Investigate Python SDK concurrency limitations

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#975 erstellt am 02.4.2026 3 Kommentare 3 Reaktionen 1 zugewiesene Person Auf GitHub ansehen

@seherv arbeitet bereits daran.

Seit 28.4.2026.

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Beschreibung

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

Vorherrschende Sprache: Python
Sterne: 272
Forks: 152
Ø Merge: 1 T. 13 Std.
Gemergte PRs (30 T.): 4

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