enes9103/my-claude-agents-skills/sql-optimization-patterns

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Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.

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npx vskill@latest install enes9103/my-claude-agents-skills/sql-optimization-patterns

SKILL.md

The same SKILL.md was published earlier as socialadsmentor/sam-agentic-harness/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_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 \*:

sql
-- 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:

sql
-- 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:

sql
-- 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
sql
-- 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

sql
-- 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;

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