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Course/Module 10/Topic 4 of 4Advanced

Performance & Optimization

Optimize the e-commerce database for production — indexing strategies, query analysis, and performance tuning for high-traffic applications.

45 minBy Priygop TeamLast updated: Feb 2026

Performance Tuning

  • EXPLAIN ANALYZE: EXPLAIN ANALYZE SELECT * FROM products WHERE category_id = 5 — shows execution plan, actual time, and rows scanned. Look for 'Seq Scan' (slow) vs 'Index Scan' (fast). If Seq Scan on large table, add an index
  • Composite Indexes: CREATE INDEX idx_orders_user_status ON orders(user_id, status) — covers queries filtering by both columns. Column order matters: leftmost column can be used alone, but not the rightmost alone
  • Partial Indexes: CREATE INDEX idx_active_products ON products(category_id) WHERE is_active = true — only indexes active products. Smaller index, faster lookups for the common case (browsing active products)
  • Query Optimization: Replace SELECT * with specific columns (reduces I/O). Use EXISTS instead of IN for subqueries (short-circuits on first match). Avoid functions on indexed columns in WHERE (invalidates index usage)
  • Connection Pooling: Don't open a new database connection per request — use PgBouncer or application-level pooling. Opening a PostgreSQL connection takes 50-100ms. Pooled connections: <1ms. Critical at scale
  • Partitioning: CREATE TABLE orders_2024 PARTITION OF orders FOR VALUES FROM ('2024-01-01') TO ('2025-01-01') — split large tables by date range. Queries on recent orders only scan the current partition, not years of history

Try It Yourself: SQL Query Builder

Try It Yourself: SQL Query BuilderHTML
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