You can just set up a separate child process for that. The main event loop which handles connections should just co-ordinate and delegate work to other programs and processes. It can await for them to complete asynchronously; that way the event loop is not blocked.
I recall people have been able to get up to around a million (idle) WebSocket connections handled by a single process.
I was able to comfortably get 20k concurrent sockets per process each churning out 1 outbound message every 3 to 5 seconds (randomized to spread out the load).
It is a good thing that Node.js forces developers to think about this because most other engines which try to hide this complexity tend to impose a significant hidden cost on the server in the form of context switching... With Node.js, there is no such cost, your process can basically have a whole CPU core for itself and it can orchestrate other processes in a maximally efficient way if you write your code correctly... Which Node.js makes very easy to do. Spawning child processes and communicating with them in Node.js is a breeze.
This article is talking about an SDK that runs in users' apps. Users can run whatever code they want, so the SDK has to find a way to keep sending the outgoing heartbeats
You can not limit yourself when you need more but still follow these patterns in any other language.
e.g., this certainly helps when the event loop is blocked, but so could FFI calls to another language for the CPU bound work. I’d only reach for a new Node thread if these didn’t pan out, because there’s usually a LOT that goes into spinning up a new node process in a container (isolating the data, making sure any bundlers and transpilers are working, making sure the worker doesn’t pull in all the app code, etc.).
Side car processes aren’t free, either. Now your processes are contending for the same pool of resources and can’t share anything, which IME means more likelihood of memory issues, esp if there isn’t anything limiting the workers your app can spawn.
Still, good article! Love seeing the ways people tackle CPU bound work loads in an otherwise I/O bound Node app.
Worker threads can be more convenient than FFI, as you don't need to compile anything, you can reuse the main application's functions, etc.
I make my comment to note tho that in many envs it’s easier to scale out than account for all the extra complications of multiple processes in a single container.
5 years ago I never would have given this comment a second thought.
Now I read it and have to wonder: when does the price of ram start showing up in the butchers bill from your cloud provider?
It's an SDK that runs in users' apps. So userland code blocks the event loop, preventing outgoing heartbeats from the SDK
Having a separate isolate in each threads spawned with the worker threads with a minimal footprint of 10MB does not seem like a high price to pay. It's not like you're going to spawn hundreds of them anyway is it? You will very likely spawn less or as much threads as your CPU cores can handle concurrently. You typically don't run a hundred of threads (OS threads) you use a thread pool and you cap the concurrency by setting a limit of maximum threads to spawn.
This is also how goroutines work under the hood, they are "green threads", an abstraction that operate on top of a much small OS thread pool.
Worker threads have constraints but most of them are intentional, and in many cases desirable.
I’d also add that SharedArrayBuffer doesn’t limit you to “shared counters or coordination primitives”. It’s just raw memory, you could store structured data in it using your own memory layout. There are libraries out there that implement higher-level data structures this way already
https://github.com/tc39/proposal-module-declarations
Unfortunately the JS standards folks have refused so far to make this situation better.
Ex. it should just be `new Worker(module { ... })`.
[1] https://docs.google.com/presentation/d/1inTcnb4hugyAvKrjFX_X...
And you can make it thread-like if you prefer by creating a “load balancer” setup to begin with to keep them CPU bound.
require('os').cpus().length
More here https://github.com/bennyschmidt/simple-node-multiprocess
Most other ways are just hiding the context switching costs and complicating monitoring IMO.
1. https://docs.platformatic.dev/docs/overview/architecture-ove...
Almost none of them treat these consistently (if they consider these at all) and all require you to work around them in strange ways.
It feels like there is a lot they could help with in the web world, especially in complex UI and moving computation off the main thread but they are just so clunky to use that almost nobody tries to work around it.
The ironic part is if bundlers, transpilers, compilers etc. weren't used at all they would probably have much more widespread use.
Move backpressure handling onto the task producer and use a SharedArrayBuffer between the producer and worker, where the worker atomically updates a work-count or current work item ID in that SharedArrayBuffer that the producer can read (atomically) to determine how far along the worker has gotten.
They’re heavy, they don’t share the entire process memory space (ie can’t reference functions), and I believe their imports are separate from each other (ie reparsed for each worker into its own memory space).
In many ways they’re closer to subprocesses in other languages, with limited shared memory.
It’s not “clean” to spin up thousands of threads, but it does work and sometimes it’s easier to write and reason about than a whole pipeline of distributing work to worked threads. I probably wouldn’t do it in a server, but in a CLI I would totally do something like spawn a thread for each file a user wants to analyze and let the OS do task scheduling for me. If they give me a thousand files, they get a thousand threads. That overhead is pretty minimal with OS threads (on Linux, Windows is a different beast).
However, apart from atomic "plain" memory no objects are directly shared (For Node/V8 they live in so called Isolated iirc) so from a logical standpoint they're kinda like a process.
The underlying reason is that in JavaScript objects are by default open to modification, ie:
const t = {x:1,y:2};
t.z = 3;
console.log(t); // => { x: 1, y: 2, z: 3 }
If you've done multithreaded C/C++ work and touched upon Erlang the JS Worker design is the logical conclusion, message passing works for small packets (work orders, structured cloning) whilst large data-shipping can be problematic with cloning.
This is why SharedArrayBuffer:s allows for no-copy sharing since the plain memory arrays they expose don't offer any security surprises in terms of code execution (spectre style attacks is another story) and also allows for work-subdivision if needed.
In terms of tradeoffs, if you’re coming from the single event loop model, they’re pretty consistent with the rest of JS. Isolation-first, explicit sharing, fewer footguns. So I think the tradeoffs are the right tradeoffs.
FWIW, traditional threads have their own tradeoffs (especially around IO). In JS that’s mostly a non-issue, so the "I need 1000s of threads" case just doesn’t come up very often.
A worker thread or Web Worker runs in its own isolate, so it needs to initialise it by parsing and executing its entry point. I'm not quite sure whether that's something that already happens but you could imagine optimising this by caching or snapshotting the initial state of an isolate when multiple workers use the same entry point, so new workers can start faster.
That cannot be done with the original main thread isolate because usually the worker environment has both different capabilities than the main isolate and a different entry point.
If I have to handle 1000 files in a small CLI I would probably just use Node.js asynchronous IO in a single thread and let it handle platform specifics for me! You’ll get very good throughput without having to handle threads yourself.
But the prices have gotten stupid: https://pcpartpicker.com/trends/price/memory/
https://appleinsider.com/articles/26/02/27/the-global-ram-an...