The gist VS NATS

Graphile worker has been great for me because it's a library that works with Postgres that allows me to queue jobs and execute them on the server without adding too many additional layers of complexity for being able to accomplish async tasks. (I'm aware of how popular bull is, but I don't want to add another data-store only for async tasks)

Bull is currently in maintenance mode, we are only fixing bugs. For new features check BullMQ, a modern rewritten implementation in Typescript. You are still very welcome to use Bull if it suits your needs, which is a safe, battle-tested library.

I use bullmq with node

You might consider using a queue for processing the request. I've found bullMQ, which works with Redis, to be a nice developer experience.

BullMQ is a pretty solid choice: https://github.com/taskforcesh/bullmq It's the successor of Bull: https://github.com/OptimalBits/bull

BullMQ

For Node.JS you can use something like BullMQ (https://github.com/taskforcesh/bullmq) and then dispatch jobs to the message queue with your worker handling the jobs. You can read about an example for Bull MQ here (https://deadsimplechat.com/blog/best-nodejs-schedulers/#2-bull)

bullmq seems like an open issue

In order to try and mitigate the OOMs. I read the Background Jobs in Node.JS with Redis blog post and implemented Heroku Data For Redis with ioredis, BullMQ and Throng,

Several message brokers, such as NATS and database queues, are not supported by OpenTelemetry (OTel) SDKs. This article will guide you on how to use context propagation explicitly with these message queues.

https://nats.io/ (Tracker removed)

> Connective Technology for Adaptive Edge & Distributed Systems

> An Introduction to NATS - The first screencast

I guess I don't need to know what it is

During our interview, we referred to NATS quite a few times! If you want to learn more about it, Sebastian suggests this tutorial series.

Pueue dumps the state of the queue to the disk as JSON every time the state changes, so when you have a lot of queued jobs this results in considerable disk io. I actually changed it to compress the state file via zstd which helped quite a bit but then eventually just moved on to running NATS [1] locally.

[1] https://nats.io/

Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.

Why wasn't NATS[1] used ?

Written in Go, single-binary deployment... there's a lot to love about NATS !

[1]https://nats.io/

require nats.io

About cost, see [1]. Also, S3 prices have been increasing and there's been a bunch of alternative offers for object store from other companies. I think people in here (HN) comment often about increasing costs of AWS offerings.

Distributed systems and consensus are inherently hard problem, but there are a lot of implementations that you can study (like Etcd that you mention, or NATS [2], which I've been playing with and looks super cool so far :-p) if you want to understand the internals, on top of many books and papers released.

Again, I never said it was "easy" to build distributed systems, I just don't think there's any esoteric knowledge to what S3 provides.

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1: https://en.wikipedia.org/wiki/Economies_of_scale

2: https://nats.io/

I am in a project were nats.io is used. Someone thought, it would be a great idea to link data in an event with data in a response using the response channel name.