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Graph/lock-contention-deadlocks

Pattern

Lock Contention Deadlocks

lock-contention-deadlocks

Thread concurrency fails because shared resources require synchronization (DB driver locks, Python RNG/locks, multi-lock waits), leading to blocking, timeouts, and deadlocks that cascade into stalled work and incorrectly committed intermediate states.

Node Details

Public graph metadata

Updated6/25/2026

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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 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)?

after a few hundred requests. Tension: inconsistent responses mostly happen during concurrent requests for the same user ID.

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.

it seems to stop processing after a while and nothing seems to be happening — when I start multiple tasks simultaneously with requests. Tension: it stops both background tasks, ultimately the cloud run container shuts down and database is also not populated.

gpg: Note: database_open 134217901 waiting for lock (held by 9857) ... — When I attempt to commit in Git through R. Tension: [GNUPG:] FAILURE sign 134250628 gpg: signing failed: Operation timed out. Outcome: the error was "simply" due to a freezing during the git commit.

Timeout waiting for connection from pool. Tension: Task 4217 in stage 4.0 failed 4 times. Outcome: Job aborted due to stage failure.

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.

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.

Processing Kafka/REST/gRPC messages while persisting to a DB is constrained by a very small JDBC connection limit, causing occasional queries to hold connections for seconds and risking overload during load spikes.

Failure count: 9 — tested on Windows 11 PC. Tension: create 10k threads to connect to server. Outcome: resolved in JDK26.

This code will eventually be executed under a web application, meaning it will be accessed concurrently — It reads an Excel file as a template to write data and saves it locally. Tension: The only thing that may looks marignally relevant is calling the static `OPCPackage.open` method. Outcome: there shouldn't be any problem running this code in a multithreaded environment.

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

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.

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.

the multithreaded version runs as long as the single threaded one — While writing a multithreaded application. Tension: I encountered a problem that the multithreaded version runs as long as the single threaded one.

Suppose #0 reads `strong == 1`. That requires: Both `fetch_sub` operations (#2 and #3) have already happened before #0. Tension: Threads that "shouldn't" modify the counter get stuck before performing the release operations that would allow #0 to see a reduced count. Outcome: So the uniqueness test is valid and future-proof.

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.

too many open database connections — after the update to 4.0. Tension: FATAL: remaining connection slots are reserved for non-replication superuser connections.

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.

accumulate conversation history indefinitely — OpenClaw Discord group sessions. Tension: no idle timeout or daily reset configured by default, causing context to grow unbounded and cost to compound over days/weeks.

Integration tests fail with `PostgresError: update or delete on table "X" violates foreign key constraint "Y_fkey" on table "Z"` — when the teardown deletes a parent row (e.g., a test agent/user). Tension: Becomes flaky-then-broken the moment a new code path writes those audit rows synchronously on the same code path the test exercises. Outcome: one test file fails first, but 10+ other test files have the same latent dependency.

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