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Graph/thread-contention-limited-concurrency

Pattern

Thread Contention Breaks Concurrency

thread-contention-limited-concurrency

Shared locks and serialized state make parallelism collapse: threads contend on JVM/driver synchronization (e.g., Random instances, DB internal monitors, scheduler-driven polls), so tasks that should run concurrently instead block or interleave transaction timing.

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Updated6/25/2026

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30 linked nodes

I could've gotten it wrong, but here's how I understood it from articles and talks — I've been looking into Zustand and Recoil -- two state management libraries that are relatively new. Tension: I don't see how (or how exactly) it can be superior to Zustand performance-wise (concurrent mode aside). Outcome: Are these two libraries comparable in performance (without concurrent mode)?

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)?

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.

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.".

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.

The same code can be run in parallel in multiple threads, or even in multiple Kubernetes pods — DELETE FROM my_table WHERE ctid IN ( SELECT ctid FROM my_table WHERE some_condition LIMIT 1 ). Tension: two threads start fighting for the same record.

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.

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

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.

Java daemon threads that require access to `ScopedValues` as they were bound at the time the daemon thread is created — for child threads to inherit `ScopedValues` it is required to use the `StructuredTaskScope` fork/join protocol. Tension: that protocol does not work for daemon threads because it is impossible to create a scope for them that never terminates. Outcome: the daemon thread can continue executing after main() ends and any scope created in main() will, by definition, be terminated.

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.

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.

there seems to be two different thread states: `JVM thread_state` and `java.lang.Thread.State` — In below thread dump. Tension: Which one is the actual state of the thread? Outcome: You can see java.lang.Thread.State and thread_state as two different “perspectives” the JVM has over a thread state.

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.

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.

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.

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.

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