PostgreSQL Optimization for Production SaaS: Indexes, Queries, and Performance

Index Strategy

Indexes are crucial for query performance, but too many indexes slow down writes. Use EXPLAIN ANALYZE to identify missing indexes, and create composite indexes for multi-column queries.

-- Analyze query performance
EXPLAIN ANALYZE 
SELECT * FROM users 
WHERE email = 'user@example.com' 
AND status = 'active';

-- Create composite index
CREATE INDEX idx_users_email_status 
ON users(email, status);

-- Partial index for filtered queries
CREATE INDEX idx_active_users 
ON users(email) 
WHERE status = 'active';

Query Optimization

Avoid N+1 queries by using JOINs and batch loading. Use LIMIT and OFFSET carefully—for large datasets, consider cursor-based pagination instead. Monitor slow query logs to identify bottlenecks.

Connection Pooling

PostgreSQL connections are expensive. Use PgBouncer or connection poolers to manage connections efficiently. Configure pool size based on your workload—typically 2-3x your CPU cores.

Partitioning

For tables with millions of rows, consider partitioning. Range partitioning works well for time-series data, while list partitioning suits categorical data. This improves query performance and makes maintenance easier.