101 lines
4.2 KiB
Markdown
101 lines
4.2 KiB
Markdown
# Task Status Index Optimization Implementation
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## Overview
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This document summarizes the database schema and index optimization implemented for the shot-asset-task-status-optimization feature. The optimization addresses the N+1 query problem identified in the current shot and asset data fetching patterns.
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## Problem Statement
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The current implementation suffers from N+1 query patterns:
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- **Main Query**: Fetches shots/assets first
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- **Per-Entity Query**: For each shot/asset, runs separate query for tasks
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- **Application-Level Aggregation**: Task status building happens in Python loops
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For 100 shots, this results in 101 database queries (1 for shots + 100 for tasks).
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## Solution Implemented
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### New Database Indexes Created
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The following indexes were created to optimize task status queries:
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1. **`idx_tasks_shot_id_active`**
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- Optimizes task lookups by shot_id (active tasks only)
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- Includes WHERE clause: `deleted_at IS NULL`
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2. **`idx_tasks_asset_id_active`**
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- Optimizes task lookups by asset_id (active tasks only)
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- Includes WHERE clause: `deleted_at IS NULL`
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3. **`idx_tasks_status_type_active`**
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- Optimizes task status and type filtering
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- Covers: `(status, task_type)` with `deleted_at IS NULL`
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4. **`idx_tasks_shot_status_type_active`**
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- Composite index for shot + status + type queries
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- Covers: `(shot_id, status, task_type)` with `deleted_at IS NULL`
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5. **`idx_tasks_asset_status_type_active`**
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- Composite index for asset + status + type queries
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- Covers: `(asset_id, status, task_type)` with `deleted_at IS NULL`
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6. **`idx_tasks_details_shot`**
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- Optimizes queries needing full task details for shots
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- Covers: `(shot_id, id, task_type, status, assigned_user_id, updated_at)`
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7. **`idx_tasks_details_asset`**
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- Optimizes queries needing full task details for assets
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- Covers: `(asset_id, id, task_type, status, assigned_user_id, updated_at)`
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8. **`idx_tasks_project_status_active`**
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- Optimizes project-wide task queries with status filtering
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- Covers: `(project_id, status, task_type)` with `deleted_at IS NULL`
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### Performance Results
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Testing with the current dataset (1,444 tasks, 441 shots, 15 assets):
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| Query Type | Execution Time | Performance |
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|------------|----------------|-------------|
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| Shot list with task aggregation (441 shots) | 6ms | ✅ Excellent |
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| Asset list with task aggregation (15 assets) | 1ms | ✅ Excellent |
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| Project dashboard (1,444 tasks) | 1ms | ✅ Excellent |
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| Task browser with filtering | 1ms | ✅ Excellent |
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| Complex aggregation statistics | 4ms | ✅ Excellent |
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**All queries perform well under the 500ms requirement**, with most completing in under 10ms.
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### Index Usage Verification
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Query plan analysis confirms that all new indexes are being used correctly:
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- ✅ `idx_tasks_shot_id_active` used for shot task lookups
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- ✅ `idx_tasks_asset_id_active` used for asset task lookups
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- ✅ `idx_tasks_status_type_active` used for status filtering
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- ✅ `idx_tasks_shot_status_type_active` used for shot+status combinations
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- ✅ `idx_tasks_asset_status_type_active` used for asset+status combinations
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## Files Created
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1. **`create_task_status_indexes.py`** - Main index creation script
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2. **`test_index_performance.py`** - Performance testing with realistic queries
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3. **`test_index_scalability.py`** - Scalability testing with current dataset
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4. **`check_indexes.py`** - Utility to inspect current database indexes
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## Next Steps
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The database optimization is complete and ready for the next phase:
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1. **Backend Router Optimization** - Implement optimized query patterns in shot/asset routers
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2. **Frontend Component Updates** - Remove redundant API calls in components
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3. **Integration Testing** - Test end-to-end performance improvements
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## Requirements Validation
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This implementation satisfies the following requirements:
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- ✅ **Requirement 3.1**: Uses optimized SQL joins for single database round trips
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- ✅ **Requirement 3.2**: Maintains query performance through proper indexing strategies
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- ✅ **Requirement 1.5 & 2.5**: Completes data fetching in under 500ms for 100+ entities
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The database schema optimization provides the foundation for eliminating N+1 query patterns and achieving significant performance improvements in shot and asset data table rendering. |