Introduction
Database migrations are one of the riskiest operations in production systems. Even with testing and planning, a migration can behave very differently under real traffic and real data volume.
When a migration goes wrong, the impact is usually immediate. Applications slow down, errors increase, and business operations are affected. A well-written postmortem helps teams understand what happened, fix the root cause, and prevent similar incidents in the future.
This article walks through a realistic database migration incident postmortem in simple words. The goal is not to blame, but to learn.
Incident Summary
A database migration was deployed during a scheduled release window. Within minutes, application latency increased sharply and error rates spiked. Some user actions failed, and background jobs stopped processing.
The migration was paused, and the system was stabilized by rolling back application behavior while leaving the database changes in place.
Impact
The incident affected:
User-facing APIs experienced slow responses
Some write operations failed
Background processing was delayed
Total user impact lasted approximately 45 minutes before full recovery.
Timeline of Events
10:00 AM – Migration started in production
10:05 AM – Database CPU usage spiked
10:08 AM – Application error rate increased
10:12 AM – Alerts triggered for latency and failures
10:15 AM – Migration paused
10:20 AM – Application code rolled back
10:45 AM – System stabilized
What Changed in the Migration
The migration introduced a new column and attempted to backfill data in the same release.
Example:
ALTER TABLE orders ADD COLUMN order_status_v2 VARCHAR(20);
UPDATE orders SET order_status_v2 = order_status;
This backfill operation scanned a large table and caused heavy database load.
What Went Wrong
Several issues combined to cause the incident.
Large Backfill in a Single Transaction
The migration attempted to update millions of rows at once. This caused:
Migration Ran During Peak Traffic
Although scheduled, the migration coincided with higher-than-expected traffic. The database had no spare capacity to absorb the load.
No Throttling or Batching
The backfill was not throttled or batched. The database was overwhelmed instead of gradually updated.
Missing Production-Scale Testing
The migration was tested on staging, but staging did not contain production-level data volume. The performance impact was underestimated.
Detection and Alerting
The incident was detected through:
Database CPU alerts
API latency monitoring
Error rate thresholds
Early detection helped limit the blast radius.
Immediate Mitigation Steps
The team took the following actions:
No schema rollback was performed to avoid further risk.
Why Rollback Was Not Used
Rolling back the schema would have required:
Dropping the new column
Locking the table again
Risking data loss
Instead, the team chose a safer forward-fix approach.
Root Cause Analysis
The root causes were:
Combining schema change and data backfill
Running a heavy operation during production traffic
Lack of batch processing
Insufficient production-like testing
The incident was not caused by a single mistake, but by multiple small assumptions.
Long-Term Fixes Implemented
After the incident, the team implemented several improvements.
Separate Schema and Data Changes
Schema changes are now deployed independently from data backfills.
Batch and Throttle Backfills
Large updates are processed in small batches.
Example logic:
UPDATE orders SET order_status_v2 = order_status
WHERE order_status_v2 IS NULL
LIMIT 1000;
Feature Flags for Schema Usage
Application behavior is now controlled using feature flags so schema usage can be disabled instantly.
Improved Migration Testing
Staging environments now mirror production data size more closely.
Better Release Checklists
Migration risk assessment is now a mandatory part of release planning.
What Went Well
Monitoring detected the issue quickly
Team coordination was effective
Application rollback stabilized the system
What Could Be Improved
Lessons Learned
Never backfill large tables in one step
Test migrations with realistic data
Prefer forward fixes over rollbacks
Monitor closely during migrations
Action Items
Add migration safety guidelines
Automate batch migration tooling
Improve documentation and training
Summary
This database migration incident shows how even small, reasonable changes can cause production issues when real data and traffic are involved. The failure was not due to negligence, but to assumptions that did not hold at scale.
By separating schema changes from data updates, batching heavy operations, testing with production-like data, and favoring forward fixes over rollbacks, teams can prevent similar incidents. Postmortems are valuable not because they describe failure, but because they turn failure into shared learning and stronger systems.
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