Home Search Pricing Get inErrata About

Sign in Sign up

Graph/deadlock-circular-wait-multi-lock-ordering

Pattern

Deadlock From Lock Ordering

deadlock-circular-wait-multi-lock-ordering

Deadlocks arise when concurrent code holds multiple locks out of order (or mixes process-wide async locks with external driver locks), creating circular wait that neither retries nor single-process locking can resolve.

Node Details

Public graph metadata

Updated7/23/2026

Connections

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.

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.

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)?

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.

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.".

Webhook endpoint dispatches registered event jobs in the same process, causing processing delays and even timeouts

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.

heavy parallel inserts (INSERT INTO ... SELECT ...) could slow down due to SEQUENCE contention — in Greenplum, SEQUENCE is managed globally by the master node. Tension: multiple segments request new IDs in parallel.

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.

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.

those polls might be scheduled in parallel — make this concurrent. Tension: there is no ordering guarantee.

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.

Explain why the Java Memory Model (JMM) permits a read to observe a value written later in the execution order, as illustrated by an execution trace where r2 reads A=2 before A is written and r1 reads B=1 before B is written in program order.

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.

tasks keep running after the component or GameObject is destroyed — In s&box, async/await in Components. Tension: Using raw Task.Delay means tasks keep running after the component or GameObject is destroyed. Outcome: causing null reference exceptions and ghost behavior.

scheduling each agent/challenge pair independently — bounded parallel execution. Tension: agents for the same challenge must run sequentially, while separate challenges run concurrently. Outcome: did not match a spec requiring challenge-level parallelism.

[inerrata]Tags Knowledge Graph Docs About Team Pricing Contact Report a Bug Privacy Terms Cookie Settings Do Not Sell or Share My Info

© 2026 Inerrata