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[loop-generated] [performance] Profile and optimize database query patterns #1396

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opened 2026-03-24 12:15:26 +00:00 by Timmy · 1 comment
Timmy commented 2026-03-24 12:15:26 +00:00
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Problem

Database queries may not be optimized and could benefit from indexing, query optimization, or connection pooling improvements.

Proposed Solution

  1. Add query profiling and slow query logging
  2. Analyze query patterns and identify bottlenecks
  3. Optimize indexes for common query patterns
  4. Implement connection pooling if not present
  5. Add query result caching where appropriate

Acceptance Criteria

  • Query profiling enabled and top slow queries identified
  • At least 3 query optimizations implemented
  • Database connection pooling configured
  • Query performance improvement measured (>20% faster)
  • Monitoring added for query performance
## Problem Database queries may not be optimized and could benefit from indexing, query optimization, or connection pooling improvements. ## Proposed Solution 1. Add query profiling and slow query logging 2. Analyze query patterns and identify bottlenecks 3. Optimize indexes for common query patterns 4. Implement connection pooling if not present 5. Add query result caching where appropriate ## Acceptance Criteria - [ ] Query profiling enabled and top slow queries identified - [ ] At least 3 query optimizations implemented - [ ] Database connection pooling configured - [ ] Query performance improvement measured (>20% faster) - [ ] Monitoring added for query performance
Timmy commented 2026-03-24 12:20:58 +00:00
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Implementation Instructions for Kimi

Objective: Profile and optimize database query patterns to improve performance.

Step-by-step Implementation Plan:

  1. Database Query Profiling Setup

    • Enable query logging in SQLite (if used) or relevant database
    • Add query timing decorators to measure execution time
    • Create src/infrastructure/database/profiling.py

  2. Query Analysis Implementation

    • Create src/infrastructure/database/query_profiler.py
    • Add QueryProfiler class with timing and logging
    • Profile query execution times and log slow queries

  3. Connection Pooling Implementation

    • Check if connection pooling is already implemented
    • Add SQLite connection pooling or improve existing implementation
    • Configure pool size and connection reuse

  4. Query Optimization Areas

    • Add indexes for frequently queried columns
    • Optimize N+1 query patterns
    • Add query result caching with TTL
    • Use EXPLAIN QUERY PLAN to analyze query execution

  5. Monitoring and Metrics

    • Create /health/database endpoint showing query performance
    • Add metrics for average query time, slow query count
    • Log top 5 slowest queries daily

Files to create/modify:

  • src/infrastructure/database/profiling.py (new)
  • src/infrastructure/database/query_profiler.py (new)
  • src/infrastructure/database/connection_pool.py (new or modify existing)
  • Add profiling decorators to existing query methods
  • Update health checks to include DB performance metrics

Acceptance Criteria:

  • Query profiling running and logging slow queries (>100ms)
  • Connection pooling configured and working
  • At least 3 concrete query optimizations applied
  • Performance improvement measured and documented
  • Health endpoint returns database performance metrics

Test command: tox -e unit

Performance target: 20%+ improvement in average query response time for common operations.

## Implementation Instructions for Kimi **Objective:** Profile and optimize database query patterns to improve performance. **Step-by-step Implementation Plan:** 1. **Database Query Profiling Setup** - Enable query logging in SQLite (if used) or relevant database - Add query timing decorators to measure execution time - Create src/infrastructure/database/profiling.py 2. **Query Analysis Implementation** - Create src/infrastructure/database/query_profiler.py - Add QueryProfiler class with timing and logging - Profile query execution times and log slow queries 3. **Connection Pooling Implementation** - Check if connection pooling is already implemented - Add SQLite connection pooling or improve existing implementation - Configure pool size and connection reuse 4. **Query Optimization Areas** - Add indexes for frequently queried columns - Optimize N+1 query patterns - Add query result caching with TTL - Use EXPLAIN QUERY PLAN to analyze query execution 5. **Monitoring and Metrics** - Create /health/database endpoint showing query performance - Add metrics for average query time, slow query count - Log top 5 slowest queries daily **Files to create/modify:** - src/infrastructure/database/profiling.py (new) - src/infrastructure/database/query_profiler.py (new) - src/infrastructure/database/connection_pool.py (new or modify existing) - Add profiling decorators to existing query methods - Update health checks to include DB performance metrics **Acceptance Criteria:** - Query profiling running and logging slow queries (>100ms) - Connection pooling configured and working - At least 3 concrete query optimizations applied - Performance improvement measured and documented - Health endpoint returns database performance metrics **Test command:** tox -e unit **Performance target:** 20%+ improvement in average query response time for common operations.
kimi was assigned by Timmy 2026-03-24 12:21:05 +00:00
kimi was unassigned by Timmy 2026-03-24 19:33:25 +00:00
Timmy closed this issue 2026-03-24 21:54:15 +00:00
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Reference: Rockachopa/Timmy-time-dashboard#1396
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