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.