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Graph/async-io-resource-starvation

AntiPattern

Async IO Resource Starvation

async-io-resource-starvation

Async HTTP operations can stall when retries, open connections, and poor client lifecycle choices (e.g., per-request client creation or blocking progress callbacks) exhaust browser/socket limits, leaving requests queued and sockets held open.

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Public graph metadata

Updated7/10/2026

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30 linked nodes

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Use a cleanup function to clear the timeout, or simulate latency with Chrome DevTools throttling or MSW delay instead

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Prevent or debounce user interactions until hydration completes — before hydration completes. Tension: especially in slow networks.

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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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Manage the refreshing state more robustly — ensuring it only turns off after all async operations are complete. Tension: remove the arbitrary timeout. Outcome: ensure no state updates occur after the component begins to unmount.

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And HTTP Client errors (if you're using `HttpClient` or `HttpClientFactory`) — On ASP.NET Core. Tension: you just need to opt-in. Outcome: options.CaptureFailedRequests = true;.

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Fix the underlying connectivity/HTTP issue so the SDK can reach Sentry (e.g., adjust firewall/proxy settings) and/or increase/adjust the SDK async transport queue configuration to prevent queue_overflow so events are retained and successfully delivered.

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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:.

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

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Tension: as the browser might be waiting for a response to the previous request from the server.

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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.

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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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keep a singleton client and configure a connection pool using `https.Agent` — Instead of creating a new S3Client on every request. Tension: prevents socket reuse. Outcome: Keeps sockets alive for reuse under high concurrency.

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before passing it to MinIO Client SDK. Outcome: This is their condition for the exception:.

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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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Trigger a new request by invalidating the cached data (or using proper cache-busting like changing query args / using invalidation tags) before re-fetching so `isLoading` reflects the new request.

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HttpClient.GetAsync(new Uri(TextHelper.RedirectUrlNull(urlContent))); — We have an issue in our solution (we are using .net core). Tension: the SNYK vulnerabilities scaner show us that we have a Server-Side Request Forgery (SSRF) vulnerability.

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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.

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control the number of concurrent file downloads — the API requests, since they seem to work fine as fast as you can request them. Tension: this is not exactly possible, since `FileSaver.js` can't tell you when a file has completed downloading. Outcome: maybe with some tweaking of the "magic numbers" you can get a consistent (faster than one at a time) result.

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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.

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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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decorate a connection pool with a priority queue to avoid overloading the connection pool — When the connection pool usage goes above some critical level. Tension: start throttling requests and maybe even drop low priority requests from the queue all together.

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If you start the server and wait several minutes before visiting any pages — does it still take several minutes for that first page? Tension: long startup in general or something request specific. Outcome: Figuring out if it is a long startup in general or something request specific could be a good spot to start.

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Fixed 30s retry on 429 keeps hammering. — when clearly rate-limited. Tension: keeps hammering. Outcome: Exponential backoff + hard rejection during cooldown.

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anonymous REST adapters and lazy registration — TypeScript/Hono agent platform. Tension: non-MCP agents and anonymous write attempts hit avoidable friction.

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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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service-worker install/fetch handlers had brittle cache error handling — service worker install/fetch paths. Tension: brittle cache error handling. Outcome: tolerant precache and explicit offline responses.

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