Database Migration Plan Skill

Produce a complete, safe database migration plan for a schema change. A migration plan is not just the SQL — it is a coordinated sequence of steps that ensures the application stays available, data stays consistent, and every step can be rolled back independently.

The expand/contract pattern is the default approach: expand the schema to support both old and new states, migrate the application, then contract to remove the old state. Never combine schema changes and data backfills in a single migration that runs during deployment.

Required Inputs

Ask for these if not already provided:

• Current schema state — the DDL or description of the table(s) as they are now
• Target schema state — the DDL or description of what the table(s) should look like after migration
• Migration reason — why this change is being made (new feature, performance fix, normalization, compliance)
• Database engine — PostgreSQL, MySQL, SQLite, CockroachDB, etc.
• Estimated data volume — approximate number of rows in affected tables
• Deployment constraints — is any downtime allowed? What is the expected traffic level during migration? Are there multiple app instances running?
• Rollback window — how long after deploy can the team roll back before the migration becomes irreversible?

Output Format

Database Migration Plan: [Migration Name]

Service: [Name] | Team: [Team name] Author: [Name] | Reviewed by: [Name / DBA] Date: [Date] | Target deploy date: [Date] Database engine: [PostgreSQL X.X / MySQL X.X] Ticket: [JIRA-XXX]

1. Migration Overview

What is changing: [1–2 sentences: the specific schema change — e.g. "Adding a non-nullable organisation_id column to the users table and backfilling it from the accounts table."]

Why: [1–2 sentences: the business or technical reason driving the change.]

Migration type: [Additive only / Additive + backfill / Column rename / Column type change / Table restructure / Index change]

Zero-downtime: [Yes — using expand/contract / No — requires maintenance window — state duration]

Estimated migration duration:

• Expand phase: [~X minutes]
• Data backfill: [~X minutes/hours — based on X rows at Y rows/second]
• Contract phase: [~X minutes after app version deployed]

2. Backward Compatibility Analysis

Before writing a single line of SQL, assess whether each change is backward compatible with the currently deployed application code.

Summary: [e.g. "This migration requires the expand/contract pattern across 3 deployment phases because steps 3 and 4 are not backward compatible."]

3. Expand/Contract Phases

Phase Overview

Phase 1 — EXPAND
  Deploy migration: add new column (nullable), create new indexes
  Old app: continues to work (ignores new column)
  New app: not yet deployed
  Duration: [~X min] | Rollback: trivial — drop new column

       │
       ▼

Phase 2 — BACKFILL + DUAL-WRITE
  Deploy app update: writes to both old and new columns
  Run backfill: populate new column for existing rows
  Validate: confirm 100% of rows have non-null new column
  Duration: [~X hours depending on data volume]
  Rollback: deploy previous app version; new column is still nullable

       │
       ▼

Phase 3 — ENFORCE + SWITCH
  Deploy migration: add NOT NULL constraint, drop old column/index
  Deploy app update: reads only from new column
  Duration: [~X min] | Rollback: requires forward-fix (constraint must be dropped first)

       │
       ▼

Phase 4 — CONTRACT (optional cleanup)
  Deploy migration: drop deprecated columns, rename if needed
  Final state matches target schema
  Rollback: not recommended — contract changes are destructive

Phase 1 — Expand Schema

Goal: Add the new column and structures without breaking the existing application. Deploy order: Run migration first, then (optionally) deploy app. Application state: Old app running; no app changes required yet.

-- Migration: 001_add_org_id_to_users.sql
BEGIN;

-- Add nullable column (safe — old app ignores it)
ALTER TABLE users
    ADD COLUMN org_id UUID NULL
        REFERENCES organisations(id) ON DELETE RESTRICT;

-- Add index NOW, not in Phase 3 — building index on large table during Phase 3 is risky
CREATE INDEX CONCURRENTLY users_org_id_idx ON users (org_id);

-- Note: CONCURRENTLY does not lock the table; safe on live traffic
-- Note: Cannot run CONCURRENTLY inside a transaction block; run separately if needed

COMMIT;

Validation after Phase 1:

-- Confirm column exists and is nullable
SELECT column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_name = 'users' AND column_name = 'org_id';
-- Expected: is_nullable = 'YES'

-- Confirm index exists
SELECT indexname, indexdef
FROM pg_indexes
WHERE tablename = 'users' AND indexname = 'users_org_id_idx';

Rollback (Phase 1 only):

BEGIN;
DROP INDEX CONCURRENTLY IF EXISTS users_org_id_idx;
ALTER TABLE users DROP COLUMN IF EXISTS org_id;
COMMIT;

Phase 2 — Backfill Existing Data

Goal: Populate the new column for all existing rows before enforcing NOT NULL. When to run: After Phase 1 is live and stable. Can be run as a background job or a one-time script. Application state: Deploy app version that dual-writes to both old and new columns.

App code change required:

// All INSERT and UPDATE operations must now set BOTH old_column and new_column
// until Phase 3 is complete. This ensures new rows are populated during the backfill window.

Backfill script — batch processing:

-- Run in batches to avoid locking. Adjust batch size based on table size and DB load.
-- Target: no single batch takes more than 5 seconds.

DO $$
DECLARE
    batch_size  INT := 1000;
    affected    INT;
BEGIN
    LOOP
        UPDATE users
        SET    org_id = accounts.organisation_id
        FROM   accounts
        WHERE  users.account_id = accounts.id
          AND  users.org_id IS NULL
        LIMIT  batch_size;

        GET DIAGNOSTICS affected = ROW_COUNT;
        EXIT WHEN affected = 0;

        -- Pause between batches to avoid saturating I/O
        PERFORM pg_sleep(0.1);
    END LOOP;
END $$;

Monitoring during backfill:

-- Check progress — run periodically during backfill
SELECT
    COUNT(*) FILTER (WHERE org_id IS NOT NULL) AS backfilled,
    COUNT(*) FILTER (WHERE org_id IS NULL)     AS remaining,
    COUNT(*)                                   AS total,
    ROUND(
        100.0 * COUNT(*) FILTER (WHERE org_id IS NOT NULL) / COUNT(*), 2
    ) AS pct_complete
FROM users;

Backfill completion validation:

-- Must return 0 before proceeding to Phase 3
SELECT COUNT(*) AS unbackfilled_rows
FROM users
WHERE org_id IS NULL;

-- Confirm no new rows written without org_id (dual-write working)
SELECT COUNT(*) AS recent_missing
FROM users
WHERE org_id IS NULL
  AND created_at > now() - INTERVAL '1 hour';

Rollback (Phase 2 — app only):

• Deploy previous app version (single-write to old column)
• org_id column remains nullable; no data is lost
• Backfilled values remain; harmless

Phase 3 — Enforce Constraints

Goal: Add NOT NULL constraint and remove dependency on the old column. Prerequisites: Phase 2