Database Migrations

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 enprojectnments.
  // 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