Database Optimization: PostgreSQL at Scale

PostgreSQL is one of the most powerful open-source databases available, but raw power means nothing without proper optimization. As your application grows from hundreds to millions of rows, the difference between a well-tuned and poorly-tuned database becomes the difference between millisecond responses and timeout errors.

Indexing is your first line of defense. Every query that appears in your slow query log should be analyzed with EXPLAIN ANALYZE. Look for sequential scans on large tables — these are almost always candidates for an index. But be strategic: every index speeds up reads at the cost of slower writes. Composite indexes should match your most common query patterns, with the most selective column first.

Connection pooling is critical at scale. PostgreSQL creates a new process for every connection, and each process consumes memory. Without a connection pooler like PgBouncer, your database will exhaust its memory long before it exhausts its computational capacity. Set your pool size based on your available RAM, not your expected concurrent users.

Partitioning large tables by date or category can dramatically improve query performance. When PostgreSQL knows a query only needs data from the last 30 days, it can skip scanning the previous 11 months entirely. Time-based partitioning is the most common pattern for event logs, analytics data, and transaction histories.

Finally, invest in monitoring. Track your query execution times, connection count, cache hit ratio, and disk I/O continuously. PostgreSQL's built-in statistics views (pg_stat_statements, pg_stat_user_tables) provide invaluable insights. A cache hit ratio below 99% on a read-heavy workload signals that you need more shared_buffers or that your working set has outgrown your available memory.