Alternatives to Docker logo

Alternatives to Docker

LXC, rkt, Kubernetes, Cloud Foundry, and Vagrant are the most popular alternatives and competitors to Docker.
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What is Docker and what are its top alternatives?

Docker is a popular platform for developers to build, ship, and run applications securely in containers. Key features of Docker include easy packaging of applications and dependencies, efficient resource utilization through containerization, and seamless deployment across different environments. However, some limitations of Docker include high memory usage, slow startup times for containers, and complexity in managing networking.

  1. Podman: Podman is an open-source container management tool that is compatible with Docker. Key features include rootless containers for increased security, Docker-compatible CLI, and support for Kubernetes integration. Pros of Podman include improved security with rootless containers and the ability to run containers without a daemon. Cons include the lack of a built-in orchestration feature like Docker Swarm.
  2. Kubernetes: Kubernetes is a powerful container orchestration platform that automates deploying, scaling, and managing containerized applications. Key features include automatic load balancing, self-healing capabilities, and support for rolling updates. Pros of Kubernetes include strong community support and scalability for large-scale deployments. Cons include a steep learning curve and complexity in setup and configuration.
  3. LXC: LXC is a lightweight containerization solution that provides an operating-system-level virtualization environment. Key features include fast boot times, low overhead, and support for running multiple container instances. Pros of LXC include low resource usage and the ability to run containers on bare-metal hardware. Cons include limited isolation compared to Docker containers.
  4. rkt: rkt is a container runtime developed by CoreOS that focuses on security and composability. Key features include a simple CLI interface, strong dependency management, and support for multiple image formats. Pros of rkt include enhanced security features such as image signature verification and a minimal attack surface. Cons include fewer plugins and a smaller ecosystem compared to Docker.
  5. CRI-O: CRI-O is a lightweight container runtime specifically designed for Kubernetes clusters. Key features include improved performance, easy integration with Kubernetes, and support for the OCI container image format. Pros of CRI-O include better performance and compatibility with Kubernetes. Cons include limited features compared to Docker, such as networking and storage options.
  6. OpenShift: OpenShift is a Kubernetes distribution with additional features for enterprise deployments, such as built-in CI/CD pipelines and developer tools. Key features include multi-tenancy support, integration with Red Hat products, and automated application scaling. Pros of OpenShift include enterprise-grade security and support, as well as extensive developer tools. Cons include licensing costs for enterprise features and potential complexity in managing large deployments.
  7. containerd: containerd is a high-performance container runtime that is designed to be embedded in larger systems such as Kubernetes. Key features include a minimalistic design, support for multiple container image formats, and container lifecycle management. Pros of containerd include stability and support from the CNCF (Cloud Native Computing Foundation). Cons include lack of advanced features compared to Docker and a more manual setup process.
  8. LXD: LXD is a system container manager that provides a more polished user experience compared to Docker. Key features include a RESTful API for managing containers, live migration capabilities, and integration with storage backends. Pros of LXD include a simple CLI interface and support for system containers for improved performance. Cons include limited features compared to Docker for application containers.
  9. Garden: Garden is a container orchestration platform that focuses on developer productivity and rapid deployment. Key features include automated dev environments, seamless CI/CD integration, and support for microservices architecture. Pros of Garden include developer-friendly workflows and easy setup for testing and deployment. Cons include limited documentation and a smaller user base compared to Docker.
  10. Vagrant: Vagrant is a tool for creating and managing lightweight, reproducible development environments. Key features include support for multiple providers (e.g., VirtualBox, VMware), easy configuration with Vagrantfiles, and efficient resource allocation. Pros of Vagrant include flexibility in creating custom development environments and support for various operating systems. Cons include slower startup times compared to Docker containers and the need for a virtualization provider.

Top Alternatives to Docker

  • LXC
    LXC

    LXC is a userspace interface for the Linux kernel containment features. Through a powerful API and simple tools, it lets Linux users easily create and manage system or application containers. ...

  • rkt
    rkt

    Rocket is a cli for running App Containers. The goal of rocket is to be composable, secure, and fast. ...

  • 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. ...

  • Cloud Foundry
    Cloud Foundry

    Cloud Foundry is an open platform as a service (PaaS) that provides a choice of clouds, developer frameworks, and application services. Cloud Foundry makes it faster and easier to build, test, deploy, and scale applications. ...

  • Vagrant
    Vagrant

    Vagrant provides the framework and configuration format to create and manage complete portable development environments. These development environments can live on your computer or in the cloud, and are portable between Windows, Mac OS X, and Linux. ...

  • Red Hat OpenShift
    Red Hat OpenShift

    OpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications. ...

  • 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. ...

  • containerd
    containerd

    An industry-standard container runtime with an emphasis on simplicity, robustness, and portability ...

Docker alternatives & related posts

LXC logo

LXC

117
223
19
Linux containers
117
223
+ 1
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PROS OF LXC
  • 5
    Easy to use
  • 4
    Lightweight
  • 3
    Simple and powerful
  • 3
    Good security
  • 2
    LGPL
  • 1
    Reliable
  • 1
    Trusted
CONS OF LXC
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    related LXC 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
    rkt logo

    rkt

    29
    112
    10
    App Container runtime
    29
    112
    + 1
    10
    PROS OF RKT
    • 5
      Security
    • 3
      Robust container portability
    • 2
      Composable containers
    CONS OF RKT
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      related rkt posts

      Kubernetes logo

      Kubernetes

      58.8K
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      677
      Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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      PROS OF KUBERNETES
      • 164
        Leading docker container management solution
      • 128
        Simple and powerful
      • 106
        Open source
      • 76
        Backed by google
      • 58
        The right abstractions
      • 25
        Scale services
      • 20
        Replication controller
      • 11
        Permission managment
      • 9
        Supports autoscaling
      • 8
        Cheap
      • 8
        Simple
      • 6
        Self-healing
      • 5
        No cloud platform lock-in
      • 5
        Promotes modern/good infrascture practice
      • 5
        Open, powerful, stable
      • 5
        Reliable
      • 4
        Scalable
      • 4
        Quick cloud setup
      • 3
        Cloud Agnostic
      • 3
        Captain of Container Ship
      • 3
        A self healing environment with rich metadata
      • 3
        Runs on azure
      • 3
        Backed by Red Hat
      • 3
        Custom and extensibility
      • 2
        Sfg
      • 2
        Gke
      • 2
        Everything of CaaS
      • 2
        Golang
      • 2
        Easy setup
      • 2
        Expandable
      CONS OF KUBERNETES
      • 16
        Steep learning curve
      • 15
        Poor workflow for development
      • 8
        Orchestrates only infrastructure
      • 4
        High resource requirements for on-prem clusters
      • 2
        Too heavy for simple systems
      • 1
        Additional vendor lock-in (Docker)
      • 1
        More moving parts to secure
      • 1
        Additional Technology Overhead

      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

      See more
      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

      See more
      Cloud Foundry logo

      Cloud Foundry

      188
      343
      5
      Deploy and scale applications in seconds on your choice of private or public cloud
      188
      343
      + 1
      5
      PROS OF CLOUD FOUNDRY
      • 2
        Perfectly aligned with springboot
      • 1
        Free distributed tracing (zipkin)
      • 1
        Application health management
      • 1
        Free service discovery (Eureka)
      CONS OF CLOUD FOUNDRY
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        related Cloud Foundry posts

        Vagrant logo

        Vagrant

        11.4K
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        A tool for building and distributing development environments
        11.4K
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        PROS OF VAGRANT
        • 352
          Development environments
        • 290
          Simple bootstraping
        • 237
          Free
        • 139
          Boxes
        • 130
          Provisioning
        • 84
          Portable
        • 81
          Synced folders
        • 69
          Reproducible
        • 51
          Ssh
        • 44
          Very flexible
        • 5
          Works well, can be replicated easily with other devs
        • 5
          Easy-to-share, easy-to-version dev configuration
        • 3
          Great
        • 3
          Just works
        • 2
          Quick way to get running
        • 1
          DRY - "Do Not Repeat Yourself"
        • 1
          Container Friendly
        • 1
          What is vagrant?
        • 1
          Good documentation
        CONS OF VAGRANT
        • 2
          Can become v complex w prod. provisioner (Salt, etc.)
        • 2
          Multiple VMs quickly eat up disk space
        • 1
          Development environment that kills your battery

        related Vagrant 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
        Tim Abbott

        We use DigitalOcean mainly to provide remote development environments for Zulip contributors in situations where developing locally using our Vagrant setup isn't practical. There's a range of reasons:

        • Situations where one needs a public IP address and SSL certificate (e.g. Facebook's OAuth system require that even for testing)
        • Giving a contributor a development environment when their computer doesn't have the few GB of free RAM needed to run one locally
        • Developer sprints, where our snapshot-based system can provision a working development environment for a potential new contributor in under a minute. This use case is particularly great because a machine that one only needs for 3 days is essentially free with Digital Ocean's pricing.
        • A backup development environment when someone's laptop is being repaired.

        One could do all of this with many hosting providers, but we've found it particularly convenient to use Digital Ocean for these applications.

        See more
        Red Hat OpenShift logo

        Red Hat OpenShift

        1.5K
        1.4K
        517
        Red Hat's free Platform as a Service (PaaS) for hosting Java, PHP, Ruby, Python, Node.js, and Perl apps
        1.5K
        1.4K
        + 1
        517
        PROS OF RED HAT OPENSHIFT
        • 99
          Good free plan
        • 63
          Open Source
        • 47
          Easy setup
        • 43
          Nodejs support
        • 42
          Well documented
        • 32
          Custom domains
        • 28
          Mongodb support
        • 27
          Clean and simple architecture
        • 25
          PHP support
        • 21
          Customizable environments
        • 11
          Ability to run CRON jobs
        • 9
          Easier than Heroku for a WordPress blog
        • 8
          Easy deployment
        • 7
          PostgreSQL support
        • 7
          Autoscaling
        • 7
          Good balance between Heroku and AWS for flexibility
        • 5
          Free, Easy Setup, Lot of Gear or D.I.Y Gear
        • 4
          Shell access to gears
        • 3
          Great Support
        • 3
          High Security
        • 3
          Logging & Metrics
        • 2
          Cloud Agnostic
        • 2
          Runs Anywhere - AWS, GCP, Azure
        • 2
          No credit card needed
        • 2
          Because it is easy to manage
        • 2
          Secure
        • 2
          Meteor support
        • 2
          Overly complicated and over engineered in majority of e
        • 2
          Golang support
        • 2
          Its free and offer custom domain usage
        • 1
          Autoscaling at a good price point
        • 1
          Easy setup and great customer support
        • 1
          MultiCloud
        • 1
          Great free plan with excellent support
        • 1
          This is the only free one among the three as of today
        CONS OF RED HAT OPENSHIFT
        • 2
          Decisions are made for you, limiting your options
        • 2
          License cost
        • 1
          Behind, sometimes severely, the upstreams

        related Red Hat OpenShift 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

        See more
        Michael Ionita

        We use Kubernetes because we decided to migrate to a hosted cluster (not AWS) and still be able to scale our clusters up and down depending on load. By wrapping it with OpenShift we are now able to easily adapt to demand but also able to separate concerns into separate Pods depending on use-cases we have.

        See more
        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
        25K
        + 1
        1.1K
        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 · 9.3M 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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