Alternatives to kops logo

Alternatives to kops

Amazon EKS, Rancher, Terraform, Helm, and minikube are the most popular alternatives and competitors to kops.
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What is kops and what are its top alternatives?

Kops is a popular open-source tool that helps with managing Kubernetes clusters on AWS. It automates the process of creating, updating, and deleting clusters, making it easier for users to work with Kubernetes on the AWS platform. Kops simplifies the deployment process with features like rolling updates, DNS management, and network configuration. However, users may find Kops challenging to configure and manage for more complex cluster setups.

  1. Kubespray: Kubespray is a community project focused on deploying Kubernetes clusters using Ansible. Key features include support for various cloud providers, high availability setup, and the ability to customize cluster configurations. Pros compared to Kops include easier customization and Ansible automation, but it may require more manual setup compared to Kops.
  2. Rancher: Rancher is a complete Kubernetes management platform that offers features like cluster provisioning, monitoring, and security. It simplifies Kubernetes management for users with features like centralized control and a user-friendly interface. Pros compared to Kops include a more comprehensive management solution, but it may be overkill for users only looking for cluster deployment.
  3. K3s: K3s is a lightweight Kubernetes distribution designed for edge computing and IoT applications. It offers a simple deployment process and a reduced footprint compared to standard Kubernetes installations. Pros compared to Kops include faster deployment speeds and a smaller resource footprint, but it may lack some advanced features found in Kops.
  4. Kubeform: Kubeform is a platform that enables users to provision and manage Kubernetes clusters across various cloud providers. It streamlines the cluster deployment process with a unified interface and built-in infrastructure support. Pros compared to Kops include multi-cloud compatibility and a more intuitive user experience, but it may have limitations in terms of cluster customization.
  5. KubeOne: KubeOne is a feature-rich Kubernetes cluster lifecycle management tool that focuses on simplicity and security. It automates the process of deploying and managing clusters while offering features like high availability and automatic updates. Pros compared to Kops include stronger security features and ease of use, but it may have a steeper learning curve for beginners.
  6. Kubeadm: Kubeadm is a Kubernetes toolkit that simplifies cluster bootstrapping and management. It offers a fast and reliable way to set up Kubernetes clusters with features like easy configuration and version compatibility. Pros compared to Kops include streamlined cluster setup and support for various Kubernetes versions, but it may require more manual intervention for cluster operations.
  7. Octopus: Octopus is a Kubernetes operator toolkit that facilitates the deployment and management of operators. It simplifies the process of installing and scaling operators in Kubernetes clusters, providing a more structured approach to cluster management. Pros compared to Kops include enhanced operator management and easier scaling, but it may be more specialized in its use case compared to Kops.
  8. KubeSphere: KubeSphere is a Kubernetes management platform that offers features like multi-tenant support, application management, and DevOps integrations. It provides a complete solution for managing Kubernetes clusters and applications in production environments. Pros compared to Kops include a more comprehensive platform with additional features, but it may be more resource-intensive and complex to set up.
  9. Eksctl: Eksctl is a command-line tool for managing Kubernetes clusters on AWS EKS. It simplifies the process of creating and managing EKS clusters with features like automated node group creation and seamless integration with other AWS services. Pros compared to Kops include native EKS support and a more streamlined setup process, but it may have limitations in terms of cluster customization.
  10. Kismatic Enterprise Toolkit: Kismatic Enterprise Toolkit is an enterprise-grade Kubernetes deployment tool that offers features like centralized cluster management, monitoring, and security. It provides a robust solution for organizations looking to deploy and manage Kubernetes clusters in production environments. Pros compared to Kops include enterprise-grade features and support, but it may be more complex to set up and manage for smaller deployments.

Top Alternatives to kops

  • Amazon EKS
    Amazon EKS

    Amazon Elastic Container Service for Kubernetes (Amazon EKS) is a managed service that makes it easy for you to run Kubernetes on AWS without needing to install and operate your own Kubernetes clusters. ...

  • Rancher
    Rancher

    Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform. ...

  • 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. ...

  • Helm
    Helm

    Helm is the best way to find, share, and use software built for Kubernetes.

  • minikube
    minikube

    It implements a local Kubernetes cluster on macOS, Linux, and Windows. Its goal is to be the tool for local Kubernetes application development and to support all Kubernetes features that fit. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

kops alternatives & related posts

Amazon EKS logo

Amazon EKS

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Highly available and scalable Kubernetes service
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PROS OF AMAZON EKS
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    Better control
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    Possibility to log in into the pods
  • 1
    Broad package manager using helm
CONS OF AMAZON EKS
    Be the first to leave a con

    related Amazon EKS posts

    We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

    We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

    We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

    You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

    See more
    Russel Werner
    Lead Engineer at StackShare · | 7 upvotes · 544.5K views

    We began our hosting journey, as many do, on Heroku because they make it easy to deploy your application and automate some of the routine tasks associated with deployments, etc. However, as our team grew and our product matured, our needs have outgrown Heroku. I will dive into the history and reasons for this in a future blog post.

    We decided to migrate our infrastructure to Kubernetes running on Amazon EKS. Although Google Kubernetes Engine has a slightly more mature Kubernetes offering and is more user-friendly; we decided to go with EKS because we already using other AWS services (including a previous migration from Heroku Postgres to AWS RDS). We are still in the process of moving our main website workloads to EKS, however we have successfully migrate all our staging and testing PR apps to run in a staging cluster. We developed a Slack chatops application (also running in the cluster) which automates all the common tasks of spinning up and managing a production-like cluster for a pull request. This allows our engineering team to iterate quickly and safely test code in a full production environment. Helm plays a central role when deploying our staging apps into the cluster. We use CircleCI to build docker containers for each PR push, which are then published to Amazon EC2 Container Service (ECR). An upgrade-operator process watches the ECR repository for new containers and then uses Helm to rollout updates to the staging environments. All this happens automatically and makes it really easy for developers to get code onto servers quickly. The immutable and isolated nature of our staging environments means that we can do anything we want in that environment and quickly re-create or restore the environment to start over.

    The next step in our journey is to migrate our production workloads to an EKS cluster and build out the CD workflows to get our containers promoted to that cluster after our QA testing is complete in our staging environments.

    See more
    Rancher logo

    Rancher

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    Open Source Platform for Running a Private Container Service
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    PROS OF RANCHER
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      Easy to use
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      Open source and totally free
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      Multi-host docker-compose support
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      Load balancing and health check included
    • 58
      Simple
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      Rolling upgrades, green/blue upgrades feature
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      Dns and service discovery out-of-the-box
    • 37
      Only requires docker
    • 34
      Multitenant and permission management
    • 29
      Easy to use and feature rich
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      Cross cloud compatible
    • 11
      Does everything needed for a docker infrastructure
    • 8
      Simple and powerful
    • 8
      Next-gen platform
    • 7
      Very Docker-friendly
    • 6
      Support Kubernetes and Swarm
    • 6
      Application catalogs with stack templates (wizards)
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      Supports Apache Mesos, Docker Swarm, and Kubernetes
    • 6
      Rolling and blue/green upgrades deployments
    • 6
      High Availability service: keeps your app up 24/7
    • 5
      Easy to use service catalog
    • 4
      Very intuitive UI
    • 4
      IaaS-vendor independent, supports hybrid/multi-cloud
    • 4
      Awesome support
    • 3
      Scalable
    • 2
      Requires less infrastructure requirements
    CONS OF RANCHER
    • 10
      Hosting Rancher can be complicated

    related Rancher posts

    Terraform logo

    Terraform

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    PROS OF TERRAFORM
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      Infrastructure as code
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      Planning
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      Simple
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      Parallelism
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      Well-documented
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      Cloud agnostic
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      It's like coding your infrastructure in simple English
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      Immutable infrastructure
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      Platform agnostic
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      Extendable
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      Automation
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      Automates infrastructure deployments
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      Portability
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      Lightweight
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      Scales to hundreds of hosts
    CONS OF TERRAFORM
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      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
    Helm logo

    Helm

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    The Kubernetes Package Manager
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    PROS OF HELM
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      Infrastructure as code
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      Open source
    • 2
      Easy setup
    • 1
      Support
    • 1
      Testa­bil­i­ty and re­pro­ducibil­i­ty
    CONS OF HELM
      Be the first to leave a con

      related Helm posts

      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
      Russel Werner
      Lead Engineer at StackShare · | 7 upvotes · 544.5K views

      We began our hosting journey, as many do, on Heroku because they make it easy to deploy your application and automate some of the routine tasks associated with deployments, etc. However, as our team grew and our product matured, our needs have outgrown Heroku. I will dive into the history and reasons for this in a future blog post.

      We decided to migrate our infrastructure to Kubernetes running on Amazon EKS. Although Google Kubernetes Engine has a slightly more mature Kubernetes offering and is more user-friendly; we decided to go with EKS because we already using other AWS services (including a previous migration from Heroku Postgres to AWS RDS). We are still in the process of moving our main website workloads to EKS, however we have successfully migrate all our staging and testing PR apps to run in a staging cluster. We developed a Slack chatops application (also running in the cluster) which automates all the common tasks of spinning up and managing a production-like cluster for a pull request. This allows our engineering team to iterate quickly and safely test code in a full production environment. Helm plays a central role when deploying our staging apps into the cluster. We use CircleCI to build docker containers for each PR push, which are then published to Amazon EC2 Container Service (ECR). An upgrade-operator process watches the ECR repository for new containers and then uses Helm to rollout updates to the staging environments. All this happens automatically and makes it really easy for developers to get code onto servers quickly. The immutable and isolated nature of our staging environments means that we can do anything we want in that environment and quickly re-create or restore the environment to start over.

      The next step in our journey is to migrate our production workloads to an EKS cluster and build out the CD workflows to get our containers promoted to that cluster after our QA testing is complete in our staging environments.

      See more
      minikube logo

      minikube

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      Local Kubernetes engine
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      PROS OF MINIKUBE
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        Let's me test k8s config locally
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        Can use same yaml config I'll use for prod deployment
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        Easy setup
      CONS OF MINIKUBE
        Be the first to leave a con

        related minikube posts

        JavaScript logo

        JavaScript

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        PROS OF JAVASCRIPT
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          Can be used on frontend/backend
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          It's everywhere
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          Lots of great frameworks
        • 896
          Fast
        • 745
          Light weight
        • 425
          Flexible
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          You can't get a device today that doesn't run js
        • 286
          Non-blocking i/o
        • 237
          Ubiquitousness
        • 191
          Expressive
        • 55
          Extended functionality to web pages
        • 49
          Relatively easy language
        • 46
          Executed on the client side
        • 30
          Relatively fast to the end user
        • 25
          Pure Javascript
        • 21
          Functional programming
        • 15
          Async
        • 13
          Full-stack
        • 12
          Its everywhere
        • 12
          Future Language of The Web
        • 12
          Setup is easy
        • 11
          JavaScript is the New PHP
        • 11
          Because I love functions
        • 10
          Like it or not, JS is part of the web standard
        • 9
          Expansive community
        • 9
          Can be used in backend, frontend and DB
        • 9
          Easy
        • 9
          Everyone use it
        • 8
          Most Popular Language in the World
        • 8
          Can be used both as frontend and backend as well
        • 8
          Powerful
        • 8
          For the good parts
        • 8
          No need to use PHP
        • 8
          Easy to hire developers
        • 7
          Love-hate relationship
        • 7
          Agile, packages simple to use
        • 7
          Its fun and fast
        • 7
          Hard not to use
        • 7
          Nice
        • 7
          Versitile
        • 7
          Evolution of C
        • 7
          Photoshop has 3 JS runtimes built in
        • 7
          It's fun
        • 7
          Popularized Class-Less Architecture & Lambdas
        • 7
          Supports lambdas and closures
        • 6
          Can be used on frontend/backend/Mobile/create PRO Ui
        • 6
          1.6K Can be used on frontend/backend
        • 6
          Client side JS uses the visitors CPU to save Server Res
        • 6
          It let's me use Babel & Typescript
        • 6
          Easy to make something
        • 5
          What to add
        • 5
          Clojurescript
        • 5
          Stockholm Syndrome
        • 5
          Function expressions are useful for callbacks
        • 5
          Scope manipulation
        • 5
          Everywhere
        • 5
          Client processing
        • 5
          Promise relationship
        • 4
          Because it is so simple and lightweight
        • 4
          Only Programming language on browser
        • 1
          Easy to learn
        • 1
          Not the best
        • 1
          Hard to learn
        • 1
          Easy to understand
        • 1
          Test
        • 1
          Test2
        • 1
          Subskill #4
        • 0
          Hard 彤
        CONS OF JAVASCRIPT
        • 22
          A constant moving target, too much churn
        • 20
          Horribly inconsistent
        • 15
          Javascript is the New PHP
        • 9
          No ability to monitor memory utilitization
        • 8
          Shows Zero output in case of ANY error
        • 7
          Thinks strange results are better than errors
        • 6
          Can be ugly
        • 3
          No GitHub
        • 2
          Slow

        related JavaScript posts

        Zach Holman

        Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

        But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

        But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

        Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

        See more
        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
        Git logo

        Git

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        Fast, scalable, distributed revision control system
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        PROS OF GIT
        • 1.4K
          Distributed version control system
        • 1.1K
          Efficient branching and merging
        • 959
          Fast
        • 845
          Open source
        • 726
          Better than svn
        • 368
          Great command-line application
        • 306
          Simple
        • 291
          Free
        • 232
          Easy to use
        • 222
          Does not require server
        • 27
          Distributed
        • 22
          Small & Fast
        • 18
          Feature based workflow
        • 15
          Staging Area
        • 13
          Most wide-spread VSC
        • 11
          Role-based codelines
        • 11
          Disposable Experimentation
        • 7
          Frictionless Context Switching
        • 6
          Data Assurance
        • 5
          Efficient
        • 4
          Just awesome
        • 3
          Github integration
        • 3
          Easy branching and merging
        • 2
          Compatible
        • 2
          Flexible
        • 2
          Possible to lose history and commits
        • 1
          Rebase supported natively; reflog; access to plumbing
        • 1
          Light
        • 1
          Team Integration
        • 1
          Fast, scalable, distributed revision control system
        • 1
          Easy
        • 1
          Flexible, easy, Safe, and fast
        • 1
          CLI is great, but the GUI tools are awesome
        • 1
          It's what you do
        • 0
          Phinx
        CONS OF GIT
        • 16
          Hard to learn
        • 11
          Inconsistent command line interface
        • 9
          Easy to lose uncommitted work
        • 7
          Worst documentation ever possibly made
        • 5
          Awful merge handling
        • 3
          Unexistent preventive security flows
        • 3
          Rebase hell
        • 2
          When --force is disabled, cannot rebase
        • 2
          Ironically even die-hard supporters screw up badly
        • 1
          Doesn't scale for big data

        related Git 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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