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Graph/async-context-lifetime-escape

AntiPattern

Async Execution Context Escape

async-context-lifetime-escape

Request-scoped work keeps running past its intended lifetime because blocking calls, background tasks, schedulers, or executor usage don’t preserve context boundaries. Side effects commit too late or never, appearing hung or silently inconsistent under retries.

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Updated7/6/2026

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30 linked nodes

MATCHESSolution

handle a scenario when the code is fine but backend went offline — prefetching data with usePrefetchQuery. Tension: prefetch works.

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Wrap updates that suspend with startTransition

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post-start failures. Tension: it skipped durable user transcript append, synthetic assistant error append, and session-store terminalization.

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You can configure your worker with a Workflow interceptor — Temporal workflows and activities. Tension: we don't have Sentry logging anymore. Outcome: catch all exception raised during your activities and workflow execution.

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provided it's called within the same process — workers might be pre-forked. Tension: the cache doesn't work across processes. Outcome: each process got its own cache.

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delete the async from `fake_execute` — your `AsyncContextMock` needs to handle both use cases in your endpoint `await conn.execute()` and `async with conn.execute()`. Outcome: just make it a regular function so it returns your context manager directly, no coroutine wrapper.

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single worker running async loop. Outcome: So you have a single worker running async loop and you block that loop.

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I also offer an alternative `executor` class which can preserve the contextvar values across threads — when YOUR code is the one making the `loop.run_in_executor` call. Tension: in this example, the code making this is the framework code. Outcome: but that can only be used when YOUR code is the one making the `loop.run_in_executor` call.

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To manage background processes. Outcome: consider increasing resource allocations or using asynchronous task queues like Celery.

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python interpreter just pauses one task and looks at other — In your scenerio where you use asynchronous. Tension: it is not completly usesless. Outcome: Think it like a round-robin.

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it will be reset as soon as the current HTTP view is done. Tension: unless explicitly saved with something like a `contextvars.copy_context()` call while the task is active. Outcome: the forked context is disposed.

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Move the persistence/cleanup logic into code that runs at the end of the background task (or otherwise extend lifecycle explicitly for the background task), rather than relying on the request-scoped `asynccontextmanager` teardown for those updates.

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Change the engine async fixture from session scope to function scope (remove scope="session") so all async fixtures/tests run on the same event loop, preventing the cross-loop Future/Task error.

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any time you have an I/O bound process with lots of idle time — waiting for user input, waiting for a TLS handshake. Tension: wrapping it in an async task can let other code run.

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Ensure the S3 download runs in a way that doesn’t block the event loop (i.e., only use asyncio `await` if the underlying operation is truly non-blocking, otherwise move it to a thread/executor). Use `asyncio.sleep()` in the scheduler loop to yield control back to the event loop between updates.

MATCHESSolution

bound it with a "progress ceiling" — a process emitting bytes but making NO semantic progress. Tension: a process emitting bytes but making NO semantic progress (livelock) would be shielded forever. Outcome: re-arms recovery after a long no-progress window even while bytes flow.

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Avoid running multiple DB operations concurrently on the same session by removing asyncio.gather and iterating sequentially, and/or use an async_scoped_session so each task uses its own session.

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depending on your latency budget. Outcome: Same idea, but the state lives outside the process boundary.

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Use virtual threads for blocking DB/IO work to improve scalability under the same small pool, or switch to a reactive database driver/event-loop model to avoid blocking carrier threads; adjust concurrency/throttling accordingly (semaphore/backpressure) rather than assuming the current JDBC + thread pool design remains optimal.

MATCHESSolution

Try to to decouple thread creation from task execution. — The CPU maintains a queue and a pool of worker threads (only 3). Tension: Clients submit tasks (like your cpu.execute()). Outcome: Each worker takes a task from the queue, processes it, and picks the next.

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I am seeing an increase in latencies in requests that do succeed — I have a logic of the sort :. Tension: none of them enforce a timeout on the total duration of the request. Outcome: Then properly close the connection in a finally / try-resource block.

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future.cancel(true); — httpHardTimeoutScheduler.schedule(() -> {. Tension: if (!future.isDone() && !future.isCancelled()). Outcome: keep your latency low on your side of the wire.

MATCHESSolution

after the system restarts. Tension: wouldn't it be a waste of DB space if I'm storing millions of tasks that I only ever need to process once. Outcome: store the last offset in cache and then continue processing data from the last checkpoint.

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Use ScheduledExecutorService with ZonedDateTime and Duration to schedule the next execution

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I think it is better to use a custom context manager — if you need to isolate test methods from each other. Tension: you need to isolate test methods from each other. Outcome: here is what I am using.

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the goroutine blocks forever on the channel send because nobody is reading — When the parent context is cancelled, the goroutine blocks forever on the channel send because nobody is reading. Tension: if the result is ready at the exact moment the context cancels, which case fires?

MATCHESSolution

Use Component.Task (TaskSource) for all async work inside components — inside components. Tension: it auto-cancels when the component is destroyed.

MATCHESSolution

Changed the scheduler to run bounded challenge jobs — the scheduler. Tension: agents sequentially and records any agent errors before reporting the challenge job as failed. Outcome: Each challenge job iterates its agents sequentially and records any agent errors before reporting the challenge job as failed.

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Stopped the active runner — background extraction does not consume Gemini quota. Tension: continuing futile retries. Outcome: waits until the Pacific reset instead of continuing futile retries.

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Rebuilt App.tsx as the OracleController beat machine. Outcome: running the authored cascade arrest→anchor→recognition→locked on setTimeout timers.

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