Backfill Job
A task that reprocesses historical events or data to close gaps after downtime or schema changes.
A backfill job replays historical data or events to fix gaps created by downtime, bugs, or schema changes. It rebuilds state so downstream systems are accurate.
Businesses run backfills after missed webhooks, failed syncs, or migrations. The job reads from a source of truth and writes corrected records to targets.
In workflows, backfills run in controlled batches with idempotent writes to avoid duplicates. They protect reporting accuracy, billing correctness, and compliance by ensuring historical data is consistent.
Frequently Asked Questions
When do I need a backfill?
After incidents that dropped or corrupted data, schema changes, or onboarding a new system that needs historical context.
How do I keep backfills safe?
Make writes idempotent, run in small batches, add rate limits, and checkpoint progress. Dry-run on a subset before full execution.
What sources should I use?
Use the system of record—primary database, data warehouse, or authoritative export. Avoid secondary caches that may be stale.
How do I avoid duplicate records?
Use upsert semantics with stable keys, and log processed IDs. Deduplicate in the target and monitor for unexpected row counts.
How should I schedule a backfill?
Run during low-traffic windows, and throttle to respect downstream rate limits. Pause if error rates spike.
What should I log?
Start/end times, batch sizes, records processed, errors by type, and checkpoints. Keep metrics for throughput and failure rate.
Can I run backfills alongside live traffic?
Yes, if you gate writes, respect rate limits, and avoid locking hot tables. Consider feature flags to pause quickly.
How do I validate success?
Reconcile counts and key fields against the source of truth, spot-check records, and rerun queries for known discrepancies.
What if dependencies fail mid-backfill?
Use checkpoints so you can resume, exponential backoff on retries, and circuit breakers to prevent cascading failures.
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