[HELP] Comprehensive analysis of edge-case race conditions within asynchronous event loop task scheduling under high concurrency -- a detailed investigation into potential memory corruption and deadlocks when multiple microtasks are enqueued simultan

I am seeking feedback from anyone who has encountered similar race conditions at scale, particularly around shared TypedArrays or SharedArrayBuffers used for IPC between worker threads where the atomic operations may not fully prevent TSO (Total Store Order) reordering issues on ARM64 architectures compared to x86_64. The specific scenario: we have a producer-consumer pattern across 12 workers, each with its own event loop, sharing a single large SharedArrayBuffer for message passing, using Atomics.compareExchange as the synchronization primitive. On our production M3 Max servers (ARM), we are seeing occasional state corruption that suggests either an ordering issue, a memory fence violation, or possibly an edge case in how Node v19.4.0 handles shared buffer reallocations during GC pressure -- I've attached three crash dumps and the relevant code snippets to this thread.

This is a genuine problem and your observation about starvation fits exactly with what we saw during our migration

Your telemetry pattern is a classic signature of microtask starvation compounded by cross-worker shared state contention — I have seen this exact 450ms oscillation profile in high-throughput ingestion pipelines where Node_modules/_internal/async_context_flow was being abused via custom AsyncResource wrappers. Let me unpack the mechanism in detail because there are at least three distinct failure modes overlapping here that your staging environment can't replicate due to lower concurrency and fewer active async resources.

First, the starvation vector you mentioned is real but more subtle than most people realize. The event loop phase progression rule in Node 18+ specifies that microtasks (process.nextTick callbacks plus promise resolutions) must be drained completely before moving on to any macrotask — setImmediate, setTimeout(0), I/O poll, anything. If you have a continuous chain where each resolved promise schedules at least one additional nextTick via an internal or explicit call, the event loop never reaches the timer phase, and your 450ms latency is simply the accumulated time it took for the microtask queue to finally empty under load. The key thing to check: are you doing anything like Promise.resolve().then(() => ...) inside a high-frequency ingestion callback that could itself be part of an upstream chain? Even one call in a hot path can propagate through your async context flow and create this feedback loop.

Second, the crossworker shared state access is almost certainly where the memory corruption vector lives if you are using Atomics with SharedArrayBuffer for some form of coordination between worker threads on ingestion. There is an edge case in Node 18+ related to how Atomic.wait/notify interacts with the event loop when a microtask checkpoint is pending — specifically, if a worker thread releases an atomic lock but the main thread has already started scheduling its microtask queue processing and hasn't reached the Atomics check point yet, you can get a brief window where both threads believe

That's a huge thread title and I think you're actually onto something real with those

I'm going to assume before I answer this that you have actually read the issue tracker, because if not, we can save ourselves both a lot of time right now. The 450ms spike profile is almost certainly event loop lag from starvation on your ingestion worker — the priority resolution

um sorry i don't really understand most of this terminology -- could someone explain what a race condition is for someone who just started learning javascript? and also why would high concurrency cause memory corruption if everything runs in one thread, i keep reading that nodejs is single threaded so i thought it was impossible.

sorry to interrupt your deep dive but could anyone also say in plain english what the starvation thing means -- like are tasks being ignored forever or something? this looks really complicated and im kind of

This is a textbook microtask starvation scenario, and frankly I'm disappointed you couldn't see it from the thread title alone. The 450ms spikes aren't "edge cases" — they are what happens when your ingestion pipeline chains promises without any setImmediate or

wait... sorry this is probably dumb and i should search first but -- what's a race condition? like when two things happen at once and break something??

also 450ms spikes sounds bad. is that because the threads are fighting over memory or something about how node handles tasks. my friend told me node has an event loop but i don't really understand it.

does this mean we should add more workers? or fewer. i genuinely cannot tell from reading this thread