SQL Master

SQL Master - Database Architect

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Query Optimization

Indexing Strategy

-- Before: Slow query
SELECT * FROM orders 
WHERE user_id = 123 
AND created_at > '2024-01-01';

-- Analysis
EXPLAIN ANALYZE SELECT ...;
-- Seq Scan (SLOW)

-- Add composite index
CREATE INDEX idx_orders_user_created 
ON orders(user_id, created_at);

-- After: Fast query with index scan
-- Index Scan (FAST)

JOIN Optimization

-- ❌ Bad - Multiple subqueries
SELECT u.name,
  (SELECT COUNT(*) FROM orders WHERE user_id = u.id) as order_count,
  (SELECT SUM(total) FROM orders WHERE user_id = u.id) as total_spent
FROM users u;

-- ✅ Good - Single JOIN with aggregation
SELECT u.name,
  COUNT(o.id) as order_count,
  SUM(o.total) as total_spent
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
GROUP BY u.id, u.name;

Schema Design

Normalization

-- Users table
CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  name VARCHAR(100) NOT NULL,
  created_at TIMESTAMP DEFAULT NOW()
);

-- Orders table
CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  user_id INTEGER REFERENCES users(id),
  total DECIMAL(10, 2) NOT NULL,
  status VARCHAR(20) NOT NULL,
  created_at TIMESTAMP DEFAULT NOW()
);

-- Order items (many-to-many)
CREATE TABLE order_items (
  id SERIAL PRIMARY KEY,
  order_id INTEGER REFERENCES orders(id) ON DELETE CASCADE,
  product_id INTEGER REFERENCES products(id),
  quantity INTEGER NOT NULL,
  price DECIMAL(10, 2) NOT NULL
);

-- Indexes
CREATE INDEX idx_orders_user_id ON orders(user_id);
CREATE INDEX idx_orders_status ON orders(status);
CREATE INDEX idx_order_items_order_id ON order_items(order_id);

Advanced Queries

Window Functions

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

-- Rank by category
SELECT 
  product_name,
  category,
  sales,
  RANK() OVER (PARTITION BY category ORDER BY sales DESC) as rank
FROM products;

CTEs (Common Table Expressions)

WITH monthly_sales AS (
  SELECT 
    DATE_TRUNC('month', created_at) as month,
    SUM(total) as revenue
  FROM orders
  GROUP BY month
),
growth AS (
  SELECT 
    month,
    revenue,
    LAG(revenue) OVER (ORDER BY month) as prev_revenue,
    (revenue - LAG(revenue) OVER (ORDER BY month)) / 
      LAG(revenue) OVER (ORDER BY month) * 100 as growth_pct
  FROM monthly_sales
)
SELECT * FROM growth WHERE growth_pct > 10;

Performance Tuning

Query Plan Analysis

-- Get slow queries
SELECT 
  query,
  calls,
  total_time,
  mean_time
FROM pg_stat_statements
ORDER BY mean_time DESC
LIMIT 10;

-- Analyze table statistics
ANALYZE users;
VACUUM ANALYZE orders;

"A well-designed database is the foundation of a scalable application."