PostgreSQL Performance Optimization

PostgreSQL Performance Optimization

Overview

Master PostgreSQL performance optimization with modern techniques for query tuning, indexing, and AI/ML workloads. With 55% of Postgres developers adopting AI tools in 2024, understanding pgvector and performance optimization is critical for building scalable data-intensive applications.

When to Use This Skill

• Slow queries requiring optimization (>100ms response time)
• Designing database schemas for high-performance applications
• Implementing vector similarity search for AI/ML features
• Scaling PostgreSQL for high-concurrency workloads
• Migrating from NoSQL to PostgreSQL for better consistency
• Optimizing ORMs (SQLAlchemy, Django ORM) for production

Core Principles

1. EXPLAIN ANALYZE is Your Best Friend

-- ALWAYS use EXPLAIN ANALYZE for slow queries
EXPLAIN (ANALYZE, BUFFERS, VERBOSE, COSTS, TIMING)
SELECT u.name, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.created_at > NOW() - INTERVAL '30 days'
GROUP BY u.id, u.name
ORDER BY order_count DESC
LIMIT 100;

-- Read output top-to-bottom, focus on:
-- 1. Seq Scan (bad for large tables) vs Index Scan (good)
-- 2. Actual time vs Planning time
-- 3. Rows estimates vs actual rows
-- 4. Buffers (disk I/O indicators)

Key Metrics:

• Planning Time: How long query planner took (<10ms ideal)
• Execution Time: Actual query runtime (<100ms for OLTP)
• Rows: Estimated vs actual (mismatches indicate stale statistics)
• Buffers: Shared hits (good), reads (disk I/O, slow)

2. Indexing Strategy

-- B-Tree Index (default, most common)
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_orders_user_created ON orders(user_id, created_at);

-- Partial Index (smaller, faster for filtered queries)
CREATE INDEX idx_active_users ON users(email)
WHERE is_active = true AND deleted_at IS NULL;

-- Covering Index (includes extra columns to avoid table lookups)
CREATE INDEX idx_orders_covering ON orders(user_id, status)
INCLUDE (created_at, total_amount);

-- GIN Index (for full-text search, JSONB, arrays)
CREATE INDEX idx_products_search ON products
USING GIN (to_tsvector('english', name || ' ' || description));

CREATE INDEX idx_tags_gin ON posts USING GIN(tags);

-- GiST Index (for geometric data, ranges, full-text)
CREATE INDEX idx_locations_gist ON stores
USING GIST (location);

-- BRIN Index (block range, time-series data)
CREATE INDEX idx_logs_created ON logs USING BRIN(created_at);