Batch Processing Best Practices for Catalog Scale
Best practices for running image jobs in high volume while maintaining throughput, traceability, and low failure rates.
Job sizing and queue strategy
Split work into predictable chunks so retries are bounded and failure impact is limited. This also improves observability and SLA tracking.
Use queue depth and runtime latency as primary health indicators. Trigger autoscaling before error rates spike.
Failure handling
Define explicit retry policies per failure class. Transient network errors should not be treated the same as deterministic rule violations.
Always emit a machine-readable audit object for each item, including stage status and compliance outcome.
Delivery workflow
Store standardized assets with stable naming rules and version references so downstream systems can consume output without ambiguity.
Use webhooks or event streams to notify dependent systems as each image completes processing.