refute VS NATS

I find straight forward, dedicated combinators much more readable and practical to use ie. for iterables (context where it makes a lot of sense) [0] example [1], runtime assertions (through refutations, which are much faster than combinators over assertions) [2], parser combinators for smallish grammars [3] etc.

In many cases vanilla/imperative js is more readable and terse, no need to bring functional fanaticism everywhere, just in places where it gives true benefits and in form that can be understood by peers.

Functional code can be beautiful and can also be unreadable/undebugable. Same with imperative code. It's great in js/ts you can pick approach where the problem is expressed more naturally and mix it at will.

[0] https://github.com/preludejs/generator

[1] https://observablehq.com/@mirek/project-euler

[2] https://github.com/preludejs/refute

[3] https://github.com/preludejs/parser

We use jsonrpc over websockets in production for many years in trading services. It works very well. We use lightweight libraries that look like this [0] and this [1]. It's lightweight, fast, type safe, easy to maintain and debug etc.

[0] https://github.com/preludejs/jsonrpc

[1] https://github.com/preludejs/refute

Types can be asserted at runtime (parsed) at IO boundaries (reading http request or response, websocket message, parsing json file etc). Once they enter statically type system they don't need to be asserted again.

The difference it makes is illusion of type-safety vs type-safety this article touches on.

You can try to bind service with client somehow but in many cases this will fail in production as you can't guarantee paired versioning, due to normal situations by design of your architecture or temporary mid-deployment state or other team doing something they were not suppose to do etc. It's hard to avoid runtime parsing in general.

Functional combinators [0] or faster [1] with predicate/assert semantics work very well with typescript, which is very pleasant language to work with.

[0] https://github.com/appliedblockchain/assert-combinators

[1] https://github.com/preludejs/refute

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.