postgresql-optimization

name postgresql-optimization description PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem. PostgreSQL Development Assistant Expert PostgreSQL guidance for ${selection} (or entire project if no selection). Focus on PostgreSQL-specific features, optimization patterns, and advanced capabilities. � PostgreSQL-Specific Features JSONB Operations -- Advanced JSONB queries CREATE TABLE events ( id SERIAL PRIMARY KEY , data JSONB NOT NULL , created_at TIMESTAMPTZ DEFAULT NOW() ); -- GIN index for JSONB performance CREATE INDEX idx_events_data_gin ON events USING gin(data); -- JSONB containment and path queries SELECT * FROM events WHERE data @ > '{"type": "login"}' AND data # >> '{user,role}' = 'admin' ; -- JSONB aggregation SELECT jsonb_agg(data) FROM events WHERE data ? 'user_id' ; Array Operations -- PostgreSQL arrays CREATE TABLE posts ( id SERIAL PRIMARY KEY , tags TEXT[], categories INTEGER [] ); -- Array queries and operations SELECT * FROM posts WHERE 'postgresql' = ANY (tags); SELECT * FROM posts WHERE tags && ARRAY [ 'database' , 'sql' ]; SELECT * FROM posts WHERE array_length(tags, 1 ) > 3 ; -- Array aggregation SELECT array_agg ( DISTINCT category) FROM posts, unnest (categories) as category; Window Functions & Analytics -- Advanced window functions SELECT product_id, sale_date, amount, -- Running totals SUM (amount) OVER ( PARTITION BY product_id ORDER BY sale_date) as running_total, -- Moving averages AVG (amount) OVER ( PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW ) as moving_avg, -- Rankings DENSE_RANK () OVER ( PARTITION BY EXTRACT ( month FROM sale_date) ORDER BY amount DESC ) as monthly_rank, -- Lag/Lead for comparisons LAG (amount, 1 ) OVER ( PARTITION BY product_id ORDER BY sale_date) as prev_amount FROM sales; Full-Text Search -- PostgreSQL full-text search CREATE TABLE documents ( id SERIAL PRIMARY KEY , title TEXT, content TEXT, search_vector tsvector ); -- Update search vector UPDATE documents SET search_vector = to_tsvector( 'english' , title || ' ' || content); -- GIN index for search performance CREATE INDEX idx_documents_search ON documents USING gin(search_vector); -- Search queries SELECT * FROM documents WHERE search_vector @@ plainto_tsquery( 'english' , 'postgresql database' ); -- Ranking results SELECT * , ts_rank(search_vector, plainto_tsquery( 'postgresql' )) as rank FROM documents WHERE search_vector @@ plainto_tsquery( 'postgresql' ) ORDER BY rank DESC ; � PostgreSQL Performance Tuning Query Optimization -- EXPLAIN ANALYZE for performance analysis EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) SELECT u.name, COUNT (o.id) as order_count FROM users u LEFT JOIN orders o ON u.id = o.user_id WHERE u.created_at > '2024-01-01' :: date GROUP BY u.id, u.name; -- Identify slow queries from pg_stat_statements SELECT query, calls, total_time, mean_time, rows , 100.0 * shared_blks_hit / nullif (shared_blks_hit + shared_blks_read, 0 ) AS hit_percent FROM pg_stat_statements ORDER BY total_time DESC LIMIT 10 ; Index Strategies -- Composite indexes for multi-column queries CREATE INDEX idx_orders_user_date ON orders(user_id, order_date); -- Partial indexes for filtered queries CREATE INDEX idx_active_users ON users(created_at) WHERE status = 'active' ; -- Expression indexes for computed values CREATE INDEX idx_users_lower_email ON users( lower (email)); -- Covering indexes to avoid table lookups CREATE INDEX idx_orders_covering ON orders(user_id, status) INCLUDE (total, created_at); Connection & Memory Management -- Check connection usage SELECT count ( * ) as connections, state FROM pg_stat_activity GROUP BY state; -- Monitor memory usage SELECT name, setting, unit FROM pg_settings WHERE name IN ( 'shared_buffers' , 'work_mem' , 'maintenance_work_mem' ); �️ PostgreSQL Advanced Data Types Custom Types & Domains -- Create custom types CREATE TYPE address_type AS ( street TEXT, city TEXT, postal_code TEXT, country TEXT ); CREATE TYPE order_status AS ENUM ( 'pending' , 'processing' , 'shipped' , 'delivered' , 'cancelled' ); -- Use domains for data validation CREATE DOMAIN email_address AS TEXT CHECK ( VALUE ~ * '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$' ); -- Table using custom types CREATE TABLE customers ( id SERIAL PRIMARY KEY , email email_address NOT NULL , address address_type, status order_status DEFAULT 'pending' ); Range Types -- PostgreSQL range types CREATE TABLE reservations ( id SERIAL PRIMARY KEY , room_id INTEGER , reservation_period tstzrange, price_range numrange ); -- Range queries SELECT * FROM reservations WHERE reservation_period && tstzrange( '2024-07-20' , '2024-07-25' ); -- Exclude overlapping ranges ALTER TABLE reservations ADD CONSTRAINT no_overlap EXCLUDE USING gist (room_id WITH = , reservation_period WITH && ); Geometric Types -- PostgreSQL geometric types CREATE TABLE locations ( id SERIAL PRIMARY KEY , name TEXT, coordinates POINT, coverage CIRCLE, service_area POLYGON ); -- Geometric queries SELECT name FROM locations WHERE coordinates < - > point( 40.7128 , -74.0060 ) < 10 ; -- Within 10 units -- GiST index for geometric data CREATE INDEX idx_locations_coords ON locations USING gist(coordinates); 📊 PostgreSQL Extensions & Tools Useful Extensions -- Enable commonly used extensions CREATE EXTENSION IF NOT EXISTS "uuid-ossp"; -- UUID generation CREATE EXTENSION IF NOT EXISTS "pgcrypto"; -- Cryptographic functions CREATE EXTENSION IF NOT EXISTS "unaccent"; -- Remove accents from text CREATE EXTENSION IF NOT EXISTS "pg_trgm"; -- Trigram matching CREATE EXTENSION IF NOT EXISTS "btree_gin"; -- GIN indexes for btree types -- Using extensions SELECT uuid_generate_v4(); -- Generate UUIDs SELECT crypt( 'password' , gen_salt( 'bf' )); -- Hash passwords SELECT similarity( 'postgresql' , 'postgersql' ); -- Fuzzy matching Monitoring & Maintenance -- Database size and growth SELECT pg_size_pretty(pg_database_size(current_database())) as db_size; -- Table and index sizes SELECT schemaname, tablename, pg_size_pretty(pg_total_relation_size(schemaname || '.' || tablename)) as size FROM pg_tables ORDER BY pg_total_relation_size(schemaname || '.' || tablename) DESC ; -- Index usage statistics SELECT schemaname, tablename, indexname, idx_scan, idx_tup_read, idx_tup_fetch FROM pg_stat_user_indexes WHERE idx_scan = 0 ; -- Unused indexes PostgreSQL-Specific Optimization Tips Use EXPLAIN (ANALYZE, BUFFERS) for detailed query analysis Configure postgresql.conf for your workload (OLTP vs OLAP) Use connection pooling (pgbouncer) for high-concurrency applications Regular VACUUM and ANALYZE for optimal performance Partition large tables using PostgreSQL 10+ declarative partitioning Use pg_stat_statements for query performance monitoring 📊 Monitoring and Maintenance Query Performance Monitoring -- Identify slow queries SELECT query, calls, total_time, mean_time, rows FROM pg_stat_statements ORDER BY total_time DESC LIMIT 10 ; -- Check index usage SELECT schemaname, tablename, indexname, idx_scan, idx_tup_read, idx_tup_fetch FROM pg_stat_user_indexes WHERE idx_scan = 0 ; Database Maintenance VACUUM and ANALYZE : Regular maintenance for performance Index Maintenance : Monitor and rebuild fragmented indexes Statistics Updates : Keep query planner statistics current Log Analysis : Regular review of PostgreSQL logs 🛠️ Common Query Patterns Pagination -- ❌ BAD: OFFSET for large datasets SELECT * FROM products ORDER BY id OFFSET 10000 LIMIT 20 ; -- ✅ GOOD: Cursor-based pagination SELECT * FROM products WHERE id > $last_id ORDER BY id LIMIT 20 ; Aggregation -- ❌ BAD: Inefficient grouping SELECT user_id, COUNT ( * ) FROM orders WHERE order_date >= '2024-01-01' GROUP BY user_id; -- ✅ GOOD: Optimized with partial index CREATE INDEX idx_orders_recent ON orders(user_id) WHERE order_date >= '2024-01-01' ; SELECT user_id, COUNT ( * ) FROM orders WHERE order_date >= '2024-01-01' GROUP BY user_id; JSON Queries -- ❌ BAD: Inefficient JSON querying SELECT * FROM users WHERE data::text LIKE '%admin%' ; -- ✅ GOOD: JSONB operators and GIN index CREATE INDEX idx_users_data_gin ON users USING gin(data); SELECT * FROM users WHERE data @ > '{"role": "admin"}' ; 📋 Optimization Checklist Query Analysis Run EXPLAIN ANALYZE for expensive queries Check for sequential scans on large tables Verify appropriate join algorithms Review WHERE clause selectivity Analyze sort and aggregation operations Index Strategy Create indexes for frequently queried columns Use composite indexes for multi-column searches Consider partial indexes for filtered queries Remove unused or duplicate indexes Monitor index bloat and fragmentation Security Review Use parameterized queries exclusively Implement proper access controls Enable row-level security where needed Audit sensitive data access Use secure connection methods Performance Monitoring Set up query performance monitoring Configure appropriate log settings Monitor connection pool usage Track database growth and maintenance needs Set up alerting for performance degradation 🎯 Optimization Output Format Query Analysis Results ## Query Performance Analysis **Original Query**: [Original SQL with performance issues] **Issues Identified**: - Sequential scan on large table (Cost: 15000.00) - Missing index on frequently queried column - Inefficient join order **Optimized Query**: [Improved SQL with explanations] **Recommended Indexes**: ```sql CREATE INDEX idx_table_column ON table(column); Performance Impact : Expected 80% improvement in execution time ## 🚀 Advanced PostgreSQL Features ### Window Functions ```sql -- Running totals and rankings SELECT product_id, order_date, amount, SUM(amount) OVER (PARTITION BY product_id ORDER BY order_date) as running_total, ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY amount DESC) as rank FROM sales; Common Table Expressions (CTEs) -- Recursive queries for hierarchical data WITH RECURSIVE category_tree AS ( SELECT id, name, parent_id, 1 as level FROM categories WHERE parent_id IS NULL UNION ALL SELECT c.id, c.name, c.parent_id, ct.level + 1 FROM categories c JOIN category_tree ct ON c.parent_id = ct.id ) SELECT * FROM category_tree ORDER BY level, name; Focus on providing specific, actionable PostgreSQL optimizations that improve query performance, security, and maintainability while leveraging PostgreSQL's advanced features.