Inngest Blog

While this does talk about durable queues, this post really hinges on a pivot to evangelizing durable workflows. It makes it sound like they're synonymous, when they're very much not. Specifically, this sentence bringing up workflows for the first time (emphasis mine):

> "Durable queues were rare when I was at Reddit, but they’re more and more popular now. Essentially, they work by combining task queues with durable workflows, helping you reliably orchestrate workflows of many parallel tasks."

That makes it sound like task queues + durable workflows = durable queues, but that's not true at all. A durable queue is literally a queue that doesn't drop messages e.g. during an unexpected shutdown. That's all. Durable workflows are a pretty different thing. A durable queue could be used just like a normal queue, but while you can't build a durable workflow on a normal queue (or at least, it would be a huge pain), a durable queue makes it vastly simpler to build a durable workflow engine. I think the article talks about durable workflows because this is DBOS, a company looking to sell durable workflow services, but also because durable workflows are considered by many to be a kind of "holy grail" of big-business applications as they seem like they can allow for you to write code that's kind of "always running", where the state in memory is persisted to a DB invisibly so that you have to think less about CRUD. The killer app of durable workflows seems to me to writing…

Re Inngest - there are a few differences:

1. Hatchet is MIT licensed and designed to be self-hosted in production, with cloud as an alternative. While the Inngest dev server is open source, it doesn't support self-hosting: https://www.inngest.com/docs/self-hosting.

2. Inngest is built on an HTTP webhook model while Hatchet is built on a long-lived, client-initiated gRPC connection. While we support HTTP webhooks for serverless environments, a core part of the Hatchet platform is built to display the health of a long-lived worker and provide worker-level metrics that can be used for autoscaling. All async runtimes that we've worked on in the past have eventually migrated off of serverless for a number of reasons, like reducing latency or having more control over things like runtime environment and DB connections. AFIAK the concept of a worker or worker health doesn't exist in Inngest.

There are the finer details which we can hash out in the other thread, but both products rely on events, tasks and durable workflows as core concepts, and there's a lot of overlap.

The other day I asked chatgpt (o3) to help me compare a bunch of task orchestration systems and arrange them according to some variables I care about (popularity, feature richness, durability, whether can be self-hosted, etc.). I ended up using https://www.inngest.com/ -- which was new to me -- and that single tool sped up my particular task by at least 10x for the week. That was a one-off project, so it won't generalize in a clean way, but I keep finding individual cases where the particular strengths of LLMs can save me a whole bunch of time. (another example: creating and evaluating responses to technical interview questions). I don't expect that these are easy to quantify, but they are significant.

This is not to disagree with the OP, but to point out that, even for engineers, the speedups might not appear where you expect. [EDIT I see like 4 other comments making the same point :)]

Inngest | San Francisco (preferred) + Remote

https://www.inngest.com is a modern queueing/durable execution platform that allows all developers to ship fast, reliable code without managing infra, queues, or state. Our users write step functions in their existing codebase, and we handle the backend infra, queueing, scaling, concurrency, throttling, rate limiting, batching, o11y, and tracing for you.

We're beginning the hiring process for:

* Infrastructure engineer: an infrastructure engineer to help build and deploy our queueing system, state store, etc. on bare metal, then eventually our own hardware.

You can see the jobs here: https://jobs.ashbyhq.com/inngest.

I wrote this last month to get ahead, and the hiring process kicks off tomorrow. HN is great!

Inngest | 3 Engineer roles | Fully-remote

Inngest is the reliability layer for modern applications. Inngest combines durable execution, events, and queues into a zero-infra platform with built-in observability. We're multi-cloud (host your code wherever you want!) and multi-language. Our product takes advanced-but-complicated reliability patterns and makes them simple to configure for any developer.

We're a team of 10 who recently raised a seed extension round. Come join us if you want to work in our fascinating problem space!

Jobs -> https://jobs.ashbyhq.com/inngest

- Staff Frontend Engineer

- Systems Engineer

- Product Engineer

Company -> https://inngest.com

what about this pattern? https://www.inngest.com/blog/python-errors-as-values

I tried it once in an sqlite DB connector with some business logic and simply checking stuff like

res: DBException | Result = db_handler.some_business_logic() if isinstance(res, DBException): return res # you can also log or even raise if this function isn't returning exceptions as values # guaranteed to be Result type here See here:

- https://gitlab.com/meehai/drpciv-flask/-/blob/main/be/db_handler.py?ref_type=heads#L186

- https://gitlab.com/meehai/drpciv-flask/-/blob/main/be/app.py?ref_type=heads#L69