Alternatives to Fabric logo
Azure Service Fabric, Ansible, Kubernetes, Chef, and Capistrano are the most popular alternatives and competitors to Fabric.
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What is Fabric?

Fabric is a Python (2.5-2.7) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks. It provides a basic suite of operations for executing local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution.
Fabric is a tool in the Server Configuration and Automation category of a tech stack.
Fabric is an open source tool with 11.7K GitHub stars and 1.8K GitHub forks. Here’s a link to Fabric's open source repository on GitHub

Fabric alternatives & related posts

Azure Service Fabric logo

Azure Service Fabric

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Distributed systems platform that simplifies build, package, deploy, and management of scalable microservices apps
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    Ansible

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    Tymoteusz Paul
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 11 upvotes · 144.6K views
    Amazon EC2
    Amazon EC2
    LXC
    LXC
    CircleCI
    CircleCI
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    Docker
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    Git
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    Vault
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    Apache Maven
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    Slack
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    Jenkins
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    TeamCity
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    Logstash
    Kibana
    Kibana
    Elasticsearch
    Elasticsearch
    Ansible
    Ansible
    VirtualBox
    VirtualBox
    Vagrant
    Vagrant

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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    Pedro Arnal Puente
    Pedro Arnal Puente
    CTO at La Cupula Music SL · | 7 upvotes · 16.8K views
    atLa Cupula Music SLLa Cupula Music SL
    Ansible
    Ansible
    Packer
    Packer
    Terraform
    Terraform
    Amazon ElastiCache
    Amazon ElastiCache
    Redis
    Redis
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    Amazon S3
    Amazon S3
    Amazon EC2
    Amazon EC2
    Debian
    Debian

    Our base infrastructure is composed of Debian based servers running in Amazon EC2 , asset storage with Amazon S3 , and Amazon RDS for Aurora and Redis under Amazon ElastiCache for data storage.

    We are starting to work in automated provisioning and management with Terraform , Packer , and Ansible .

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    Kubernetes logo

    Kubernetes

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    Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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    Yshay Yaacobi
    Yshay Yaacobi
    Software Engineer · | 27 upvotes · 159.3K views
    atSolutoSoluto
    Docker Swarm
    Docker Swarm
    Kubernetes
    Kubernetes
    Visual Studio Code
    Visual Studio Code
    Go
    Go
    TypeScript
    TypeScript
    JavaScript
    JavaScript
    C#
    C#
    F#
    F#
    .NET
    .NET

    Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

    Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

    After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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    GitHub
    GitHub
    nginx
    nginx
    ESLint
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    AVA
    AVA
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    Semantic UI React
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    Redux
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    React
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    Node.js
    FeathersJS
    FeathersJS
    Heroku
    Heroku
    Amazon EC2
    Amazon EC2
    Kubernetes
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    Jenkins
    Jenkins
    Docker Compose
    Docker Compose
    Docker
    Docker
    #Frontend
    #Stack
    #Backend
    #Containers
    #Containerized

    Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.

    We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.

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    Marcel Kornegoor
    Marcel Kornegoor
    CTO at AT Computing · | 5 upvotes · 46.5K views
    atAT ComputingAT Computing
    Python
    Python
    Chef
    Chef
    Puppet Labs
    Puppet Labs
    Ansible
    Ansible
    Google Compute Engine
    Google Compute Engine
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    Kubernetes
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    Docker
    GitHub
    GitHub
    VirtualBox
    VirtualBox
    Jenkins
    Jenkins
    Visual Studio Code
    Visual Studio Code
    Fedora
    Fedora
    Red Hat Enterprise Linux
    Red Hat Enterprise Linux
    Debian
    Debian
    CentOS
    CentOS
    Ubuntu
    Ubuntu
    Linux
    Linux
    #ATComputing

    Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.

    For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.

    For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.

    Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.

    See more
    Capistrano logo

    Capistrano

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    A remote server automation and deployment tool written in Ruby
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    Julien DeFrance
    Julien DeFrance
    Full Stack Engineering Manager at ValiMail · | 16 upvotes · 183.5K views
    atSmartZipSmartZip
    Amazon DynamoDB
    Amazon DynamoDB
    Ruby
    Ruby
    Node.js
    Node.js
    AWS Lambda
    AWS Lambda
    New Relic
    New Relic
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Elasticsearch
    Elasticsearch
    Superset
    Superset
    Amazon Quicksight
    Amazon Quicksight
    Amazon Redshift
    Amazon Redshift
    Zapier
    Zapier
    Segment
    Segment
    Amazon CloudFront
    Amazon CloudFront
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    Memcached
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon RDS for Aurora
    Amazon RDS for Aurora
    MySQL
    MySQL
    Amazon RDS
    Amazon RDS
    Amazon S3
    Amazon S3
    Docker
    Docker
    Capistrano
    Capistrano
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Rails API
    Rails API
    Rails
    Rails
    Algolia
    Algolia

    Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

    I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

    For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

    Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

    Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

    Future improvements / technology decisions included:

    Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

    As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

    One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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    Kir Shatrov
    Kir Shatrov
    Production Engineer at Shopify · | 13 upvotes · 15.9K views
    atShopifyShopify
    kubernetes-deploy
    kubernetes-deploy
    Capistrano
    Capistrano
    Heroku
    Heroku
    Shipit
    Shipit
    #PlatformAsAService
    #ApplicationHosting
    #ContainerTools
    #BuildTestDeploy

    Shipit, our deployment tool, is at the heart of Continuous Delivery at Shopify. Shipit is an orchestrator that runs and tracks progress of any deploy script that you provide for a project. It supports deploying to Rubygems, Pip, Heroku and Capistrano out of the box. For us, it's mostly kubernetes-deploy or Capistrano for legacy projects.

    We use a slightly tweaked GitHub flow, with feature development going in branches and the master branch being the source of truth for the state of things in production. When your PR is ready, you add it to the Merge Queue in ShipIt. The idea behind the Merge Queue is to control the rate of code that is being merged to master branch. In the busy hours, we have many developers who want to merge the PRs, but at the same time we don't want to introduce too many changes to the system at the same time. Merge Queue limits deploys to 5-10 commits at a time, which makes it easier to identify issues and roll back in case we notice any unexpected behaviour after the deploy.

    We use a browser extension to make Merge Queue play nicely with the Merge button on GitHub:

    Both Shipit and kubernetes-deploy are open source, and we've heard quite a few success stories from companies who have adopted our flow.

    #BuildTestDeploy #ContainerTools #ApplicationHosting #PlatformAsAService

    See more

    related Puppet Labs posts

    Marcel Kornegoor
    Marcel Kornegoor
    CTO at AT Computing · | 5 upvotes · 46.5K views
    atAT ComputingAT Computing
    Python
    Python
    Chef
    Chef
    Puppet Labs
    Puppet Labs
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    Ansible
    Google Compute Engine
    Google Compute Engine
    Kubernetes
    Kubernetes
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    Docker
    GitHub
    GitHub
    VirtualBox
    VirtualBox
    Jenkins
    Jenkins
    Visual Studio Code
    Visual Studio Code
    Fedora
    Fedora
    Red Hat Enterprise Linux
    Red Hat Enterprise Linux
    Debian
    Debian
    CentOS
    CentOS
    Ubuntu
    Ubuntu
    Linux
    Linux
    #ATComputing

    Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.

    For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.

    For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.

    Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.

    See more
    StackShare Editors
    StackShare Editors
    Ansible
    Ansible
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    Puppet Labs
    Salt
    Salt

    By 2014, the DevOps team at Lyft decided to port their infrastructure code from Puppet to Salt. At that point, the Puppet code based included around "10,000 lines of spaghetti-code,” which was unfamiliar and challenging to the relatively new members of the DevOps team.

    “The DevOps team felt that the Puppet infrastructure was too difficult to pick up quickly and would be impossible to introduce to [their] developers as the tool they’d use to manage their own services.”

    To determine a path forward, the team assessed both Ansible and Salt, exploring four key areas: simplicity/ease of use, maturity, performance, and community.

    They found that “Salt’s execution and state module support is more mature than Ansible’s, overall,” and that “Salt was faster than Ansible for state/playbook runs.” And while both have high levels of community support, Salt exceeded expectations in terms of friendless and responsiveness to opened issues.

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    Salt logo

    Salt

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    StackShare Editors
    StackShare Editors
    Ansible
    Ansible
    Puppet Labs
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    Salt
    Salt

    By 2014, the DevOps team at Lyft decided to port their infrastructure code from Puppet to Salt. At that point, the Puppet code based included around "10,000 lines of spaghetti-code,” which was unfamiliar and challenging to the relatively new members of the DevOps team.

    “The DevOps team felt that the Puppet infrastructure was too difficult to pick up quickly and would be impossible to introduce to [their] developers as the tool they’d use to manage their own services.”

    To determine a path forward, the team assessed both Ansible and Salt, exploring four key areas: simplicity/ease of use, maturity, performance, and community.

    They found that “Salt’s execution and state module support is more mature than Ansible’s, overall,” and that “Salt was faster than Ansible for state/playbook runs.” And while both have high levels of community support, Salt exceeded expectations in terms of friendless and responsiveness to opened issues.

    See more
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    EasyEngine

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      Mina

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      Rundeck

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      A platform for Self-Service Operations
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        cPanel

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        Create an exceptional hosting experience
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        AWX

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          Shipit

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          Kir Shatrov
          Kir Shatrov
          Production Engineer at Shopify · | 13 upvotes · 15.9K views
          atShopifyShopify
          kubernetes-deploy
          kubernetes-deploy
          Capistrano
          Capistrano
          Heroku
          Heroku
          Shipit
          Shipit
          #PlatformAsAService
          #ApplicationHosting
          #ContainerTools
          #BuildTestDeploy

          Shipit, our deployment tool, is at the heart of Continuous Delivery at Shopify. Shipit is an orchestrator that runs and tracks progress of any deploy script that you provide for a project. It supports deploying to Rubygems, Pip, Heroku and Capistrano out of the box. For us, it's mostly kubernetes-deploy or Capistrano for legacy projects.

          We use a slightly tweaked GitHub flow, with feature development going in branches and the master branch being the source of truth for the state of things in production. When your PR is ready, you add it to the Merge Queue in ShipIt. The idea behind the Merge Queue is to control the rate of code that is being merged to master branch. In the busy hours, we have many developers who want to merge the PRs, but at the same time we don't want to introduce too many changes to the system at the same time. Merge Queue limits deploys to 5-10 commits at a time, which makes it easier to identify issues and roll back in case we notice any unexpected behaviour after the deploy.

          We use a browser extension to make Merge Queue play nicely with the Merge button on GitHub:

          Both Shipit and kubernetes-deploy are open source, and we've heard quite a few success stories from companies who have adopted our flow.

          #BuildTestDeploy #ContainerTools #ApplicationHosting #PlatformAsAService

          See more
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          ServerPilot

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            Webmin

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                Juju

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