Python Batch Worker

Worker Flow

1. Pull batch items from queue.
2. Create and start jobs with concurrency limit.
3. Poll statuses in async loop.
4. Write outputs and usage metrics to DB.

Operational Advice

• Use dead-letter queue for repeated failures.
• Expose worker metrics for observability.
• Gracefully shutdown workers to avoid job loss.

Operational Patterns

• Use queue-based orchestration for sustained throughput.
• Separate latency-sensitive traffic from bulk processing workloads.
• Centralize webhook/event consumption through one idempotent handler.
• Use dead-letter queue for repeated failures and manual replay.

Go-Live Checklist

1. Load test with realistic clip duration distribution.
2. Validate alerting for error rate, queue lag, and failed jobs.
3. Confirm credit accounting and billing UX alignment.
4. Prepare escalation template containing job_id + payload summary.

Python Worker Scaling Strategy

1. Start with bounded concurrency and tune by model profile.
2. Separate polling coroutine pool from create/start tasks.
3. Track queue lag and auto-scale workers on demand.

When to Use Python Batch Worker

Python Batch Worker belongs to the Examples & Integrations section and covers batch process queues with controlled concurrency.

The page is written for developers and operators who need predictable video background removal behavior in production, not just a one-off demo request.

• Use this example when python batch worker matches your deployment shape or automation pattern.
• Start with the reference flow, then replace storage, queue, and database pieces with your production stack.
• Keep one small fixture video for smoke tests and one difficult video for quality regression checks.

Implementation Notes

Before you promote this workflow, test it with at least one short clip, one longer clip, and one visually difficult clip from your actual product or customer segment.

For support and debugging, persist the original input reference, selected model, output format, credit usage, and final job status alongside your internal user or project id.

• Do not copy example credentials, callback URLs, or storage paths directly into production.
• Do not run high-volume jobs synchronously from user-facing request handlers.
• Do not skip observability; every example should emit enough state to replay or debug failed jobs.

Related Documentation

FAQ