PostgreSQL Query Optimization

PostgreSQL is known as the "world's most advanced open source relational database," and for good reason. It offers incredibly powerful tools for optimization.

Understanding the Query Planner

Before optimizing, you must understand *how* Postgres executes your query. The EXPLAIN ANALYZE command runs the query and returns the execution plan with timing information.

EXPLAIN ANALYZE 
SELECT * FROM orders WHERE user_id = 500;

This output will tell you if it used an Index Scan (good) or a Seq Scan (bad, scanned the whole table). It also shows the "cost," which is an arbitrary number representing the effort required.

Advanced Indexing

Beyond standard B-tree indexes, Postgres supports specialized index types:

Example: If you frequently query only "active" users, why index the inactive ones?

-- Only index users where status is 'active'
CREATE INDEX idx_active_users ON users(email) 
WHERE status = 'active';

Optimization Techniques

1. Avoid SELECT *

Selecting all columns fetches more data than needed, preventing "Index Only Scans."

-- Bad
SELECT * FROM users WHERE id = 1;

-- Good
SELECT id, email, name FROM users WHERE id = 1;

2. Use CTEs (Common Table Expressions) for readability

CTEs (WITH clauses) make complex queries readable.

WITH monthly_orders AS (
  SELECT user_id, SUM(total) as spent
  FROM orders
  WHERE created_at > NOW() - INTERVAL '1 month'
  GROUP BY user_id
)
SELECT u.name, mo.spent
FROM users u
JOIN monthly_orders mo ON u.id = mo.user_id
WHERE mo.spent > 1000;

3. Connection Pooling

Postgres has a significant overhead for each new connection. In high-traffic apps, you *must* use a connection pooler like PgBouncer. It keeps a pool of open connections and reuses them, allowing you to handle thousands of concurrent requests with only a few hundred actual database connections.