gleam
argo
gleam | argo | |
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1 | 43 | |
3,373 | 14,314 | |
- | 0.9% | |
6.3 | 9.8 | |
7 days ago | 4 days ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gleam
argo
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StackStorm โ IFTTT for Ops
Like Argo Workflows?
https://github.com/argoproj/argo-workflows
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Creators of Argo CD Release New OSS Project Kargo for Next Gen Gitops
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...
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Practical Tips for Refactoring Release CI using GitHub Actions
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.
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(Not) to Write a Pipeline
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.
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what technologies are people using for job scheduling in/with k8s?
Argo Workflows + Argo Events
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What are some good self-hosted CI/CD tools where pipeline steps run in docker containers?
Drone, or Tekton, Argo Workflows if youโre on k8s
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job scheduling for scientific computing on k8s?
Check out Argo Workflows.
- Orchestration poll
- What's the best way to inject a yaml file into an Argo workflow step?
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Which build system do you use?
go-git has a lot of bugs and is not actively maintained. The bug even affects Argo Workflow, which caused our data pipeline to fail unexpectedly (reference: https://github.com/argoproj/argo-workflows/issues/10091)
What are some alternatives?
glow - Glow is an easy-to-use distributed computation system written in Go, similar to Hadoop Map Reduce, Spark, Flink, Storm, etc. I am also working on another similar pure Go system, https://github.com/chrislusf/gleam , which is more flexible and more performant.
temporal - Temporal service
tendermint - โ Tendermint Core (BFT Consensus) in Go
keda - KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
hprose - Hprose is a cross-language RPC. This project is Hprose for Golang.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
torrent - Full-featured BitTorrent client package and utilities
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
rpcx - Best microservices framework in Go, like alibaba Dubbo, but with more features, Scale easily. Try it. Test it. If you feel it's better, use it! ๐๐๐ฏ๐ๆ๐๐ฎ๐๐๐จ, ๐๐จ๐ฅ๐๐ง๐ ๆ๐ซ๐ฉ๐๐ฑ! build for cloud!
StackStorm - StackStorm (aka "IFTTT for Ops") is event-driven automation for auto-remediation, incident responses, troubleshooting, deployments, and more for DevOps and SREs. Includes rules engine, workflow, 160 integration packs with 6000+ actions (see https://exchange.stackstorm.org) and ChatOps. Installer at https://docs.stackstorm.com/install/index.html
DHT - BitTorrent DHT Protocol && DHT Spider.
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.