{"kind":"Skill","metadata":{"namespace":"community","name":"postgresql-optimization","version":"0.1.0"},"spec":{"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.","files":{"SKILL.md":"---\nname: postgresql-optimization\ndescription: '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.'\n---\n\n# PostgreSQL Development Assistant\n\nExpert PostgreSQL guidance for ${selection} (or entire project if no selection). Focus on PostgreSQL-specific features, optimization patterns, and advanced capabilities.\n\n## � PostgreSQL-Specific Features\n\n### JSONB Operations\n```sql\n-- Advanced JSONB queries\nCREATE TABLE events (\n    id SERIAL PRIMARY KEY,\n    data JSONB NOT NULL,\n    created_at TIMESTAMPTZ DEFAULT NOW()\n);\n\n-- GIN index for JSONB performance\nCREATE INDEX idx_events_data_gin ON events USING gin(data);\n\n-- JSONB containment and path queries\nSELECT * FROM events \nWHERE data @\u003e '{\"type\": \"login\"}'\n  AND data #\u003e\u003e '{user,role}' = 'admin';\n\n-- JSONB aggregation\nSELECT jsonb_agg(data) FROM events WHERE data ? 'user_id';\n```\n\n### Array Operations\n```sql\n-- PostgreSQL arrays\nCREATE TABLE posts (\n    id SERIAL PRIMARY KEY,\n    tags TEXT[],\n    categories INTEGER[]\n);\n\n-- Array queries and operations\nSELECT * FROM posts WHERE 'postgresql' = ANY(tags);\nSELECT * FROM posts WHERE tags \u0026\u0026 ARRAY['database', 'sql'];\nSELECT * FROM posts WHERE array_length(tags, 1) \u003e 3;\n\n-- Array aggregation\nSELECT array_agg(DISTINCT category) FROM posts, unnest(categories) as category;\n```\n\n### Window Functions \u0026 Analytics\n```sql\n-- Advanced window functions\nSELECT \n    product_id,\n    sale_date,\n    amount,\n    -- Running totals\n    SUM(amount) OVER (PARTITION BY product_id ORDER BY sale_date) as running_total,\n    -- Moving averages\n    AVG(amount) OVER (PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) as moving_avg,\n    -- Rankings\n    DENSE_RANK() OVER (PARTITION BY EXTRACT(month FROM sale_date) ORDER BY amount DESC) as monthly_rank,\n    -- Lag/Lead for comparisons\n    LAG(amount, 1) OVER (PARTITION BY product_id ORDER BY sale_date) as prev_amount\nFROM sales;\n```\n\n### Full-Text Search\n```sql\n-- PostgreSQL full-text search\nCREATE TABLE documents (\n    id SERIAL PRIMARY KEY,\n    title TEXT,\n    content TEXT,\n    search_vector tsvector\n);\n\n-- Update search vector\nUPDATE documents \nSET search_vector = to_tsvector('english', title || ' ' || content);\n\n-- GIN index for search performance\nCREATE INDEX idx_documents_search ON documents USING gin(search_vector);\n\n-- Search queries\nSELECT * FROM documents \nWHERE search_vector @@ plainto_tsquery('english', 'postgresql database');\n\n-- Ranking results\nSELECT *, ts_rank(search_vector, plainto_tsquery('postgresql')) as rank\nFROM documents \nWHERE search_vector @@ plainto_tsquery('postgresql')\nORDER BY rank DESC;\n```\n\n## � PostgreSQL Performance Tuning\n\n### Query Optimization\n```sql\n-- EXPLAIN ANALYZE for performance analysis\nEXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) \nSELECT u.name, COUNT(o.id) as order_count\nFROM users u\nLEFT JOIN orders o ON u.id = o.user_id\nWHERE u.created_at \u003e '2024-01-01'::date\nGROUP BY u.id, u.name;\n\n-- Identify slow queries from pg_stat_statements\nSELECT query, calls, total_time, mean_time, rows,\n       100.0 * shared_blks_hit / nullif(shared_blks_hit + shared_blks_read, 0) AS hit_percent\nFROM pg_stat_statements \nORDER BY total_time DESC \nLIMIT 10;\n```\n\n### Index Strategies\n```sql\n-- Composite indexes for multi-column queries\nCREATE INDEX idx_orders_user_date ON orders(user_id, order_date);\n\n-- Partial indexes for filtered queries\nCREATE INDEX idx_active_users ON users(created_at) WHERE status = 'active';\n\n-- Expression indexes for computed values\nCREATE INDEX idx_users_lower_email ON users(lower(email));\n\n-- Covering indexes to avoid table lookups\nCREATE INDEX idx_orders_covering ON orders(user_id, status) INCLUDE (total, created_at);\n```\n\n### Connection \u0026 Memory Management\n```sql\n-- Check connection usage\nSELECT count(*) as connections, state \nFROM pg_stat_activity \nGROUP BY state;\n\n-- Monitor memory usage\nSELECT name, setting, unit \nFROM pg_settings \nWHERE name IN ('shared_buffers', 'work_mem', 'maintenance_work_mem');\n```\n\n## �️ PostgreSQL Advanced Data Types\n\n### Custom Types \u0026 Domains\n```sql\n-- Create custom types\nCREATE TYPE address_type AS (\n    street TEXT,\n    city TEXT,\n    postal_code TEXT,\n    country TEXT\n);\n\nCREATE TYPE order_status AS ENUM ('pending', 'processing', 'shipped', 'delivered', 'cancelled');\n\n-- Use domains for data validation\nCREATE DOMAIN email_address AS TEXT \nCHECK (VALUE ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}$');\n\n-- Table using custom types\nCREATE TABLE customers (\n    id SERIAL PRIMARY KEY,\n    email email_address NOT NULL,\n    address address_type,\n    status order_status DEFAULT 'pending'\n);\n```\n\n### Range Types\n```sql\n-- PostgreSQL range types\nCREATE TABLE reservations (\n    id SERIAL PRIMARY KEY,\n    room_id INTEGER,\n    reservation_period tstzrange,\n    price_range numrange\n);\n\n-- Range queries\nSELECT * FROM reservations \nWHERE reservation_period \u0026\u0026 tstzrange('2024-07-20', '2024-07-25');\n\n-- Exclude overlapping ranges\nALTER TABLE reservations \nADD CONSTRAINT no_overlap \nEXCLUDE USING gist (room_id WITH =, reservation_period WITH \u0026\u0026);\n```\n\n### Geometric Types\n```sql\n-- PostgreSQL geometric types\nCREATE TABLE locations (\n    id SERIAL PRIMARY KEY,\n    name TEXT,\n    coordinates POINT,\n    coverage CIRCLE,\n    service_area POLYGON\n);\n\n-- Geometric queries\nSELECT name FROM locations \nWHERE coordinates \u003c-\u003e point(40.7128, -74.0060) \u003c 10; -- Within 10 units\n\n-- GiST index for geometric data\nCREATE INDEX idx_locations_coords ON locations USING gist(coordinates);\n```\n\n## 📊 PostgreSQL Extensions \u0026 Tools\n\n### Useful Extensions\n```sql\n-- Enable commonly used extensions\nCREATE EXTENSION IF NOT EXISTS \"uuid-ossp\";    -- UUID generation\nCREATE EXTENSION IF NOT EXISTS \"pgcrypto\";     -- Cryptographic functions\nCREATE EXTENSION IF NOT EXISTS \"unaccent\";     -- Remove accents from text\nCREATE EXTENSION IF NOT EXISTS \"pg_trgm\";      -- Trigram matching\nCREATE EXTENSION IF NOT EXISTS \"btree_gin\";    -- GIN indexes for btree types\n\n-- Using extensions\nSELECT uuid_generate_v4();                     -- Generate UUIDs\nSELECT crypt('password', gen_salt('bf'));      -- Hash passwords\nSELECT similarity('postgresql', 'postgersql'); -- Fuzzy matching\n```\n\n### Monitoring \u0026 Maintenance\n```sql\n-- Database size and growth\nSELECT pg_size_pretty(pg_database_size(current_database())) as db_size;\n\n-- Table and index sizes\nSELECT schemaname, tablename,\n       pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) as size\nFROM pg_tables \nORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;\n\n-- Index usage statistics\nSELECT schemaname, tablename, indexname, idx_scan, idx_tup_read, idx_tup_fetch\nFROM pg_stat_user_indexes \nWHERE idx_scan = 0;  -- Unused indexes\n```\n\n### PostgreSQL-Specific Optimization Tips\n- **Use EXPLAIN (ANALYZE, BUFFERS)** for detailed query analysis\n- **Configure postgresql.conf** for your workload (OLTP vs OLAP)\n- **Use connection pooling** (pgbouncer) for high-concurrency applications\n- **Regular VACUUM and ANALYZE** for optimal performance\n- **Partition large tables** using PostgreSQL 10+ declarative partitioning\n- **Use pg_stat_statements** for query performance monitoring\n\n## 📊 Monitoring and Maintenance\n\n### Query Performance Monitoring\n```sql\n-- Identify slow queries\nSELECT query, calls, total_time, mean_time, rows\nFROM pg_stat_statements \nORDER BY total_time DESC \nLIMIT 10;\n\n-- Check index usage\nSELECT schemaname, tablename, indexname, idx_scan, idx_tup_read, idx_tup_fetch\nFROM pg_stat_user_indexes \nWHERE idx_scan = 0;\n```\n\n### Database Maintenance\n- **VACUUM and ANALYZE**: Regular maintenance for performance\n- **Index Maintenance**: Monitor and rebuild fragmented indexes\n- **Statistics Updates**: Keep query planner statistics current\n- **Log Analysis**: Regular review of PostgreSQL logs\n\n## 🛠️ Common Query Patterns\n\n### Pagination\n```sql\n-- ❌ BAD: OFFSET for large datasets\nSELECT * FROM products ORDER BY id OFFSET 10000 LIMIT 20;\n\n-- ✅ GOOD: Cursor-based pagination\nSELECT * FROM products \nWHERE id \u003e $last_id \nORDER BY id \nLIMIT 20;\n```\n\n### Aggregation\n```sql\n-- ❌ BAD: Inefficient grouping\nSELECT user_id, COUNT(*) \nFROM orders \nWHERE order_date \u003e= '2024-01-01' \nGROUP BY user_id;\n\n-- ✅ GOOD: Optimized with partial index\nCREATE INDEX idx_orders_recent ON orders(user_id) \nWHERE order_date \u003e= '2024-01-01';\n\nSELECT user_id, COUNT(*) \nFROM orders \nWHERE order_date \u003e= '2024-01-01' \nGROUP BY user_id;\n```\n\n### JSON Queries\n```sql\n-- ❌ BAD: Inefficient JSON querying\nSELECT * FROM users WHERE data::text LIKE '%admin%';\n\n-- ✅ GOOD: JSONB operators and GIN index\nCREATE INDEX idx_users_data_gin ON users USING gin(data);\n\nSELECT * FROM users WHERE data @\u003e '{\"role\": \"admin\"}';\n```\n\n## 📋 Optimization Checklist\n\n### Query Analysis\n- [ ] Run EXPLAIN ANALYZE for expensive queries\n- [ ] Check for sequential scans on large tables\n- [ ] Verify appropriate join algorithms\n- [ ] Review WHERE clause selectivity\n- [ ] Analyze sort and aggregation operations\n\n### Index Strategy\n- [ ] Create indexes for frequently queried columns\n- [ ] Use composite indexes for multi-column searches\n- [ ] Consider partial indexes for filtered queries\n- [ ] Remove unused or duplicate indexes\n- [ ] Monitor index bloat and fragmentation\n\n### Security Review\n- [ ] Use parameterized queries exclusively\n- [ ] Implement proper access controls\n- [ ] Enable row-level security where needed\n- [ ] Audit sensitive data access\n- [ ] Use secure connection methods\n\n### Performance Monitoring\n- [ ] Set up query performance monitoring\n- [ ] Configure appropriate log settings\n- [ ] Monitor connection pool usage\n- [ ] Track database growth and maintenance needs\n- [ ] Set up alerting for performance degradation\n\n## 🎯 Optimization Output Format\n\n### Query Analysis Results\n```\n## Query Performance Analysis\n\n**Original Query**:\n[Original SQL with performance issues]\n\n**Issues Identified**:\n- Sequential scan on large table (Cost: 15000.00)\n- Missing index on frequently queried column\n- Inefficient join order\n\n**Optimized Query**:\n[Improved SQL with explanations]\n\n**Recommended Indexes**:\n```sql\nCREATE INDEX idx_table_column ON table(column);\n```\n\n**Performance Impact**: Expected 80% improvement in execution time\n```\n\n## 🚀 Advanced PostgreSQL Features\n\n### Window Functions\n```sql\n-- Running totals and rankings\nSELECT \n    product_id,\n    order_date,\n    amount,\n    SUM(amount) OVER (PARTITION BY product_id ORDER BY order_date) as running_total,\n    ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY amount DESC) as rank\nFROM sales;\n```\n\n### Common Table Expressions (CTEs)\n```sql\n-- Recursive queries for hierarchical data\nWITH RECURSIVE category_tree AS (\n    SELECT id, name, parent_id, 1 as level\n    FROM categories \n    WHERE parent_id IS NULL\n    \n    UNION ALL\n    \n    SELECT c.id, c.name, c.parent_id, ct.level + 1\n    FROM categories c\n    JOIN category_tree ct ON c.parent_id = ct.id\n)\nSELECT * FROM category_tree ORDER BY level, name;\n```\n\nFocus on providing specific, actionable PostgreSQL optimizations that improve query performance, security, and maintainability while leveraging PostgreSQL's advanced features.\n"},"import":{"commit_sha":"541b7819d8c3545c6df122491af4fa1eae415779","imported_at":"2026-05-18T20:05:35Z","license_text":"MIT License\n\nCopyright GitHub, Inc.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.","owner":"github","repo":"github/awesome-copilot","source_url":"https://github.com/github/awesome-copilot/tree/541b7819d8c3545c6df122491af4fa1eae415779/plugins/database-data-management/skills/postgresql-optimization"}},"content_hash":[125,192,159,7,30,77,213,181,149,250,54,85,139,177,162,71,221,128,140,90,192,114,254,34,221,74,255,190,39,21,170,55],"trust_level":"unsigned","yanked":false}