Database Migration Patterns

Database Migration Patterns

Safe, reversible database schema changes for production systems.

When to Activate

• Creating or altering database tables
• Adding/removing columns or indexes
• Running data migrations (backfill, transform)
• Planning zero-downtime schema changes
• Setting up migration tooling for a new project

Core Principles

1. Every change is a migration — never alter production databases manually
2. Migrations are forward-only in production — rollbacks use new forward migrations
3. Schema and data migrations are separate — never mix DDL and DML in one migration
4. Test migrations against production-sized data — a migration that works on 100 rows may lock on 10M
5. Migrations are immutable once deployed — never edit a migration that has run in production

Migration Safety Checklist

Before applying any migration:

• Migration has both UP and DOWN (or is explicitly marked irreversible)
• No full table locks on large tables (use concurrent operations)
• New columns have defaults or are nullable (never add NOT NULL without default)
• Indexes created concurrently (not inline with CREATE TABLE for existing tables)
• Data backfill is a separate migration from schema change
• Tested against a copy of production data
• Rollback plan documented

PostgreSQL Patterns

Adding a Column Safely

-- GOOD: Nullable column, no lock
ALTER TABLE users ADD COLUMN avatar_url TEXT;

-- GOOD: Column with default (Postgres 11+ is instant, no rewrite)
ALTER TABLE users ADD COLUMN is_active BOOLEAN NOT NULL DEFAULT true;

-- BAD: NOT NULL without default on existing table (requires full rewrite)
ALTER TABLE users ADD COLUMN role TEXT NOT NULL;
-- This locks the table and rewrites every row

Adding an Index Without Downtime

-- BAD: Blocks writes on large tables
CREATE INDEX idx_users_email ON users (email);

-- GOOD: Non-blocking, allows concurrent writes
CREATE INDEX CONCURRENTLY idx_users_email ON users (email);

-- Note: CONCURRENTLY cannot run inside a transaction block
-- Most migration tools need special handling for this

Renaming a Column (Zero-Downtime)

Never rename directly in production. Use the expand-contract pattern:

-- Step 1: Add new column (migration 001)
ALTER TABLE users ADD COLUMN display_name TEXT;

-- Step 2: Backfill data (migration 002, data migration)
UPDATE users SET display_name = username WHERE display_name IS NULL;

-- Step 3: Update application code to read/write both columns
-- Deploy application changes

-- Step 4: Stop writing to old column, drop it (migration 003)
ALTER TABLE users DROP COLUMN username;

Removing a Column Safely

-- Step 1: Remove all application references to the column
-- Step 2: Deploy application without the column reference
-- Step 3: Drop column in next migration
ALTER TABLE orders DROP COLUMN legacy_status;

-- For Django: use SeparateDatabaseAndState to remove from model
-- without generating DROP COLUMN (then drop in next migration)

Large Data Migrations

-- BAD: Updates all rows in one transaction (locks table)
UPDATE users SET normalized_email = LOWER(email);

-- GOOD: Batch update with progress
DO $$
DECLARE
  batch_size INT := 10000;
  rows_updated INT;
BEGIN
  LOOP
    UPDATE users
    SET normalized_email = LOWER(email)
    WHERE id IN (
      SELECT id FROM users
      WHERE normalized_email IS NULL
      LIMIT batch_size
      FOR UPDATE SKIP LOCKED
    );
    GET DIAGNOSTICS rows_updated = ROW_COUNT;
    RAISE NOTICE 'Updated % rows', rows_updated;
    EXIT WHEN rows_updated = 0;
    COMMIT;
  END LOOP;
END $$;

Prisma (TypeScript/Node.js)

Workflow

# Create migration from schema changes
npx prisma migrate dev --name add_user_avatar

# Apply pending migrations in production
npx prisma migrate deploy

# Reset database (dev only)
npx prisma migrate reset

# Generate client after schema changes
npx prisma generate

Schema Example

model User {
  id        String   @id @default(cuid())
  email     String   @unique
  name      String?
  avatarUrl String?  @map("avatar_url")
  createdAt DateTime @default(now()) @map("created_at")
  updatedAt DateTime @updatedAt @map("updated_at")
  orders    Order[]

  @@map("users")
  @@index([email])
}

Custom SQL Migration

For operations Prisma cannot express (concurrent indexes, data backfills):

# Create empty migration, then edit the SQL manually
npx prisma migrate dev --create-only --name add_email_index

-- migrations/20240115_add_email_index/migration.sql
-- Prisma cannot generate CONCURRENTLY, so we write it manually
CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_users_email ON users (email);

Drizzle (TypeScript/Node.js)

Workflow

# Generate migration from schema changes
npx drizzle-kit generate

# Apply migrations
npx drizzle-kit migrate

# Push schema directly (dev only, no migration file)
npx drizzle-kit push

Schema Example

import { pgTable, text, timestamp, uuid, boolean } from "drizzle-orm/pg-core";

export const users = pgTable("users", {
  id: uuid("id").primaryKey().defaultRandom(),
  email: text("email").notNull().unique(),
  name: text("name"),
  isActive: boolean("is_active").notNull().default(true),
  createdAt: timestamp("created_at").notNull().defaultNow(),
  updatedAt: timestamp("updated_at").notNull().defaultNow(),
});

Kysely (TypeScript/Node.js)

Workflow (kysely-ctl)

# Initialize config file (kysely.config.ts)
kysely init

# Create a new migration file
kysely migrate make add_user_avatar

# Apply all pending migrations
kysely migrate latest

# Rollback last migration
kysely migrate down

# Show migration status
kysely migrate list

Migration File

// migrations/2024_01_15_001_create_user_profile.ts
import { type Kysely, sql } from 'kysely'

// IMPORTANT: Always use Kysely<any>, not your typed DB interface.
// Migrations are frozen in time and must not depend on current schema types.
export async function up(db: Kysely<any>): Promise<void> {
  await db.schema
    .createTable('user_profile')
    .addColumn('id', 'serial', (col) => col.primaryKey())
    .addColumn('email', 'varchar(255)', (col) => col.notNull().unique())
    .addColumn('avatar_url', 'text')
    .addColumn('created_at', 'timestamp', (col) =>
      col.defaultTo(sql`now()`).notNull()
    )
    .execute()

  await db.schema
    .createIndex('idx_user_profile_avatar')
    .on('user_profile')
    .column('avatar_url')
    .execute()
}

export async function down(db: Kysely<any>): Promise<void> {
  await db.schema.dropTable('user_profile').execute()
}

Programmatic Migrator

import { Migrator, FileMigrationProvider } from 'kysely'
import { promises as fs } from 'fs'
import * as path from 'path'
// ESM only — CJS can use __dirname directly
import { fileURLToPath } from 'url'
const migrationFolder = path.join(
  path.dirname(fileURLToPath(import.meta.url)),
  './migrations',
)

// `db` is your Kysely<any> database instance
const migrator = new Migrator({
  db,
  provider: new FileMigrationProvider({
    fs,
    path,
    migrationFolder,
  }),
  // WARNING: Only enable in development. Disables timestamp-ordering
  // validation, which can cause schema drift between environments.
  // allowUnorderedMigrations: true,
})

const { error, results } = await migrator.migrateToLatest()

results?.forEach((it) => {
  if (it.status === 'Success') {
    console.log(`migration "${it.migrationName}" executed successfully`)
  } else if (it.status === 'Error') {
    console.error(`failed to execute migration "${it.migrationName}"`)
  }
})

if (error) {
  console.error('migration failed', error)
  process.exit(1)
}

Django (Python)

Workflow

# Generate migration from model changes
python manage.py makemigrations

# Apply migrations
python manage.py migrate

# Show migration status
python manage.py showmigrations

# Generate empty migration for custom SQL
python manage.py makemigrations --empty app_name -n description

Data Migration

from django.db import migrations

def backfill_display_names(apps, schema_editor):
    User = apps.get_model("accounts", "User")
    batch_size = 5000
    users = User.objects.filter(display_name="")
    while users.exists():
        batch = list(users[:batch_size])
        for user in batch:
            user.display_name = user.username
        User.objects.bulk_update(batch, ["display_name"], batch_size=batch_size)

def reverse_backfill(apps, schema_editor):
    pass  # Data migration, no reverse needed

class Migration(migrations.Migration):
    dependencies = [("accounts", "0015_add_display_name")]

    operations = [
        migrations.RunPython(backfill_display_names, reverse_backfill),
    ]

SeparateDatabaseAndState

Remove a column from the Django model without dropping it from the database immediately:

class Migration(migrations.Migration):
    operations = [
        migrations.SeparateDatabaseAndState(
            state_operations=[
                migrations.RemoveField(model_name="user", name="legacy_field"),
            ],
            database_operations=[],  # Don't touch the DB yet
        ),
    ]

golang-migrate (Go)

Workflow

# Create migration pair
migrate create -ext sql -dir migrations -seq add_user_avatar

# Apply all pending migrations
migrate -path migrations -database "$DATABASE_URL" up

# Rollback last migration
migrate -path migrations -database "$DATABASE_URL" down 1

# Force version (fix dirty state)
migrate -path migrations -database "$DATABASE_URL" force VERSION

Migration Files

-- migrations/000003_add_user_avatar.up.sql
ALTER TABLE users ADD COLUMN avatar_url TEXT;
CREATE INDEX CONCURRENTLY idx_users_avatar ON users (avatar_url) WHERE avatar_url IS NOT NULL;

-- migrations/000003_add_user_avatar.down.sql
DROP INDEX IF EXISTS idx_users_avatar;
ALTER TABLE users DROP COLUMN IF EXISTS avatar_url;

Zero-Downtime Migration Strategy

For critical production changes, follow the expand-contract pattern:

Phase 1: EXPAND
  - Add new column/table (nullable or with default)
  - Deploy: app writes to BOTH old and new
  - Backfill existing data

Phase 2: MIGRATE
  - Deploy: app reads from NEW, writes to BOTH
  - Verify data consistency

Phase 3: CONTRACT
  - Deploy: app only uses NEW
  - Drop old column/table in separate migration

Timeline Example

Day 1: Migration adds new_status column (nullable)
Day 1: Deploy app v2 — writes to both status and new_status
Day 2: Run backfill migration for existing rows
Day 3: Deploy app v3 — reads from new_status only
Day 7: Migration drops old status column

Anti-Patterns

Anti-Pattern	Why It Fails	Better Approach
Manual SQL in production	No audit trail, unrepeatable	Always use migration files
Editing deployed migrations	Causes drift between environments	Create new migration instead
NOT NULL without default	Locks table, rewrites all rows	Add nullable, backfill, then add constraint
Inline index on large table	Blocks writes during build	CREATE INDEX CONCURRENTLY
Schema + data in one migration	Hard to rollback, long transactions	Separate migrations
Dropping column before removing code	Application errors on missing column	Remove code first, drop column next deploy