Intent
Chunk synchronization work to handle scale, rate limits, and long runs.
When to use
- Data volumes are too large for single-pass syncs.
- APIs enforce rate limits or timeouts.
- You need resumable jobs with progress tracking.
Core mechanics
- Segment the dataset into deterministic batches.
- Process each batch with retry and backoff.
- Persist checkpoints and summary reports.
Implementation checklist
- Define batch size and ordering key.
- Implement checkpoint storage and resume logic.
- Add retries with exponential backoff.
- Record per-batch outcomes and totals.
- Publish a final summary report.
Failure modes and mitigations
- Partial syncs -> persist checkpoints and idempotent updates.
- Rate limits -> throttle and back off.
- Duplicate processing -> use stable batch keys.
Observability and validation
- Batch counts, success/failure totals, and runtime.
- Per-batch error logs and retry counts.
Artifacts
- Checkpoint file or table.
- Batch summary report.
- Error report with failed records.