Investigate Python SDK concurrency limitations
@seherv ci sta già lavorando.
Dal 28/4/2026.
Valutazione
Questa issue non è ancora stata valutata.
Descrizione
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
- Lingua principale
- Python
- Stelle
- 272
- Fork
- 152
- Merge medio
- 3g 21h
- PR unite (30g)
- 5
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