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Add comprehensive performance improvement plan documents
Co-authored-by: willysoft <63505597+willysoft@users.noreply.github.com>
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‎.github/ISSUE_TEMPLATE/performance-improvement-plan.md‎

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---
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name: Performance Improvement Plan
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about: Comprehensive plan for AutoQuery performance optimizations
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title: '[PERFORMANCE] Performance Improvement Plan Implementation'
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labels: enhancement, performance
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assignees: ''
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---
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## 概述 (Overview)
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本 Issue 追蹤 AutoQuery 函式庫的效能改進計畫實施進度。
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This issue tracks the implementation progress of the AutoQuery library performance improvement plan.
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## 相關文件 (Related Documentation)
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- 📄 [Performance Improvement Plan (繁體中文)](/PERFORMANCE_IMPROVEMENT_PLAN.md)
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- 📄 [Performance Improvement Plan (English)](/docs/PERFORMANCE_IMPROVEMENT_PLAN_EN.md)
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## 實施進度 (Implementation Progress)
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### Phase 1: Quick Wins (1-2 months)
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- [ ] **String Parsing Optimization** - Reduce GC pressure and improve parsing performance
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- Use `Span<char>` for zero-allocation string parsing
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- Cache parsing results for `Fields` and `Sort` parameters
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- Estimated impact: 30-40% reduction in GC pressure, 40-50% faster string processing
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- [ ] **Expression Tree Compilation Cache Warming** - Reduce cold start latency
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- Implement warmup mechanism during application startup
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- Use `Lazy<T>` to ensure single compilation per expression
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- Add API for manual pre-compilation of common queries
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- Estimated impact: 50-70% reduction in first-query latency
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- [ ] **Expand Benchmark Test Suite** - Better performance visibility
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- Add cold start performance tests
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- Add concurrency tests
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- Add memory performance tests
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- Add large dataset tests (100K+, 1M+ records)
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### Phase 2: Core Improvements (2-3 months)
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- [ ] **Batch Filter Optimization** - Improve complex query performance
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- Implement expression optimizer with constant folding
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- Add dead code elimination
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- Implement query plan caching
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- Estimated impact: 20-30% improvement for complex queries
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- [ ] **Memory Pooling** - Reduce GC pressure
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- Use `ArrayPool<T>` and `MemoryPool<T>` for temporary buffers
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- Implement object pools for heavy objects
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- Estimated impact: 25-35% reduction in GC pressure
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- [ ] **AsyncEnumerable Support** - Better async/await integration
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- Add `IAsyncEnumerable<T>` support
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- Implement async query extension methods
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- Add cancellation token support
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- Estimated impact: Better memory usage for large datasets
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### Phase 3: Advanced Features (3-6 months)
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- [ ] **Source Generator Integration** - Eliminate runtime overhead
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- Implement C# Source Generator
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- Generate compile-time optimized code
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- Add opt-in mechanism
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- Estimated impact: Eliminate runtime compilation overhead
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- [ ] **Smart Caching Strategy** - Prevent unbounded cache growth
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- Implement LRU/LFU caching
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- Add configurable cache size limits
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- Add cache statistics API
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- Estimated impact: Prevent memory leaks in long-running apps
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- [ ] **Query Plan Visualization Tools** - Better debugging
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- Implement query plan visualization
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- Add performance analysis API
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- Add diagnostic logging
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- Estimated impact: Improved developer experience
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### Phase 4: Specialized Optimizations (As Needed)
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- [ ] **SIMD Optimization** - Hardware acceleration
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- Identify SIMD-friendly hot paths
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- Implement vectorized filtering operations
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- Estimated impact: 2-4x improvement in specific scenarios
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- [ ] **Custom Scenario Optimizations** - Target specific use cases
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- Identify common usage patterns
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- Implement specialized optimizations
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- TBD based on user feedback
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## 貢獻指南 (How to Contribute)
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如果您想貢獻其中任何一項改進，請：
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If you'd like to contribute any of these improvements, please:
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1. 在此 Issue 下留言表達意願 / Comment on this issue to express interest
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2. 開啟新的 Issue 討論具體實作細節 / Open a new issue to discuss implementation details
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3. 提交 PR 時請包含：
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- Benchmark 測試結果比較 / Benchmark comparison results
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- 效能測試報告 / Performance test report
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- 相關文件更新 / Documentation updates
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- 確保所有測試通過 / Ensure all tests pass
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## 成功指標 (Success Metrics)
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每項優化都將測量：
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- 效能提升百分比 / Performance improvement percentage
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- 記憶體影響 / Memory impact
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- 向後相容性 / Backward compatibility
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- 開發者體驗 / Developer experience
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## 相關資源 (Resources)
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- [BenchmarkDotNet Documentation](https://benchmarkdotnet.org/)
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- [.NET Performance Tips](https://learn.microsoft.com/en-us/dotnet/framework/performance/)
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- [Expression Trees Best Practices](https://learn.microsoft.com/en-us/dotnet/csharp/programming-guide/concepts/expression-trees/)
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- [C# Source Generators](https://learn.microsoft.com/en-us/dotnet/roslyn-sdk/source-generators-overview)
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## 更新日誌 (Update Log)
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<!-- 請在這裡記錄重要的進度更新 -->
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<!-- Please record important progress updates here -->
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- 2026-02-04: 創建效能改進計畫 / Created performance improvement plan

‎HOW_TO_CREATE_ISSUE.md‎

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# 如何創建效能改進計畫 Issue (How to Create Performance Improvement Plan Issue)
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## 概述 (Overview)
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由於自動化工具無法直接在 GitHub 上創建 Issue，請按照以下步驟手動創建效能改進計畫的 Issue。
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Since the automation tool cannot directly create GitHub issues, please follow these steps to manually create the performance improvement plan issue.
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## 已準備的文件 (Prepared Documents)
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以下文件已經為您準備好：
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The following documents have been prepared for you:
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1. **效能改進計畫 (繁體中文)**: `/PERFORMANCE_IMPROVEMENT_PLAN.md`
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- 詳細的效能改進計畫，包含問題分析、建議方案和實施路線圖
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2. **Performance Improvement Plan (English)**: `/docs/PERFORMANCE_IMPROVEMENT_PLAN_EN.md`
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- Comprehensive performance improvement plan with detailed analysis and roadmap
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3. **GitHub Issue 範本**: `.github/ISSUE_TEMPLATE/performance-improvement-plan.md`
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- 可直接使用的 GitHub Issue 範本
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## 創建 Issue 的步驟 (Steps to Create Issue)
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### 方法一：使用 Issue 範本 (Method 1: Use Issue Template)
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1. 前往 GitHub 儲存庫頁面
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- Go to the GitHub repository page
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2. 點擊 "Issues" 標籤
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- Click on the "Issues" tab
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3. 點擊 "New issue" 按鈕
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- Click the "New issue" button
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4. 選擇 "Performance Improvement Plan" 範本
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- Select the "Performance Improvement Plan" template
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5. 檢查並提交 Issue
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- Review and submit the issue
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### 方法二：手動創建 (Method 2: Manual Creation)
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1. 前往 GitHub 儲存庫頁面
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- Go to the GitHub repository page
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2. 點擊 "Issues" → "New issue"
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- Click "Issues" → "New issue"
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3. 使用以下資訊：
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- Use the following information:
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**標題 (Title)**:
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```
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[PERFORMANCE] Performance Improvement Plan Implementation
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```
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**標籤 (Labels)**:
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- `enhancement`
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- `performance`
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**內容 (Body)**:
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- 複製 `.github/ISSUE_TEMPLATE/performance-improvement-plan.md` 的內容
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- Copy the content from `.github/ISSUE_TEMPLATE/performance-improvement-plan.md`
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4. 提交 Issue
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- Submit the issue
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## 文件說明 (Document Description)
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### PERFORMANCE_IMPROVEMENT_PLAN.md (繁體中文版)
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這個文件包含：
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- **目前狀態分析**: 現有的效能優化和基準測試結果
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- **改進機會**: 按優先級分類的 9 項改進建議
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- 高優先級 (3項): 快取預熱、批次過濾優化、字串解析優化
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- 中優先級 (3項): AsyncEnumerable 支援、記憶體池化、Source Generator
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- 低優先級 (3項): SIMD 優化、查詢視覺化工具、智能快取策略
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- **效能測試計畫**: 擴展的基準測試場景
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- **實施路線圖**: 分為 4 個階段的實施計畫
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- **貢獻指南**: 如何參與改進工作
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### PERFORMANCE_IMPROVEMENT_PLAN_EN.md (English Version)
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This document includes:
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- **Current State Analysis**: Existing optimizations and benchmark results
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- **Improvement Opportunities**: 9 improvement suggestions categorized by priority
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- High Priority (3): Cache warming, batch filter optimization, string parsing
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- Medium Priority (3): AsyncEnumerable support, memory pooling, source generators
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- Low Priority (3): SIMD optimization, visualization tools, smart caching
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- **Performance Testing Plan**: Extended benchmark scenarios
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- **Implementation Roadmap**: 4-phase implementation plan
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- **Contribution Guidelines**: How to participate in improvements
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## 主要改進項目摘要 (Key Improvements Summary)
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### 第一階段 (Phase 1) - 快速勝利
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1. **字串解析最佳化**
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- 使用 `Span<char>` 減少記憶體分配
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- 預期效益: GC 壓力減少 30-40%
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2. **Expression Tree 快取預熱**
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- 減少冷啟動延遲
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- 預期效益: 第一次查詢延遲減少 50-70%
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3. **擴展基準測試**
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- 冷啟動測試、併發測試、大型資料集測試
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### 第二階段 (Phase 2) - 核心改進
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4. **批次過濾條件最佳化**
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- 預期效益: 複雜查詢效能提升 20-30%
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5. **記憶體池化**
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- 預期效益: GC 壓力減少 25-35%
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6. **AsyncEnumerable 支援**
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- 改善大型資料集處理效率
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### 第三階段 (Phase 3) - 進階功能
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7. **Source Generator 整合**
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- 消除執行時編譯開銷
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8. **智能快取策略**
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- 防止記憶體洩漏
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9. **查詢計畫視覺化工具**
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- 改善開發者體驗
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## 後續步驟 (Next Steps)
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1. ✅ 創建 GitHub Issue (使用上述方法)
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2. ⬜ 開始實施第一階段的改進
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3. ⬜ 收集社群反饋
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4. ⬜ 根據優先級和社群需求調整計畫
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## 聯繫方式 (Contact)
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如有任何問題或需要協助，請：
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- 在創建的 Issue 中留言討論
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- 開啟新的 Discussion
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- 提交 Pull Request
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---
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*祝效能改進工作順利！*
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*Good luck with the performance improvements!*

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