------------------- +@pytest.fixture(scope="session") +def sdk() -> str: + raw = os.environ.get("SDK", "python").strip().lower() + return SDK_ALIASES.get(raw, raw) + + +@pytest.fixture(scope="session") +def app_port() -> int: + return int(os.environ.get("APP_PORT", "9080")) + + +@pytest.fixture(scope="session") +def app_dir() -> Path: + return Path(os.environ.get("APP_WORKDIR", "/app")) + + +@pytest.fixture(scope="session") +def base_url(app_port) -> str: + return f"http://localhost:{app_port}" + + +# --------------------------------------------------------------------- +# HTTP client +# --------------------------------------------------------------------- +class Api: + """Thin HTTP helper with a short timeout. + + A hung request must fail the check rather than stall the whole verifier + until Harbor's task timeout kills it with no diagnosis. + """ + + def __init__(self, base_url: str, timeout: float = 20.0): + self.base_url = base_url.rstrip("/") + self.timeout = timeout + self.session = requests.Session() + + def _url(self, path: str) -> str: + return f"{self.base_url}{path if path.startswith('/') else '/' + path}" + + def get(self, path, **kw): + return self.session.get(self._url(path), timeout=self.timeout, **kw) + + def post(self, path, **kw): + return self.session.post(self._url(path), timeout=self.timeout, **kw) + + +@pytest.fixture(scope="session") +def api(base_url) -> Api: + return Api(base_url) + + +# --------------------------------------------------------------------- +# database clients — the verifier's OWN, independent of the agent's +# --------------------------------------------------------------------- +@pytest.fixture(scope="session") +def mongo_uri() -> str: + host = os.environ.get("DOCUMENTDB_HOST", "localhost") + port = os.environ.get("DOCUMENTDB_PORT", "10260") + user = os.environ.get("DOCUMENTDB_USER", "docdbadmin") + pwd = os.environ.get("DOCUMENTDB_PASSWORD", "") + if not pwd: + pytest.fail( + "DOCUMENTDB_PASSWORD is not set in the verifier environment. " + "start-documentdb generates it and runner.sh must export it." + ) + from urllib.parse import quote_plus + return ( + f"mongodb://{quote_plus(user)}:{quote_plus(pwd)}@{host}:{port}/" + "?authMechanism=SCRAM-SHA-256&tls=true&tlsAllowInvalidCertificates=true" + "&directConnection=true" + ) + + +@pytest.fixture(scope="session") +def mongo(mongo_uri): + """The verifier's own pymongo client. + + Deliberately independent of the agent's client: every persistence claim is + checked by reading the database ourselves, so an app that answers HTTP from + an in-memory dict cannot pass. + """ + from pymongo import MongoClient + client = MongoClient(mongo_uri, serverSelectionTimeoutMS=20000) + client.admin.command("ping") + yield client + client.close() + + +@pytest.fixture(scope="session") +def db(mongo): + return mongo[database_name()] + + +@pytest.fixture(scope="session") +def pg(): + """Connection to the PostgreSQL engine underneath DocumentDB. + + This is the second observation plane, and the thing that makes this + benchmark stronger than a Mongo-API-only grader: index usage and scan + behaviour can be proven from the engine's own statistics rather than + inferred from source code. + """ + psycopg = pytest.importorskip("psycopg", reason="psycopg not installed") + conn = psycopg.connect( + host=os.environ.get("DOCUMENTDB_HOST", "localhost"), + port=int(os.environ.get("PG_PORT", "9712")), + user=os.environ.get("PG_USER", "documentdb"), + dbname=os.environ.get("PG_DB", "postgres"), + connect_timeout=20, + ) + conn.autocommit = True + yield conn + conn.close() + + +# --------------------------------------------------------------------- +# seeding through the agent's API +# --------------------------------------------------------------------- +@pytest.fixture(scope="session") +def seed_roots(api): + """POST the contract's seed rows through the agent's own API. + + Seeding through the API (rather than writing to the database directly) is + what makes the later reads meaningful: it tests the agent's write path. + """ + created = {} + for root in ROOTS: + create = root.get("create") + if not create: + continue + rows = [] + for row in root["seed"]: + resp = api.post(create["path"], json=row) + rows.append({"row": row, "status": resp.status_code, + "body": _safe_json(resp)}) + created[root["name"]] = rows + # Give the engine a moment to make writes visible to an independent reader. + time.sleep(1.0) + return created + + +def _safe_json(resp): + try: + return resp.json() + except Exception: + return None + + +@pytest.fixture(scope="session") +def root_persisted(db, seed_roots): + """Read every seeded id back with the VERIFIER's own client.""" + out = {} + for root in ROOTS: + coll = db[collection_name(root)] + docs = {} + for row in root["seed"]: + docs[row["id"]] = _find_by_logical_id(coll, row["id"]) + out[root["name"]] = docs + return out + + +def _find_by_logical_id(coll, logical_id): + """Find a document by the id the API was given. + + The agent may store it as `_id` or as a separate `id` field; both are + legitimate modelling choices, so accept either rather than dictating one. + """ + doc = coll.find_one({"_id": logical_id}) + if doc is None: + doc = coll.find_one({"id": logical_id}) + return doc + + +def emulator_docs_for_id(db, root, logical_id): + return _find_by_logical_id(db[collection_name(root)], logical_id) + + +# --------------------------------------------------------------------- +# realistic volume, for the engine checks only +# --------------------------------------------------------------------- +FILLER_COUNT = int(os.environ.get("VERIFIER_FILLER_DOCS", "20000")) + + +@pytest.fixture(scope="session") +def bulk_filler(db, seed_roots): + """Bulk-load filler documents so the engine checks are meaningful. + + WHY THIS IS NECESSARY, not a workaround: on a 4-document collection a + sequential scan is genuinely the cheaper plan, and PostgreSQL is right to + choose it. Grading index usage at that size would test nothing — the oracle + itself would fail. The task tells the agent the service runs against + "millions of orders", so the verifier must grade at a size where that + statement has consequences. + + Written by the VERIFIER directly, not through the API: this is background + volume, not a test of the agent's write path. Filler uses a disjoint + customer_id namespace (FILLER-*) so it cannot affect the filtered-list + assertions in check_api. + """ + from pymongo import InsertOne + + inserted = {} + for root in ROOTS: + coll = db[collection_name(root)] + existing = coll.count_documents({"customer_id": {"$regex": "^FILLER-"}}) + if existing >= FILLER_COUNT: + inserted[root["name"]] = existing + continue + + ops, batch = [], 0 + for i in range(FILLER_COUNT - existing): + ops.append(InsertOne({ + "_id": f"filler-{i}", + "customer_id": f"FILLER-{i % 4000}", + "status": ["shipped", "pending", "cancelled"][i % 3], + "amount": float((i % 500) + 10), + "items": ["filler"], + })) + if len(ops) >= 2000: + coll.bulk_write(ops, ordered=False) + batch += len(ops) + ops = [] + if ops: + coll.bulk_write(ops, ordered=False) + batch += len(ops) + inserted[root["name"]] = batch + + # ANALYZE so the planner costs against real statistics rather than the + # defaults it assumed when the table was tiny. + try: + import psycopg + with psycopg.connect( + host=os.environ.get("DOCUMENTDB_HOST", "localhost"), + port=int(os.environ.get("PG_PORT", "9712")), + user=os.environ.get("PG_USER", "documentdb"), + dbname=os.environ.get("PG_DB", "postgres"), + connect_timeout=20, + ) as conn: + conn.autocommit = True + with conn.cursor() as cur: + cur.execute("ANALYZE") + except Exception: + # Statistics are an optimisation, not a correctness requirement. + pass + + return inserted + + +# --------------------------------------------------------------------- +# source scanning (for the WEAKER, static half of the grader) +# +# ANTI-GAMING: comments are stripped before any regex runs, so an agent +# cannot satisfy a check by writing "# uses a singleton MongoClient" in a +# comment. String literals are deliberately NOT stripped, so patterns in +# check_source.py must require code adjacency (`foo\s*=`, `Foo\s*\(`) +# rather than a bare keyword a log message could satisfy by accident. +# --------------------------------------------------------------------- +SOURCE_SUFFIXES = { + "python": {".py"}, + "dotnet": {".cs", ".csproj"}, + "java": {".java", ".xml", ".gradle"}, + "nodejs": {".js", ".ts", ".mjs", ".cjs", ".json"}, + "go": {".go", ".mod"}, +} +SKIP_DIRS = { + ".git", "node_modules", "__pycache__", ".venv", "venv", "env", + "bin", "obj", "target", "dist", "build", ".pytest_cache", "site-packages", +} + +_TRIPLE_DOUBLE = re.compile(r'"""(?:.|\n)*?"""') +_TRIPLE_SINGLE = re.compile(r"'''(?:.|\n)*?'''") +_HASH_LINE = re.compile(r"#[^\n]*") +_BLOCK_C = re.compile(r"/\*(?:.|\n)*?\*/") +_SLASH_LINE = re.compile(r"//[^\n]*") +_XML_COMMENT = re.compile(r"") + + +def _strip_comments(text: str, sdk: str) -> str: + if sdk == "python": + text = _TRIPLE_DOUBLE.sub("", text) + text = _TRIPLE_SINGLE.sub("", text) + return _HASH_LINE.sub("", text) + if sdk == "go": + text = _BLOCK_C.sub("", text) + return _SLASH_LINE.sub("", text) + text = _XML_COMMENT.sub("", text) + text = _BLOCK_C.sub("", text) + return _SLASH_LINE.sub("", text) + + +@pytest.fixture(scope="session") +def workdir(app_dir) -> Path: + return app_dir + + +@pytest.fixture(scope="session") +def source_files(sdk, workdir) -> list: + suffixes = SOURCE_SUFFIXES.get(sdk, {".py"}) + out = [] + if not workdir.exists(): + return out + for p in workdir.rglob("*"): + if not p.is_file() or any(part in SKIP_DIRS for part in p.parts): + continue + if p.suffix in suffixes: + out.append(p) + return out + + +@pytest.fixture(scope="session") +def source_text(sdk, source_files) -> str: + """All source concatenated, comments stripped.""" + chunks = [] + for p in source_files: + try: + chunks.append(_strip_comments(p.read_text(encoding="utf-8", errors="ignore"), sdk)) + except OSError: + pass + return "\n".join(chunks) + + +@pytest.fixture(scope="session") +def source_text_raw(source_files) -> str: + """Source WITHOUT comment stripping. + + Used only where a comment genuinely counts as a finding — a hardcoded + credential is a leak whether or not it sits in a comment. + """ + chunks = [] + for p in source_files: + try: + chunks.append(p.read_text(encoding="utf-8", errors="ignore")) + except OSError: + pass + return "\n".join(chunks) diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/harvest_metrics.py b/benchmarks/documentdb-sdk-skills/shared/verifier/harvest_metrics.py new file mode 100644 index 0000000..f61f819 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/harvest_metrics.py @@ -0,0 +1,343 @@ +#!/usr/bin/env python3 +"""Harvest agent token usage and emit MSBench custom metrics. + +WHY +--- +MSBench's reward is binary: did the submission comply, yes or no. That answers +"is the kit effective?" but not "what did it cost?" — and cost is half the GTM +story. It also cannot tell us *which kinds of task* are expensive, which is +exactly the comparison we want across a growing task matrix. + +MSBench has a first-class hook for this: an agent or verifier may write +`custom_metrics.json` into the instance output directory (`$OUTPUT_DIR`, +default `/output`). MSBench ingests it, derives a schema, and surfaces the +values per instance in `msbench-cli report --output .json` and in CSV. +Real-world examples from the platform's own tests: `Tool_Calls_Total`, +`Files_Changed`, `msbench.agent_elapsed_time_sec`. + +The `msbench-agent-github-copilot-cli` agent does NOT emit token metrics, so +we harvest them ourselves from the Copilot CLI's own session store, which the +agent leaves behind in the container. + +WHAT IT READS +------------- +`~/.copilot/session-store.db` (SQLite), table `assistant_usage_events` — one +row per model request, carrying input/output/cache/reasoning token counts and +`total_nano_aiu` (AI credits x 1e9). + +THE MEASUREMENT TRAP THIS AVOIDS +-------------------------------- +`cache_read_tokens` is a SUBSET of `input_tokens` (verified against a real +store: 0 of 6,348 rows had cache_read > input). Skills are sent once and then +read from cache, so raw `input_tokens` overstates their marginal cost by +roughly 20x. We therefore emit `tokens_fresh_input = input - cache_read` as +the honest "billed at full rate" figure, alongside the raw counts and the +cache share, so a reader cannot accidentally quote the misleading number. + +All emitted values are NUMERIC. MSBench infers a `numeric` schema from the +values, and a string would produce an inconsistent schema across instances. +Model identifiers, API endpoints, reasoning effort, agent identity, and the +observable Copilot CLI version are encoded losslessly into six-byte integer +chunks. `report.py` reconstructs them and refuses to publish a treatment/control +comparison when model provenance is missing or differs. + +This is intentionally self-contained (stdlib only, no imports from the kit): +it has to run inside a minimal task container. `testing/scenarios/benchmark-metrics/` +asserts it agrees with `evals/harness/token_usage.py` on identical input, so +the two cost definitions cannot drift apart. +""" +from __future__ import annotations + +import argparse +import json +import os +import shutil +import sqlite3 +import subprocess +import sys +import tempfile +from pathlib import Path + +NANO_AIU_PER_CREDIT = 1_000_000_000 +TEXT_CHUNK_BYTES = 6 +AGENT_IDENTITY = "msbench-agent-github-copilot-cli" + +# Where the Copilot CLI keeps its session store. The agent may run as root or +# as another user depending on how the runner invokes it, so try each. +CANDIDATE_STORES = [ + "~/.copilot/session-store.db", + "/root/.copilot/session-store.db", + "/home/agent/.copilot/session-store.db", + "/agent/.copilot/session-store.db", +] + +ROLLUP_SQL = """ +SELECT + COUNT(*) AS requests, + COUNT(DISTINCT turn_index) AS turns, + COALESCE(SUM(input_tokens), 0) AS input_tokens, + COALESCE(SUM(output_tokens), 0) AS output_tokens, + COALESCE(SUM(cache_read_tokens), 0) AS cache_read_tokens, + COALESCE(SUM(cache_write_tokens), 0) AS cache_write_tokens, + COALESCE(SUM(reasoning_tokens), 0) AS reasoning_tokens, + COALESCE(SUM(total_nano_aiu), 0) AS nano_aiu, + COALESCE(SUM(duration_ms), 0) AS duration_ms +FROM assistant_usage_events +""" + + +def encode_text_metrics(prefix: str, value: str) -> dict[str, int]: + """Encode UTF-8 losslessly as JSON-safe integers. + + MSBench custom metrics are numeric-only. Six bytes fit below 2^48, so every + chunk remains exactly representable by both JSON numbers and IEEE-754 + doubles while still allowing the report to reconstruct the exact string. + """ + data = value.encode("utf-8") + metrics = { + f"{prefix}_available": 1 if data else 0, + f"{prefix}_utf8_len": len(data), + f"{prefix}_chunk_count": (len(data) + TEXT_CHUNK_BYTES - 1) + // TEXT_CHUNK_BYTES, + } + for index in range(0, len(data), TEXT_CHUNK_BYTES): + chunk = data[index : index + TEXT_CHUNK_BYTES] + metrics[f"{prefix}_chunk_{index // TEXT_CHUNK_BYTES:03d}"] = int.from_bytes( + chunk, "big" + ) + return metrics + + +def observed_values(conn: sqlite3.Connection, column: str) -> list[str]: + allowed = {"model", "api_endpoint", "reasoning_effort"} + if column not in allowed: + raise ValueError(f"unsupported provenance column: {column}") + try: + rows = conn.execute( + f"SELECT DISTINCT {column} FROM assistant_usage_events " + f"WHERE {column} IS NOT NULL AND {column} <> '' ORDER BY {column}" + ) + except sqlite3.OperationalError: + return [] + return [str(row[0]) for row in rows] + + +def copilot_cli_version() -> str: + executable = shutil.which("copilot") + if not executable: + return "" + try: + result = subprocess.run( + [executable, "--version"], + capture_output=True, + text=True, + timeout=10, + check=False, + ) + except (OSError, subprocess.TimeoutExpired): + return "" + return (result.stdout or result.stderr).strip().splitlines()[0] + + +def find_store(explicit: str | None = None) -> Path | None: + if explicit: + p = Path(explicit).expanduser() + return p if p.is_file() else None + for candidate in CANDIDATE_STORES: + p = Path(candidate).expanduser() + if p.is_file(): + return p + return None + + +def read_usage(db_path: Path) -> dict: + """Roll up usage from a private snapshot of the store. + + The store is copied with its -wal/-shm sidecars before reading: copying + only the .db would silently lose any transaction still sitting in the + write-ahead log, which is exactly where the final turns live. + """ + tmpdir = Path(tempfile.mkdtemp(prefix="tokenharvest-")) + try: + snapshot = tmpdir / db_path.name + shutil.copy2(db_path, snapshot) + for suffix in ("-wal", "-shm"): + sidecar = db_path.with_name(db_path.name + suffix) + if sidecar.exists(): + shutil.copy2(sidecar, snapshot.with_name(snapshot.name + suffix)) + + conn = sqlite3.connect(f"file:{snapshot}?mode=ro", uri=True) + conn.row_factory = sqlite3.Row + try: + tables = { + r[0] for r in conn.execute( + "SELECT name FROM sqlite_master WHERE type='table'" + ) + } + if "assistant_usage_events" not in tables: + return {} + row = dict(conn.execute(ROLLUP_SQL).fetchone() or {}) + model_ids = observed_values(conn, "model") + api_endpoints = observed_values(conn, "api_endpoint") + reasoning_efforts = observed_values(conn, "reasoning_effort") + finally: + conn.close() + finally: + shutil.rmtree(tmpdir, ignore_errors=True) + + if not row or not row.get("requests"): + return {} + + inp = int(row["input_tokens"]) + cache = int(row["cache_read_tokens"]) + fresh = max(inp - cache, 0) + metrics = { + "agent_requests": int(row["requests"]), + "agent_turns": int(row["turns"]), + "tokens_input": inp, + "tokens_output": int(row["output_tokens"]), + "tokens_cache_read": cache, + "tokens_cache_write": int(row["cache_write_tokens"]), + "tokens_reasoning": int(row["reasoning_tokens"]), + # The honest marginal cost: input actually billed at the uncached rate. + "tokens_fresh_input": fresh, + "tokens_billable_total": fresh + int(row["output_tokens"]), + "cache_read_share_pct": round(100.0 * cache / inp, 2) if inp else 0.0, + "ai_credits": round(int(row["nano_aiu"]) / NANO_AIU_PER_CREDIT, 4), + "agent_wall_secs": round(int(row["duration_ms"]) / 1000.0, 1), + "model_count": len(model_ids), + "api_endpoint_count": len(api_endpoints), + "reasoning_effort_count": len(reasoning_efforts), + } + metrics.update(encode_text_metrics("model_ids", "\n".join(model_ids))) + metrics.update(encode_text_metrics("api_endpoints", "\n".join(api_endpoints))) + metrics.update( + encode_text_metrics("reasoning_efforts", "\n".join(reasoning_efforts)) + ) + metrics.update(encode_text_metrics("agent_identity", AGENT_IDENTITY)) + metrics.update(encode_text_metrics("copilot_cli_version", copilot_cli_version())) + return metrics + + +def read_ctrf(path: Path) -> dict: + """Per-check-category pass/fail counts from the verifier's CTRF report. + + This is what makes the token numbers actionable: it lets us ask which + CATEGORY of requirement (api / behavior / documentdb / engine / source / + skills) an expensive run was actually failing, rather than only how much it + cost overall. + """ + if not path.is_file(): + return {} + try: + data = json.loads(path.read_text()) + except (json.JSONDecodeError, UnicodeDecodeError): + return {} + + tests = (data.get("results") or {}).get("tests") or [] + out: dict[str, float] = {} + total = passed = 0 + per_category: dict[str, list[int]] = {} + + for test in tests: + name = str(test.get("name", "")) + status = str(test.get("status", "")).lower() + if status == "skipped": + continue + total += 1 + ok = 1 if status == "passed" else 0 + passed += ok + category = "other" + for key in ("api", "behavior", "documentdb", "engine", "source", "skills"): + if f"check_{key}" in name: + category = key + break + bucket = per_category.setdefault(category, [0, 0]) + bucket[0] += ok + bucket[1] += 1 + + out["checks_total"] = total + out["checks_passed"] = passed + out["checks_failed"] = total - passed + for category, (ok, tot) in sorted(per_category.items()): + out[f"checks_{category}_passed"] = ok + out[f"checks_{category}_total"] = tot + return out + + +def build_metrics(store: Path | None, ctrf: Path, reward_file: Path) -> dict: + metrics: dict[str, float] = {} + + usage = read_usage(store) if store else {} + metrics.update(usage) + # Explicitly record whether token data was available. Without this, a run + # where harvesting failed is indistinguishable from a run that used no + # tokens — and a silent zero would corrupt any average computed over it. + metrics["tokens_available"] = 1 if usage else 0 + + metrics.update(read_ctrf(ctrf)) + + reward = 0 + if reward_file.is_file(): + try: + reward = int((reward_file.read_text().strip() or "0")[:1]) + except ValueError: + reward = 0 + metrics["reward"] = reward + + # Cost-to-outcome, only for a run that actually succeeded. Emitting it for + # a failed run would make giving up early look cheap. + if reward == 1 and usage: + metrics["credits_to_green"] = usage["ai_credits"] + metrics["turns_to_green"] = usage["agent_turns"] + metrics["billable_tokens_to_green"] = usage["tokens_billable_total"] + + return metrics + + +def main(argv=None) -> int: + p = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + p.add_argument("--store", default=os.environ.get("COPILOT_SESSION_STORE"), + help="path to session-store.db (default: autodetect)") + p.add_argument("--ctrf", + default=os.path.join( + os.environ.get("VERIFIER_LOG_DIR", "/logs/verifier"), + "ctrf.json")) + p.add_argument("--reward", + default=os.path.join( + os.environ.get("VERIFIER_LOG_DIR", "/logs/verifier"), + "reward.txt")) + p.add_argument("--output-dir", default=os.environ.get("OUTPUT_DIR", "/output")) + args = p.parse_args(argv) + + store = find_store(args.store) + metrics = build_metrics(store, Path(args.ctrf), Path(args.reward)) + + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + out_file = out_dir / "custom_metrics.json" + + # Merge rather than overwrite: the agent may have written its own metrics + # into the same file, and clobbering them would lose data MSBench expects. + existing = {} + if out_file.is_file(): + try: + existing = json.loads(out_file.read_text()) + except (json.JSONDecodeError, UnicodeDecodeError): + existing = {} + existing.update(metrics) + out_file.write_text(json.dumps(existing, indent=2, sort_keys=True)) + + print(f"[harvest] wrote {out_file}") + if store: + print(f"[harvest] session store: {store}") + else: + print("[harvest] NOTE: no Copilot session store found; token metrics " + "omitted and tokens_available=0.", file=sys.stderr) + for k in sorted(metrics): + print(f"[harvest] {k} = {metrics[k]}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/benchmarks/documentdb-sdk-skills/shared/verifier/runner.sh b/benchmarks/documentdb-sdk-skills/shared/verifier/runner.sh new file mode 100755 index 0000000..bd9de22 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/shared/verifier/runner.sh @@ -0,0 +1,193 @@ +#!/usr/bin/env bash +# Shared verifier runner. Each task's tests/test.sh calls this with the task's +# SDK name as $1. +# +# Responsibilities: +# 1. Start DocumentDB Local (idempotent) and export the generated password. +# 2. Ensure the agent produced /app/build.sh and /app/run.sh. +# 3. Run /app/build.sh. +# 4. Run /app/run.sh in the background. +# 5. Wait for the agent's /health endpoint. +# 6. Run pytest over /verifier/check_*.py + /tests/checks.py. +# 7. Write 0 or 1 to /logs/verifier/reward.txt. +# +# ALWAYS writes reward.txt, defaulting to 0, so an early failure is recorded as +# a failure rather than as a missing file the harness has to interpret. + +set -uo pipefail + +SDK="${1:-}" +if [ -z "$SDK" ]; then + echo "[runner] usage: runner.sh " >&2 + exit 2 +fi +export SDK + +LOG_DIR="${VERIFIER_LOG_DIR:-/logs/verifier}" +APP_DIR="${APP_WORKDIR:-/app}" +APP_PORT="${APP_PORT:-9080}" +mkdir -p "$LOG_DIR" +REWARD_FILE="$LOG_DIR/reward.txt" +echo "0" > "$REWARD_FILE" + +section() { + echo + echo "============================================================" + echo "[runner] $*" + echo "============================================================" +} + +fail() { + echo "[runner] FAIL: $*" >&2 + echo "0" > "$REWARD_FILE" + exit 0 # exit 0: the REWARD is the verdict, not the process exit code +} + +# --------------------------------------------------------------------- +section "1. Start DocumentDB" +# --------------------------------------------------------------------- +# The password is generated HERE, in the parent, not inside start-documentdb. +# A child process cannot export a variable back to its caller, and both the +# agent's app and the verifier must end up with the same credential. +# It is generated per container and never baked into the image — check_skills.py +# fails any submission with a hardcoded credential, so the benchmark must not +# model bad practice itself. +if [ -z "${DOCUMENTDB_PASSWORD:-}" ]; then + DOCUMENTDB_PASSWORD="Bench$(head -c 12 /dev/urandom | od -An -tx1 | tr -d ' \n')" +fi +export DOCUMENTDB_PASSWORD + +if ! start-documentdb; then + fail "DocumentDB did not start. See $LOG_DIR/documentdb.log" +fi + +# --------------------------------------------------------------------- +section "2. Check the agent's deliverables" +# --------------------------------------------------------------------- +for f in build.sh run.sh; do + if [ ! -f "$APP_DIR/$f" ]; then + fail "$APP_DIR/$f is missing. The task requires both build.sh and run.sh." + fi + chmod +x "$APP_DIR/$f" 2>/dev/null || true +done + +# --------------------------------------------------------------------- +section "3. Build" +# --------------------------------------------------------------------- +( cd "$APP_DIR" && ./build.sh ) >"$LOG_DIR/build.log" 2>&1 +BUILD_RC=$? +if [ $BUILD_RC -ne 0 ]; then + echo "[runner] build.sh exited $BUILD_RC; last 40 lines:" >&2 + tail -40 "$LOG_DIR/build.log" >&2 + fail "build failed" +fi + +# --------------------------------------------------------------------- +section "4. Run" +# --------------------------------------------------------------------- +( cd "$APP_DIR" && ./run.sh ) >"$LOG_DIR/app.log" 2>&1 & +APP_PID=$! + +# shellcheck disable=SC2317 # invoked indirectly by the EXIT trap below +cleanup() { + # Stop the agent's app when the verifier exits, however it exits — success, + # early `fail`, or a timeout. Without this the app keeps holding the port + # and the database connection after grading finishes. + if kill -0 "$APP_PID" 2>/dev/null; then + kill "$APP_PID" 2>/dev/null || true + fi +} +trap cleanup EXIT + +# --------------------------------------------------------------------- +section "5. Wait for /health" +# --------------------------------------------------------------------- +READY=0 +for i in $(seq 1 90); do + if ! kill -0 "$APP_PID" 2>/dev/null; then + echo "[runner] the app exited during start-up; last 40 lines:" >&2 + tail -40 "$LOG_DIR/app.log" >&2 + fail "app process died before becoming healthy" + fi + if curl -fsS "http://localhost:${APP_PORT}/health" >/dev/null 2>&1; then + echo "[runner] healthy after ${i}s" + READY=1 + break + fi + sleep 1 +done +[ "$READY" -eq 1 ] || { + tail -40 "$LOG_DIR/app.log" >&2 + fail "app did not become healthy on port ${APP_PORT} within 90s" +} + +# --------------------------------------------------------------------- +section "6. Grade" +# --------------------------------------------------------------------- +# Prefer the isolated verifier venv so the agent's build.sh cannot shadow a +# verifier dependency (e.g. by installing a different pymongo system-wide). +PY=/opt/verifier-venv/bin/python +[ -x "$PY" ] || PY=python3 + +# The task's own checks are copied INTO /verifier before running. +# +# pytest only applies a conftest.py to tests at or below its directory, so a +# checks.py sitting in /tests cannot see the shared fixtures in +# /verifier/conftest.py — it fails with "fixture 'sdk' not found". Moving the +# file under /verifier puts it in the same fixture scope as everything else, +# which is also conceptually right: the verifier owns grading. +if [ -f /tests/checks.py ]; then + cp /tests/checks.py /verifier/task_checks.py + TASK_CHECKS=/verifier/task_checks.py +else + TASK_CHECKS="" + echo "[runner] WARNING: /tests/checks.py not found; task-specific checks skipped" >&2 +fi + +"$PY" -m pytest \ + --ctrf "$LOG_DIR/ctrf.json" \ + -rA -v \ + -p no:cacheprovider \ + /verifier/check_api.py \ + /verifier/check_behavior.py \ + /verifier/check_documentdb.py \ + /verifier/check_engine.py \ + /verifier/check_source.py \ + /verifier/check_skills.py \ + $TASK_CHECKS \ + 2>&1 | tee "$LOG_DIR/pytest.log" + +PYTEST_RC=${PIPESTATUS[0]} + +# --------------------------------------------------------------------- +section "7. Reward" +# --------------------------------------------------------------------- +if [ "$PYTEST_RC" -eq 0 ]; then + echo "1" > "$REWARD_FILE" + echo "[runner] REWARD=1 (all mandatory checks passed)" +else + echo "0" > "$REWARD_FILE" + echo "[runner] REWARD=0 (pytest exit $PYTEST_RC)" +fi + +# --------------------------------------------------------------------- +section "8. Cost metrics" +# --------------------------------------------------------------------- +# MSBench's reward is binary — it says whether the submission complied, not +# what it cost. Token usage is harvested from the Copilot CLI's own session +# store (which the agent leaves in the container) and written to +# $OUTPUT_DIR/custom_metrics.json, MSBench's first-class per-instance hook. +# This is what lets us compare cost ACROSS tasks later. +# +# Advisory: a failure here must never change the reward. The submission has +# already been graded, and losing cost telemetry is not a grading outcome. +"$PY" /verifier/harvest_metrics.py \ + --ctrf "$LOG_DIR/ctrf.json" \ + --reward "$REWARD_FILE" \ + --output-dir "${OUTPUT_DIR:-/output}" \ + >>"$LOG_DIR/metrics.log" 2>&1 \ + || echo "[runner] WARNING: metric harvest failed; see $LOG_DIR/metrics.log" >&2 + +# The reward file is the verdict. Exiting non-zero here would make Harbor treat +# a legitimately-failed submission as a broken verifier. +exit 0 diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/Dockerfile b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/Dockerfile new file mode 100644 index 0000000..b4aeba2 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/Dockerfile @@ -0,0 +1,16 @@ +FROM documentdb-orders-base:latest + +ENV SDK="python" + +# Bake the reference implementation in. solve.sh copies it into /app when the +# oracle runs. The agent under evaluation never sees /reference. +COPY environment/reference /reference + +# Pre-install the reference impl's dependencies from the vendored wheels, so +# the oracle's /app/build.sh works with NO internet access during grading — +# which is what the task instruction promises the agent. +COPY .wheels /opt/wheels +RUN pip3 install --no-cache-dir --no-index --find-links=/opt/wheels \ + -r /reference/requirements.txt + +WORKDIR /app diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/app.py b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/app.py new file mode 100644 index 0000000..4994c63 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/app.py @@ -0,0 +1,168 @@ +"""Reference Orders API — the ORACLE for the orders-api-python task. + +This is what a correct submission looks like: it is what `solve.sh` copies into +/app to prove the grader can be satisfied. It exists to validate the benchmark, +not to be shown to the agent under evaluation. + +Every property the verifier grades is deliberate here: + + * ONE MongoClient, created at import and reused (check_source, check_engine). + A client owns a connection pool; building one per request exhausts server + connections and defeats pooling entirely. + * Explicit pool/timeout options, so a database blip is a fast error rather + than a 30-second hang (check_source). + * Every connection setting read from the environment; nothing hardcoded + (check_skills). + * ONE compound index {customer_id: 1, created_at: -1} — NOT that plus a + separate {customer_id: 1}. The compound index already serves the equality + lookup as its prefix, so adding the single-field one would be pure write + tax, which is exactly what the kit's indexing skill warns against. + * ESR ordering: the equality key (customer_id) precedes the sort key + (created_at) (check_documentdb). + * Documents carry a type discriminator, a schemaVersion and an ISO-8601 + created_at, so the collection can evolve (check_documentdb). + * amount stays numeric and items stays an ordered array (check_behavior). +""" +from __future__ import annotations + +import os +from datetime import datetime, timezone +from typing import Any +from urllib.parse import quote_plus + +from fastapi import FastAPI, HTTPException, Query +from fastapi.responses import JSONResponse +from pydantic import BaseModel, Field +from pymongo import ASCENDING, DESCENDING, MongoClient +from pymongo.errors import DuplicateKeyError + +SCHEMA_VERSION = 1 +DOC_TYPE = "order" + + +def _require_env(name: str, default: str | None = None) -> str: + value = os.environ.get(name, default) + if value is None or value == "": + raise RuntimeError( + f"{name} is not set. Every connection setting is supplied through " + f"the environment so the same build can target any deployment." + ) + return value + + +def _build_uri() -> str: + host = _require_env("DOCUMENTDB_HOST", "localhost") + port = _require_env("DOCUMENTDB_PORT", "10260") + user = _require_env("DOCUMENTDB_USER") + password = _require_env("DOCUMENTDB_PASSWORD") + # Credentials are URL-encoded: a generated password may contain characters + # that would otherwise terminate the URI early. + return ( + f"mongodb://{quote_plus(user)}:{quote_plus(password)}@{host}:{port}/" + "?authMechanism=SCRAM-SHA-256&directConnection=true" + ) + + +# -------------------------------------------------------------------------- +# ONE client for the process lifetime. +# -------------------------------------------------------------------------- +client = MongoClient( + _build_uri(), + tls=True, + # The local container presents a self-signed certificate. This is driven by + # an env var so a cloud deployment cannot silently inherit the relaxed + # setting: DOCUMENTDB_TLS_INSECURE is only set in the dev container. + tlsAllowInvalidCertificates=os.environ.get( + "DOCUMENTDB_TLS_INSECURE", "true" + ).lower() == "true", + maxPoolSize=100, + minPoolSize=5, + serverSelectionTimeoutMS=10000, + connectTimeoutMS=10000, + socketTimeoutMS=30000, + retryWrites=True, +) + +db = client[_require_env("DOCUMENTDB_DATABASE", "ordersdb")] +orders = db[_require_env("DOCUMENTDB_ORDERS_COLLECTION", "orders")] + +app = FastAPI(title="Orders API") + + +@app.on_event("startup") +def ensure_indexes() -> None: + """Create the one index the access pattern needs. + + {customer_id: 1, created_at: -1} follows ESR: the equality field leads, the + sort field follows. Its customer_id prefix also serves the plain equality + lookup, so a separate {customer_id: 1} index would add write cost and buy + nothing. + """ + orders.create_index( + [("customer_id", ASCENDING), ("created_at", DESCENDING)], + name="customer_id_1_created_at_-1", + ) + + +class OrderIn(BaseModel): + id: str + customer_id: str + status: str + amount: float + items: list[str] = Field(default_factory=list) + + +def _to_document(order: OrderIn) -> dict[str, Any]: + return { + "_id": order.id, + "id": order.id, + "customer_id": order.customer_id, + "status": order.status, + "amount": float(order.amount), + "items": list(order.items), + # Schema-evolution metadata: without these, migrating a live collection + # later means guessing which documents are old. + "type": DOC_TYPE, + "schemaVersion": SCHEMA_VERSION, + "created_at": datetime.now(timezone.utc).isoformat(), + } + + +def _to_response(doc: dict[str, Any]) -> dict[str, Any]: + out = dict(doc) + out["id"] = out.get("id", out.get("_id")) + out.pop("_id", None) + return out + + +@app.get("/health") +def health() -> dict[str, str]: + return {"status": "ok"} + + +@app.post("/orders", status_code=201) +def create_order(order: OrderIn): + try: + orders.insert_one(_to_document(order)) + except DuplicateKeyError: + # Reject rather than overwrite: an upsert here would silently destroy + # the existing order. + raise HTTPException(status_code=409, detail=f"order {order.id} exists") + return _to_response(orders.find_one({"_id": order.id})) + + +@app.get("/orders/{order_id}") +def get_order(order_id: str): + doc = orders.find_one({"_id": order_id}) + if doc is None: + raise HTTPException(status_code=404, detail=f"order {order_id} not found") + return _to_response(doc) + + +@app.get("/orders") +def list_orders(customer_id: str | None = Query(default=None)): + query = {"customer_id": customer_id} if customer_id else {} + # Sorted by created_at so the compound index serves filter and order + # together rather than requiring a separate sort step. + cursor = orders.find(query).sort("created_at", DESCENDING) + return JSONResponse([_to_response(d) for d in cursor]) diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/build.sh b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/build.sh new file mode 100755 index 0000000..231a888 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/build.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash +set -euo pipefail +# Installs from the wheels baked into the image: the task runs with no internet. +pip3 install --no-cache-dir --no-index --find-links=/opt/wheels -r /app/requirements.txt diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/requirements.txt b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/requirements.txt new file mode 100644 index 0000000..9bf8315 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/requirements.txt @@ -0,0 +1,4 @@ +fastapi==0.115.4 +uvicorn[standard]==0.32.0 +pymongo==4.10.1 +pydantic==2.9.2 diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/run.sh b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/run.sh new file mode 100755 index 0000000..51cc753 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/environment/reference/run.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash +set -euo pipefail +cd /app +exec uvicorn app:app --host 0.0.0.0 --port "${APP_PORT:-9080}" diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/instruction.md b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/instruction.md new file mode 100644 index 0000000..dcf3a08 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/instruction.md @@ -0,0 +1,75 @@ +# Orders API + +Build a small HTTP service that stores customer orders in **Azure DocumentDB** +(MongoDB-compatible). + +## Deliverables + +Put your source in `/app`, together with two executable scripts: + +- `/app/build.sh` — installs dependencies. Run once, before the service starts. +- `/app/run.sh` — starts the service in the foreground on `$APP_PORT`. + +## Connection + +The database is already running in this container. Read every connection +setting from the environment — do not hardcode any of them: + +| Variable | Meaning | +|---|---| +| `DOCUMENTDB_HOST` | hostname (`localhost`) | +| `DOCUMENTDB_PORT` | port (`10260`) | +| `DOCUMENTDB_USER` | username | +| `DOCUMENTDB_PASSWORD` | password, generated per container | +| `DOCUMENTDB_DATABASE` | database name | +| `DOCUMENTDB_ORDERS_COLLECTION` | collection name | +| `APP_PORT` | port your service must listen on | + +The endpoint uses TLS with a self-signed certificate and SCRAM-SHA-256 +authentication. + +## API + +### `GET /health` +Returns `200` when the service is up. + +### `POST /orders` +Creates an order. Request body: + +```json +{ + "id": "o-alpha", + "customer_id": "CUST-001", + "status": "shipped", + "amount": 120.5, + "items": ["widget", "gasket"] +} +``` + +- `201` (or `200`) on success. +- `409` if an order with that `id` already exists. It must **not** be created + twice or overwritten. + +### `GET /orders/{id}` +Returns the order as JSON, including `customer_id`, `status`, `amount` and +`items`. `404` if it does not exist. + +### `GET /orders?customer_id=CUST-001` +Returns a JSON array of every order for that customer, and only those. + +## Field requirements + +- `amount` is a number, not a string. +- `items` is an array of strings and must round-trip in order. +- Each stored order must record when it was created. + +## Scale + +The service is expected to run against collections of millions of orders. The +`customer_id` lookup is the hot path and runs constantly. + +## Notes + +- The container has no internet access during grading; `build.sh` may install + from the local package cache. +- Python 3, `pymongo` and `mongosh` are already installed in the image. diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/solution/solve.sh b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/solution/solve.sh new file mode 100755 index 0000000..428d746 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/solution/solve.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env bash +# Oracle solution for orders-api-python. +# Copies the reference implementation into the agent's workspace. +set -euo pipefail + +mkdir -p /app +cp -r /reference/. /app/ +chmod +x /app/build.sh /app/run.sh + +echo "[solve] reference impl copied to /app:" +ls -la /app diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/task.toml b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/task.toml new file mode 100644 index 0000000..0e3ab99 --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/task.toml @@ -0,0 +1,24 @@ +version = "1.0" + +[metadata] +author_name = "documentdb-sdk-skills-bench" +author_email = "noreply@example.com" +difficulty = "medium" +category = "skills-compliance" +tags = ["documentdb", "mongodb", "python", "sdk-best-practices", "azure"] + +[verifier] +# 900s allows: DocumentDB start (~10-30s) + build + app start + bulk filler +# load (20k docs) + ~30 pytest checks. +timeout_sec = 900.0 + +[agent] +# 1 hour for the agent to author 4 endpoints plus the data modelling and +# indexing decisions. +timeout_sec = 3600.0 + +[environment] +build_timeout_sec = 1800.0 +cpus = 4 +memory = "8G" +storage = "20G" diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/tests/checks.py b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/tests/checks.py new file mode 100644 index 0000000..956b7af --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/tests/checks.py @@ -0,0 +1,15 @@ +"""orders-api-python task-specific checks. + +The shared library (/verifier/check_*.py) already covers the whole rubric for +Python. This file is intentionally tiny — add Python-specific assertions here +if the skill set grows a Python-only rule. + +It also guarantees pytest has a file to collect from /tests even when no +task-specific assertions exist, so a missing /tests/checks.py cannot silently +reduce the graded surface. +""" +from __future__ import annotations + + +def test_python_task_marker(sdk): + assert sdk == "python" diff --git a/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/tests/test.sh b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/tests/test.sh new file mode 100755 index 0000000..bc4fcab --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/tasks/orders-api-python/tests/test.sh @@ -0,0 +1,6 @@ +#!/usr/bin/env bash +# Harbor verifier entrypoint for orders-api-python. +# Delegates to the shared runner, passing the SDK name. +set -uo pipefail + +exec /verifier/runner.sh python diff --git a/benchmarks/documentdb-sdk-skills/verify-controls.sh b/benchmarks/documentdb-sdk-skills/verify-controls.sh new file mode 100755 index 0000000..24a114c --- /dev/null +++ b/benchmarks/documentdb-sdk-skills/verify-controls.sh @@ -0,0 +1,148 @@ +#!/usr/bin/env bash +# verify-controls.sh — reproduce every grader-validation number in docs/REPORT.md. +# +# Runs the three control submissions against the built task image and asserts +# the expected reward for each. This is the positive/negative control pair that +# makes the benchmark trustworthy: +# +# oracle must score 1 — otherwise the grader cannot be satisfied and every +# measured score is meaningless +# empty must score 0 — weak, but proves missing deliverables are noticed +# naive must score 0 — THE important one: a fully working app with no +# DocumentDB best practices must still fail +# +# A grader that cannot fail is worthless; a grader that cannot pass is broken. +# This checks both directions, plus the case in between that actually matters. +# +# Usage: +# bash build.sh # once, to build the images +# bash verify-controls.sh # ~10 min: each run seeds 20k documents +# bash verify-controls.sh --only naive + +set -uo pipefail + +HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +TASK_DIR="$HERE/tasks/orders-api-python" +IMAGE="${TASK_TAG:-documentdb-orders-api-python:latest}" +ONLY="" +RESULT_JSON="" +while [ $# -gt 0 ]; do + case "$1" in + --only) ONLY="${2:-}"; shift 2;; + --output) RESULT_JSON="${2:-}"; shift 2;; + *) echo "unknown option: $1" >&2; exit 2;; + esac +done + +if ! docker image inspect "$IMAGE" >/dev/null 2>&1; then + echo "Image $IMAGE not found. Run: bash build.sh" >&2 + exit 1 +fi + +FAILURES=0 +RESULTS_TMP="$(mktemp)" +trap 'rm -f "$RESULTS_TMP"' EXIT + +# run_control +run_control() { + local name="$1" expected="$2" setup="$3" + if [ -n "$ONLY" ] && [ "$ONLY" != "$name" ]; then return 0; fi + + echo + echo "══════════════════════════════════════════════════════════════" + echo " control: $name (expect reward=$expected)" + echo "══════════════════════════════════════════════════════════════" + + local out + out=$(docker run --rm \ + -v "$TASK_DIR/tests:/tests:ro" \ + -v "$TASK_DIR/solution:/solution:ro" \ + -v "$HERE/controls:/controls:ro" \ + "$IMAGE" \ + bash -c "$setup /tests/test.sh >/dev/null 2>&1; \ + echo \"REWARD=\$(cat /logs/verifier/reward.txt)\"; \ + python3 -c \" +import json +try: + d = json.load(open('/output/custom_metrics.json')) +except Exception: + raise SystemExit +print('CHECKS=%s/%s' % (d.get('checks_passed'), d.get('checks_total'))) +for c in ['api','behavior','documentdb','engine','source','skills']: + p, t = d.get('checks_%s_passed' % c), d.get('checks_%s_total' % c) + if t: print(' %-12s %s/%s' % (c, p, t)) +\"" 2>&1) + + echo "$out" + + local actual checks + actual=$(echo "$out" | sed -n 's/^REWARD=//p' | tail -1) + checks=$(echo "$out" | sed -n 's/^CHECKS=//p' | tail -1) + # Record for the machine-readable artifact. Categories are captured from the + # container's own custom_metrics.json, not re-derived here, so the committed + # result cannot drift from what the verifier actually reported. + printf '%s\t%s\t%s\t%s\n' "$name" "$expected" "$actual" "${checks:-}" >> "$RESULTS_TMP" + if [ "$actual" = "$expected" ]; then + echo " ✅ $name: reward=$actual as expected" + else + echo " ❌ $name: reward=$actual, EXPECTED $expected" >&2 + FAILURES=$((FAILURES + 1)) + fi +} + +# The oracle: copies the reference implementation into /app. +run_control oracle 1 '/solution/solve.sh >/dev/null 2>&1 &&' + +# Empty: nothing in /app at all. +run_control empty 0 'true &&' + +# Naive: functional, but follows no DocumentDB best practice. +run_control naive 0 'mkdir -p /app && cp -r /controls/naive-python/. /app/ && chmod +x /app/*.sh &&' + +if [ -n "$RESULT_JSON" ]; then + mkdir -p "$(dirname "$RESULT_JSON")" + KIT_COMMIT="$(git -C "$HERE" rev-parse --short HEAD 2>/dev/null || echo unknown)" + python3 - "$RESULTS_TMP" "$RESULT_JSON" "$KIT_COMMIT" "$IMAGE" <<'PYEOF' +import json, subprocess, sys, datetime +rows_path, out_path, commit, image = sys.argv[1:5] +controls = {} +for line in open(rows_path): + name, expected, actual, checks = (line.rstrip("\n").split("\t") + ["", "", "", ""])[:4] + passed = total = None + if checks and "/" in checks: + p, t = checks.split("/", 1) + passed, total = int(p), int(t) + controls[name] = { + "expected_reward": int(expected), + "actual_reward": int(actual) if actual.isdigit() else None, + "checks_passed": passed, + "checks_total": total, + "as_expected": actual == expected, + } +artifact = { + "kind": "controls-validation", + "provenance": { + "date": datetime.date.today().isoformat(), + "kit_commit": commit, + "image_tag": image, + "host": "local docker", + "command": "bash verify-controls.sh", + }, + "controls": controls, + "all_as_expected": all(c["as_expected"] for c in controls.values()), +} +json.dump(artifact, open(out_path, "w"), indent=2, sort_keys=True) +open(out_path, "a").write("\n") +print(f" wrote {out_path}") +PYEOF +fi + +echo +echo "══════════════════════════════════════════════════════════════" +if [ "$FAILURES" -eq 0 ]; then + echo " ✅ all controls behaved as expected" + exit 0 +fi +echo " ❌ $FAILURES control(s) did not behave as expected" >&2 +echo " The grader is not trustworthy until this is resolved." >&2 +exit 1 diff --git a/compat/.gitignore b/compat/.gitignore new file mode 100644 index 0000000..cad09be --- /dev/null +++ b/compat/.gitignore @@ -0,0 +1,2 @@ +node_modules/ +results/*.json diff --git a/compat/PROMPT.md b/compat/PROMPT.md new file mode 100644 index 0000000..264cc6f --- /dev/null +++ b/compat/PROMPT.md @@ -0,0 +1,12 @@ +# Prompt + +Create a small Node.js e-commerce project for Azure DocumentDB using the +official `mongodb` driver. + +- Read the connection from `MONGODB_URI`. +- Reuse one `MongoClient`. +- Seed deterministic `customers`, `products`, `orders`, `order_items`, and + `inventory` collections. +- Add one filtered `find()` query and three `$lookup` queries. +- Add `npm test` with exact expected results. +- Do not hardcode credentials or add database-specific branches. diff --git a/compat/QUICKSTART.md b/compat/QUICKSTART.md new file mode 100644 index 0000000..11fda75 --- /dev/null +++ b/compat/QUICKSTART.md @@ -0,0 +1,104 @@ +# Azure DocumentDB e-commerce quick-start + +This is a MongoDB-compatible Node.js project running **entirely on Azure +DocumentDB**. It uses the official `mongodb` driver and standard query syntax; +no MongoDB server is required. + +> Azure DocumentDB and this agent kit are in public preview. This is a local +> compatibility demonstration, not production deployment guidance. + +The minimal project-generation prompt is in [`PROMPT.md`](PROMPT.md). + +## What is tested + +The deterministic seed creates: + +| Collection | Documents | +|---|---:| +| `customers` | 12 | +| `products` | 16 | +| `orders` | 48 | +| `order_items` | 96 | +| `inventory` | 32 | + +The four exact-result checks cover: + +1. A filtered and sorted `find()`. +2. Orders grouped by customer, then joined to `customers` with `$lookup`. +3. One order joined to its customer and `order_items`. +4. Products joined to `inventory`. + +The workload uses `$match`, `$group`, `$lookup`, `$unwind`, `$project`, +`$sort`, `$limit`, `$size`, and `$sum`. + +## 1. Install + +```bash +cd compat +npm ci +``` + +The source creates one process-wide `MongoClient`. Authentication and TLS are +configured entirely through `MONGODB_URI`. + +## 2. Start DocumentDB Local + +```bash +export DOCDB_PASSWORD='' + +docker rm -f documentdb-compat 2>/dev/null || true +docker run -d --name documentdb-compat \ + -p 127.0.0.1:10261:10260 \ + ghcr.io/microsoft/documentdb/documentdb-local:latest \ + --username docdbadmin \ + --password "$DOCDB_PASSWORD" +``` + +Wait for the gateway port: + +```bash +until (echo >/dev/tcp/127.0.0.1/10261) 2>/dev/null; do sleep 1; done +``` + +## 3. Run the end-to-end workflow + +```bash +ENCODED_PASSWORD=$(node -p 'encodeURIComponent(process.argv[1])' "$DOCDB_PASSWORD") +export MONGODB_URI="mongodb://docdbadmin:${ENCODED_PASSWORD}@127.0.0.1:10261/?tls=true&tlsAllowInvalidCertificates=true" + +npm test + +RESULT_FILE=results/documentdb.json npm run query >/dev/null +sha256sum results/documentdb.json +``` + +For Azure-hosted DocumentDB, replace `MONGODB_URI` with the Azure connection +string and do not enable `tlsAllowInvalidCertificates`. + +## Result + +```text +{"database":"ecommerce_compat","counts":{"customers":12,"products":16,"orders":48,"order_items":96,"inventory":32}} +{"status":"PASS","checks":4,"query_shapes":{"find":1,"lookup_aggregations":3}} +ELAPSED_SECONDS=12.22 +``` + +The elapsed time is one local functional run started as soon as the gateway +port opened; it includes the remaining driver wait for full readiness. It is +recorded for reproducibility, not as a performance benchmark. + +## What this demonstrates + +| Surface | Result | +|---|---| +| Official Node.js `mongodb` driver | PASS | +| Deterministic seed and index creation | PASS | +| Filter + projection + sort | PASS | +| `$group` + customer `$lookup` | PASS | +| Order + customer + item `$lookup` | PASS | +| Product + inventory `$lookup` | PASS | +| Exact expected output | PASS | + +This demonstrates the tested MongoDB-compatible surface only; it does not imply +that every MongoDB feature is supported. Check advanced operators against the +[DocumentDB compatibility documentation](https://learn.microsoft.com/azure/documentdb/compatibility). diff --git a/compat/package-lock.json b/compat/package-lock.json new file mode 100644 index 0000000..5a29f62 --- /dev/null +++ b/compat/package-lock.json @@ -0,0 +1,162 @@ +{ + "name": "documentdb-ecommerce-compat", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "documentdb-ecommerce-compat", + "version": "1.0.0", + "dependencies": { + "mongodb": "6.20.0" + } + }, + "node_modules/@mongodb-js/saslprep": { + "version": "1.5.0", + "resolved": "https://registry.npmjs.org/@mongodb-js/saslprep/-/saslprep-1.5.0.tgz", + "integrity": "sha512-Hk1SKJCMcCos38+vqDnZzlIo4XRj9yCGzYkjB4LcqpeXRIYfia1UWTz+VrueLxoU+uSRJzgkufxoRZg8gi52YA==", + "license": "MIT", + "dependencies": { + "sparse-bitfield": "^3.0.3" + } + }, + "node_modules/@types/webidl-conversions": { + "version": "7.0.3", + "resolved": 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+ } + }, + "node_modules/whatwg-url": { + "version": "14.2.0", + "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-14.2.0.tgz", + "integrity": "sha512-De72GdQZzNTUBBChsXueQUnPKDkg/5A5zp7pFDuQAj5UFoENpiACU0wlCvzpAGnTkj++ihpKwKyYewn/XNUbKw==", + "license": "MIT", + "dependencies": { + "tr46": "^5.1.0", + "webidl-conversions": "^7.0.0" + }, + "engines": { + "node": ">=18" + } + } + } +} diff --git a/compat/package.json b/compat/package.json new file mode 100644 index 0000000..baf4221 --- /dev/null +++ b/compat/package.json @@ -0,0 +1,16 @@ +{ + "name": "documentdb-ecommerce-compat", + "version": "1.0.0", + "private": true, + "description": "A MongoDB-compatible e-commerce workload running end-to-end on Azure DocumentDB", + "type": "module", + "scripts": { + "seed": "node scripts/seed.js", + "query": "node scripts/run-queries.js", + "verify": "node scripts/verify.js", + "test": "npm run seed && npm run verify" + }, + "dependencies": { + "mongodb": "6.20.0" + } +} diff --git a/compat/scripts/run-queries.js b/compat/scripts/run-queries.js new file mode 100644 index 0000000..37b8871 --- /dev/null +++ b/compat/scripts/run-queries.js @@ -0,0 +1,14 @@ +import { mkdir, writeFile } from "node:fs/promises"; +import { dirname } from "node:path"; +import { withDatabase } from "../src/database.js"; +import { runQueries } from "../src/queries.js"; + +const result = await withDatabase(runQueries); +const json = `${JSON.stringify(result, null, 2)}\n`; + +if (process.env.RESULT_FILE) { + await mkdir(dirname(process.env.RESULT_FILE), { recursive: true }); + await writeFile(process.env.RESULT_FILE, json); +} + +process.stdout.write(json); diff --git a/compat/scripts/seed.js b/compat/scripts/seed.js new file mode 100644 index 0000000..145c168 --- /dev/null +++ b/compat/scripts/seed.js @@ -0,0 +1,115 @@ +import { withDatabase } from "../src/database.js"; + +const statuses = ["pending", "confirmed", "shipped", "delivered", "cancelled"]; +const tiers = ["bronze", "silver", "gold", "platinum"]; +const categories = ["electronics", "home", "sports", "books"]; + +await withDatabase(async (db) => { + const collections = [ + "customers", + "products", + "orders", + "order_items", + "inventory" + ]; + + for (const collection of collections) { + await db.collection(collection).deleteMany({}); + } + + const customers = Array.from({ length: 12 }, (_, index) => { + const id = index + 1; + return { + customer_id: `CUST_${String(id).padStart(3, "0")}`, + name: `Customer ${id}`, + tier: tiers[index % tiers.length], + address: { + city: ["Seattle", "Portland", "Austin"][index % 3], + country: "US" + } + }; + }); + + const products = Array.from({ length: 16 }, (_, index) => { + const id = index + 1; + return { + product_id: `PROD_${String(id).padStart(3, "0")}`, + name: `Product ${id}`, + category: categories[index % categories.length], + price: 15 + id * 7 + }; + }); + + const orders = []; + const orderItems = []; + + for (let index = 0; index < 48; index += 1) { + const id = index + 1; + const orderId = `ORD_${String(id).padStart(3, "0")}`; + const customerId = `CUST_${String((index % 12) + 1).padStart(3, "0")}`; + const itemCount = (index % 3) + 1; + let total = 0; + + for (let itemIndex = 0; itemIndex < itemCount; itemIndex += 1) { + const productNumber = ((index * 3 + itemIndex) % 16) + 1; + const quantity = itemIndex + 1; + const unitPrice = 15 + productNumber * 7; + total += quantity * unitPrice; + orderItems.push({ + order_id: orderId, + product_id: `PROD_${String(productNumber).padStart(3, "0")}`, + quantity, + unit_price: unitPrice + }); + } + + orders.push({ + order_id: orderId, + customer_id: customerId, + status: statuses[index % statuses.length], + total_amount: total, + created_at: new Date(Date.UTC(2025, index % 12, (index % 27) + 1)) + }); + } + + const inventory = []; + for (let productIndex = 0; productIndex < products.length; productIndex += 1) { + for (let warehouseIndex = 0; warehouseIndex < 2; warehouseIndex += 1) { + inventory.push({ + product_id: products[productIndex].product_id, + warehouse_id: `WH_${warehouseIndex + 1}`, + quantity: (productIndex + 1) * 9 + warehouseIndex * 4 + }); + } + } + + await db.collection("customers").insertMany(customers); + await db.collection("products").insertMany(products); + await db.collection("orders").insertMany(orders); + await db.collection("order_items").insertMany(orderItems); + await db.collection("inventory").insertMany(inventory); + + await Promise.all([ + db.collection("customers").createIndex({ customer_id: 1 }, { unique: true }), + db.collection("products").createIndex({ product_id: 1 }, { unique: true }), + db + .collection("orders") + .createIndex({ customer_id: 1, status: 1, created_at: -1 }), + db.collection("orders").createIndex({ order_id: 1 }, { unique: true }), + db.collection("order_items").createIndex({ order_id: 1 }), + db.collection("inventory").createIndex({ product_id: 1 }) + ]); + + console.log( + JSON.stringify({ + database: db.databaseName, + counts: { + customers: customers.length, + products: products.length, + orders: orders.length, + order_items: orderItems.length, + inventory: inventory.length + } + }) + ); +}); diff --git a/compat/scripts/verify.js b/compat/scripts/verify.js new file mode 100644 index 0000000..8eeabdc --- /dev/null +++ b/compat/scripts/verify.js @@ -0,0 +1,143 @@ +import assert from "node:assert/strict"; +import { withDatabase } from "../src/database.js"; +import { runQueries } from "../src/queries.js"; + +const result = await withDatabase(runQueries); + +assert.deepEqual( + result.recentShippedOrders.map((order) => order.order_id), + ["ORD_003"], + "recent shipped orders changed" +); + +assert.deepEqual( + result.customerOrderSummary.map( + ({ customer_id, customer_name, tier, order_count, revenue }) => ({ + customer_id, + customer_name, + tier, + order_count, + revenue + }) + ), + [ + { + customer_id: "CUST_012", + customer_name: "Customer 12", + tier: "platinum", + order_count: 4, + revenue: 1928 + }, + { + customer_id: "CUST_003", + customer_name: "Customer 3", + tier: "gold", + order_count: 4, + revenue: 1760 + }, + { + customer_id: "CUST_009", + customer_name: "Customer 9", + tier: "bronze", + order_count: 4, + revenue: 1760 + }, + { + customer_id: "CUST_006", + customer_name: "Customer 6", + tier: "silver", + order_count: 4, + revenue: 1704 + }, + { + customer_id: "CUST_011", + customer_name: "Customer 11", + tier: "gold", + order_count: 4, + revenue: 992 + } + ], + "customer revenue join changed" +); + +assert.equal(result.orderDetails.length, 1); +assert.deepEqual( + { + order_id: result.orderDetails[0].order_id, + customer_id: result.orderDetails[0].customer_id, + customer_name: result.orderDetails[0].customer_name, + status: result.orderDetails[0].status, + total_amount: result.orderDetails[0].total_amount, + item_count: result.orderDetails[0].item_count + }, + { + order_id: "ORD_018", + customer_id: "CUST_006", + customer_name: "Customer 6", + status: "shipped", + total_amount: 314, + item_count: 3 + }, + "order/customer/items join changed" +); + +assert.deepEqual( + result.productInventory.map( + ({ product_id, product_name, category, warehouse_count, total_quantity }) => ({ + product_id, + product_name, + category, + warehouse_count, + total_quantity + }) + ), + [ + { + product_id: "PROD_001", + product_name: "Product 1", + category: "electronics", + warehouse_count: 2, + total_quantity: 22 + }, + { + product_id: "PROD_002", + product_name: "Product 2", + category: "home", + warehouse_count: 2, + total_quantity: 40 + }, + { + product_id: "PROD_003", + product_name: "Product 3", + category: "sports", + warehouse_count: 2, + total_quantity: 58 + }, + { + product_id: "PROD_004", + product_name: "Product 4", + category: "books", + warehouse_count: 2, + total_quantity: 76 + }, + { + product_id: "PROD_005", + product_name: "Product 5", + category: "electronics", + warehouse_count: 2, + total_quantity: 94 + } + ], + "product/inventory join changed" +); + +console.log( + JSON.stringify({ + status: "PASS", + checks: 4, + query_shapes: { + find: 1, + lookup_aggregations: 3 + } + }) +); diff --git a/compat/src/database.js b/compat/src/database.js new file mode 100644 index 0000000..4e45aa3 --- /dev/null +++ b/compat/src/database.js @@ -0,0 +1,28 @@ +import { MongoClient } from "mongodb"; + +const uri = process.env.MONGODB_URI; + +if (!uri) { + throw new Error("MONGODB_URI is required"); +} + +export const databaseName = + process.env.MONGODB_DATABASE || "ecommerce_compat"; + +// One client and one pool for the process lifetime. TLS, authentication, and +// endpoint differences belong in MONGODB_URI, not in database-specific code. +export const client = new MongoClient(uri, { + serverSelectionTimeoutMS: 10_000, + connectTimeoutMS: 10_000, + socketTimeoutMS: 30_000, + maxPoolSize: 20 +}); + +export async function withDatabase(operation) { + await client.connect(); + try { + return await operation(client.db(databaseName)); + } finally { + await client.close(); + } +} diff --git a/compat/src/queries.js b/compat/src/queries.js new file mode 100644 index 0000000..9efc6f5 --- /dev/null +++ b/compat/src/queries.js @@ -0,0 +1,149 @@ +export async function runQueries(db) { + const recentShippedOrders = await db + .collection("orders") + .find( + { customer_id: "CUST_003", status: "shipped" }, + { + projection: { + _id: 0, + order_id: 1, + customer_id: 1, + status: 1, + total_amount: 1, + created_at: 1 + } + } + ) + .sort({ created_at: -1 }) + .toArray(); + + // SQL analogue: + // SELECT c.name, COUNT(*), SUM(o.total_amount) + // FROM orders o JOIN customers c USING (customer_id) + // GROUP BY c.name ORDER BY SUM(o.total_amount) DESC LIMIT 5; + const customerOrderSummary = await db + .collection("orders") + .aggregate([ + { + $group: { + _id: "$customer_id", + order_count: { $sum: 1 }, + revenue: { $sum: "$total_amount" } + } + }, + { + $lookup: { + from: "customers", + localField: "_id", + foreignField: "customer_id", + as: "customer" + } + }, + { $unwind: "$customer" }, + { + $project: { + _id: 0, + customer_id: "$_id", + customer_name: "$customer.name", + tier: "$customer.tier", + order_count: 1, + revenue: 1 + } + }, + { $sort: { revenue: -1, customer_id: 1 } }, + { $limit: 5 } + ]) + .toArray(); + + // SQL analogue: + // SELECT o.*, c.name, oi.* + // FROM orders o + // JOIN customers c USING (customer_id) + // JOIN order_items oi USING (order_id) + // WHERE o.order_id = 'ORD_018'; + const orderDetails = await db + .collection("orders") + .aggregate([ + { $match: { order_id: "ORD_018" } }, + { + $lookup: { + from: "customers", + localField: "customer_id", + foreignField: "customer_id", + as: "customer" + } + }, + { $unwind: "$customer" }, + { + $lookup: { + from: "order_items", + localField: "order_id", + foreignField: "order_id", + as: "items" + } + }, + { + $project: { + _id: 0, + order_id: 1, + customer_id: 1, + customer_name: "$customer.name", + status: 1, + total_amount: 1, + item_count: { $size: "$items" }, + items: { + product_id: 1, + quantity: 1, + unit_price: 1 + } + } + } + ]) + .toArray(); + + // SQL analogue: + // SELECT p.product_id, p.name, SUM(i.quantity) + // FROM products p JOIN inventory i USING (product_id) + // GROUP BY p.product_id, p.name ORDER BY SUM(i.quantity), p.product_id; + const productInventory = await db + .collection("products") + .aggregate([ + { + $lookup: { + from: "inventory", + localField: "product_id", + foreignField: "product_id", + as: "stock" + } + }, + { + $project: { + _id: 0, + product_id: 1, + product_name: "$name", + category: 1, + warehouse_count: { $size: "$stock" }, + total_quantity: { $sum: "$stock.quantity" } + } + }, + { $sort: { total_quantity: 1, product_id: 1 } }, + { $limit: 5 } + ]) + .toArray(); + + // $lookup does not guarantee array ordering. Normalize client-side so exact + // result verification does not mistake an allowed order difference for a + // query failure. + for (const order of orderDetails) { + order.items.sort((left, right) => + left.product_id.localeCompare(right.product_id) + ); + } + + return { + recentShippedOrders, + customerOrderSummary, + orderDetails, + productInventory + }; +} diff --git a/docs/DIAGNOSTICS.md b/docs/DIAGNOSTICS.md index cd3cd2f..46e967e 100644 --- a/docs/DIAGNOSTICS.md +++ b/docs/DIAGNOSTICS.md @@ -15,7 +15,6 @@ and `python3` on the host — no MCP server, no cloud, no API keys. | Knowledge-base router | [`knowledge-base/`](../knowledge-base/README.md) | Deterministic NL question → exact script (no LLM at routing time). | | Demo datasets | [`scenarios/ecommerce/`](../scenarios/ecommerce/), [`scenarios/contoso/`](../scenarios/contoso/README.md) | Seeders that plant the problems the tools find. | | Regression tests | [`testing/`](../testing/README.md) | Fixture-first contracts that guard the scripts. | -| Token study | [`token-tests/`](../token-tests/README.md) | Measured token savings of scripts vs text-skill workflows. | --- @@ -29,11 +28,11 @@ password; the scripts read it from `DB_USER` (default `docdbadmin`) and docker run -dt --name documentdb-local \ -p 10260:10260 \ -e USERNAME=docdbadmin \ - -e PASSWORD=Test1234 \ + -e PASSWORD=[YOUR_PASSWORD] \ ghcr.io/microsoft/documentdb/documentdb-local:latest # the scripts require a password — export it once (or pass --password each time) -export DB_PASSWORD=Test1234 +export DB_PASSWORD=[YOUR_PASSWORD] # preflight: confirm the engine answers (should print "1") docker exec documentdb-local psql -h localhost -p 9712 -U documentdb -d postgres -tAc "SELECT 1" @@ -70,6 +69,25 @@ bash scripts/perf-advisor.sh --db ecommerce bash scripts/data-integrity-check.sh --db ecommerce ``` +### Windows + +Each diagnostic has a Python entry point and a PowerShell convenience wrapper: + +```powershell +python scripts\document-bloat-advisor.py --db contoso +python scripts\toast-split-advisor.py --db contoso +.\scripts\index-redundancy-finder.ps1 --db ecommerce +.\scripts\db-config-advisor.ps1 --db contoso +.\scripts\perf-advisor.ps1 --db ecommerce +.\scripts\data-integrity-check.ps1 --db ecommerce +``` + +Prerequisites are Python 3.10+ and Docker Desktop with the target Linux +DocumentDB container running. Git Bash and WSL are not required. The portable +launcher copies the selected read-only script into the container and runs it +there, preserving the same CLI flags and JSON output contract as the Bash entry +point. + Add `--json` to any of them for a compact machine-readable result (what the router and agents consume): @@ -108,15 +126,6 @@ bash scripts/document-bloat-advisor.sh --db contoso # opportunities now clea bash testing/run.sh # fixture-first contracts; auto-creates a venv ``` -## 6. Reproduce the token study (optional) - -```bash -cd token-tests -bash token-ab-measure.sh | python3 summarize.py # see RESULTS.md for the table -``` - ---- - ## Connection defaults | Setting | Default | Override | diff --git a/docs/SKILLS.md b/docs/SKILLS.md index a657508..7df967b 100644 --- a/docs/SKILLS.md +++ b/docs/SKILLS.md @@ -63,8 +63,7 @@ Companion to the toolbox: the `data-modeling` skill's [`model-large-field-split`](../skills/data-modeling/model-large-field-split.md) rule explains the TOAST anti-pattern, and its analyzer [`scripts/toast-split-advisor.sh`](../scripts/toast-split-advisor.sh) measures it. -The scripts are guarded by the regression suite in [`../testing/`](../testing/README.md), -and their token efficiency is measured in [`../token-tests/RESULTS.md`](../token-tests/RESULTS.md). +The scripts are guarded by the regression suite in [`../testing/`](../testing/README.md). ## Use when diff --git a/docs/TESTING.md b/docs/TESTING.md new file mode 100644 index 0000000..988ebc7 --- /dev/null +++ b/docs/TESTING.md @@ -0,0 +1,376 @@ +# Testing the agent kit + +The kit is tested in **three complementary suites**. The first two have +opposite success criteria — conflating them is the main design mistake to avoid +— and the third is the publication layer. + +| | **Diagnostic Regression Suite** | **Cross-Model Skill Evaluations** | **MSBench Skill-Efficacy Benchmark** | +|---|---|---|---| +| Subject | diagnostic scripts and router | text skills driving an agent | code produced by an agent | +| Question | "Is the answer stable and repeatable?" | "Is the output good, and what did it cost?" | "Does the produced application satisfy the fixed rubric?" | +| Success | identical canonicalised JSON and exact contracts | beats its control arm over repeated model runs | treatment/control `pass@k` delta | +| Runtime | ~2 min, free | minutes to hours, costs AI credits | slow, Docker/gated platform access | +| Home | [`testing/`](../testing/) | [`evals/`](../evals/) | [`benchmarks/`](../benchmarks/documentdb-sdk-skills/README.md) | +| CI | every PR | dispatch/monthly | PR validation, manual publication | + +The Diagnostic Regression Suite and Cross-Model Skill Evaluations support +iteration. The MSBench Skill-Efficacy Benchmark produces the externally +citable result. + +A diagnostic script is a **tool**: the same database must give the same answer +every time. An agent is not: it must be measured as a distribution against a +control. Applying either standard to the other produces nonsense. + +--- + +## Prerequisites + +The `static` scenarios in the Diagnostic Regression Suite need **nothing at +all**. Everything else needs a running container. + +```bash +# 1. Start DocumentDB Local +docker run -d --name documentdb-local \ + -p 10260:10260 -p 9712:9712 \ + -e USERNAME=docdbadmin -e PASSWORD='' \ + ghcr.io/microsoft/documentdb/documentdb-local:latest + +# 2. Install mongosh INTO the container — the image does not ship it +MV=2.3.8 +curl -sSL "https://downloads.mongodb.com/compass/mongosh-${MV}-linux-x64.tgz" -o /tmp/mongosh.tgz +tar xzf /tmp/mongosh.tgz -C /tmp +docker cp "/tmp/mongosh-${MV}-linux-x64/bin/mongosh" documentdb-local:/usr/local/bin/mongosh +docker cp "/tmp/mongosh-${MV}-linux-x64/bin/mongosh_crypt_v1.so" documentdb-local:/usr/local/lib/ +docker exec documentdb-local mongosh --version + +# 3. Export the password the container was created with +export DB_PASSWORD='' +``` + +> **Step 2 is not optional.** The `documentdb-local` image ships `psql` but **no +> `mongosh`**, and every diagnostic script talks to the database through +> `docker exec mongosh`. Skipping it makes every scenario fail with +> `executable file not found`, which looks like a broken test rather than a +> missing dependency. + +Forgot the password? + +```bash +docker inspect documentdb-local \ + --format '{{range .Config.Env}}{{println .}}{{end}}' | grep PASSWORD +``` + +> **A wrong password reports as `MongoServerError: Invalid key`** — an *auth* +> failure, not a malformed document. The suite now detects this up front and +> aborts once with an actionable message instead of producing ~30 cryptic +> failures. + +--- + +## Diagnostic Regression Suite + +```bash +bash testing/run.sh # everything (~2 min) -> 86 passed +``` + +`run.sh` creates `testing-venv/` on first use and passes extra arguments +straight through to pytest. + +### No container, no credentials, no network + +These run in well under a second and are the first CI job: + +```bash +cd testing && pytest scenarios/token-accounting scenarios/evals-config +``` + +They override the container fixture with a no-op. Use them for a fast check that +the cost metrics and the Cross-Model Skill Evaluations configs are still sane. + +### Individual scenarios + +```bash +bash testing/run.sh scenarios/determinism # scripts are reproducible +bash testing/run.sh scenarios/remediation-effect # the advice actually works +bash testing/run.sh scenarios/kb-router # routing contract +bash testing/run.sh -m determinism # by marker +bash testing/run.sh -k redundancy -v # by name +``` + +Markers: `determinism`, `remediation`, `tokens`, `evalsconfig`, `kbrouter`, +`redundancy`, `integrity`, `perf`, `healthy_indexes`, `jsoncontract`. + +### What the two headline scenarios prove + +**`scenarios/determinism`** runs every script 3× against one untouched database +and asserts the canonicalised `--json` is identical. It found and drove the fix +of **four real nondeterminism bugs** — two `$sample` calls, a latency threshold +that decided list *membership*, and an `explain()` probe built from an arbitrary +`findOne()`. See [its SCENARIO.md](../testing/scenarios/determinism/SCENARIO.md). + +**`scenarios/remediation-effect`** is the strongest grading rung available: +rather than asking a model whether the advice is good, it **applies** the advice +and measures the database. + +``` +tool detects the defect -> fix DERIVED FROM THE TOOL'S OUTPUT -> plan improves + -> results unchanged -> no new redundancy -> the tool agrees it is fixed +``` + +The metric is **scan amplification** (`totalDocsExamined / nReturned`): 2000× → +1×. Raw "documents examined" is the wrong metric on this engine — see +[its SCENARIO.md](../testing/scenarios/remediation-effect/SCENARIO.md). + +### Reproducing a determinism check by hand + +```bash +for i in 1 2 3; do + bash scripts/data-integrity-check.sh --db test_determinism --json > /tmp/r$i.json +done +diff /tmp/r1.json /tmp/r2.json && diff /tmp/r2.json /tmp/r3.json && echo IDENTICAL +``` + +Only `--json` is expected to be stable. The human report embeds a wall-clock +banner and measured latencies by design. + +--- + +## Cross-Model Skill Evaluations + +Runs on [Vally](https://microsoft.github.io/vally/). **Everything below the +`lint`/`plan` line costs AI credits.** + +```bash +cd evals && npm ci +``` + +The suite has **two** evals: `documentdb-skills/eval.yaml` asks whether the +right skill fires (binary, reproducible), and +`documentdb-quality/quality-eval.yaml` asks whether the guidance is any good +(blinded cross-model judge panel, not reproducible by construction). + +### Free — always do these first + +```bash +npm run lint:all # validate both eval specs +npm run experiment:plan # resolve the 3x2 triggering matrix +npm run experiment:quality:plan # resolve the quality treatment-vs-control matrix +npm run eval:mock # triggering eval against the mock executor +``` + +> ⚠️ **The mock executor invokes no skills** (`Skills used 0`). Positive-trigger +> stimuli therefore always fail and anti-trigger stimuli pass **vacuously**. The +> mock validates plumbing, not behaviour — never quote a mock result. + +### Paid — real models + +Requires `COPILOT_SDK_AUTH_TOKEN`. + +```bash +# One cell, to sanity-check cost before committing to the matrix +npx vally experiment run documentdb-skills.experiment.yaml \ + --variant 'model=claude-opus-5,skills=kit' + +# The full matrix: 3 models x {kit, control} x 5 runs +npm run experiment + +# Guidance QUALITY, treatment vs control, judged by a blinded 3-vendor panel +npm run experiment:quality +``` + +The quality experiment is 2 arms x 4 stimuli x 3 runs = 24 agent runs, each +graded by 3 judges = 72 judge calls. Run a single cell first. + +The matrix is **3 models × 2 arms × 5 runs = 30 trials per stimulus**. Start +with one cell. + +### What "treatment" vs "control" means + +The intervention is **the kit's skills being installed and discoverable** — +nothing else differs. The control has no skill files, so there is nothing for +the agent to read; the treatment has them on disk but is **never told to use +them**. For cost, that makes the input-token delta the literal price of the kit +being available and used. + +### Two rules for reading Cross-Model Skill Evaluations results + +1. **Only deltas are publishable.** An absolute pass-rate is uninterpretable — + "90% passed" means nothing without knowing what a bare agent scores on the + same stimuli. Every model runs with the kit and with nothing. +2. **The agent is never told to use the skills.** They are made discoverable as + a user would have them installed, and no prompt mentions them. This measures + the kit's *organic* effect, not a hinted best case. + +### Cost accounting + +```bash +python3 evals/harness/token_usage.py sessions --limit 10 + +python3 evals/harness/token_usage.py report --session \ + --task orders-api --model claude-opus-5 --arm skills --run 1 \ + --outcome-passed 40 --outcome-total 40 --out results/opus-skills-1.json + +python3 evals/harness/token_usage.py compare results/*.json +``` + +``` +| Model | Arm | N | Pass rate | Credits (mean±sd) | Turns | Fresh input tok | Cache share | Credits/pass | +|---|---|---|---|---|---|---|---|---| +| claude-opus-5 | control | 3 | 67% | 812.4±31.2 | 9.0±1.0 | 402,118 | 95% | 1218.6 | +| claude-opus-5 | skills | 3 | 100% | 941.7±18.5 | 6.3±0.6 | 486,930 | 95% | 941.7 | +``` + +Three things this table is built to stop you getting wrong: + +- **Never quote raw input tokens as the cost of a skill.** `cache_read_tokens` + is a *subset* of `input_tokens`, and ~95% of input is cache reads, so the + skill payload is amortised. `Fresh input tok` is the honestly-billed number. +- **A failed run reports no cost-to-green**, otherwise giving up early would + look like efficiency. `Credits/pass` divides by *passes*, not runs — failed + attempts are part of the price. +- **Cells with n < 3 are flagged ⚠️.** A mean over one non-deterministic agent + run is an anecdote. + +--- + +## MSBench Skill-Efficacy Benchmark + +The publication layer. See +[`benchmarks/documentdb-sdk-skills/README.md`](../benchmarks/documentdb-sdk-skills/README.md). + +### Free — runs on every PR + +```bash +cd testing && pytest scenarios/benchmark-config scenarios/benchmark-metrics +``` + +Validates the registration files, the Harbor task layout, that the instruction +contains **no solution hints**, and that the cost metrics agree with the +Cross-Model Skill Evaluation cost module. +No Docker, no MSBench access, no network. + +### Local — needs Docker + +```bash +cd benchmarks/documentdb-sdk-skills +docker build -f shared/base/Dockerfile -t documentdb-orders-base:latest . +cd tasks/orders-api-python +docker build -f environment/Dockerfile -t documentdb-orders-api-python:latest . + +# positive control: the oracle must score 1 +docker run --rm documentdb-orders-api-python:latest \ + bash -c '/solution/solve.sh && /tests/test.sh; cat /logs/verifier/reward.txt' + +# negative control: an empty /app must score 0 +docker run --rm documentdb-orders-api-python:latest \ + bash -c '/tests/test.sh; cat /logs/verifier/reward.txt' +``` + +Both controls matter. A grader that cannot be satisfied makes every score +meaningless; a grader that never fails is worthless. + +### Gated — needs internal MSBench access + +```bash +pip install keyring artifacts-keyring +pip install msbench-cli --index-url=https://pkgs.dev.azure.com/devdiv/_packaging/MicrosoftSweBench/pypi/simple/ +az login + +# BOTH arms — a treatment score without its control is not a result +msbench-cli run --benchmark documentdb-sdk-skills --pass_at_k 5 ... +msbench-cli run --benchmark documentdb-sdk-skills-noskills --pass_at_k 5 ... + +msbench-cli report --run_id --output report.json # includes token cost +``` + +> `pip install msbench` installs an **unrelated** package. The real tool is +> `msbench-cli` from the private feed. + +### The effectiveness report + +```bash +msbench-cli report --run_id --output treatment.json +msbench-cli report --run_id --output control.json +python3 benchmarks/documentdb-sdk-skills/report.py \ + --treatment treatment.json --control control.json +``` + +`report.py` refuses to run on a single arm: an absolute pass rate cannot +distinguish an effective kit from an easy task. Current status and the full +provenance of every number are in +[`benchmarks/documentdb-sdk-skills/docs/REPORT.md`](../benchmarks/documentdb-sdk-skills/docs/REPORT.md). + +### Cost per task + +The verifier writes MSBench `custom_metrics.json` with per-task token usage, so +runs can be compared across tasks: + +| Metric | Why | +|---|---| +| `tokens_fresh_input` | input billed at the uncached rate — the honest cost | +| `cache_read_share_pct` | how much the skill payload was amortised | +| `ai_credits` | the money number | +| `credits_to_green` | cost of a **passing** run only | +| `checks__passed/_total` | which rung an expensive run was failing | +| `tokens_available` | `0` when harvesting failed, so a gap is never read as free | + +--- + +## The grading ladder + +Pick the **strongest criterion the scenario allows** — strongest meaning most +reproducible and hardest to satisfy accidentally. + +This is about fit, not about avoiding judges. LLM-as-a-judge is a normal grader +and the right tool for qualities that are genuinely matters of degree (is the +explanation clear? is the guidance well-targeted?), which is why Cross-Model Skill Evaluations use one. It just cannot serve a +scenario whose purpose is to prove **repeatability**, +because the same input can score differently across runs. Where an outcome is +measurable in the system, measure it; where it is a judgement, judge it. + +| Rung | Criterion | Used by | +|---|---|---| +| 1 | Determinism (identical output) | `scenarios/determinism` | +| 2 | **Before/after measured delta** | `scenarios/remediation-effect` | +| 3 | Structural assertion | `scenarios/*-contract`, `evals-config` | +| 4 | Semantic equivalence | query-generation scenarios | +| 5 | Behavioural (does it run and do the right thing?) | future app-level evals | +| 6 | Contract conformance | `scenarios/json-contract` | +| 7 | LLM-as-a-judge | qualitative dimensions — blinded, cross-model, reported next to an objective score | + +Improvement metrics are gameable, so they are **paired with regression guards** — +`remediation-effect` runs `index-redundancy-finder.sh` to make sure "just index +everything" fails, and asserts the result set is unchanged. + +--- + +## CI + +| Workflow | Trigger | Cost | +|---|---|---| +| [`diagnostic-regression.yml`](../.github/workflows/diagnostic-regression.yml) | push, PR, dispatch | free | +| [`skill-evaluations.yml`](../.github/workflows/skill-evaluations.yml) | dispatch, monthly | credits | +| [`skill-efficacy-benchmark.yml`](../.github/workflows/skill-efficacy-benchmark.yml) | PR (validate only), dispatch | free / Docker / gated | + +The `validate` job in Cross-Model Skill Evaluations (static guards + +`vally lint` + matrix resolution) always runs and is free, so a broken skill +path is caught **before** any credits are spent. That matters: +`vally --dry-run` validates matrix structure but **not** skill paths, and a +treatment arm whose skills fail to load is silently just a second control arm. + +Use the `dry_run` input to validate the matrix from CI for free. + +--- + +## Adding a scenario + +See [`testing/CREATE-SCENARIO.md`](../testing/CREATE-SCENARIO.md) and the +template at `testing/scenarios/_scenario-template/`. + +Two rules learned the hard way: + +- **Seed deterministically.** No `Math.random()` in a fixture — use a seeded + LCG. A test that cannot be reproduced cannot be debugged. +- **Prove the test can fail.** Inject a mutation and confirm it is caught. Every + scenario here documents the mutations it was verified against; a green suite + that cannot fail proves nothing. diff --git a/evals/.gitignore b/evals/.gitignore new file mode 100644 index 0000000..a3020d2 --- /dev/null +++ b/evals/.gitignore @@ -0,0 +1,5 @@ +# Vally run artifacts (trajectories, results, cache) — never committed +vally-results/ +.vally-cache/ +node_modules/ +results/ diff --git a/evals/README.md b/evals/README.md new file mode 100644 index 0000000..c8272e0 --- /dev/null +++ b/evals/README.md @@ -0,0 +1,165 @@ +# Cross-Model Skill Evaluations + +> **Two folders, two questions.** This repo tests two fundamentally different +> things, and conflating them is the mistake to avoid: +> +> | Folder | Suite | Question | Success looks like | +> |---|---|---|---| +> | [`../testing/`](../testing/) | **Diagnostic Regression Suite** | Do the **diagnostic scripts** return the *same* answer every time? | byte-identical `--json` across 3 runs | +> | **`evals/`** (here) | **Cross-Model Skill Evaluations** | Do the **text skills** make an agent produce *better* output, and at what cost? | higher quality / lower cost than a control run | +> +> The Diagnostic Regression Suite is free, fast, and runs on every PR. +> Cross-Model Skill Evaluations cost AI credits and are run deliberately. + +Cross-Model Skill Evaluations are built on **[Vally](https://aka.ms/vally)** (`@microsoft/vally-cli`, +MIT, pinned in [`package.json`](package.json)) — Microsoft's evaluation platform +for AI agents. Vally gives us, natively: multi-model runs, skills on/off +variants, an objective **`skill-invocation`** grader, real token/AI-credit +accounting from API `response.usage`, and LLM-judge graders for the cases where +nothing objective exists. + +## Layout + +``` +evals/ +├── package.json # pins @microsoft/vally-cli +├── documentdb-skills/ +│ └── eval.yaml # stimuli + graders (the contract) +└── vally-results/ # run artifacts — gitignored, never committed +``` + +## Prerequisites + +```bash +cd evals && npm install +export VALLY_TELEMETRY_OPTOUT=1 DO_NOT_TRACK=1 # see "Telemetry" below +``` + +**For real (non-mock) runs** the Copilot SDK executor needs an auth token in the +environment (`COPILOT_SDK_AUTH_TOKEN`); without it a run fails with +`Session was not created with authentication info or custom provider`. +Real runs consume AI credits. + +## Running + +```bash +npm run lint # static validation — no agent, no cost +npm run eval:mock # mock executor — free, but see the WARNING below +npm run eval # real model — costs AI credits, needs auth + +# filter by tag +npx vally eval -e documentdb-skills/eval.yaml --tag kind=anti-trigger +``` + +## ⚠️ The mock executor cannot score skill activation + +`--executor mock` never invokes skills (`Skills used 0`). That means: + +- **positive-trigger stimuli always FAIL on mock**, and +- **anti-trigger stimuli always PASS on mock — vacuously.** + +So a green anti-trigger result on mock proves *nothing*, exactly like a +diagnostic script that "passes" determinism by finding nothing (see +[`../testing/scenarios/determinism/`](../testing/scenarios/determinism/)). + +**Use mock only to validate the plumbing** — that the spec parses, skill paths +resolve, graders are wired. **Any real signal requires a real executor.** + +## What Phase 1 covers + +Skill **routing**: does the right skill activate for a given phrasing, and does +**no** DocumentDB skill activate for an unrelated question? + +This was chosen deliberately as the first eval because it is: +- the **cheapest** useful signal, +- graded **objectively** (`skill-invocation` is binary — no LLM judge needed), and +- otherwise only ever verified by hand. + +| Stimulus | Tag | Asserts | +|---|---|---| +| slow query on `db.orders` | `kind=trigger` | `documentdb-query-optimizer` **required** | +| array field + 30-day expiry | `kind=trigger` | `documentdb-indexing` **required** | +| pool exhaustion / timeouts | `kind=trigger` | `documentdb-connection` **required** | +| PostgreSQL partial index | `kind=anti-trigger` | **no** DocumentDB skill called | +| CSS centering | `kind=anti-trigger` | **no** DocumentDB skill called | + +### Honest measurement: skills are never hinted + +`environment.skills` makes the skills *discoverable* exactly as an installed user +would have them — the prompt **never tells the agent to use them**. This measures +the skill's **organic** effect rather than a hinted best case. (Protocol borrowed +from the MSBench `cosmos-sdk-skills` runner.) + +## The two evals + +| Eval | Question | Grader | Reproducible? | +|---|---|---|---| +| [`documentdb-skills/eval.yaml`](documentdb-skills/eval.yaml) | does the **right skill fire**? | `skill-invocation` (binary) | yes | +| [`documentdb-quality/quality-eval.yaml`](documentdb-quality/quality-eval.yaml) | is the **guidance any good**? | blinded cross-model `panel` | **no, by construction** | + +The first is cheap and exact but cannot tell good advice from bad — a skill can +fire perfectly and still give poor guidance. The second closes that gap and is +the one place in the kit where an LLM judge is the right instrument, because +"was this explanation clear and correct" is genuinely a matter of degree. + +### How the judge is kept honest + +Five guards, each enforced by a test in +`testing/scenarios/evals-config/` so they cannot quietly regress: + +| Guard | Why | +|---|---| +| **3 judges, 3 vendors** | models favour their own output; no model may be the sole judge of a run it could have produced | +| **median, not mean** | one miscalibrated judge must not swing the verdict | +| **blinded prompt** | a judge told which arm it is grading will find reasons to agree | +| **anchored criteria + per-stimulus rubric** | "rate 1–5" is not a measurement; both runs must be scored against the same yardstick | +| **correctness is a `required` gate** | a fluent, confident, WRONG answer must fail however well it reads | + +`technical_correctness` also carries more weight than any presentational +criterion, so style can never outvote accuracy. + +> ⚠️ **The mock executor is close to useless for this eval.** It invokes no +> skills *and* cannot call the judge models, so every stimulus scores 0 with +> zero tokens. `npm run eval:quality:mock` validates that the config loads and +> the stimuli execute — nothing about quality. Never quote a mock score. + +> ⚠️ **Always publish the quality score next to the objective ones.** It is not +> reproducible run-to-run. If the judge and the objective measures disagree +> systematically, trust the objective ones and fix the rubric. + +## Grading policy — match the grader to the claim + +LLM-as-a-judge is a legitimate and widely used grader, and Cross-Model Skill +Evaluations rely on one for the qualitative dimensions no assertion can +capture. The ordering below is +about **fit**: prefer a criterion that is reproducible and hard to satisfy by +accident, and reach for a judge when the thing being graded is genuinely a +matter of degree rather than a fact about the system. + +Prefer, in order: + +1. deterministic / exact match → `../testing/` (Diagnostic Regression Suite) +2. **measured before/after delta** → apply the advice, re-measure (`run-command`) +3. structural assertion on the artifact → `program`, `file-matches` +4. semantic equivalence vs a golden output +5. **behavioural assertion** → `skill-invocation`, `tool-call`, `metric-threshold` ← *Phase 1 lives here* +6. contract conformance +7. LLM-as-a-judge → for qualitative dimensions (clarity, targeting, tone); blinded, cross-model panel, and always reported next to an objective score so its reliability is visible + +A useful trick: convert 7 → 2/3 by **constraining the ask**. "Explain how to +speed this up" is judge-only prose; "emit the exact `createIndex(...)` you +recommend" is executable, so it can be applied and measured. + +## Telemetry + +Vally collects pseudonymous usage telemetry (including a persistent device +identifier) **by default**. Always export `VALLY_TELEMETRY_OPTOUT=1` / +`DO_NOT_TRACK=1` — the npm scripts assume you have. + +## Next (not yet built) + +- Real-model matrix: `claude-opus-5` × `gpt-5.6-sol` × `gemini-3.1-pro-preview`, + skills vs control, `--runs 5` (`vally experiment` + `vally compare`) +- Before/after graders that apply a recommended index and re-measure `explain()`, + paired with `index-redundancy-finder.sh --json` as an anti-gaming guard +- CI: Diagnostic Regression Suite every PR; Cross-Model Skill Evaluations on dispatch/schedule diff --git a/evals/documentdb-quality/quality-eval.yaml b/evals/documentdb-quality/quality-eval.yaml new file mode 100644 index 0000000..c5003e5 --- /dev/null +++ b/evals/documentdb-quality/quality-eval.yaml @@ -0,0 +1,249 @@ +# quality-eval.yaml — Cross-Model Skill Evaluations: is the GUIDANCE any good? +# +# WHY THIS FILE EXISTS +# -------------------- +# Everything else the kit measures is conformance: +# +# Diagnostic Regression Suite (testing/) do the scripts return a stable answer? +# Cross-Model Skill Evaluations (eval.yaml) does the RIGHT SKILL FIRE for a given phrasing? +# MSBench Skill-Efficacy Benchmark (benchmarks/) does the produced code satisfy 30 fixed checks? +# +# None of those can tell you whether the advice was actually GOOD. A skill can +# trigger perfectly and give poor guidance; code can satisfy every rule in our +# rubric and still be the wrong answer to the user's question. This eval closes +# that gap, and it is the ONE place in the kit where an LLM judge is the right +# instrument — because "was this explanation clear and correct" is genuinely a +# matter of degree, not a fact about the system. +# +# HOW IT IS KEPT HONEST +# --------------------- +# A judge is easy to do badly. Five guards, all load-bearing: +# +# 1. ANCHORED CRITERIA, not vibes. Five named dimensions with written +# descriptions and explicit weights, so "4/5" means something specific and +# two runs are scored against the same yardstick. +# 2. CROSS-MODEL PANEL. Three judges from three vendors. Models favour their +# own output (self-preference bias), so no model may be the sole judge of a +# run it could have produced. +# 3. MEDIAN, not mean. One miscalibrated judge cannot drag the verdict; a +# mean would let it. +# 4. BLINDING. The judges are told nothing about arms, skills, or which +# condition produced the answer. If a judge knows which answer is +# "supposed" to be better, it will find reasons. +# 5. CORRECTNESS GATES. technical_correctness and documentdb_specificity are +# `required`, so a fluent, confident, WRONG answer fails no matter how well +# it scores on clarity. Prose quality must never outvote accuracy. +# +# READING THE RESULT +# ------------------ +# This score is NOT reproducible run-to-run, by construction. Always publish it +# NEXT TO the objective numbers (skill-triggering from eval.yaml, rubric +# conformance from the MSBench benchmark), never instead of them. If the judge +# and the objective measures disagree systematically, trust the objective ones +# and fix the rubric. +# +# Run: +# npm run lint:quality +# npm run eval:quality # costs AI credits — 3 judges per stimulus + +name: documentdb-guidance-quality +description: >- + Quality of the guidance an agent gives for Azure DocumentDB questions, scored + by a blinded cross-model panel against an anchored rubric. Complements the + skill-triggering eval (does the right skill fire) and the MSBench benchmark + (does the produced code conform), neither of which can assess whether the + advice itself was good. +type: capability + +defaults: + timeout: 5m + # Agent output varies run to run and so does a judge's reading of it. One + # sample is an anecdote; the median of 3 is a weak but honest signal. + runs: 3 + +environment: + # Same organic-discovery protocol as the triggering eval: the skills are + # discoverable exactly as a user would have them installed, and no prompt + # mentions them. The experiment file swaps this list for [] in the control + # arm, which is the only difference between arms. + skills: + - ../../skills/query-optimizer + - ../../skills/query-optimization + - ../../skills/indexing + - ../../skills/connection + - ../../skills/data-modeling + - ../../skills/vector-search + +# --------------------------------------------------------------------------- +# The panel, defined once and reused by every stimulus via a YAML anchor. +# --------------------------------------------------------------------------- +x-panel: &quality_panel + type: panel + config: + # Three vendors. A model must never be the only judge of output it could + # plausibly have produced itself. + models: + - model: claude-opus-5 + reasoning_effort: medium + - model: gpt-5.6-sol + reasoning_effort: medium + - model: gemini-3.1-pro-preview + reasoning_effort: medium + + # Median: robust to a single miscalibrated or malfunctioning judge. + aggregation: median + scoring: scale_1_5 + + # 0.6 on the normalised [0,1] scale is 4/5 — "good, with minor gaps". + # Deliberately not 3/5: a merely adequate answer to a DocumentDB question + # is not what having the kit installed is supposed to produce. + threshold: 0.6 + overall_threshold: 0.6 + + evidence: [trajectory] + + prompt: | + You are grading the quality of technical guidance about Azure DocumentDB + (a MongoDB-compatible database built on PostgreSQL). + + BLINDING — read carefully. + You are NOT told, and must not try to infer, how this answer was + produced, what tooling or documentation the responder had available, or + how any other answer scored. Judge only the text in front of you. + Speculating about the source is grounds for an unreliable score. + + HOW TO SCORE + * Correctness outranks polish. A confident, fluent answer that would + mislead a user is a LOW score, however well written. + * Reward specificity over generic advice. "Add an index" is weak; naming + the fields, the order, and why, is strong. + * Penalise plausible-but-wrong details more harshly than omissions. A + user can look up what is missing; they cannot easily detect what is + subtly incorrect. + * Judge what is written, not what you assume was meant. + * Do not reward length. A short, correct, complete answer scores higher + than a long one padded with caveats. + + DocumentDB is MongoDB-compatible but NOT MongoDB. Advice that is only + true of community MongoDB, or that recommends unsupported features, is a + correctness failure, not a nitpick. + + criteria: + # --- gates: an answer that fails either of these fails overall --- + - name: technical_correctness + description: >- + Is every technical claim accurate for Azure DocumentDB? Index + behaviour, query semantics, driver behaviour, limits. Any statement + that would mislead a practitioner is a failure here, regardless of + how well the rest reads. + weight: 3 + required: true + + - name: documentdb_specificity + description: >- + Is the guidance specific to Azure DocumentDB rather than generic + MongoDB advice that happens to be adjacent? Correctly reflects where + DocumentDB differs (its PostgreSQL engine, cosmosSearch vector + indexes, createSearchIndexes, cluster tiers, Entra auth). + Recommending a MongoDB feature DocumentDB does not support fails + this criterion. + weight: 2 + required: true + + # --- quality dimensions: weighted, but not gates --- + - name: actionability + description: >- + Could a competent engineer act on this immediately? Concrete + commands, index definitions or configuration, as opposed to + describing what they ought to do in the abstract. + weight: 2 + required: false + + - name: diagnostic_reasoning + description: >- + Does it explain HOW to establish the cause rather than jumping to a + fix? For performance questions specifically, does it reach for + explain() or measurement before prescribing an index? + weight: 2 + required: false + + - name: completeness + description: >- + Does it cover the trade-offs a practitioner would hit next — write + cost of an index, memory, cardinality, when the advice does NOT + apply? Missing caveats that would bite in production lose marks here. + weight: 1 + required: false + +stimuli: + # ------------------------------------------------------------------------- + # Each prompt is a real question a user would ask, phrased WITHOUT naming the + # solution — otherwise the judge grades instruction-following, not guidance. + # ------------------------------------------------------------------------- + - name: slow-query-guidance-quality + tags: { kind: quality, area: query-perf } + prompt: | + This query against my Azure DocumentDB collection has got slower as the + collection grew to a few million documents: + + db.orders.find({ status: "shipped", customer_id: "CUST_004087" }) + .sort({ created_at: -1 }) + + Why is it slow, and what should I do about it? + rubric: + - Establishes the cause with explain() or measurement before prescribing a fix + - Recommends a compound index and gets the key ORDER right (equality before sort) + - Explains why that order matters rather than only asserting it + - Mentions the cost of the index (writes, storage) or when it would not help + graders: + - *quality_panel + + - name: index-choice-guidance-quality + tags: { kind: quality, area: indexing } + prompt: | + I have an Azure DocumentDB collection where each document has an array of + tags, and I also need documents to expire 30 days after creation. How + should I set up indexing? + rubric: + - Identifies that an index on an array field is a multikey index, and what that implies + - Recommends a TTL index for expiry, with the correct field type requirement + - Warns about the pitfalls (e.g. multikey index size, TTL granularity) + - Does not recommend a feature Azure DocumentDB does not support + graders: + - *quality_panel + + - name: connection-pool-guidance-quality + tags: { kind: quality, area: connection } + prompt: | + My Node.js service talking to Azure DocumentDB throws connection-pool + exhaustion errors and timeouts under load. What is going wrong and how do + I fix it? + rubric: + - Identifies client-per-request (or pool misuse) as the likely cause + - Prescribes a singleton MongoClient reused across requests + - Gives concrete pool/timeout settings rather than "tune the pool" + - Explains what the settings do, so the reader can adapt them + graders: + - *quality_panel + + - name: data-modelling-guidance-quality + tags: { kind: quality, area: data-modeling } + prompt: | + I am designing an Azure DocumentDB schema for an e-commerce app. Orders + have line items, and customers have addresses. Should I embed these or + keep them in separate collections? + rubric: + - Answers with the trade-off and the criteria for choosing, not a blanket rule + - Mentions the 16 MB document limit as a hard constraint on embedding + - Considers access patterns and unbounded growth (line items, addresses) + - Gives a concrete recommendation for THIS case rather than only theory + graders: + - *quality_panel + +scoring: + weights: + panel: 1.0 + # Every stimulus must clear the bar. A mean across stimuli would let a strong + # answer on one topic hide a misleading answer on another — and a misleading + # answer is precisely what we most need to catch. + threshold: 1.0 diff --git a/evals/documentdb-skills.experiment.yaml b/evals/documentdb-skills.experiment.yaml new file mode 100644 index 0000000..188bddf --- /dev/null +++ b/evals/documentdb-skills.experiment.yaml @@ -0,0 +1,113 @@ +# documentdb-skills.experiment.yaml — Cross-Model Skill Evaluations cross-model matrix +# +# THE QUESTION THIS ANSWERS +# ------------------------- +# Not "is the kit good?" (unfalsifiable), but: +# +# Does installing the DocumentDB agent kit change what an agent does, +# across models, by more than run-to-run noise — and what does it cost? +# +# WHAT "TREATMENT" AND "CONTROL" MEAN +# ----------------------------------- +# The INTERVENTION being measured is: the agent kit's skills are installed and +# discoverable. That is the only thing that differs between the two arms. +# +# control -> environment.skills = [] (nothing to discover or read) +# kit -> environment.skills = [...] (the skills, never mentioned) +# +# Model, prompt, stimuli, graders and run count are held identical, so any +# difference in quality or token cost is attributable to the kit. +# +# For TOKENS specifically: the control has no skill files, so its input is just +# the prompt and its own work. The treatment reads a SKILL.md when it judges one +# relevant, and those bytes become input tokens. The delta is the price of the +# kit being available and used. +# +# THE CONTROL ARM IS THE WHOLE POINT +# ---------------------------------- +# An absolute score is uninterpretable. "90% of stimuli passed" means nothing +# without knowing what a bare agent scores on the same stimuli. Every model is +# therefore run twice: once with the skills installed, once with none. +# +# Any published number MUST be a skills-vs-control delta. A single-arm number is +# not a result. +# +# THE AGENT IS NEVER TOLD TO USE THE SKILLS +# ----------------------------------------- +# The skills are made discoverable exactly as a user would have them installed, +# and the prompts contain no hint to use them. This measures the kit's *organic* +# effect — does an agent that merely HAS the kit apply it? — rather than a +# hinted best case, which would be a much easier and much less honest question. +# (Protocol borrowed from the Cosmos MSBench runner; see the plan, §2d.) +# +# COST +# ---- +# Vally reports tokens and nano-AIU per trial. Cross-check with +# `evals/harness/token_usage.py`, which reads the same run from the Copilot CLI +# session store, and remember that raw input tokens overstate skill cost by +# roughly 20x because the payload is cached after first use. +# +# RUN +# --- +# npm run experiment:plan # --dry-run: validates the matrix, costs nothing +# npm run experiment # 3 models x 2 arms x 5 runs = 30 trials PER +# # stimulus. Costs real AI credits. +# +# vally experiment run documentdb-skills.experiment.yaml \ +# --variant claude-opus-5-skills # one cell only + +name: documentdb-skills-cross-model + +evals: + - documentdb-skills/eval.yaml + +overrides: + # N per cell. Agent output is non-deterministic, so a single run is an + # anecdote. 5 is the smallest N that gives a usable spread without making the + # matrix (3 x 2 x 5 = 30 trials per stimulus) unaffordable. + runs: 5 + timeout: 5m + +execution: + workers: 3 + +# --------------------------------------------------------------------------- +# The matrix. Everything NOT listed in these axes is validated to be identical +# across variants, so a difference in results cannot come from an accidental +# config drift between arms. +# --------------------------------------------------------------------------- +matrix: + model: + path: /defaults/model + values: + - claude-opus-5 + - gpt-5.6-sol + - gemini-3.1-pro-preview + + skills: + path: /environment/skills + values: + # CONTROL: a bare agent. Same prompt, same model, no kit. + - control: [] + # TREATMENT: the kit installed, discoverable, unmentioned. + # + # Keep this list in sync with `environment.skills` in the eval spec. The + # `evals-config` scenario in testing/ asserts every path here exists — + # Vally's --dry-run does NOT validate skill paths, so a typo or a skill + # that only exists on another branch would silently produce a "control" + # run mislabelled as treatment, which is the worst possible failure mode + # for an A/B test. + - kit: + - ../skills/query-optimizer + - ../skills/query-optimization + - ../skills/indexing + - ../skills/connection + - ../skills/data-modeling + - ../skills/vector-search + +# The baseline every delta is measured against. Cross-model rows are reported +# too, but the meaningful comparison is each model against ITS OWN control — +# comparing gemini-kit to claude-control would conflate two changes at once. +baseline: + model: claude-opus-5 + skills: control diff --git a/evals/documentdb-skills/eval.yaml b/evals/documentdb-skills/eval.yaml new file mode 100644 index 0000000..63728df --- /dev/null +++ b/evals/documentdb-skills/eval.yaml @@ -0,0 +1,123 @@ +# eval.yaml — DocumentDB skill-efficacy eval (Cross-Model Skill Evaluations) +# +# Phase 1 scope: does the RIGHT skill activate for a given phrasing, and — just +# as important — does NO DocumentDB skill activate for an unrelated question? +# +# ⚠️ SCOPE LIMIT — READ THIS BEFORE QUOTING ANY RESULT FROM THIS EVAL. +# Every grader here is `skill-invocation`: it checks that a skill FIRED, never +# that the guidance it produced was any GOOD. Nothing in this file, and nothing +# in the MSBench benchmark (which grades rubric conformance with deterministic +# checks), assesses the QUALITY of the advice. +# +# A skill can trigger perfectly and still give poor advice, and this eval would +# report a pass. That question is answered by its sibling, +# ../documentdb-quality/quality-eval.yaml, which scores the guidance itself with +# a blinded cross-model judge panel. Claims sourced from THIS file must be +# limited to routing behaviour. +# +# Why start here (see the grading ladder in the plan): +# * it is the cheapest useful signal (runs on the free `mock` executor), +# * it is graded OBJECTIVELY (`skill-invocation` = binary), no LLM judge, +# * and it regression-tests skill routing, which is otherwise only ever +# checked by hand. +# +# Run: +# npm run lint # static validation, no agent, no cost +# npm run eval:mock # execute against the mock executor (free) +# npm run eval # execute against a real model (costs AI credits) + +name: documentdb-skills +description: >- + Skill-activation contract for the Azure DocumentDB agent kit: the correct + skill must activate for DocumentDB questions, and no DocumentDB skill may + activate for questions about other databases or unrelated topics. +type: capability + +defaults: + timeout: 3m + +environment: + # Skills are made discoverable to the agent exactly as a user would have them + # installed — the agent is NEVER told to use them. This measures the skill's + # *organic* effect rather than a hinted best case. + skills: + - ../../skills/query-optimizer + - ../../skills/indexing + - ../../skills/connection + - ../../skills/data-modeling + - ../../skills/vector-search + +stimuli: + # --------------------------------------------------------------------- + # Positive triggers — the right skill must activate + # --------------------------------------------------------------------- + - name: slow-query-should-trigger-optimizer + tags: { kind: trigger, area: query-perf } + prompt: | + This query against my Azure DocumentDB collection has become slow as the + collection grew. Why is it slow and how do I fix it? + + db.orders.find({ status: "shipped", customer_id: "CUST_004087" }) + .sort({ created_at: -1 }) + graders: + - type: skill-invocation + config: + required: [documentdb-query-optimizer] + + - name: index-type-choice-should-trigger-indexing + tags: { kind: trigger, area: indexing } + prompt: | + I have an Azure DocumentDB collection where each document has an array of + tags, and I also need to expire documents after 30 days. Which index types + should I create, and why? + graders: + - type: skill-invocation + config: + required: [documentdb-indexing] + + - name: pool-exhaustion-should-trigger-connection + tags: { kind: trigger, area: connection } + prompt: | + My Node.js service talking to Azure DocumentDB is throwing connection pool + exhaustion errors and timeouts under load. How should I configure the + MongoClient? + graders: + - type: skill-invocation + config: + required: [documentdb-connection] + + # --------------------------------------------------------------------- + # Anti-triggers — NO DocumentDB skill may activate + # (this is the half that usually goes untested) + # --------------------------------------------------------------------- + - name: postgres-question-should-not-trigger + tags: { kind: anti-trigger } + prompt: "How do I create a partial index in PostgreSQL for a users table?" + graders: + - type: skill-invocation + config: + disallowed: + - documentdb-query-optimizer + - documentdb-indexing + - documentdb-connection + - documentdb-data-modeling + - documentdb-vector-search + + - name: unrelated-question-should-not-trigger + tags: { kind: anti-trigger } + prompt: "How do I center a div horizontally and vertically with CSS grid?" + graders: + - type: skill-invocation + config: + disallowed: + - documentdb-query-optimizer + - documentdb-indexing + - documentdb-connection + - documentdb-data-modeling + - documentdb-vector-search + +scoring: + weights: + skill-invocation: 1.0 + # Skill activation is binary and objective: every stimulus must pass. + threshold: 1.0 diff --git a/evals/harness/token_usage.py b/evals/harness/token_usage.py new file mode 100644 index 0000000..c2581bd --- /dev/null +++ b/evals/harness/token_usage.py @@ -0,0 +1,380 @@ +#!/usr/bin/env python3 +"""Task-level token / AI-credit accounting for Cross-Model Skill Evaluations. + +WHY THIS EXISTS +--------------- +Cross-Model Skill Evaluations ask two questions about the text skills: *is the +output good* and *what did it cost*. Vally answers both, but only when a real +model executor is available. This module answers the cost half from data the +Copilot CLI already writes locally, so cost can be measured today and +cross-checked against Vally later. It is stdlib-only and read-only. + +Source: ``~/.copilot/session-store.db`` (SQLite), table +``assistant_usage_events`` — one row per model request: + + session_id, turn_index, agent_id, parent_tool_call_id, model, + input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, + reasoning_tokens, total_nano_aiu, request_multiplier, duration_ms, ... + +THE MEASUREMENT TRAP THIS MODULE IS BUILT TO AVOID +-------------------------------------------------- +Skills add input tokens by construction — they are extra context. So +"skills use more tokens" is trivially true and says nothing about whether they +are worth it. Two corrections make the number honest: + +1. ``cache_read_tokens`` is a SUBSET of ``input_tokens`` (verified: 0 of 6,348 + rows had cache_read > input). A skill payload is sent once and then read from + cache, so raw ``input_tokens`` massively overstates its marginal cost. We + report ``fresh_input = input - cache_read`` as the tokens actually paid for + at full rate, and ``cache_read_share`` to show the amortisation. + +2. The comparison that matters is COST-TO-OUTCOME, not cost-per-turn. A run that + costs more per turn but needs fewer turns to reach a passing result is + cheaper. Hence ``credits_to_green`` / ``turns_to_green``, which require an + outcome to be supplied alongside the usage data. + +``total_nano_aiu`` is the money column (AI Credits, 1e9 nano-AIU = 1 credit) and +already accounts for ``request_multiplier``, so it is comparable across models +in a way raw token counts are not. + +USAGE +----- + # what sessions exist + python3 token_usage.py sessions --limit 10 + + # one task's usage, as a JSON artifact + python3 token_usage.py report --session \ + --task orders-api --model claude-opus-5 --arm skills --run 3 \ + --outcome-passed 38 --outcome-total 40 + + # aggregate many artifacts into the arm-vs-arm comparison table + python3 token_usage.py compare results/*.json +""" + +from __future__ import annotations + +import argparse +import json +import os +import shutil +import sqlite3 +import statistics +import sys +import tempfile +from pathlib import Path + +DEFAULT_DB = Path.home() / ".copilot" / "session-store.db" + +NANO_AIU_PER_CREDIT = 1_000_000_000 + + +# -------------------------------------------------------------------------- +# database access +# -------------------------------------------------------------------------- +def _open_readonly(db_path: Path) -> tuple[sqlite3.Connection, Path]: + """Open a private snapshot of the session store. + + The CLI may be writing to this database while we read it. Opening the live + file read-only can still fail or read a torn state mid-checkpoint, so we + copy the database together with its -wal and -shm sidecars into a temp dir + and read the copy. Copying only the .db would silently lose every + transaction still sitting in the write-ahead log. + """ + if not db_path.exists(): + raise SystemExit( + f"Session store not found: {db_path}\n" + "Token accounting requires generation to be driven by the Copilot " + "CLI (see the plan, §3b path A). Pass --db if it lives elsewhere." + ) + + tmpdir = Path(tempfile.mkdtemp(prefix="tokenusage-")) + snapshot = tmpdir / db_path.name + shutil.copy2(db_path, snapshot) + for suffix in ("-wal", "-shm"): + sidecar = db_path.with_name(db_path.name + suffix) + if sidecar.exists(): + shutil.copy2(sidecar, snapshot.with_name(snapshot.name + suffix)) + + conn = sqlite3.connect(f"file:{snapshot}?mode=ro", uri=True) + conn.row_factory = sqlite3.Row + return conn, tmpdir + + +def _cleanup(conn: sqlite3.Connection, tmpdir: Path | None) -> None: + conn.close() + if tmpdir: + shutil.rmtree(tmpdir, ignore_errors=True) + + +# -------------------------------------------------------------------------- +# core rollup +# -------------------------------------------------------------------------- +_ROLLUP_SQL = """ +SELECT + COUNT(*) AS requests, + COUNT(DISTINCT turn_index) AS turns, + COALESCE(SUM(input_tokens), 0) AS input_tokens, + COALESCE(SUM(output_tokens), 0) AS output_tokens, + COALESCE(SUM(cache_read_tokens), 0) AS cache_read_tokens, + COALESCE(SUM(cache_write_tokens), 0) AS cache_write_tokens, + COALESCE(SUM(reasoning_tokens), 0) AS reasoning_tokens, + COALESCE(SUM(total_nano_aiu), 0) AS nano_aiu, + COALESCE(SUM(duration_ms), 0) AS duration_ms +FROM assistant_usage_events +WHERE session_id = ? +""" + + +def _derive(row: dict) -> dict: + """Add the derived metrics that make the raw counters interpretable.""" + inp = row["input_tokens"] + cache = row["cache_read_tokens"] + # cache_read is a subset of input (verified), so this is the input actually + # billed at the uncached rate — the real marginal cost of the context. + fresh = max(inp - cache, 0) + row["fresh_input_tokens"] = fresh + row["cache_read_share"] = round(cache / inp, 4) if inp else 0.0 + row["credits"] = round(row["nano_aiu"] / NANO_AIU_PER_CREDIT, 4) + row["wall_secs"] = round(row["duration_ms"] / 1000.0, 1) + return row + + +def session_usage(conn: sqlite3.Connection, session_id: str) -> dict | None: + row = conn.execute(_ROLLUP_SQL, (session_id,)).fetchone() + if row is None or row["requests"] == 0: + return None + return _derive(dict(row)) + + +def by_model(conn: sqlite3.Connection, session_id: str) -> list[dict]: + """Per-model split. A session can span models; costs are not comparable + across them, so never sum credits across models without saying so.""" + sql = _ROLLUP_SQL.replace( + "FROM assistant_usage_events", ", model FROM assistant_usage_events" + ) + " GROUP BY model ORDER BY model" + return [_derive(dict(r)) for r in conn.execute(sql, (session_id,))] + + +def by_agent(conn: sqlite3.Connection, session_id: str) -> list[dict]: + """Main-agent vs subagent split. + + Subagent spend is real spend. Reporting only the main agent would understate + the cost of any skill that delegates. + """ + sql = _ROLLUP_SQL.replace( + "FROM assistant_usage_events", + ", COALESCE(agent_id, 'main') AS agent FROM assistant_usage_events", + ) + " GROUP BY agent ORDER BY agent" + return [_derive(dict(r)) for r in conn.execute(sql, (session_id,))] + + +def list_sessions(conn: sqlite3.Connection, limit: int) -> list[dict]: + sql = """ + SELECT s.id, s.repository, s.branch, s.created_at, + COUNT(u.id) AS requests, + COALESCE(SUM(u.total_nano_aiu), 0) AS nano_aiu + FROM sessions s + LEFT JOIN assistant_usage_events u ON u.session_id = s.id + GROUP BY s.id + HAVING requests > 0 + ORDER BY s.created_at DESC + LIMIT ? + """ + out = [] + for r in conn.execute(sql, (limit,)): + d = dict(r) + d["credits"] = round(d.pop("nano_aiu") / NANO_AIU_PER_CREDIT, 2) + out.append(d) + return out + + +# -------------------------------------------------------------------------- +# report artifact +# -------------------------------------------------------------------------- +def build_report(conn: sqlite3.Connection, session_id: str, task: dict, + outcome: dict | None) -> dict: + usage = session_usage(conn, session_id) + if usage is None: + raise SystemExit( + f"No usage rows for session {session_id!r}.\n" + "Check the id with: token_usage.py sessions" + ) + + models = by_model(conn, session_id) + report = { + "task": task, + "session_id": session_id, + "models": [m["model"] for m in models], + "usage": usage, + "by_model": models, + "by_agent": by_agent(conn, session_id), + } + + if outcome: + report["outcome"] = outcome + green = outcome.get("green") + # Cost-to-green is only meaningful for a run that actually reached + # green. Emitting it for a failed run would flatter the failure — a run + # that gives up early looks "cheap". Left null instead, deliberately. + report["cost_to_green"] = { + "green": green, + "credits_to_green": usage["credits"] if green else None, + "turns_to_green": usage["turns"] if green else None, + "tokens_to_green": ( + usage["fresh_input_tokens"] + usage["output_tokens"] + if green else None + ), + } + return report + + +# -------------------------------------------------------------------------- +# comparison across runs +# -------------------------------------------------------------------------- +def _mean_sd(values: list[float]) -> dict: + if not values: + return {"n": 0, "mean": None, "sd": None} + return { + "n": len(values), + "mean": round(statistics.mean(values), 2), + "sd": round(statistics.stdev(values), 2) if len(values) > 1 else 0.0, + } + + +def compare(reports: list[dict]) -> dict: + """Group run artifacts by (model, arm) and summarise each cell. + + N per cell is reported explicitly: agent runs are non-deterministic, and a + mean over 1 run is not a result. Cells with n < 3 are flagged rather than + quietly presented as if they were comparable. + """ + cells: dict[tuple[str, str], list[dict]] = {} + for r in reports: + key = (r["task"].get("model") or "?", r["task"].get("arm") or "?") + cells.setdefault(key, []).append(r) + + rows = [] + for (model, arm), runs in sorted(cells.items()): + passed = [r for r in runs if r.get("outcome", {}).get("green")] + credits = [r["usage"]["credits"] for r in runs] + turns = [float(r["usage"]["turns"]) for r in runs] + fresh = [float(r["usage"]["fresh_input_tokens"]) for r in runs] + share = [r["usage"]["cache_read_share"] for r in runs] + rows.append({ + "model": model, + "arm": arm, + "runs": len(runs), + "underpowered": len(runs) < 3, + "pass_rate": round(len(passed) / len(runs), 3) if runs else None, + "credits": _mean_sd(credits), + "turns": _mean_sd(turns), + "fresh_input_tokens": _mean_sd(fresh), + "cache_read_share": _mean_sd(share), + # The ROI number: what one passing result costs. Undefined when + # nothing passed — reported as null, never as 0 or infinity. + "credits_per_pass": ( + round(sum(credits) / len(passed), 2) if passed else None + ), + }) + return {"cells": rows} + + +def render_table(comparison: dict) -> str: + hdr = ( + "| Model | Arm | N | Pass rate | Credits (mean±sd) | Turns | " + "Fresh input tok | Cache share | Credits/pass |" + ) + sep = "|---|---|---|---|---|---|---|---|---|" + lines = [hdr, sep] + for c in comparison["cells"]: + flag = " ⚠️" if c["underpowered"] else "" + cr, tn, fi, cs = (c["credits"], c["turns"], + c["fresh_input_tokens"], c["cache_read_share"]) + pass_rate = "—" if c["pass_rate"] is None else f"{c['pass_rate']:.0%}" + cpp = "—" if c["credits_per_pass"] is None else c["credits_per_pass"] + lines.append( + f"| {c['model']} | {c['arm']} | {c['runs']}{flag} | {pass_rate} | " + f"{cr['mean']}±{cr['sd']} | {tn['mean']}±{tn['sd']} | " + f"{fi['mean']:,.0f} | {cs['mean']:.0%} | {cpp} |" + ) + if any(c["underpowered"] for c in comparison["cells"]): + lines.append("") + lines.append("⚠️ = fewer than 3 runs in this cell; treat as indicative only.") + return "\n".join(lines) + + +# -------------------------------------------------------------------------- +# CLI +# -------------------------------------------------------------------------- +def main(argv=None) -> int: + p = argparse.ArgumentParser( + description="Task-level token / AI-credit accounting for Cross-Model Skill Evaluations." + ) + p.add_argument("--db", type=Path, default=DEFAULT_DB, + help=f"session store path (default: {DEFAULT_DB})") + sub = p.add_subparsers(dest="cmd", required=True) + + s = sub.add_parser("sessions", help="list sessions that have usage data") + s.add_argument("--limit", type=int, default=20) + + r = sub.add_parser("report", help="emit a per-task usage artifact") + r.add_argument("--session", required=True) + r.add_argument("--task", default=None, help="scenario / task name") + r.add_argument("--model", default=None) + r.add_argument("--arm", default=None, choices=["skills", "control"]) + r.add_argument("--run", type=int, default=None) + r.add_argument("--outcome-passed", type=int, default=None) + r.add_argument("--outcome-total", type=int, default=None) + r.add_argument("--out", type=Path, default=None) + + c = sub.add_parser("compare", help="aggregate report artifacts into a table") + c.add_argument("reports", nargs="+", type=Path) + c.add_argument("--json", action="store_true", help="emit JSON, not markdown") + + args = p.parse_args(argv) + + if args.cmd == "compare": + reports = [json.loads(f.read_text()) for f in args.reports] + result = compare(reports) + print(json.dumps(result, indent=2) if args.json else render_table(result)) + return 0 + + conn, tmpdir = _open_readonly(args.db) + try: + if args.cmd == "sessions": + for row in list_sessions(conn, args.limit): + print(f"{row['id']} {row['created_at']} " + f"{row['requests']:>5} req {row['credits']:>10.2f} cr " + f"{row.get('repository') or ''}") + return 0 + + outcome = None + if args.outcome_total is not None: + passed = args.outcome_passed or 0 + outcome = { + "tests_passed": passed, + "tests_total": args.outcome_total, + "green": passed == args.outcome_total and args.outcome_total > 0, + } + + report = build_report( + conn, + args.session, + {"task": args.task, "model": args.model, + "arm": args.arm, "run": args.run}, + outcome, + ) + text = json.dumps(report, indent=2) + if args.out: + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(text) + print(f"wrote {args.out}", file=sys.stderr) + else: + print(text) + return 0 + finally: + _cleanup(conn, tmpdir) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/evals/package-lock.json b/evals/package-lock.json new file mode 100644 index 0000000..19525be --- /dev/null +++ b/evals/package-lock.json @@ -0,0 +1,1867 @@ +{ + "name": "documentdb-agent-kit-evals", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + 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documentdb-skills/eval.yaml", + "eval:mock": "vally eval -e documentdb-skills/eval.yaml --executor mock", + "eval": "vally eval -e documentdb-skills/eval.yaml", + "experiment:plan": "vally experiment run documentdb-skills.experiment.yaml --dry-run", + "experiment": "vally experiment run documentdb-skills.experiment.yaml --compare", + "tokens": "python3 harness/token_usage.py", + "lint:quality": "vally lint -e documentdb-quality/quality-eval.yaml", + "eval:quality": "vally eval -e documentdb-quality/quality-eval.yaml", + "eval:quality:mock": "vally eval -e documentdb-quality/quality-eval.yaml --executor mock", + "experiment:quality:plan": "vally experiment run quality.experiment.yaml --dry-run", + "experiment:quality": "vally experiment run quality.experiment.yaml --compare", + "lint:all": "npm run lint && npm run lint:quality" + }, + "devDependencies": { + "@microsoft/vally-cli": "0.13.0" + } +} diff --git a/evals/quality.experiment.yaml b/evals/quality.experiment.yaml new file mode 100644 index 0000000..1e77f20 --- /dev/null +++ b/evals/quality.experiment.yaml @@ -0,0 +1,60 @@ +# quality.experiment.yaml — is the guidance BETTER with the kit installed? +# +# THE QUESTION +# ------------ +# `documentdb-skills.experiment.yaml` asks whether the right skill fires. +# This one asks the harder question: **is the resulting advice any good, and is +# it better than an agent without the kit would have given?** +# +# WHY A CONTROL ARM MATTERS EVEN MORE HERE THAN ELSEWHERE +# ------------------------------------------------------- +# Frontier models already know a lot about MongoDB. A bare agent will produce a +# perfectly respectable answer to "why is my query slow" — so a treatment-only +# score of 4.2/5 proves nothing whatsoever. The claim we want to make is +# comparative ("the kit makes the guidance better"), so the measurement has to +# be comparative too. +# +# It is entirely possible this experiment shows a SMALL delta. That is a real +# finding and must be reported as one: it would mean the kit's value lies in +# DocumentDB-specific correctness (which the `documentdb_specificity` gate +# isolates) rather than in general answer quality. +# +# COST +# ---- +# 2 arms x 4 stimuli x 3 runs = 24 agent runs, each graded by 3 judges = 72 +# judge calls. Start with `--variant` on a single cell before running the lot. +# +# Run: +# npm run experiment:quality:plan # free — resolve and print +# npm run experiment:quality # costs AI credits + +name: documentdb-guidance-quality-vs-control + +evals: + - documentdb-quality/quality-eval.yaml + +overrides: + runs: 3 + timeout: 5m + +execution: + workers: 2 + +matrix: + skills: + path: /environment/skills + values: + # CONTROL: a bare agent answering from its own knowledge. + - control: [] + # TREATMENT: the kit installed, discoverable, never mentioned. + - kit: + - ../skills/query-optimizer + - ../skills/query-optimization + - ../skills/indexing + - ../skills/connection + - ../skills/data-modeling + - ../skills/vector-search + +# The delta is measured against the unskilled agent. +baseline: + skills: control diff --git a/examples/azure-deployment/deploy.sh b/examples/azure-deployment/deploy.sh old mode 100644 new mode 100755 diff --git a/scenarios/contoso/scaling-benchmark/README.md b/scenarios/contoso/scaling-benchmark/README.md new file mode 100644 index 0000000..bfb26a3 --- /dev/null +++ b/scenarios/contoso/scaling-benchmark/README.md @@ -0,0 +1,39 @@ +# Scaling benchmark (optional / advanced) + +> **Not part of the quickstart.** These scripts seed **multiple** databases at +> increasing scale (x1, x2, x4, x6, x8, x16) and run repeated benchmarks. They +> take time and disk, and are meant for investigating how the TOAST detoast tax +> grows with data size — not for a first run. For the ready-to-run demo use the +> single base-size dataset via [`../seed.sh`](../seed.sh). + +## Contents + +| File | What it does | +|------|-------------| +| `scale-bench2.sh` | Kill-tolerant harness: seeds `contoso_x{1,2,4,6,8,16}` and times the BI query suite at each scale. | +| `contoso-probe.js` | Single-query survivable timing probe (used by the harness). | +| `toast-index-bench.sh` | Sweeps index configurations (`_id`-only, single, compound, covering-attempt, group-only) before/after the schema split, recording PostgreSQL block traffic (the detoast tax). | +| `toast-index-probe.js` | The per-config explain + timing probe the bench runs. | +| `results.tsv` | Captured scaling results (x1…x16). **Generated output — git-ignored; recreated by `scale-bench2.sh`.** | +| `diagnostic-report-contoso-scaling.md` | Full write-up of the scaling + before/after-split findings. | +| `bench-queries.js`, `bench-seed-deterministic.js` | Older engine-comparison benchmark (MongoDB vs DocumentDB) — deterministic seed + query timing. | + +## Key finding (summary) + +Under the co-located schema the canonical BI aggregation's PostgreSQL block reads +scale with the TOASTed text, not the scalars it queries; a **covering index +cannot avoid it** (it still `FETCH`es and detoasts the full document). Splitting +the large text into a side collection keyed by `_id` collapses block reads +(~75× on the selective query, ~57× full-scan) — **schema shape beats index +tuning** for this class of problem. Details in +`diagnostic-report-contoso-scaling.md`. + +## Run (advanced) + +```bash +# seeds contoso_x1..x16 and benchmarks each (slow; needs the container up) +bash scenarios/contoso/scaling-benchmark/scale-bench2.sh + +# before/after-split index matrix on a chosen scaled DB +bash scenarios/contoso/scaling-benchmark/toast-index-bench.sh --db contoso_x16 --phase before +``` diff --git a/scenarios/contoso/scaling-benchmark/bench-queries.js b/scenarios/contoso/scaling-benchmark/bench-queries.js new file mode 100644 index 0000000..343bc57 --- /dev/null +++ b/scenarios/contoso/scaling-benchmark/bench-queries.js @@ -0,0 +1,77 @@ +// Benchmark queries — same on both engines +var results = []; + +function time(label, fn) { + var start = Date.now(); + var count = 0; + try { count = fn(); } catch(e) { count = -1; } + var ms = Date.now() - start; + results.push({label: label, ms: ms, count: count}); + print(" " + ms + "ms\t" + count + "\t" + label); + return ms; +} + +print("═══ POINT QUERIES (find by indexed/unindexed field) ═══"); +time("find orders by order_id", function() { return db.orders.find({order_id:"ORD_025000"}).toArray().length; }); +time("find orders by customer_id", function() { return db.orders.find({customer_id:"CUST_002500"}).toArray().length; }); +time("find orders by status=shipped", function() { return db.orders.find({status:"shipped"}).limit(100).toArray().length; }); +time("find orders by status+date sort", function() { return db.orders.find({status:"pending"}).sort({created_at:-1}).limit(20).toArray().length; }); +time("find orders by city", function() { return db.orders.find({shipping_city:"Seattle"}).limit(100).toArray().length; }); +time("find order_items by order_id", function() { return db.order_items.find({order_id:"ORD_025000"}).toArray().length; }); +time("find order_items by product_id", function() { return db.order_items.find({product_id:"PROD_001000"}).toArray().length; }); +time("find customers by customer_id", function() { return db.customers.find({customer_id:"CUST_002500"}).toArray().length; }); +time("find customers by email", function() { return db.customers.find({email:"cust2500@example.com"}).toArray().length; }); +time("find customers by tier+active", function() { return db.customers.find({tier:"gold",is_active:true}).limit(50).toArray().length; }); +time("find products by category+price", function() { return db.products.find({category_id:"CAT_005",price:{$gt:100,$lt:300}}).toArray().length; }); +time("find products by brand", function() { return db.products.find({brand:"TechPro"}).limit(50).toArray().length; }); +time("find reviews by product_id", function() { return db.reviews.find({product_id:"PROD_001000"}).toArray().length; }); +time("find reviews by rating>=4", function() { return db.reviews.find({rating:{$gte:4}}).limit(100).toArray().length; }); + +print(""); +print("═══ COUNT QUERIES ═══"); +time("count orders", function() { return db.orders.countDocuments(); }); +time("count order_items", function() { return db.order_items.countDocuments(); }); +time("count reviews", function() { return db.reviews.countDocuments(); }); +time("count customers", function() { return db.customers.countDocuments(); }); + +print(""); +print("═══ AGGREGATION QUERIES ═══"); +time("agg: revenue by status", function() { return db.orders.aggregate([{$group:{_id:"$status",total:{$sum:"$total_amount"},count:{$sum:1}}}]).toArray().length; }); +time("agg: revenue by city (delivered)", function() { return db.orders.aggregate([{$match:{status:"delivered"}},{$group:{_id:"$shipping_city",revenue:{$sum:"$total_amount"}}}]).toArray().length; }); +time("agg: top 10 customers by spend", function() { return db.orders.aggregate([{$group:{_id:"$customer_id",total:{$sum:"$total_amount"}}},{$sort:{total:-1}},{$limit:10}]).toArray().length; }); +time("agg: top 10 products by qty sold", function() { return db.order_items.aggregate([{$group:{_id:"$product_id",totalQty:{$sum:"$quantity"}}},{$sort:{totalQty:-1}},{$limit:10}]).toArray().length; }); +time("agg: avg rating per product (top10)", function() { return db.reviews.aggregate([{$group:{_id:"$product_id",avgRating:{$avg:"$rating"},count:{$sum:1}}},{$sort:{count:-1}},{$limit:10}]).toArray().length; }); +time("agg: orders per month", function() { return db.orders.aggregate([{$group:{_id:{$month:"$created_at"},count:{$sum:1},revenue:{$sum:"$total_amount"}}},{$sort:{_id:1}}]).toArray().length; }); + +print(""); +print("═══ LOOKUP / JOIN QUERIES ═══"); +time("lookup: order + items (1 order)", function() { return db.orders.aggregate([{$match:{order_id:"ORD_025000"}},{$lookup:{from:"order_items",localField:"order_id",foreignField:"order_id",as:"items"}}]).toArray().length; }); +time("lookup: customer orders (1 cust)", function() { return db.orders.aggregate([{$match:{customer_id:"CUST_002500"}},{$lookup:{from:"order_items",localField:"order_id",foreignField:"order_id",as:"items"}},{$limit:10}]).toArray().length; }); + +print(""); +print("═══ EXPLAIN CHECKS (COLLSCAN vs IXSCAN) ═══"); +var explainChecks = [ + ["orders", {order_id:"ORD_025000"}], + ["orders", {status:"shipped"}], + ["orders", {shipping_city:"Seattle"}], + ["order_items", {order_id:"ORD_025000"}], + ["order_items", {product_id:"PROD_001000"}], + ["customers", {customer_id:"CUST_002500"}], + ["customers", {email:"cust2500@example.com"}], + ["products", {category_id:"CAT_005",price:{$gt:100}}], + ["reviews", {product_id:"PROD_001000"}], + ["reviews", {rating:{$gte:4}}] +]; +explainChecks.forEach(function(c) { + var plan = db.runCommand({explain:{find:c[0],filter:c[1],limit:1},verbosity:"executionStats"}); + var wp = (plan.queryPlanner||{}).winningPlan||{}; + var es = plan.executionStats||{}; + var stage = wp.stage || (wp.inputStage||{}).stage || "?"; + print(" " + stage + "\t" + (es.totalDocsExamined||0) + " docs\t" + c[0] + " " + JSON.stringify(c[1]).substring(0,50)); +}); + +print(""); +var totalMs = results.reduce(function(s,r){return s+r.ms;},0); +var slow = results.filter(function(r){return r.ms>100;}).length; +var moderate = results.filter(function(r){return r.ms>20&&r.ms<=100;}).length; +print("TOTAL: " + totalMs + "ms across " + results.length + " queries (" + slow + " slow, " + moderate + " moderate)"); diff --git a/scenarios/contoso/scaling-benchmark/bench-seed-deterministic.js b/scenarios/contoso/scaling-benchmark/bench-seed-deterministic.js new file mode 100644 index 0000000..58690ab --- /dev/null +++ b/scenarios/contoso/scaling-benchmark/bench-seed-deterministic.js @@ -0,0 +1,92 @@ +// Seed identical ecommerce data — deterministic so both DBs get same docs +var rng_seed = 42; +function rng() { rng_seed = (rng_seed * 1103515245 + 12345) & 0x7fffffff; return rng_seed / 0x7fffffff; } +function rngInt(min,max) { return min + Math.floor(rng() * (max-min+1)); } +function pad(n,w) { var s=String(n); while(s.length0.1,loyalty_points:rngInt(0,10000), + address:{city:cities[rngInt(0,9)],state:"ST",zip:pad(rngInt(10000,99999),5)}, + username:"user"+pad(i,6),created_at:new Date(2024,rngInt(0,11),rngInt(1,28))}); + } + db.customers.insertMany(custs); +} +print(" Customers: "+db.customers.countDocuments()); + +// Products +var brands=["TechPro","GadgetX","SmartHome","EcoLife","FitGear","StyleCo","NatureWell","PowerMax","CoolTech","SafeGuard"]; +var prods=[]; +for (var i=0;i<2000;i++) { + prods.push({product_id:"PROD_"+pad(i,6),name:"Product "+i,category_id:"CAT_"+pad(rngInt(1,10),3), + supplier_id:"SUP_"+pad(rngInt(1,50),3),brand:brands[rngInt(0,9)],price:Math.round(rng()*500*100)/100, + cost:Math.round(rng()*200*100)/100,active:rng()>0.05,tags:["tag"+rngInt(1,20),"tag"+rngInt(1,20)]}); +} +db.products.insertMany(prods); +print(" Products: "+db.products.countDocuments()); + +// Inventory +var whs=["WH_EAST","WH_WEST","WH_CENTRAL"]; +var invs=[]; +for (var i=0;i<2000;i++) { + var nw=rngInt(1,3); + for (var w=0;w0.7?Math.round(rng()*20*100)/100:0}); + } + ords.push({order_id:"ORD_"+pad(i,6),customer_id:"CUST_"+pad(rngInt(0,4999),6), + status:statuses[rngInt(0,4)],payment_method:payments[rngInt(0,3)], + total_amount:Math.round(total*100)/100, + shipping_city:cities[rngInt(0,9)], + created_at:new Date(2024,rngInt(0,11),rngInt(1,28))}); + } + db.orders.insertMany(ords); + db.order_items.insertMany(items); +} +print(" Orders: "+db.orders.countDocuments()+", Order Items: "+db.order_items.countDocuments()); + +// Reviews +for (var batch=0;batch<25;batch++) { + var revs=[]; + for (var i=batch*1000;i<(batch+1)*1000;i++) { + revs.push({review_id:"REV_"+pad(i,6),product_id:"PROD_"+pad(rngInt(0,1999),6), + customer_id:"CUST_"+pad(rngInt(0,4999),6),order_id:"ORD_"+pad(rngInt(0,49999),6), + rating:rngInt(1,5),title:"Review "+i,text:"This is review text for item "+i, + helpful_votes:rngInt(0,50),created_at:new Date(2024,rngInt(0,11),rngInt(1,28))}); + } + db.reviews.insertMany(revs); +} +print(" Reviews: "+db.reviews.countDocuments()); + +print("SEED COMPLETE"); diff --git a/scenarios/contoso/scaling-benchmark/contoso-probe.js b/scenarios/contoso/scaling-benchmark/contoso-probe.js new file mode 100644 index 0000000..15604a7 --- /dev/null +++ b/scenarios/contoso/scaling-benchmark/contoso-probe.js @@ -0,0 +1,21 @@ +// contoso-probe.js — minimal, survivable timing probe. +// Runs ONE canonical BI query (open pipeline value by territory: full scan + +// $group over opportunities — the query that pays the detoast tax) REPS times +// and prints the MIN wall-clock ms. Sub-second even at large scale, so it +// completes between container cycles in a flaky environment. +// Env: CONTOSO_DB, QUERY_REPS (default 7) +var DB = process.env.CONTOSO_DB || "contoso_x1"; +var REPS = parseInt(process.env.QUERY_REPS || "7"); +var d = db.getSiblingDB(DB); +var best = Infinity, rows = 0; +for (var r = 0; r < REPS; r++) { + var t = Date.now(); + rows = d.opportunities.aggregate([ + { $match: { state: "open" } }, + { $group: { _id: "$territory_id", pipeline: { $sum: "$est_value" }, deals: { $sum: 1 } } }, + { $sort: { pipeline: -1 } } + ]).toArray().length; + var ms = Date.now() - t; + if (ms < best) best = ms; +} +print("PROBE " + DB + " min_ms=" + best + " reps=" + REPS + " opps=" + d.opportunities.countDocuments() + " groups=" + rows); diff --git a/scenarios/contoso/scaling-benchmark/diagnostic-report-contoso-scaling.md b/scenarios/contoso/scaling-benchmark/diagnostic-report-contoso-scaling.md new file mode 100644 index 0000000..b73ed5f --- /dev/null +++ b/scenarios/contoso/scaling-benchmark/diagnostic-report-contoso-scaling.md @@ -0,0 +1,237 @@ +# DocumentDB Agent-Kit — Contoso Scaling & Diagnostic Report + +**Generated:** 2026-07-02 05:20 UTC +**Container:** `documentdb-local` — `ghcr.io/documentdb/documentdb-local:latest` +**Scenario dir:** `documentdb-agent-kit/scenarios/contoso/` +**New diagnostic tools:** `scripts/document-bloat-advisor.sh`, `scripts/db-config-advisor.sh` + +> Filename note: the request spelled this `diagnoistic-report-contodo`; saved with the +> corrected spelling `diagnostic-report-contoso` so it is discoverable. + +--- + +## 1. Dataset — Contoso "Dynamics 365 Sales" model + +Source: `mscottsewell/ContosoBI → Contoso - Sales - Current Release`. That release is the +**Dynamics 365 Sales (CRM)** demo (the numbered import files are Campaigns, Territories, +Accounts, Opportunities). The bundled `.xlsx` are password-encrypted, so the model was +reproduced faithfully as documents, keeping **all entities and relationships**: + +``` +territories (dim) +users (salespeople) -> territory_id +products (dim) (moderate text: description ~400B) +campaigns (BIG text: content ~4KB) [scaled] +accounts -> territory_id, owner_id (BIG text: profile ~3.5KB) [scaled] +opportunities -> account_id, campaign_id, owner_id, territory_id [scaled] + line_items:[ -> product_id ] + narrative ~3.5KB + activity_log ~2.5KB <-- large text CO-LOCATED (the anti-pattern) +``` + +**Text extension (MongoDB/DocumentDB-friendly):** each large entity carries realistic, +low-compressibility free-text. Scale factor multiplies products/campaigns/accounts and the +fact-like `opportunities` (base = 500 opportunities per scale unit). + +Scale folders/datasets generated: **x1, x2, x4, x6, x8, x16** as databases +`contoso_x1 … contoso_x16` (seeder: `contoso-seed.js`, deterministic + resumable). + +--- + +## 2. The DocumentDB-specific anti-pattern (not generic bad code) + +DocumentDB stores each document as **one BSON column** in PostgreSQL. A large, +low-compressibility field is pushed **out-of-line into a TOAST table**. Because the whole +document is a single column, **reading any scalar field detoasts the entire document** — so +co-locating big text with fields your BI queries aggregate imposes a per-scan **detoast tax**. + +Measured proof (validation probe): a collection of 1,000 docs with a varied ~6 KB text field → +**heap 80 KB, TOAST 8,000 KB**. The same field filled with *compressible* text (`"x"×6000`) → +**TOAST 0** (compressed inline). So the effect is specific to *realistic* large text. + +Measured on the Contoso `opportunities` collection (x1): **heap 40 KB vs TOAST 3,880 KB — 99 % +of the collection is text the BI queries never read.** + +This is **not** generic "SELECT *" advice: in DocumentDB **projection does not avoid the tax** +(the document is detoasted server-side *before* projection). The correct fix is a **schema +split** — move the big text into a side collection keyed by `_id`. + +The suite intentionally ships this suboptimal schema first, then shows the tools finding it. + +--- + +## 3. Business query suite (`contoso-queries.js`) + +Seven realistic BI aggregations, each needing **only scalar fields** (so each pays the detoast +tax under the co-located schema): + +| # | Query | Business question | +|---|-------|-------------------| +| Q1 | `pipeline_by_territory` | Open pipeline value by territory | +| Q2 | `value_by_stage` | Deal count & value by sales stage | +| Q3 | `monthly_bookings` | Won bookings trend by month | +| Q4 | `top_accounts` | Top accounts by pipeline | +| Q5 | `product_mix` | Revenue by product (line items) | +| Q6 | `rep_leaderboard` | Won value by sales rep | +| Q7 | `avg_deal_by_industry` | Avg deal size by industry (join to accounts) | + +Metric = **min wall-clock over 5 repetitions** (min = least noise). + +--- + +## 4. Scaling test — the bottleneck the curve reveals + +Same query suite, increasing dataset size (co-located schema). Backing-Postgres +`opportunities` heap/TOAST captured per scale. + +| Scale | Opps | Total (ms) | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Heap | TOAST | +|------:|-----:|-----------:|---:|---:|---:|---:|---:|---:|---:|-----:|------:| +| x1 | 500 | **76** | 8 | 9 | 7 | 8 | 11 | 7 | 26 | 40 KB | 3.97 MB | +| x2 | 1000 | **95** | 12 | 14 | 11 | 11ᵃ| 17 | 11 | 41 | 73 KB | 7.98 MB | +| x4 | 2000 | **225** | 21 | 26 | 20 | 27 | 31 | 19 | 81 | 139 KB | 16.6 MB | +| x6 | 3000 | **243** | 26 | 31 | 26 | 31 | 45 | 25 | 59 | 221 KB | 23.8 MB | +| x8 | 4000 | **413** | 39 | 48 | 37 | 55 | 58 | 37 | 139 | 278 KB | 31.9 MB | +| x16 | 8000 | **708** | 74 | 99 | 77 | 111| 115| 74 | 158 | 557 KB | 63.5 MB | + +ᵃ x2/Q4 = parsing artifact in the harness; the total (95 ms) is correct. + +**Bottleneck revealed:** query latency grows roughly **linearly with dataset size** +(76 ms → 708 ms as data grows 16×), tracking the **TOAST volume** (3.97 MB → 63.5 MB, exactly +16×). Every BI aggregation must detoast the full text of every opportunity it scans, even +though it uses none of it. Note the heap stays tiny (40 → 557 KB) — the cost is **all detoast**, +which no index can fix. + +(With `shared_buffers = 128 MB` the working set stays cached even at x16, so this is a +**CPU/buffer-traffic** tax from detoasting, not disk I/O — it would additionally become disk I/O +once the working set exceeds cache.) + +--- + +## 5. Diagnostics detect the problem (x8, BEFORE) + +### 5a. `document-bloat-advisor.sh --db contoso_x8` +``` +⚠️ opportunities heap=272KB TOAST=31144KB (TOAST ratio 0.991) avgObjSize=6342B + dominant fields: narrative:3502B, activity_log:2502B, line_items:174B + Fix: move 'narrative' (+ other large text) into side collection + 'opportunities_text' keyed by _id; keep opportunities scalar-only. +⚠️ accounts heap=88KB TOAST=4800KB (ratio 0.982) profile:3502B +⚠️ campaigns heap=8KB TOAST=256KB (ratio 0.970) content:4002B +✅ products heap=256KB TOAST=0KB (ratio 0.000) — no bloat +Flagged 3 collections; 36,200KB of TOASTed text detoasted on every scan. +``` +The tool cross-references MongoDB `avgObjSize` with PostgreSQL heap/TOAST sizes, names the +offending fields, and prescribes the DocumentDB-specific schema-split fix. `products` +(compressible/short description) is correctly **not** flagged. + +### 5b. `db-config-advisor.sh --db contoso_x8` +``` +shared_buffers = 128 MB (source: configuration file) +Working set for 'contoso_x8' = 36.9 MB (heap 0.6 + TOAST 35.4 + idx 0.3) TOAST share = 96% +Cache hit: heap 100% / TOAST 100% / index 100% (a few TOAST blocks read from disk) +• TOAST is 96% of the working set. Removing it (schema split) would cut the working set to + 1.6 MB, letting the hot data stay resident WITHOUT changing config. +``` +Every statement is tied to a **measured** number — no generic "set shared_buffers to 25 % of +RAM." The advisor's recommendation (shrink the working set before touching config) points back +to the bloat fix. + +--- + +## 6. Before / After — applying the diagnostic-recommended fix + +Fix applied by `contoso-split-fix.js` (the remediation the advisor recommends): move +`narrative` + `activity_log` into `opportunities_text` (keyed by `_id`), `$unset` them from +`opportunities`, then `VACUUM`. + +### 6a. Query latency (x8, 4,000 opportunities, min of 5 reps) + +| Query | Before (ms) | After (ms) | Speed-up | +|-------|------------:|-----------:|---------:| +| pipeline_by_territory | 39 | 8 | **4.9×** | +| value_by_stage | 48 | 9 | **5.3×** | +| monthly_bookings | 37 | 7 | **5.3×** | +| top_accounts | 55 | 10 | **5.5×** | +| product_mix | 58 | 32 | 1.8× | +| rep_leaderboard | 37 | 6 | **6.2×** | +| avg_deal_by_industry | 139 | 45 | 3.1× | +| **TOTAL** | **413** | **117** | **3.5×** | + +Pure `opportunities` scans got **~5× faster**. The two smaller gains are instructive: +`product_mix` still unwinds `line_items` (kept in the doc), and `avg_deal_by_industry` **joins +`accounts`, which is still bloated** — i.e., the remaining tax comes from collections not yet +fixed, reinforcing the advisor's other findings. + +### 6b. Storage / working set (opportunities) + +| State | opportunities heap | opportunities TOAST | BI hot-path size | +|-------|-------------------:|--------------------:|-----------------:| +| Before | 272 KB | 31,144 KB | **≈ 31.4 MB** | +| After | 2,024 KB | **0 KB** | **≈ 2.0 MB** (‑15.7×) | + +`document-bloat-advisor.sh` after split + vacuum: +``` +✅ opportunities heap=2024KB TOAST=0KB (ratio 0.000) — no bloat +⚠️ opportunities_text (now holds the 31 MB text — COLD, read only for detail views) +``` + +**The text is preserved, just relocated** off the hot BI path. Total stored bytes are similar; +what changed is that BI aggregations now scan a 2 MB inline heap instead of detoasting 31 MB — +hence the 3.5–6× latency win. The fix is an **application/schema change** (write text to +`opportunities_text`, read it only on detail views), exactly the kind of guidance +`index-redundancy-finder.sh` gives for indexes. + +--- + +## 7. What the two new tools add to the kit + +| Tool | Layer | What it measures (facts only) | Actionable output | +|------|-------|-------------------------------|-------------------| +| `document-bloat-advisor.sh` | Mongo + PG | avgObjSize, per-collection heap vs TOAST, dominant fields | Which collection/field to split into a side collection (app-code fix) | +| `db-config-advisor.sh` | PG | shared_buffers, working set (heap+TOAST+idx), measured cache-hit, TOAST share | Evidence-based: shrink working set (bloat fix) vs raise `shared_buffers` toward the *measured* working set | + +Both follow the kit's principle established earlier: **report measured facts and concrete +structural issues; never emit generic rules-of-thumb.** The config advisor deliberately does +not prescribe "25 % of RAM"; it derives everything from the workload's own numbers. + +--- + +## 8. Reproduce + +```bash +# seed one scale (resumable) +docker cp scenarios/contoso/contoso-seed.js :/tmp/ +CONTOSO_DB=contoso_x8 CONTOSO_SCALE=8 mongosh ... --file /tmp/contoso-seed.js + +# full scaling benchmark (kill-tolerant) +bash scenarios/contoso/scale-bench2.sh "1 2 4 6 8 16" + +# diagnose +bash scripts/document-bloat-advisor.sh --db contoso_x8 +bash scripts/db-config-advisor.sh --db contoso_x8 + +# apply the fix, re-measure +CONTOSO_DB=contoso_x8 mongosh ... --file /tmp/contoso-split-fix.js +``` + +--- + +## 9. Environment note (transparency) + +The shared Docker host repeatedly killed the container; worse, `documentdb-local` re-runs its +sample-data init on every start and **crash-loops** (dup-key on `01-users.js`) once `/data` is +populated. Recovery: `docker commit` (no external volume exists — commit preserves `/data`), +then run with `-e SKIP_INIT_DATA=true -e INIT_DATA_PATH=/tmp/noinit` to skip init. After that +the container was stable and all six scales seeded and benched cleanly. The seeder and harness +were made **resumable/kill-tolerant** so partial seeds converge across restarts. + +--- + +## 10. Headline + +- **Bottleneck:** BI query latency scales linearly with data because large co-located text is + detoasted on every scan (x1 76 ms → x16 708 ms; TOAST 3.97 → 63.5 MB). +- **Diagnosis:** `document-bloat-advisor.sh` pinpoints `opportunities` at 99 % TOAST and names + the fields; `db-config-advisor.sh` shows TOAST is 96 % of the working set. +- **Fix (app/schema):** split large text into a side collection → **3.5× faster** query suite + (individual scans up to **6× faster**), hot working set **15.7× smaller**, zero TOAST on the + hot path — with no config change and no data loss. diff --git a/scenarios/contoso/scaling-benchmark/scale-bench2.sh b/scenarios/contoso/scaling-benchmark/scale-bench2.sh new file mode 100755 index 0000000..90b6020 --- /dev/null +++ b/scenarios/contoso/scaling-benchmark/scale-bench2.sh @@ -0,0 +1,73 @@ +#!/usr/bin/env bash +# scale-bench2.sh — kill-tolerant Contoso scaling benchmark. +# Seeding is resumable, so each scale is retried (ensuring the container up +# every attempt) until opportunities == 500*scale, then benched. +set -uo pipefail + +SCALES="${1:-1 2 4 6 8 16}" +DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +CONT="${CONTAINER:-documentdb-local}" +DB_USER="${DB_USER:-docdbadmin}" +PASSWORD="${DB_PASSWORD:-}" +[[ -z "$PASSWORD" ]] && { echo "Error: no password. Set DB_PASSWORD (e.g. export DB_PASSWORD='')." >&2; exit 1; } +A=(-u "$DB_USER" -p "$PASSWORD" --authenticationMechanism SCRAM-SHA-256 --tls --tlsAllowInvalidCertificates --quiet) +RESULTS="$DIR/results.tsv" +QREPS="${QREPS:-5}" +MAXTRY="${MAXTRY:-40}" + +ensure(){ for i in $(seq 1 120); do docker start "$CONT" >/dev/null 2>&1; docker exec "$CONT" mongosh "localhost:10260/admin" "${A[@]}" --eval "db.runCommand({ping:1}).ok" 2>/dev/null | grep -q 1 && return 0; sleep 1; done; return 1; } +oppcount(){ docker exec "$CONT" mongosh "localhost:10260/$1" "${A[@]}" --eval "print(db.opportunities.countDocuments())" 2>/dev/null | tr -dc '0-9'; } + +ensure || { echo "not ready"; exit 1; } +docker cp "$DIR/../contoso-seed.js" "$CONT:/tmp/contoso-seed.js" >/dev/null 2>&1 +docker cp "$DIR/../contoso-queries.js" "$CONT:/tmp/contoso-queries.js" >/dev/null 2>&1 + +: > "${RESULTS}.tmp" 2>/dev/null || true +echo -e "scale\topp_count\ttotal_min_ms\tpipeline\tstage\tmonthly\ttopacct\tprodmix\treplead\tavgindustry\theap_bytes\ttoast_bytes" > "$RESULTS" + +for S in $SCALES; do + DB="contoso_x${S}"; EXP=$((500*S)) + echo "── x${S} (${DB}) target opps=${EXP} ──" + # seed-until-complete + try=0; cnt=0 + while [[ "$try" -lt "$MAXTRY" ]]; do + try=$((try+1)) + ensure || { echo " attempt ${try}: not ready"; continue; } + cnt=$(oppcount "$DB"); cnt="${cnt:-0}" + [[ "$cnt" == "$EXP" ]] && { echo " seeded (${cnt}) after ${try} attempt(s)"; break; } + echo " attempt ${try}: have ${cnt}, seeding..." + docker exec -e CONTOSO_DB="$DB" -e CONTOSO_SCALE="$S" "$CONT" mongosh "localhost:10260/${DB}" "${A[@]}" --file /tmp/contoso-seed.js >/dev/null 2>&1 || true + done + cnt=$(oppcount "$DB"); cnt="${cnt:-0}" + [[ "$cnt" != "$EXP" ]] && { echo " WARN: ${DB} incomplete (${cnt}/${EXP}); recording anyway"; } + + # bench with retry (need a QSUMMARY line) + OUT=""; btry=0 + while [[ "$btry" -lt 12 ]]; do + btry=$((btry+1)); ensure || continue + OUT=$(docker exec -e CONTOSO_DB="$DB" -e QUERY_REPS="$QREPS" "$CONT" mongosh "localhost:10260/${DB}" "${A[@]}" --file /tmp/contoso-queries.js 2>/dev/null) + echo "$OUT" | grep -q QSUMMARY && break + done + getq(){ echo "$OUT" | awk -v q="$1" '/QRESULT/ && $0 ~ q {n=$0; sub(/.*"ms":/,"",n); sub(/[,}].*/,"",n); print n; exit}'; } + TOTAL=$(echo "$OUT" | awk '/QSUMMARY/{n=$0; sub(/.*total_min_ms":/,"",n); sub(/[,}].*/,"",n); print n; exit}') + Q1=$(getq pipeline_by_territory); Q2=$(getq value_by_stage); Q3=$(getq monthly_bookings) + Q4=$(getq top_accounts); Q5=$(getq product_mix); Q6=$(getq rep_leaderboard); Q7=$(getq avg_deal_by_industry) + + # sizes with retry + SZ=""; ztry=0 + while [[ "$ztry" -lt 12 ]]; do + ztry=$((ztry+1)); ensure || continue + SZ=$(docker exec "$CONT" psql -h localhost -p 9712 -U documentdb -d postgres -t --no-align -F $'\t' -c " + SELECT pg_relation_size(t.oid), COALESCE(pg_relation_size(NULLIF(t.reltoastrelid,0)),0) + FROM documentdb_api_catalog.collections c + JOIN pg_class t ON t.oid=('documentdb_data.documents_'||c.collection_id)::regclass + WHERE c.database_name='${DB}' AND c.collection_name='opportunities';" 2>/dev/null | grep -vE '^SET$' | head -1) + [[ -n "$SZ" ]] && break + done + HEAP=$(echo "$SZ" | cut -f1); TOAST=$(echo "$SZ" | cut -f2) + + echo -e "${S}\t${cnt}\t${TOTAL}\t${Q1}\t${Q2}\t${Q3}\t${Q4}\t${Q5}\t${Q6}\t${Q7}\t${HEAP}\t${TOAST}" >> "$RESULTS" + echo " => opps=${cnt} total_min_ms=${TOTAL} heap=${HEAP} toast=${TOAST}" +done + +echo ""; echo "=== ${RESULTS} ==="; cat "$RESULTS" diff --git a/scenarios/contoso/scaling-benchmark/toast-index-bench.sh b/scenarios/contoso/scaling-benchmark/toast-index-bench.sh new file mode 100755 index 0000000..a734434 --- /dev/null +++ b/scenarios/contoso/scaling-benchmark/toast-index-bench.sh @@ -0,0 +1,111 @@ +#!/usr/bin/env bash +# toast-index-bench.sh — sweep a matrix of index configurations against the +# canonical Contoso BI queries and record the detoast tax (PG block traffic + +# docs examined + timing). Run it once BEFORE the schema split and once AFTER to +# get an apples-to-apples before/after table at identical row counts. +# +# Emits a TSV to stdout (one row per index-config x query) plus per-config +# heap/TOAST bytes. Meant to be captured into a report log. +# +# Usage: +# bash toast-index-bench.sh --db contoso_x16 --phase before +# bash toast-index-bench.sh --db contoso_x16 --phase after +set -uo pipefail + +CONTAINER="${CONTAINER:-documentdb-local}" +PORT="${PORT:-10260}" +PG_PORT="${PG_PORT:-9712}" +PG_USER="${PG_USER:-documentdb}" +DB_USER="${DB_USER:-docdbadmin}" +PASSWORD="${DB_PASSWORD:-}" +DB="contoso_x16" +PHASE="before" +REPS="${QUERY_REPS:-7}" + +while [[ $# -gt 0 ]]; do + case "$1" in + --db) DB="$2"; shift 2;; + --phase) PHASE="$2"; shift 2;; + --reps) REPS="$2"; shift 2;; + *) echo "unknown arg $1" >&2; exit 2;; + esac +done + +[[ -z "$PASSWORD" ]] && { echo "Error: no password. Set DB_PASSWORD or pass --password (e.g. export DB_PASSWORD='')." >&2; exit 1; } + +mongo() { + docker exec "$CONTAINER" mongosh "localhost:${PORT}/${DB}" \ + -u "$DB_USER" -p "$PASSWORD" --authenticationMechanism SCRAM-SHA-256 \ + --tls --tlsAllowInvalidCertificates --quiet --eval "$1" 2>/dev/null +} +psql_q() { + docker exec "$CONTAINER" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d postgres \ + -t --no-align -F $'\t' -c "$1" 2>/dev/null | grep -vE '^(SET|)$' +} + +# copy the probe into the container once +docker cp "$(dirname "$0")/toast-index-probe.js" "${CONTAINER}:/tmp/toast-index-probe.js" >/dev/null 2>&1 + +run_probe() { # $1=query $2=label + docker exec -e CONTOSO_DB="$DB" -e QUERY="$1" -e IDX_LABEL="$2" -e QUERY_REPS="$REPS" \ + "$CONTAINER" mongosh "localhost:${PORT}/${DB}" \ + -u "$DB_USER" -p "$PASSWORD" --authenticationMechanism SCRAM-SHA-256 \ + --tls --tlsAllowInvalidCertificates --quiet --file /tmp/toast-index-probe.js 2>/dev/null \ + | grep '^IDXRESULT' | sed 's/^IDXRESULT //' +} + +# drop every secondary index (keep _id_) so each config starts clean +reset_indexes() { + mongo ' + db.opportunities.getIndexes().forEach(function(i){ + if (i.name !== "_id_") { try { db.opportunities.dropIndex(i.name); } catch(e){} } + }); + ' >/dev/null +} + +heap_toast() { # -> "heap_bytestoast_bytes" + psql_q " + SELECT pg_relation_size(t.oid), + COALESCE(pg_relation_size(NULLIF(t.reltoastrelid,0)),0) + FROM documentdb_api_catalog.collections c + JOIN pg_class t ON t.oid = ('documentdb_data.documents_'||c.collection_id)::regclass + WHERE c.database_name='${DB}' AND c.collection_name='opportunities';" +} + +# ── index configurations to sweep ────────────────────────────────────────── +# label|createIndex spec ("" = none, baseline _id_ only) +CONFIGS=( + "baseline_id_only|" + "single_state|{state:1}" + "compound_state_terr|{state:1,territory_id:1}" + "covering_attempt|{state:1,territory_id:1,est_value:1}" + "group_key_only|{territory_id:1}" +) + +read -r HEAP TOAST <<<"$(heap_toast)" +echo "# phase=${PHASE} db=${DB} opps heap_bytes=${HEAP} toast_bytes=${TOAST} reps=${REPS}" +printf 'phase\tquery\tconfig\tindex_used\tstage\tdocs_examined\tblocks_total\tblocks_mib\tplan_ms\tmin_ms\trows\n' + +for QUERY in selective fullscan; do + for entry in "${CONFIGS[@]}"; do + label="${entry%%|*}" + spec="${entry#*|}" + reset_indexes + if [[ -n "$spec" ]]; then + mongo "db.opportunities.createIndex(${spec});" >/dev/null + fi + json="$(run_probe "$QUERY" "$label")" + [[ -z "$json" ]] && { echo -e "${PHASE}\t${QUERY}\t${label}\tERROR"; continue; } + # extract fields with python (stdlib) for robustness + echo "$json" | python3 -c ' +import sys, json +d = json.load(sys.stdin) +print("\t".join(str(x) for x in [ + "'"$PHASE"'", d["query"], d["idx"], d["index_used"], d["stage"], + d["docs_examined"], d["blocks_total"], d["blocks_mib"], + d["plan_ms"], d["min_ms"], d["rows"] +]))' + done +done + +reset_indexes diff --git a/scenarios/contoso/scaling-benchmark/toast-index-probe.js b/scenarios/contoso/scaling-benchmark/toast-index-probe.js new file mode 100644 index 0000000..bae754b --- /dev/null +++ b/scenarios/contoso/scaling-benchmark/toast-index-probe.js @@ -0,0 +1,89 @@ +// toast-index-probe.js — measure the detoast tax for ONE query under whatever +// index currently exists on opportunities. Emits a single JSON line so a shell +// driver can sweep a matrix of {index config} x {schema state}. +// +// The key evidence is NOT just wall-clock ms (noisy) but the PostgreSQL block +// traffic the executor reports: numBlocksFromCache + numBlocksFromDisk. Under +// the co-located schema every matched document is detoasted, so a query that +// touches only scalar fields still reads the entire TOAST chain — the block +// count explodes. After the schema split the same query reads far fewer blocks. +// +// Env: +// CONTOSO_DB database (default contoso_x16) +// QUERY which query: "selective" (default) | "fullscan" +// QUERY_REPS timed reps, report MIN ms (default 7) +// IDX_LABEL label of the current index config (passed through to output) + +var DB = process.env.CONTOSO_DB || "contoso_x16"; +var QUERY = process.env.QUERY || "selective"; +var REPS = parseInt(process.env.QUERY_REPS || "7"); +var LABEL = process.env.IDX_LABEL || "unknown"; +var d = db.getSiblingDB(DB); + +// The two aggregations both read ONLY scalar fields (never narrative/activity_log). +function pipeline() { + if (QUERY === "fullscan") { + // no $match -> every document participates -> detoast on 100% of docs + return [ + { $group: { _id: "$sales_stage", n: { $sum: 1 }, value: { $sum: "$est_value" } } }, + { $sort: { value: -1 } } + ]; + } + // selective: ~1/3 of docs (state:open) -> an index on state can prune keys, + // but every surviving doc is still FETCHed and detoasted (co-located schema). + return [ + { $match: { state: "open" } }, + { $group: { _id: "$territory_id", pipeline: { $sum: "$est_value" }, deals: { $sum: 1 } } }, + { $sort: { pipeline: -1 } } + ]; +} + +function longVal(x) { return (x && typeof x === "object" && "low" in x) ? x.low : x; } + +// ── explain: pull plan stage + docs examined + PG block traffic ───────────── +var e = d.opportunities.explain("executionStats").aggregate(pipeline()); +var cur = null; +(e.stages || []).forEach(function (s) { if (s.$cursor) cur = s.$cursor; }); + +var stage = "?", docsExamined = -1, blocksCache = -1, blocksDisk = -1, planMs = -1, idxName = ""; +if (cur) { + var wp = cur.queryPlanner && cur.queryPlanner.winningPlan; + if (wp) { + stage = wp.stage || "?"; + // index name may live on this stage or a child inputStage + var node = wp; + while (node && !idxName) { + if (node.indexName) idxName = node.indexName; + node = node.inputStage; + } + } + var es = cur.executionStats; + if (es) { + docsExamined = longVal(es.totalDocsExamined); + planMs = longVal(es.executionTimeMillis); + var exec = es.executionStages; + if (exec) { + blocksCache = longVal(exec.numBlocksFromCache); + blocksDisk = longVal(exec.numBlocksFromDisk); + } + } +} + +// ── timed run: report MIN wall-clock ms over REPS ─────────────────────────── +var best = Infinity, rows = 0; +for (var r = 0; r < REPS; r++) { + var t = Date.now(); + rows = d.opportunities.aggregate(pipeline()).toArray().length; + var ms = Date.now() - t; + if (ms < best) best = ms; +} + +var blocksTotal = (blocksCache < 0 ? 0 : blocksCache) + (blocksDisk < 0 ? 0 : blocksDisk); +print("IDXRESULT " + JSON.stringify({ + db: DB, query: QUERY, idx: LABEL, index_used: idxName || stage, + stage: stage, docs_examined: docsExamined, + blocks_cache: blocksCache, blocks_disk: blocksDisk, blocks_total: blocksTotal, + blocks_mib: Math.round(blocksTotal * 8 / 1024 * 10) / 10, + plan_ms: planMs, min_ms: best, rows: rows, + opps: d.opportunities.countDocuments() +})); diff --git a/scenarios/ecommerce-advanced/.gitignore b/scenarios/ecommerce-advanced/.gitignore new file mode 100644 index 0000000..6efc548 --- /dev/null +++ b/scenarios/ecommerce-advanced/.gitignore @@ -0,0 +1,2 @@ +node_modules/ +.tmp/ diff --git a/scenarios/ecommerce-advanced/README.md b/scenarios/ecommerce-advanced/README.md new file mode 100644 index 0000000..1792483 --- /dev/null +++ b/scenarios/ecommerce-advanced/README.md @@ -0,0 +1,152 @@ +# Advanced e-commerce compatibility dataset + +Deterministic, committed fixtures for testing three higher-risk +MongoDB-compatible surfaces in Azure DocumentDB: + +1. Multi-document transactions +2. Change streams +3. Advanced aggregation stages and operators + +The dataset is generated from source, validated without a database, and +committed as JSONL so the exact same bytes can later be loaded into MongoDB and +DocumentDB. + +> These fixtures are for compatibility testing, not production sample data. + +## Dataset + +| Collection | Records | Test purpose | +|---|---:|---| +| `customers` | 200 | `$lookup`, segmentation, nested addresses | +| `products` | 100 | arrays, nested attributes, `$bucket`, `$sortArray` | +| `inventory` | 300 | transactional stock updates and contention | +| `orders` | 2,000 | grouping, window functions, date operators | +| `order_items` | 6,000 | one-to-many and correlated pipeline `$lookup` | +| `payments` | 2,000 | transaction consistency across collections | +| `returns` | 120 | `$unionWith` activity streams | +| `daily_sales` | 84 | 90-day series with 6 missing days and 3 null values for `$densify` / `$fill` | +| `stream_orders` | 2 | baseline documents for change-stream operations | + +Additional case definitions: + +| File | Cases | +|---|---:| +| `data/cases/transactions.json` | 4 | +| `data/cases/change-streams.json` | 4 operations | +| `data/cases/aggregations.json` | 7 | + +## Transaction fixtures + +All cases target one known inventory record in `WH_EAST`: + +| Case | Product | Starting stock | Expected | +|---|---|---:|---| +| Successful purchase | `PROD_0001` | 10 | commit, stock becomes 8 | +| Concurrent last unit | `PROD_0002` | 1 | exactly one of two transactions commits | +| Insufficient stock | `PROD_0003` | 0 | rollback, no order/payment | +| Forced exception | `PROD_0004` | 25 | rollback after payment insert, stock remains 25 | + +## Change-stream sequence + +`data/cases/change-streams.json` defines a deterministic sequence against +`stream_orders`: + +```text +insert → update → replace → delete +``` + +The later compatibility test should normalize server-specific fields such as +resume token, `clusterTime`, and `wallTime`, then compare operation type, +namespace, document key, update description, and full document. + +## Advanced aggregation coverage + +The case catalog covers: + +- `$facet` +- Pipeline `$lookup` with `let` and `$expr` +- `$setWindowFields` +- `$densify` and `$fill` +- `$unionWith` +- `$bucket` +- `$dateTrunc` +- Array expressions: `$map`, `$filter`, `$reduce`, `$sortArray` +- `$merge` + +## Reproduce the committed data + +```bash +cd scenarios/ecommerce-advanced +npm ci + +# Rewrite data/ from the deterministic generator. +npm run generate + +# Validate hashes, counts, foreign keys, totals, and all edge-case fixtures. +npm run validate + +# Generate twice in clean directories and require byte-identical output. +npm run check-determinism +``` + +Expected: + +```text +{"status":"PASS","dataset":"ecommerce-advanced","version":"1.0.0",...} +PASS: two clean generations are byte-identical +``` + +The generator uses no wall clock and no `Math.random()`. Its seed, fixed +generation timestamp, and data epoch are recorded in `data/manifest.json`, +along with record counts, byte counts, and SHA-256 for every generated file. + +### Generation result + +Generated and validated on 2026-09-01: + +```text +collections: + customers=200 + products=100 + inventory=300 + orders=2000 + order_items=6000 + payments=2000 + returns=120 + daily_sales=84 + stream_orders=2 + +test cases: + transactions=4 + change_streams=4 + aggregations=7 + +generated files=13 +generated payload=2,225,666 bytes +validation=PASS +two-clean-generation diff=PASS +``` + +## Load into a MongoDB-compatible endpoint + +```bash +export MONGODB_URI='' +export MONGODB_DATABASE='ecommerce_advanced' +npm run load +``` + +`load.mjs` converts the committed `{"$date":"..."}` values to BSON dates, +loads every collection, creates the indexes from `data/indexes.json`, and +verifies final counts. It drops all source collections first and also clears +the write-producing test targets `customer_summaries` and `stream_events`, so +previous `$merge` or change-stream runs cannot leak into the next test. + +The committed files were also loaded into DocumentDB Local. All nine collection +counts matched the manifest, `orders.placed_at` and `daily_sales.sales_date` +were BSON dates, and every declared secondary index was created. + +## Why this size + +The dataset is large enough to exercise joins, grouping, windows, and +contention without turning compatibility tests into load tests. Correctness is +the purpose here; performance testing should use a separately scaled fixture. diff --git a/scenarios/ecommerce-advanced/data/cases/aggregations.json b/scenarios/ecommerce-advanced/data/cases/aggregations.json new file mode 100644 index 0000000..7ddc76e --- /dev/null +++ b/scenarios/ecommerce-advanced/data/cases/aggregations.json @@ -0,0 +1,95 @@ +[ + { + "case_id": "facet-order-dashboard", + "purpose": "$facet with status counts, revenue totals, and top customers.", + "collections": [ + "orders" + ], + "stages": [ + "$match", + "$facet", + "$group", + "$sort", + "$limit" + ] + }, + { + "case_id": "pipeline-lookup-order-items", + "purpose": "Correlated $lookup using let and $expr.", + "collections": [ + "orders", + "order_items" + ], + "stages": [ + "$match", + "$lookup", + "$expr", + "$project" + ] + }, + { + "case_id": "window-customer-running-total", + "purpose": "$setWindowFields partitioned by customer and sorted by date.", + "collections": [ + "orders" + ], + "stages": [ + "$setWindowFields", + "$sum", + "$documentNumber" + ] + }, + { + "case_id": "daily-sales-densify-fill", + "purpose": "$densify missing days and $fill intentional null revenue values.", + "collections": [ + "daily_sales" + ], + "stages": [ + "$densify", + "$fill", + "$dateTrunc" + ] + }, + { + "case_id": "order-return-union", + "purpose": "$unionWith orders and returns into one activity stream.", + "collections": [ + "orders", + "returns" + ], + "stages": [ + "$unionWith", + "$project", + "$sort", + "$limit" + ] + }, + { + "case_id": "product-price-buckets", + "purpose": "$bucket price bands and aggregate product tag arrays.", + "collections": [ + "products" + ], + "stages": [ + "$bucket", + "$map", + "$filter", + "$reduce", + "$sortArray" + ] + }, + { + "case_id": "materialize-customer-summary", + "purpose": "$merge a repeatable customer summary into a target collection.", + "collections": [ + "orders", + "customer_summaries" + ], + "stages": [ + "$group", + "$set", + "$merge" + ] + } +] diff --git a/scenarios/ecommerce-advanced/data/cases/change-streams.json b/scenarios/ecommerce-advanced/data/cases/change-streams.json new file mode 100644 index 0000000..713e2f6 --- /dev/null +++ b/scenarios/ecommerce-advanced/data/cases/change-streams.json @@ -0,0 +1,64 @@ +[ + { + "sequence": 1, + "operation": "insert", + "collection": "stream_orders", + "document": { + "order_id": "STREAM_NEW_001", + "customer_id": "CUST_0003", + "status": "pending", + "total_amount": 55, + "version": 1, + "updated_at": { + "$date": "2025-03-31T22:00:00.000Z" + } + }, + "expected_operation_type": "insert" + }, + { + "sequence": 2, + "operation": "update", + "collection": "stream_orders", + "filter": { + "order_id": "STREAM_BASE_001" + }, + "update": { + "$set": { + "status": "shipped", + "version": 2, + "updated_at": { + "$date": "2025-03-31T23:00:00.000Z" + } + } + }, + "expected_operation_type": "update" + }, + { + "sequence": 3, + "operation": "replace", + "collection": "stream_orders", + "filter": { + "order_id": "STREAM_BASE_002" + }, + "replacement": { + "order_id": "STREAM_BASE_002", + "customer_id": "CUST_0002", + "status": "delivered", + "total_amount": 40, + "version": 2, + "updated_at": { + "$date": "2025-03-31T23:00:00.000Z" + } + }, + "expected_operation_type": "replace" + }, + { + "sequence": 4, + "operation": "delete", + "collection": "stream_orders", + "filter": { + "order_id": "STREAM_NEW_001" + }, + "expected_operation_type": "delete" + } +] diff --git a/scenarios/ecommerce-advanced/data/cases/transactions.json b/scenarios/ecommerce-advanced/data/cases/transactions.json new file mode 100644 index 0000000..adbc307 --- /dev/null +++ b/scenarios/ecommerce-advanced/data/cases/transactions.json @@ -0,0 +1,53 @@ +[ + { + "case_id": "commit-purchase", + "description": "Insert order, item, and payment; decrement stock atomically.", + "product_id": "PROD_0001", + "warehouse_id": "WH_EAST", + "quantity": 2, + "initial_quantity": 10, + "expected_quantity": 8, + "order_id": "TXN_ORDER_COMMIT", + "payment_id": "TXN_PAYMENT_COMMIT", + "expected": "commit" + }, + { + "case_id": "forced-rollback", + "description": "Throw after payment insert; all writes and stock update roll back.", + "product_id": "PROD_0004", + "warehouse_id": "WH_EAST", + "quantity": 3, + "initial_quantity": 25, + "expected_quantity": 25, + "order_id": "TXN_ORDER_ROLLBACK", + "payment_id": "TXN_PAYMENT_ROLLBACK", + "failure_after": "payment_insert", + "expected": "rollback" + }, + { + "case_id": "insufficient-stock", + "description": "Reject purchase and leave no order/payment when inventory is zero.", + "product_id": "PROD_0003", + "warehouse_id": "WH_EAST", + "quantity": 1, + "initial_quantity": 0, + "expected_quantity": 0, + "order_id": "TXN_ORDER_NO_STOCK", + "payment_id": "TXN_PAYMENT_NO_STOCK", + "expected": "rollback" + }, + { + "case_id": "concurrent-last-unit", + "description": "Two concurrent purchases target the last unit; exactly one commits.", + "product_id": "PROD_0002", + "warehouse_id": "WH_EAST", + "quantity": 1, + "initial_quantity": 1, + "expected_quantity": 0, + "competing_order_ids": [ + "TXN_ORDER_RACE_A", + "TXN_ORDER_RACE_B" + ], + "expected_commits": 1 + } +] diff --git a/scenarios/ecommerce-advanced/data/collections/customers.jsonl b/scenarios/ecommerce-advanced/data/collections/customers.jsonl new file mode 100644 index 0000000..e054312 --- /dev/null +++ b/scenarios/ecommerce-advanced/data/collections/customers.jsonl @@ -0,0 +1,200 @@ +{"customer_id":"CUST_0001","name":"Customer 1","email":"customer1@example.test","tier":"bronze","region":"amer","address":{"city":"Seattle","country":"CA","postal_code":"10000"},"segments":["frequent","promotion"],"created_at":{"$date":"2024-01-02T00:00:00.000Z"}} +{"customer_id":"CUST_0002","name":"Customer 2","email":"customer2@example.test","tier":"silver","region":"emea","address":{"city":"Portland","country":"US","postal_code":"17919"},"segments":["standard","full-price"],"created_at":{"$date":"2024-01-01T00:00:00.000Z"}} +{"customer_id":"CUST_0003","name":"Customer 3","email":"customer3@example.test","tier":"gold","region":"apac","address":{"city":"Austin","country":"US","postal_code":"25838"},"segments":["standard","full-price"],"created_at":{"$date":"2023-12-31T00:00:00.000Z"}} +{"customer_id":"CUST_0004","name":"Customer 4","email":"customer4@example.test","tier":"platinum","region":"canada","address":{"city":"Toronto","country":"US","postal_code":"33757"},"segments":["frequent","full-price"],"created_at":{"$date":"2023-12-30T00:00:00.000Z"}} +{"customer_id":"CUST_0005","name":"Customer 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+1,17 @@ +{ + "name": "documentdb-ecommerce-advanced-data", + "version": "1.0.0", + "private": true, + "description": "Deterministic ecommerce fixtures for transactions, change streams, and advanced MQL compatibility tests", + "type": "module", + "scripts": { + "generate": "node scripts/generate.mjs", + "validate": "node scripts/validate.mjs", + "check-determinism": "bash scripts/check-determinism.sh", + "load": "node scripts/load.mjs", + "test": "npm run validate && npm run check-determinism" + }, + "dependencies": { + "mongodb": "6.20.0" + } +} diff --git a/scenarios/ecommerce-advanced/scripts/check-determinism.sh b/scenarios/ecommerce-advanced/scripts/check-determinism.sh new file mode 100755 index 0000000..35ca60d --- /dev/null +++ b/scenarios/ecommerce-advanced/scripts/check-determinism.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env bash +# Generate the dataset twice in clean directories and require byte-identical +# output. A deterministic fixture that cannot reproduce itself is not a test +# fixture. + +set -euo pipefail + +HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +ROOT="$(cd "$HERE/.." && pwd)" +TMP="$ROOT/.tmp" + +rm -rf "$TMP" +mkdir -p "$TMP/one" "$TMP/two" +trap 'rm -rf "$TMP"' EXIT + +node "$HERE/generate.mjs" --output "$TMP/one" >/dev/null +node "$HERE/generate.mjs" --output "$TMP/two" >/dev/null + +diff -ru "$TMP/one" "$TMP/two" +echo "PASS: two clean generations are byte-identical" + diff --git a/scenarios/ecommerce-advanced/scripts/generate.mjs b/scenarios/ecommerce-advanced/scripts/generate.mjs new file mode 100644 index 0000000..53589b2 --- /dev/null +++ b/scenarios/ecommerce-advanced/scripts/generate.mjs @@ -0,0 +1,563 @@ +import { createHash } from "node:crypto"; +import { + mkdir, + readdir, + rm, + writeFile +} from "node:fs/promises"; +import { dirname, join, resolve } from "node:path"; +import { fileURLToPath } from "node:url"; + +const here = dirname(fileURLToPath(import.meta.url)); +const scenarioRoot = resolve(here, ".."); + +const outputArgument = process.argv.indexOf("--output"); +const outputRoot = + outputArgument >= 0 + ? resolve(process.argv[outputArgument + 1]) + : join(scenarioRoot, "data"); + +const DATASET_VERSION = "1.0.0"; +const SEED = 20260901; +const GENERATED_AT = "2026-09-01T00:00:00.000Z"; +const DATA_EPOCH = "2025-01-01T00:00:00.000Z"; +const DAY_MS = 86_400_000; + +const collectionsRoot = join(outputRoot, "collections"); +const casesRoot = join(outputRoot, "cases"); + +function dateValue(day, hour = 0) { + return { + $date: new Date( + Date.parse(DATA_EPOCH) + day * DAY_MS + hour * 3_600_000 + ).toISOString() + }; +} + +function round2(value) { + return Math.round((value + Number.EPSILON) * 100) / 100; +} + +function padded(prefix, value, width = 4) { + return `${prefix}_${String(value).padStart(width, "0")}`; +} + +function stableJson(value) { + return `${JSON.stringify(value, null, 2)}\n`; +} + +function jsonLine(value) { + return `${JSON.stringify(value)}\n`; +} + +function sha256(content) { + return createHash("sha256").update(content).digest("hex"); +} + +async function writeJsonl(relativePath, rows) { + const content = rows.map(jsonLine).join(""); + const path = join(outputRoot, relativePath); + await mkdir(dirname(path), { recursive: true }); + await writeFile(path, content); + return { + path: relativePath, + records: rows.length, + bytes: Buffer.byteLength(content), + sha256: sha256(content) + }; +} + +async function writeJson(relativePath, value) { + const content = stableJson(value); + const path = join(outputRoot, relativePath); + await mkdir(dirname(path), { recursive: true }); + await writeFile(path, content); + return { + path: relativePath, + records: Array.isArray(value) ? value.length : 1, + bytes: Buffer.byteLength(content), + sha256: sha256(content) + }; +} + +await rm(outputRoot, { recursive: true, force: true }); +await mkdir(collectionsRoot, { recursive: true }); +await mkdir(casesRoot, { recursive: true }); + +const regions = ["amer", "emea", "apac", "canada"]; +const cities = ["Seattle", "Portland", "Austin", "Toronto", "London", "Tokyo"]; +const tiers = ["bronze", "silver", "gold", "platinum"]; +const categories = ["electronics", "home", "sports", "books", "outdoor"]; +const brands = ["Northwind", "Contoso", "Fabrikam", "Adventure", "Litware"]; +const statuses = ["pending", "confirmed", "shipped", "delivered", "cancelled"]; +const channels = ["web", "mobile", "marketplace"]; +const paymentMethods = ["card", "bank_transfer", "wallet"]; +const warehouses = ["WH_EAST", "WH_WEST", "WH_CENTRAL"]; + +const customers = Array.from({ length: 200 }, (_, index) => { + const number = index + 1; + return { + customer_id: padded("CUST", number), + name: `Customer ${number}`, + email: `customer${number}@example.test`, + tier: tiers[index % tiers.length], + region: regions[index % regions.length], + address: { + city: cities[index % cities.length], + country: index % 7 === 0 ? "CA" : "US", + postal_code: String(10000 + ((index * 7919) % 89999)) + }, + segments: [ + index % 3 === 0 ? "frequent" : "standard", + index % 5 === 0 ? "promotion" : "full-price" + ], + created_at: dateValue(-(365 + (index % 730))) + }; +}); + +const products = Array.from({ length: 100 }, (_, index) => { + const number = index + 1; + const price = round2(12 + number * 3.75); + return { + product_id: padded("PROD", number), + name: `Product ${number}`, + category: categories[index % categories.length], + brand: brands[index % brands.length], + price, + cost: round2(price * (0.42 + (index % 4) * 0.06)), + active: index % 17 !== 0, + tags: [ + categories[index % categories.length], + index % 2 === 0 ? "featured" : "standard", + index % 7 === 0 ? "seasonal" : "evergreen" + ], + attributes: { + color: ["black", "blue", "green", "red"][index % 4], + size: ["small", "medium", "large"][index % 3], + weight_kg: round2(0.25 + (index % 12) * 0.18) + }, + ratings: { + average: round2(3.2 + (index % 18) / 10), + count: 15 + ((index * 37) % 480) + }, + created_at: dateValue(-(180 + (index % 365))) + }; +}); + +const inventory = []; +for (let productIndex = 0; productIndex < products.length; productIndex += 1) { + for (let warehouseIndex = 0; warehouseIndex < warehouses.length; warehouseIndex += 1) { + const productNumber = productIndex + 1; + let quantity = 20 + ((productNumber * 17 + warehouseIndex * 23) % 180); + + // Transaction edge cases all target WH_EAST. + if (warehouseIndex === 0 && productNumber === 1) quantity = 10; + if (warehouseIndex === 0 && productNumber === 2) quantity = 1; + if (warehouseIndex === 0 && productNumber === 3) quantity = 0; + if (warehouseIndex === 0 && productNumber === 4) quantity = 25; + + inventory.push({ + inventory_id: `${padded("PROD", productNumber)}:${warehouses[warehouseIndex]}`, + product_id: padded("PROD", productNumber), + warehouse_id: warehouses[warehouseIndex], + quantity, + reserved: quantity === 0 ? 0 : Math.min(quantity, (productNumber + warehouseIndex) % 6), + reorder_point: 12 + (productNumber % 18), + version: 1, + updated_at: dateValue(89, warehouseIndex) + }); + } +} + +const orders = []; +const orderItems = []; +const payments = []; + +for (let orderIndex = 0; orderIndex < 2000; orderIndex += 1) { + const orderNumber = orderIndex + 1; + const orderId = padded("ORD", orderNumber, 6); + const customerNumber = ((orderIndex * 17) % customers.length) + 1; + const itemCount = (orderIndex % 5) + 1; + let total = 0; + + for (let itemIndex = 0; itemIndex < itemCount; itemIndex += 1) { + const productNumber = + ((orderIndex * 11 + itemIndex * 7) % products.length) + 1; + const product = products[productNumber - 1]; + const quantity = (itemIndex % 3) + 1; + const discountRate = [0, 0, 0.05, 0.1, 0.15][ + (orderIndex + itemIndex) % 5 + ]; + const lineTotal = round2(product.price * quantity * (1 - discountRate)); + total = round2(total + lineTotal); + + orderItems.push({ + order_item_id: `${orderId}:${itemIndex + 1}`, + order_id: orderId, + line_number: itemIndex + 1, + product_id: product.product_id, + quantity, + unit_price: product.price, + discount_rate: discountRate, + line_total: lineTotal + }); + } + + const status = statuses[orderIndex % statuses.length]; + const placedDay = orderIndex % 90; + orders.push({ + order_id: orderId, + customer_id: padded("CUST", customerNumber), + status, + region: regions[orderIndex % regions.length], + channel: channels[orderIndex % channels.length], + currency: "USD", + item_count: itemCount, + total_amount: total, + placed_at: dateValue(placedDay, orderIndex % 20), + updated_at: dateValue(placedDay, (orderIndex % 20) + 1), + shipping: { + city: cities[orderIndex % cities.length], + method: ["standard", "express", "pickup"][orderIndex % 3] + } + }); + + payments.push({ + payment_id: padded("PAY", orderNumber, 6), + order_id: orderId, + customer_id: padded("CUST", customerNumber), + amount: total, + currency: "USD", + method: paymentMethods[orderIndex % paymentMethods.length], + status: status === "cancelled" ? "reversed" : "captured", + processed_at: dateValue(placedDay, (orderIndex % 20) + 2) + }); +} + +const deliveredOrders = orders.filter((order) => order.status === "delivered"); +const returns = deliveredOrders.slice(0, 120).map((order, index) => { + const firstItem = orderItems.find( + (item) => item.order_id === order.order_id && item.line_number === 1 + ); + return { + return_id: padded("RET", index + 1, 5), + order_id: order.order_id, + customer_id: order.customer_id, + product_id: firstItem.product_id, + amount: firstItem.line_total, + reason: ["damaged", "wrong_item", "changed_mind"][index % 3], + status: ["requested", "approved", "refunded"][index % 3], + created_at: dateValue(60 + (index % 30), 12) + }; +}); + +const missingSalesDays = new Set([7, 19, 36, 57, 78, 85]); +const nullRevenueDays = new Set([14, 43, 69]); +const dailySales = []; + +for (let day = 0; day < 90; day += 1) { + if (missingSalesDays.has(day)) continue; + // Revenue is formula-derived rather than queried, keeping generation + // independent of a database engine. + const revenue = round2( + orders + .filter((_, orderIndex) => orderIndex % 90 === day) + .reduce((sum, order) => sum + order.total_amount, 0) + ); + dailySales.push({ + sales_date: dateValue(day), + revenue: nullRevenueDays.has(day) ? null : revenue, + order_count: orders.filter((_, orderIndex) => orderIndex % 90 === day).length, + units: orderItems + .filter((item) => { + const orderNumber = Number(item.order_id.slice(4)); + return (orderNumber - 1) % 90 === day; + }) + .reduce((sum, item) => sum + item.quantity, 0), + promotion_active: day % 14 < 4, + note: nullRevenueDays.has(day) ? "intentional null for $fill" : null + }); +} + +const streamOrders = [ + { + order_id: "STREAM_BASE_001", + customer_id: "CUST_0001", + status: "pending", + total_amount: 25, + version: 1, + updated_at: dateValue(89, 20) + }, + { + order_id: "STREAM_BASE_002", + customer_id: "CUST_0002", + status: "confirmed", + total_amount: 40, + version: 1, + updated_at: dateValue(89, 21) + } +]; + +const transactionCases = [ + { + case_id: "commit-purchase", + description: "Insert order, item, and payment; decrement stock atomically.", + product_id: "PROD_0001", + warehouse_id: "WH_EAST", + quantity: 2, + initial_quantity: 10, + expected_quantity: 8, + order_id: "TXN_ORDER_COMMIT", + payment_id: "TXN_PAYMENT_COMMIT", + expected: "commit" + }, + { + case_id: "forced-rollback", + description: "Throw after payment insert; all writes and stock update roll back.", + product_id: "PROD_0004", + warehouse_id: "WH_EAST", + quantity: 3, + initial_quantity: 25, + expected_quantity: 25, + order_id: "TXN_ORDER_ROLLBACK", + payment_id: "TXN_PAYMENT_ROLLBACK", + failure_after: "payment_insert", + expected: "rollback" + }, + { + case_id: "insufficient-stock", + description: "Reject purchase and leave no order/payment when inventory is zero.", + product_id: "PROD_0003", + warehouse_id: "WH_EAST", + quantity: 1, + initial_quantity: 0, + expected_quantity: 0, + order_id: "TXN_ORDER_NO_STOCK", + payment_id: "TXN_PAYMENT_NO_STOCK", + expected: "rollback" + }, + { + case_id: "concurrent-last-unit", + description: "Two concurrent purchases target the last unit; exactly one commits.", + product_id: "PROD_0002", + warehouse_id: "WH_EAST", + quantity: 1, + initial_quantity: 1, + expected_quantity: 0, + competing_order_ids: ["TXN_ORDER_RACE_A", "TXN_ORDER_RACE_B"], + expected_commits: 1 + } +]; + +const changeStreamCases = [ + { + sequence: 1, + operation: "insert", + collection: "stream_orders", + document: { + order_id: "STREAM_NEW_001", + customer_id: "CUST_0003", + status: "pending", + total_amount: 55, + version: 1, + updated_at: dateValue(89, 22) + }, + expected_operation_type: "insert" + }, + { + sequence: 2, + operation: "update", + collection: "stream_orders", + filter: { order_id: "STREAM_BASE_001" }, + update: { + $set: { + status: "shipped", + version: 2, + updated_at: dateValue(89, 23) + } + }, + expected_operation_type: "update" + }, + { + sequence: 3, + operation: "replace", + collection: "stream_orders", + filter: { order_id: "STREAM_BASE_002" }, + replacement: { + order_id: "STREAM_BASE_002", + customer_id: "CUST_0002", + status: "delivered", + total_amount: 40, + version: 2, + updated_at: dateValue(89, 23) + }, + expected_operation_type: "replace" + }, + { + sequence: 4, + operation: "delete", + collection: "stream_orders", + filter: { order_id: "STREAM_NEW_001" }, + expected_operation_type: "delete" + } +]; + +const aggregationCases = [ + { + case_id: "facet-order-dashboard", + purpose: "$facet with status counts, revenue totals, and top customers.", + collections: ["orders"], + stages: ["$match", "$facet", "$group", "$sort", "$limit"] + }, + { + case_id: "pipeline-lookup-order-items", + purpose: "Correlated $lookup using let and $expr.", + collections: ["orders", "order_items"], + stages: ["$match", "$lookup", "$expr", "$project"] + }, + { + case_id: "window-customer-running-total", + purpose: "$setWindowFields partitioned by customer and sorted by date.", + collections: ["orders"], + stages: ["$setWindowFields", "$sum", "$documentNumber"] + }, + { + case_id: "daily-sales-densify-fill", + purpose: "$densify missing days and $fill intentional null revenue values.", + collections: ["daily_sales"], + stages: ["$densify", "$fill", "$dateTrunc"] + }, + { + case_id: "order-return-union", + purpose: "$unionWith orders and returns into one activity stream.", + collections: ["orders", "returns"], + stages: ["$unionWith", "$project", "$sort", "$limit"] + }, + { + case_id: "product-price-buckets", + purpose: "$bucket price bands and aggregate product tag arrays.", + collections: ["products"], + stages: ["$bucket", "$map", "$filter", "$reduce", "$sortArray"] + }, + { + case_id: "materialize-customer-summary", + purpose: "$merge a repeatable customer summary into a target collection.", + collections: ["orders", "customer_summaries"], + stages: ["$group", "$set", "$merge"] + } +]; + +const indexes = { + customers: [ + { name: "customer_id_1", key: { customer_id: 1 }, unique: true }, + { name: "region_1_tier_1", key: { region: 1, tier: 1 } } + ], + products: [ + { name: "product_id_1", key: { product_id: 1 }, unique: true }, + { name: "category_1_price_1", key: { category: 1, price: 1 } }, + { name: "tags_1", key: { tags: 1 } } + ], + inventory: [ + { + name: "product_id_1_warehouse_id_1", + key: { product_id: 1, warehouse_id: 1 }, + unique: true + } + ], + orders: [ + { name: "order_id_1", key: { order_id: 1 }, unique: true }, + { + name: "customer_id_1_placed_at_-1", + key: { customer_id: 1, placed_at: -1 } + }, + { name: "status_1_placed_at_-1", key: { status: 1, placed_at: -1 } } + ], + order_items: [ + { + name: "order_id_1_line_number_1", + key: { order_id: 1, line_number: 1 }, + unique: true + }, + { name: "product_id_1", key: { product_id: 1 } } + ], + payments: [ + { name: "payment_id_1", key: { payment_id: 1 }, unique: true }, + { name: "order_id_1", key: { order_id: 1 }, unique: true } + ], + returns: [ + { name: "return_id_1", key: { return_id: 1 }, unique: true }, + { name: "order_id_1", key: { order_id: 1 } } + ], + daily_sales: [{ name: "sales_date_1", key: { sales_date: 1 }, unique: true }], + stream_orders: [ + { name: "order_id_1", key: { order_id: 1 }, unique: true } + ] +}; + +const files = []; +files.push(await writeJsonl("collections/customers.jsonl", customers)); +files.push(await writeJsonl("collections/products.jsonl", products)); +files.push(await writeJsonl("collections/inventory.jsonl", inventory)); +files.push(await writeJsonl("collections/orders.jsonl", orders)); +files.push(await writeJsonl("collections/order_items.jsonl", orderItems)); +files.push(await writeJsonl("collections/payments.jsonl", payments)); +files.push(await writeJsonl("collections/returns.jsonl", returns)); +files.push(await writeJsonl("collections/daily_sales.jsonl", dailySales)); +files.push(await writeJsonl("collections/stream_orders.jsonl", streamOrders)); +files.push(await writeJson("cases/transactions.json", transactionCases)); +files.push(await writeJson("cases/change-streams.json", changeStreamCases)); +files.push(await writeJson("cases/aggregations.json", aggregationCases)); +files.push(await writeJson("indexes.json", indexes)); + +const manifest = { + dataset: "ecommerce-advanced", + version: DATASET_VERSION, + generator: "scripts/generate.mjs", + seed: SEED, + generated_at: GENERATED_AT, + data_epoch: DATA_EPOCH, + date_encoding: "Extended JSON {$date: ISO-8601}", + files: files.sort((left, right) => left.path.localeCompare(right.path)), + collection_counts: { + customers: customers.length, + products: products.length, + inventory: inventory.length, + orders: orders.length, + order_items: orderItems.length, + payments: payments.length, + returns: returns.length, + daily_sales: dailySales.length, + stream_orders: streamOrders.length + }, + test_case_counts: { + transactions: transactionCases.length, + change_streams: changeStreamCases.length, + aggregations: aggregationCases.length + }, + empty_output_collections: ["customer_summaries", "stream_events"], + transaction_fixture_products: { + commit_purchase: "PROD_0001:WH_EAST", + concurrent_last_unit: "PROD_0002:WH_EAST", + insufficient_stock: "PROD_0003:WH_EAST", + forced_rollback: "PROD_0004:WH_EAST" + }, + daily_sales: { + calendar_days: 90, + stored_days: dailySales.length, + missing_day_offsets: [...missingSalesDays], + null_revenue_day_offsets: [...nullRevenueDays] + } +}; + +await writeFile(join(outputRoot, "manifest.json"), stableJson(manifest)); + +const totalBytes = manifest.files.reduce((sum, file) => sum + file.bytes, 0); +console.log( + JSON.stringify({ + output: outputRoot, + collections: manifest.collection_counts, + test_cases: manifest.test_case_counts, + files: manifest.files.length, + bytes: totalBytes + }) +); diff --git a/scenarios/ecommerce-advanced/scripts/load.mjs b/scenarios/ecommerce-advanced/scripts/load.mjs new file mode 100644 index 0000000..6ac97c0 --- /dev/null +++ b/scenarios/ecommerce-advanced/scripts/load.mjs @@ -0,0 +1,101 @@ +import { readFile } from "node:fs/promises"; +import { dirname, join, resolve } from "node:path"; +import { fileURLToPath } from "node:url"; +import { MongoClient } from "mongodb"; + +const uri = process.env.MONGODB_URI; +if (!uri) throw new Error("MONGODB_URI is required"); + +const databaseName = process.env.MONGODB_DATABASE || "ecommerce_advanced"; +const here = dirname(fileURLToPath(import.meta.url)); +const dataRoot = resolve(here, "..", "data"); + +function reviveExtendedJson(value) { + if (Array.isArray(value)) return value.map(reviveExtendedJson); + if (value && typeof value === "object") { + if ( + Object.keys(value).length === 1 && + typeof value.$date === "string" + ) { + return new Date(value.$date); + } + return Object.fromEntries( + Object.entries(value).map(([key, nested]) => [ + key, + reviveExtendedJson(nested) + ]) + ); + } + return value; +} + +async function readJson(relativePath) { + return JSON.parse(await readFile(join(dataRoot, relativePath), "utf8")); +} + +async function readJsonl(relativePath) { + const content = await readFile(join(dataRoot, relativePath), "utf8"); + return content + .split("\n") + .filter(Boolean) + .map((line) => reviveExtendedJson(JSON.parse(line))); +} + +const manifest = await readJson("manifest.json"); +const indexes = await readJson("indexes.json"); +const client = new MongoClient(uri, { + serverSelectionTimeoutMS: 10_000, + connectTimeoutMS: 10_000, + socketTimeoutMS: 60_000, + maxPoolSize: 10 +}); + +await client.connect(); +try { + const db = client.db(databaseName); + + // Remove write-producing test outputs from earlier runs. Dataset loading must + // restore a clean baseline, not leave $merge or change-stream artifacts. + for (const collectionName of manifest.empty_output_collections || []) { + await db.collection(collectionName).drop().catch((error) => { + if (error.codeName !== "NamespaceNotFound") throw error; + }); + } + + for (const [collectionName, count] of Object.entries( + manifest.collection_counts + )) { + await db.collection(collectionName).drop().catch((error) => { + if (error.codeName !== "NamespaceNotFound") throw error; + }); + const collection = db.collection(collectionName); + const rows = await readJsonl(`collections/${collectionName}.jsonl`); + if (rows.length) { + await collection.insertMany(rows, { ordered: true }); + } + + for (const index of indexes[collectionName] || []) { + const { key, ...options } = index; + await collection.createIndex(key, options); + } + + const actual = await collection.countDocuments(); + if (actual !== count) { + throw new Error( + `${collectionName}: loaded ${actual} records, expected ${count}` + ); + } + console.log(`${collectionName}: ${actual}`); + } + + console.log( + JSON.stringify({ + status: "PASS", + database: databaseName, + dataset: manifest.dataset, + version: manifest.version + }) + ); +} finally { + await client.close(); +} diff --git a/scenarios/ecommerce-advanced/scripts/validate.mjs b/scenarios/ecommerce-advanced/scripts/validate.mjs new file mode 100644 index 0000000..d276d2e --- /dev/null +++ b/scenarios/ecommerce-advanced/scripts/validate.mjs @@ -0,0 +1,276 @@ +import { createHash } from "node:crypto"; +import { readFile } from "node:fs/promises"; +import { dirname, join, resolve } from "node:path"; +import { fileURLToPath } from "node:url"; + +const here = dirname(fileURLToPath(import.meta.url)); +const scenarioRoot = resolve(here, ".."); +const dataRoot = resolve(process.argv[2] || join(scenarioRoot, "data")); + +function sha256(content) { + return createHash("sha256").update(content).digest("hex"); +} + +async function readJson(relativePath) { + return JSON.parse(await readFile(join(dataRoot, relativePath), "utf8")); +} + +async function readJsonl(relativePath) { + const content = await readFile(join(dataRoot, relativePath), "utf8"); + return content + .split("\n") + .filter(Boolean) + .map((line, index) => { + try { + return JSON.parse(line); + } catch (error) { + throw new Error(`${relativePath}:${index + 1}: ${error.message}`); + } + }); +} + +function assert(condition, message) { + if (!condition) throw new Error(message); +} + +function unique(rows, field, collection) { + const values = rows.map((row) => row[field]); + assert( + new Set(values).size === values.length, + `${collection}.${field} contains duplicates` + ); +} + +function asDate(value, path) { + assert(value && typeof value.$date === "string", `${path} is not Extended JSON date`); + const milliseconds = Date.parse(value.$date); + assert(Number.isFinite(milliseconds), `${path} is not a valid ISO-8601 date`); + return milliseconds; +} + +const manifest = await readJson("manifest.json"); +assert( + JSON.stringify(manifest.empty_output_collections) === + JSON.stringify(["customer_summaries", "stream_events"]), + "clean output collection list changed" +); + +for (const file of manifest.files) { + const content = await readFile(join(dataRoot, file.path)); + assert(content.length === file.bytes, `${file.path}: byte count changed`); + assert(sha256(content) === file.sha256, `${file.path}: SHA-256 changed`); +} + +const customers = await readJsonl("collections/customers.jsonl"); +const products = await readJsonl("collections/products.jsonl"); +const inventory = await readJsonl("collections/inventory.jsonl"); +const orders = await readJsonl("collections/orders.jsonl"); +const orderItems = await readJsonl("collections/order_items.jsonl"); +const payments = await readJsonl("collections/payments.jsonl"); +const returns = await readJsonl("collections/returns.jsonl"); +const dailySales = await readJsonl("collections/daily_sales.jsonl"); +const streamOrders = await readJsonl("collections/stream_orders.jsonl"); +const transactionCases = await readJson("cases/transactions.json"); +const changeStreamCases = await readJson("cases/change-streams.json"); +const aggregationCases = await readJson("cases/aggregations.json"); +const indexes = await readJson("indexes.json"); + +const collections = { + customers, + products, + inventory, + orders, + order_items: orderItems, + payments, + returns, + daily_sales: dailySales, + stream_orders: streamOrders +}; + +for (const [name, expected] of Object.entries(manifest.collection_counts)) { + assert(collections[name].length === expected, `${name}: expected ${expected} records`); +} + +unique(customers, "customer_id", "customers"); +unique(products, "product_id", "products"); +unique(inventory, "inventory_id", "inventory"); +unique(orders, "order_id", "orders"); +unique(orderItems, "order_item_id", "order_items"); +unique(payments, "payment_id", "payments"); +unique(returns, "return_id", "returns"); +unique(streamOrders, "order_id", "stream_orders"); + +const customerIds = new Set(customers.map((row) => row.customer_id)); +const productIds = new Set(products.map((row) => row.product_id)); +const orderIds = new Set(orders.map((row) => row.order_id)); + +for (const order of orders) { + assert(customerIds.has(order.customer_id), `orders: missing customer ${order.customer_id}`); + asDate(order.placed_at, `${order.order_id}.placed_at`); + asDate(order.updated_at, `${order.order_id}.updated_at`); +} + +for (const item of orderItems) { + assert(orderIds.has(item.order_id), `order_items: missing order ${item.order_id}`); + assert(productIds.has(item.product_id), `order_items: missing product ${item.product_id}`); +} + +for (const payment of payments) { + assert(orderIds.has(payment.order_id), `payments: missing order ${payment.order_id}`); + assert( + customerIds.has(payment.customer_id), + `payments: missing customer ${payment.customer_id}` + ); +} + +for (const returnRow of returns) { + assert(orderIds.has(returnRow.order_id), `returns: missing order ${returnRow.order_id}`); + assert( + customerIds.has(returnRow.customer_id), + `returns: missing customer ${returnRow.customer_id}` + ); + assert( + productIds.has(returnRow.product_id), + `returns: missing product ${returnRow.product_id}` + ); +} + +for (const row of inventory) { + assert(productIds.has(row.product_id), `inventory: missing product ${row.product_id}`); + assert(row.quantity >= 0 && row.reserved >= 0, `${row.inventory_id}: negative stock`); + assert(row.reserved <= row.quantity, `${row.inventory_id}: reserved exceeds quantity`); +} + +const itemsByOrder = new Map(); +for (const item of orderItems) { + const items = itemsByOrder.get(item.order_id) || []; + items.push(item); + itemsByOrder.set(item.order_id, items); +} + +const paymentsByOrder = new Map(payments.map((payment) => [payment.order_id, payment])); +for (const order of orders) { + const items = itemsByOrder.get(order.order_id) || []; + const itemTotal = Math.round( + items.reduce((sum, item) => sum + item.line_total, 0) * 100 + ) / 100; + assert(items.length === order.item_count, `${order.order_id}: item_count mismatch`); + assert(itemTotal === order.total_amount, `${order.order_id}: total mismatch`); + assert( + paymentsByOrder.get(order.order_id)?.amount === order.total_amount, + `${order.order_id}: payment amount mismatch` + ); +} + +const inventoryById = new Map(inventory.map((row) => [row.inventory_id, row])); +for (const testCase of transactionCases) { + const key = `${testCase.product_id}:${testCase.warehouse_id}`; + const row = inventoryById.get(key); + assert(row, `${testCase.case_id}: inventory fixture ${key} missing`); + assert( + row.quantity === testCase.initial_quantity, + `${testCase.case_id}: expected initial quantity ${testCase.initial_quantity}, got ${row.quantity}` + ); +} + +assert( + transactionCases.find((testCase) => testCase.case_id === "concurrent-last-unit") + ?.expected_commits === 1, + "concurrent transaction case must require exactly one commit" +); + +assert(changeStreamCases.length === 4, "expected four change-stream operations"); +assert( + changeStreamCases.map((testCase) => testCase.operation).join(",") === + "insert,update,replace,delete", + "change-stream operation sequence changed" +); + +const salesDates = dailySales.map((row, index) => + asDate(row.sales_date, `daily_sales[${index}].sales_date`) +); +assert(new Set(salesDates).size === salesDates.length, "daily_sales dates are not unique"); +const dataEpoch = Date.parse(manifest.data_epoch); +const storedDayOffsets = new Set( + salesDates.map((date) => Math.round((date - dataEpoch) / 86_400_000)) +); +const actualMissingDayOffsets = Array.from( + { length: manifest.daily_sales.calendar_days }, + (_, day) => day +).filter((day) => !storedDayOffsets.has(day)); +assert( + JSON.stringify(actualMissingDayOffsets) === + JSON.stringify(manifest.daily_sales.missing_day_offsets), + "daily_sales missing-day offsets changed" +); +const actualNullRevenueOffsets = dailySales + .filter((row) => row.revenue === null) + .map((row) => + Math.round((Date.parse(row.sales_date.$date) - dataEpoch) / 86_400_000) + ); +assert( + JSON.stringify(actualNullRevenueOffsets) === + JSON.stringify(manifest.daily_sales.null_revenue_day_offsets), + "daily_sales null-revenue offsets changed" +); +assert( + dailySales.filter((row) => row.revenue === null).length === + manifest.daily_sales.null_revenue_day_offsets.length, + "daily_sales null-revenue fixture count changed" +); +assert( + manifest.daily_sales.calendar_days - dailySales.length === + manifest.daily_sales.missing_day_offsets.length, + "daily_sales missing-day fixture count changed" +); + +const requiredAdvancedStages = new Set([ + "$facet", + "$lookup", + "$expr", + "$setWindowFields", + "$densify", + "$fill", + "$unionWith", + "$bucket", + "$sortArray", + "$merge" +]); +const documentedStages = new Set( + aggregationCases.flatMap((testCase) => testCase.stages) +); +for (const stage of requiredAdvancedStages) { + assert(documentedStages.has(stage), `advanced aggregation case missing ${stage}`); +} + +for (const collection of Object.keys(collections)) { + assert(indexes[collection], `indexes.json has no entry for ${collection}`); +} + +assert( + manifest.test_case_counts.transactions === transactionCases.length && + manifest.test_case_counts.change_streams === changeStreamCases.length && + manifest.test_case_counts.aggregations === aggregationCases.length, + "test case counts changed" +); + +console.log( + JSON.stringify({ + status: "PASS", + dataset: manifest.dataset, + version: manifest.version, + files_verified: manifest.files.length + 1, + collection_counts: manifest.collection_counts, + test_case_counts: manifest.test_case_counts, + invariants: { + foreign_keys: "PASS", + order_totals: "PASS", + payment_totals: "PASS", + time_series_gaps: "PASS", + transaction_fixtures: "PASS", + change_stream_sequence: "PASS", + advanced_aggregation_coverage: "PASS", + hashes: "PASS" + } + }) +); diff --git a/scenarios/ecommerce/README.md b/scenarios/ecommerce/README.md index 87913d8..d0f4b89 100644 --- a/scenarios/ecommerce/README.md +++ b/scenarios/ecommerce/README.md @@ -10,6 +10,8 @@ the diagnostic toolbox: `perf-advisor.sh`, `index-redundancy-finder.sh`, and | File | What it is | |------|-----------| | `seed.sh` | Self-contained seeder (embeds the data-generation script; ~50K orders, ~150K order_items). | +| [`../../compat/`](../../compat/QUICKSTART.md) | MongoDB-compatible Node.js workload running end-to-end on DocumentDB, including three `$lookup` queries and exact-result verification. | +| [`../ecommerce-advanced/`](../ecommerce-advanced/README.md) | Deterministic committed data for transactions, change streams, and advanced aggregation compatibility tests. | ## Run diff --git a/scripts/data-integrity-check.ps1 b/scripts/data-integrity-check.ps1 new file mode 100644 index 0000000..e1c9a1a --- /dev/null +++ b/scripts/data-integrity-check.ps1 @@ -0,0 +1,4 @@ +$ErrorActionPreference = "Stop" +& (Join-Path $PSScriptRoot "invoke-diagnostic.ps1") ` + -Diagnostic "data-integrity-check" @args +exit $LASTEXITCODE diff --git a/scripts/data-integrity-check.py b/scripts/data-integrity-check.py new file mode 100644 index 0000000..bd9ff1b --- /dev/null +++ b/scripts/data-integrity-check.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +from portable_diagnostic import main + +if __name__ == "__main__": + raise SystemExit(main("data-integrity-check")) diff --git a/scripts/data-integrity-check.sh b/scripts/data-integrity-check.sh index 50af17a..40ba1fc 100755 --- a/scripts/data-integrity-check.sh +++ b/scripts/data-integrity-check.sh @@ -22,6 +22,9 @@ # an agent/router can consume the verdict without the full human report. set -uo pipefail +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source "$SCRIPT_DIR/diagnostic-runtime.sh" + CONTAINER_NAME="${CONTAINER_NAME:-documentdb-local}" PORT="${PORT:-10260}" USER="${DB_USER:-docdbadmin}" @@ -56,7 +59,7 @@ done [[ -z "$PASSWORD" ]] && { echo "Error: no password. Set DB_PASSWORD or pass --password (local demo: export DB_PASSWORD=Test1234)." >&2; exit 1; } run_mongosh() { - docker exec -u documentdb "$CONTAINER_NAME" mongosh \ + docdb_exec_as documentdb mongosh \ "localhost:${PORT}/${DB}" -u "$USER" -p "$PASSWORD" \ --authenticationMechanism SCRAM-SHA-256 --tls --tlsAllowInvalidCertificates \ --quiet --eval "$1" 2>/dev/null @@ -67,7 +70,35 @@ hecho() { [[ "$JSON" == "1" ]] || echo "$@"; } # JS prelude: JSON_MODE toggles whether the shared check bodies print the human # report (out) or only their machine-readable fragment. -JS_MODE="var JSON_MODE=$([[ "$JSON" == "1" ]] && echo true || echo false); function out(s){ if(!JSON_MODE) print(s); }" +# +# It also defines detSample() — a DETERMINISTIC sampler used instead of +# `$sample`. `$sample` draws a different random subset on every run, so the +# reported type counts changed between two runs against an unchanged database +# (measured: amount {number:87,string:13} then {number:85,string:15}). The +# finding was stable but the numbers were not, which makes the output +# unreproducible and impossible to diff in CI. +# +# detSample() takes half the documents from each end of _id order. Head+tail +# rather than head-only is deliberate: mixed types usually arrive from schema +# drift over time, so the oldest and newest documents are exactly where the +# disagreement lives. Sampling only the head would systematically miss a type +# change introduced after the collection was created. +JS_PRELUDE=' +function detSample(coll, size) { + var half = Math.ceil(size / 2); + var head = db[coll].find().sort({ _id: 1 }).limit(half).toArray(); + var tail = db[coll].find().sort({ _id: -1 }).limit(size - half).toArray(); + var seen = {}, picked = []; + head.concat(tail).forEach(function (d) { + var k = String(d._id); + if (seen[k]) return; + seen[k] = 1; + picked.push(d); + }); + return picked; +} +' +JS_MODE="var JSON_MODE=$([[ "$JSON" == "1" ]] && echo true || echo false); function out(s){ if(!JSON_MODE) print(s); } $JS_PRELUDE" # ── Shared check bodies (single source of truth for both modes) ───────────── read -r -d '' CHECK1_JS <<'JS' @@ -80,7 +111,7 @@ var collSet = new Set(colls); // Strategy: find fields ending in _id (but not _id itself), and check // if there is a matching collection (singular or plural) colls.forEach(function(c) { - var sample = db[c].aggregate([{$sample:{size:20}}]).toArray(); + var sample = detSample(c, 20); if (sample.length === 0) return; // Gather all top-level fields that end with "_id" (excluding _id) @@ -106,8 +137,10 @@ colls.forEach(function(c) { } if (!targetColl) return; - // Check if the target collection actually has this field (or _id) - var targetSample = db[targetColl].findOne(); + // Check if the target collection actually has this field (or _id). + // First-by-_id rather than findOne(): an arbitrary document could be + // missing an optional field and flip the result between runs. + var targetSample = db[targetColl].find().sort({_id:1}).limit(1).toArray()[0]; if (!targetSample) return; var targetField = targetSample[fk] !== undefined ? fk : null; if (!targetField && fk === targetColl.replace(/s$/, "") + "_id") { @@ -153,7 +186,7 @@ colls.forEach(function(c) { if (count < 5) return; var sampleSize = Math.min(100, count); - var sample = db[c].aggregate([{$sample:{size:sampleSize}}]).toArray(); + var sample = detSample(c, sampleSize); if (sample.length === 0) return; // Record the scalar BSON type seen for each field across sampled docs diff --git a/scripts/db-config-advisor.ps1 b/scripts/db-config-advisor.ps1 new file mode 100644 index 0000000..7bcb3ce --- /dev/null +++ b/scripts/db-config-advisor.ps1 @@ -0,0 +1,4 @@ +$ErrorActionPreference = "Stop" +& (Join-Path $PSScriptRoot "invoke-diagnostic.ps1") ` + -Diagnostic "db-config-advisor" @args +exit $LASTEXITCODE diff --git a/scripts/db-config-advisor.py b/scripts/db-config-advisor.py new file mode 100644 index 0000000..1c9d16e --- /dev/null +++ b/scripts/db-config-advisor.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +from portable_diagnostic import main + +if __name__ == "__main__": + raise SystemExit(main("db-config-advisor")) diff --git a/scripts/db-config-advisor.sh b/scripts/db-config-advisor.sh index 2aa5314..bb3eb6a 100755 --- a/scripts/db-config-advisor.sh +++ b/scripts/db-config-advisor.sh @@ -24,6 +24,9 @@ # "SELECT pg_stat_reset()"), run your workload, then re-run this tool. set -uo pipefail +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source "$SCRIPT_DIR/diagnostic-runtime.sh" + CONTAINER_NAME="${CONTAINER_NAME:-documentdb-local}" PG_PORT="${PG_PORT:-9712}" PG_USER="${PG_USER:-documentdb}" @@ -44,7 +47,7 @@ done [[ -z "$DB" ]] && { echo "Error: --db is required" >&2; exit 1; } run_psql() { - docker exec "$CONTAINER_NAME" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ + docdb_exec_as "" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ -t --no-align -F $'\t' -c "$1" 2>/dev/null | grep -vE '^SET$' } diff --git a/scripts/diagnostic-runtime.sh b/scripts/diagnostic-runtime.sh new file mode 100755 index 0000000..4690bc0 --- /dev/null +++ b/scripts/diagnostic-runtime.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +# Shared command transport for DocumentDB diagnostics. +# +# Normal Linux invocation runs Docker from the host. The portable Python +# launcher copies the script into the DocumentDB container and sets +# DOCDB_DIRECT=1, so the same diagnostic logic calls mongosh/psql directly. + +docdb_exec_as() { + local user="$1" + shift + if [[ "${DOCDB_DIRECT:-0}" == "1" ]]; then + "$@" + elif [[ -n "$user" ]]; then + docker exec -u "$user" "$CONTAINER_NAME" "$@" + else + docker exec "$CONTAINER_NAME" "$@" + fi +} + +docdb_exec_stdin_as() { + local user="$1" + shift + if [[ "${DOCDB_DIRECT:-0}" == "1" ]]; then + "$@" + elif [[ -n "$user" ]]; then + docker exec -i -u "$user" "$CONTAINER_NAME" "$@" + else + docker exec -i "$CONTAINER_NAME" "$@" + fi +} diff --git a/scripts/document-bloat-advisor.ps1 b/scripts/document-bloat-advisor.ps1 new file mode 100644 index 0000000..e42e7dd --- /dev/null +++ b/scripts/document-bloat-advisor.ps1 @@ -0,0 +1,4 @@ +$ErrorActionPreference = "Stop" +& (Join-Path $PSScriptRoot "invoke-diagnostic.ps1") ` + -Diagnostic "document-bloat-advisor" @args +exit $LASTEXITCODE diff --git a/scripts/document-bloat-advisor.py b/scripts/document-bloat-advisor.py new file mode 100644 index 0000000..f665c18 --- /dev/null +++ b/scripts/document-bloat-advisor.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +from portable_diagnostic import main + +if __name__ == "__main__": + raise SystemExit(main("document-bloat-advisor")) diff --git a/scripts/document-bloat-advisor.sh b/scripts/document-bloat-advisor.sh index b0c59b9..fc812dd 100755 --- a/scripts/document-bloat-advisor.sh +++ b/scripts/document-bloat-advisor.sh @@ -26,6 +26,9 @@ # [--min-total-kb 256] ignore collections smaller than this set -uo pipefail +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source "$SCRIPT_DIR/diagnostic-runtime.sh" + CONTAINER_NAME="${CONTAINER_NAME:-documentdb-local}" PORT="${PORT:-10260}" PG_PORT="${PG_PORT:-9712}" @@ -54,15 +57,24 @@ while [[ $# -gt 0 ]]; do done [[ -z "$DB" ]] && { echo "Error: --db is required" >&2; exit 1; } [[ -z "$PASSWORD" ]] && { echo "Error: no password. Set DB_PASSWORD or pass --password (local demo: export DB_PASSWORD=Test1234)." >&2; exit 1; } +[[ "$MIN_TOTAL_KB" =~ ^[0-9]+$ ]] || { echo "Error: --min-total-kb must be a non-negative integer" >&2; exit 2; } +[[ "$TOAST_RATIO" =~ ^[0-9]+([.][0-9]+)?$ ]] || { echo "Error: --toast-ratio must be a non-negative number" >&2; exit 2; } run_mongosh() { - docker exec "$CONTAINER_NAME" mongosh "localhost:${PORT}/${DB}" \ + local program="$1" + shift + docdb_exec_as "" env "$@" mongosh "localhost:${PORT}/${DB}" \ -u "$DB_USER_" -p "$PASSWORD" --authenticationMechanism SCRAM-SHA-256 \ - --tls --tlsAllowInvalidCertificates --quiet --eval "$1" 2>/dev/null + --tls --tlsAllowInvalidCertificates --quiet --eval "$program" 2>/dev/null } run_psql() { - docker exec "$CONTAINER_NAME" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ - -t --no-align -F $'\t' -c "$1" 2>/dev/null | grep -vE '^(SET|)$' + local query="$1" + shift + printf '%s\n' "$query" | + docdb_exec_stdin_as "" psql -h localhost -p "$PG_PORT" -U "$PG_USER" \ + -d "$PG_DB" -v ON_ERROR_STOP=1 -t --no-align -F $'\t' "$@" \ + 2>/dev/null | + grep -vE '^(SET|)$' } # ── Per-collection heap/TOAST from PostgreSQL (measured facts) ────────────── @@ -74,9 +86,9 @@ SELECT c.collection_name, pg_total_relation_size(t.oid) FROM documentdb_api_catalog.collections c JOIN pg_class t ON t.oid = ('documentdb_data.documents_' || c.collection_id)::regclass -WHERE c.database_name = '${DB}' +WHERE c.database_name = :'db_name' ORDER BY pg_total_relation_size(t.oid) DESC; -") +" --set=db_name="$DB") if [[ -z "$SIZES" ]]; then echo "No collections found for database '${DB}' (is it seeded? is the container up?)" >&2 @@ -99,7 +111,6 @@ total_toast=0 while IFS=$'\t' read -r coll heap toast total; do [[ -z "$coll" ]] && continue - total_kb=$(( total / 1024 )) (( total < MIN_TOTAL_KB * 1024 )) && continue # toast ratio via awk (float) ratio=$(awk -v t="$toast" -v h="$heap" 'BEGIN{ d=t+h; if(d<=0){print 0}else{printf "%.3f", t/d} }') @@ -110,11 +121,30 @@ while IFS=$'\t' read -r coll heap toast total; do if [[ "$over" == "1" ]]; then flagged=$((flagged+1)) total_toast=$(( total_toast + toast )) - # MongoDB avgObjSize + dominant top-level fields (sampled) - FIELDS=$(run_mongosh ' - var s = db.'"$coll"'.stats(); + # MongoDB avgObjSize + dominant top-level fields. + # Deterministic sampler (head+tail of _id order), NOT `$sample`: + # a random subset made the reported average field sizes wobble between + # runs (measured: "title:13B" vs "title:12B" on an unchanged database), + # which made the advisor's output impossible to diff. + if ! FIELDS=$(run_mongosh ' + function detSample(collection, size) { + var half = Math.ceil(size / 2); + var head = collection.find().sort({ _id: 1 }).limit(half).toArray(); + var tail = collection.find().sort({ _id: -1 }).limit(size - half).toArray(); + var seen = {}, picked = []; + head.concat(tail).forEach(function (d) { + var k = String(d._id); + if (seen[k]) return; + seen[k] = 1; + picked.push(d); + }); + return picked; + } + var coll = process.env.DOCDB_COLLECTION; + var collection = db.getCollection(coll); + var s = collection.stats(); var avg = s.avgObjSize || 0; - var docs = db.'"$coll"'.aggregate([{$sample:{size:20}}]).toArray(); + var docs = detSample(collection, 20); var acc = {}; docs.forEach(function(doc){ Object.keys(doc).forEach(function(k){ @@ -126,13 +156,38 @@ while IFS=$'\t' read -r coll heap toast total; do }); var n = docs.length || 1; var arr = Object.keys(acc).map(function(k){ return {f:k, avg:Math.round(acc[k]/n)}; }); - arr.sort(function(a,b){ return b.avg - a.avg; }); + // Sort by size DESC, then by field name ASC so equal-sized fields + // never swap places between runs. + arr.sort(function(a,b){ return (b.avg - a.avg) || (a.f < b.f ? -1 : a.f > b.f ? 1 : 0); }); print("AVG " + avg); arr.slice(0,3).forEach(function(x){ print("FLD " + x.f + " " + x.avg); }); - ') - avgobj=$(echo "$FIELDS" | awk '/^AVG/{print $2}') + var top = arr.length ? arr[0].f : ""; + var dominant = arr.slice(0,3).map(function(x) { + return x.f + ":" + x.avg + "B"; + }).join(","); + print("FINDING " + JSON.stringify({ + collection: coll, + heap_bytes: Number(process.env.DOCDB_HEAP_BYTES), + toast_bytes: Number(process.env.DOCDB_TOAST_BYTES), + toast_ratio: Number(process.env.DOCDB_TOAST_RATIO), + avg_obj_size: avg, + dominant_fields: dominant, + recommended_split_field: top, + fix: "move large text to side collection " + coll + "_text keyed by _id" + })); + ' "DOCDB_COLLECTION=$coll" "DOCDB_HEAP_BYTES=$heap" \ + "DOCDB_TOAST_BYTES=$toast" "DOCDB_TOAST_RATIO=$ratio"); then + echo "Failed to sample collection '$coll'" >&2 + exit 1 + fi + avgobj=$(echo "$FIELDS" | awk '/^AVG/{print $2; exit}') bigfields=$(echo "$FIELDS" | awk '/^FLD/{print $2":"$3"B"}' | paste -sd, -) topfield=$(echo "$FIELDS" | awk '/^FLD/{print $2; exit}') + finding_json=$(printf '%s\n' "$FIELDS" | sed -n 's/^FINDING //p' | tail -n 1) + if [[ -z "$finding_json" ]]; then + echo "Failed to serialize finding for collection '$coll'" >&2 + exit 1 + fi emit_human " ⚠️ ${coll}" emit_human " heap=${heap_kb}KB TOAST=${toast_kb}KB (TOAST ratio ${ratio}) avgObjSize=${avgobj}B" @@ -146,8 +201,7 @@ while IFS=$'\t' read -r coll heap toast total; do # JSON finding [[ $first -eq 0 ]] && FINDINGS_JSON+="," first=0 - FINDINGS_JSON+=$(printf '{"collection":"%s","heap_bytes":%s,"toast_bytes":%s,"toast_ratio":%s,"avg_obj_size":%s,"dominant_fields":"%s","recommended_split_field":"%s","fix":"move large text to side collection %s_text keyed by _id"}' \ - "$coll" "$heap" "$toast" "$ratio" "${avgobj:-0}" "$bigfields" "$topfield" "$coll") + FINDINGS_JSON+="$finding_json" else emit_human " ✅ ${coll} heap=${heap_kb}KB TOAST=${toast_kb}KB (ratio ${ratio}) — no bloat" fi diff --git a/scripts/index-redundancy-finder.ps1 b/scripts/index-redundancy-finder.ps1 new file mode 100644 index 0000000..e00f418 --- /dev/null +++ b/scripts/index-redundancy-finder.ps1 @@ -0,0 +1,4 @@ +$ErrorActionPreference = "Stop" +& (Join-Path $PSScriptRoot "invoke-diagnostic.ps1") ` + -Diagnostic "index-redundancy-finder" @args +exit $LASTEXITCODE diff --git a/scripts/index-redundancy-finder.py b/scripts/index-redundancy-finder.py new file mode 100644 index 0000000..b880f7f --- /dev/null +++ b/scripts/index-redundancy-finder.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +from portable_diagnostic import main + +if __name__ == "__main__": + raise SystemExit(main("index-redundancy-finder")) diff --git a/scripts/index-redundancy-finder.sh b/scripts/index-redundancy-finder.sh index 8b4a714..1f2af1d 100755 --- a/scripts/index-redundancy-finder.sh +++ b/scripts/index-redundancy-finder.sh @@ -24,6 +24,7 @@ set -uo pipefail SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source "$SCRIPT_DIR/diagnostic-runtime.sh" CONTAINER_NAME="${CONTAINER_NAME:-documentdb-local}" PORT="${PORT:-10260}" @@ -79,14 +80,14 @@ done # ── Helpers ─────────────────────────────────────────────────────────── run_mongosh() { local target_db="${2:-$DB}" - docker exec -u documentdb "$CONTAINER_NAME" mongosh \ + docdb_exec_as documentdb mongosh \ "localhost:${PORT}/${target_db}" -u "$USER" -p "$PASSWORD" \ --authenticationMechanism SCRAM-SHA-256 --tls --tlsAllowInvalidCertificates \ --quiet --eval "$1" 2>/dev/null } run_psql() { - docker exec "$CONTAINER_NAME" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ + docdb_exec_as "" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ -t -A -F $'\t' -c "$1" 2>/dev/null | grep -v "^SET$" } diff --git a/scripts/index-redundancy-render.py b/scripts/index-redundancy-render.py index 489d40c..0428260 100644 --- a/scripts/index-redundancy-render.py +++ b/scripts/index-redundancy-render.py @@ -10,15 +10,15 @@ import json import sys from collections import defaultdict +from typing import TextIO SEV_ORDER = {"HIGH": 0, "MEDIUM": 1, "LOW": 2} SEV_ICON = {"HIGH": "🔴", "MEDIUM": "🟡", "LOW": "🔵"} -def main(): - data = json.load(sys.stdin) +def render(data: list[dict], output: TextIO = sys.stdout): if not data: - print(" ✅ No redundant or unused indexes found") + print(" ✅ No redundant or unused indexes found", file=output) return data.sort(key=lambda f: (SEV_ORDER.get(f["severity"], 3), f["collection"], f["rule"])) @@ -31,17 +31,30 @@ def main(): for f in data: counts[f["severity"]] = counts.get(f["severity"], 0) + 1 - print(f' Found {len(data)} finding(s): {counts["HIGH"]} HIGH, ' - f'{counts["MEDIUM"]} MEDIUM, {counts["LOW"]} LOW') - print() + print( + f' Found {len(data)} finding(s): {counts["HIGH"]} HIGH, ' + f'{counts["MEDIUM"]} MEDIUM, {counts["LOW"]} LOW', + file=output, + ) + print(file=output) for coll in sorted(by_coll): - print(f" ── {coll} ──") + print(f" ── {coll} ──", file=output) for f in by_coll[coll]: icon = SEV_ICON.get(f["severity"], " ") - print(f' {icon} [{f["severity"]}] {f["rule"]:18s} {f["index"]}') - print(f' → {f["reason"]}') - print(f' 💡 db.{coll}.dropIndex("{f["index"]}")') - print() + print( + f' {icon} [{f["severity"]}] {f["rule"]:18s} {f["index"]}', + file=output, + ) + print(f' → {f["reason"]}', file=output) + print( + f' 💡 db.{coll}.dropIndex("{f["index"]}")', + file=output, + ) + print(file=output) + + +def main(): + render(json.load(sys.stdin)) if __name__ == "__main__": diff --git a/scripts/index_redundancy_render.py b/scripts/index_redundancy_render.py new file mode 100644 index 0000000..0a26e8c --- /dev/null +++ b/scripts/index_redundancy_render.py @@ -0,0 +1,15 @@ +"""Importable renderer shim for the hyphenated CLI module.""" + +from __future__ import annotations + +import importlib.util +from pathlib import Path + +_PATH = Path(__file__).with_name("index-redundancy-render.py") +_SPEC = importlib.util.spec_from_file_location("index_redundancy_renderer", _PATH) +if _SPEC is None or _SPEC.loader is None: + raise ImportError(f"cannot load renderer: {_PATH}") +_MODULE = importlib.util.module_from_spec(_SPEC) +_SPEC.loader.exec_module(_MODULE) + +render = _MODULE.render diff --git a/scripts/invoke-diagnostic.ps1 b/scripts/invoke-diagnostic.ps1 new file mode 100644 index 0000000..d317cdf --- /dev/null +++ b/scripts/invoke-diagnostic.ps1 @@ -0,0 +1,31 @@ +param( + [Parameter(Mandatory = $true)] + [string]$Diagnostic, + + [Parameter(ValueFromRemainingArguments = $true)] + [string[]]$DiagnosticArgs +) + +$ErrorActionPreference = "Stop" +$ScriptPath = Join-Path $PSScriptRoot "$Diagnostic.py" + +$PyLauncher = Get-Command py -ErrorAction SilentlyContinue +if ($PyLauncher) { + & $PyLauncher.Source -3 -c "import sys; raise SystemExit(sys.version_info < (3, 10))" + if ($LASTEXITCODE -eq 0) { + & $PyLauncher.Source -3 $ScriptPath @DiagnosticArgs + exit $LASTEXITCODE + } +} + +$Python = Get-Command python -ErrorAction SilentlyContinue +if ($Python) { + & $Python.Source -c "import sys; raise SystemExit(sys.version_info < (3, 10))" + if ($LASTEXITCODE -eq 0) { + & $Python.Source $ScriptPath @DiagnosticArgs + exit $LASTEXITCODE + } +} + +Write-Error "Python 3.10 or newer is required. Install Python and ensure 'py' or 'python' is on PATH." +exit 1 diff --git a/scripts/perf-advisor.ps1 b/scripts/perf-advisor.ps1 new file mode 100644 index 0000000..76d7c9f --- /dev/null +++ b/scripts/perf-advisor.ps1 @@ -0,0 +1,4 @@ +$ErrorActionPreference = "Stop" +& (Join-Path $PSScriptRoot "invoke-diagnostic.ps1") ` + -Diagnostic "perf-advisor" @args +exit $LASTEXITCODE diff --git a/scripts/perf-advisor.py b/scripts/perf-advisor.py new file mode 100644 index 0000000..b81695c --- /dev/null +++ b/scripts/perf-advisor.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +from portable_diagnostic import main + +if __name__ == "__main__": + raise SystemExit(main("perf-advisor")) diff --git a/scripts/perf-advisor.sh b/scripts/perf-advisor.sh index 6bddea1..3c0e920 100755 --- a/scripts/perf-advisor.sh +++ b/scripts/perf-advisor.sh @@ -32,6 +32,9 @@ # bash scripts/perf-advisor.sh --db myapp --all-dbs # scan all databases set -uo pipefail +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source "$SCRIPT_DIR/diagnostic-runtime.sh" + CONTAINER_NAME="${CONTAINER_NAME:-documentdb-local}" PORT="${PORT:-10260}" PG_PORT="${PG_PORT:-9712}" @@ -76,25 +79,25 @@ done # ── Helper functions ────────────────────────────────────────────────── run_mongosh() { local target_db="${2:-$DB}" - docker exec -u documentdb "$CONTAINER_NAME" mongosh \ + docdb_exec_as documentdb mongosh \ "localhost:${PORT}/${target_db}" -u "$USER" -p "$PASSWORD" \ --authenticationMechanism SCRAM-SHA-256 --tls --tlsAllowInvalidCertificates \ --quiet --eval "$1" 2>/dev/null } run_psql() { - docker exec "$CONTAINER_NAME" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ + docdb_exec_as "" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ -t --no-align -c "$1" 2>/dev/null } run_psql_pretty() { - docker exec "$CONTAINER_NAME" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ + docdb_exec_as "" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ -c "$1" 2>/dev/null | grep -v "^SET$" } # psql that returns a single scalar/line with no formatting (for JSON assembly) run_psql_raw() { - docker exec "$CONTAINER_NAME" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ + docdb_exec_as "" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ -t -A -c "$1" 2>/dev/null } @@ -124,7 +127,8 @@ fi if [[ "$JSON" == "1" ]]; then # Mongo-layer findings per database (index health, COLLSCAN audit, slow queries). read -r -d '' MONGO_JSON_JS <<'JS' -var out = { db: db.getName(), collections: [], index_health: [], collscans: [], slow_queries: [] }; +var out = { db: db.getName(), collections: [], index_health: [], collscans: [], + query_timings: [], slow_queries: [] }; var colls = db.getCollectionNames().sort(); // --- overview + index health --- @@ -153,6 +157,14 @@ colls.forEach(function(c) { }); // --- COLLSCAN audit (same logic as the human report) --- +// probeDoc() picks the FIRST document by _id rather than `findOne()`, which +// returns an arbitrary document. The probe VALUES are taken from this document, +// so an arbitrary pick made the audit nondeterministic: a numeric probe built +// from `val/2` scanned a different fraction of the collection depending on +// which document turned up, which changed both `docs_scanned` and — because +// PostgreSQL costs the two plans differently — sometimes the chosen plan, and +// therefore whether the query was reported as a collection scan at all. +function probeDoc(c){ var a=db[c].find().sort({_id:1}).limit(1).toArray(); return a.length?a[0]:null; } function effectiveIndex(node){ var g=0; while(node&&g++<50){ if(node.stage==="COLLSCAN")return "__COLLSCAN__"; if(node.stage==="IXSCAN")return node.indexName||"?"; node=node.inputStage||(node.inputStages?node.inputStages[0]:null);} return null; } function testQuery(coll, field, label, filter, indexedFields){ try { @@ -165,31 +177,65 @@ function testQuery(coll, field, label, filter, indexedFields){ } colls.forEach(function(c) { var count=db[c].estimatedDocumentCount(); if (count<10) return; - var sample=db[c].findOne(); if (!sample) return; + var sample=probeDoc(c); if (!sample) return; var indexedFields={}; db[c].getIndexes().forEach(function(ix){var k=Object.keys(ix.key||{}); if(k.length)indexedFields[k[0]]=true;}); Object.keys(sample).filter(function(k){return k!=="_id";}).forEach(function(field){ var val=sample[field]; - if (typeof val==="string" && val.length<100) testQuery(c,field,"find {"+field+":\"...\"}", JSON.parse("{\""+field+"\":\""+val+"\"}"), indexedFields); - else if (typeof val==="number") testQuery(c,field,"find {"+field+":{$gt:...}}", JSON.parse("{\""+field+"\":{\"$gt\":"+(val/2)+"}}"), indexedFields); - else if (typeof val==="boolean") testQuery(c,field,"find {"+field+":"+val+"}", JSON.parse("{\""+field+"\":"+val+"}"), indexedFields); + var filter={}; + if (typeof val==="string" && val.length<100) { + filter[field]=val; + testQuery(c,field,"find {"+field+":\"...\"}",filter,indexedFields); + } else if (typeof val==="number") { + filter[field]={$gt:val/2}; + testQuery(c,field,"find {"+field+":{$gt:...}}",filter,indexedFields); + } else if (typeof val==="boolean") { + filter[field]=val; + testQuery(c,field,"find {"+field+":"+val+"}",filter,indexedFields); + } }); }); -// --- query timing (report anything >50ms) --- -function timeQuery(coll,label,fn){ var s=Date.now(); var n=0; try{n=fn();}catch(e){n=-1;} var ms=Date.now()-s; if(ms>50) out.slow_queries.push({collection:coll, query:label, ms:ms, results:n}); } +// --- query timing --- +// +// `query_timings` records EVERY probe that was run, whether fast or slow. Its +// membership is therefore deterministic: the same database always yields the +// same set of probes in the same order, and only the measured `ms` moves. +// +// `slow_queries` is the >threshold subset, kept for backward compatibility. It +// is INHERENTLY volatile — whether a query crosses 50 ms depends on cache +// warmth and machine load, so the same database can produce different +// membership on two consecutive runs (measured). Treat it as a measurement, +// not a finding; filter `query_timings` by `ms` yourself if you need a +// different threshold. +var SLOW_MS = 50; +function timeQuery(coll,label,fn){ + var s=Date.now(); var n=0; try{n=fn();}catch(e){n=-1;} var ms=Date.now()-s; + out.query_timings.push({collection:coll, query:label, ms:ms, results:n}); + if (ms>SLOW_MS) out.slow_queries.push({collection:coll, query:label, ms:ms, results:n}); +} colls.forEach(function(c){ var count=db[c].estimatedDocumentCount(); if (count<100) return; - var sample=db[c].findOne(); if (!sample) return; + var sample=probeDoc(c); if (!sample) return; timeQuery(c,"countDocuments()",function(){return db[c].countDocuments();}); - var sf=Object.keys(sample).filter(function(k){return k!=="_id";}).find(function(f){return typeof sample[f]==="string"&&sample[f].length<50;}); + // Pick the probe field deterministically: `findOne()` key order is stable + // for a given document, but sort the candidates anyway so the chosen field + // cannot depend on storage-layer key ordering. + var sf=Object.keys(sample).filter(function(k){return k!=="_id";}).sort() + .find(function(f){return typeof sample[f]==="string"&&sample[f].length<50;}); if (sf){ var v=sample[sf]; - timeQuery(c,"find({"+sf+":...})",function(){return db[c].find(JSON.parse("{\""+sf+"\":\""+v+"\"}")).count();}); + var filter={}; filter[sf]=v; + timeQuery(c,"find({"+sf+":...})",function(){return db[c].find(filter).count();}); timeQuery(c,"aggregate $group by "+sf,function(){return db[c].aggregate([{$group:{_id:"$"+sf,n:{$sum:1}}}]).toArray().length;}); } }); out.summary = { collections: out.collections.length, index_health_findings: out.index_health.length, - collscan_patterns: out.collscans.length, slow_queries: out.slow_queries.length }; + collscan_patterns: out.collscans.length, + // deterministic: how many probes ran + query_probes: out.query_timings.length, + // volatile: how many crossed the latency threshold this run + slow_queries: out.slow_queries.length, + slow_threshold_ms: SLOW_MS }; print("MONGOJSON " + JSON.stringify(out)); JS @@ -392,8 +438,9 @@ colls.forEach(function(c) { if (count < 10) return; // skip tiny collections print(" Testing " + c + " (" + count + " docs)..."); - // Sample a document to discover fields - var sample = db[c].findOne(); + // Sample a document to discover fields. First-by-_id, not findOne(): an + // arbitrary document gives arbitrary probe values and an unstable report. + var sample = db[c].find().sort({_id:1}).limit(1).toArray()[0]; if (!sample) return; var fields = Object.keys(sample).filter(function(k) { return k !== "_id"; }); @@ -409,12 +456,16 @@ colls.forEach(function(c) { // Test equality/range filter on each top-level string/number field fields.forEach(function(field) { var val = sample[field]; + var filter = {}; if (typeof val === "string" && val.length < 100) { - testQuery(c, field, "find {" + field + ":\"...\"}", JSON.parse("{\"" + field + "\":\"" + val + "\"}"), null, indexedFields); + filter[field] = val; + testQuery(c, field, "find {" + field + ":\"...\"}", filter, null, indexedFields); } else if (typeof val === "number") { - testQuery(c, field, "find {" + field + ":{$gt:...}}", JSON.parse("{\"" + field + "\":{\"$gt\":" + (val/2) + "}}"), null, indexedFields); + filter[field] = {$gt: val/2}; + testQuery(c, field, "find {" + field + ":{$gt:...}}", filter, null, indexedFields); } else if (typeof val === "boolean") { - testQuery(c, field, "find {" + field + ":" + val + "}", JSON.parse("{\"" + field + "\":" + val + "}"), null, indexedFields); + filter[field] = val; + testQuery(c, field, "find {" + field + ":" + val + "}", filter, null, indexedFields); } }); // NOTE: a full-collection $group aggregation always scans every document by @@ -452,8 +503,9 @@ colls.forEach(function(c) { if (count < 100) return; print(" ── " + c + " (" + count + " docs) ──"); - // Sample to get realistic filter values - var sample = db[c].findOne(); + // Sample to get realistic filter values (first-by-_id, so the same database + // always produces the same probes) + var sample = db[c].find().sort({_id:1}).limit(1).toArray()[0]; if (!sample) return; // Test count (full scan baseline) @@ -464,8 +516,9 @@ colls.forEach(function(c) { var strField = fields.find(function(f) { return typeof sample[f] === "string" && sample[f].length < 50; }); if (strField) { var val = sample[strField]; + var filter = {}; filter[strField] = val; timeQuery(c, "find({" + strField + ":\"" + val.substring(0,20) + "...\"})", function() { - return db[c].find(JSON.parse("{\"" + strField + "\":\"" + val + "\"}")).count(); + return db[c].find(filter).count(); }); } diff --git a/scripts/portable_diagnostic.py b/scripts/portable_diagnostic.py new file mode 100644 index 0000000..95906b5 --- /dev/null +++ b/scripts/portable_diagnostic.py @@ -0,0 +1,278 @@ +#!/usr/bin/env python3 +"""Cross-platform launcher for the Bash-based DocumentDB diagnostics. + +Windows hosts do not need Bash, awk, sed, or grep. The launcher copies the +selected diagnostic and its shared runtime into the Linux DocumentDB container, +then executes the existing diagnostic logic there. Docker Desktop and Python +3.10+ are the only host requirements. +""" + +from __future__ import annotations + +import json +import os +import subprocess +import sys +import tempfile +import uuid +from collections import defaultdict +from datetime import datetime +from pathlib import Path +from typing import TextIO + +SCRIPT_DIR = Path(__file__).resolve().parent +SUPPORTED = { + "data-integrity-check", + "db-config-advisor", + "document-bloat-advisor", + "index-redundancy-finder", + "perf-advisor", + "toast-split-advisor", +} + + +class DiagnosticError(RuntimeError): + """A portable diagnostic could not be launched or completed.""" + + +def option_value(args: list[str], name: str, default: str) -> str: + try: + index = args.index(name) + except ValueError: + return default + if index + 1 >= len(args): + raise DiagnosticError(f"{name} requires a value") + return args[index + 1] + + +def container_name(args: list[str]) -> str: + return option_value( + args, + "--container", + os.environ.get("CONTAINER_NAME") + or os.environ.get("CONTAINER") + or "documentdb-local", + ) + + +def docker_environment() -> dict[str, str]: + names = ( + "DB_PASSWORD", + "DB_USER", + "PORT", + "PG_PORT", + "PG_USER", + "PG_DB", + "DEBUG", + ) + return {name: os.environ[name] for name in names if name in os.environ} + + +def docker_exec_command( + container: str, + remote_script: str, + args: list[str], + extra_env: dict[str, str] | None = None, +) -> list[str]: + environment = {"DOCDB_DIRECT": "1", **docker_environment(), **(extra_env or {})} + command = ["docker", "exec", "-i", "-u", "documentdb"] + for name, value in environment.items(): + command.extend(["-e", f"{name}={value}"]) + command.extend([container, "/bin/bash", remote_script, *args]) + return command + + +def _run(command: list[str], **kwargs) -> subprocess.CompletedProcess[str]: + try: + return subprocess.run( + command, text=True, encoding="utf-8", errors="replace", **kwargs + ) + except FileNotFoundError as error: + raise DiagnosticError( + "Docker CLI was not found. Install Docker Desktop and ensure " + "'docker' is available on PATH." + ) from error + + +def _copy(container: str, source: Path, destination: str) -> None: + result = _run( + ["docker", "cp", str(source), f"{container}:{destination}"], + capture_output=True, + ) + if result.returncode != 0: + raise DiagnosticError(result.stderr.strip() or f"docker cp failed: {source}") + + +def normalized_shell_copy(source: Path, directory: Path) -> Path: + """Create an LF-only copy so Git-for-Windows CRLF checkout is harmless.""" + target = directory / source.name + content = source.read_text(encoding="utf-8").replace("\r\n", "\n").replace( + "\r", "\n" + ) + target.write_text(content, encoding="utf-8", newline="\n") + return target + + +def _render_index_findings( + payload: str, args: list[str], container: str, output: TextIO +) -> None: + from index_redundancy_render import render + + findings = json.loads(payload) + by_database: dict[str, list[dict]] = defaultdict(list) + for finding in findings: + by_database[finding.get("db", "")].append(finding) + + target = option_value(args, "--db", "ALL") if "--all-dbs" not in args else "ALL" + print("╔" + "═" * 66 + "╗", file=output) + print("║ DocumentDB Index Redundancy Finder".ljust(67) + "║", file=output) + print(f"║ Database: {target}".ljust(67) + "║", file=output) + print(f"║ Container: {container}".ljust(67) + "║", file=output) + timestamp = datetime.now().strftime("%Y%m%d%H%M%S") + print(f"║ Timestamp: {timestamp}".ljust(67) + "║", file=output) + print("╚" + "═" * 66 + "╝", file=output) + print(file=output) + + databases = sorted(by_database) or ([target] if target != "ALL" else []) + for database in databases: + print("┌" + "─" * 62 + "┐", file=output) + print(f"│ Database: {database}".ljust(63) + "│", file=output) + print("└" + "─" * 62 + "┘", file=output) + print(file=output) + render(by_database.get(database, []), output) + + print(file=output) + print("═" * 67, file=output) + print(" Legend:", file=output) + print(" 🔴 HIGH — Safe to drop (validated by index spec / catalog)", file=output) + print(" 🟡 MEDIUM — Likely safe (zero usage validated at both layers)", file=output) + print(" 🔵 LOW — Review query patterns before dropping", file=output) + print("═" * 67, file=output) + + +def _render_toast_report(payload: str, args: list[str], output: TextIO) -> None: + from toast_split_advisor_render import render + + render( + data=payload, + json_mode="--json" in args, + db_name=option_value(args, "--db", ""), + field_min=int(option_value(args, "--field-min-bytes", "1024")), + inline_threshold=2000, + toast_ratio=float(option_value(args, "--toast-ratio", "0.5")), + min_kb=option_value(args, "--min-total-kb", "256"), + output=output, + ) + + +def run_named( + name: str, + args: list[str], + *, + stdout: TextIO = sys.stdout, + stderr: TextIO = sys.stderr, +) -> int: + if name not in SUPPORTED: + raise DiagnosticError(f"unsupported diagnostic: {name}") + + if any(arg in ("-h", "--help") for arg in args): + print( + f"Usage: python {name}.py [the same options as {name}.sh]\n" + "Runs the diagnostic through Docker without requiring Bash on the host.", + file=stdout, + ) + return 0 + + container = container_name(args) + remote_dir = f"/tmp/documentdb-agent-kit-{uuid.uuid4().hex}" + remote_script = f"{remote_dir}/{name}.sh" + script = SCRIPT_DIR / f"{name}.sh" + runtime = SCRIPT_DIR / "diagnostic-runtime.sh" + + create = _run( + ["docker", "exec", "-u", "root", container, "mkdir", "-p", remote_dir], + capture_output=True, + ) + if create.returncode != 0: + raise DiagnosticError( + create.stderr.strip() + or f"cannot prepare portable diagnostic in container '{container}'" + ) + + try: + with tempfile.TemporaryDirectory(prefix="documentdb-agent-kit-") as temp: + temp_dir = Path(temp) + _copy( + container, + normalized_shell_copy(runtime, temp_dir), + f"{remote_dir}/diagnostic-runtime.sh", + ) + _copy( + container, + normalized_shell_copy(script, temp_dir), + remote_script, + ) + + inner_args = list(args) + extra_env: dict[str, str] = {} + render = None + if name == "index-redundancy-finder" and "--json" not in inner_args: + inner_args.append("--json") + render = lambda payload, stream: _render_index_findings( + payload, args, container, stream + ) + elif name == "toast-split-advisor": + extra_env["DOCDB_PORTABLE_RAW"] = "1" + render = lambda payload, stream: _render_toast_report( + payload, args, stream + ) + + result = _run( + docker_exec_command( + container, remote_script, inner_args, extra_env + ), + capture_output=True, + ) + if result.stderr: + stderr.write(result.stderr) + if result.returncode != 0: + if result.stdout: + stdout.write(result.stdout) + return result.returncode + + if render is None: + stdout.write(result.stdout) + else: + render(result.stdout, stdout) + return 0 + finally: + _run( + ["docker", "exec", "-u", "root", container, "rm", "-rf", remote_dir], + capture_output=True, + ) + + +def main(name: str | None = None) -> int: + for stream in (sys.stdout, sys.stderr): + reconfigure = getattr(stream, "reconfigure", None) + if reconfigure is not None: + reconfigure(encoding="utf-8") + selected = name + args = sys.argv[1:] + if selected is None: + if not args: + print( + "Usage: python portable_diagnostic.py [options]", + file=sys.stderr, + ) + return 2 + selected, args = args[0], args[1:] + try: + return run_named(selected, args) + except (DiagnosticError, json.JSONDecodeError, ValueError) as error: + print(f"Error: {error}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/toast-split-advisor-render.py b/scripts/toast-split-advisor-render.py index 95105c1..a0a837f 100644 --- a/scripts/toast-split-advisor-render.py +++ b/scripts/toast-split-advisor-render.py @@ -11,138 +11,253 @@ TOAST_RATIO, MIN_KB. """ -import os import json +import os +import sys +from typing import TextIO + +def render( + *, + data: str, + json_mode: bool, + db_name: str, + field_min: int, + inline_threshold: int, + toast_ratio: float, + min_kb: str, + output: TextIO = sys.stdout, +) -> None: + findings = [] + clean = [] + + for line in data.splitlines(): + if not line.strip(): + continue + parts = line.split("\t", 4) + if len(parts) < 5: + continue + coll, heap, toast, total, tail = parts + heap, toast, total = int(heap), int(toast), int(total) + ratio = (toast / (heap + toast)) if (heap + toast) > 0 else 0.0 + + if tail == "CLEAN": + clean.append( + { + "collection": coll, + "heap_bytes": heap, + "toast_bytes": toast, + "toast_ratio": round(ratio, 4), + } + ) + continue + + field_data = json.loads(tail) + avg = field_data.get("avg_obj_size", 0) or 0 + fields = field_data.get("fields", []) + candidates = [f for f in fields if f.get("b", 0) >= field_min] + largest_only = (not candidates) and fields + show = candidates if candidates else (fields[:1] if fields else []) + moved_bytes = sum(f["b"] for f in candidates) + projected_hot = max(avg - moved_bytes, 0) + + findings.append( + { + "collection": coll, + "heap_bytes": heap, + "toast_bytes": toast, + "total_bytes": total, + "toast_ratio": round(ratio, 4), + "avg_obj_size": avg, + "sampled_docs": field_data.get("sampled", 0), + "split_candidates": [ + { + "field": f["f"], + "avg_bytes": f["b"], + "pct_of_doc": round(100.0 * f["b"] / avg, 1) + if avg > 0 + else 0.0, + } + for f in show + ], + "candidate_threshold_bytes": field_min, + "no_field_over_threshold": bool(largest_only), + "projected_hot_avg_bytes": projected_hot, + "projected_stays_inline": projected_hot < inline_threshold, + "recommended_side_collection": coll + "_ext", + "side_collection_key": "_id", + "note": "ANALYSIS ONLY — no data moved. Apply the split yourself.", + } + ) + + if json_mode: + print( + json.dumps( + { + "db": db_name, + "flagged": len(findings), + "findings": findings, + "clean": clean, + "analysis_only": True, + } + ), + file=output, + ) + return + + bar = "═" * 70 + print(bar, file=output) + print( + " DocumentDB TOAST Split Advisor (ANALYSIS ONLY — does not modify data)", + file=output, + ) + print( + f" Database: {db_name} flag TOAST ratio > {toast_ratio}, min {min_kb}KB, " + f"candidate field >= {field_min}B", + file=output, + ) + print(bar, file=output) + print(file=output) + + for finding in findings: + heap_kb = finding["heap_bytes"] // 1024 + toast_kb = finding["toast_bytes"] // 1024 + print(f" ⚠️ {finding['collection']}", file=output) + print( + f" heap={heap_kb}KB TOAST={toast_kb}KB " + f"(TOAST ratio {finding['toast_ratio']}) " + f"avgObjSize={finding['avg_obj_size']}B " + f"[sampled {finding['sampled_docs']} docs]", + file=output, + ) + if finding["no_field_over_threshold"]: + big = ( + finding["split_candidates"][0] + if finding["split_candidates"] + else None + ) + if big: + print( + f" no single field >= " + f"{finding['candidate_threshold_bytes']}B; largest is " + f"'{big['field']}' ({big['avg_bytes']}B, " + f"{big['pct_of_doc']}% of doc).", + file=output, + ) + print( + " Bloat is spread across fields — review the schema " + "rather than a single split.", + file=output, + ) + else: + print( + " split candidate field(s) to MOVE into a side collection:", + file=output, + ) + for candidate in finding["split_candidates"]: + print( + f" • {candidate['field']:<24} " + f"~{candidate['avg_bytes']}B/doc " + f"({candidate['pct_of_doc']}% of document)", + file=output, + ) + inline = ( + "stays INLINE (TOAST≈0) ✅" + if finding["projected_stays_inline"] + else "still large — consider moving more fields ⚠️" + ) + print( + f" after moving these, hot doc ≈ " + f"{finding['projected_hot_avg_bytes']}B → {inline}", + file=output, + ) + print( + f" → create '{finding['recommended_side_collection']}' " + f"keyed by '{finding['side_collection_key']}' holding those " + f"fields; keep '{finding['collection']}' scalar-only.", + file=output, + ) + print(file=output) + + for item in clean: + print( + f" ✅ {item['collection']} heap={item['heap_bytes']//1024}KB " + f"TOAST={item['toast_bytes']//1024}KB " + f"(ratio {item['toast_ratio']}) — no bloat", + file=output, + ) + + print(file=output) + print("─" * 70, file=output) + if not findings: + print( + " ✅ No large-document/TOAST bloat detected — nothing to split.", + file=output, + ) + return + + print( + f" Flagged {len(findings)} collection(s). " + "This tool ONLY reports guidance.", + file=output, + ) + print(file=output) + print( + " Applying the split safely (DO NOT run a naive bulk update on a large", + file=output, + ) + print( + " collection — it bursts WAL, holds locks, and leaves dead-tuple bloat):", + file=output, + ) + print( + " 1. Copy the candidate field(s) into the side collection, keyed by _id,", + file=output, + ) + print( + " in _id-range BATCHES (e.g. 10k docs) — copy BEFORE removing.", + file=output, + ) + print( + " 2. Verify per batch that the side collection has the row, THEN $unset", + file=output, + ) + print( + " the field(s) from the hot collection for that batch.", + file=output, + ) + print( + " 3. After migration, VACUUM (FULL, ANALYZE) the hot table to reclaim", + file=output, + ) + print( + " space (takes an exclusive lock + ~2x disk — schedule a window).", + file=output, + ) + print( + " 4. Update the application to read the field(s) from the side", + file=output, + ) + print( + " collection on demand (extra _id lookup) — split only wins when the", + file=output, + ) + print( + " big field is read far less often than the scalars are scanned.", + file=output, + ) + + +def main() -> None: + render( + data=os.environ.get("REPORT_DATA", ""), + json_mode=os.environ.get("JSON_MODE", "0") == "1", + db_name=os.environ.get("DB_NAME", ""), + field_min=int(os.environ.get("FIELD_MIN", "1024")), + inline_threshold=int(os.environ.get("INLINE_THR", "2000")), + toast_ratio=float(os.environ.get("TOAST_RATIO", "0.5")), + min_kb=os.environ.get("MIN_KB", "256"), + ) + -data = os.environ.get("REPORT_DATA", "") -json_mode = os.environ.get("JSON_MODE", "0") == "1" -db_name = os.environ.get("DB_NAME", "") -field_min = int(os.environ.get("FIELD_MIN", "1024")) -inline_thr = int(os.environ.get("INLINE_THR", "2000")) -toast_ratio= float(os.environ.get("TOAST_RATIO", "0.5")) -min_kb = os.environ.get("MIN_KB", "256") - -findings = [] # flagged collections with split guidance -clean = [] # collections with no TOAST bloat - -for line in data.splitlines(): - if not line.strip(): - continue - parts = line.split("\t", 4) - if len(parts) < 5: - continue - coll, heap, toast, total, tail = parts - heap, toast, total = int(heap), int(toast), int(total) - ratio = (toast / (heap + toast)) if (heap + toast) > 0 else 0.0 - - if tail == "CLEAN": - clean.append({"collection": coll, "heap_bytes": heap, "toast_bytes": toast, - "toast_ratio": round(ratio, 4)}) - continue - - fj = json.loads(tail) - avg = fj.get("avg_obj_size", 0) or 0 - fields = fj.get("fields", []) - - # Split candidates: the largest fields that individually exceed the threshold. - # These are what get pushed to TOAST and should move to a side collection. - candidates = [f for f in fields if f.get("b", 0) >= field_min] - # If nothing crosses the threshold, still surface the single largest field so - # the operator sees where the weight is (bloat may be spread across fields). - largest_only = (not candidates) and fields - show = candidates if candidates else (fields[:1] if fields else []) - - moved_bytes = sum(f["b"] for f in candidates) - projected_hot = max(avg - moved_bytes, 0) - stays_inline = projected_hot < inline_thr - - def pct(b): - return round(100.0 * b / avg, 1) if avg > 0 else 0.0 - - findings.append({ - "collection": coll, - "heap_bytes": heap, - "toast_bytes": toast, - "total_bytes": total, - "toast_ratio": round(ratio, 4), - "avg_obj_size": avg, - "sampled_docs": fj.get("sampled", 0), - "split_candidates": [ - {"field": f["f"], "avg_bytes": f["b"], "pct_of_doc": pct(f["b"])} - for f in show - ], - "candidate_threshold_bytes": field_min, - "no_field_over_threshold": bool(largest_only), - "projected_hot_avg_bytes": projected_hot, - "projected_stays_inline": stays_inline, - "recommended_side_collection": coll + "_ext", - "side_collection_key": "_id", - "note": "ANALYSIS ONLY — no data moved. Apply the split yourself.", - }) - -if json_mode: - print(json.dumps({ - "db": db_name, - "flagged": len(findings), - "findings": findings, - "clean": clean, - "analysis_only": True, - })) - raise SystemExit(0) - -# ── Human report ──────────────────────────────────────────────────────────── -bar = "═" * 70 -print(bar) -print(" DocumentDB TOAST Split Advisor (ANALYSIS ONLY — does not modify data)") -print(f" Database: {db_name} flag TOAST ratio > {toast_ratio}, min {min_kb}KB, " - f"candidate field >= {field_min}B") -print(bar) -print() - -for f in findings: - hk, tk = f["heap_bytes"] // 1024, f["toast_bytes"] // 1024 - print(f" ⚠️ {f['collection']}") - print(f" heap={hk}KB TOAST={tk}KB (TOAST ratio {f['toast_ratio']}) " - f"avgObjSize={f['avg_obj_size']}B [sampled {f['sampled_docs']} docs]") - if f["no_field_over_threshold"]: - big = f["split_candidates"][0] if f["split_candidates"] else None - if big: - print(f" no single field >= {f['candidate_threshold_bytes']}B; largest is " - f"'{big['field']}' ({big['avg_bytes']}B, {big['pct_of_doc']}% of doc).") - print(" Bloat is spread across fields — review the schema rather than a" - " single split.") - else: - print(f" split candidate field(s) to MOVE into a side collection:") - for c in f["split_candidates"]: - print(f" • {c['field']:<24} ~{c['avg_bytes']}B/doc " - f"({c['pct_of_doc']}% of document)") - inline = ("stays INLINE (TOAST≈0) ✅" if f["projected_stays_inline"] - else "still large — consider moving more fields ⚠️") - print(f" after moving these, hot doc ≈ {f['projected_hot_avg_bytes']}B → {inline}") - print(f" → create '{f['recommended_side_collection']}' keyed by " - f"'{f['side_collection_key']}' holding those fields; keep " - f"'{f['collection']}' scalar-only.") - print() - -for c in clean: - print(f" ✅ {c['collection']} heap={c['heap_bytes']//1024}KB " - f"TOAST={c['toast_bytes']//1024}KB (ratio {c['toast_ratio']}) — no bloat") - -print() -print("─" * 70) -if not findings: - print(" ✅ No large-document/TOAST bloat detected — nothing to split.") -else: - print(f" Flagged {len(findings)} collection(s). This tool ONLY reports guidance.") - print() - print(" Applying the split safely (DO NOT run a naive bulk update on a large") - print(" collection — it bursts WAL, holds locks, and leaves dead-tuple bloat):") - print(" 1. Copy the candidate field(s) into the side collection, keyed by _id,") - print(" in _id-range BATCHES (e.g. 10k docs) — copy BEFORE removing.") - print(" 2. Verify per batch that the side collection has the row, THEN $unset") - print(" the field(s) from the hot collection for that batch.") - print(" 3. After migration, VACUUM (FULL, ANALYZE) the hot table to reclaim") - print(" space (takes an exclusive lock + ~2x disk — schedule a window).") - print(" 4. Update the application to read the field(s) from the side") - print(" collection on demand (extra _id lookup) — split only wins when the") - print(" big field is read far less often than the scalars are scanned.") +if __name__ == "__main__": + main() diff --git a/scripts/toast-split-advisor.ps1 b/scripts/toast-split-advisor.ps1 new file mode 100644 index 0000000..7f17abd --- /dev/null +++ b/scripts/toast-split-advisor.ps1 @@ -0,0 +1,4 @@ +$ErrorActionPreference = "Stop" +& (Join-Path $PSScriptRoot "invoke-diagnostic.ps1") ` + -Diagnostic "toast-split-advisor" @args +exit $LASTEXITCODE diff --git a/scripts/toast-split-advisor.py b/scripts/toast-split-advisor.py new file mode 100644 index 0000000..53e0173 --- /dev/null +++ b/scripts/toast-split-advisor.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +from portable_diagnostic import main + +if __name__ == "__main__": + raise SystemExit(main("toast-split-advisor")) diff --git a/scripts/toast-split-advisor.sh b/scripts/toast-split-advisor.sh index 46fc3bd..d565a25 100755 --- a/scripts/toast-split-advisor.sh +++ b/scripts/toast-split-advisor.sh @@ -32,6 +32,7 @@ set -uo pipefail SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source "$SCRIPT_DIR/diagnostic-runtime.sh" CONTAINER_NAME="${CONTAINER_NAME:-documentdb-local}" PORT="${PORT:-10260}" @@ -68,21 +69,30 @@ while [[ $# -gt 0 ]]; do done [[ -z "$DB" ]] && { echo "Error: --db is required" >&2; exit 1; } [[ -z "$PASSWORD" ]] && { echo "Error: no password. Set DB_PASSWORD or pass --password (local demo: export DB_PASSWORD=Test1234)." >&2; exit 1; } +[[ "$SAMPLE" =~ ^[1-9][0-9]*$ ]] || { echo "Error: --sample must be a positive integer" >&2; exit 2; } +[[ "$MIN_TOTAL_KB" =~ ^[0-9]+$ ]] || { echo "Error: --min-total-kb must be a non-negative integer" >&2; exit 2; } +[[ "$FIELD_MIN_BYTES" =~ ^[0-9]+$ ]] || { echo "Error: --field-min-bytes must be a non-negative integer" >&2; exit 2; } +[[ "$TOAST_RATIO" =~ ^[0-9]+([.][0-9]+)?$ ]] || { echo "Error: --toast-ratio must be a non-negative number" >&2; exit 2; } run_mongosh() { - docker exec "$CONTAINER_NAME" mongosh "localhost:${PORT}/${DB}" \ + local program="$1" + shift + docdb_exec_as "" env "$@" mongosh "localhost:${PORT}/${DB}" \ -u "$DB_USER_" -p "$PASSWORD" --authenticationMechanism SCRAM-SHA-256 \ - --tls --tlsAllowInvalidCertificates --quiet --eval "$1" 2>/dev/null + --tls --tlsAllowInvalidCertificates --quiet --eval "$program" 2>/dev/null } run_psql() { - docker exec "$CONTAINER_NAME" psql -h localhost -p "$PG_PORT" -U "$PG_USER" -d "$PG_DB" \ - -t --no-align -F $'\t' -c "$1" 2>/dev/null | grep -vE '^(SET|)$' + local query="$1" + shift + printf '%s\n' "$query" | + docdb_exec_stdin_as "" psql -h localhost -p "$PG_PORT" -U "$PG_USER" \ + -d "$PG_DB" -v ON_ERROR_STOP=1 -t --no-align -F $'\t' "$@" \ + 2>/dev/null | + grep -vE '^(SET|)$' } # ── Per-collection heap/TOAST from PostgreSQL (measured facts) ────────────── # tab-separated: collectionheap_bytestoast_bytestotal_bytes -COLL_FILTER="" -[[ -n "$ONE_COLL" ]] && COLL_FILTER="AND c.collection_name = '${ONE_COLL}'" SIZES=$(run_psql " SELECT c.collection_name, pg_relation_size(t.oid), @@ -90,9 +100,11 @@ SELECT c.collection_name, pg_total_relation_size(t.oid) FROM documentdb_api_catalog.collections c JOIN pg_class t ON t.oid = ('documentdb_data.documents_' || c.collection_id)::regclass -WHERE c.database_name = '${DB}' ${COLL_FILTER} +WHERE c.database_name = :'db_name' + AND (NULLIF(:'collection_name', '') IS NULL + OR c.collection_name = :'collection_name') ORDER BY pg_total_relation_size(t.oid) DESC; -") +" --set=db_name="$DB" --set=collection_name="$ONE_COLL") if [[ -z "$SIZES" ]]; then echo "No collections found for database '${DB}' (is it seeded? is the container up?)" >&2 @@ -106,9 +118,15 @@ fi field_json() { local coll="$1" run_mongosh ' - var s = db.'"$coll"'.stats(); + var coll = process.env.DOCDB_COLLECTION; + var sample = Number(process.env.DOCDB_SAMPLE); + if (!Number.isSafeInteger(sample) || sample < 1) { + throw new Error("invalid sample size"); + } + var collection = db.getCollection(coll); + var s = collection.stats(); var avg = s.avgObjSize || 0; - var docs = db.'"$coll"'.aggregate([{$sample:{size:'"$SAMPLE"'}}]).toArray(); + var docs = collection.aggregate([{$sample:{size:sample}}]).toArray(); var acc = {}, n = docs.length || 1; docs.forEach(function(doc){ Object.keys(doc).forEach(function(k){ @@ -120,7 +138,8 @@ field_json() { var fields = Object.keys(acc).map(function(k){ return {f:k, b:Math.round(acc[k]/n)}; }); fields.sort(function(a,b){ return b.b - a.b; }); print("FIELDJSON " + JSON.stringify({avg_obj_size: avg, sampled: n, fields: fields})); - ' | sed -n 's/^FIELDJSON //p' + ' "DOCDB_COLLECTION=$coll" "DOCDB_SAMPLE=$SAMPLE" | + sed -n 's/^FIELDJSON //p' } # ── Collect one record per collection into a buffer for a single Python pass ─ @@ -135,8 +154,10 @@ while IFS=$'\t' read -r coll heap toast total; do ratio=$(awk -v t="$toast" -v h="$heap" 'BEGIN{ d=t+h; if(d<=0){print 0}else{printf "%.4f", t/d} }') over=$(awk -v r="$ratio" -v thr="$TOAST_RATIO" 'BEGIN{ print (r>thr)?1:0 }') if [[ "$over" == "1" ]]; then - fj=$(field_json "$coll") - [[ -z "$fj" ]] && fj='{"avg_obj_size":0,"sampled":0,"fields":[]}' + if ! fj=$(field_json "$coll") || [[ -z "$fj" ]]; then + echo "Failed to sample collection '$coll'" >&2 + exit 1 + fi BUFFER+="${coll}"$'\t'"${heap}"$'\t'"${toast}"$'\t'"${total}"$'\t'"${fj}"$'\n' else BUFFER+="${coll}"$'\t'"${heap}"$'\t'"${toast}"$'\t'"${total}"$'\t'"CLEAN"$'\n' @@ -148,6 +169,11 @@ if [[ "${DEBUG:-0}" == "1" ]]; then echo "── DEBUG: BUFFER ──"; printf '%s' "$BUFFER" | cat -A; } >&2 fi +if [[ "${DOCDB_PORTABLE_RAW:-0}" == "1" ]]; then + printf '%s' "$BUFFER" + exit 0 +fi + # ── Build the report in Python (robust JSON assembly + float math) ────────── REPORT_DATA="$BUFFER" JSON_MODE="$JSON" DB_NAME="$DB" \ FIELD_MIN="$FIELD_MIN_BYTES" INLINE_THR="$TOAST_INLINE_THRESHOLD" \ diff --git a/scripts/toast_split_advisor_render.py b/scripts/toast_split_advisor_render.py new file mode 100644 index 0000000..0c4bf8e --- /dev/null +++ b/scripts/toast_split_advisor_render.py @@ -0,0 +1,15 @@ +"""Importable renderer shim for the hyphenated CLI module.""" + +from __future__ import annotations + +import importlib.util +from pathlib import Path + +_PATH = Path(__file__).with_name("toast-split-advisor-render.py") +_SPEC = importlib.util.spec_from_file_location("toast_split_advisor_renderer", _PATH) +if _SPEC is None or _SPEC.loader is None: + raise ImportError(f"cannot load renderer: {_PATH}") +_MODULE = importlib.util.module_from_spec(_SPEC) +_SPEC.loader.exec_module(_MODULE) + +render = _MODULE.render diff --git a/testing/README.md b/testing/README.md index dfd5b2e..2154630 100644 --- a/testing/README.md +++ b/testing/README.md @@ -1,4 +1,13 @@ -# DocumentDB Agent-Kit — Regression Test Framework +# Diagnostic Regression Suite + +> **This is the Diagnostic Regression Suite — the deterministic half.** The kit tests two different +> things: `testing/` (here) proves the **diagnostic scripts** return the *same* +> answer every time, while [`../evals/`](../evals/) measures whether the **text +> skills** make an agent produce *better* output, and at what token cost. +> The Diagnostic Regression Suite is free and runs on every PR; +> Cross-Model Skill Evaluations cost AI credits. +> See [`scenarios/determinism/`](scenarios/determinism/) for the repeatability +> contract. This framework guards the agent-kit's **diagnostic skills/scripts** against regression. It is modelled on the [Cosmos DB agent-kit `testing-v2`] framework @@ -18,7 +27,7 @@ testing free-form LLM output. ``` seed fixture (known issues) ──► run kit script ──► assert vs contract - fixture.js scripts/*.sh expected-findings.yaml + fixture.js scripts/*.py or *.sh expected-findings.yaml ``` ## Layout @@ -76,11 +85,23 @@ cd testing && ../testing-venv/bin/python -m pytest scenarios/ecommerce-redundant cd testing && ../testing-venv/bin/python -m pytest --container my-docdb --keep-db ``` +On Windows, use Python or the matching PowerShell launcher: + +```powershell +python scripts\perf-advisor.py --db ecommerce --json +.\scripts\perf-advisor.ps1 --db ecommerce --json +``` + +The portable launcher requires Python 3.10+ and Docker Desktop, but does not +require Git Bash or WSL. It executes the existing diagnostic logic inside the +Linux DocumentDB container. The Diagnostic Regression Suite uses these Python +launchers on every platform. + If the container isn't running, the whole suite **skips** (it does not fail). ## The regression loop (for skill/script changes) -1. Edit a skill rule or a `scripts/*.sh` diagnostic. +1. Edit a skill rule or a diagnostic under `scripts/` (`.sh`, `.py`, or `.ps1`). 2. Run `pytest`. Green = behavior preserved. 3. A red test means the change altered detection behavior. Either: - it's a **bug** → fix the script, or diff --git a/testing/conftest.py b/testing/conftest.py index 470a349..ebf31b8 100644 --- a/testing/conftest.py +++ b/testing/conftest.py @@ -28,18 +28,40 @@ def container_name(request): return request.config.getoption("--container") or kit.CONTAINER -@pytest.fixture(scope="session", autouse=True) +@pytest.fixture(scope="session") def require_container(container_name): - """Skip everything if the DocumentDB container isn't running or no password - is configured (credentials are never baked in — set DOCDB_PASSWORD or - DB_PASSWORD).""" + """Validate DocumentDB access for fixtures that explicitly depend on it. + + Container-independent tests must not request this fixture. Credentials are + never baked in; set DOCDB_PASSWORD or DB_PASSWORD for live scenarios. + """ if not kit.DB_PASSWORD: pytest.skip("No DB password configured — set DOCDB_PASSWORD or DB_PASSWORD " - "(local demo: export DB_PASSWORD=Test1234)") + "(e.g. export DB_PASSWORD='')") if not kit.container_running(container_name): pytest.skip(f"DocumentDB container '{container_name}' is not running " f"(start it, or pass --container / set DOCDB_CONTAINER)") + ok, detail = kit.can_authenticate(container_name) + if not ok: + # A bad credential is fatal for the whole session: aborting once with a + # clear message beats ~50 identical setup errors. + pytest.exit( + "Cannot authenticate to the DocumentDB container " + f"'{container_name}' as user '{kit.DB_USER}'.\n\n" + f" The server said: {detail}\n\n" + " NOTE: DocumentDB reports a wrong password as " + "'MongoServerError: Invalid key' — it is an AUTH failure, not a " + "malformed document.\n\n" + " Fix: export the password the container was created with, e.g.\n" + " docker inspect " + container_name + + " --format '{{range .Config.Env}}{{println .}}{{end}}' | grep PASSWORD\n" + " export DB_PASSWORD=''\n" + " Or recreate the container with a password you choose.", + returncode=2, + ) + return container_name + @pytest.fixture def expected(request): diff --git a/testing/harness/canonicalise.py b/testing/harness/canonicalise.py new file mode 100644 index 0000000..5e21e29 --- /dev/null +++ b/testing/harness/canonicalise.py @@ -0,0 +1,94 @@ +"""Canonicalisation helpers for the Diagnostic Regression Suite. + +A diagnostic script is "deterministic" when the SAME database state yields the +SAME `--json` result. Two kinds of legitimate drift must be normalised away +first, or the assertion produces false failures: + +1. **Volatile values** — live measurements (query latency, PostgreSQL hit ratios + and scan counters). Removed entirely. +2. **Volatile ordering** — lists that are sorted by one of those volatile values + can legitimately reorder. Sorted into a stable order; contents still compared. + +Everything else is compared exactly, so a real nondeterminism bug (unsorted +output, dict ordering, sampling, parallel workers) still fails loudly. +""" + +import json +import re + + +def canonicalise(value, volatile_fields, order_insensitive_lists=(), + sampled_count_maps=(), sampled_size_strings=(), _key=None): + """Return a copy of `value` with volatile data normalised away. + + Args: + value: parsed JSON (dict / list / scalar). + volatile_fields: key names whose values are live measurements. + order_insensitive_lists: key names whose list order is derived from a + volatile metric and must therefore be normalised. + sampled_count_maps: key names holding a {category: count} map produced by + *sampling*. The categories are a stable finding; the counts are not, + so counts are blanked while the category keys are preserved. + sampled_size_strings: key names holding a summary string with sampled + byte sizes (e.g. "body:4002B,notes:2002B"). The sizes are blanked; + the field names and their order — the actual finding — are kept. + _key: the key this value was found under (internal recursion detail). + """ + volatile = set(volatile_fields) + order_insensitive = set(order_insensitive_lists) + sampled_maps = set(sampled_count_maps) + sampled_sizes = set(sampled_size_strings) + + if isinstance(value, dict): + if _key in sampled_maps: + # Keep WHICH categories were observed (the actual finding); drop the + # sampled counts (which vary run to run). + return {k: "" for k in sorted(value)} + return { + k: canonicalise(v, volatile, order_insensitive, sampled_maps, + sampled_sizes, _key=k) + for k, v in sorted(value.items()) + if k not in volatile + } + + if isinstance(value, list): + items = [ + canonicalise(v, volatile, order_insensitive, sampled_maps, + sampled_sizes, _key=_key) + for v in value + ] + if _key in order_insensitive: + # Stable, content-based ordering — independent of how the script + # happened to sort them on this run. + items.sort(key=lambda item: json.dumps(item, sort_keys=True)) + return items + + if isinstance(value, str) and _key in sampled_sizes: + # "body:4002B,notes:2002B,title:13B" -> "body:,notes:,title:" + return re.sub(r"\d+B\b", "", value) + + return value + + +def canonical_json(value, volatile_fields, order_insensitive_lists=(), + sampled_count_maps=(), sampled_size_strings=()): + """Canonicalise and render as a stable JSON string for comparison/diffing.""" + return json.dumps( + canonicalise(value, volatile_fields, order_insensitive_lists, + sampled_count_maps, sampled_size_strings), + sort_keys=True, + indent=2, + ) + + +def is_empty_result(canonical): + """True when a canonicalised result carries no findings at all. + + Guards against 'false determinism': a script that reports nothing is + trivially identical across runs, which proves nothing. + """ + if canonical is None: + return True + if isinstance(canonical, (list, dict)): + return len(canonical) == 0 + return False diff --git a/testing/harness/conftest_base.py b/testing/harness/conftest_base.py index 5c79888..fc0487b 100644 --- a/testing/harness/conftest_base.py +++ b/testing/harness/conftest_base.py @@ -26,7 +26,8 @@ def make_seeded_db_fixture(db_name, scenario_dir, fixture_filename="fixture.js") fixture = Path(scenario_dir) / fixture_filename @pytest.fixture(scope="session") - def seeded_db(request, container_name): + def seeded_db(request, require_container): + container_name = require_container if not fixture.exists(): raise FileNotFoundError(f"fixture not found: {fixture}") kit.drop_db(db_name, container=container_name) diff --git a/testing/harness/kit.py b/testing/harness/kit.py index 86db1ff..16e2877 100644 --- a/testing/harness/kit.py +++ b/testing/harness/kit.py @@ -18,6 +18,7 @@ import json import os import subprocess +import sys from dataclasses import dataclass, field from pathlib import Path @@ -33,6 +34,8 @@ CONTAINER = os.environ.get("DOCDB_CONTAINER", "documentdb-local") PORT = os.environ.get("DOCDB_PORT", "10260") PG_PORT = os.environ.get("DOCDB_PG_PORT", "9712") +PG_USER = os.environ.get("DOCDB_PG_USER", "documentdb") +PG_DB = os.environ.get("DOCDB_PG_DB", "postgres") DB_USER = os.environ.get("DOCDB_USER", "docdbadmin") DB_PASSWORD = os.environ.get("DOCDB_PASSWORD") or os.environ.get("DB_PASSWORD") or "" @@ -86,6 +89,47 @@ def mongosh_eval(db, js, container=CONTAINER, timeout=240): return (p.stdout or "") + (p.stderr or "") +def can_authenticate(container=CONTAINER, timeout=60): + """Return (ok, detail) for a credential preflight against the container. + + DocumentDB reports a bad SCRAM credential as the *very* misleading + `MongoServerError: Invalid key`, which otherwise surfaces much later as a + confusing "fixture seed failed" on every scenario. Checking once, up front, + turns ~30 cryptic failures into one actionable message. + """ + out = mongosh_eval("admin", "db.runCommand({ping:1}).ok", + container=container, timeout=timeout) + if "1" in (out or "").split(): + return True, "" + return False, (out or "").strip()[:300] + + +def analyze(container=CONTAINER, timeout=180): + """Refresh PostgreSQL planner statistics. + + Called after seeding. Immediately after a bulk insert the planner is working + from stale (or absent) statistics, so its cost estimates — and therefore the + PLAN IT CHOOSES — can differ from the plan it will choose a moment later + once autovacuum has analysed the table. + + That is a real source of test flakiness rather than a theoretical one: the + determinism suite failed roughly 2 runs in 7 because a probe on a + non-leading index column was planned as an index scan in one run and a + collection scan in another, purely because background ANALYZE landed + between them. Settling statistics up front removes the race, and it is what + any plan-sensitive measurement should do. + + Best-effort: a failure here is not a test failure, only a missed + optimisation, so it is reported and swallowed. + """ + p = docker_exec( + ["psql", "-h", "localhost", "-p", PG_PORT, "-U", PG_USER, + "-d", PG_DB, "-q", "-c", "ANALYZE"], + timeout=timeout, container=container, + ) + return p.returncode == 0 + + def seed(db, fixture_path, container=CONTAINER, timeout=300): """Copy a .js fixture into the container and execute it against `db`.""" fixture_path = Path(fixture_path) @@ -99,6 +143,8 @@ def seed(db, fixture_path, container=CONTAINER, timeout=300): out = (p.stdout or "") + (p.stderr or "") if p.returncode != 0: raise RuntimeError(f"fixture seed failed (rc={p.returncode}):\n{out}") + # Settle planner statistics before anything measures a query plan. + analyze(container=container) return out @@ -107,17 +153,24 @@ def drop_db(db, container=CONTAINER): # --- Running agent-kit scripts -------------------------------------------- -def run_script(name, *args, want_json=False, timeout=300): - """Run scripts/ with args. If want_json, parse stdout as JSON. +def run_script(name, *args, want_json=False, timeout=300, portable=True): + """Run a diagnostic with args. If want_json, parse stdout as JSON. Passes the harness connection config to the script via environment (the scripts read DB_PASSWORD / DB_USER / CONTAINER_NAME / PORT / PG_PORT), so no - credentials are baked into either the scripts or this harness. + credentials are baked into either the scripts or this harness. Prefer the + portable Python entry point so Linux CI exercises the same host-side code + used by Windows and PowerShell. """ script = SCRIPTS_DIR / name if not script.exists(): raise FileNotFoundError(f"script not found: {script}") - cmd = ["bash", str(script), *args] + portable_script = script.with_suffix(".py") + cmd = ( + [sys.executable, str(portable_script), *args] + if portable and portable_script.exists() + else ["bash", str(script), *args] + ) env = { **os.environ, "DB_PASSWORD": DB_PASSWORD, diff --git a/testing/pytest.ini b/testing/pytest.ini index fce050b..1056e73 100644 --- a/testing/pytest.ini +++ b/testing/pytest.ini @@ -13,3 +13,12 @@ markers = healthy_indexes: false-positive guard (well-indexed DB must produce zero redundancy findings) jsoncontract: --json output shape/validity guard for all diagnostic scripts kbrouter: knowledge-base router (kb_route.py) routing/scoring contract tests + determinism: Loop A — diagnostic scripts must return identical --json across repeated runs + tokens: Loop B cost accounting (token_usage.py) — pure arithmetic, no container needed + remediation: before/after grading — the kit's advice must measurably improve the database + evalsconfig: static validation of the Loop B eval + experiment configs (no container) + benchmarkmetrics: Loop C — MSBench cost-metric harvester (no container needed) + benchmarkconfig: Loop C — MSBench benchmark structure and registration (no container) + routeefficiency: text-skills vs diagnostic-scripts route comparison (no container) + advanceddata: committed ecommerce-advanced dataset generation and invariant checks + portable: cross-platform diagnostic launcher contracts (no container) diff --git a/testing/scenarios/benchmark-config/conftest.py b/testing/scenarios/benchmark-config/conftest.py new file mode 100644 index 0000000..f00de7a --- /dev/null +++ b/testing/scenarios/benchmark-config/conftest.py @@ -0,0 +1,9 @@ +"""Scenario conftest: benchmark config validation needs no database.""" + +import pytest + + +@pytest.fixture(scope="session", autouse=True) +def require_container(): + """No-op override — this scenario only reads files.""" + return None diff --git a/testing/scenarios/benchmark-config/tests/test_benchmark_config.py b/testing/scenarios/benchmark-config/tests/test_benchmark_config.py new file mode 100644 index 0000000..3bd6116 --- /dev/null +++ b/testing/scenarios/benchmark-config/tests/test_benchmark_config.py @@ -0,0 +1,679 @@ +"""Static validation of the MSBench benchmark (MSBench Skill-Efficacy Benchmark). + +WHY THIS EXISTS +--------------- +MSBench's feedback loop is slow and expensive: build images, push to an +internal ACR, submit a run, wait. A typo in a registration file or a broken +arm split should not cost a build cycle to discover — and a *silently* broken +arm split would not be discovered at all, it would just produce a wrong +published number. + +These tests need no Docker, no MSBench access and no network, so they run on +every PR. They pin the things the live platform validates (and a few it does +not). + +The registration schema was read from the live central benchmarks repo, whose +`skillsbench` / `skillsbenchnoskills` pair is the convention this benchmark +follows. +""" +from __future__ import annotations + +import json +import re +import tomllib +from pathlib import Path + +import pytest + +import kit + +pytestmark = pytest.mark.benchmarkconfig + +BENCH = kit.REPO_DIR / "benchmarks" / "documentdb-sdk-skills" +REG = BENCH / "msbench-registration" +TREATMENT = "documentdb-sdk-skills" +CONTROL = "documentdb-sdk-skills-noskills" +BENCHMARK_WORKFLOW = ( + kit.REPO_DIR / ".github" / "workflows" / "skill-efficacy-benchmark.yml" +) + +# The keys the live platform's registry.json files carry. Cosmos' registration +# has none of this — it predates the requirement — which is exactly why it is +# asserted here. +REQUIRED_REGISTRY_KEYS = { + "id", "name", "owner", "creator", "contactEmail", "description", + "source", "format", "category", "license", "languages", "platforms", +} + + +def _json(path: Path) -> dict: + assert path.is_file(), f"missing: {path}" + return json.loads(path.read_text()) + + +def _toml(path: Path) -> dict: + assert path.is_file(), f"missing: {path}" + return tomllib.loads(path.read_text()) + + +def _jsonl(path: Path) -> list[dict]: + assert path.is_file(), f"missing: {path}" + return [json.loads(line) for line in path.read_text().splitlines() if line.strip()] + + +# --------------------------------------------------------------------------- +# registration files +# --------------------------------------------------------------------------- +@pytest.mark.parametrize("arm", [TREATMENT, CONTROL]) +def test_registry_json_has_the_required_keys(arm): + reg = _json(REG / arm / "registry.json") + missing = REQUIRED_REGISTRY_KEYS - set(reg) + assert not missing, ( + f"{arm}/registry.json is missing {sorted(missing)}. The live platform " + f"requires these; copying the Cosmos registration (which has no " + f"registry.json at all) would be rejected." + ) + assert reg["id"] == arm, f"registry id {reg['id']!r} != folder {arm!r}" + assert reg["format"] == "Harbor" + + +@pytest.mark.parametrize("arm", [TREATMENT, CONTROL]) +def test_loader_name_matches_the_arm(arm): + loader = _toml(REG / arm / "benchmark_loaders.toml") + assert loader["benchmark"]["name"] == arm + assert loader["benchmark"]["path"] == "dataset.jsonl" + + +@pytest.mark.parametrize("arm", [TREATMENT, CONTROL]) +def test_dataset_rows_are_well_formed(arm): + rows = _jsonl(REG / arm / "dataset.jsonl") + assert rows, f"{arm}/dataset.jsonl is empty" + for row in rows: + for key in ("benchmark", "instance_id", "image_tag", + "problem_statement", "task_style"): + assert key in row, f"{arm} dataset row missing {key!r}: {row}" + assert row["benchmark"] == arm + assert row["image_tag"].startswith(f"{arm}."), ( + f"image_tag {row['image_tag']!r} must be namespaced by the " + f"benchmark name, or the two arms will collide in the registry." + ) + + +def test_the_two_arms_grade_the_same_task(): + """The arms must differ ONLY in whether skills are installed. + + If the control ran a different task, the delta between them would measure + the task difference rather than the kit. + """ + t = _jsonl(REG / TREATMENT / "dataset.jsonl") + c = _jsonl(REG / CONTROL / "dataset.jsonl") + assert [r["instance_id"] for r in t] == [r["instance_id"] for r in c] + assert [r["problem_statement"] for r in t] == [r["problem_statement"] for r in c] + assert [r["sdk"] for r in t] == [r["sdk"] for r in c] + + +def test_control_arm_is_described_as_a_control(): + reg = _json(REG / CONTROL / "registry.json") + blob = (reg["description"] + " " + reg.get("notes", "")).lower() + assert "control" in blob and "no " in blob, ( + "The control arm's registry entry must say it is a control with no " + "skills, or a reader will treat its score as a standalone result." + ) + + +# --------------------------------------------------------------------------- +# the task +# --------------------------------------------------------------------------- +TASK = BENCH / "tasks" / "orders-api-python" + + +def test_task_has_the_harbor_layout(): + for rel in ("task.toml", "instruction.md", "environment/Dockerfile", + "solution/solve.sh", "tests/test.sh", "tests/checks.py"): + assert (TASK / rel).exists(), f"task is missing {rel}" + + +def test_instruction_never_hints_at_the_skills(): + """The organic-discovery protocol. + + The task statement may describe REQUIREMENTS (including scale), but must + not name the solution. Mentioning indexes, ESR or connection pooling would + hand the agent the very thing the benchmark is measuring, and both arms + would score the same. + """ + text = (TASK / "instruction.md").read_text().lower() + banned = [ + "create_index", "createindex", "compound index", "esr", + "connection pool", "singleton", "best practice", "skill", + "schemaversion", "discriminator", + ] + found = [b for b in banned if b in text] + assert not found, ( + f"instruction.md hints at the solution: {found}. The agent must derive " + f"these from its installed skills, not from the prompt — otherwise the " + f"control arm scores the same and the benchmark measures nothing." + ) + + +def test_instruction_states_the_scale_requirement(): + """Requirements are fair game and necessary: without a scale signal, not + creating an index is a defensible engineering choice rather than a miss.""" + text = (TASK / "instruction.md").read_text().lower() + assert "million" in text or "scale" in text, ( + "instruction.md gives no scale signal, so grading index usage would be " + "unfair — on a small collection, no index is the right answer." + ) + + +def test_task_toml_declares_timeouts(): + task = _toml(TASK / "task.toml") + assert task["verifier"]["timeout_sec"] > 0 + assert task["agent"]["timeout_sec"] > 0 + assert task["environment"]["build_timeout_sec"] > 0 + + +# --------------------------------------------------------------------------- +# curation config +# --------------------------------------------------------------------------- +def test_no_verification_is_skipped(): + """Under a BINARY reward, a flaky check corrupts the entire signal. + + The platform's own skillsbench config carries several + `skip = "oracle", reason = "Non-deterministic: ..."` entries. A check that + cannot be made deterministic must be REMOVED, not skipped — so this must + stay empty, and changing it should require a deliberate argument. + """ + cfg = _toml(BENCH / "orders.toml") + skips = cfg.get("tasks", {}).get("skip-verification", {}) + assert not skips, ( + f"verification skips declared: {skips}. Make the check deterministic " + f"or delete it; do not skip it under a binary reward." + ) + + +def test_curation_registries_match_the_platform(): + cfg = _toml(BENCH / "orders.toml") + assert cfg["docker"]["prod"]["registry"] == "codeexecservice.azurecr.io" + assert cfg["docker"]["staging"]["registry"] == "msbenchstaging.azurecr.io" + for profile in ("local", "staging", "prod"): + assert cfg["docker"][profile]["benchmark"] == TREATMENT + + +# --------------------------------------------------------------------------- +# verifier wiring +# --------------------------------------------------------------------------- +VERIFIER = BENCH / "shared" / "verifier" + + +def test_every_check_module_is_graded(): + """A check module that exists but is never passed to pytest silently + reduces the graded surface, and nothing else would reveal it.""" + runner = (VERIFIER / "runner.sh").read_text() + modules = sorted(p.name for p in VERIFIER.glob("check_*.py")) + assert modules, "no check modules found" + missing = [m for m in modules if m not in runner] + assert not missing, ( + f"these check modules exist but runner.sh never runs them: {missing}" + ) + + +def test_runner_writes_a_reward_before_it_can_fail(): + """Harbor reads reward.txt. If an early failure left it absent, the harness + would have to guess — so it is written as 0 up front.""" + runner = (VERIFIER / "runner.sh").read_text() + reward_init = runner.index('echo "0" > "$REWARD_FILE"') + first_fail = runner.index("start-documentdb") + assert reward_init < first_fail, ( + "runner.sh must default reward.txt to 0 before the first step that can fail" + ) + + +def test_metric_harvest_cannot_change_the_reward(): + """Cost telemetry is advisory. Losing it must never turn a passing + submission into a failing one.""" + runner = (VERIFIER / "runner.sh").read_text() + idx = runner.index("harvest_metrics.py") + tail = runner[idx:] + assert "||" in tail and "WARNING" in tail, ( + "the harvest step must tolerate failure (|| warn), not abort the run" + ) + assert runner.index('echo "1" > "$REWARD_FILE"') < idx, ( + "the reward must be decided before metrics are harvested" + ) + + +def test_contract_declares_the_engine_expectations(): + """The engine checks are this benchmark's differentiator; a contract that + omitted them would silently skip them.""" + contract = _json(BENCH / "shared" / "contracts" / "orders.json") + root = contract["roots"][0] + assert root["engine"]["forbid_full_scan_for"], "no engine expectations" + assert root["engine"]["max_scan_amplification"] > 0 + assert root["indexing"]["esr_compound"]["equality"] + assert root["indexing"]["esr_compound"]["sort"] + + +def test_runner_fails_loudly_when_the_treatment_arm_has_no_skills(): + ces = (BENCH / "shared" / "ces" / "runner.sh").read_text() + assert "FATAL" in ces and "exit 1" in ces, ( + "the agent runner must abort when SKILLS_ARM=kit but the skills are " + "missing; otherwise the treatment arm silently becomes a control arm" + ) + + +# --------------------------------------------------------------------------- +# interpreter compatibility +# --------------------------------------------------------------------------- +def test_verifier_modules_parse_under_python_310(): + """The task image is Ubuntu 22.04 => CPython 3.10. + + This caught a real bug: an f-string using 3.12-only nested quoting parsed + fine on the dev machine (3.12) and blew up inside the container with + `SyntaxError: unterminated string literal`, which only surfaced after a + full image build and run. Feature-version parsing catches it in CI in + milliseconds instead. + """ + import ast + + modules = sorted((VERIFIER).glob("*.py")) + assert modules, "no verifier modules found" + failures = [] + for path in modules: + try: + ast.parse(path.read_text(), filename=str(path), + feature_version=(3, 10)) + except SyntaxError as exc: + failures.append(f"{path.name}:{exc.lineno}: {exc.msg}") + assert not failures, ( + "verifier modules are not valid Python 3.10 (the task image's " + "interpreter):\n " + "\n ".join(failures) + ) + + +def test_reference_app_parses_under_python_310(): + import ast + + app = TASK / "environment" / "reference" / "app.py" + try: + ast.parse(app.read_text(), filename=str(app), feature_version=(3, 10)) + except SyntaxError as exc: + pytest.fail(f"reference app is not valid Python 3.10: " + f"line {exc.lineno}: {exc.msg}") + + +def test_verifier_has_no_catastrophically_backtracking_regexes(): + """Guard against a real bug that cost a full build-and-run cycle to find. + + check_source.py originally detected per-request client construction with a + regex containing NESTED QUANTIFIERS over lines: + + (?:[^\\n]*\\n(?:[ \\t]+[^\\n]*\\n)*?)*? + + On a 6 KB file it never returned — the verifier hung for minutes and the + run had to be killed. It is now done with `ast`, which is exact and ~1000x + faster. + + A quantifier applied to a group that itself contains a quantifier is the + signature of the problem, so it is banned outright in this directory. + """ + import re as _re + + # A group ending in a quantifier, immediately followed by another quantifier. + nested = _re.compile(r"\)[*+]\??[*+]|\*\?\)\*|\)\*\?\)") + offenders = [] + for path in sorted(VERIFIER.glob("*.py")): + for lineno, line in enumerate(path.read_text().splitlines(), 1): + stripped = line.strip() + if stripped.startswith("#") or '"""' in stripped: + continue + if nested.search(line): + offenders.append(f"{path.name}:{lineno}") + assert not offenders, ( + "possible catastrophic-backtracking regex (a quantified group followed " + f"by another quantifier) at {offenders}. Use ast for source analysis." + ) + + +# --------------------------------------------------------------------------- +# credential detection must not fire on correct code +# --------------------------------------------------------------------------- +def _credential_findings(source: str) -> list[str]: + """Apply check_skills.py's credential patterns to a source string.""" + import re as _re + import importlib.util + + spec = importlib.util.spec_from_file_location( + "_cs", VERIFIER / "check_skills.py") + mod = importlib.util.module_from_spec(spec) + # check_skills imports pytest at module scope; the test venv has it. + spec.loader.exec_module(mod) + out = [] + for pattern, label in mod._CREDENTIAL_PATTERNS: + for m in _re.finditer(pattern, source): + out.append(f"{label}: {m.group(0)[:40]}") + return out + + +def test_credential_check_does_not_fire_on_env_driven_uris(): + """A false positive here is worse than no check at all. + + This exact pattern failed the reference implementation: the URI is an + f-string TEMPLATE reading credentials from the environment — the correct + thing to do — but the password character class matched the interpolation + placeholder and flagged it as a hardcoded secret. + """ + good = ''' +from urllib.parse import quote_plus +user = os.environ["DOCUMENTDB_USER"] +password = os.environ["DOCUMENTDB_PASSWORD"] +uri = f"mongodb://{quote_plus(user)}:{quote_plus(password)}@{host}:{port}/" +uri2 = "mongodb://%s:%s@%s" % (user, password, host) +pwd = os.getenv("DOCUMENTDB_PASSWORD") +''' + assert not _credential_findings(good), ( + "the credential check fires on correct, env-driven code: " + f"{_credential_findings(good)}" + ) + + +def test_credential_check_still_catches_real_secrets(): + """The other half: a check that never fires is worthless.""" + bad_uri = 'client = MongoClient("mongodb://admin:Sup3rSecret@db:10260/")' + bad_literal = 'password = "Sup3rSecret"' + assert _credential_findings(bad_uri), "missed a URI-embedded password" + assert _credential_findings(bad_literal), "missed a hardcoded password literal" + + +# --------------------------------------------------------------------------- +# singleton-client detection +# --------------------------------------------------------------------------- +def _client_offenders(tmp_path, source: str) -> list[str]: + import importlib.util + + spec = importlib.util.spec_from_file_location( + "_csrc", VERIFIER / "check_source.py") + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + f = tmp_path / "app.py" + f.write_text(source) + return mod._client_calls_inside_functions([f]) + + +def test_singleton_client_detector_accepts_module_level_client(tmp_path): + good = ''' +from pymongo import MongoClient +client = MongoClient("mongodb://host") +db = client["x"] + +@app.get("/orders") +def list_orders(): + return list(db.orders.find({})) +''' + assert not _client_offenders(tmp_path, good) + + +def test_singleton_client_detector_catches_client_hidden_in_a_helper(tmp_path): + """Found by a negative control, not by inspection. + + An earlier version of this check only looked inside route-decorated + functions. A deliberately naive submission that built a client in a plain + helper called by every route PASSED, even though it creates a new + connection pool per request — the exact anti-pattern the check exists for. + """ + sneaky = ''' +from pymongo import MongoClient + +def coll(): + c = MongoClient("mongodb://host") + return c["db"]["orders"] + +@app.get("/orders") +def list_orders(): + return list(coll().find({})) +''' + offenders = _client_offenders(tmp_path, sneaky) + assert offenders, ( + "a MongoClient built inside a helper called per request was not " + "detected; the check would pass a submission that creates a new " + "connection pool on every call" + ) + + +def test_singleton_client_detector_allows_a_memoised_factory(tmp_path): + """A cached factory really is a singleton, and rejecting it would punish a + legitimate pattern.""" + cached = ''' +from functools import lru_cache +from pymongo import MongoClient + +@lru_cache(maxsize=1) +def get_client(): + return MongoClient("mongodb://host") +''' + assert not _client_offenders(tmp_path, cached) + + +# --------------------------------------------------------------------------- +# reproducibility of the published numbers +# --------------------------------------------------------------------------- +def test_control_submissions_are_committed(): + """The report cites the naive control's score as evidence. + + That fixture originally lived only in /tmp, which made the headline + discrimination result impossible for anyone else to reproduce. Evidence + that cannot be re-run is an assertion, not a measurement. + """ + naive = BENCH / "controls" / "naive-python" + for rel in ("app.py", "build.sh", "run.sh", "requirements.txt"): + assert (naive / rel).is_file(), f"naive control is missing {rel}" + + +def test_naive_control_is_functional_but_not_best_practice(): + """The fixture only proves anything if it is genuinely a WORKING app that + merely ignores best practice. If someone 'fixes' it, it stops testing the + thing it exists to test.""" + src = (BENCH / "controls" / "naive-python" / "app.py").read_text() + # functional: implements the full contract + for endpoint in ("/health", "/orders"): + assert endpoint in src, f"naive control no longer serves {endpoint}" + assert "insert_one" in src and "find_one" in src, ( + "naive control must really persist to DocumentDB, or it stops being a " + "test of best practice and becomes a test of basic competence" + ) + # but deliberately wrong in the ways the kit teaches + assert "create_index" not in src, ( + "naive control now creates an index; it no longer demonstrates the gap" + ) + assert "schemaVersion" not in src, "naive control now sets schemaVersion" + + +def test_verify_controls_script_checks_both_directions(): + """A grader that cannot fail is worthless; one that cannot pass is broken. + The reproduction script must assert both, plus the case in between.""" + script = (BENCH / "verify-controls.sh").read_text() + for name, expected in (("oracle", "1"), ("empty", "0"), ("naive", "0")): + assert f"run_control {name} {expected}" in script, ( + f"verify-controls.sh does not assert {name} -> reward {expected}" + ) + assert "exit 1" in script, "the script must fail the build when a control deviates" + + +def test_oracle_workflow_uses_the_supported_build_entrypoint(): + """A clean checkout has no generated .skills/ or .wheels/ directories. + + The base Dockerfile copies both, so invoking `docker build` directly from + the workflow fails before the oracle runs. `build.sh` is the single + supported entry point because it stages both inputs and copies the wheels + into the task-image context. + """ + workflow = BENCHMARK_WORKFLOW.read_text() + build_step = workflow.index("run: bash build.sh") + oracle_step = workflow.index("- name: Oracle must score 1") + + assert build_step < oracle_step + assert "docker build -f shared/base/Dockerfile" not in workflow + assert "docker build -f environment/Dockerfile" not in workflow + assert "run: bash verify-controls.sh --only oracle" in workflow + assert "run: bash verify-controls.sh --only empty" in workflow + assert "docker run --rm --name oracle" not in workflow + assert "docker run --rm --name empty" not in workflow + + +def test_azure_login_action_is_pinned_to_an_immutable_commit(): + workflow = BENCHMARK_WORKFLOW.read_text() + match = re.search(r"uses:\s*azure/login@([0-9a-f]{40})(?:\s+#\s*(\S+))?", workflow) + + assert match, ( + "azure/login must be pinned to a full 40-character commit SHA, not a " + "mutable major-version tag" + ) + assert match.group(1) == "7184910d9eb2b1c5e48f7073824a90609bb9b6d6" + assert match.group(2) == "v2.3.1" + + +def test_build_entrypoint_stages_every_generated_docker_input(): + dockerfile = (BENCH / "shared" / "base" / "Dockerfile").read_text() + build = (BENCH / "build.sh").read_text() + + assert "COPY .wheels " in dockerfile + assert "COPY .skills " in dockerfile + assert 'bash shared/base/vendor-wheels.sh .wheels' in build + assert 'cp -r "$REPO/skills/." .skills/' in build + assert 'cp -r .wheels tasks/orders-api-python/.wheels' in build + + +# --------------------------------------------------------------------------- +# committed results +# +# Results are checked in so a number in a deck can be traced to the run that +# produced it. These enforce the two properties that make a committed result +# worth anything: it is interpretable later, and it is not one-armed. +# --------------------------------------------------------------------------- +RESULTS = BENCH / "results" + +REQUIRED_PROVENANCE = {"date", "kit_commit"} +# Effectiveness results additionally need enough to re-run and to compare +# like-for-like. +REQUIRED_EFFECTIVENESS_PROVENANCE = REQUIRED_PROVENANCE | { + "run_id", "benchmark", "image_tag", "dataset_version", "model", "pass_at_k", +} + + +def _result_files(): + return sorted(RESULTS.glob("*.json")) if RESULTS.is_dir() else [] + + +def test_results_directory_documents_its_contract(): + """The naming/provenance contract must be documented — but only once there + are results to interpret. + + This originally hard-required results/README.md, which made a CONFIG + validation depend on DATA being present: deleting stale artifacts, or + cloning fresh, failed the suite. Results are generated output and are + legitimately prunable; the benchmark's configuration is valid either way. + + The contract itself now lives in the benchmark README, so it survives + deletion of the data directory. + """ + if not _result_files(): + pytest.skip("no committed results yet; nothing to interpret") + assert (RESULTS / "README.md").is_file(), ( + "results/ contains artifacts but no README defining the naming and " + "provenance contract — committed numbers become uninterpretable " + "folklore without it" + ) + + +def test_every_committed_result_has_provenance(): + """A number without provenance cannot be interpreted a year later. + + Which skills did the agent have? Which task image? Which model? Without + those, the result is a rumour with a decimal point. + """ + for path in _result_files(): + data = json.loads(path.read_text()) + prov = data.get("provenance") + assert prov, f"{path.name} has no provenance block" + missing = REQUIRED_PROVENANCE - set(prov) + assert not missing, f"{path.name} provenance missing {sorted(missing)}" + + +def test_effectiveness_results_are_committed_in_arm_pairs(): + """The important one. + + A treatment score with no control is not a result: 80% resolved could mean + an excellent kit or an easy task, and the number cannot distinguish them. + Committing one arm alone would let exactly that get quoted. + + (cosmosdb-agent-kit's committed batch results are all `*-skills.*` with no + control counterpart, so a delta cannot be computed from that repo. This + test is a deliberate divergence.) + """ + treatments = {p.name.replace("-treatment.json", "") + for p in _result_files() if p.name.endswith("-treatment.json")} + controls = {p.name.replace("-control.json", "") + for p in _result_files() if p.name.endswith("-control.json")} + + orphan_treatments = sorted(treatments - controls) + orphan_controls = sorted(controls - treatments) + assert not orphan_treatments, ( + f"treatment results committed with no matching control: " + f"{orphan_treatments}. Publish both arms or neither." + ) + assert not orphan_controls, ( + f"control results committed with no matching treatment: {orphan_controls}" + ) + + +def test_effectiveness_results_carry_full_provenance(): + """Effectiveness results need more than date+commit: without run_id, + image_tag, dataset_version, model and pass_at_k they cannot be re-run or + compared like-for-like.""" + for path in _result_files(): + data = json.loads(path.read_text()) + if data.get("kind") != "effectiveness": + continue + missing = REQUIRED_EFFECTIVENESS_PROVENANCE - set(data.get("provenance", {})) + assert not missing, ( + f"{path.name} is an effectiveness result but its provenance is " + f"missing {sorted(missing)}" + ) + + +def test_committed_results_have_a_readable_companion(): + """Every machine-readable result needs a human-readable one beside it, or + it will only ever be read by a script.""" + for path in _result_files(): + if path.name == "README.md": + continue + companion = path.with_suffix(".md") + assert companion.is_file(), ( + f"{path.name} has no {companion.name}; commit the rendered report " + f"alongside the raw data" + ) + + +def test_controls_validation_result_matches_the_report(): + """The committed artifact and the prose in REPORT.md must not drift. + + If someone updates one and forgets the other, the document stops describing + the evidence it cites. + """ + path = RESULTS / "2026-08-17-controls-validation.json" + if not path.is_file(): + pytest.skip("no controls-validation artifact committed") + data = json.loads(path.read_text()) + controls = data["controls"] + assert controls["oracle"]["actual_reward"] == 1 + assert controls["empty"]["actual_reward"] == 0 + assert controls["naive"]["actual_reward"] == 0 + assert data["all_as_expected"] is True + + report = (BENCH / "docs" / "REPORT.md").read_text() + naive = controls["naive"] + assert f"{naive['checks_passed']} / {naive['checks_total']}" in report or \ + f"{naive['checks_passed']}/{naive['checks_total']}" in report, ( + f"REPORT.md does not mention the naive control's committed score " + f"({naive['checks_passed']}/{naive['checks_total']}) — the document and " + f"the artifact have drifted apart" + ) diff --git a/testing/scenarios/benchmark-metrics/conftest.py b/testing/scenarios/benchmark-metrics/conftest.py new file mode 100644 index 0000000..6493a77 --- /dev/null +++ b/testing/scenarios/benchmark-metrics/conftest.py @@ -0,0 +1,16 @@ +"""Scenario conftest: benchmark metric validation needs no database.""" + +import sys +from pathlib import Path + +import pytest + +REPO = Path(__file__).resolve().parents[3] +sys.path.insert(0, str(REPO / "evals" / "harness")) +sys.path.insert(0, str(REPO / "benchmarks" / "documentdb-sdk-skills" / "shared" / "verifier")) + + +@pytest.fixture(scope="session", autouse=True) +def require_container(): + """No-op override — this scenario is infrastructure-free.""" + return None diff --git a/testing/scenarios/benchmark-metrics/tests/test_harvest_metrics.py b/testing/scenarios/benchmark-metrics/tests/test_harvest_metrics.py new file mode 100644 index 0000000..019171f --- /dev/null +++ b/testing/scenarios/benchmark-metrics/tests/test_harvest_metrics.py @@ -0,0 +1,509 @@ +"""Contract tests for the MSBench cost-metric harvester (MSBench Skill-Efficacy Benchmark). + +WHY THIS SCENARIO EXISTS +------------------------ +MSBench's reward is binary: complied, or did not. That cannot answer "what did +it cost?" or "which kind of task is expensive?", which is half of what we want +from the benchmark. `harvest_metrics.py` fills that gap by writing MSBench's +per-instance `custom_metrics.json`. + +Two failure modes make those numbers dangerous rather than merely absent, and +both are pinned here: + +1. **Silent divergence from the Cross-Model Skill Evaluation cost module.** + We now have two places that compute "what did the agent cost" — + `evals/harness/token_usage.py` (Cross-Model Skill Evaluations) and + `benchmarks/.../harvest_metrics.py` (MSBench Skill-Efficacy Benchmark). + They must agree, or we will publish two different cost figures for the same + run. They cannot share an import (the harvester has to run inside a minimal + task container), so consistency is pinned by TEST instead of by import path. + +2. **A zero that means "not measured".** If harvesting fails, a missing metric + is honest; a silent 0 corrupts every average computed over it. + +Infrastructure-free: synthetic SQLite, no container, no credentials, no +network. Runs on every PR. +""" +from __future__ import annotations + +import json +import sqlite3 +from pathlib import Path + +import pytest + +import harvest_metrics as hm +import token_usage as tu + +pytestmark = pytest.mark.benchmarkmetrics + + +# --------------------------------------------------------------------------- +# synthetic session store (shared shape with the token-accounting scenario) +# --------------------------------------------------------------------------- +def _make_store(tmp_path: Path, rows) -> Path: + db = tmp_path / "session-store.db" + conn = sqlite3.connect(db) + conn.execute("""CREATE TABLE sessions ( + id TEXT, cwd TEXT, repository TEXT, host_type TEXT, branch TEXT, + summary TEXT, created_at TEXT, updated_at TEXT)""") + conn.execute("""CREATE TABLE assistant_usage_events ( + id INTEGER PRIMARY KEY, session_id TEXT, turn_index INTEGER, + agent_id TEXT, parent_tool_call_id TEXT, model TEXT, + input_tokens INTEGER, output_tokens INTEGER, cache_read_tokens INTEGER, + cache_write_tokens INTEGER, reasoning_tokens INTEGER, + total_nano_aiu INTEGER, request_multiplier REAL, duration_ms INTEGER, + time_to_first_token_ms INTEGER, inter_token_latency_ms INTEGER, + initiator TEXT, api_endpoint TEXT, reasoning_effort TEXT, + finish_reason TEXT, content_filter_triggered INTEGER, + token_details_json TEXT, created_at TEXT)""") + conn.execute("INSERT INTO sessions (id, created_at) VALUES ('s1','2026-08-13T00:00:00Z')") + conn.executemany( + """INSERT INTO assistant_usage_events + (session_id, turn_index, agent_id, model, input_tokens, output_tokens, + cache_read_tokens, cache_write_tokens, reasoning_tokens, + total_nano_aiu, duration_ms) + VALUES (:session_id,:turn_index,:agent_id,:model,:input_tokens, + :output_tokens,:cache_read_tokens,:cache_write_tokens, + :reasoning_tokens,:total_nano_aiu,:duration_ms)""", + rows, + ) + conn.commit() + conn.close() + return db + + +def _row(**kw): + base = dict(session_id="s1", turn_index=0, agent_id=None, model="m1", + input_tokens=1000, output_tokens=100, cache_read_tokens=0, + cache_write_tokens=0, reasoning_tokens=0, + total_nano_aiu=1_000_000_000, duration_ms=1000) + base.update(kw) + return base + + +@pytest.fixture +def store(tmp_path): + return _make_store(tmp_path, [ + _row(turn_index=0, input_tokens=1000, cache_read_tokens=0, + output_tokens=100, total_nano_aiu=2_000_000_000, duration_ms=1500), + _row(turn_index=1, input_tokens=5000, cache_read_tokens=4500, + output_tokens=200, total_nano_aiu=3_000_000_000, duration_ms=2500), + _row(turn_index=1, input_tokens=5000, cache_read_tokens=4800, + output_tokens=50, total_nano_aiu=1_000_000_000, duration_ms=500, + agent_id="explore"), + ]) + + +def _ctrf(tmp_path, tests) -> Path: + p = tmp_path / "ctrf.json" + p.write_text(json.dumps({"results": {"tests": tests}})) + return p + + +def _reward(tmp_path, value) -> Path: + p = tmp_path / "reward.txt" + p.write_text(str(value)) + return p + + +# --------------------------------------------------------------------------- +# THE consistency guarantee +# --------------------------------------------------------------------------- +def test_harvester_agrees_with_the_skill_evaluation_cost_module(store): + """The two cost implementations must produce identical numbers. + + They deliberately do not share code — the harvester must be self-contained + to run inside a minimal task container — so this test is what stops them + drifting into two different published cost figures for the same run. + """ + harvested = hm.read_usage(store) + + conn, tmp = tu._open_readonly(store) + try: + skill_evaluation = tu._derive(dict(conn.execute( + tu._ROLLUP_SQL.replace("WHERE session_id = ?", "") + ).fetchone())) + finally: + tu._cleanup(conn, tmp) + + for hkey, tkey in [ + ("tokens_input", "input_tokens"), + ("tokens_output", "output_tokens"), + ("tokens_cache_read", "cache_read_tokens"), + ("tokens_fresh_input", "fresh_input_tokens"), + ("tokens_reasoning", "reasoning_tokens"), + ("ai_credits", "credits"), + ("agent_turns", "turns"), + ("agent_requests", "requests"), + ]: + assert harvested[hkey] == pytest.approx(skill_evaluation[tkey]), ( + f"cost definitions have drifted: harvest_metrics.{hkey}=" + f"{harvested[hkey]} but token_usage.{tkey}={skill_evaluation[tkey]}" + ) + + +def test_fresh_input_subtracts_cache_reads(store): + """The honesty metric: cache_read is a SUBSET of input, so a skill payload + sent once and then cached must not be billed at full rate in our reporting.""" + m = hm.read_usage(store) + assert m["tokens_cache_read"] == 9300 + assert m["tokens_fresh_input"] == 11000 - 9300 + assert m["cache_read_share_pct"] == pytest.approx(9300 / 11000 * 100, abs=0.01) + + +def test_turns_counts_distinct_turns_not_requests(store): + """A subagent request shares its parent's turn index; counting requests as + turns would overstate how many round trips the task needed.""" + m = hm.read_usage(store) + assert m["agent_requests"] == 3 + assert m["agent_turns"] == 2 + + +def test_model_identity_is_captured_as_numeric_metrics(store): + metrics = hm.read_usage(store) + assert metrics["model_count"] == 1 + encoded = { + key: value for key, value in metrics.items() if key.startswith("model_ids_") + } + assert encoded["model_ids_available"] == 1 + assert all( + isinstance(value, (int, float)) and not isinstance(value, bool) + for value in encoded.values() + ) + assert all( + value < 2**53 + for key, value in encoded.items() + if "_chunk_" in key + ) + + +def test_multiple_models_are_recorded_not_silently_merged(tmp_path): + mixed = _make_store( + tmp_path, + [ + _row(turn_index=0, model="model-a"), + _row(turn_index=1, model="model-b"), + ], + ) + metrics = hm.read_usage(mixed) + assert metrics["model_count"] == 2 + + +# --------------------------------------------------------------------------- +# missing data must not read as zero cost +# --------------------------------------------------------------------------- +def test_missing_store_is_flagged_not_zeroed(tmp_path): + metrics = hm.build_metrics( + None, _ctrf(tmp_path, []), _reward(tmp_path, 0) + ) + assert metrics["tokens_available"] == 0 + assert "tokens_input" not in metrics, ( + "A failed harvest must OMIT token metrics, not report 0. A silent zero " + "would drag down every average computed over it." + ) + + +def test_present_store_is_flagged_available(store, tmp_path): + metrics = hm.build_metrics(store, _ctrf(tmp_path, []), _reward(tmp_path, 1)) + assert metrics["tokens_available"] == 1 + assert metrics["tokens_input"] > 0 + + +# --------------------------------------------------------------------------- +# outcome gating +# --------------------------------------------------------------------------- +def test_failed_run_reports_no_cost_to_green(store, tmp_path): + """Otherwise an agent that gives up early looks like the cheapest one.""" + metrics = hm.build_metrics(store, _ctrf(tmp_path, []), _reward(tmp_path, 0)) + assert metrics["reward"] == 0 + for key in ("credits_to_green", "turns_to_green", "billable_tokens_to_green"): + assert key not in metrics + + +def test_green_run_reports_cost_to_green(store, tmp_path): + metrics = hm.build_metrics(store, _ctrf(tmp_path, []), _reward(tmp_path, 1)) + assert metrics["reward"] == 1 + assert metrics["credits_to_green"] == pytest.approx(6.0) + assert metrics["turns_to_green"] == 2 + # billable = fresh input + output, NOT raw input + assert metrics["billable_tokens_to_green"] == 1700 + 350 + + +def test_missing_reward_file_counts_as_failure(store, tmp_path): + metrics = hm.build_metrics(store, _ctrf(tmp_path, []), tmp_path / "nope.txt") + assert metrics["reward"] == 0 + + +# --------------------------------------------------------------------------- +# CTRF category breakdown — what makes the cost numbers actionable +# --------------------------------------------------------------------------- +def test_check_categories_are_broken_out(tmp_path): + ctrf = _ctrf(tmp_path, [ + {"name": "/verifier/check_api.py::test_a", "status": "passed"}, + {"name": "/verifier/check_api.py::test_b", "status": "failed"}, + {"name": "/verifier/check_engine.py::test_c", "status": "passed"}, + {"name": "/verifier/check_behavior.py::test_d", "status": "passed"}, + ]) + m = hm.read_ctrf(ctrf) + assert m["checks_total"] == 4 + assert m["checks_passed"] == 3 + assert m["checks_failed"] == 1 + assert m["checks_api_passed"] == 1 and m["checks_api_total"] == 2 + assert m["checks_engine_passed"] == 1 + assert m["checks_behavior_passed"] == 1 + + +def test_skipped_checks_are_excluded_from_totals(tmp_path): + """A skipped check was not graded. Counting it as a pass would inflate the + score; counting it as a failure would punish a legitimately N/A rule.""" + ctrf = _ctrf(tmp_path, [ + {"name": "/verifier/check_api.py::test_a", "status": "passed"}, + {"name": "/verifier/check_source.py::test_b", "status": "skipped"}, + ]) + m = hm.read_ctrf(ctrf) + assert m["checks_total"] == 1 + assert "checks_source_total" not in m + + +def test_malformed_ctrf_does_not_crash_the_harvest(tmp_path): + """Losing telemetry must never change a grading outcome.""" + bad = tmp_path / "ctrf.json" + bad.write_text("not json") + assert hm.read_ctrf(bad) == {} + + +# --------------------------------------------------------------------------- +# MSBench output contract +# --------------------------------------------------------------------------- +def test_all_emitted_values_are_numeric(store, tmp_path): + """MSBench infers a `numeric` schema from the values and validates that the + schema is consistent across instances. A single string would break it.""" + metrics = hm.build_metrics(store, _ctrf(tmp_path, [ + {"name": "/verifier/check_api.py::t", "status": "passed"}, + ]), _reward(tmp_path, 1)) + bad = {k: v for k, v in metrics.items() + if not isinstance(v, (int, float)) or isinstance(v, bool)} + assert not bad, f"non-numeric custom metrics would break MSBench's schema: {bad}" + + +def test_writes_custom_metrics_json_to_the_output_dir(store, tmp_path): + out = tmp_path / "output" + rc = hm.main([ + "--store", str(store), + "--ctrf", str(_ctrf(tmp_path, [])), + "--reward", str(_reward(tmp_path, 1)), + "--output-dir", str(out), + ]) + assert rc == 0 + written = json.loads((out / "custom_metrics.json").read_text()) + assert written["reward"] == 1 + assert written["tokens_available"] == 1 + + +def test_existing_metrics_are_merged_not_clobbered(store, tmp_path): + """The agent may have written its own metrics to the same file; destroying + them would lose data MSBench expects to ingest.""" + out = tmp_path / "output" + out.mkdir() + (out / "custom_metrics.json").write_text(json.dumps({"Tool_Calls_Total": 56})) + hm.main([ + "--store", str(store), + "--ctrf", str(_ctrf(tmp_path, [])), + "--reward", str(_reward(tmp_path, 1)), + "--output-dir", str(out), + ]) + written = json.loads((out / "custom_metrics.json").read_text()) + assert written["Tool_Calls_Total"] == 56 + assert written["tokens_available"] == 1 + + +def test_reader_does_not_mutate_the_session_store(store): + """The store may be live. Snapshot, never open it for writing.""" + before = store.read_bytes() + hm.read_usage(store) + assert store.read_bytes() == before + + +# --------------------------------------------------------------------------- +# report generator (MSBench Skill-Efficacy Benchmark effectiveness report) +# --------------------------------------------------------------------------- +import importlib.util as _ilu +from pathlib import Path as _Path + +# tests/ -> benchmark-metrics/ -> scenarios/ -> testing/ -> repo root +_REPORT_PY = (_Path(__file__).resolve().parents[4] + / "benchmarks" / "documentdb-sdk-skills" / "report.py") + + +def _report_mod(): + spec = _ilu.spec_from_file_location("_report", _REPORT_PY) + mod = _ilu.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + + +def _msbench_report(benchmark, resolved, values_by_instance): + """Shape mirrors `msbench-cli report --output .json` (msbench.report 2.0).""" + return { + "schema": {"id": "msbench.report", "version": "2.0.0"}, + "resolved": {benchmark: resolved}, + "custom_metrics": { + benchmark: {k: {"values": v} for k, v in values_by_instance.items()} + }, + } + + +def _model_values(model="m1"): + return hm.encode_text_metrics("model_ids", model) + + +def test_report_counts_only_true_as_a_pass(): + """`resolved` values may be bool OR the string "error". Counting "error" as + anything but a failure would silently inflate the published pass rate.""" + m = _report_mod() + rep = _msbench_report( + "documentdb-sdk-skills", + {"a": True, "b": False, "c": "error"}, + {"a": {"tokens_available": 1}, "b": {"tokens_available": 1}, + "c": {"tokens_available": 1}}, + ) + s = m.summarise(rep, "documentdb-sdk-skills") + assert s["instances"] == 3 + assert s["passed"] == 1 + assert s["pass_rate"] == pytest.approx(1 / 3) + + +def test_report_flags_instances_with_no_token_data(): + """A missing cost figure must be visible, not averaged in as zero.""" + m = _report_mod() + rep = _msbench_report( + "documentdb-sdk-skills", + {"a": True, "b": True}, + {"a": {"tokens_available": 1, "ai_credits": 10.0}, + "b": {"tokens_available": 0}}, + ) + s = m.summarise(rep, "documentdb-sdk-skills") + assert s["missing_token_data"] == 1 + # the mean is over the ONE instance that reported, not diluted by a zero + assert s["cost"]["ai_credits"] == pytest.approx(10.0) + assert s["cost_n"]["ai_credits"] == 1 + + +def test_report_requires_both_arms(): + """A one-armed report is uninterpretable: an absolute pass rate cannot + distinguish an effective kit from an easy task.""" + m = _report_mod() + with pytest.raises(SystemExit): + m.main(["--treatment", str(_REPORT_PY)]) # no --control + + +def test_report_decodes_the_exact_observed_model(): + m = _report_mod() + values = _model_values("claude-opus-5") + assert m.decode_text_metrics(values, "model_ids") == "claude-opus-5" + + +def test_report_rejects_missing_model_provenance(): + m = _report_mod() + treatment = m.summarise( + _msbench_report( + "documentdb-sdk-skills", + {"a": True}, + {"a": {"tokens_available": 1}}, + ), + "documentdb-sdk-skills", + ) + control_values = {"tokens_available": 1, **_model_values("m1")} + control = m.summarise( + _msbench_report( + "documentdb-sdk-skills-noskills", + {"a": True}, + {"a": control_values}, + ), + "documentdb-sdk-skills-noskills", + ) + with pytest.raises(SystemExit, match="missing observed model provenance"): + m.require_comparable_model_provenance(treatment, control) + + +def test_report_rejects_different_models_between_arms(): + m = _report_mod() + treatment = m.summarise( + _msbench_report( + "documentdb-sdk-skills", + {"a": True}, + {"a": {"tokens_available": 1, **_model_values("model-a")}}, + ), + "documentdb-sdk-skills", + ) + control = m.summarise( + _msbench_report( + "documentdb-sdk-skills-noskills", + {"a": True}, + {"a": {"tokens_available": 1, **_model_values("model-b")}}, + ), + "documentdb-sdk-skills-noskills", + ) + with pytest.raises(SystemExit, match="different models"): + m.require_comparable_model_provenance(treatment, control) + + +def test_report_accepts_the_same_observed_model(): + m = _report_mod() + values = {"tokens_available": 1, **_model_values("gpt-5.6-sol")} + treatment = m.summarise( + _msbench_report("documentdb-sdk-skills", {"a": True}, {"a": values}), + "documentdb-sdk-skills", + ) + control = m.summarise( + _msbench_report( + "documentdb-sdk-skills-noskills", {"a": True}, {"a": values} + ), + "documentdb-sdk-skills-noskills", + ) + assert ( + m.require_comparable_model_provenance(treatment, control) + == "gpt-5.6-sol" + ) + + +def test_credits_per_passing_result_charges_failures_to_successes(tmp_path): + """5 runs at 100 credits with 1 pass costs 500 per success, not 100.""" + m = _report_mod() + rep = _msbench_report( + "documentdb-sdk-skills", + {f"i{n}": (n == 0) for n in range(5)}, + {f"i{n}": {"tokens_available": 1, "ai_credits": 100.0} for n in range(5)}, + ) + s = m.summarise(rep, "documentdb-sdk-skills") + ctl = m.summarise( + _msbench_report("documentdb-sdk-skills-noskills", + {"i0": True}, + {"i0": {"tokens_available": 1, "ai_credits": 100.0}}), + "documentdb-sdk-skills-noskills") + m.render(s, ctl) # populates _cpp + assert s["_cpp"] == pytest.approx(500.0) + + +def test_report_renders_the_headline_and_category_tables(): + m = _report_mod() + t = m.summarise(_msbench_report( + "documentdb-sdk-skills", {"a": True}, + {"a": {"tokens_available": 1, "ai_credits": 5.0, + **_model_values("m1"), + "checks_engine_passed": 4, "checks_engine_total": 4}}), + "documentdb-sdk-skills") + c = m.summarise(_msbench_report( + "documentdb-sdk-skills-noskills", {"a": False}, + {"a": {"tokens_available": 1, "ai_credits": 4.0, + **_model_values("m1"), + "checks_engine_passed": 0, "checks_engine_total": 4}}), + "documentdb-sdk-skills-noskills") + out = m.render(t, c) + assert "Pass rate" in out + assert "`engine`" in out + assert "Credits per passing result" in out + assert "Observed model identifier" in out + # the reader must be steered away from raw input tokens + assert "fresh" in out.lower() diff --git a/testing/scenarios/determinism/SCENARIO.md b/testing/scenarios/determinism/SCENARIO.md new file mode 100644 index 0000000..4c8b7d9 --- /dev/null +++ b/testing/scenarios/determinism/SCENARIO.md @@ -0,0 +1,131 @@ +# Scenario: Determinism (Diagnostic Regression Suite) + +> **The deterministic half of the kit's testing.** The diagnostic scripts are +> *tools*, not agents: given the same database state they must return the same +> answer every time. This scenario proves that, and fails loudly when they don't. + +## Goal + +For every diagnostic script, run it **3 times** against one untouched database +and assert the `--json` results are identical after canonicalisation. + +Determinism is asserted on `--json` only. The human-readable output deliberately +embeds a wall-clock banner (`Timestamp: …`) and measured latencies, so it is not +byte-stable and is not part of this contract. + +## What the fixture plants + +[`fixture.js`](fixture.js) seeds `test_determinism` so **every** script produces +non-empty output — otherwise the assertion would be vacuous (see below): + +| Collection | Plants | Exercises | +|---|---|---| +| `accounts` | prefix-redundant + duplicate indexes | `index-redundancy-finder.sh` | +| `invoices` | orphaned `account_id` refs, mixed-type `amount` | `data-integrity-check.sh` | +| `articles` | ~6 KB of **incompressible** text per doc | `document-bloat-advisor.sh` | +| `events` | no secondary indexes | `perf-advisor.sh` | +| — | (PG config/cache always reported) | `db-config-advisor.sh` | + +Two fixture details that matter: + +- **The fixture is itself reproducible.** It uses a fixed-seed Lehmer LCG, never + `Math.random()` — a determinism scenario cannot be seeded non-deterministically. +- **The large text is incompressible.** PostgreSQL compresses (pglz) before + storing out of line, so repeated filler text shrinks away and never TOASTs. + Random-ish characters really do land in TOAST, which is what the bloat advisor + looks for. + +## The three traps this suite avoids + +1. **False determinism.** A script that finds *nothing* returns the same empty + result every run — which proves nothing. `must_find` asserts each script + actually reported something. +2. **Over-broad normalisation.** The volatile allowlist is small, **measured** + (not guessed), and documented with the observed drift that justifies each + entry. Drift outside it fails the test. +3. **A test that cannot fail.** `test_injected_drift_is_detected` is a negative + control. Verified end-to-end by temporarily emitting `$RANDOM` from + `db-config-advisor.sh` — the suite failed, as it must. + +## Contract + +[`expected-findings.yaml`](expected-findings.yaml): + +| Key | Meaning | +|---|---| +| `runs` | how many times each script is run (3) | +| `scripts[].must_find` | output must be non-empty (anti-false-determinism) | +| `volatile_fields` | live measurements removed before comparison | +| `order_insensitive_lists` | lists sorted by a volatile metric, so order-normalised | +| `sampled_count_maps` | **now empty** — kept so re-introducing a normalisation is a reviewable change | +| `sampled_size_strings` | **now empty** — same reason | + +## Findings this suite produced — and what was done about them + +The suite caught **four real nondeterminism bugs**. All four have been **fixed at +the source** rather than normalised away. The history is kept because it is the +evidence that this suite does something. + +| # | Script | Bug | Fix | +|---|---|---|---| +| 1 | `data-integrity-check.sh` | `$sample` for type-consistency → counts wobbled (`{"number":87,"string":13}` vs `{"number":85,"string":15}`) | deterministic sampler | +| 2 | `document-bloat-advisor.sh` | `$sample` for `dominant_fields` → sizes wobbled (`title:13B` vs `title:12B`) | deterministic sampler + tie-broken sort | +| 3 | `perf-advisor.sh` | `slow_queries` membership decided by a wall-clock threshold → *which* queries qualified changed with cache warmth | split into `query_timings` (deterministic membership) + `slow_queries` (measurement) | +| 4 | `perf-advisor.sh` | COLLSCAN audit built probe values from `findOne()`, i.e. an **arbitrary** document → a numeric probe of `val/2` scanned a different fraction each run, changing `docs_scanned` and sometimes the chosen plan | probe the first document by `_id` | +| 5 | `perf-advisor.sh` | `index_health[].unused` reads `$indexStats` access counters — which the tool's **own probe queries mutate**, and which the engine updates **asynchronously** | treated as a measurement (allowlisted); `redundant` still asserted | + +| 6 | *the harness* | The planner chose **IXSCAN in one run and COLLSCAN in the next** for the same probe, because table statistics were still stale from the bulk seed and background `ANALYZE` landed mid-suite | `kit.seed()` now runs `ANALYZE` after seeding | + +Bugs 5 and 6 are the subtle ones: the suite passed in isolation and in most full +runs, but failed about **2 runs in 7** under load. An intermittently-failing +determinism test is worse than none — it trains people to re-run until green. + +Bug 6 is worth dwelling on because it was **not a bug in the scripts at all**. +`perf-advisor` was behaving correctly and reporting exactly what the engine told +it; the engine's answer legitimately changed as PostgreSQL's cost estimates +improved. Immediately after a bulk insert the planner is working from stale +statistics, so a probe on a non-leading index column was costed as an index scan +in one run and a sequential scan in another. + +The fix belongs in the harness, not the tool: `kit.seed()` now issues `ANALYZE` +so every scenario measures against settled statistics. Verified by running the +full suite **6 consecutive times with no failures**, against 2 failures in 7 +before. + +The general lesson: when a determinism test flakes, the first question is +whether the *tool* is unstable or whether the *environment it observes* is. Only +the first is a bug to fix in the tool. + +Bug 4 is the interesting one: it was **invisible until bug 3 was fixed**. While +the timing noise was being normalised away, it masked a genuine logic defect +underneath. That is the argument for keeping the volatile-field allowlist as +small as possible — every entry can hide a real bug. + +### The deterministic sampler + +Both samplers now take half the documents from **each end** of `_id` order +instead of `$sample`. Head+tail rather than head-only is deliberate: mixed types +usually arrive from schema drift over time, so the oldest and newest documents +are exactly where the disagreement lives. Sampling only the head would +systematically miss a type change introduced after the collection was created. + +Verified after the fix: 3 consecutive runs byte-identical, with the findings +still detected (`invoices.amount {"number":86,"string":14}`; +`dominant_fields "body:4002B,notes:2002B,title:12B"`). + +### What remains volatile — and why that is correct + +`slow_queries` and the PostgreSQL live counters stay in the allowlist. These are +**measurements, not findings**: whether a query crosses 50 ms genuinely depends +on cache warmth and machine load. The fix was not to force them stable but to +stop *deriving structure* from them — hence `query_timings`, whose membership and +`results` counts are deterministic while only `ms` moves. + +## Run + +```bash +export DB_PASSWORD='' +cd testing && pytest scenarios/determinism +``` + +Requires a running `documentdb-local` container (see the repo README). diff --git a/testing/scenarios/determinism/conftest.py b/testing/scenarios/determinism/conftest.py new file mode 100644 index 0000000..6d5d656 --- /dev/null +++ b/testing/scenarios/determinism/conftest.py @@ -0,0 +1,10 @@ +"""Scenario conftest: seed the determinism fixture into its own database.""" + +from pathlib import Path + +from conftest_base import make_seeded_db_fixture + +seeded_db = make_seeded_db_fixture( + "test_determinism", + Path(__file__).resolve().parent, +) diff --git a/testing/scenarios/determinism/expected-findings.yaml b/testing/scenarios/determinism/expected-findings.yaml new file mode 100644 index 0000000..69d3e1a --- /dev/null +++ b/testing/scenarios/determinism/expected-findings.yaml @@ -0,0 +1,136 @@ +# expected-findings.yaml — determinism (Diagnostic Regression Suite) +# +# Contract: every diagnostic script, given the SAME database state, must return +# the SAME `--json` result on repeated runs. +# +# Determinism is asserted on `--json` only. The human-readable output embeds a +# wall-clock banner (`Timestamp: ...`) and measured latencies, so it is not — and +# should not be — byte-stable. +# +# Scope of the guarantee: a FIXED database state. Some findings legitimately +# depend on live PostgreSQL counters (e.g. `idx_scan`, which resets when the +# container restarts), so this scenario seeds its own database and does not +# exercise it between the repeated runs. + +database: test_determinism + +# How many times each script is run and compared. All runs must agree. +runs: 3 + +# Scripts under test. `must_find` = the canonicalised output must be non-empty +# (i.e. the script actually reported something). Without this guard a script that +# finds NOTHING would trivially "pass" determinism — the false-determinism trap. +scripts: + - name: index-redundancy-finder.sh + must_find: true + - name: data-integrity-check.sh + must_find: true + - name: document-bloat-advisor.sh + must_find: true + - name: perf-advisor.sh + must_find: true + - name: db-config-advisor.sh + must_find: true + +# --------------------------------------------------------------------------- +# Volatile fields — MEASURED, not guessed. +# +# These keys hold live measurements that legitimately change between two runs +# against an unchanged database. They are removed before comparison. Everything +# else must be byte-stable; unexplained drift fails the test loudly. +# +# Observed drift that motivated each entry (perf-advisor.sh --json, two +# consecutive runs on an unchanged DB): +# slow_queries[].ms 57 -> 61, 473 -> 452, 429 -> 309, 174 -> 168 +# pg.cache_top[].idx_hit_pct 95.65 -> 96.43, 98.55 -> 98.70 +# pg.scan_mix[].seq_scan 85 -> 93 (and idx_scan / idx_scan_pct with it) +# +# Keep this list SHORT and reviewed. Growing it silently is how a real +# nondeterminism bug gets hidden. +# --------------------------------------------------------------------------- +volatile_fields: + # wall-clock / duration + - timestamp + - generated_at + - duration_ms + - elapsed_ms + - ms + # `index_health[].unused` is derived from `$indexStats` accesses.ops — a LIVE + # counter — and perf-advisor's OWN probe queries touch the very indexes it is + # reporting on. Worse, the counter is updated ASYNCHRONOUSLY, so whether run N + # observes the effect of run N-1's probes is a race. + # + # Symptom that exposed it: the suite passed in isolation and in most full + # runs, but failed roughly 2 runs in 7 under load. A test that fails + # intermittently is worse than no test — it trains people to re-run until + # green — so the measurement is separated from the finding here. + # + # What is still asserted: which indexes EXIST and which are REDUNDANT + # (`index_health[].redundant`) — those are structural facts. Only "has this + # index been read recently", which is a live measurement, is dropped. + - unused + # PostgreSQL live counters and ratios + - seq_scan + - idx_scan + - idx_scan_pct + - heap_hit_pct + - idx_hit_pct + - heap_disk_reads + - numBlocksFromCache + - numBlocksFromDisk + # `perf-advisor.sh` reports query latency two ways: + # * `query_timings` — EVERY probe it ran. Membership and `results` counts are + # deterministic; only the measured `ms` moves. This is what the determinism + # contract asserts. + # * `slow_queries` — the subset over the latency threshold. Whether a query + # crosses 50 ms depends on cache warmth and machine load, so its MEMBERSHIP + # legitimately changes between runs. It is a measurement, not a finding. + # + # Originally `slow_queries` was the only timing output, which made the whole + # timing section unassertable. Splitting it (see scripts/perf-advisor.sh) means + # the deterministic part is now checked rather than discarded. + - slow_queries + +# Lists whose ORDER is derived from a volatile metric (they are sorted by a live +# counter, so two runs can legitimately order them differently). Such lists are +# order-normalised before comparison; their *contents* still must match. +# +# Observed: pg.scan_mix reordered between runs (reviews/inventory swapped) purely +# because their seq_scan counters crossed over. +order_insensitive_lists: + - cache_top + - scan_mix + +# --------------------------------------------------------------------------- +# Sampled values — NOW EMPTY, because the sampling was made deterministic. +# +# History (kept deliberately: it is the evidence that this suite works). +# This scenario found two real nondeterminism bugs on its very first run: +# +# 1. `data-integrity-check.sh` used `$sample` for field-type consistency, so +# two runs on an UNCHANGED database disagreed on the counts: +# invoices.amount {"number": 87, "string": 13} +# invoices.amount {"number": 85, "string": 15} +# +# 2. `document-bloat-advisor.sh` used `$sample` for `dominant_fields`, so the +# average field sizes wobbled: +# "body:4002B,notes:2002B,title:13B" +# "body:4002B,notes:2002B,title:12B" +# +# Both were initially NORMALISED AWAY here (counts and byte sizes blanked) and +# recorded as follow-ups. They have since been FIXED at the source: both scripts +# now use a deterministic sampler — half the documents from each end of `_id` +# order — instead of `$sample`. Head+tail rather than head-only is deliberate: +# mixed types usually come from schema drift over time, so the oldest and newest +# documents are exactly where the disagreement lives. +# +# Verified after the fix: 3 consecutive runs are byte-identical, with the +# findings still detected (invoices.amount {"number": 86, "string": 14}; +# dominant_fields "body:4002B,notes:2002B,title:12B"). +# +# These lists are kept — empty — so that re-introducing a normalisation is a +# visible, reviewable change rather than a silent one. +# --------------------------------------------------------------------------- +sampled_count_maps: [] + +sampled_size_strings: [] diff --git a/testing/scenarios/determinism/fixture.js b/testing/scenarios/determinism/fixture.js new file mode 100644 index 0000000..2f7f0c1 --- /dev/null +++ b/testing/scenarios/determinism/fixture.js @@ -0,0 +1,109 @@ +// fixture.js — determinism +// Seeds a database that makes EVERY diagnostic script produce NON-EMPTY output. +// +// That matters: a script trivially looks "deterministic" when it finds nothing +// (empty output is identical every run). This fixture plants real findings for +// each script so the determinism assertion is meaningful: +// +// index-redundancy-finder <- redundant / duplicate / reverse-variant indexes +// data-integrity-check <- orphaned references + mixed field types +// document-bloat-advisor <- large INCOMPRESSIBLE text (really lands in TOAST) +// perf-advisor <- unindexed collections => collection-scan patterns +// db-config-advisor <- always reports PG config/cache (no planting needed) +// +// The fixture itself must be REPRODUCIBLE — a determinism scenario cannot be +// seeded with Math.random(). All "random-looking" data below comes from a +// deterministic LCG with a fixed seed, so re-seeding yields identical data. + +// ---- deterministic PRNG (fixed seed; NOT Math.random) --------------------- +var _seed = 20260810; +function rnd() { // Lehmer / Park-Miller LCG + _seed = (_seed * 48271) % 2147483647; + return _seed / 2147483647; +} +function ri(n) { return Math.floor(rnd() * n); } + +// Incompressible text pool. PostgreSQL compresses (pglz) before moving a value +// out of line, so a repeated sentence would shrink to nothing and never TOAST. +// A pseudo-random character pool does not compress, so the value really is +// stored out-of-line — which is what document-bloat-advisor looks for. +var CH = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; +var POOL = ""; +while (POOL.length < 24000) POOL += CH.charAt(ri(CH.length)); +function text(n) { return POOL.substr(ri(POOL.length - n - 1), n); } + +["accounts", "invoices", "articles", "events"].forEach(function (c) { + try { db[c].drop(); } catch (e) {} +}); + + +// ---- accounts: redundant indexes ------------------------------------------ +var bulk = []; +for (var i = 1; i <= 400; i++) { + bulk.push({ + account_id: i, + email: "a" + i + "@example.test", + tenant_id: (i % 20) + 1, + status: ["active", "inactive", "pending"][i % 3] + }); +} +db.accounts.insertMany(bulk); +db.accounts.createIndex({ tenant_id: 1 }); // prefix-redundant +db.accounts.createIndex({ tenant_id: 1, status: 1 }); // keep +db.accounts.createIndex({ email: 1 }); // duplicate +db.accounts.createIndex({ email: 1 }, { unique: true, name: "email_unique" }); + +// ---- invoices: orphaned refs + mixed types (data integrity) --------------- +bulk = []; +for (var i = 1; i <= 300; i++) { + // every 10th invoice points at an account that does not exist + var acct = (i % 10 === 0) ? 99000 + i : (i % 400) + 1; + bulk.push({ + invoice_id: i, + account_id: acct, + // deliberately mixed type: some amounts are strings + amount: (i % 7 === 0) ? String(i * 3) : i * 3, + created_at: new Date(2024, i % 12, (i % 28) + 1) + }); +} +db.invoices.insertMany(bulk); + +// ---- articles: large INCOMPRESSIBLE text (document bloat / TOAST) --------- +// ~4 KB of incompressible text per doc, well above PostgreSQL's ~2 KB +// out-of-line threshold, and enough total volume to clear the advisor's +// --min-total-kb floor with a TOAST ratio above its default 0.5. +bulk = []; +for (var i = 1; i <= 250; i++) { + bulk.push({ + article_id: i, + title: "Article " + i, + author_id: (i % 40) + 1, + body: text(4000), + notes: text(2000) + }); +} +db.articles.insertMany(bulk); + +// ---- events: no secondary indexes -> collection scans --------------------- +bulk = []; +for (var i = 1; i <= 800; i++) { + bulk.push({ + event_id: i, + kind: ["click", "view", "purchase"][i % 3], + user_id: (i % 150) + 1, + payload_size: (i % 900) + 100 + }); +} +db.events.insertMany(bulk); + +// ---- traffic so "keep" indexes show real reads --------------------------- +for (var i = 1; i <= 60; i++) { + db.accounts.find({ tenant_id: (i % 20) + 1, status: "active" }).limit(1).toArray(); + db.accounts.find({ email: "a" + i + "@example.test" }).limit(1).toArray(); + db.events.find({ kind: "purchase" }).limit(1).toArray(); +} + +print("FIXTURE_READY determinism"); +db.getCollectionNames().sort().forEach(function (c) { + print(" " + c + ": " + db[c].countDocuments() + " docs, " + db[c].getIndexes().length + " indexes"); +}); diff --git a/testing/scenarios/determinism/tests/test_determinism.py b/testing/scenarios/determinism/tests/test_determinism.py new file mode 100644 index 0000000..e15b132 --- /dev/null +++ b/testing/scenarios/determinism/tests/test_determinism.py @@ -0,0 +1,163 @@ +"""Determinism contract (Diagnostic Regression Suite) — the deterministic half of the kit's testing. + +The diagnostic scripts are *tools*, not agents: given the same database state +they must return the same answer every time. This scenario runs each script +N times (N from expected-findings.yaml) and asserts the canonicalised `--json` +results are identical. + +Three traps this suite is explicitly designed to avoid: + +1. **False determinism** — a script that finds nothing is identical every run. + `must_find` asserts the output is non-empty, so the comparison is meaningful. +2. **Over-broad normalisation** — the volatile-field allowlist is small, + measured, and documented in expected-findings.yaml. Drift outside it fails. +3. **A test that cannot fail** — `test_injected_drift_is_detected` is a negative + control proving the comparison actually catches a difference. +""" + +from pathlib import Path + +import pytest +import yaml + +import kit +from canonicalise import canonical_json, canonicalise, is_empty_result + +_SPEC = yaml.safe_load( + (Path(__file__).resolve().parents[1] / "expected-findings.yaml").read_text() +) +RUNS = _SPEC["runs"] +SCRIPTS = _SPEC["scripts"] +VOLATILE = _SPEC["volatile_fields"] +ORDER_INSENSITIVE = _SPEC.get("order_insensitive_lists", []) +SAMPLED_MAPS = _SPEC.get("sampled_count_maps", []) +SAMPLED_SIZES = _SPEC.get("sampled_size_strings", []) +SCRIPT_IDS = [s["name"] for s in SCRIPTS] + + +@pytest.fixture(scope="session") +def repeated_runs(seeded_db): + """Run every script RUNS times against the same, untouched database. + + Nothing else touches the database between runs, so any difference is the + script's own nondeterminism. + """ + results = {} + for spec in SCRIPTS: + name = spec["name"] + results[name] = [ + kit.run_script(name, "--db", seeded_db, "--json", want_json=True) + for _ in range(RUNS) + ] + return results + + +@pytest.mark.determinism +@pytest.mark.parametrize("script", SCRIPT_IDS) +def test_script_output_is_deterministic(repeated_runs, script): + """Same DB state -> identical canonicalised --json across N runs.""" + runs = repeated_runs[script] + + for i, r in enumerate(runs): + assert r.returncode == 0, ( + f"{script} run {i + 1} exited {r.returncode}\nstderr: {r.stderr[:400]}" + ) + assert r.json is not None, ( + f"{script} run {i + 1} did not emit valid JSON.\n" + f"stdout[:400]: {r.stdout[:400]}" + ) + + baseline = canonical_json(runs[0].json, VOLATILE, ORDER_INSENSITIVE, SAMPLED_MAPS, SAMPLED_SIZES) + for i, r in enumerate(runs[1:], start=2): + current = canonical_json(r.json, VOLATILE, ORDER_INSENSITIVE, SAMPLED_MAPS, SAMPLED_SIZES) + if current != baseline: + import difflib + + diff = "\n".join( + list( + difflib.unified_diff( + baseline.splitlines(), + current.splitlines(), + fromfile="run 1", + tofile=f"run {i}", + lineterm="", + ) + )[:40] + ) + pytest.fail( + f"{script} is NOT deterministic: run 1 and run {i} differ after " + f"canonicalisation.\n" + f"Either the script has a real nondeterminism bug, or a new live " + f"measurement needs adding to `volatile_fields` in " + f"expected-findings.yaml (review it — do not grow the list " + f"silently).\n\n{diff}" + ) + + +@pytest.mark.determinism +@pytest.mark.parametrize( + "script", [s["name"] for s in SCRIPTS if s.get("must_find")] +) +def test_findings_are_non_empty(repeated_runs, script): + """Guard against 'false determinism' — an empty result proves nothing.""" + canonical = canonicalise(repeated_runs[script][0].json, VOLATILE, ORDER_INSENSITIVE, SAMPLED_MAPS, SAMPLED_SIZES) + assert not is_empty_result(canonical), ( + f"{script} produced an EMPTY result on the determinism fixture, so its " + f"determinism assertion is vacuous. Fix fixture.js to plant a finding " + f"this script detects, or set must_find: false with a reason." + ) + + +@pytest.mark.determinism +def test_injected_drift_is_detected(): + """Negative control: the comparison must actually be able to fail. + + A determinism test that can never fail is worse than none at all. + """ + a = {"findings": [{"collection": "accounts", "rule": "PREFIX_REDUNDANT"}]} + b = {"findings": [{"collection": "accounts", "rule": "EXACT_DUPLICATE"}]} + assert canonical_json(a, VOLATILE, ORDER_INSENSITIVE, SAMPLED_MAPS, SAMPLED_SIZES) != canonical_json( + b, VOLATILE, ORDER_INSENSITIVE, SAMPLED_MAPS, SAMPLED_SIZES + ), "canonicalisation is too aggressive — it erased a real difference" + + +@pytest.mark.determinism +def test_volatile_normalisation_is_targeted(): + """The allowlist must remove live measurements — and nothing structural.""" + payload = { + "collection": "orders", + "rule": "PREFIX_REDUNDANT", + "ms": 57, + "scan_mix": [{"collection": "b", "seq_scan": 9}, {"collection": "a", "seq_scan": 1}], + } + out = canonicalise(payload, VOLATILE, ORDER_INSENSITIVE, SAMPLED_MAPS, SAMPLED_SIZES) + + # volatile values removed ... + assert "ms" not in out + assert all("seq_scan" not in entry for entry in out["scan_mix"]) + # ... structural facts preserved ... + assert out["collection"] == "orders" + assert out["rule"] == "PREFIX_REDUNDANT" + # ... and volatile ordering normalised (a before b, regardless of input order) + assert [e["collection"] for e in out["scan_mix"]] == ["a", "b"] + + +@pytest.mark.determinism +def test_sampled_counts_normalised_but_finding_preserved(): + """`$sample`-derived counts are blanked; the categories (the finding) survive. + + `data-integrity-check.sh` samples documents to detect mixed field types, so + the counts wobble run to run. What matters — that `amount` holds BOTH a + number and a string — must still be asserted, and a *different* set of + categories must still be detected as a difference. + """ + run1 = {"field": "amount", "types": {"number": 87, "string": 13}} + run2 = {"field": "amount", "types": {"number": 85, "string": 15}} + same_finding_diff_counts = {"types"} + assert canonical_json(run1, VOLATILE, ORDER_INSENSITIVE, same_finding_diff_counts) == \ + canonical_json(run2, VOLATILE, ORDER_INSENSITIVE, same_finding_diff_counts) + + # but a genuinely different set of types is still caught + run3 = {"field": "amount", "types": {"number": 100}} + assert canonical_json(run1, VOLATILE, ORDER_INSENSITIVE, same_finding_diff_counts) != \ + canonical_json(run3, VOLATILE, ORDER_INSENSITIVE, same_finding_diff_counts) diff --git a/testing/scenarios/ecommerce-advanced-data/conftest.py b/testing/scenarios/ecommerce-advanced-data/conftest.py new file mode 100644 index 0000000..347a253 --- /dev/null +++ b/testing/scenarios/ecommerce-advanced-data/conftest.py @@ -0,0 +1,9 @@ +"""This scenario validates committed files and needs no database container.""" + +import pytest + + +@pytest.fixture(scope="session", autouse=True) +def require_container(): + return None + diff --git a/testing/scenarios/ecommerce-advanced-data/tests/test_dataset.py b/testing/scenarios/ecommerce-advanced-data/tests/test_dataset.py new file mode 100644 index 0000000..60056ac --- /dev/null +++ b/testing/scenarios/ecommerce-advanced-data/tests/test_dataset.py @@ -0,0 +1,80 @@ +"""Regression guards for the committed ecommerce-advanced dataset.""" + +import hashlib +import json +import shutil +import subprocess +from pathlib import Path + +import pytest + +import kit + + +pytestmark = pytest.mark.advanceddata + +SCENARIO = kit.REPO_DIR / "scenarios" / "ecommerce-advanced" +DATA = SCENARIO / "data" + + +def read_json(path): + return json.loads(path.read_text()) + + +def read_jsonl(path): + return [json.loads(line) for line in path.read_text().splitlines() if line] + + +def test_committed_files_match_manifest(): + manifest = read_json(DATA / "manifest.json") + + assert manifest["seed"] == 20260901 + assert manifest["generated_at"] == "2026-09-01T00:00:00.000Z" + assert manifest["data_epoch"] == "2025-01-01T00:00:00.000Z" + assert manifest["collection_counts"] == { + "customers": 200, + "products": 100, + "inventory": 300, + "orders": 2000, + "order_items": 6000, + "payments": 2000, + "returns": 120, + "daily_sales": 84, + "stream_orders": 2, + } + + for spec in manifest["files"]: + content = (DATA / spec["path"]).read_bytes() + assert len(content) == spec["bytes"], spec["path"] + assert hashlib.sha256(content).hexdigest() == spec["sha256"], spec["path"] + + +def test_transaction_and_time_series_edge_cases_are_present(): + cases = read_json(DATA / "cases" / "transactions.json") + by_id = {case["case_id"]: case for case in cases} + + assert by_id["commit-purchase"]["initial_quantity"] == 10 + assert by_id["commit-purchase"]["expected_quantity"] == 8 + assert by_id["concurrent-last-unit"]["initial_quantity"] == 1 + assert by_id["concurrent-last-unit"]["expected_commits"] == 1 + assert by_id["insufficient-stock"]["initial_quantity"] == 0 + assert by_id["forced-rollback"]["failure_after"] == "payment_insert" + + daily_sales = read_jsonl(DATA / "collections" / "daily_sales.jsonl") + assert len(daily_sales) == 84 + assert sum(row["revenue"] is None for row in daily_sales) == 3 + + +def test_generator_reproduces_byte_identical_output(): + if not shutil.which("node"): + pytest.skip("Node.js is required to run the deterministic generator") + + result = subprocess.run( + ["bash", "scripts/check-determinism.sh"], + cwd=SCENARIO, + capture_output=True, + text=True, + timeout=120, + ) + assert result.returncode == 0, result.stderr + assert "byte-identical" in result.stdout diff --git a/testing/scenarios/ecommerce-missing-index/expected-findings.yaml b/testing/scenarios/ecommerce-missing-index/expected-findings.yaml index 095125d..6249de1 100644 --- a/testing/scenarios/ecommerce-missing-index/expected-findings.yaml +++ b/testing/scenarios/ecommerce-missing-index/expected-findings.yaml @@ -5,4 +5,5 @@ database: test_ecom_missing_index script: perf-advisor.sh unindexed_collection: events +escaped_collection: escaped_values min_collscan_patterns: 1 diff --git a/testing/scenarios/ecommerce-missing-index/fixture.js b/testing/scenarios/ecommerce-missing-index/fixture.js index 47629f6..b4f8c59 100644 --- a/testing/scenarios/ecommerce-missing-index/fixture.js +++ b/testing/scenarios/ecommerce-missing-index/fixture.js @@ -6,7 +6,7 @@ // Planted issue (answer key — see expected-findings.yaml): // events.event_type / events.device_id : no supporting index -> COLLSCAN -["events", "products"].forEach(function (c) { +["events", "products", "escaped_values"].forEach(function (c) { try { db[c].drop(); } catch (e) {} }); @@ -37,8 +37,24 @@ db.products.createIndex({ sku: 1 }); db.products.createIndex({ category: 1 }); db.products.createIndex({ price: 1 }); +// ---- escaped_values: prove probe filters do not concatenate JSON strings ---- +// Both the field name and sampled value contain JSON-significant characters. +// Building `JSON.parse("{\"" + field + ... )` throws before testQuery runs and +// used to make perf-advisor silently lose every Mongo-layer finding. +bulk = []; +for (var i = 1; i <= 20; i++) { + var doc = { _id: i }; + doc['path"key\\segment'] = + i === 1 ? 'say "hello" \\ root' : "ordinary-" + i; + bulk.push(doc); +} +db.escaped_values.insertMany(bulk); +// (intentionally NO secondary indexes) + print("FIXTURE_READY missing-index"); print(" events: " + db.events.countDocuments() + " docs, " + db.events.getIndexes().length + " indexes"); print(" products: " + db.products.countDocuments() + " docs, " + db.products.getIndexes().length + " indexes"); +print(" escaped_values: " + db.escaped_values.countDocuments() + " docs, " + + db.escaped_values.getIndexes().length + " indexes"); diff --git a/testing/scenarios/ecommerce-missing-index/tests/test_perf_contract.py b/testing/scenarios/ecommerce-missing-index/tests/test_perf_contract.py index 9d74eaf..b00ac93 100644 --- a/testing/scenarios/ecommerce-missing-index/tests/test_perf_contract.py +++ b/testing/scenarios/ecommerce-missing-index/tests/test_perf_contract.py @@ -18,6 +18,16 @@ def report(seeded_db): return res.stdout + res.stderr +@pytest.fixture(scope="session") +def json_report(seeded_db): + res = kit.run_script( + "perf-advisor.sh", "--db", seeded_db, "--json", want_json=True + ) + assert res.returncode == 0, f"script failed (rc={res.returncode})\n{res.stderr}" + assert res.json is not None, f"invalid JSON output:\n{res.stdout[:400]}" + return res.json + + @pytest.mark.perf def test_collscan_reported(report, expected): assert "COLLSCAN" in report, "expected at least one COLLSCAN marker" @@ -34,3 +44,15 @@ def test_unindexed_collection_flagged(report, expected): assert re.search(rf"COLLSCAN:\s*{re.escape(coll)}\b", report), ( f"expected a COLLSCAN against the unindexed '{coll}' collection" ) + + +def test_json_significant_field_and_value_do_not_abort_audit( + report, json_report, expected +): + coll = expected["escaped_collection"] + assert re.search(rf"COLLSCAN:\s*{re.escape(coll)}\b", report) + database = json_report["mongo"][0] + assert any(item["collection"] == coll for item in database["collscans"]), ( + "a quote/backslash in the sampled field or value must not erase Mongo " + "findings from JSON mode" + ) diff --git a/testing/scenarios/evals-config/conftest.py b/testing/scenarios/evals-config/conftest.py new file mode 100644 index 0000000..abad31f --- /dev/null +++ b/testing/scenarios/evals-config/conftest.py @@ -0,0 +1,9 @@ +"""Scenario conftest: eval-config validation needs no database.""" + +import pytest + + +@pytest.fixture(scope="session", autouse=True) +def require_container(): + """No-op override — this scenario only reads YAML files.""" + return None diff --git a/testing/scenarios/evals-config/tests/test_evals_config.py b/testing/scenarios/evals-config/tests/test_evals_config.py new file mode 100644 index 0000000..ebd20f9 --- /dev/null +++ b/testing/scenarios/evals-config/tests/test_evals_config.py @@ -0,0 +1,379 @@ +"""Static validation of the Cross-Model Skill Evaluations eval + experiment configs. + +WHY THIS EXISTS +--------------- +`vally experiment run --dry-run` validates the matrix structure but does NOT +check that the skill paths point at anything real. That gap produced a live bug +while this scenario's sibling files were being written: the experiment +referenced `../skills/query-performance-tuning`, which exists on a different +branch. The dry run passed. + +That is the worst possible failure mode for an A/B test. A treatment arm whose +skills silently fail to load is just a second control arm — and it would have +produced a confident, published, completely wrong conclusion of "the kit makes +no difference". + +These tests are cheap, need no container, no credentials and no network, and +they run on every PR. +""" + +from pathlib import Path + +import pytest +import yaml + +import kit + +pytestmark = pytest.mark.evalsconfig + +EVALS_DIR = kit.REPO_DIR / "evals" +EVAL_SPEC = EVALS_DIR / "documentdb-skills" / "eval.yaml" +EXPERIMENT = EVALS_DIR / "documentdb-skills.experiment.yaml" + + +def _load(path: Path) -> dict: + assert path.exists(), f"missing config: {path}" + return yaml.safe_load(path.read_text()) + + +@pytest.fixture(scope="module") +def spec(): + return _load(EVAL_SPEC) + + +@pytest.fixture(scope="module") +def experiment(): + return _load(EXPERIMENT) + + +# --------------------------------------------------------------------------- +# skill paths must resolve +# --------------------------------------------------------------------------- +def _skill_dir_is_valid(p: Path) -> bool: + return p.is_dir() and (p / "SKILL.md").is_file() + + +def test_eval_skill_paths_exist(spec): + """Paths in an eval spec resolve relative to the eval file's directory.""" + base = EVAL_SPEC.parent + skills = spec["environment"]["skills"] + assert skills, "eval spec declares no skills — the treatment arm is empty" + missing = [s for s in skills if not _skill_dir_is_valid((base / s).resolve())] + assert not missing, ( + f"eval.yaml references skills that do not exist or have no SKILL.md: " + f"{missing}" + ) + + +def _kit_skills(experiment) -> list[str]: + for entry in experiment["matrix"]["skills"]["values"]: + if isinstance(entry, dict) and "kit" in entry: + return entry["kit"] + pytest.fail("experiment matrix has no 'kit' (treatment) arm") + + +def test_experiment_skill_paths_exist(experiment): + """Paths inside variant overrides resolve relative to the EXPERIMENT file's + directory — a different base than the eval spec uses. Getting this wrong is + easy and silent, which is exactly why it is asserted.""" + base = EXPERIMENT.parent + missing = [ + s for s in _kit_skills(experiment) + if not _skill_dir_is_valid((base / s).resolve()) + ] + assert not missing, ( + f"experiment treatment arm references skills that do not exist: {missing}\n" + "A treatment arm whose skills fail to load is a second control arm." + ) + + +def test_treatment_and_eval_skill_sets_agree(spec, experiment): + """The two files must describe the same kit. + + If they drift, `vally eval` and `vally experiment run` measure different + things while reporting under the same name. + """ + eval_names = {Path(s).name for s in spec["environment"]["skills"]} + exp_names = {Path(s).name for s in _kit_skills(experiment)} + assert eval_names <= exp_names, ( + f"eval.yaml declares skills the experiment's treatment arm omits: " + f"{sorted(eval_names - exp_names)}" + ) + + +# --------------------------------------------------------------------------- +# the experiment must be a valid controlled comparison +# --------------------------------------------------------------------------- +def test_control_arm_is_empty(experiment): + """The control must have NO skills. A 'control' that loads part of the kit + measures nothing.""" + values = experiment["matrix"]["skills"]["values"] + control = next( + (v["control"] for v in values if isinstance(v, dict) and "control" in v), + None, + ) + assert control is not None, "experiment matrix has no 'control' arm" + assert control == [], f"control arm is not empty: {control}" + + +def test_baseline_is_a_control_cell(experiment): + """Deltas are measured against the baseline, so the baseline must be an + unskilled arm — otherwise every reported delta is against a treated run.""" + assert experiment["baseline"]["skills"] == "control", ( + "baseline must sit in the control arm" + ) + + +def test_models_are_declared_and_distinct(experiment): + models = experiment["matrix"]["model"]["values"] + assert len(models) >= 2, "a cross-model comparison needs at least 2 models" + assert len(set(models)) == len(models), f"duplicate models: {models}" + assert experiment["baseline"]["model"] in models + + +def test_runs_per_cell_is_enough_for_a_spread(experiment): + """Agent output is non-deterministic. N=1 is an anecdote, not a measurement. + + The token harness flags cells with n<3; the experiment should not be + configured to produce them in the first place. + """ + runs = experiment.get("overrides", {}).get("runs") + assert runs is not None, "experiment does not pin `runs`" + assert runs >= 3, f"runs={runs} is too few to report a mean and spread" + + +def test_matrix_expands_to_every_model_arm_cell(experiment): + models = experiment["matrix"]["model"]["values"] + arms = experiment["matrix"]["skills"]["values"] + assert len(models) * len(arms) == 6, ( + "expected a 3-model x 2-arm matrix; update this test deliberately if " + "the design changes" + ) + + +# --------------------------------------------------------------------------- +# the eval spec must remain an honest measurement +# --------------------------------------------------------------------------- +def test_prompts_never_mention_the_skills(spec): + """The organic-discovery protocol: the agent is never told to use the kit. + + Hinting turns 'does an installed kit get applied?' into the much easier and + much less interesting 'can the model follow an instruction?'. + """ + banned = ("use the skill", "using the skill", "documentdb-query-optimizer", + "documentdb-indexing", "documentdb-connection", "agent kit", + "skills/") + offenders = [] + for stim in spec["stimuli"]: + prompt = stim.get("prompt", "").lower() + for phrase in banned: + if phrase in prompt: + offenders.append((stim["name"], phrase)) + assert not offenders, ( + f"stimulus prompts hint at the skills, which invalidates the " + f"organic-discovery protocol: {offenders}" + ) + + +def test_anti_trigger_stimuli_exist(spec): + """Half of routing quality is NOT firing on unrelated questions. An eval + with only positive triggers rewards a kit that triggers on everything.""" + kinds = [s.get("tags", {}).get("kind") for s in spec["stimuli"]] + assert "anti-trigger" in kinds, "no anti-trigger stimuli — over-triggering " + assert "trigger" in kinds, "no positive-trigger stimuli" + + +def test_every_stimulus_has_a_grader(spec): + for stim in spec["stimuli"]: + assert stim.get("graders"), f"stimulus {stim['name']!r} has no grader" + + +def test_graders_are_objective(spec): + """Phase 1 stimuli should be graded reproducibly. + + Not a prohibition on LLM judges — they are a normal grader and + Cross-Model Skill Evaluations are expected to use one for qualitative + dimensions. But the Phase 1 stimuli are + all skill-TRIGGERING checks, which have a definite right answer, so a + reproducible grader is the correct fit and a judge here would add cost and + variance for nothing. + + The allowlist is a speed bump, not a wall: adding a judge should be a + deliberate, reviewed change that records which stimulus needs it and why. + """ + reproducible = {"skill-invocation", "run-command", "file-exists", "regex"} + others = [] + for stim in spec["stimuli"]: + for g in stim["graders"]: + if g["type"] not in reproducible: + others.append((stim["name"], g["type"])) + assert not others, ( + f"graders outside the reproducible set are in use: {others}. If a " + f"judge is genuinely the right fit for that stimulus, add its type to " + f"the allowlist in this test and note why." + ) + + +# --------------------------------------------------------------------------- +# quality eval — the LLM-judge panel +# +# A judge is easy to do badly, and a badly-built one produces confident numbers +# that are wrong. These pin the anti-bias properties that make the score worth +# reporting at all. They are cheap, need no model, and run on every PR. +# --------------------------------------------------------------------------- +QUALITY_SPEC = EVALS_DIR / "documentdb-quality" / "quality-eval.yaml" +QUALITY_EXPERIMENT = EVALS_DIR / "quality.experiment.yaml" + + +@pytest.fixture(scope="module") +def quality(): + return _load(QUALITY_SPEC) + + +def _panels(spec): + out = [] + for stim in spec["stimuli"]: + for g in stim["graders"]: + if g["type"] == "panel": + out.append((stim["name"], g["config"])) + return out + + +def test_quality_eval_uses_a_multi_vendor_panel(quality): + """Self-preference bias is real: models rate their own output higher. + + A single-model judge on a cross-model comparison would systematically + favour one arm. Requiring judges from 3 distinct vendors makes that + impossible for any one of them to swing alone. + """ + for name, cfg in _panels(quality): + models = [m["model"] if isinstance(m, dict) else m for m in cfg["models"]] + assert len(models) >= 3, f"{name}: panel has only {models}" + vendors = {m.split("-")[0] for m in models} + assert len(vendors) >= 3, ( + f"{name}: judges come from too few vendors ({sorted(vendors)}). " + f"A model must never be the sole judge of output it could have " + f"produced itself." + ) + + +def test_quality_panel_aggregates_by_median(quality): + """A mean lets one miscalibrated judge drag the verdict; a median does not.""" + for name, cfg in _panels(quality): + assert cfg.get("aggregation") == "median", ( + f"{name}: aggregation is {cfg.get('aggregation')!r}, expected " + f"'median' — one outlier judge must not decide the result." + ) + + +def test_correctness_criteria_are_gates(quality): + """A fluent, confident, WRONG answer must fail. + + technical_correctness and documentdb_specificity are marked `required`, so + they gate the verdict: prose quality can never outvote accuracy. + """ + must_gate = {"technical_correctness", "documentdb_specificity"} + for name, cfg in _panels(quality): + criteria = {c["name"]: c for c in cfg["criteria"]} + missing = must_gate - set(criteria) + assert not missing, f"{name}: missing criteria {sorted(missing)}" + for key in must_gate: + assert criteria[key].get("required") is True, ( + f"{name}: {key} is not a required gate, so an answer could " + f"pass on style while being wrong." + ) + + +def test_correctness_outweighs_style(quality): + """Weight, not just gating: correctness must carry more than any single + presentational dimension.""" + for name, cfg in _panels(quality): + criteria = {c["name"]: c.get("weight", 1) for c in cfg["criteria"]} + correctness = criteria["technical_correctness"] + for other, weight in criteria.items(): + if other in ("technical_correctness", "documentdb_specificity"): + continue + assert correctness > weight, ( + f"{name}: technical_correctness (weight {correctness}) does not " + f"outweigh {other} (weight {weight})." + ) + + +def test_judges_are_blinded(quality): + """If a judge knows which arm produced an answer, it will find reasons. + + The prompt must instruct the judge not to infer provenance, and must not + itself leak the experiment's vocabulary. + """ + for name, cfg in _panels(quality): + prompt = cfg.get("prompt", "") + assert prompt, f"{name}: panel has no judge prompt" + assert "BLINDING" in prompt.upper(), ( + f"{name}: judge prompt carries no blinding instruction" + ) + leaked = [w for w in ("treatment arm", "control arm", "skills installed", + "with the kit", "agent kit") + if w in prompt.lower()] + assert not leaked, ( + f"{name}: judge prompt leaks experiment vocabulary {leaked}; the " + f"judge must not know which condition it is grading." + ) + + +def test_every_quality_stimulus_has_an_anchored_rubric(quality): + """"Rate this 1-5" is not a rubric. Each stimulus states what a good answer + contains, so two runs are scored against the same yardstick.""" + for stim in quality["stimuli"]: + rubric = stim.get("rubric") + assert rubric and len(rubric) >= 3, ( + f"{stim['name']}: needs an anchored rubric of at least 3 criteria, " + f"got {rubric!r}" + ) + + +def test_quality_prompts_do_not_name_the_solution(quality): + """Same organic-discovery rule as the triggering eval: if the prompt says + "use a compound index", the judge grades instruction-following instead of + guidance quality, and both arms score alike.""" + banned = ("compound index", "createindex", "create_index", "esr", + "singleton", "ttl index", "multikey", "skill") + offenders = [] + for stim in quality["stimuli"]: + prompt = stim["prompt"].lower() + offenders += [(stim["name"], b) for b in banned if b in prompt] + assert not offenders, ( + f"quality prompts name the solution {offenders}; the answer must come " + f"from the responder, not the question." + ) + + +def test_quality_runs_are_repeated(quality): + """Both agent output and judge scoring vary. n=1 is an anecdote.""" + runs = quality.get("defaults", {}).get("runs") + assert runs and runs >= 3, f"runs={runs}; need >= 3 for a usable median" + + +def test_quality_experiment_has_a_control_arm(): + """The decisive one for this eval. + + Frontier models already answer MongoDB questions well, so a treatment-only + quality score proves nothing. Only the delta against an unskilled agent + supports the claim "the kit makes the guidance better". + """ + exp = _load(QUALITY_EXPERIMENT) + values = exp["matrix"]["skills"]["values"] + control = next((v["control"] for v in values + if isinstance(v, dict) and "control" in v), None) + assert control == [], f"control arm must be empty, got {control!r}" + assert exp["baseline"]["skills"] == "control" + + +def test_quality_experiment_skill_paths_exist(): + """Same silent-failure trap as the triggering experiment: a treatment arm + whose skills do not load is just a second control arm.""" + exp = _load(QUALITY_EXPERIMENT) + kit = next((v["kit"] for v in exp["matrix"]["skills"]["values"] + if isinstance(v, dict) and "kit" in v), []) + base = QUALITY_EXPERIMENT.parent + missing = [s for s in kit if not _skill_dir_is_valid((base / s).resolve())] + assert not missing, f"quality experiment references missing skills: {missing}" diff --git a/testing/scenarios/json-contract/expected-findings.yaml b/testing/scenarios/json-contract/expected-findings.yaml index fb794a2..0e6d267 100644 --- a/testing/scenarios/json-contract/expected-findings.yaml +++ b/testing/scenarios/json-contract/expected-findings.yaml @@ -10,6 +10,9 @@ database: test_json_contract shape: document-bloat-advisor.sh: type: list + toast-split-advisor.sh: + type: object + keys: [db, flagged, findings, clean, analysis_only] index-redundancy-finder.sh: type: list db-config-advisor.sh: diff --git a/testing/scenarios/json-contract/fixture.js b/testing/scenarios/json-contract/fixture.js index 4f8a038..5a28dbc 100644 --- a/testing/scenarios/json-contract/fixture.js +++ b/testing/scenarios/json-contract/fixture.js @@ -11,7 +11,17 @@ // This is a SHAPE fixture: the tests assert JSON validity + structure, never // specific finding counts, so the data does not need to be deterministic. -["customers", "orders", "documents"].forEach(function (c) { +var ADVERSARIAL_COLLECTION = + "safe;db.getCollection('diagnostic_sentinel').drop();db.safe"; + +[ + "customers", + "orders", + "documents", + "safe", + "diagnostic_sentinel", + ADVERSARIAL_COLLECTION +].forEach(function (c) { try { db[c].drop(); } catch (e) {} }); @@ -47,6 +57,9 @@ for (var i = 1; i <= 120; i++) { bulk.push({ _id: i, kind: "note", amount: i * 10, blob: txt(6000) }); } db.documents.insertMany(bulk); +db.safe.insertOne({ _id: 1, marker: "safe" }); +db.diagnostic_sentinel.insertOne({ _id: 1, marker: "must-survive" }); +db.getCollection(ADVERSARIAL_COLLECTION).insertMany(bulk); print("FIXTURE_READY json-contract"); db.getCollectionNames().sort().forEach(function (c) { diff --git a/testing/scenarios/json-contract/tests/test_json_contract.py b/testing/scenarios/json-contract/tests/test_json_contract.py index 2a1d203..0ef1364 100644 --- a/testing/scenarios/json-contract/tests/test_json_contract.py +++ b/testing/scenarios/json-contract/tests/test_json_contract.py @@ -18,6 +18,9 @@ ) SHAPE = _SPEC["shape"] TYPEMAP = {"list": list, "object": dict} +ADVERSARIAL_COLLECTION = ( + "safe;db.getCollection('diagnostic_sentinel').drop();db.safe" +) @pytest.fixture(scope="session") @@ -30,6 +33,17 @@ def json_outputs(seeded_db): return out +@pytest.fixture(scope="session") +def quoted_database(request, require_container): + name = "test_json_'contract" + fixture = Path(__file__).resolve().parents[1] / "fixture.js" + kit.drop_db(name, container=require_container) + kit.seed(name, fixture, container=require_container) + yield name + if not request.config.getoption("--keep-db"): + kit.drop_db(name, container=require_container) + + @pytest.mark.jsoncontract @pytest.mark.parametrize("script", list(SHAPE)) def test_emits_valid_json_of_expected_shape(json_outputs, script): @@ -61,13 +75,96 @@ def test_json_stdout_is_pure_json(json_outputs): ) +@pytest.mark.jsoncontract +@pytest.mark.parametrize("script", list(SHAPE)) +def test_bash_and_portable_entry_points_have_same_json_shape( + json_outputs, seeded_db, script +): + portable = json_outputs[script] + bash = kit.run_script( + script, + "--db", + seeded_db, + "--json", + want_json=True, + portable=False, + ) + assert bash.returncode == 0, ( + f"{script} Bash entry point failed (rc={bash.returncode})\n" + f"{bash.stderr[:400]}" + ) + assert bash.json is not None, ( + f"{script} Bash entry point emitted invalid JSON:\n{bash.stdout[:400]}" + ) + assert type(bash.json) is type(portable.json) + if isinstance(portable.json, dict): + assert set(bash.json) == set(portable.json) + + +@pytest.mark.jsoncontract +@pytest.mark.parametrize( + ("script", "extra_args"), + [ + ("document-bloat-advisor.sh", []), + ( + "toast-split-advisor.sh", + ["--collection", ADVERSARIAL_COLLECTION, "--sample", "5"], + ), + ], +) +def test_catalog_names_are_passed_as_data(quoted_database, script, extra_args): + result = kit.run_script( + script, + "--db", + quoted_database, + "--min-total-kb", + "0", + "--toast-ratio", + "0", + *extra_args, + "--json", + want_json=True, + portable=False, + ) + assert result.returncode == 0, ( + f"{script} failed for a quoted collection name:\n{result.stderr[:400]}" + ) + assert result.json is not None, result.stdout[:400] + findings = ( + result.json + if script == "document-bloat-advisor.sh" + else result.json["findings"] + ) + quoted = [ + finding for finding in findings + if finding["collection"] == ADVERSARIAL_COLLECTION + ] + assert quoted, f"{script} did not preserve the exact collection name" + assert quoted[0]["avg_obj_size"] > 0, ( + f"{script} did not sample the quoted collection" + ) + sentinel = kit.mongosh_eval( + quoted_database, + 'db.getCollection("diagnostic_sentinel").countDocuments({})', + ) + assert "1" in sentinel.split(), ( + f"{script} executed JavaScript embedded in the collection name" + ) + + @pytest.mark.jsoncontract def test_perf_advisor_nested_shape(json_outputs): d = json_outputs["perf-advisor.sh"].json assert isinstance(d["mongo"], list) and d["mongo"], "perf: mongo[] is empty" m = d["mongo"][0] - for key in ("collections", "index_health", "collscans", "slow_queries", "summary"): + for key in ("collections", "index_health", "collscans", + "query_timings", "slow_queries", "summary"): assert key in m, f"perf mongo[0] missing '{key}'" + # query_timings is the deterministic view (every probe); slow_queries is the + # threshold-filtered subset of it, so it can never be the larger of the two. + assert len(m["slow_queries"]) <= len(m["query_timings"]), ( + "perf: slow_queries must be a subset of query_timings" + ) assert isinstance(d["pg"], dict), "perf: pg is not an object" for key in ("config", "cache_top", "scan_mix", "unused_pg_indexes", "blocked_queries"): assert key in d["pg"], f"perf pg missing '{key}'" diff --git a/testing/scenarios/portable-cli/conftest.py b/testing/scenarios/portable-cli/conftest.py new file mode 100644 index 0000000..85f45f1 --- /dev/null +++ b/testing/scenarios/portable-cli/conftest.py @@ -0,0 +1,8 @@ +"""Portable CLI tests need no live database.""" + +import pytest + + +@pytest.fixture(scope="session", autouse=True) +def require_container(): + return None diff --git a/testing/scenarios/portable-cli/tests/test_portable_cli.py b/testing/scenarios/portable-cli/tests/test_portable_cli.py new file mode 100644 index 0000000..03c4bd6 --- /dev/null +++ b/testing/scenarios/portable-cli/tests/test_portable_cli.py @@ -0,0 +1,147 @@ +from __future__ import annotations + +import io +import os +import subprocess +import sys +from pathlib import Path + +import pytest + +SCRIPTS = Path(__file__).resolve().parents[4] / "scripts" +REPO = SCRIPTS.parent +sys.path.insert(0, str(SCRIPTS)) + +import portable_diagnostic as portable # noqa: E402 + + +def test_every_diagnostic_has_python_and_powershell_entry_points(): + for name in sorted(portable.SUPPORTED): + assert (SCRIPTS / f"{name}.sh").is_file() + assert (SCRIPTS / f"{name}.py").is_file() + assert (SCRIPTS / f"{name}.ps1").is_file() + + +@pytest.mark.skipif(os.name == "nt", reason="Windows does not expose Unix mode bits") +def test_all_tracked_shell_files_are_executable(): + result = subprocess.run( + ["git", "ls-files", "*.sh"], + cwd=REPO, + capture_output=True, + text=True, + check=True, + ) + missing = [] + for relative in result.stdout.splitlines(): + path = REPO / relative + if path.stat().st_mode & 0o111 == 0: + missing.append(relative) + assert not missing, f"shell files missing executable mode: {missing}" + + +def test_container_option_overrides_environment(monkeypatch): + monkeypatch.setenv("CONTAINER_NAME", "from-env") + assert portable.container_name(["--container", "from-cli"]) == "from-cli" + assert portable.container_name([]) == "from-env" + + +def test_docker_command_uses_argument_list_and_direct_mode(monkeypatch): + monkeypatch.setenv("DB_PASSWORD", "secret with spaces") + command = portable.docker_exec_command( + "docdb", + "/tmp/kit/perf-advisor.sh", + ["--db", "sales data", "--json"], + ) + assert command[:6] == [ + "docker", + "exec", + "-i", + "-u", + "documentdb", + "-e", + ] + assert "DOCDB_DIRECT=1" in command + assert "DB_PASSWORD=secret with spaces" in command + assert command[-3:] == ["--db", "sales data", "--json"] + + +def test_help_does_not_require_docker(): + output = io.StringIO() + assert portable.run_named("perf-advisor", ["--help"], stdout=output) == 0 + assert "without requiring Bash on the host" in output.getvalue() + + +def test_missing_docker_has_actionable_error(monkeypatch): + def missing(*args, **kwargs): + raise FileNotFoundError + + monkeypatch.setattr(subprocess, "run", missing) + with pytest.raises(portable.DiagnosticError, match="Docker CLI was not found"): + portable.run_named("perf-advisor", ["--db", "sales", "--json"]) + + +def test_powershell_wrappers_forward_all_arguments(): + for name in sorted(portable.SUPPORTED): + text = (SCRIPTS / f"{name}.ps1").read_text() + assert f'-Diagnostic "{name}"' in text + assert "@args" in text + assert "exit $LASTEXITCODE" in text + + +def test_crlf_shell_checkout_is_normalized(tmp_path): + source = tmp_path / "diagnostic.sh" + source.write_bytes(b"#!/usr/bin/env bash\r\nset -uo pipefail\r\necho ok\r\n") + output_dir = tmp_path / "normalized" + output_dir.mkdir() + + normalized = portable.normalized_shell_copy(source, output_dir) + + assert normalized.read_bytes() == ( + b"#!/usr/bin/env bash\nset -uo pipefail\necho ok\n" + ) + + +def test_ci_and_benchmark_pin_the_same_mongosh_version(): + workflow = ( + REPO / ".github" / "workflows" / "diagnostic-regression.yml" + ).read_text() + dockerfile = ( + REPO + / "benchmarks" + / "documentdb-sdk-skills" + / "shared" + / "base" + / "Dockerfile" + ).read_text() + + assert 'MONGOSH_VERSION: "2.3.8"' in workflow + assert "ARG MONGOSH_VERSION=2.3.8" in dockerfile + digest = "23edb768189663aaa9732a2340a25b5fc05a314940538809a7840be7f2ce221f" + assert f'MONGOSH_SHA256: "{digest}"' in workflow + assert f"ARG MONGOSH_SHA256={digest}" in dockerfile + assert "sha256sum -c -" in workflow + assert "sha256sum -c -" in dockerfile + assert "INSTALLED_VERSION=" in workflow + assert 'Expected mongosh $MV' in workflow + + +def test_ci_and_benchmark_pin_documentdb_image_digest(): + workflow = ( + REPO / ".github" / "workflows" / "diagnostic-regression.yml" + ).read_text() + dockerfile = ( + REPO + / "benchmarks" + / "documentdb-sdk-skills" + / "shared" + / "base" + / "Dockerfile" + ).read_text() + image = ( + "ghcr.io/microsoft/documentdb/documentdb-local" + "@sha256:0fcf634531c1917ad0855ff9f4354aca0a5c5d8e435f08250568deb37eeb0ad5" + ) + + assert f'DOCUMENTDB_LOCAL_IMAGE: "{image}"' in workflow + assert f"FROM {image}" in dockerfile + assert "documentdb-local:latest" not in workflow diff --git a/testing/scenarios/remediation-effect/SCENARIO.md b/testing/scenarios/remediation-effect/SCENARIO.md new file mode 100644 index 0000000..b73843c --- /dev/null +++ b/testing/scenarios/remediation-effect/SCENARIO.md @@ -0,0 +1,113 @@ +# Scenario: remediation effect (before/after grading) + +**Suite:** Diagnostic Regression Suite +**Grading rung:** *measured before/after delta* — chosen because this scenario's +job is to prove determinism, which a judge cannot do. + +## The idea + +LLM-as-a-judge is a normal and useful grader, and this kit uses it in Cross-Model Skill Evaluations for +things that are genuinely a matter of degree — is the explanation clear, is the +guidance well-targeted. What it cannot give us is **repeatability**: the same +input can score differently across runs, so a judge is the wrong instrument for +a suite whose whole purpose is to show that the diagnostic scripts return a +stable, reproducible answer. + +For diagnostics we happen to have a stronger option available, so we use it: +**the database is the oracle.** This scenario applies the kit's advice and +measures whether the database actually improved — a number, not an opinion. + +The rule of thumb across the kit is to pick the strongest criterion the scenario +*allows*, not to avoid judges on principle: + +| Situation | Grader | +|---|---| +| The outcome is measurable in the system (plans, counters, result sets) | measure it — this scenario | +| The outcome is structural (an index exists, a key order) | assert it | +| The outcome is irreducibly qualitative (was the advice well explained?) | LLM-as-a-judge, blinded and cross-model (Cross-Model Skill Evaluations) | + +Judges are used where they are the right tool; here a measurement was available, +and a measurement is what a determinism proof needs. + +## The chain it closes + +| Step | Assertion | +|---|---| +| 1. The defect is real | `COLLSCAN`, ≥100× scan amplification, full-collection read | +| 2. The kit diagnoses it | `perf-advisor.sh --json` reports the collection scan | +| 3. The fix is **derived from that output** | field parsed from `collscans[].query`, not hardcoded | +| 4. The plan improves | `IXSCAN`, amplification ≤2×, ≥50× better | +| 5. Results are unchanged | identical `order_id` set before and after | +| 6. Nothing got worse | `index-redundancy-finder.sh` reports no new redundancy | +| 7. The tool agrees | `perf-advisor.sh` no longer reports that scan | + +**Step 3 is what makes this a test of the kit rather than a test of PostgreSQL.** +If the `collscans` reporting were removed from `perf-advisor.sh`, this suite +could no longer derive a fix and would fail — which is the correct outcome. + +## The metric: scan amplification + +`totalDocsExamined / nReturned`. + +The obvious metric — "documents examined dropped" — is **wrong on this engine**. +Measured here, DocumentDB's planner picks an index scan even for very +unselective predicates, so raw `examined` mostly reflects how many rows the +query legitimately matches: + +| Query | Plan | Examined | Returned | +|---|---|---|---| +| `amount > 490` (selective) | IXSCAN | 760 | 760 | +| `amount > 10` (unselective) | IXSCAN | 19,960 | 19,960 | + +Both are healthy — they read only rows they return. Scan amplification captures +that independently of selectivity: + +| State | Examined | Returned | Amplification | +|---|---|---|---| +| before (no index) | 20,000 | 10 | **2000×** | +| after (index) | 10 | 10 | **1×** | + +It is also exactly the ratio the `documentdb-query-performance-tuning` skill +teaches users to read out of `explain()` — so the test grades the same thing the +documentation does. + +## Anti-gaming + +An improvement metric alone is trivially gamed: index every field and everything +looks fast. Three guards make that cost something: + +- **`index-redundancy-finder.sh` is run as a paired regression guard** — the + remediation may not introduce structural redundancy (`EXACT_DUPLICATE`, + `PREFIX_REDUNDANT`, or `REVERSE_VARIANT`). Live-counter findings such as + `WRITE_TAX` are excluded: a brand-new index can briefly report zero reads + until asynchronous usage counters propagate, even when `explain()` already + shows `IXSCAN`. +- **Result sets must be identical** — a faster query returning different rows is + not a fix. +- **The before-state must genuinely be slow** — otherwise every later assertion + passes for free. + +## Verification performed + +A green suite proves nothing unless it can fail. Two mutations were injected: + +| Mutation | Result | +|---|---| +| Skip the remediation entirely | **3 tests fail** (plan, amplification, tool-confirms) | +| Game the metric: add `{customer_id}`, `{customer_id,status}`, `{customer_id,status,amount}` | **anti-gaming guard fails** with `PREFIX_REDUNDANT` + `WRITE_TAX` | + +## Known limitation + +Only defects the local container can actually demonstrate are graded here. +**Index-backed sort and covered queries are excluded**: the DocumentDB gateway +pins the experimental GUCs that enable them (`enableIndexOrderbyPushdown`, +`enableNewCompositeIndexOpClass`, `defaultUseCompositeOpClass`) off per session, +and `ALTER SYSTEM` plus an index rebuild does not change the Mongo-API plan. +Grading advice we cannot demonstrate would be worse than not grading it. + +## Run + +```bash +export DB_PASSWORD='' +cd testing && pytest scenarios/remediation-effect +``` diff --git a/testing/scenarios/remediation-effect/conftest.py b/testing/scenarios/remediation-effect/conftest.py new file mode 100644 index 0000000..7c72993 --- /dev/null +++ b/testing/scenarios/remediation-effect/conftest.py @@ -0,0 +1,10 @@ +"""Scenario conftest: seed the remediation-effect fixture.""" + +from pathlib import Path + +from conftest_base import make_seeded_db_fixture + +seeded_db = make_seeded_db_fixture( + "test_remediation_effect", + Path(__file__).resolve().parent, +) diff --git a/testing/scenarios/remediation-effect/expected-findings.yaml b/testing/scenarios/remediation-effect/expected-findings.yaml new file mode 100644 index 0000000..3c73d37 --- /dev/null +++ b/testing/scenarios/remediation-effect/expected-findings.yaml @@ -0,0 +1,67 @@ +# expected-findings.yaml — remediation effect (before/after grading) +# +# This scenario implements the STRONGEST rung of the grading ladder: instead of +# asking a model to judge whether advice is good, it APPLIES the advice and +# measures whether the database got better. +# +# The chain being closed: +# tool detects a defect -> advice is applied -> the defect is measurably +# gone -> the tool confirms it -> nothing else got worse +# +# Every link is asserted. A break anywhere fails the suite. + +database: test_remediation_effect +collection: orders +documents: 20000 + +# --------------------------------------------------------------------------- +# The metric: SCAN AMPLIFICATION = totalDocsExamined / nReturned +# +# "documents examined dropped" is the obvious metric and it is the wrong one. +# Measured on this container, DocumentDB's planner picks an index scan even for +# very unselective predicates, so raw `examined` is dominated by how many rows +# the query legitimately matches, not by whether the index helped: +# +# amount > 490 (selective) IXSCAN examined=760 returned=760 +# amount > 10 (unselective) IXSCAN examined=19960 returned=19960 +# +# Both are healthy — they read only rows they return. Scan amplification +# captures exactly that, independent of selectivity: how many documents were +# read for each document actually returned. +# +# before (no index): examined=20000, returned=10 -> 2000.0x +# after (index): examined=10, returned=10 -> 1.0x +# +# This is also precisely the ratio the query-performance-tuning skill teaches +# users to read out of explain(), so the test grades the same thing the docs do. +# --------------------------------------------------------------------------- +before: + # The defect must be real, or "improvement" is meaningless. + min_scan_amplification: 100.0 + required_stage: COLLSCAN + +after: + # A healthy plan reads roughly what it returns. 2.0 leaves headroom for + # index-vs-heap bookkeeping without admitting a genuinely wasteful scan. + max_scan_amplification: 2.0 + required_stage: IXSCAN + # And the improvement must be large, not a rounding artifact. + min_improvement_factor: 50.0 + +# --------------------------------------------------------------------------- +# Anti-gaming guards. +# +# An improvement metric on its own is trivially gameable: index every field and +# every query looks fast. These guards make "just add indexes" cost something, +# which is what the indexing skill actually teaches. +# --------------------------------------------------------------------------- +guards: + # The remediation must not introduce indexes the kit's own redundancy finder + # would flag. Advice that fixes one problem by creating another is not advice. + no_new_redundancy: true + # The tool that reported the defect must stop reporting it afterwards. + # Without this, a "fix" that satisfies the metric but not the tool would pass. + tool_confirms_fix: true + # Data must be unchanged: a "faster" query that returns different results is + # not a fix. Same documents in, same documents out. + results_unchanged: true diff --git a/testing/scenarios/remediation-effect/fixture.js b/testing/scenarios/remediation-effect/fixture.js new file mode 100644 index 0000000..1f12a56 --- /dev/null +++ b/testing/scenarios/remediation-effect/fixture.js @@ -0,0 +1,42 @@ +// fixture.js — remediation effect +// +// Seeds a collection with a REAL, measurable performance defect: a moderately +// large `orders` collection with NO secondary indexes, so every filtered query +// degenerates into a collection scan that reads the whole table to return a +// handful of rows. +// +// The point of this scenario is not to prove that indexes work — everyone knows +// that. It is to prove that THE KIT'S OWN ADVICE, applied literally, produces a +// measurable improvement, and that the kit's tools then confirm the fix. So the +// fixture must contain a defect the diagnostic scripts actually detect. +// +// Deterministic by construction: no Math.random() anywhere, so the same seed +// yields the same collection every time and the before/after numbers are +// reproducible. + +db.orders.drop(); + +var STATUSES = ["shipped", "pending", "cancelled"]; +var N = 20000; + +var bulk = []; +for (var i = 1; i <= N; i++) { + bulk.push({ + order_id: i, + // 2000 distinct customers over 20000 orders => 10 orders each. + // Selective enough that a collection scan is obviously wasteful: + // 20000 documents read to return 10. + customer_id: "C" + (i % 2000), + status: STATUSES[i % 3], + amount: (i % 500) + 10, + region: ["emea", "amer", "apac"][i % 3] + }); + if (bulk.length === 5000) { db.orders.insertMany(bulk); bulk = []; } +} +if (bulk.length) db.orders.insertMany(bulk); + +// Deliberately NO secondary indexes: _id only. This is the "before" state. + +print("FIXTURE_READY remediation-effect"); +print(" orders: " + db.orders.countDocuments() + " docs, " + + db.orders.getIndexes().length + " index(es)"); diff --git a/testing/scenarios/remediation-effect/tests/test_remediation_effect.py b/testing/scenarios/remediation-effect/tests/test_remediation_effect.py new file mode 100644 index 0000000..e284af7 --- /dev/null +++ b/testing/scenarios/remediation-effect/tests/test_remediation_effect.py @@ -0,0 +1,286 @@ +"""Before/after remediation grading — a measured delta rather than a judgement. + +WHY THIS SCENARIO EXISTS +------------------------ +"Did the skill help?" is often answered by asking a model to judge the answer, +and that is a reasonable grader for qualities that are matters of degree. This +kit uses one in Cross-Model Skill Evaluations for exactly that. + +It is the wrong instrument HERE, though, for a specific reason: this suite lives +in the deterministic loop, and its job is to show the diagnostic scripts return +a stable, reproducible answer. A judge cannot demonstrate repeatability — the +same input can score differently across runs. + +For diagnostics we have a stronger option available, so we take it: the database +is the oracle. We apply the advice and MEASURE whether the database improved. + + 1. the tool detects a real defect (else "improvement" is meaningless) + 2. the fix is DERIVED FROM THE TOOL'S OWN OUTPUT, not hardcoded here + 3. applying it measurably improves the plan + 4. the tool then agrees the defect is gone + 5. nothing else got worse (no new redundancy, identical results) + +Step 2 is what makes this a test of the KIT rather than a test of PostgreSQL. +If someone deletes the `collscans` reporting from perf-advisor, this suite stops +being able to derive a fix and fails — as it should. + +THE METRIC +---------- +Scan amplification = totalDocsExamined / nReturned. See expected-findings.yaml +for why raw "documents examined" is the wrong metric on this engine. +""" + +import json +import re +from pathlib import Path + +import pytest +import yaml + +import kit + +_SPEC = yaml.safe_load( + (Path(__file__).resolve().parents[1] / "expected-findings.yaml").read_text() +) +COLL = _SPEC["collection"] +DOCS = _SPEC["documents"] +BEFORE = _SPEC["before"] +AFTER = _SPEC["after"] +GUARDS = _SPEC["guards"] + +pytestmark = pytest.mark.remediation + + +# --------------------------------------------------------------------------- +# helpers +# --------------------------------------------------------------------------- +def _num(v): + """mongosh renders 64-bit ints as {low, high, unsigned}; flatten to int.""" + if isinstance(v, dict) and "low" in v: + return (v["high"] << 32) | (v["low"] & 0xFFFFFFFF) + return int(v) + + +_EXPLAIN_JS = """ +function stages(node) { + var s = [], g = 0; + while (node && g++ < 30) { + s.push(node.stage); + node = node.inputStage || (node.inputStages ? node.inputStages[0] : null); + } + return s; +} +var p = db.runCommand({explain: {find: "%(coll)s", filter: %(filter)s}, + verbosity: "executionStats"}); +var es = p.executionStats || {}; +print("EXPLAIN " + JSON.stringify({ + stages: stages((p.queryPlanner || {}).winningPlan), + examined: Number(es.totalDocsExamined || 0), + returned: Number(es.nReturned || 0) +})); +""" + + +def explain(db, filt: dict) -> dict: + js = _EXPLAIN_JS % {"coll": COLL, "filter": json.dumps(filt)} + out = kit.mongosh_eval(db, js) + m = re.search(r"EXPLAIN (\{.*\})", out) + assert m, f"could not parse explain output:\n{out[:600]}" + d = json.loads(m.group(1)) + d["examined"] = _num(d["examined"]) + d["returned"] = _num(d["returned"]) + # Amplification is undefined for an empty result set; such a probe cannot + # grade anything, so treat it as a broken probe rather than a pass. + d["amplification"] = ( + d["examined"] / d["returned"] if d["returned"] else float("inf") + ) + return d + + +def fetch_ids(db, filt: dict) -> list: + js = ( + 'print("IDS " + JSON.stringify(' + f'db.{COLL}.find({json.dumps(filt)}, {{order_id: 1, _id: 0}})' + '.toArray().map(function (d) {{ return d.order_id; }}).sort(function (a, b) {{ return a - b; }})' + "));" + ).replace("{{", "{").replace("}}", "}") + out = kit.mongosh_eval(db, js) + m = re.search(r"IDS (\[.*\])", out) + assert m, f"could not parse ids:\n{out[:600]}" + return json.loads(m.group(1)) + + +# The query under test. Equality on a selective field is the canonical case the +# query-optimizer skill addresses, and it is one the local engine can genuinely +# demonstrate (index-backed SORT and covered queries cannot be reproduced here — +# the gateway pins the experimental GUCs that enable them). +PROBE_FILTER = {"customer_id": "C42"} + + +# --------------------------------------------------------------------------- +# step 1 — the defect must be real, and the tool must find it +# --------------------------------------------------------------------------- +@pytest.fixture(scope="module") +def baseline(seeded_db): + return explain(seeded_db, PROBE_FILTER) + + +def test_defect_is_real(baseline): + """Guards against a vacuous 'improvement': if the before-state were already + healthy, every later assertion would pass for free.""" + assert BEFORE["required_stage"] in baseline["stages"], ( + f"expected a {BEFORE['required_stage']} before remediation, " + f"got {baseline['stages']}" + ) + assert baseline["returned"] > 0, "probe matched nothing; it cannot grade" + assert baseline["amplification"] >= BEFORE["min_scan_amplification"], ( + f"before-state is not actually slow: amplification=" + f"{baseline['amplification']:.1f}x " + f"(examined={baseline['examined']}, returned={baseline['returned']})" + ) + assert baseline["examined"] >= DOCS * 0.9, ( + "expected a full-collection read before remediation" + ) + + +@pytest.fixture(scope="module") +def advice(seeded_db): + """The kit's OWN diagnosis, parsed from perf-advisor's JSON. + + Deriving the remediation from here — rather than hardcoding + `createIndex({customer_id: 1})` — is what makes this a test of the kit. + """ + r = kit.run_script("perf-advisor.sh", "--db", seeded_db, "--json", + want_json=True) + assert r.returncode == 0 and r.json, f"perf-advisor failed: {r.stderr[:300]}" + collscans = r.json["mongo"][0]["collscans"] + fields = [] + for finding in collscans: + if finding["collection"] != COLL: + continue + # e.g. 'find {status:"..."}' / 'find {amount:{$gt:...}}' -> field name + m = re.search(r"find \{(\w+):", finding["query"]) + if m and m.group(1) not in fields: + fields.append(m.group(1)) + return fields + + +def test_tool_reports_the_defect(advice): + """The kit must diagnose the collection scans itself. If this fails, the + remediation below has nothing to be derived from.""" + assert advice, ( + "perf-advisor reported no collection scans on a collection with no " + "secondary indexes — the diagnosis, not the fix, is broken" + ) + + +# --------------------------------------------------------------------------- +# step 2 — apply the advice, measure the effect +# --------------------------------------------------------------------------- +@pytest.fixture(scope="module") +def remediated(seeded_db, baseline, advice): + """Apply the fix the kit's advice implies, then re-measure. + + Only the field under test is indexed. Indexing every reported field would + inflate the result and quietly violate the indexing skill's own guidance + about unused indexes. + """ + field = list(PROBE_FILTER)[0] + before_ids = fetch_ids(seeded_db, PROBE_FILTER) + kit.mongosh_eval(seeded_db, f'db.{COLL}.createIndex({{{field}: 1}});') + return { + "field": field, + "before_ids": before_ids, + "after_ids": fetch_ids(seeded_db, PROBE_FILTER), + "plan": explain(seeded_db, PROBE_FILTER), + } + + +def test_plan_switches_to_an_index_scan(remediated): + assert AFTER["required_stage"] in remediated["plan"]["stages"], ( + f"expected {AFTER['required_stage']} after creating an index on " + f"{remediated['field']}, got {remediated['plan']['stages']}" + ) + + +def test_scan_amplification_collapses(baseline, remediated): + """The headline before/after number — fully objective, no judge involved.""" + after = remediated["plan"] + assert after["amplification"] <= AFTER["max_scan_amplification"], ( + f"still reading {after['amplification']:.1f} documents per returned " + f"document (examined={after['examined']}, returned={after['returned']})" + ) + factor = baseline["amplification"] / after["amplification"] + assert factor >= AFTER["min_improvement_factor"], ( + f"improvement of only {factor:.1f}x " + f"({baseline['amplification']:.1f}x -> {after['amplification']:.1f}x)" + ) + + +# --------------------------------------------------------------------------- +# step 3 — anti-gaming guards +# --------------------------------------------------------------------------- +def test_results_are_unchanged(remediated): + """A faster query that returns different rows is not a fix. This is the + guard that stops 'optimisation' from silently changing behaviour.""" + assert remediated["before_ids"] == remediated["after_ids"], ( + "remediation changed the result set: " + f"{len(remediated['before_ids'])} rows before, " + f"{len(remediated['after_ids'])} after" + ) + assert remediated["before_ids"], "probe returned nothing; it cannot grade" + + +def test_no_redundant_index_was_introduced(seeded_db, remediated): + """'Just add indexes' must cost something. + + Speed metrics are trivially gameable by indexing everything, so the kit's + own redundancy finder is run as a paired regression guard. + + Only STRUCTURAL redundancy rules are relevant here. `WRITE_TAX` and + `UNUSED_VERIFIED` depend on live usage counters, and a newly-created index + can be reported as unused until those asynchronous counters propagate. An + immediate standalone reproduction exposed exactly that race: the same + healthy index was briefly labelled WRITE_TAX even though the query plan was + IXSCAN. Treating that as structural redundancy would make this test + timing-dependent and conflate two different questions. + """ + if not GUARDS.get("no_new_redundancy"): + pytest.skip("guard disabled in expected-findings.yaml") + r = kit.run_script("index-redundancy-finder.sh", "--db", seeded_db, "--json", + want_json=True) + assert r.returncode == 0 and r.json is not None, ( + f"index-redundancy-finder failed: {r.stderr[:300]}" + ) + findings = r.json if isinstance(r.json, list) else r.json.get("findings", []) + structural_rules = {"EXACT_DUPLICATE", "PREFIX_REDUNDANT", "REVERSE_VARIANT"} + offending = [ + f for f in findings + if f.get("collection") == COLL and f.get("rule") in structural_rules + ] + assert not offending, ( + f"remediation introduced redundant indexes: {json.dumps(offending)[:400]}" + ) + + +def test_tool_confirms_the_fix(seeded_db, remediated): + """Close the loop: the tool that reported the defect must stop reporting it. + + Without this, a remediation could satisfy the metric while the kit still + told the user their database was broken. + """ + if not GUARDS.get("tool_confirms_fix"): + pytest.skip("guard disabled in expected-findings.yaml") + r = kit.run_script("perf-advisor.sh", "--db", seeded_db, "--json", + want_json=True) + assert r.returncode == 0 and r.json, f"perf-advisor failed: {r.stderr[:300]}" + still = [ + f for f in r.json["mongo"][0]["collscans"] + if f["collection"] == COLL + and re.search(r"find \{(\w+):", f["query"]) + and re.search(r"find \{(\w+):", f["query"]).group(1) == remediated["field"] + ] + assert not still, ( + f"perf-advisor still reports a collection scan on " + f"{remediated['field']} after it was indexed: {still}" + ) diff --git a/testing/scenarios/route-efficiency/conftest.py b/testing/scenarios/route-efficiency/conftest.py new file mode 100644 index 0000000..be6e637 --- /dev/null +++ b/testing/scenarios/route-efficiency/conftest.py @@ -0,0 +1,15 @@ +"""Scenario conftest: route-efficiency config validation needs no database.""" + +import sys +from pathlib import Path + +import pytest + +REPO = Path(__file__).resolve().parents[3] +sys.path.insert(0, str(REPO / "benchmarks" / "documentdb-route-efficiency" / "shared" / "verifier")) + + +@pytest.fixture(scope="session", autouse=True) +def require_container(): + """No-op override — this scenario only reads files and grades JSON.""" + return None diff --git a/testing/scenarios/route-efficiency/tests/test_route_efficiency.py b/testing/scenarios/route-efficiency/tests/test_route_efficiency.py new file mode 100644 index 0000000..83b0e2f --- /dev/null +++ b/testing/scenarios/route-efficiency/tests/test_route_efficiency.py @@ -0,0 +1,355 @@ +"""Guards for the route-efficiency benchmark (text skills vs diagnostic scripts). +""" +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import pytest +import yaml + +import kit + +pytestmark = pytest.mark.routeefficiency + +BENCH = kit.REPO_DIR / "benchmarks" / "documentdb-route-efficiency" +TASK = BENCH / "tasks" / "index-redundancy-diagnosis" +sys.path.insert(0, str(BENCH / "shared" / "verifier")) +import check_parity # noqa: E402 + + +@pytest.fixture(scope="module") +def expected(): + return yaml.safe_load((TASK / "expected-findings.yaml").read_text()) + + +# --------------------------------------------------------------------------- +# arm design +# --------------------------------------------------------------------------- +def test_arms_are_mutually_exclusive_and_asserted(): + """A leftover file turns one arm into the other, and the run would still + complete and still report a number — a confident comparison of an arm + against itself. The installer must verify, not just copy.""" + script = (BENCH / "shared" / "arms" / "install-arm.sh").read_text() + assert "route-text" in script and "route-script" in script + assert "rm -rf" in script, "the installer must clear the previous arm" + assert script.count("FATAL") >= 3, ( + "the installer must fail loudly when an arm is contaminated or empty" + ) + assert "exit 1" in script + + +def test_instruction_does_not_reveal_the_route(): + """Both arms get the identical prompt. Naming a script or a skill would + steer one arm and turn the comparison into instruction-following.""" + text = (TASK / "instruction.md").read_text().lower() + for leak in ("kb-route", "script", "skill", "toolbox", "advisor", "finder"): + assert leak not in text, f"instruction leaks the route: {leak!r}" + + +def test_harness_refuses_to_fabricate_runs(): + """Without an agent the harness must stop, not emit plausible numbers. + + A silent fallback here would produce something that looks like a + measurement and is not — worse than producing nothing. + """ + script = (BENCH / "run-comparison.sh").read_text() + assert "AGENT_CMD is not set" in script + assert "exit 2" in script + + +def test_harness_resets_every_shared_surface(): + """Each of these is a leak that would favour whichever arm ran second.""" + script = (BENCH / "run-comparison.sh").read_text() + assert "drop_db" in script, "each run needs its own database" + assert "ANALYZE" in script, "planner statistics must be settled after seeding" + assert "arm_order" in script, "arm order must be randomised across iterations" + assert 'rm -rf "$run_out"' in script, "a stale finding.json must not be graded" + + +def test_fixture_is_deterministic(): + """Both arms must see byte-identical data, or a cost difference could be a + data difference.""" + # Strip comments first: the fixture's own docstring says "no Math.random()", + # and matching that would be a false positive — the check must look at code. + src = (TASK / "fixture.js").read_text() + code = "\n".join(line.split("//")[0] for line in src.splitlines()) + assert "Math.random" not in code, ( + "fixture uses Math.random(); both arms must see byte-identical data or " + "a cost difference could be a data difference" + ) + + +# --------------------------------------------------------------------------- +# parity grading — the gate on every cost number +# --------------------------------------------------------------------------- +def test_parity_accepts_the_correct_finding(expected): + r = check_parity.grade( + {"redundant_indexes": ["tenant_id_1", "email_1"], + "reason": "tenant_id_1 is a prefix of the compound index; " + "email_1 duplicates email_unique"}, + expected) + assert r["parity"] is True, r + + +def test_parity_rejects_a_missed_finding(expected): + r = check_parity.grade( + {"redundant_indexes": ["tenant_id_1"], "reason": "prefix"}, expected) + assert r["parity"] is False and r["missed"] == ["email_1"] + + +def test_parity_rejects_naming_a_healthy_index(expected): + """Over-reporting is not correctness.""" + r = check_parity.grade( + {"redundant_indexes": ["tenant_id_1", "email_1", "email_unique"], + "reason": "prefix and duplicate"}, expected) + assert r["parity"] is False and "email_unique" in r["false_positives"] + + +def test_parity_rejects_listing_every_index(expected): + """The obvious attack: an arm could 'win' parity by naming everything.""" + r = check_parity.grade( + {"redundant_indexes": ["tenant_id_1", "email_1", + "tenant_id_1_status_1", "customer_id_1", "_id_"], + "reason": "prefix duplicate"}, expected) + assert r["parity"] is False and len(r["false_positives"]) >= 3 + + +def test_parity_requires_a_stated_mechanism(expected): + """Right names with no reasoning is a lucky guess, not a diagnosis.""" + r = check_parity.grade( + {"redundant_indexes": ["tenant_id_1", "email_1"], + "reason": "these should be dropped"}, expected) + assert r["parity"] is False and r["unjustified"] + + +def test_parity_tolerates_formatting_differences(expected): + """Failing over `accounts.tenant_id_1` would grade prose, not correctness.""" + r = check_parity.grade( + {"redundant_indexes": ["accounts.tenant_id_1", " EMAIL_1 "], + "reason": "prefix of the compound index; duplicate key"}, expected) + assert r["parity"] is True, r + + +# --------------------------------------------------------------------------- +# cost aggregation +# --------------------------------------------------------------------------- +def _summarizer(): + import importlib.util + spec = importlib.util.spec_from_file_location("_sum", BENCH / "summarize.py") + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + + +def test_cost_excludes_runs_that_failed_parity(): + """THE headline rule. + + A route that is cheaper because it answered wrong has saved nothing. + Averaging its tokens in would manufacture a saving out of a failure — and + the failing run is typically the cheapest, so the bias is large. + """ + m = _summarizer() + runs = [ + {"arm": "route-script", "parity": True, "tokens_available": 1, + "tokens_fresh_input": 10000, "ai_credits": 10.0}, + {"arm": "route-script", "parity": False, "tokens_available": 1, + "tokens_fresh_input": 100, "ai_credits": 0.1}, # wrong AND cheapest + {"arm": "route-text", "parity": True, "tokens_available": 1, + "tokens_fresh_input": 50000, "ai_credits": 50.0}, + ] + s = m.summarise(runs) + assert s["route-script"]["cost"]["tokens_fresh_input"] == 10000, ( + "the parity-failing run was averaged into the cost, inventing a saving" + ) + assert s["route-script"]["parity_rate"] == pytest.approx(0.5) + + +def test_missing_token_data_is_excluded_not_zeroed(): + m = _summarizer() + runs = [ + {"arm": "route-text", "parity": True, "tokens_available": 1, + "tokens_fresh_input": 40000}, + {"arm": "route-text", "parity": True, "tokens_available": 0}, + ] + s = m.summarise(runs) + assert s["route-text"]["cost"]["tokens_fresh_input"] == 40000 + assert s["route-text"]["missing_token_data"] == 1 + + +def test_report_warns_when_the_cheaper_route_is_less_correct(): + """If the script route wins on tokens but loses on correctness, the saving + must be flagged as suspect rather than published as a win.""" + m = _summarizer() + runs = [ + {"arm": "route-text", "parity": True, "tokens_available": 1, + "tokens_fresh_input": 50000, "ai_credits": 50.0}, + {"arm": "route-script", "parity": True, "tokens_available": 1, + "tokens_fresh_input": 9000, "ai_credits": 9.0}, + {"arm": "route-script", "parity": False, "tokens_available": 1, + "tokens_fresh_input": 500, "ai_credits": 0.5}, + ] + out = m.render(m.summarise(runs), runs) + assert "suspect" in out.lower() + + +# --------------------------------------------------------------------------- +# cross-model cost in USD +# +# One token of Gemini 3.1 Pro is not one token of GPT-5.6 Sol: output differs +# 2.5x ($12 vs $30 / MTok) and input 2.5x ($2 vs $5). A cross-model table in raw +# tokens would rank tokenisers and verbosity, not cost. +# --------------------------------------------------------------------------- +import pricing # noqa: E402 + + +def test_every_matrix_model_has_published_pricing(): + """An unpriced model must raise, not fall back to a default rate — a + guessed rate produces a plausible dollar figure nothing can identify as + invented.""" + for model in ("claude-opus-5", "gpt-5.6-sol", "gemini-3.1-pro-preview"): + assert model in pricing.PRICING, f"no pricing for matrix model {model}" + assert pricing.PRICING[model]["source"].startswith("http") + with pytest.raises(pricing.UnknownModel): + pricing.cost_usd({"input_tokens": 1000, "output_tokens": 10}, "no-such-model") + + +def test_cached_input_is_billed_at_the_discounted_rate(): + """~90% discount on cache reads is why the fresh/cached split matters. + + Billing all input at the full rate would overstate a skill payload's cost + by roughly 10x, since it is sent once and then read from cache. + """ + for model in ("claude-opus-5", "gpt-5.6-sol", "gemini-3.1-pro-preview"): + p = pricing.PRICING[model] + assert p["cached_input"] < p["input"] / 2, ( + f"{model}: cached input is not discounted" + ) + all_fresh = pricing.cost_usd( + {"input_tokens": 1_000_000, "cache_read_tokens": 0, "output_tokens": 0}, + "claude-opus-5") + all_cached = pricing.cost_usd( + {"input_tokens": 1_000_000, "cache_read_tokens": 1_000_000, "output_tokens": 0}, + "claude-opus-5") + assert all_fresh["usd_total"] == pytest.approx(5.00) + assert all_cached["usd_total"] == pytest.approx(0.50) + + +def test_models_are_not_interchangeable_on_cost(): + """The reason this module exists: identical usage costs materially + different amounts, so tokens alone cannot rank models.""" + usage = {"input_tokens": 100_000, "cache_read_tokens": 90_000, + "output_tokens": 5_000} + costs = {m: pricing.cost_usd(usage, m)["usd_total"] + for m in ("claude-opus-5", "gpt-5.6-sol", "gemini-3.1-pro-preview")} + assert costs["gemini-3.1-pro-preview"] < costs["claude-opus-5"] + assert costs["claude-opus-5"] < costs["gpt-5.6-sol"] + spread = max(costs.values()) / min(costs.values()) + assert spread > 1.5, ( + f"identical usage differs by only {spread:.2f}x across models; if that " + f"is really true the pricing table is probably stale ({costs})" + ) + + +def test_long_context_tier_is_applied(): + """Both Sol and Gemini reprice the WHOLE request above a threshold, not + just the overflow. Missing this understates a long run's cost.""" + below = pricing.cost_usd( + {"input_tokens": 100_000, "cache_read_tokens": 0, "output_tokens": 1000}, + "gemini-3.1-pro-preview") + above = pricing.cost_usd( + {"input_tokens": 300_000, "cache_read_tokens": 0, "output_tokens": 1000}, + "gemini-3.1-pro-preview") + assert below["long_context_tier"] is False + assert above["long_context_tier"] is True + assert above["rates_usd_per_mtok"]["input"] > below["rates_usd_per_mtok"]["input"] + + +def test_pricing_table_records_when_it_was_retrieved(): + """Rates change. A stale table produces wrong dollar figures with no other + symptom, so the retrieval date must travel with every priced result.""" + assert pricing.PRICING_RETRIEVED + priced = pricing.cost_usd( + {"input_tokens": 1000, "output_tokens": 10}, "claude-opus-5") + assert priced["pricing_retrieved"] == pricing.PRICING_RETRIEVED + assert priced["pricing_source"].startswith("http") + + +def test_published_rates_agree_with_copilot_credits(): + """Cross-check against a second, independent cost source. + + Copilot's `total_nano_aiu` is token-based per-model billing at + 1 credit = $0.01. Pricing real usage with the published rates and dividing + by (credits x $0.01) gives ~1.0 for every model measured. A future + divergence means either the rate table has gone stale or Copilot changed + its billing — both worth catching. + """ + import sqlite3 + store = Path.home() / ".copilot" / "session-store.db" + if not store.is_file(): + pytest.skip("no local session store to cross-check against") + conn = sqlite3.connect(f"file:{store}?mode=ro", uri=True) + checked = 0 + try: + for model in ("claude-opus-5", "gpt-5.6-sol", "gemini-3.1-pro-preview"): + rows = list(conn.execute( + "SELECT input_tokens, cache_read_tokens, output_tokens, total_nano_aiu " + "FROM assistant_usage_events " + "WHERE model = ? AND total_nano_aiu > 0 LIMIT 100", (model,))) + if len(rows) < 5: + continue + ratios = [] + for inp, cache, out, aiu in rows: + usd = pricing.cost_usd( + {"input_tokens": inp, "cache_read_tokens": cache, + "output_tokens": out}, model)["usd_total"] + credits_usd = (aiu / 1e9) * 0.01 + if credits_usd > 0: + ratios.append(usd / credits_usd) + if not ratios: + continue + checked += 1 + median = sorted(ratios)[len(ratios) // 2] + assert 0.8 < median < 1.25, ( + f"{model}: published rates and Copilot credits disagree by " + f"{median:.2f}x. Either PRICING is stale (retrieved " + f"{pricing.PRICING_RETRIEVED}) or billing changed." + ) + finally: + conn.close() + if not checked: + pytest.skip("not enough local usage rows to cross-check") + + +def test_prompt_is_actually_passed_to_the_agent(): + """Regression: the harness once built the prompt into a lowercase variable + while the agent command referenced an uppercase one that was never set. + + Every agent would have received an EMPTY task, `|| true` would have hidden + it, every run would have failed parity, and the conclusion would have been + "neither route can do this" — a confident, wholly wrong result from a + one-character-class typo. Static analysis caught it (SC2034). + """ + src = (BENCH / "run-comparison.sh").read_text() + assert 'PROMPT="$(sed' in src, "the prompt must be assigned to PROMPT" + assert "export PROMPT" in src, "PROMPT must be exported for AGENT_CMD" + assert '<<<"$PROMPT"' in src, "the prompt must also reach the agent on stdin" + assert 'if [ -z "$PROMPT" ]' in src, ( + "an empty prompt must abort the run rather than silently grading a " + "no-op as a failed answer" + ) + + +def test_no_shell_script_has_lint_findings(): + """CI lints every script under benchmarks/. Keeping this here means a + finding shows up in the normal test run too, not only in CI.""" + import shutil + import subprocess + if not shutil.which("shellcheck"): + pytest.skip("shellcheck not installed") + scripts = sorted(str(p) for p in (kit.REPO_DIR / "benchmarks").rglob("*.sh")) + assert scripts, "no shell scripts found under benchmarks/" + proc = subprocess.run(["shellcheck", "-e", "SC1091", *scripts], + capture_output=True, text=True, timeout=180) + assert proc.returncode == 0, f"shellcheck findings:\n{proc.stdout[:2000]}" diff --git a/testing/scenarios/token-accounting/SCENARIO.md b/testing/scenarios/token-accounting/SCENARIO.md new file mode 100644 index 0000000..3ac3bbb --- /dev/null +++ b/testing/scenarios/token-accounting/SCENARIO.md @@ -0,0 +1,58 @@ +# Scenario: token accounting + +**Suite:** Diagnostic Regression Suite — guarding a Cross-Model Skill +Evaluations component. +**Infrastructure:** none. No container, no credentials, no network. + +## What it guards + +`evals/harness/token_usage.py`, which turns the Copilot CLI's local session +store into per-task cost artifacts (tokens, AI credits, cost-to-green) and +aggregates them into the model × arm comparison table. + +These numbers are the ones most likely to end up in a GTM claim, so they are +also the ones where a quietly wrong denominator does the most damage. Every test +here pins one specific way the cost story could mislead. + +## The four traps being pinned + +| Trap | Why it matters | Test | +|---|---|---| +| Counting cached input as spend | `cache_read_tokens` is a **subset** of `input_tokens` (verified: 0 of 6,348 real rows had cache_read > input). Skills are sent once then read from cache, so raw input overstates their marginal cost by ~20× | `test_fresh_input_subtracts_cache_reads` | +| Cost-to-green on a failed run | A run that gives up early is cheap. Letting it report a cost-to-green makes failure look like efficiency | `test_failed_run_reports_no_cost_to_green` | +| Credits-per-pass when nothing passed | Must be **undefined** — not `0` (looks free) and not a crash | `test_credits_per_pass_is_none_when_nothing_passed` | +| A single run presented as a mean | Agent output is non-deterministic; n=1 is not a result | `test_underpowered_cells_are_flagged` | + +Two further properties are asserted because they were easy to get wrong: + +- **Failed attempts are charged to the successes.** Three runs at 10 credits + with one pass means a passing result cost **30**, not 10. +- **Subagent spend is reported separately** rather than dropped, so a skill that + delegates cannot hide its cost. + +## Why it lives in `testing/` and not `evals/` + +The module belongs to Cross-Model Skill Evaluations (it measures agent cost), +but it is pure stdlib arithmetic over a synthetic SQLite file — perfectly +deterministic. The Diagnostic Regression Suite is where deterministic things +are proven, so that is where its contract lives. + +The scenario's `conftest.py` overrides the root `require_container` fixture with +a no-op, which is what makes it infrastructure-free. That is deliberate: it lets +CI run this on every PR without Docker or credentials. + +## Verification performed + +Beyond "the tests pass", two mutations were injected to confirm the suite can +actually fail: + +| Mutation | Caught by | +|---|---| +| `fresh = inp` (stop subtracting cache reads) | 3 tests | +| `credits_per_pass` divided by *runs* instead of *passes* | 1 test | + +## Run + +```bash +cd testing && pytest scenarios/token-accounting # no container needed +``` diff --git a/testing/scenarios/token-accounting/conftest.py b/testing/scenarios/token-accounting/conftest.py new file mode 100644 index 0000000..acfccd9 --- /dev/null +++ b/testing/scenarios/token-accounting/conftest.py @@ -0,0 +1,26 @@ +"""Scenario conftest: token accounting needs NO database. + +The root conftest has an autouse fixture that skips (or aborts) when the +DocumentDB container is unavailable. This scenario tests pure arithmetic over a +synthetic SQLite file, so it overrides that fixture with a no-op. Defining a +fixture of the same name in a nested conftest shadows the parent's. + +The practical benefit: these tests run in CI on every PR with no Docker, no +container and no credentials. +""" + +import sys +from pathlib import Path + +import pytest + +# The module under test lives in the Cross-Model Skill Evaluations folder (it measures agent cost), but +# it is plain stdlib Python, so Diagnostic Regression Suite can and should regression-test it. +HARNESS = Path(__file__).resolve().parents[3] / "evals" / "harness" +sys.path.insert(0, str(HARNESS)) + + +@pytest.fixture(scope="session", autouse=True) +def require_container(): + """No-op override — this scenario is infrastructure-free.""" + return None diff --git a/testing/scenarios/token-accounting/tests/test_token_usage.py b/testing/scenarios/token-accounting/tests/test_token_usage.py new file mode 100644 index 0000000..4c1db72 --- /dev/null +++ b/testing/scenarios/token-accounting/tests/test_token_usage.py @@ -0,0 +1,291 @@ +"""Contract tests for evals/harness/token_usage.py (Cross-Model Skill Evaluations cost accounting). + +These tests exist because the cost numbers are the ones that will end up in a +GTM claim, and a quietly wrong denominator is the easiest way to publish a false +result. Each test pins one thing that could silently mislead: + +* cache_read is a SUBSET of input, so "fresh input" must subtract it +* a run that never went green must NOT report a cost-to-green +* credits-per-pass must be undefined (not 0, not infinity) when nothing passed +* a single run must not be presented as a comparable mean + +Everything runs against a synthetic SQLite file — no container, no credentials, +no network. +""" + +import json +import sqlite3 +import sys +from pathlib import Path + +import pytest + +import token_usage as tu + +pytestmark = pytest.mark.tokens + + +# --------------------------------------------------------------------------- +# synthetic session store +# --------------------------------------------------------------------------- +def _make_store(tmp_path: Path, rows, sessions=("s1",)) -> Path: + """Build a minimal session-store.db with the two tables we read.""" + db = tmp_path / "session-store.db" + conn = sqlite3.connect(db) + conn.execute("""CREATE TABLE sessions ( + id TEXT, cwd TEXT, repository TEXT, host_type TEXT, branch TEXT, + summary TEXT, created_at TEXT, updated_at TEXT)""") + conn.execute("""CREATE TABLE assistant_usage_events ( + id INTEGER PRIMARY KEY, session_id TEXT, turn_index INTEGER, + agent_id TEXT, parent_tool_call_id TEXT, model TEXT, + input_tokens INTEGER, output_tokens INTEGER, cache_read_tokens INTEGER, + cache_write_tokens INTEGER, reasoning_tokens INTEGER, + total_nano_aiu INTEGER, request_multiplier REAL, duration_ms INTEGER, + time_to_first_token_ms INTEGER, inter_token_latency_ms INTEGER, + initiator TEXT, api_endpoint TEXT, reasoning_effort TEXT, + finish_reason TEXT, content_filter_triggered INTEGER, + token_details_json TEXT, created_at TEXT)""") + for sid in sessions: + conn.execute("INSERT INTO sessions (id, created_at) VALUES (?, ?)", + (sid, "2026-08-12T00:00:00Z")) + conn.executemany( + """INSERT INTO assistant_usage_events + (session_id, turn_index, agent_id, model, input_tokens, + output_tokens, cache_read_tokens, cache_write_tokens, + reasoning_tokens, total_nano_aiu, duration_ms) + VALUES (:session_id, :turn_index, :agent_id, :model, :input_tokens, + :output_tokens, :cache_read_tokens, :cache_write_tokens, + :reasoning_tokens, :total_nano_aiu, :duration_ms)""", + rows, + ) + conn.commit() + conn.close() + return db + + +def _row(**kw): + base = dict(session_id="s1", turn_index=0, agent_id=None, model="m1", + input_tokens=1000, output_tokens=100, cache_read_tokens=0, + cache_write_tokens=0, reasoning_tokens=0, + total_nano_aiu=1_000_000_000, duration_ms=1000) + base.update(kw) + return base + + +@pytest.fixture +def store(tmp_path): + rows = [ + _row(turn_index=0, input_tokens=1000, cache_read_tokens=0, + output_tokens=100, total_nano_aiu=2_000_000_000, duration_ms=1500), + _row(turn_index=1, input_tokens=5000, cache_read_tokens=4500, + output_tokens=200, total_nano_aiu=3_000_000_000, duration_ms=2500), + _row(turn_index=1, input_tokens=5000, cache_read_tokens=4800, + output_tokens=50, total_nano_aiu=1_000_000_000, duration_ms=500, + agent_id="explore"), + ] + return _make_store(tmp_path, rows) + + +# --------------------------------------------------------------------------- +# rollup arithmetic +# --------------------------------------------------------------------------- +def test_rollup_sums_every_request(store): + conn, tmp = tu._open_readonly(store) + try: + u = tu.session_usage(conn, "s1") + finally: + tu._cleanup(conn, tmp) + + assert u["requests"] == 3 + # turn_index 1 appears twice (main + subagent) but is ONE turn + assert u["turns"] == 2 + assert u["input_tokens"] == 11000 + assert u["output_tokens"] == 350 + + +def test_fresh_input_subtracts_cache_reads(store): + """The headline honesty check. + + cache_read_tokens is a subset of input_tokens (verified against the real + store: 0 of 6,348 rows had cache_read > input). Reporting raw input as the + cost of a skill would overstate it by an order of magnitude. + """ + conn, tmp = tu._open_readonly(store) + try: + u = tu.session_usage(conn, "s1") + finally: + tu._cleanup(conn, tmp) + + assert u["cache_read_tokens"] == 9300 + assert u["fresh_input_tokens"] == 11000 - 9300 + assert u["cache_read_share"] == pytest.approx(9300 / 11000, abs=1e-4) + + +def test_credits_convert_from_nano_aiu(store): + conn, tmp = tu._open_readonly(store) + try: + u = tu.session_usage(conn, "s1") + finally: + tu._cleanup(conn, tmp) + assert u["credits"] == pytest.approx(6.0) + + +def test_subagent_spend_is_reported_separately(store): + """Subagent cost is real cost. Reporting only the main agent would + understate any skill that delegates.""" + conn, tmp = tu._open_readonly(store) + try: + agents = {a["agent"]: a for a in tu.by_agent(conn, "s1")} + finally: + tu._cleanup(conn, tmp) + + assert set(agents) == {"main", "explore"} + assert agents["explore"]["credits"] == pytest.approx(1.0) + assert agents["main"]["credits"] == pytest.approx(5.0) + + +def test_fresh_input_never_negative(tmp_path): + """Defensive: if the provider ever reports cache_read > input, the metric + must clamp rather than produce a negative 'cost'.""" + db = _make_store(tmp_path, [_row(input_tokens=100, cache_read_tokens=500)]) + conn, tmp = tu._open_readonly(db) + try: + u = tu.session_usage(conn, "s1") + finally: + tu._cleanup(conn, tmp) + assert u["fresh_input_tokens"] == 0 + + +# --------------------------------------------------------------------------- +# outcome handling +# --------------------------------------------------------------------------- +def test_failed_run_reports_no_cost_to_green(store): + """A run that gave up early is cheap. If we let it report a cost-to-green, + failure would look like efficiency.""" + conn, tmp = tu._open_readonly(store) + try: + rep = tu.build_report(conn, "s1", {"model": "m1", "arm": "skills"}, + {"tests_passed": 3, "tests_total": 10, + "green": False}) + finally: + tu._cleanup(conn, tmp) + + assert rep["cost_to_green"]["green"] is False + assert rep["cost_to_green"]["credits_to_green"] is None + assert rep["cost_to_green"]["turns_to_green"] is None + + +def test_green_run_reports_cost_to_green(store): + conn, tmp = tu._open_readonly(store) + try: + rep = tu.build_report(conn, "s1", {"model": "m1", "arm": "skills"}, + {"tests_passed": 10, "tests_total": 10, + "green": True}) + finally: + tu._cleanup(conn, tmp) + + assert rep["cost_to_green"]["credits_to_green"] == pytest.approx(6.0) + assert rep["cost_to_green"]["turns_to_green"] == 2 + # tokens_to_green uses FRESH input, not raw input + assert rep["cost_to_green"]["tokens_to_green"] == 1700 + 350 + + +def test_missing_session_fails_loudly(store): + conn, tmp = tu._open_readonly(store) + try: + with pytest.raises(SystemExit): + tu.build_report(conn, "does-not-exist", {}, None) + finally: + tu._cleanup(conn, tmp) + + +# --------------------------------------------------------------------------- +# comparison +# --------------------------------------------------------------------------- +def _report(model, arm, credits, turns, green, fresh=1000): + return { + "task": {"model": model, "arm": arm}, + "usage": {"credits": credits, "turns": turns, + "fresh_input_tokens": fresh, "cache_read_share": 0.9}, + "outcome": {"green": green}, + } + + +def test_compare_groups_by_model_and_arm(): + reports = [ + _report("m1", "skills", 10, 5, True), + _report("m1", "skills", 12, 6, True), + _report("m1", "control", 8, 9, False), + ] + cells = {(c["model"], c["arm"]): c for c in tu.compare(reports)["cells"]} + assert set(cells) == {("m1", "skills"), ("m1", "control")} + assert cells[("m1", "skills")]["runs"] == 2 + assert cells[("m1", "skills")]["pass_rate"] == 1.0 + assert cells[("m1", "control")]["pass_rate"] == 0.0 + + +def test_credits_per_pass_is_none_when_nothing_passed(): + """Must not be 0 (looks free) or a divide-by-zero crash.""" + cells = tu.compare([_report("m1", "control", 8, 9, False)])["cells"] + assert cells[0]["credits_per_pass"] is None + + +def test_credits_per_pass_charges_failures_to_the_successes(): + """Three runs costing 10 credits each, one of which passed, means a passing + result cost 30 — not 10. Failed attempts are part of the price.""" + reports = [ + _report("m1", "skills", 10, 5, True), + _report("m1", "skills", 10, 5, False), + _report("m1", "skills", 10, 5, False), + ] + cells = tu.compare(reports)["cells"] + assert cells[0]["credits_per_pass"] == pytest.approx(30.0) + + +def test_underpowered_cells_are_flagged(): + """Agent runs are non-deterministic; a mean over one run is not a result.""" + cells = tu.compare([_report("m1", "skills", 10, 5, True)])["cells"] + assert cells[0]["underpowered"] is True + assert cells[0]["runs"] == 1 + + reports = [_report("m1", "skills", 10, 5, True) for _ in range(3)] + assert tu.compare(reports)["cells"][0]["underpowered"] is False + + +def test_render_table_marks_underpowered_and_undefined(): + table = tu.render_table(tu.compare([_report("m1", "control", 8, 9, False)])) + assert "⚠️" in table + assert "| — |" in table # credits/pass undefined, not 0 + + +def test_standard_deviation_is_reported(): + """A mean without a spread invites over-claiming a difference that is noise.""" + reports = [ + _report("m1", "skills", 10, 5, True), + _report("m1", "skills", 20, 5, True), + ] + cell = tu.compare(reports)["cells"][0] + assert cell["credits"]["mean"] == pytest.approx(15.0) + assert cell["credits"]["sd"] > 0 + + +# --------------------------------------------------------------------------- +# snapshot safety +# --------------------------------------------------------------------------- +def test_reader_does_not_lock_or_mutate_the_source(store): + """The CLI may be writing to the store while we read it. We must snapshot, + never open the live file for writing.""" + before = store.read_bytes() + conn, tmp = tu._open_readonly(store) + try: + tu.session_usage(conn, "s1") + finally: + tu._cleanup(conn, tmp) + assert store.read_bytes() == before + assert not tmp.exists(), "temp snapshot must be cleaned up" + + +def test_missing_store_gives_an_actionable_error(tmp_path): + with pytest.raises(SystemExit) as e: + tu._open_readonly(tmp_path / "nope.db") + assert "Copilot CLI" in str(e.value)