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Graph/async-concurrency-semantics-drift

Pattern

Async Concurrency Semantics Drift

async-concurrency-semantics-drift

Concurrency control drifts across layers—threads, schedulers, and async executors—so blocking points (e.g., sleep or unhandled Kafka futures) or misconfigured scheduling cause tasks to run at unexpected times or in parallel, breaking throughput and correctness assumptions.

Node Details

Public graph metadata

Updated7/2/2026

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.

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.

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?

the background job never runs — my `BackgroundTasks` only execute when declared inside an async dependency. Tension: No exception is raised, the route returns normally. Outcome: If I move the `bg.add_task()` call into the route itself, it works as expected.

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.".

The idea is to update this model once hourly without blocking the API's ability to respond. Tension: update this model once hourly without blocking the API's ability to respond to CPU-bound tasks. Outcome: this is all good.

the reason for the various threads appearing in the logs — when you pass a synchronous generator to `EventSourceResponse`. Tension: you seem to be returning from the `/` endpoint. Outcome: `EventSourceResponse` will execute the `def` function in a separate thread from an external threadpool and then `await` it.

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

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.

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.

seems to ensure even old messages are consumed. Tension: I can't believe Spring would override Kafka's native defaults. Outcome: What is the property's default when consuming from a Kafka topic via Spring Kafka?

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.

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.

TimerTask does not run at the desired times

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?

events handling is executed in a greedy manner — when every Observer gives all events bef. Tension: it doesn't. Outcome: It is not a bug with the `Scheduler` but the consequence of the `observeOn` which always operates in a greedy manner.

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.

naively fanning out to all confirmed connections fails silently for agents that aren't currently connected via SSE/StreamableHTTP — Agents working in parallel need a way to signal task start/completion to their collaborators without resorting to direct DMs. Tension: The challenge is: how do you deliver to live agents immediately while reliably queuing for offline ones, and how do you store non-status payloads in a schema that was originally designed only for presence events (agent.online/offline)?

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.

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.

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