Database Design

Schema Design Principles

Normalization Guidelines

sql
-- 1NF: Atomic values, no repeating groups
-- 2NF: No partial dependencies on composite keys
-- 3NF: No transitive dependencies

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

-- Addresses table (separate entity)
CREATE TABLE addresses (
  id SERIAL PRIMARY KEY,
  user_id INTEGER REFERENCES users(id) ON DELETE CASCADE,
  street VARCHAR(255),
  city VARCHAR(100),
  country VARCHAR(100),
  is_primary BOOLEAN DEFAULT false
);

Denormalization for Performance

sql
-- When read performance matters more than write consistency
CREATE TABLE order_summaries (
  id SERIAL PRIMARY KEY,
  order_id INTEGER REFERENCES orders(id),
  customer_name VARCHAR(255),  -- Denormalized from customers
  total_amount DECIMAL(10,2),
  item_count INTEGER,
  last_updated TIMESTAMPTZ DEFAULT NOW()
);

Index Design

Common Index Patterns

sql
-- B-tree (default) for equality and range queries
CREATE INDEX idx_users_email ON users(email);

-- Composite index (order matters!)
CREATE INDEX idx_orders_user_date ON orders(user_id, created_at DESC);

-- Partial index for specific conditions
CREATE INDEX idx_active_users ON users(email) WHERE deleted_at IS NULL;

-- GIN index for array/JSONB columns
CREATE INDEX idx_posts_tags ON posts USING GIN(tags);

-- Covering index (includes additional columns)
CREATE INDEX idx_orders_covering ON orders(user_id) INCLUDE (total, status);

Index Analysis

sql
-- Check index usage
SELECT
  schemaname, tablename, indexname,
  idx_scan, idx_tup_read, idx_tup_fetch
FROM pg_stat_user_indexes
ORDER BY idx_scan DESC;

-- Find missing indexes
SELECT
  relname, seq_scan, seq_tup_read,
  idx_scan, idx_tup_fetch
FROM pg_stat_user_tables
WHERE seq_scan > idx_scan
ORDER BY seq_tup_read DESC;

Migration Patterns

Safe Migration Template

sql
-- Always use transactions
BEGIN;

-- Add column with default (non-blocking in PG 11+)
ALTER TABLE users ADD COLUMN status VARCHAR(20) DEFAULT 'active';

-- Create index concurrently (doesn't lock table)
CREATE INDEX CONCURRENTLY idx_users_status ON users(status);

-- Backfill data in batches
UPDATE users SET status = 'active' WHERE status IS NULL AND id BETWEEN 1 AND 10000;

COMMIT;

Zero-Downtime Migrations

1. Add new column (nullable)
2. Deploy code that writes to both columns
3. Backfill old data
4. Deploy code that reads from new column
5. Remove old column

Query Optimization

EXPLAIN Analysis

sql
-- Always use EXPLAIN ANALYZE
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT * FROM orders WHERE user_id = 123 AND status = 'pending';

-- Key metrics to watch:
-- - Seq Scan vs Index Scan
-- - Actual rows vs Estimated rows
-- - Buffers: shared hit vs read

Common Optimizations

sql
-- Use EXISTS instead of IN for large sets
SELECT * FROM users u
WHERE EXISTS (SELECT 1 FROM orders o WHERE o.user_id = u.id);

-- Pagination with keyset (cursor) instead of OFFSET
SELECT * FROM posts
WHERE created_at < '2024-01-01'
ORDER BY created_at DESC
LIMIT 20;

-- Use CTEs for complex queries
WITH active_users AS (
  SELECT id FROM users WHERE last_login > NOW() - INTERVAL '30 days'
)
SELECT * FROM orders WHERE user_id IN (SELECT id FROM active_users);

Constraints & Data Integrity

sql
-- Primary key
ALTER TABLE users ADD PRIMARY KEY (id);

-- Foreign key with cascade
ALTER TABLE orders ADD CONSTRAINT fk_orders_user
  FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE;

-- Check constraint
ALTER TABLE products ADD CONSTRAINT chk_price_positive
  CHECK (price >= 0);

-- Unique constraint
ALTER TABLE users ADD CONSTRAINT uniq_users_email UNIQUE (email);

-- Exclusion constraint (no overlapping ranges)
ALTER TABLE reservations ADD CONSTRAINT excl_no_overlap
  EXCLUDE USING gist (room_id WITH =, tsrange(start_time, end_time) WITH &&);

Best Practices

• Use UUIDs for public-facing IDs, SERIAL/BIGSERIAL for internal
• Always add created_at and updated_at timestamps
• Use soft deletes (deleted_at) for important data
• Design for eventual consistency in distributed systems
• Document schema decisions in migration files
• Test migrations on production-size data before deploying