temporal VS argo

Since I'm in a ranting mode -- here's a good example: you're limited to _ONE_ IO per shard in the history service:

https://github.com/temporalio/temporal/blob/e22e6304b3c4a409...

https://github.com/temporalio/temporal/blob/e22e6304b3c4a409...

Temporal does a crazy amount of database operations and all of these are behind that mutex.

Oh, and you can't change the shard count on existing clusters.

Great stuff.

GitHub: github.com/temporalio/temporal

Repository: Trigger.dev · Temporal Documentation: Trigger.dev docs · Temporal docs License: Varies by engine (Trigger.dev: Apache 2.0; Temporal: MIT) Free Tier: Varies by engine — hosted usage tiers, self-hosted deployments, and managed-cloud limits all differ Best for: Any ingest workload where “what failed and why” needs to be answerable, upstreams are flaky, or you’re running at a scale where silent failures are unacceptable.

Temporal employee here. I'm very surprised by your comment.

It's true that we recently had a Series D and that VC firms recognize the value of what we do. The Temporal Server software is 100% open source (MIT license: https://github.com/temporalio/temporal/blob/main/LICENSE). It's totally free and you don't even need to fill out a registration form, just download precompiled binaries from GitHub or clone the repo and build it yourself. You can self-host it anywhere you like, no restrictions on scale or commercial usage. We offer SaaS (Temporal Cloud), which customers can choose as an alternative self-hosting, based on their needs. The migration path is bi-directional, so not a trap by any definition.

Regarding AI, Temporal is widely used in that space, but that does not negate the thousands of other companies that use Temporal for other things (e.g., order management systems, customer onboarding, loan origination, money movement, cloud infrastructure management, and so on). In fact, our growth in the AI market came about because companies who were already using Temporal for other use cases realized that it also solved the problems they encountered in their AI projects.

And to your last point, we've made dozens of enhancements to the product (here's a small sample: https://temporal.io/blog/categories/product-news). I'd encourage you to follow the news from next week's Replay conference (https://replay.temporal.io/) because we'll be announcing many more.

Temporal docs: https://docs.temporal.io/

The architecture lets you scale workers independently, deploy new workflow versions without downtime, and run the Temporal Service either self-hosted or as Temporal Cloud (their managed offering). The project has roughly 19,000 GitHub stars on the temporalio/temporal repository, which tells you the community traction is real.

i’m not associated with temporal, no does the link above have any referrer nonsense in there. it’s not wrong to point to a proper resource that can explain and demonstrate my argument better than a couple of words could. temporal is open source[0] so maybe a github link would have been more palatable?

[0]: https://github.com/temporalio/temporal

Temporal Documentation

Argo has a CLI, which provides a convenient interface for submitting, monitoring, and recording your workflows. You can download the CLI from GitHub Releases. Use the version that matches the Argo release installed in your Kubernetes cluster.

Remember to meet the prerequisites, including AWS cli, kubectl, terraform and Argo Workflow CLI.

Like Argo Workflows?

https://github.com/argoproj/argo-workflows

Dagger looks more comparable to Argo Workflows: https://argoproj.github.io/argo-workflows/ That's the first of the Argo projects, which can run multi-step workflows within containers on Kubernetes.

For what it's worth, my colleagues and I have had great luck with Argo Workflows and wrote up a blog post about some of its advantages a few years ago: https://www.interline.io/blog/scaling-openstreetmap-data-wor...

Despite other alternatives like Circle CI, Travis CI, GitLab CI or even self-hosted options using open-source projects like Tekton or Argo Workflow, the reason for choosing GitHub Actions was straightforward: GitHub Actions, in conjunction with the GitHub ecosystem, offers a user-friendly experience and access to a rich software marketplace.

author seems to be describing the kind of patterns you might make with https://argoproj.github.io/argo-workflows/ . or see for example https://github.com/couler-proj/couler , which is an sdk for describing tasks that may be submitted to different workflow engines on the backend.

it's a little confusing to me that the author seems to object to "pipelines" and then equate them with messaging-queues. for me at least, "pipeline" vs "workflow-engine" vs "scheduler" are all basically synonyms in this context. those things may or may not be implemented with a message-queue for persistence, but the persistence layer itself is usually below the level of abstraction that $current_problem is really concerned with. like the author says, eventually you have to track state/timestamps/logs, but you get that from the beginning if you start with a workflow engine.

i agree with author that message-queues should not be a knee-jerk response to most problems because the LoE for edge-cases/observability/monitoring is huge. (maybe reach for a queue only if you may actually overwhelm whatever the "scheduler" can handle.) but don't build the scheduler from scratch either.. use argowf, kubeflow, or a more opinionated framework like airflow, mlflow, databricks, aws lamda or step-functions. all/any of these should have config or api that's robust enough to express rate-limit/retry stuff. almost any of these choices has better observability out-of-the-box than you can easily get from a queue. but most importantly.. they provide idioms for handling failure that data-science folks and junior devs can work with. the right way to structure code is just much more clear and things like structuring messages/events, subclassing workers, repeating/retrying tasks, is just harder to mess up.

Argo Workflows + Argo Events

Drone, or Tekton, Argo Workflows if you’re on k8s

Check out Argo Workflows.