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 sc
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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.
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.
Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.
Understanding EXPLAIN output is fundamental to optimization.
PostgreSQL EXPLAIN:
-- Basic explain
EXPLAIN SELECT * FROM users WHERE email = '[email protected]';
-- With actual execution stats
EXPLAIN ANALYZE
SELECT * FROM users WHERE email = '[email protected]';
-- 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:
Indexes are the most powerful optimization tool.
Index Types:
-- 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);
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) = '[email protected]';
-- 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) = '[email protected]';
-- Or store normalized data
SELECT * FROM users WHERE email = '[email protected]';
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 pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
-- 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;
LIKE '%abc' can't use index-- 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;