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Graph/cross-layer-deadlock-circular-wait

Pattern

Cross-Layer Deadlock Loops

cross-layer-deadlock-circular-wait

Locking mismatched resources across concurrency layers—event loops, OS/network limits, Redis/DB drivers, and global interpreter locks—creates circular waits that stall progress, and naive retry/locking may not help, leaving systems hung or failing under load.

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Updated7/29/2026

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30 linked nodes

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.

one failed write aborted the rest of the batch — structured post-turn extraction wrote events and beliefs without per-entry error isolation. Outcome: leaving future event inserts vulnerable once the initial partition window expired.

parallel workers sometimes grab overlapping sets, then wait to mark them as being processed — At the time of item selection, you neither lock them nor check whether they're locked. Tension: someone else could've already grabbed a subset of those. Outcome: duplicate their work on the items they happened to share.

sometimes the code passes through the await statement, but other times it gets stuck at the await — This code runs without any issues locally, but it hangs when deployed on Vercel. Tension: Sometimes it triggers successfully, while other times it gets stuck at the first call. Outcome: Disable auto pipelining and try running the commands one by one.

"android::Looper::pollInner" — we are using Sentry as crash reporting tool. Tension: it just shows a bunch of stacktrace lines that cannot help me to find the cause. Outcome: high number of crashes.

the cache doesn't work across processes — workers might be pre-forked. Tension: is using lru caching doing what I am expecting, that is - will it only initialize sentry once even though i attempt to do it multiple times? Outcome: provided it's called within the same process.

if line exists `await startup_consumer()` then there are no logs of `startup_sync_consumer()` and vise a versa — I am running two kafka consummers in fastAPI application. Tension: Looks like each consumer blocks event loop. Outcome: How to fix it?

sqlite3.OperationalError: database is locked. — I trigger `/session_a` and `/session_b` almost simultaneously. Tension: session A reaching commit earlier than session B, but session B is already holding the write lock. Outcome: A will inevitably fail with a database is locked error.

facing error that `event loop is closed` — I have been working on yet another API using FastAPI and trying to write test cases for the APIs. Tension: using `asyncpg` driver/library to connect to the main db and for testcases using `aiosqlite` driver/library. Outcome: re-run the tests and report back on if it mitigates the issue.

the two requests are executed "in parallel." — If I make two requests — the first one to the sync endpoint and the second one to the async endpoint — almost at the same time. Tension: I expected the event loop to be blocked. Outcome: the two requests are executed "in parallel.".

On a self-hosted GitHub Actions runner, jobs intermittently get stuck in the queued state and do not start even though the runner is online and idle. This happens randomly on various workflow steps/jobs.

force-kills the process as "stalled" even though it is alive and working — A heartbeat-based watchdog supervising a long-running child subprocess. Outcome: the run is killed.

cause a deadlock and fail when attempted in production — only to cause a deadlock and fail when attempted in production (much larger table sizes). Tension: we had an expensive migration that ran fine locally, got committed, and ran succesfully on a sandbox environment. Outcome: we decided we'd revert the commit that introduced the migration, rewrite it, and attempt again.

Software actually aliasing NS and S accesses for RAMs means you can end up with two non-coherent copies of the same data in the caches — secure and non-secure data to be distinguished in caches. Tension: which can desync because of the lack of coherency. Outcome: This is why secure world page tables can be set up with NS=1 pages.

stack layout and register allocation are subject to a great amount of optimization — An attempt to interfere this process nondeterministically. Tension: benchmarks will become useless. Outcome: nondeterministic performance.

Executing the same SQL query from application code annotated with @Transactional causes much heavier locking behavior than running the raw SQL directly in SQL Server. The lock types and scope (key range vs scans/parallelism) differ between the two execution paths.

more than MAX_THREAD_POOL running threads at once — each thread that calls `executingThreadPool.add` immediately uses `executingThreadPool.poll`. Tension: your locks don't prevent more than MAX_THREAD_POOL running threads at once. Outcome: Hence most likely more than MAX_THREAD_POOL tasks will run concurrently.

Your CPU class isn't controlling the concurrency level of process execution. — The threads you start in CPUDemo are doing their own processing. Tension: the CPU isn't starting the "processing" threads itself. Outcome: Try to to decouple thread creation from task execution.

But it doesn't work like that. — I wanted to try power of virtual threads in Java in a simple application which consists of many tasks. Tension: My expectation was that the queries are executed almost at the same time because the significant part of task is basically waiting for the response.

what are the correct values of three highlighted parameters? — when it comes to intrusive lockless stacks (single-linked lists). Tension: I've seen various permutations of highlighted parameters. Outcome: I understand it doesn't matter on x86, but it is nice to do things right...

Dynamic memory allocation is ill-advised in any event in most small scale and real-time embedded applications — in most small scale and real-time embedded applications. Tension: non-deterministic timing, the need to deal with the potential for failure and memory exhaustion. Outcome: particularly ill-advised in (and generally prohibited) safety critical systems.

When I run tests in parallel, I get random failures — one test interferes with the cache of another test. Tension: one test interferes with the cache of another test.

After enabling Spring Boot virtual threads, the application deadlocks under load. Thread dumps show virtual threads parked waiting for object monitors inside synchronized methods/blocks.

Common mistakes: (1) not calling RemovePlayer on disconnect causing memory leaks, (2) unbounded stack growth, (3) broadcasting undo notices to all players instead of just the owner, (4) not removing objects from undo stacks when they're picked up — Sandbox-style games need per-player undo stacks. Tension: allowing players to undo weapons out of other players' hands.

Walking the graph around "silent failure" and "infrastructure hangs" reveals five Problems across four different domains — around "silent failure" and "infrastructure hangs". Tension: that share the same unextr.

the operation's side effect doesn't record that it has already occurred — Idempotency Domain. Tension: re-execution produces duplicate effects.

Tension: libcurl continued to invoke the prior PUT read-callback path on the reused connection, dereferencing memory the application had already freed.

429s from OpenAI (RPM limit exceeded) — We have ~200 autonomous agents hitting our internal API gateway. Tension: Cascading retries amplifying the load. Outcome: P99 latency spikes to 30+ seconds.

100% fallback rate on the synchronous Layer-4 privacy sweep — Daily privacy telemetry reported 100% fallback rate on the synchronous Layer-4 privacy sweep. Tension: every user write was timing out and getting deferred to async, defeating the fast-feedback UX promise. Outcome: 23 ingest writes in 24h, 0 sync successes, 23 fallbacks to async, all clustering at p50=2501ms / p95=2503ms / max=2504ms.

Daemon-witnessed triage counts (edge attrs.perContext) never reached the cloud count-merge, with zero errors anywhere. — Two independent layers each silently dropped the field. Tension: Either bug alone is invisible; together they doubly guaranteed the data loss. Outcome: with zero errors anywhere.

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