sql-optimization-patterns

Imported from wshobson/agents (plugins/developer-essentials/skills/sql-optimization-patterns/SKILL.md) via skills.sh. Install upstream with npx skills add wshobson/agents --skill sql-optimization-patterns. Copyright stays with the author.

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:

-- 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

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

-- Composite index (order matters!)
CREATE INDEX idx_orders_user_status ON orders(user_id, status);

-- Partial index (index subset of rows)
CREATE INDEX idx_active_users ON users(email)
WHERE status = 'active';

-- Expression index
CREATE INDEX idx_users_lower_email ON users(LOWER(email));

-- Covering index (include additional columns)
CREATE INDEX idx_users_email_covering ON users(email)
INCLUDE (name, created_at);

-- Full-text search index
CREATE INDEX idx_posts_search ON posts
USING GIN(to_tsvector('english', title || ' ' || body));

-- JSONB index
CREATE INDEX idx_metadata ON events USING GIN(metadata);

3. Query Optimization Patterns

Avoid SELECT *:

-- Bad: Fetches unnecessary columns
SELECT * FROM users WHERE id = 123;

-- Good: Fetch only what you need
SELECT id, email, name FROM users WHERE id = 123;

Use WHERE Clause Efficiently:

-- Bad: Function prevents index usage
SELECT * FROM users WHERE LOWER(email) = 'user@example.com';

-- Good: Create functional index or use exact match
CREATE INDEX idx_users_email_lower ON users(LOWER(email));
-- Then:
SELECT * FROM users WHERE LOWER(email) = 'user@example.com';

-- Or store normalized data
SELECT * FROM users WHERE email = 'user@example.com';

Optimize JOINs:

-- Bad: Cartesian product then filter
SELECT u.name, o.total
FROM users u, orders o
WHERE u.id = o.user_id AND u.created_at > '2024-01-01';

-- Good: Filter before join
SELECT u.name, o.total
FROM users u
JOIN orders o ON u.id = o.user_id
WHERE u.created_at > '2024-01-01';

-- Better: Filter both tables
SELECT u.name, o.total
FROM (SELECT * FROM users WHERE created_at > '2024-01-01') u
JOIN orders o ON u.id = o.user_id;

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

1. Index Selectively: Too many indexes slow down writes
2. Monitor Query Performance: Use slow query logs
3. Keep Statistics Updated: Run ANALYZE regularly
4. Use Appropriate Data Types: Smaller types = better performance
5. Normalize Thoughtfully: Balance normalization vs performance
6. Cache Frequently Accessed Data: Use application-level caching
7. Connection Pooling: Reuse database connections
8. Regular Maintenance: VACUUM, ANALYZE, rebuild indexes

-- Update statistics
ANALYZE users;
ANALYZE VERBOSE orders;

-- Vacuum (PostgreSQL)
VACUUM ANALYZE users;
VACUUM FULL users;  -- Reclaim space (locks table)

-- Reindex
REINDEX INDEX idx_users_email;
REINDEX TABLE users;

Common Pitfalls

• Over-Indexing: Each index slows down INSERT/UPDATE/DELETE
• Unused Indexes: Waste space and slow writes
• Missing Indexes: Slow queries, full table scans
• Implicit Type Conversion: Prevents index usage
• OR Conditions: Can't use indexes efficiently
• LIKE with Leading Wildcard: LIKE '%abc' can't use index
• Function in WHERE: Prevents index usage unless functional index exists

Monitoring Queries

-- Find slow queries (PostgreSQL)
SELECT query, calls, total_time, mean_time
FROM pg_stat_statements
ORDER BY mean_time DESC
LIMIT 10;

-- Find missing indexes (PostgreSQL)
SELECT
    schemaname,
    tablename,
    seq_scan,
    seq_tup_read,
    idx_scan,
    seq_tup_read / seq_scan AS avg_seq_tup_read
FROM pg_stat_user_tables
WHERE seq_scan > 0
ORDER BY seq_tup_read DESC
LIMIT 10;

-- Find unused indexes (PostgreSQL)
SELECT
    schemaname,
    tablename,
    indexname,
    idx_scan,
    idx_tup_read,
    idx_tup_fetch
FROM pg_stat_user_indexes
WHERE idx_scan = 0
ORDER BY pg_relation_size(indexrelid) DESC;