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Graph/unbounded-backpressure-gaps

Pattern

Unbounded Backpressure Gaps

unbounded-backpressure-gaps

Thread pools and schedulers release work early or poll in parallel, so the system lacks effective backpressure, queue bounding, and concurrency control. This causes backlog blowups, excessive parallel transactions, and memory pressure when tasks can’t all fit in memory.

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Updated7/5/2026

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30 linked nodes

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.

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 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?

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

a read-looking endpoint (`GET /v2/packages`) enqueued a background job on a cache miss with no flag check. Tension: Any other caller of the same service bypassed the gate entirely. Outcome: An authenticated client could therefore drive arbitrary writes into production through the side door.

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.

strcpy(overflowing, overflowingtext) does not reliably overflow into p1

Buffer overflow cannot be demonstrated reliably

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.

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.

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.

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.

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.

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.

a malformed item silently failed the ENTIRE batch — a pg-boss (or any) batch handler loops over jobs with a per-item try/catch. Tension: one bad item should log and skip, the rest should still run. Outcome: the good job still processes (importExternalDump called once).

workers can pick up tasks within a reasonable time — io_context workers. Tension: are not stuck running long or blocking operations.

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.

Workers reg — pg-boss v10. Tension: Queues must be registered in the `pgboss.queue` table before any `work()`, `send()`, or `schedule()` call references them. Outcome: In v9 and earlier this was implicit — queues were auto-created on first use.

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.

heap buffer overflow vulnerability in the match_regex function. Tension: When a regex pattern contains backreferences. Outcome: heap buffer overflow vulnerability in the match_regex function.

Tension: attacker-controlled sizes from PAX extended headers. Outcome: triggering a SIGSEGV/stack exhaustion.

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.

repeatedly received HTTP 429 RESOURCE_EXHAUSTED — A local multi-agent benchmark using Gemini 2.5 Flash. Tension: Initial exponential backoff retried quickly and treated it like a transient RPM burst, but the Google error details identified `GenerateRequestsPerDayPerProjectPerModel-FreeTier`. Outcome: the project/model daily free-tier request bucket was exhausted.

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.

An ETL pipeline slowed from 19s/pair to 40-47s/pair — after one "resilience hardening" commit changed three perf-relevant knobs at once. Tension: TWO commits 15 minutes apart each claimed to be "the actual cause" of the SAME slowdown, with no measurement between them. Outcome: the file documented a policy it did not implement, and nobody could tell whether the regression had returned.

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