fn VS NATS

Not always. Check out how Oracle Cloud does it. It's a hosted version of an open source stack called fn, which you can run fully locally via a simple CLI tool.

https://fnproject.io

There's not much info out there but I'll describe what I got from reading the blog posts and searching for it.

"Serverless" stuff like the (proprietary) Lambda or (open source, https://fnproject.io/) OCI Cloud Functions are based on a few ideas:

1. Use Linux syscalls+x86 as the target ABI/ISA. Thus programs are Docker containers and because the kernel is a bit too much C to trust, maybe also custom virtual machines for sandboxing.

2. Because downloading a full blown Linux userspace and starting it up inside a new virtual machine can be slow, then add a variety of hacks on top to try and make a start/stop model look like an always on service. For example by having always-on instances (which means serverless now has servers again), by using Docker layers and other stuff.

GraalOS asks the following question: what if we toss Linux and x86 as the API? Is there a way to do server-side computing better?

This question just leads to more questions:

• What do we replace it with?

• What are the benefits?

GraalOS starts by saying, let's replace Linux/native code with the Java specifications instead. This gives you a relatively large and consistent yet open source surface area for doing all the server-side basics you need like IO, threading, memory management and so on. You can then layer Truffle (from the same team) on top to get other languages like JavaScript, Python, Ruby, WASM, Rust or C++ (via LLVM bitcode) and so on. All of these running on top of the JVM rather than Linux.

In this model the JVM isn't an operating system, exactly, but it might as well be because you don't have access to the underlying kernel at all. There's no way to make system calls in this model that aren't mediated by the standard libraries of your language. And this is enforced via two very different sandboxing technologies:

1. The server controls the compiler.

2. Intel MPX and whatever the AMD equivalent is.

Controlling the compiler is how you implement software level sandboxing. Because all code running on the CPU is created by your own compiler which the developer cannot choose (like in a browser), you can implement and impose whatever policies you like. The most obvious is no syscalls, no unsafe memory accesses and so on. But then you may want more than that, for example, how do you stop Spectre attacks extracting secrets from the address space? To which the answer can be the CPU's "Memory Protection Keys". This is a very, very fast and lightweight way to do a kind of in-process context switch. You can associate page ranges with a "key" and then put that key into a special register to control what memory ranges are currently accessible. It's like an additional set of permissions over what the kernel has set up. Because you control the compiler, you can ensure that only system code can alter the current memory protection key, and then this lets you compile and execute untrusted code without worrying about speculation attacks.

So that's the theory, what's the benefits?

The first benefit is that you don't need containers anymore. GraalVM has the "native image" tool that produces native Linux standalone executables from JVM apps, like Go does. And those JVM apps can be interpreters or JIT compilers for other Truffle languages as well. So now, you no longer need to drag around half an Ubuntu install for each app you run. It means programs can be moved between servers way faster because there's less to copy, and anyway Oracle Cloud has notoriously excellent networking, from what I've read, so new instances can be spun up much faster than before. And native images start ~instantly because they are fully native code and have a persisted heap snapshot computed as part of the build process, so they effectively start already initialized. And then finally in some cases they can do snapshotting post-startup too, for fast suspend/resume, and the compiler knows how to do MPX keys.

So with all this done, you can produce a server side infrastructure in which programs are just shared libraries loaded and unloaded into pre-warmed HTTP servers, yet still isolated and protected from each other, and because things are way faster you can actually just start and stop these servers genuinely on demand on a per-request basis. There's no need to charge users for idle minutes as you try to avoid a shutdown/startup cycle. In turn that means a lot of complexity just boils away.

BTW, I just checked and it turns out that Oracle's "free cloud" deal applies to functions as well. You get like 2 million free activations a month or something, and 400k "gigabyte memory-seconds". So if that pricing is sustained with this then it means a lot of types of JVM servers will just be completely free to host, because native image also reduces memory consumption a lot.

At least that's my guess as to what's going on. But it's not launched yet, just announced. I guess we'll have to see what it's like for real when it's available.

Functions: Scalable, multi-tenant serverless functions based on Fn

OpenFAAS or FN Project are options

Install fn (refer to https://fnproject.io/ for latest instructions)

I disagree, microservices are an architectural concept related to the software, not to the infrastructure.

Whether you are using containers or VPS or serverless or bare metal for your infrastructure, that's completely unrelated to the concept of microservices: you can deploy either a monolith or microservices in any of the above.

As an example you can deploy a monolith on Lambda[1] or you can deploy microservices on bare metal using one of the several self managed serverless engines available[2].

[1] see e.g. https://claudiajs.com/tutorials/serverless-express.html or https://blog.logrocket.com/zappa-and-aws-lambda-for-serverle...

[2] see e.g. https://fnproject.io/ and https://knative.dev/

Still, for today I would like to talk to you about the FN project, an open-source alternative.

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.