This guide explains how to change database schemas without taking your product offline. You’ll learn the “expand-and-contract” pattern, safe backfills, traffic controls, rollback tactics, and a test checklist you can apply to Postgres, MySQL, and cloud databases. Copy the steps into your runbook, adapt naming to your stack, and cut risk during releases.

Why zero-downtime matters

A schema tweak that feels small in a branch can stall checkouts, break reports, or spike 500s once it meets production load. Release windows, maintenance banners, and “please refresh” messages cost trust and revenue. Zero-downtime techniques help you ship structural changes while requests keep flowing. The goal is simple: no user notices your database surgery.

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The core pattern: expand-and-contract

Think of migrations as two moves rather than one:

1. Expand (forward-compatible): Add new structures while old ones still work. Examples:

Add a nullable column rather than replacing a field.

Create a new table that mirrors writes from the legacy table.

Add an index concurrently to avoid blocking.

Introduce a view or feature flag that lets the app read old or new paths.

2. Contract (cleanup): After traffic runs safely on the new shape, remove the old column, table, or code path. Contracting is never urgent; leave it until metrics look boring for a full cycle.

This pattern reduces blast radius by making the system valid in both states during the transition.

Pre-flight checklist (copy/paste to your runbook)

Change ticket: One sentence goal, owner, rollback note, deadline.

Data map: Exact tables/columns touched, expected row counts, and hot partitions.

Traffic profile: Peak hours, batch jobs, and long-running queries that might collide.

Safety switches: Feature flags, read/write toggles, and a one-click rollback plan.

Idempotency: Scripts can be re-run without corrupting data.

Backfill strategy: Batches, chunk sizes, and retry rules.

Observability: Dashboards for errors, latency, queue depth, and replication lag.

Comms: Who gets pinged if metrics drift engineering lead, SRE, product owner.

Safe steps for common schema changes

1) Adding a column

Add the nullable column first.

Ship the app so it writes both the legacy value and the new column.

Backfill historical rows in small chunks (e.g., 10k) with controlled sleep between batches.

Gradually shift reads to the new column behind a flag.

When reads are stable and backfill is complete, lock the flag to “new”.

Drop the old column during a calm period.

2) Renaming a column

Add the new column; start dual writes.

Backfill from old → new with checksums to verify.

Switch reads to the new column; keep dual writes for a while.

Freeze old writes; archive the old column for a set period.

Remove the old column after audit passes.

3) Splitting a table

Create the target tables with proper keys and indexes.

Introduce an app layer that writes to both the original and the new targets (shadow writes).

Migrate historical rows in windows; throttle to protect replicas.

Toggle reads table-by-table or endpoint-by-endpoint.

Retire the original table when reads are fully moved.

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How to backfill without hurting production

Chunking: Use primary key ranges or time windows. Keep each batch under your lock and I/O thresholds.

Low-priority I/O: Use DB-specific settings to reduce contention.

Retry budget: Retries with jitter; skip and log stubborn rows, then sweep later.

Checksums: Compare sample hashes before and after to confirm accuracy.

Pause switch: A feature flag that stops backfill instantly if metrics drift.

Online schema change tools and patterns

Postgres:

• • CREATE INDEX CONCURRENTLY to avoid blocking writes.

  • Logical replication for table moves or versioned schemas.

  • Avoid broad ALTER TABLE ... SET NOT NULL until data is fully backfilled; use NOT VALID constraints first, then validate.

MySQL:

gh-ost or pt-online-schema-change for large table changes.

Watch replica lag and cutover windows; run on a staging replica first.

Cloud databases:

Check provider docs for online DDL nuances, quotas, and throttling.

Pre-provision extra capacity during heavy backfills.

Traffic controls that save you during cutovers

Feature flags: Route a small percentage of reads to the new path; ramp from 1% → 5% → 25% → 100%.

Dual reads (compare mode): Read both versions for a sample of requests; log diffs for investigation.

Shadow writes: Write to the new store in the background without serving from it yet.

Circuit breakers: If error rate, p95 latency, or replica lag crosses a threshold, flip back automatically.

Rollback: design it before you ship

A rollback is not a message in Slack; it’s a reversible sequence:

Keep old structures available until you’ve watched a full business cycle (usually days).

Maintain dual writes for a cooling-off period so the old path stays fresh.

Keep a backfill reverse plan (new → old) for the brief window where you might go back.

Document the exact commands, flags, and dashboards to check while rolling back.

Observability that matters

Set clear SLOs for the migration window:

• Error rate: e.g., 5xx ≤ 0.5% per service.

• Latency: p95 within 10% of baseline.

• Replication lag: below your read-after-write needs.

• Backfill speed: predictable rows/minute without starving live traffic.

Prepare dashboards ahead of time and pin them in your war room channel. Aim for boring graphs; boring is victory.

People and process

• One owner, many helpers: A single decision maker avoids indecision during pressure.

• Dry runs: Rehearse on production-like data (scrubbed) to reveal slow queries, sequence gaps, and deadlocks.

• Change window: Pick periods with known load patterns; avoid payroll, sale events, or month-end accounting.

• Post-migration audit: Confirm data counts, indexes, and permissions; write a short note with learnings.

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