sql-optimization-patterns

# SQL Optimization Patterns

Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.

## When to Use This Skill

- Debugging slow-running queries
- Designing performant database schemas
- Optimizing application response times
- Reducing database load and costs
- Improving scalability for growing datasets
- Analyzing EXPLAIN query plans
- Implementing efficient indexes
- Resolving N+1 query problems

## Core Concepts

### 1. Query Execution Plans (EXPLAIN)

Understanding EXPLAIN output is fundamental to optimization.

**PostgreSQL EXPLAIN:**

```sql
-- Basic explain
EXPLAIN SELECT * FROM users WHERE email = 'user@example.com';

-- With actual execution stats
EXPLAIN ANALYZE
SELECT * FROM users WHERE email = 'user@example.com';

-- Verbose output with more details
EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
SELECT u.*, o.order_total
FROM users u
JOIN orders o ON u.id = o.user_id
WHERE u.created_at > NOW() - INTERVAL '30 days';
```

**Key Metrics to Watch:**

- **Seq Scan**: Full table scan (usually slow for large tables)
- **Index Scan**: Using index (good)
- **Index Only Scan**: Using index without touching table (best)
- **Nested Loop**: Join method (okay for small datasets)
- **Hash Join**: Join method (good for larger datasets)
- **Merge Join**: Join method (good for sorted data)
- **Cost**: Estimated query cost (lower is better)
- **Rows**: Estimated rows returned
- **Actual Time**: Real execution time

### 2. Index Strategies

Indexes are the most powerful optimization tool.

**Index Types:**

- **B-Tree**: Default, good for equality and range queries
- **Hash**: Only for equality (=) comparisons
- **GIN**: Full-text search, array queries, JSONB
- **GiST**: Geometric data, full-text search
- **BRIN**: Block Range INdex for very large tables with correlation

```sql
-- Standard B-Tree index
CREATE INDEX idx_users_email ON users(email);

-- Composite index (order matters!)
CREATE INDEX idx_o