How do you set CPU, memory, timeout, and concurrency for a render worker?
Measure representative jobs and choose resource limits that protect both throughput and reliability.
How do you set CPU, memory, timeout, and concurrency for a render worker?
Resource settings should follow measurements. A short sample may hide the peak memory and duration of a longer or more complex render.
Measure the whole job
Include input download, decoding, rendering, output upload, and validation. Observe peak resource use and compare several representative media sizes. Temporary files and concurrent processes can increase memory or storage needs beyond the encoder alone.
Set bounded execution
Choose timeout and concurrency settings supported by the selected cloud service. Leave room for expected variability without allowing stuck work to run indefinitely. Increasing concurrency can overload memory or downstream services even when CPU is available. Test near the intended workload limit and verify that resource exhaustion becomes a clear failed attempt with recoverable state. Reassess settings after changing resolution, fonts, filters, or output duration.
Further reading: Cloud Run job resource configuration.
Account for the full attempt lifecycle, including transfers and validation, according to the service’s semantics.
No. Resource contention and downstream limits can make jobs slower or less reliable.
When workload size, render settings, dependencies, or observed resource behavior changes.
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