Optimizing Database Performance in Production

Database performance issues are responsible for a significant portion of production incidents. Here are the techniques I’ve used to diagnose and fix performance problems in real applications.

Start with Monitoring

Before optimizing anything, establish a baseline. Use your database’s built-in slow query log to identify the worst offenders. In PostgreSQL:

-- Enable slow query logging
SET log_min_duration_statement = 1000; -- Log queries taking > 1 second

Index Strategically

Indexes speed up reads but slow down writes. The key is identifying the right indexes for your access patterns.

Common Index Patterns

• B-tree indexes: Best for equality and range queries
• Composite indexes: Index multiple columns for multi-condition queries
• Partial indexes: Index only rows matching a condition

-- Composite index for common query pattern
CREATE INDEX idx_orders_user_status 
ON orders(user_id, status) 
WHERE status != 'cancelled';

Avoid N+1 Queries

The N+1 query problem is one of the most common performance killers. It happens when loading a list and then making separate queries for related data.

// Bad: N+1 queries
const posts = await db.posts.findMany();
for (const post of posts) {
  const author = await db.users.findById(post.authorId); // N queries!
}

// Good: Single query with join
const posts = await db.posts.findMany({
  include: { author: true }
});

Use Connection Pooling

Database connections are expensive to create. Always use a connection pool in production applications.

Conclusion

Database optimization is an ongoing process. Regular monitoring, thoughtful indexing, and avoiding common anti-patterns will keep your application performing well as it scales.