Parallel Agent Competition in Git Worktrees

AGENTHUB OPTIMIZATION CHALLENGE: Parallel Query Tuning

PROJECT SETUP
  Git worktrees spawned: 3 isolated branches (optimize-v1, optimize-v2, optimize-v3)
  Query baseline: SELECT * FROM orders WHERE status='pending' AND created_at > NOW()-INTERVAL 30 DAY
  Current performance: 2.8 seconds, 512MB memory, 1.2M rows scanned
  Target: Sub-1 second, minimal memory, <100k rows scanned
  Database: PostgreSQL 14
  Test dataset: Real production snapshot (safe read-only copy)

APPROACH 1: INDEX + QUERY RESTRUCTURE (Branch: optimize-v1)
  Strategy: Add composite index on status + created_at; rewrite query with index hints
  Changes:
    CREATE INDEX idx_orders_status_created ON orders(status, created_at DESC)
    Rewrite: SELECT id, status FROM orders WHERE status='pending' AND created_at > NOW()-INTERVAL 30 DAY
    Remove: Unnecessary SELECT * → only fetch needed columns
  Result: 0.34 seconds, 45MB memory, 8.2k rows scanned
  Trade-off: Index build time 120 seconds; ongoing write overhead minimal

APPROACH 2: MATERIALIZED VIEW + CACHE (Branch: optimize-v2)
  Strategy: Pre-aggregate pending orders in materialized view, refresh every 1 hour
  Changes:
    CREATE MATERIALIZED VIEW pending_orders_30d AS SELECT id, status, created_at FROM orders WHERE status='pending' AND created_at > NOW()-INTERVAL 30 DAY
    Query: SELECT * FROM pending_orders_30d
    Scheduled refresh: Every 60 minutes via cron
  Result: 0.08 seconds (cache hit), 12MB memory, 0 index scans
  Trade-off: Data staleness (max 60 min old); refresh cost amortized over 60 queries

APPROACH 3: SHARDING BY DATE PARTITION (Branch: optimize-v3)
  Strategy: Partition table by created_at month; direct query to relevant partition only
  Changes:
    ALTER TABLE orders PARTITION BY RANGE (YEAR(created_at), MONTH(created_at))
    Query planner automatically prunes non-matching partitions
    SELECT * FROM orders_202607 WHERE status='pending'
  Result: 0.42 seconds, 38MB memory, 150k rows scanned
  Trade-off: More complex schema management; benefits scale with larger date ranges

RESULTS RANKING
  Winner: Approach 2 (Materialized View)
    Speed: 0.08s vs 0.34s vs 0.42s
    Memory: 12MB (99% reduction)
    Trade-off acceptable: 60-min staleness is tolerable for pending orders
    Implementation risk: Low; standard pattern
    
  Runner-up: Approach 1 (Index)
    Speed: 0.34s (12x improvement)
    No staleness: Real-time data
    Risk: Slightly higher maintenance overhead
    Best if materialized view refresh SLA breached
    
  Third: Approach 3 (Partitioning)
    Speed: 0.42s improvement
    Complex schema: Ongoing maintenance cost
    Best for: Multi-year historical queries, not current-pending-only queries

RECOMMENDATION
  Ship Approach 2 (materialized view) to production
  Implement Approach 1 (index) as fallback if refresh fails
  Monitor staleness complaints; if frequent, switch to Approach 1
  
  Merged branch: optimize-v2 into main
  Closed branches: optimize-v1 (preserved for reference), optimize-v3 (archived)
  
  Estimated production speedup: 34x faster query execution, 98% memory reduction