PostgreSQL Optimization Guide for Web Applications

PostgreSQL is the most popular database for modern web applications, powering everything from small MVPs to large-scale SaaS platforms. As your application grows, poorly optimized database queries become the primary bottleneck affecting user experience. Slow queries cause pages to load slowly, APIs to time out, and users to leave. Understanding basic PostgreSQL optimization can dramatically improve your application's performance without expensive infrastructure upgrades.

The EXPLAIN command is your most important tool for understanding query performance. Run `EXPLAIN ANALYZE` before any slow query to see the execution plan PostgreSQL uses. The output shows you whether indexes are being used, how many rows are scanned, and where time is spent. Look for sequential scans on large tables, which indicate a missing index. The actual execution time shown by ANALYZE tells you exactly how long each step takes, helping you identify the bottleneck.

Indexes are the single most impactful optimization for most applications. Create indexes on columns used in WHERE clauses, JOIN conditions, and ORDER BY statements. A composite index on multiple columns can serve queries that filter on any prefix of those columns. However, indexes slow down writes because they must be updated with every insert and update. Use partial indexes for queries that filter on a specific condition. Monitor your index usage with `pg_stat_user_indexes` and remove unused indexes.

Each PostgreSQL connection consumes server memory, and opening new connections is expensive. Without connection pooling, a traffic spike can exhaust your database connections and crash your application. Use a connection pooler like PgBouncer or the built-in pooling in your ORM. Supabase includes PgBouncer by default through its connection pooling feature. Set your pool size based on your database server's resources, typically keeping it well below the maximum connection limit.

Select only the columns you need instead of using SELECT *. Use LIMIT and pagination for queries that could return many rows. Avoid N+1 query problems by using JOINs or batch loading instead of looping queries. Cache frequently accessed read data using Redis or application-level caching. For complex reporting queries, consider creating materialized views that pre-compute results and refresh on a schedule.

PostgreSQL requires regular maintenance for optimal performance. The VACUUM process reclaims storage from deleted and updated rows. ANALYZE updates the query planner's statistics, helping it choose better execution plans. Modern PostgreSQL runs autovacuum by default, but heavily updated tables may need more aggressive vacuum settings. Monitor table and index bloat over time. Regular backups are essential, not just for disaster recovery but also for the ability to test optimizations safely.

Database optimization is both an art and a science that requires understanding your specific query patterns and data distribution. AI-generated code often produces inefficient queries that work fine with small datasets but collapse under production load. SpringCode profiles your PostgreSQL performance, identifies slow queries, adds appropriate indexes, and implements connection pooling to ensure your database can handle growth.