postgresql

Production PostgreSQL expertise from Amazon/Netflix engineers. Use when designing database schemas, writing complex SQL queries, optimizing query performance with EXPLAIN ANALYZE, creating indexes, se

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PostgreSQL - Production Database Expertise

Purpose: Master PostgreSQL for building scalable, reliable data storage systems

Agent: Amazon Cloud Architect / Netflix Backend Architect Use When: Designing database schemas, optimizing queries, or building data-intensive applications

Overview

PostgreSQL is the world's most advanced open-source relational database, offering ACID compliance, complex queries, and extensibility.

Core Features:

  • ACID compliance (reliability)
  • Advanced data types (JSON, arrays, hstore)
  • Full-text search
  • Geospatial data (PostGIS)
  • Transactions and concurrency
  • Extensible (custom functions, types)

Core Concepts

1. Data Types

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  name TEXT NOT NULL,
  age INTEGER CHECK (age >= 0),
  balance DECIMAL(10, 2) DEFAULT 0.00,
  is_active BOOLEAN DEFAULT true,
  metadata JSONB,
  tags TEXT[],
  created_at TIMESTAMP DEFAULT NOW(),
  updated_at TIMESTAMP DEFAULT NOW()
);

2. Indexes for Performance

-- B-tree index (default, good for = and range queries)
CREATE INDEX idx_users_email ON users(email);

-- Partial index (index subset of rows)
CREATE INDEX idx_active_users ON users(email)
WHERE is_active = true;

-- Composite index (multiple columns)
CREATE INDEX idx_users_name_email ON users(name, email);

-- GIN index (for JSONB, arrays, full-text search)
CREATE INDEX idx_users_metadata ON users USING GIN(metadata);
CREATE INDEX idx_users_tags ON users USING GIN(tags);

-- Check indexes
\d users

3. Constraints

CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  user_id INTEGER NOT NULL REFERENCES users(id) ON DELETE CASCADE,
  total DECIMAL(10, 2) NOT NULL CHECK (total >= 0),
  status VARCHAR(20) NOT NULL CHECK (status IN ('pending', 'paid', 'shipped')),
  created_at TIMESTAMP DEFAULT NOW(),
  CONSTRAINT unique_user_order UNIQUE (user_id, created_at)
);

4. Relationships

-- One-to-Many
CREATE TABLE posts (
  id SERIAL PRIMARY KEY,
  user_id INTEGER REFERENCES users(id),
  title TEXT NOT NULL,
  content TEXT
);

-- Many-to-Many
CREATE TABLE posts_tags (
  post_id INTEGER REFERENCES posts(id),
  tag_id INTEGER REFERENCES tags(id),
  PRIMARY KEY (post_id, tag_id)
);

-- Join query
SELECT posts.*, users.name as author_name
FROM posts
INNER JOIN users ON posts.user_id = users.id;

Query Optimization

1. Use EXPLAIN ANALYZE

-- Check query performance
EXPLAIN ANALYZE
SELECT * FROM users WHERE email = 'john@example.com';

-- Look for:
-- - Seq Scan (bad) → add index
-- - Index Scan (good)
-- - Execution time

2. Avoid N+1 Queries

-- ❌ Bad: N+1 problem (multiple queries)
SELECT * FROM users; -- 1 query
-- Then for each user:
SELECT * FROM posts WHERE user_id = ?; -- N queries

-- ✅ Good: Single query with JOIN
SELECT users.*,
       json_agg(posts) as posts
FROM users
LEFT JOIN posts ON posts.user_id = users.id
GROUP BY users.id;

3. Use Proper Indexes

-- Slow without index
SELECT * FROM orders WHERE created_at > '2024-01-01';

-- Fast with index
CREATE INDEX idx_orders_created_at ON orders(created_at);

4. Limit Results

-- Pagination
SELECT * FROM users
ORDER BY created_at DESC
LIMIT 20 OFFSET 40; -- Page 3 (20 per page)

-- Better: Use cursor-based pagination
SELECT * FROM users
WHERE id > 100 -- Last ID from previous page
ORDER BY id
LIMIT 20;

Advanced Features

1. JSONB Operations

-- Insert JSONB data
INSERT INTO users (email, name, metadata)
VALUES ('john@example.com', 'John', '{"age": 30, "city": "NYC"}');

-- Query JSONB
SELECT * FROM users
WHERE metadata->>'city' = 'NYC';

-- Update JSONB
UPDATE users
SET metadata = metadata || '{"premium": true}'
WHERE id = 1;

-- JSONB index for fast queries
CREATE INDEX idx_users_metadata_city
ON users((metadata->>'city'));

2. Array Operations

-- Insert array
INSERT INTO users (email, name, tags)
VALUES ('jane@example.com', 'Jane', ARRAY['admin', 'developer']);

-- Query array
SELECT * FROM users WHERE 'admin' = ANY(tags);
SELECT * FROM users WHERE tags @> ARRAY['admin']; -- Contains

-- Array functions
SELECT * FROM users WHERE array_length(tags, 1) > 2;

3. Full-Text Search

-- Add tsvector column
ALTER TABLE posts ADD COLUMN search_vector tsvector;

-- Create index
CREATE INDEX idx_posts_search
ON posts USING GIN(search_vector);

-- Update search vector
UPDATE posts
SET search_vector = to_tsvector('english', title || ' ' || content);

-- Search
SELECT * FROM posts
WHERE search_vector @@ to_tsquery('english', 'postgresql & performance');

-- With ranking
SELECT *, ts_rank(search_vector, query) AS rank
FROM posts, to_tsquery('english', 'postgresql') query
WHERE search_vector @@ query
ORDER BY rank DESC;

4. Transactions

BEGIN;

UPDATE accounts SET balance = balance - 100 WHERE id = 1;
UPDATE accounts SET balance = balance + 100 WHERE id = 2;

-- If everything is OK
COMMIT;

-- If error occurred
-- ROLLBACK;

5. Window Functions

-- Row number
SELECT
  name,
  salary,
  ROW_NUMBER() OVER (ORDER BY salary DESC) as rank
FROM employees;

-- Running total
SELECT
  date,
  amount,
  SUM(amount) OVER (ORDER BY date) as running_total
FROM sales;

-- Partition by category
SELECT
  category,
  product,
  price,
  AVG(price) OVER (PARTITION BY category) as avg_category_price
FROM products;

Best Practices

1. Use Connection Pooling

// With Prisma
const prisma = new PrismaClient({
  datasources: {
    db: {
      url: process.env.DATABASE_URL
    }
  },
  // Connection pool settings
  log: ['query', 'error', 'warn'],
})

// With node-postgres
import { Pool } from 'pg'

const pool = new Pool({
  host: 'localhost',
  port: 5432,
  database: 'mydb',
  user: 'postgres',
  password: 'password',
  max: 20, // Max clients in pool
  idleTimeoutMillis: 30000,
  connectionTimeoutMillis: 2000,
})

const result = await pool.query('SELECT * FROM users WHERE id = $1', [1])

2. Use Prepared Statements (Prevent SQL Injection)

-- ❌ Bad: SQL injection vulnerability
SELECT * FROM users WHERE email = 'user_input';

-- ✅ Good: Parameterized query
SELECT * FROM users WHERE email = $1;

// Node.js example
const email = req.body.email

// ✅ Safe from SQL injection
const result = await pool.query(
  'SELECT * FROM users WHERE email = $1',
  [email]
)

3. Regular VACUUM and ANALYZE

-- Reclaim storage and update statistics
VACUUM ANALYZE users;

-- Auto-vacuum (configure in postgresql.conf)
autovacuum = on

4. Monitor Performance

-- Long-running queries
SELECT pid, now() - query_start as duration, query
FROM pg_stat_activity
WHERE state = 'active'
ORDER BY duration DESC;

-- Kill long query
SELECT pg_terminate_backend(pid);

-- Table sizes
SELECT
  schemaname,
  tablename,
  pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) AS size
FROM pg_tables
WHERE schemaname = 'public'
ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;

Common Patterns

Soft Delete

ALTER TABLE users ADD COLUMN deleted_at TIMESTAMP;

-- Soft delete
UPDATE users SET deleted_at = NOW() WHERE id = 1;

-- Query only active records
SELECT * FROM users WHERE deleted_at IS NULL;

-- Create view for active records
CREATE VIEW active_users AS
SELECT * FROM users WHERE deleted_at IS NULL;

Audit Trail

CREATE TABLE audit_log (
  id SERIAL PRIMARY KEY,
  table_name VARCHAR(50),
  record_id INTEGER,
  action VARCHAR(20), -- INSERT, UPDATE, DELETE
  old_data JSONB,
  new_data JSONB,
  user_id INTEGER,
  created_at TIMESTAMP DEFAULT NOW()
);

-- Trigger function
CREATE OR REPLACE FUNCTION audit_trigger_func()
RETURNS TRIGGER AS $$
BEGIN
  INSERT INTO audit_log (table_name, record_id, action, old_data, new_data)
  VALUES (
    TG_TABLE_NAME,
    NEW.id,
    TG_OP,
    row_to_json(OLD),
    row_to_json(NEW)
  );
  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

-- Attach trigger
CREATE TRIGGER users_audit_trigger
AFTER INSERT OR UPDATE OR DELETE ON users
FOR EACH ROW EXECUTE FUNCTION audit_trigger_func();

UUID Primary Keys

CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

CREATE TABLE products (
  id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
  name TEXT NOT NULL,
  price DECIMAL(10, 2)
);

Migrations

-- migrations/001_create_users.sql
CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  name TEXT NOT NULL
);

-- migrations/002_add_created_at.sql
ALTER TABLE users ADD COLUMN created_at TIMESTAMP DEFAULT NOW();

-- migrations/003_add_index.sql
CREATE INDEX idx_users_email ON users(email);

With Prisma:

npx prisma migrate dev --name add_user_table
npx prisma migrate deploy # Production

Backup & Restore

# Backup
pg_dump -U postgres mydb > backup.sql
pg_dump -U postgres -Fc mydb > backup.dump  # Compressed

# Restore
psql -U postgres mydb < backup.sql
pg_restore -U postgres -d mydb backup.dump

# Backup to remote
pg_dump -U postgres mydb | gzip | ssh user@remote 'cat > backup.sql.gz'

Remember: PostgreSQL is powerful but needs tuning. Use indexes wisely, avoid N+1 queries, and monitor performance regularly.

Created: 2026-02-04 Maintained By: Amazon Cloud Architect Version: PostgreSQL 14+

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