argo-rollouts
argo
argo-rollouts | argo | |
---|---|---|
9 | 43 | |
2,499 | 14,314 | |
2.1% | 0.9% | |
9.5 | 9.8 | |
about 17 hours ago | 3 days ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
argo-rollouts
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Gateway API is now in Beta; new project formed for service mesh APIs
And support is on it's way for Argo Rollouts 🎉 https://github.com/argoproj/argo-rollouts/pull/2004
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Progressive Delivery with Argo Rollouts : Blue-Green Deployment
curl -LO https://github.com/argoproj/argo-rollouts/releases/latest/download/kubectl-argo-rollouts-linux-amd64 chmod +x ./kubectl-argo-rollouts-linux-amd64 sudo mv ./kubectl-argo-rollouts-linux-amd64 /usr/local/bin/kubectl-argo-rollouts kubectl argo rollouts version
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Progressive Delivery with Argo Rollouts: Canary Deployment
kubectl create namespace argo-rollouts kubectl apply -n argo-rollouts -f https://github.com/argoproj/argo-rollouts/releases/latest/download/install.yaml
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how do you auto deploy to kubernetes with auto rollbacks?
You can see in why argo section here what are the limitations of rolling updates and how rollouts solves it: https://github.com/argoproj/argo-rollouts
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Best Practices for Argo CD
Argo Rollouts
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Canary deployments
Check the public users list https://github.com/argoproj/argo-rollouts/blob/master/USERS.md
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argo-rollouts VS flagger - a user suggested alternative
2 projects | 25 Jan 2022
- argoproj/argo-rollouts: Progressive Delivery for Kubernetes
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Automation assistants: GitOps tools in comparison
Supplementary GitOps operators can also be used for deployment strategies, such as canary releases, A/B tests, and blue/green deployments, which have now been grouped under the term “progressive delivery”. The resources of most GitOps operators are not sufficient for this. One solution is Flagger. The tool that was launched by Weaveworks is now being developed as part of the Flux project. The Argo project also has an operator for this use case: Argo Rollouts. Both offer CRs for implementing progressive delivery strategies in interaction with various ingress controllers and service meshes.
argo
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StackStorm – 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 you’re 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?
flagger - Progressive delivery Kubernetes operator (Canary, A/B Testing and Blue/Green deployments)
temporal - Temporal service
Flux - Successor: https://github.com/fluxcd/flux2
keda - KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
fleet - Deploy workloads from Git to large fleets of Kubernetes clusters
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
terraform-k8s - Terraform Cloud Operator for Kubernetes
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
flux2 - Open and extensible continuous delivery solution for Kubernetes. Powered by GitOps Toolkit.
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
argo-cd - Declarative Continuous Deployment for Kubernetes
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.