Migration patterns

Assume: the service is running, writes are arriving, you cannot take a maintenance window. Most production migrations live here.

The universal rule

Never couple a schema change to a code change in the same deploy. They fail independently, and you need each to be reversible independently.

Expand → Migrate → Contract

The safe three-phase dance for any non-trivial change:

1. Expand — add the new shape alongside the old. No caller depends on it yet.
2. Migrate — move data to the new shape. Switch readers. Switch writers. Backfill anything left.
3. Contract — drop the old shape once nothing reads or writes it for a cooling-off period.

Each phase is a separate deploy. Each is independently revertable.

Common change patterns

Add a column

• Safe: ALTER TABLE ... ADD COLUMN ... NULL. No lock (or brief, depending on engine).
• Danger: NOT NULL with no default on a large table — full-table rewrite, long lock. Use a default (cheap if metadata-only in your engine) or expand/migrate/contract: add nullable → backfill → add NOT NULL constraint.

Drop a column

• Ship code that stops reading it. Deploy. Wait a release.
• Ship code that stops writing it. Deploy. Wait a release.
• Drop the column.
• Never reverse this order.

Rename a column

• Effectively: add new → dual-write → backfill → switch reads → stop writing old → drop old.
• Never ALTER TABLE ... RENAME COLUMN while code is live. Callers break.

Change column type

• Add a new column with the target type.
• Dual-write (writers write both).
• Backfill the new column.
• Switch readers to the new column.
• Stop writing the old.
• Drop the old.

Add an index on a large table

• Postgres: CREATE INDEX CONCURRENTLY — no table lock. Monitor for failure.
• MySQL: online DDL since 5.6 for most index adds. Check ALGORITHM=INPLACE, LOCK=NONE.
• Never just CREATE INDEX on a hot table without CONCURRENTLY / online algorithm.

Add a foreign key

• Add the column first, without the constraint.
• Backfill valid values.
• Add the constraint NOT VALID (Postgres) so new rows are checked.
• VALIDATE CONSTRAINT later during low traffic.

Partitioning

• Create the new partitioned table alongside.
• Shadow-write to both.
• Backfill.
• Switch reads.
• Drop the old.

Backfill patterns

• Batch in chunks by primary key range. Size the chunk so a single chunk finishes in ~1s.
• Sleep between chunks. Leave room for user traffic and replication lag.
• Checkpoint progress in a table or file; a restart should resume, not re-scan.
• Track replica lag on Postgres/MySQL. If lag climbs, slow down.
• Avoid UPDATE ... WHERE condition on the whole table in one shot. It will bite.

Sketch:

-- in a loop, with checkpointing and sleep
UPDATE users
SET email_lower = LOWER(email)
WHERE id > $last_id
  AND id <= $last_id + 10000
  AND email_lower IS NULL;

Dual-write safely

When writers must update both old and new:

• Apply the write to both inside the same transaction if they're in the same DB.
• If cross-store, use an outbox table — write to outbox in the transaction, publish async.
• Reject writes where the dual-write fails, unless the business accepts loss on the new path during rollout.

Shadow-read for confidence

Before switching reads:

• Run both queries (old and new), compare results, log mismatches.
• Fix mismatches in the backfill, not in the read path.
• Only cut over when mismatch rate ≈ 0 for a sustained window.

Reversibility

Every migration has a reverse. Write both up and down migrations. For destructive ops (drops, renames), the reverse might be "restore from backup" — state that explicitly, and get a snapshot before you run it.

Checklist before you run it

• Ran on a restored copy of prod-sized data? Measured duration.
• Identified locking behavior on this engine, for this size.
• Backup / snapshot present and verified.
• Rollback path documented.
• Oncall notified. Deploy freeze if needed.
• Feature flag / flagged query gate, so readers/writers can switch without redeploy.

Anti-patterns

• Migrations mixed into feature PRs. Can't revert independently.
• DELETE FROM ... WHERE ... to "clean up" at scale — use batched archival instead.
• CREATE INDEX without CONCURRENTLY on a live Postgres table — locks writes.
• SELECT * in backfill queries — read only what you need.
• Running a migration in the ORM's auto-migrate mode in production. Use explicit migration files, versioned and reviewed.