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Investigate Python SDK concurrency limitations

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#975 3 commentaires 3 réactions 1 personne assignée Voir sur GitHub

@seherv y travaille déjà.

Depuis le 28/4/2026.

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Description

P1

The Python Dapr SDK currently relies on threadpool-based execution for workflow and activity processing. This model has known limitations:

  • The GIL (Global Interpreter Lock) constrains true parallelism for CPU-bound work
  • Thread pool exhaustion under high concurrency can delay sidecar acknowledgments

Native asyncio execution would allow the SDK to handle a higher volume of concurrent workflow tasks without the overhead and contention introduced by thread management, reducing sidecar response latency under sustained load.

Goals

  • Quantify the throughput and latency characteristics of the current threadpool implementation under sustained workflow load
  • Rewrite workflow and activity execution to use native asyncio
  • Validate that the rewrite improves sidecar response latency and throughput under load conditions

Tasks

  • Benchmark current threadpool implementation — measure workflow throughput, activity concurrency, and sidecar acknowledgment latency under sustained load
  • Document failure conditions — identify load thresholds at which sidecar response times begin to degrade
  • Design asyncio execution model — propose the replacement architecture for workflow and activity dispatch
  • Implement asyncio rewrite — replace threadpool-based execution with native asyncio in the workflow worker
  • Ensure existing workflow and activity behavior is preserved
  • Load validate — re-run benchmarks against the rewritten implementation under production-representative load and confirm improvement in sidecar response latency and throughput
  • Document operational guidance — update SDK documentation with concurrency configuration recommendations for workflow-heavy deployments
  • Write Dapr OSS blog on findings
Langage dominant
Python
Étoiles
272
Forks
152
Merge moyen
1 j 13 h
PR mergées (30 j)
4

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