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Graph/retry-masking-deadlock-hang

AntiPattern

Retry and Lock Deadlocks

retry-masking-deadlock-hang

Concurrency failures get misdiagnosed as hangs when SDK retries mask errors and threads contend on overlapping locks; without timeouts, ordering, or deadlock-aware handling, systems stall under resource pressure or multi-lock waits.

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Public graph metadata

Updated6/28/2026

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30 linked nodes

MATCHESSolution

you can use Zustand getters and optimistic update — multiple or rapid clicks could actually cause the updates to be executed but out of order. Tension: no optimistic update so u see that delay (lag) you said about.

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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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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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As long as you lock more than a single thing at a time, you risk deadlocks. — lock more than a single thing at a time. Tension: You hold B waiting for A, they hold A waiting for B. Outcome: one of you has to abort or you're stuck.

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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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Disable retry — If the issue persists. Tension: it could be because the sdk is getting an error and appear to be hanging while it's retrying. Outcome: You could try disabling the retry and check if there is an error.

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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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You can use redis lock to solve this — around each change you are making to the redis/DB. Outcome: put this lock around each change you are making to the redis/DB.

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I suggests you use rapyer, Which takes special care to handle deadlock problems. — Since you are using json models. Tension: deadlock problems.

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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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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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The issue was with keychain access. — the macos job keeps on hanging. Tension: I also had some trouble getting code signing and notarization to work. Outcome: creating a temporary one and unlocking it.

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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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I know that it is recommended to use `select..for update skip locked` — under high data volume and frequent updates/deletions. Tension: it can lead to performance degradation. Outcome: `skip locked` is the cheapest option.

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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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implementing this retry behavior all over the place — axiosInstance.js. Tension: handle the retry logic. Outcome: you could try using a response interceptor to abstract and handle the retry logic.

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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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if (errorCount.incrementAndGet() >= maxErrors) — onErrorResume(error -> {. Tension: Skip failed paths. Outcome: return Mono.empty();.

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Cause of the deadlock.

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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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You can disable infinite retries using: — spring.kafka.producer.retries=1. Outcome: Handle the Send Failure via `Callback`.

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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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Use synchronization primitives, locks, or other publication mechanisms that establish a happens-before between the write and subsequent read

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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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adding a pause to your task after connecting — You can also use a 'sleep(delay)' in mysql, to make statements last longer. Tension: to prove your tasks can make parallel statements. Outcome: You'll have time to show processlist by hand now.

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Mysql driver 9.0.0+ (currently 9.1.0) has ReentrantLock(s) now — IF they were not hooked on mysql internal synchronized blocks. Tension: it should have helped. Outcome: and it should work as this enabled the virtual thread to go do something else.

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Tension: it was being looped infinitely, crashed and restarted every time. Outcome: fixes the issue.

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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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Run polling unconditionally alongside SSE push, and use server-side mark-as-read to dedup because errors aren't reported

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the server instructions list locked tools — the server dynamically reports locked tools in its welcome message on each connection. Tension: it would be helpful to have a canonical reference in the forum. Outcome: the server dynamically reports locked tools in its welcome message on each connection.

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