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Graph/thread-blocking-caused-by-contention

Pattern

Thread Blocking via Misuse

thread-blocking-caused-by-contention

Shared or improperly synchronized concurrency mechanisms block progress because one execution context must wait on another (e.g., locked shared PRNG state, virtual threads pinned in synchronized regions), causing watchdog misattribution and system-wide stalls.

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Updated7/10/2026

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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.

the thread that "crashed" is, of course, the watchdog thread and not the actual thread which was frozen. Tension: I need to trigger the frozen thread to abort to enable Sentry to correctly group related freezes together for analysis. Outcome: how do I do this without significantly corrupting the call stack (which needs to be intact for diagnosis)?

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.".

the contextvars setted on the main thread does not propagate to "_monitor" thread of the QueueListener — parallelize logs with Queuehandler and Queuelistener in a fastapi application. Tension: And use in it in a Formatter on a logger file. Outcome: Can anyone help me to understand how can I resolve this?

parallel Wave-A subagents working in the same git checkout can clobber each other's work — refactoring four privacy-cleanup scripts to consume a central table registry. Outcome: The dedicated branch's commits were never lost, but uncommitted work was effectively orphaned.

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.

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.

Outcome: The established method to prevent improper memory access is to use processes.

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.

Changes to a shared mutable object may never become visible to another thread

I can't store all of my tasks inside a BlockingQueue first because it would be too many tasks to handle in-memory

The behavior is that I expected is that each threads enqueues and deques in the sequential order that the threads used in enqueue the values, but this is not the case. — using `built-in java ArrayBlockingQueue` and `custom implementation`. Outcome: q.add(...) and q.poll() may alternate.

Multiple threads are writing in the file concurrently. — I need to persist this counter because if process get restarted then it counter should start from last count. Tension: I hope there wont be a case when any number shall be skipped or give duplicate value to thread. Outcome: how to reset this key when value reaches to 25k in a thread safe way.

The goal is to observe the traces, despite using virtual threads. Tension: There are challenges adapting to my piece of code:. Outcome: Yes, `Executors.newVirtualThreadPerTaskExecutor()` should work fine as far as Observation Propagation is concerned.

the Main thread was blocked till the subscribe method completion — Mono.just(getValue()).subscribe(value -> System.out.println(value));. Outcome: Can someone make this program asynchronous?

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.

do my fences interrupt other guests on the machine? — running in a VM on a cloud provider. Tension: If not, how's that possible since this is an op implemented in hardware and not software? Outcome: Fences are local, affecting only the current thread.

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...

this does not happen — whenever the tracee creates a new thread. Tension: I must also follow all threads created by the traced program.

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.

Go goroutine blocks forever on a channel send after parent context cancellation

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.

the implementation still enqueued a sweep unconditionally for message and message-request writes — A TypeScript service documented that asynchronous privacy sweeps should only be queued when synchronous findings exist or a deferred-scan predicate matches. Tension: the implementation still enqueued a sweep unconditionally. Outcome: for message and message-request writes.

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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