Alternatives to Vagrant logo

Alternatives to Vagrant

VirtualBox, Ansible, Packer, Terraform, and OpenStack are the most popular alternatives and competitors to Vagrant.
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What is Vagrant and what are its top alternatives?

Vagrant is an open-source tool that focuses on creating and managing lightweight, reproducible, and portable development environments. It allows developers to set up virtual machines with specific configurations, making it easier to maintain consistency across different environments. Vagrant also integrates with various virtualization software such as VirtualBox, VMware, and Docker. However, Vagrant can sometimes be slow in spinning up and provisioning virtual machines, and its configuration files can be verbose and complex.

  1. Docker: Docker provides lightweight containers for applications, offering a more efficient and faster alternative to virtual machines. Key features include easy scalability, portability, and compatibility with various operating systems. Pros include faster startup times and less resource consumption compared to Vagrant. However, Docker containers may not fully replace the need for virtual machines in all scenarios.

  2. Packer: Packer is a tool for creating machine images for multiple platforms from a single source configuration. Key features include support for multiple builders, such as VirtualBox and AWS, and automated image builds. Pros include faster image creation and consistency across different environments. However, Packer may require additional setup compared to Vagrant.

  3. Ansible: Ansible is a configuration management tool that focuses on automation and simplicity. Key features include agentless architecture, idempotency, and playbooks for defining automation tasks. Pros include ease of use and fast deployment of configurations. However, Ansible may not provide the same level of environment isolation as Vagrant.

  4. Terraform: Terraform is an infrastructure as code tool that enables you to manage and provision infrastructure across various cloud providers. Key features include declarative configuration and support for multiple providers. Pros include scalability and multi-cloud support. However, Terraform focuses more on infrastructure provisioning rather than development environments.

  5. VirtualBox: VirtualBox is a powerful, open-source virtualization product for x86 hardware. Key features include support for various guest operating systems and hardware virtualization. Pros include ease of use and compatibility with Vagrant. However, VirtualBox may be resource-intensive compared to other alternatives.

  6. Kubernetes: Kubernetes is an open-source container orchestration platform that automates deployment, scaling, and management of containerized applications. Key features include high availability, self-healing, and flexibility. Pros include scalability and efficiency in managing containerized workloads. However, Kubernetes may be overly complex for simple development environments.

  7. Nomad: Nomad is a cluster manager and scheduler designed for microservices and batch processing workloads. Key features include easy deployment, autoscaling, and support for multiple types of workloads. Pros include simplicity and flexibility in managing distributed systems. However, Nomad may not offer the same level of configuration options as Vagrant.

  8. Chef: Chef is a configuration management tool that automates the process of defining, deploying, and managing infrastructure. Key features include a domain-specific language for writing configurations and support for continuous automation. Pros include scalability and reproducibility of infrastructure. However, Chef may require a steeper learning curve compared to Vagrant.

  9. OpenStack: OpenStack is an open-source cloud computing platform that allows you to create and manage large pools of compute, storage, and networking resources. Key features include modularity, scalability, and support for multiple hypervisors. Pros include flexibility and control over cloud infrastructure. However, OpenStack may be more suited for production environments rather than development.

  10. Vagrant Manager: Vagrant Manager is a Mac OS X status bar menu app for managing Vagrant VMs. Key features include quick access to VMs, automatic SSH tunneling, and VM collaboration. Pros include a user-friendly interface and simplified VM management. However, Vagrant Manager may have limited functionality compared to the command-line interface of Vagrant.

Top Alternatives to Vagrant

  • VirtualBox
    VirtualBox

    VirtualBox is a powerful x86 and AMD64/Intel64 virtualization product for enterprise as well as home use. Not only is VirtualBox an extremely feature rich, high performance product for enterprise customers, it is also the only professional solution that is freely available as Open Source Software under the terms of the GNU General Public License (GPL) version 2. ...

  • Ansible
    Ansible

    Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use. ...

  • Packer
    Packer

    Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images. ...

  • 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. ...

  • OpenStack
    OpenStack

    OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources throughout a datacenter, all managed through a dashboard that gives administrators control while empowering their users to provision resources through a web interface. ...

  • 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 ...

  • 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. ...

  • 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. ...

Vagrant alternatives & related posts

VirtualBox logo

VirtualBox

30.4K
25K
1.1K
Run nearly any operating system on a single machine and to freely switch between OS instances running simultaneously
30.4K
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PROS OF VIRTUALBOX
  • 358
    Free
  • 231
    Easy
  • 169
    Default for vagrant
  • 110
    Fast
  • 73
    Starts quickly
  • 45
    Open-source
  • 42
    Running in background
  • 41
    Simple, yet comprehensive
  • 27
    Default for boot2docker
  • 22
    Extensive customization
  • 3
    Free to use
  • 2
    Mouse integration
  • 2
    Easy tool
  • 2
    Cross-platform
CONS OF VIRTUALBOX
    Be the first to leave a con

    related VirtualBox posts

    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.2M 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.

    See more
    Ansible logo

    Ansible

    18.8K
    15.3K
    1.3K
    Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
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    PROS OF ANSIBLE
    • 284
      Agentless
    • 210
      Great configuration
    • 199
      Simple
    • 176
      Powerful
    • 155
      Easy to learn
    • 69
      Flexible
    • 55
      Doesn't get in the way of getting s--- done
    • 35
      Makes sense
    • 30
      Super efficient and flexible
    • 27
      Powerful
    • 11
      Dynamic Inventory
    • 9
      Backed by Red Hat
    • 7
      Works with AWS
    • 6
      Cloud Oriented
    • 6
      Easy to maintain
    • 4
      Vagrant provisioner
    • 4
      Simple and powerful
    • 4
      Multi language
    • 4
      Simple
    • 4
      Because SSH
    • 4
      Procedural or declarative, or both
    • 4
      Easy
    • 3
      Consistency
    • 2
      Well-documented
    • 2
      Masterless
    • 2
      Debugging is simple
    • 2
      Merge hash to get final configuration similar to hiera
    • 2
      Fast as hell
    • 1
      Manage any OS
    • 1
      Work on windows, but difficult to manage
    • 1
      Certified Content
    CONS OF ANSIBLE
    • 8
      Dangerous
    • 5
      Hard to install
    • 3
      Doesn't Run on Windows
    • 3
      Bloated
    • 3
      Backward compatibility
    • 2
      No immutable infrastructure

    related Ansible 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
    Sebastian Gębski

    Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.

    See more
    Packer logo

    Packer

    582
    561
    42
    Create identical machine images for multiple platforms from a single source configuration
    582
    561
    + 1
    42
    PROS OF PACKER
    • 27
      Cross platform builds
    • 9
      Vm creation automation
    • 4
      Bake in security
    • 1
      Good documentation
    • 1
      Easy to use
    CONS OF PACKER
      Be the first to leave a con

      related Packer posts

      John Kodumal

      LaunchDarkly is almost a five year old company, and our methodology for deploying was state of the art... for 2014. We recently undertook a project to modernize the way we #deploy our software, moving from Ansible-based deploy scripts that executed on our local machines, to using Spinnaker (along with Terraform and Packer) as the basis of our deployment system. We've been using Armory's enterprise Spinnaker offering to make this project a reality.

      See more
      Terraform logo

      Terraform

      18K
      14.2K
      345
      Describe your complete infrastructure as code and build resources across providers
      18K
      14.2K
      + 1
      345
      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
      OpenStack logo

      OpenStack

      777
      1.1K
      130
      Open source software for building private and public clouds
      777
      1.1K
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      130
      PROS OF OPENSTACK
      • 56
        Private cloud
      • 38
        Avoid vendor lock-in
      • 22
        Flexible in use
      • 6
        Industry leader
      • 4
        Supported by many companies in top500
      • 4
        Robust architecture
      CONS OF OPENSTACK
        Be the first to leave a con

        related OpenStack posts

        Shared insights
        on
        UbuntuUbuntuOpenStackOpenStackCentOSCentOS
        at

        Hello guys

        I am confused between choosing CentOS7 or centos8 for OpenStack tripleo undercloud deployment. Which one should I use? There is another option to use OpenStack, Ubuntu, or MicroStack.

        We wanted to use this deployment to build our home cloud or private cloud infrastructure. I heard that centOS is always the best choice through a little research, but still not sure. As centos8 from Redhat is not supported for OpenStack tripleo deployments anymore, I had to upgrade to CentosStream.

        See more
        Docker logo

        Docker

        170.7K
        137.3K
        3.9K
        Enterprise Container Platform for High-Velocity Innovation.
        170.7K
        137.3K
        + 1
        3.9K
        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 · 9.2M 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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        Puppet Labs

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        CONS OF PUPPET LABS
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        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.

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        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.

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        Kubernetes

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        CONS OF KUBERNETES
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        • 2
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        related Kubernetes posts

        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.

        Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

        https://eng.uber.com/distributed-tracing/

        (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

        Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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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.

        #BigData #AWS #DataScience #DataEngineering

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