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Graph/async-http-blocking-middleware-mismatch

AntiPattern

Async I/O Blocking Mismatch

async-http-blocking-middleware-mismatch

Blocking operations in async request handlers—waiting for long subprocesses, streaming with incorrectly scoped async clients, or assuming HTTP middleware behavior—stall the event loop or never hit logs, breaking FastAPI/ASGI streaming, WebSockets, and Docker connectivity expectations.

Node Details

Public graph metadata

Updated6/25/2026

Connections

30 linked nodes

MATCHESSolution

Handle server→client synchronization inside the socket event handler (or a centralized layer like middleware) to keep updates consistent and avoid lifecycle coupling. Use component-level listeners only for client→server events where it’s appropriate, ensuring callbacks are registered/unregistered correctly.

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

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maybe you can swap your post to axios, because it has a timeout property — if you only want to do this once. Tension: some requests take too long to download content (about a minute). Outcome: can you in anyway batch your requests, so you are sending more requests in order to receive smaller responses?

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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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If you convert all operations to perform asynchronously (using `aiosqlite`) , the issue will be mitigated. — When a route in FastAPI is listed as asynchronous (`async def`). Tension: that doesn't fully solve the inherent single writer problem of Sqlite. Outcome: Here's a fully operational update of the FastAPI server logic:.

MATCHESSolution

use httpx or aiohttp e.g: — async def test_api(request: Request):. Tension: requests(which is synchronous) inside async function. Outcome: async with client.stream("POST", "http://127.0.0.1:8000/test_api",json={} ) as response: async for chunk in response.aiter_bytes(chunk_size=1234): yield chunk.

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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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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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Using `asyncio.sleep()` in a loop is perfectly fine. Tension: every time you `await` the sleep, you effectively yield control back to the event loop. Outcome: calling `asyncio.sleep(0)` does not sleep at all, but only yields control back to the event loop.

MATCHESSolution

use `pytest_asyncio` and the async fixture — If you don't want to utilize an `AsyncClient` like the one by `httpx`. Tension: ensuring that the FastAPI app's lifespan context correctly works.

MATCHESSolution

Ensure the server yields to the asyncio event loop while processing (e.g., add an `await asyncio.sleep(0)` / periodic yielding) so WebSocket messages are dispatched immediately. Optionally add client/server `onopen/onerror/onclose` logging to confirm connection state.

MATCHESSolution

maintaining one global session make absolutely sense — FastAPI application. Tension: Every `boto3.Session()` call is costing me 50ms-100ms in latency. Outcome: session = boto3.Session() # ← global reusable boto3 session.

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ASGIMiddleware. Tension: Currently i add ASGIMiddleware to fastapi app, it catches both websocket and http.

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I have solved it using the lifespan of FastAPI. — use it inside the routes. Tension: I couldn't identify where the problem is. Outcome: I have just initialized the Pubsub client in LifeSpan.

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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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making sure you pace your requests to stay below those limits — API rate limits. Tension: allowing room for your normal operations, too. Outcome: handle any 429 responses gracefully to retry.

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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.

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An async function was passed to useActionState.

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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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Async-iterate the Result instead of awaiting `.records` — The driver's Result is async-iterable. Tension: Combined with a streaming HTTP response, records flow end-to-end without any single buffer. Outcome: records flow end-to-end without any single buffer.

MATCHESSolution

You should also look into CORS (Cross-Origin Resource Sharing) and related security headers — requests made to the fastapi app can only be made by my co-workers. Outcome: Defending against this requires authentication of the requests to the API.

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What to do once the request stream size exceeded the limit? — I'm building a file upload handling library in Node.js. Tension: I want to know how to close the request so that no further data can be sent over the wire and waste the server resources. Outcome: you must use promise object which is stable to handle asyncronous tasks.

MATCHESSolution

How can I get hold of the query metrics in this async setup? — The Couchbase documentation mentions to switch to the Reactive and Async APIs. Tension: the blocking API is really just a wrapper: it contains both the rows and the metadata (including metrics). In the reactive API this is split. Outcome: you can also call `result.metaData()` to access metrics, warnings, and the rest of the metadata.

MATCHESSolution

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

Use asynchronous sending with callbacks. — If you're using KafkaTemplate.send() without handling the ListenableFuture returned. Tension: you're effectively blocking the calling thread until the message is acknowledged by the Kafka broker. Outcome: The addCallback method allows you to execute code when the send operation succeeds or fails, without blocking the main thread.

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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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Rewrite the test as an async test and run it with an async test runner (e.g., pytest.mark.asyncio), using await on the async ORM methods (acreate/acount) instead of asyncio.run().

MATCHESSolution

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

Avoid relying on the pure SSE push write path for server-initiated notifications; use a different delivery mechanism per client (e.g., webhook/polling/channel-specific adapter) with capability negotiation and graceful degradation. Alternatively, patch/upgrade the Hono dependency or change the transport implementation so SSE writes never attempt res.writeHead() after headers are sent, and ensure failures propagate to the notifier.

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