Alternatives to Ansible logo

Alternatives to Ansible

Puppet Labs, Chef, Salt, Terraform, and Jenkins are the most popular alternatives and competitors to Ansible.
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What is Ansible and what are its top alternatives?

Ansible is an open-source IT automation tool that allows you to automate the configuration, management, and deployment of applications and systems. It uses a simple YAML-based language to describe configurations and automation tasks, making it easy to understand and use. Ansible is agentless, meaning it doesn't require any software to be installed on the managed hosts. However, one limitation of Ansible is it can become slower as the number of managed hosts increases.

  1. Chef: Chef is a powerful automation platform that uses a domain-specific language for writing system configurations. It offers features like infrastructure as code, automated testing, and compliance automation. However, compared to Ansible, Chef has a steeper learning curve and requires more setup.
  2. Puppet: Puppet is another popular automation tool that focuses on configuration management and orchestration. It provides a declarative language for describing system configurations and offers a wide range of pre-built modules. One drawback of Puppet is its reliance on a master-agent architecture, which can introduce points of failure.
  3. SaltStack: SaltStack is a powerful automation and remote execution tool that uses Python as its configuration language. It offers features like event-driven automation, remote execution, and configuration management. However, setting up and configuring SaltStack can be complex compared to Ansible.
  4. Terraform: Terraform is a tool for building, changing, and versioning infrastructure efficiently. It allows you to describe infrastructure as code using a simple configuration language and supports multiple cloud providers. One downside of Terraform compared to Ansible is that it focuses more on infrastructure provisioning rather than configuration management.
  5. Juju: Juju is a cloud orchestration tool that focuses on modeling and deploying applications. It uses charms, which are pre-configured packages of code that automate the deployment and operation of applications. Juju is more application-centric compared to Ansible's system-centric approach.
  6. Rundeck: Rundeck is a self-service operations platform that allows you to run automation tasks on any node in your infrastructure. It offers features like job scheduling, access control, and workflow orchestration. Rundeck provides a user-friendly interface for running automation tasks compared to Ansible's command-line interface.
  7. Bcfg2: Bcfg2 is an open-source configuration management tool that emphasizes security and host introspection. It uses a client-server architecture to manage system configurations and supports various system types. One drawback of Bcfg2 is its complexity and lack of community support compared to Ansible.
  8. Fabric: Fabric is a Python-based library for automating system administration tasks. It allows you to run commands on remote servers over SSH and provides tools for executing shell commands, transferring files, and managing connections. Fabric is more lightweight and Python-centric compared to Ansible.
  9. Otter: Otter is an automation platform that focuses on configuration management, software deployment, and release automation. It provides a visual designer for creating workflows and offers integrations with various tools and systems. Otter is more user-friendly and visually-oriented compared to Ansible's YAML-based configuration files.
  10. Foreman: Foreman is a complete lifecycle management tool for physical and virtual servers. It provides features like provisioning, configuration management, monitoring, and reporting. Foreman integrates with tools like Puppet and Ansible, making it a versatile platform for managing infrastructure. However, Foreman can be complex to set up and configure compared to Ansible.

Top Alternatives to Ansible

  • Puppet Labs
    Puppet Labs

    Puppet is an automated administrative engine for your Linux, Unix, and Windows systems and performs administrative tasks (such as adding users, installing packages, and updating server configurations) based on a centralized specification. ...

  • Chef
    Chef

    Chef enables you to manage and scale cloud infrastructure with no downtime or interruptions. Freely move applications and configurations from one cloud to another. Chef is integrated with all major cloud providers including Amazon EC2, VMWare, IBM Smartcloud, Rackspace, OpenStack, Windows Azure, HP Cloud, Google Compute Engine, Joyent Cloud and others. ...

  • Salt
    Salt

    Salt is a new approach to infrastructure management. Easy enough to get running in minutes, scalable enough to manage tens of thousands of servers, and fast enough to communicate with them in seconds. Salt delivers a dynamic communication bus for infrastructures that can be used for orchestration, remote execution, configuration management and much more. ...

  • Terraform
    Terraform

    With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...

  • Jenkins
    Jenkins

    In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project. ...

  • AWS CloudFormation
    AWS CloudFormation

    You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work. ...

  • Docker
    Docker

    The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application — from legacy to what comes next — and securely run them anywhere ...

  • Kubernetes
    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

Ansible alternatives & related posts

Puppet Labs logo

Puppet Labs

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787
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Server automation framework and application
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PROS OF PUPPET LABS
  • 52
    Devops
  • 44
    Automate it
  • 26
    Reusable components
  • 21
    Dynamic and idempotent server configuration
  • 18
    Great community
  • 12
    Very scalable
  • 12
    Cloud management
  • 10
    Easy to maintain
  • 9
    Free tier
  • 6
    Works with Amazon EC2
  • 4
    Declarative
  • 4
    Ruby
  • 3
    Works with Azure
  • 3
    Works with OpenStack
  • 2
    Nginx
  • 1
    Ease of use
CONS OF PUPPET LABS
  • 3
    Steep learning curve
  • 1
    Customs types idempotence

related Puppet Labs posts

Shared insights
on
SaltSaltPuppet LabsPuppet LabsAnsibleAnsible
at

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
Marcel Kornegoor

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
Chef logo

Chef

1.3K
1.1K
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Build, destroy and rebuild servers on any public or private cloud
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PROS OF CHEF
  • 110
    Dynamic and idempotent server configuration
  • 76
    Reusable components
  • 47
    Integration testing with Vagrant
  • 43
    Repeatable
  • 30
    Mock testing with Chefspec
  • 14
    Ruby
  • 8
    Can package cookbooks to guarantee repeatability
  • 7
    Works with AWS
  • 3
    Has marketplace where you get readymade cookbooks
  • 3
    Matured product with good community support
  • 2
    Less declarative more procedural
  • 2
    Open source configuration mgmt made easy(ish)
CONS OF CHEF
    Be the first to leave a con

    related Chef posts

    In late 2013, the Operations Engineering team at PagerDuty was made up of 4 engineers, and was comprised of generalists, each of whom had one or two areas of depth. Although the Operations Team ran its own on-call, each engineering team at PagerDuty also participated on the pager.

    The Operations Engineering Team owned 150+ servers spanning multiple cloud providers, and used Chef to automate their infrastructure across the various cloud providers with a mix of completely custom cookbooks and customized community cookbooks.

    Custom cookbooks were managed by Berkshelf, andach custom cookbook contained its own tests based on ChefSpec 3, coupled with Rspec.

    Jenkins was used to GitHub for new changes and to handle unit testing of those features.

    See more
    Marcel Kornegoor

    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
    Salt logo

    Salt

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    Fast, scalable and flexible software for data center automation
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    PROS OF SALT
    • 46
      Flexible
    • 30
      Easy
    • 27
      Remote execution
    • 24
      Enormously flexible
    • 12
      Great plugin API
    • 10
      Python
    • 5
      Extensible
    • 3
      Scalable
    • 2
      nginx
    • 1
      Vagrant provisioner
    • 1
      HipChat
    • 1
      Best IaaC
    • 1
      Automatisation
    • 1
      Parallel Execution
    CONS OF SALT
    • 1
      Bloated
    • 1
      Dangerous
    • 1
      No immutable infrastructure

    related Salt posts

    Shared insights
    on
    SaltSaltPuppet LabsPuppet LabsAnsibleAnsible
    at

    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
    Terraform logo

    Terraform

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    Describe your complete infrastructure as code and build resources across providers
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    PROS OF TERRAFORM
    • 122
      Infrastructure as code
    • 73
      Declarative syntax
    • 45
      Planning
    • 28
      Simple
    • 24
      Parallelism
    • 8
      Well-documented
    • 8
      Cloud agnostic
    • 6
      It's like coding your infrastructure in simple English
    • 6
      Immutable infrastructure
    • 5
      Platform agnostic
    • 4
      Extendable
    • 4
      Automation
    • 4
      Automates infrastructure deployments
    • 4
      Portability
    • 2
      Lightweight
    • 2
      Scales to hundreds of hosts
    CONS OF TERRAFORM
    • 1
      Doesn't have full support to GKE

    related Terraform posts

    Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.

    Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!

    Check Out My Architecture: CLICK ME

    Check out the GitHub repo attached

    See more
    Emanuel Evans
    Senior Architect at Rainforest QA · | 20 upvotes · 1.5M views

    We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

    We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

    Read the blog post to go more in depth.

    See more
    Jenkins logo

    Jenkins

    57.6K
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    An extendable open source continuous integration server
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    PROS OF JENKINS
    • 523
      Hosted internally
    • 469
      Free open source
    • 318
      Great to build, deploy or launch anything async
    • 243
      Tons of integrations
    • 211
      Rich set of plugins with good documentation
    • 111
      Has support for build pipelines
    • 68
      Easy setup
    • 66
      It is open-source
    • 53
      Workflow plugin
    • 13
      Configuration as code
    • 12
      Very powerful tool
    • 11
      Many Plugins
    • 10
      Continuous Integration
    • 10
      Great flexibility
    • 9
      Git and Maven integration is better
    • 8
      100% free and open source
    • 7
      Slack Integration (plugin)
    • 7
      Github integration
    • 6
      Self-hosted GitLab Integration (plugin)
    • 6
      Easy customisation
    • 5
      Pipeline API
    • 5
      Docker support
    • 4
      Fast builds
    • 4
      Hosted Externally
    • 4
      Excellent docker integration
    • 4
      Platform idnependency
    • 3
      AWS Integration
    • 3
      JOBDSL
    • 3
      It's Everywhere
    • 3
      Customizable
    • 3
      Can be run as a Docker container
    • 3
      It`w worked
    • 2
      Loose Coupling
    • 2
      NodeJS Support
    • 2
      Build PR Branch Only
    • 2
      Easily extendable with seamless integration
    • 2
      PHP Support
    • 2
      Ruby/Rails Support
    • 2
      Universal controller
    CONS OF JENKINS
    • 13
      Workarounds needed for basic requirements
    • 10
      Groovy with cumbersome syntax
    • 8
      Plugins compatibility issues
    • 7
      Lack of support
    • 7
      Limited abilities with declarative pipelines
    • 5
      No YAML syntax
    • 4
      Too tied to plugins versions

    related Jenkins posts

    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 23 upvotes · 8.3M views

    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.

    See more
    Thierry Schellenbach

    Releasing new versions of our services is done by Travis CI. Travis first runs our test suite. Once it passes, it publishes a new release binary to GitHub.

    Common tasks such as installing dependencies for the Go project, or building a binary are automated using plain old Makefiles. (We know, crazy old school, right?) Our binaries are compressed using UPX.

    Travis has come a long way over the past years. I used to prefer Jenkins in some cases since it was easier to debug broken builds. With the addition of the aptly named “debug build” button, Travis is now the clear winner. It’s easy to use and free for open source, with no need to maintain anything.

    #ContinuousIntegration #CodeCollaborationVersionControl

    See more
    AWS CloudFormation logo

    AWS CloudFormation

    1.5K
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    Create and manage a collection of related AWS resources
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    PROS OF AWS CLOUDFORMATION
    • 43
      Automates infrastructure deployments
    • 21
      Declarative infrastructure and deployment
    • 13
      No more clicking around
    • 3
      Any Operative System you want
    • 3
      Atomic
    • 3
      Infrastructure as code
    • 1
      CDK makes it truly infrastructure-as-code
    • 1
      Automates Infrastructure Deployment
    • 0
      K8s
    CONS OF AWS CLOUDFORMATION
    • 4
      Brittle
    • 2
      No RBAC and policies in templates

    related AWS CloudFormation posts

    Joseph Kunzler
    DevOps Engineer at Tillable · | 9 upvotes · 202K views

    We use Terraform because we needed a way to automate the process of building and deploying feature branches. We wanted to hide the complexity such that when a dev creates a PR, it triggers a build and deployment without the dev having to worry about any of the 'plumbing' going on behind the scenes. Terraform allows us to automate the process of provisioning DNS records, Amazon S3 buckets, Amazon EC2 instances and AWS Elastic Load Balancing (ELB)'s. It also makes it easy to tear it all down when finished. We also like that it supports multiple clouds, which is why we chose to use it over AWS CloudFormation.

    See more
    Bram Verdonck

    Yesterday we moved away from using CloudFlare towards Amazon Route 53 for a few reasons. Although CloudFlare is a great platform, once you reach almost a 100% AWS Service integration, it makes it hard to still use CloudFlare in the stack. Also being able to use Aliases for DNS makes it faster because instead of doing a CNAME and an A record lookup, you will be able to receive the A records from the end services directly. We always loved working with CloudFlare , especially for DNS as we already used Amazon CloudFront for CDN. But having everything within AWS makes it "cleaner" when deploying automatically using AWS CloudFormation. All that aside, the main reason for moving towards Amazon Route 53 for DNS is the ability to do geolocation and latency based DNS responses. Doing this outside the AWS console would increase the complexity.

    See more
    Docker logo

    Docker

    170.8K
    137.4K
    3.9K
    Enterprise Container Platform for High-Velocity Innovation.
    170.8K
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    PROS OF DOCKER
    • 823
      Rapid integration and build up
    • 691
      Isolation
    • 521
      Open source
    • 505
      Testa­bil­i­ty and re­pro­ducibil­i­ty
    • 460
      Lightweight
    • 218
      Standardization
    • 185
      Scalable
    • 106
      Upgrading / down­grad­ing / ap­pli­ca­tion versions
    • 88
      Security
    • 85
      Private paas environments
    • 34
      Portability
    • 26
      Limit resource usage
    • 17
      Game changer
    • 16
      I love the way docker has changed virtualization
    • 14
      Fast
    • 12
      Concurrency
    • 8
      Docker's Compose tools
    • 6
      Easy setup
    • 6
      Fast and Portable
    • 5
      Because its fun
    • 4
      Makes shipping to production very simple
    • 3
      Highly useful
    • 3
      It's dope
    • 2
      Very easy to setup integrate and build
    • 2
      HIgh Throughput
    • 2
      Package the environment with the application
    • 2
      Does a nice job hogging memory
    • 2
      Open source and highly configurable
    • 2
      Simplicity, isolation, resource effective
    • 2
      MacOS support FAKE
    • 2
      Its cool
    • 2
      Docker hub for the FTW
    • 2
      Super
    • 0
      Asdfd
    CONS OF DOCKER
    • 8
      New versions == broken features
    • 6
      Unreliable networking
    • 6
      Documentation not always in sync
    • 4
      Moves quickly
    • 3
      Not Secure

    related Docker posts

    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M views

    Our whole DevOps stack consists of the following tools:

    • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
    • Respectively Git as revision control system
    • SourceTree as Git GUI
    • Visual Studio Code as IDE
    • CircleCI for continuous integration (automatize development process)
    • Prettier / TSLint / ESLint as code linter
    • SonarQube as quality gate
    • Docker as container management (incl. Docker Compose for multi-container application management)
    • VirtualBox for operating system simulation tests
    • Kubernetes as cluster management for docker containers
    • Heroku for deploying in test environments
    • nginx as web server (preferably used as facade server in production environment)
    • SSLMate (using OpenSSL) for certificate management
    • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
    • PostgreSQL as preferred database system
    • Redis as preferred in-memory database/store (great for caching)

    The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

    • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
    • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
    • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
    • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
    • Scalability: All-in-one framework for distributed systems.
    • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
    See more
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 23 upvotes · 8.3M views

    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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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M views

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

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    Ashish Singh
    Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

    To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

    Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

    We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

    Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

    Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

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