Alternatives to minikube logo

Alternatives to minikube

Kubernetes, Docker Compose, Docker, Kind, and k3s are the most popular alternatives and competitors to minikube.
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What is minikube and what are its top alternatives?

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.
minikube is a tool in the Container Tools category of a tech stack.
minikube is an open source tool with 28.6K GitHub stars and 4.8K GitHub forks. Here’s a link to minikube's open source repository on GitHub

Top Alternatives to minikube

  • 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. ...

  • Docker Compose
    Docker Compose

    With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running. ...

  • 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 ...

  • Kind
    Kind

    It is a tool for running local Kubernetes clusters using Docker container “nodes”. It was primarily designed for testing Kubernetes itself, but may be used for local development or CI. ...

  • k3s
    k3s

    Certified Kubernetes distribution designed for production workloads in unattended, resource-constrained, remote locations or inside IoT appliances. Supports something as small as a Raspberry Pi or as large as an AWS a1.4xlarge 32GiB server. ...

  • 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. ...

minikube alternatives & related posts

Kubernetes logo

Kubernetes

58.9K
51K
677
Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
58.9K
51K
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677
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 · 10.9M 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
Docker Compose logo

Docker Compose

21.3K
16.1K
501
Define and run multi-container applications with Docker
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16.1K
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PROS OF DOCKER COMPOSE
  • 123
    Multi-container descriptor
  • 110
    Fast development environment setup
  • 79
    Easy linking of containers
  • 68
    Simple yaml configuration
  • 60
    Easy setup
  • 16
    Yml or yaml format
  • 12
    Use Standard Docker API
  • 8
    Open source
  • 5
    Go from template to application in minutes
  • 5
    Can choose Discovery Backend
  • 4
    Scalable
  • 4
    Easy configuration
  • 4
    Kubernetes integration
  • 3
    Quick and easy
CONS OF DOCKER COMPOSE
  • 9
    Tied to single machine
  • 5
    Still very volatile, changing syntax often

related Docker Compose posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M 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

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

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

See more
Docker logo

Docker

171.3K
137.7K
3.9K
Enterprise Container Platform for High-Velocity Innovation.
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137.7K
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PROS OF DOCKER
  • 823
    Rapid integration and build up
  • 691
    Isolation
  • 521
    Open source
  • 505
    Testa­bil­i­ty and re­pro­ducibil­i­ty
  • 460
    Lightweight
  • 218
    Standardization
  • 185
    Scalable
  • 106
    Upgrading / down­grad­ing / ap­pli­ca­tion versions
  • 88
    Security
  • 85
    Private paas environments
  • 34
    Portability
  • 26
    Limit resource usage
  • 17
    Game changer
  • 16
    I love the way docker has changed virtualization
  • 14
    Fast
  • 12
    Concurrency
  • 8
    Docker's Compose tools
  • 6
    Easy setup
  • 6
    Fast and Portable
  • 5
    Because its fun
  • 4
    Makes shipping to production very simple
  • 3
    Highly useful
  • 3
    It's dope
  • 2
    Very easy to setup integrate and build
  • 2
    HIgh Throughput
  • 2
    Package the environment with the application
  • 2
    Does a nice job hogging memory
  • 2
    Open source and highly configurable
  • 2
    Simplicity, isolation, resource effective
  • 2
    MacOS support FAKE
  • 2
    Its cool
  • 2
    Docker hub for the FTW
  • 2
    Super
  • 0
    Asdfd
CONS OF DOCKER
  • 8
    New versions == broken features
  • 6
    Unreliable networking
  • 6
    Documentation not always in sync
  • 4
    Moves quickly
  • 3
    Not Secure

related Docker posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M 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 · 8.7M 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
Kind logo

Kind

29
59
0
Run local Kubernetes clusters using Docker
29
59
+ 1
0
PROS OF KIND
    Be the first to leave a pro
    CONS OF KIND
      Be the first to leave a con

      related Kind posts

      k3s logo

      k3s

      93
      242
      16
      Lightweight Kubernetes. 5 less than k8s (by Rancher Labs)
      93
      242
      + 1
      16
      PROS OF K3S
      • 6
        Lightweight
      • 4
        Easy
      • 2
        Replication Controller
      • 2
        Scale Services
      • 2
        Open Source
      CONS OF K3S
        Be the first to leave a con

        related k3s posts

        Enzo Gehlen
        Web Developer at IXCSoft · | 2 upvotes · 30.4K views
        Shared insights
        on
        k3sk3sK3dK3d

        Would you use K3d/k3s in an LXC container?

        See more
        JavaScript logo

        JavaScript

        352.4K
        268.2K
        8.1K
        Lightweight, interpreted, object-oriented language with first-class functions
        352.4K
        268.2K
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        PROS OF JAVASCRIPT
        • 1.7K
          Can be used on frontend/backend
        • 1.5K
          It's everywhere
        • 1.2K
          Lots of great frameworks
        • 897
          Fast
        • 745
          Light weight
        • 425
          Flexible
        • 392
          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
          Setup is easy
        • 12
          Future Language of The Web
        • 12
          Its everywhere
        • 11
          Because I love functions
        • 11
          JavaScript is the New PHP
        • 10
          Like it or not, JS is part of the web standard
        • 9
          Expansive community
        • 9
          Everyone use it
        • 9
          Can be used in backend, frontend and DB
        • 9
          Easy
        • 8
          Most Popular Language in the World
        • 8
          Powerful
        • 8
          Can be used both as frontend and backend as well
        • 8
          For the good parts
        • 8
          No need to use PHP
        • 8
          Easy to hire developers
        • 7
          Agile, packages simple to use
        • 7
          Love-hate relationship
        • 7
          Photoshop has 3 JS runtimes built in
        • 7
          Evolution of C
        • 7
          It's fun
        • 7
          Hard not to use
        • 7
          Versitile
        • 7
          Its fun and fast
        • 7
          Nice
        • 7
          Popularized Class-Less Architecture & Lambdas
        • 7
          Supports lambdas and closures
        • 6
          It let's me use Babel & Typescript
        • 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
          Easy to make something
        • 5
          Clojurescript
        • 5
          Promise relationship
        • 5
          Stockholm Syndrome
        • 5
          Function expressions are useful for callbacks
        • 5
          Scope manipulation
        • 5
          Everywhere
        • 5
          Client processing
        • 5
          What to add
        • 4
          Because it is so simple and lightweight
        • 4
          Only Programming language on browser
        • 1
          Test
        • 1
          Hard to learn
        • 1
          Test2
        • 1
          Not the best
        • 1
          Easy to understand
        • 1
          Subskill #4
        • 1
          Easy to learn
        • 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 · 10.9M 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

        291.4K
        174.8K
        6.6K
        Fast, scalable, distributed revision control system
        291.4K
        174.8K
        + 1
        6.6K
        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 · 9.7M 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.
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        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 8.7M 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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        Johnny Bell

        I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

        I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

        I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

        Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

        Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

        With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

        If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

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        Russel Werner
        Lead Engineer at StackShare · | 32 upvotes · 2.5M views

        StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

        Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

        #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

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