Alternatives to Composer logo

Alternatives to Composer

Docker Compose, Docker, Kubernetes, Docker Swarm, and Elementor are the most popular alternatives and competitors to Composer.
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What is Composer and what are its top alternatives?

Composer is a dependency management tool for PHP that allows users to easily manage packages and libraries within their projects. It simplifies the process of including external dependencies and ensures that the required versions are installed correctly. However, Composer has some limitations such as slow performance when dealing with a large number of dependencies and potential conflicts between packages.

  1. Pip: Pip is a package manager for Python that allows users to easily install and manage libraries and dependencies. It features a large repository of packages and supports virtual environments for project isolation. Pros include a vast library of packages and easy installation process, while a limitation is the lack of support for local dependencies.
  2. npm: npm is the default package manager for Node.js and a popular choice for managing JavaScript dependencies. It offers a wide range of packages and tools for frontend development. Pros include a large ecosystem and fast package installation, while a con is the occasional version conflicts.
  3. Yarn: Yarn is an alternative package manager for JavaScript that aims to improve upon npm's speed and reliability. It features offline package installation and deterministic dependency resolution. Pros include faster installation times and consistent package versions, while a limitation is the lack of support for some npm features.
  4. NuGet: NuGet is a package manager for the .NET framework that allows users to easily add libraries and dependencies to their projects. It supports both .NET Core and the full .NET framework. Pros include a vast library of packages and easy integration with Visual Studio, while a limitation is the occasional package version conflicts.
  5. Bundler: Bundler is a dependency manager for Ruby that helps users manage gems and versions within their projects. It ensures that the correct gem versions are installed and resolves dependencies automatically. Pros include easy gem management and version locking, while a limitation is slow installation times for large projects.
  6. CocoaPods: CocoaPods is a dependency manager for Swift and Objective-C projects on iOS and macOS. It simplifies the process of adding third-party libraries and frameworks to Xcode projects. Pros include a large repository of libraries and easy integration with Xcode, while a limitation is the occasional compatibility issues with Xcode updates.
  7. Maven: Maven is a build automation tool for Java projects that also serves as a dependency manager. It manages project dependencies, builds, and documentation generation. Pros include a robust build system and extensive plugin support, while a limitation is the complex configuration required for some projects.
  8. Gradle: Gradle is another build automation tool for Java projects that includes built-in support for dependency management. It offers a flexible and highly configurable build system. Pros include fast build times and support for multi-project builds, while a limitation is the learning curve for more advanced features.
  9. Pipenv: Pipenv is a tool that combines pip and virtualenv into one package for managing Python dependencies and virtual environments. It ensures deterministic package installation and supports automatic dependency resolution. Pros include simplified dependency management and enhanced security through hash checking, while a limitation is occasional conflicts with existing virtual environments.
  10. Carthage: Carthage is a decentralized dependency manager for iOS and Mac development that focuses on simplicity and reliability. It allows users to specify precisely which dependencies to include in their projects and builds them from source. Pros include faster builds and cleaner project setups, while a limitation is the lack of support for dynamic frameworks.

Top Alternatives to Composer

  • 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 ...

  • 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 Swarm
    Docker Swarm

    Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself. ...

  • Elementor
    Elementor

    Create beautiful websites using a simple, intuitive drag and drop Interface.It offers pixel perfect design, yet produces 100% clean code. Take your design vision and turn it into a stunning custom-made website. It's fast and easy. ...

  • Conductor
    Conductor

    Conductor is an orchestration engine that runs in the cloud.

  • 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. ...

Composer alternatives & related posts

Docker Compose logo

Docker Compose

21.6K
16.3K
501
Define and run multi-container applications with Docker
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+ 1
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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 · 11.1M 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

174.2K
140K
3.9K
Enterprise Container Platform for High-Velocity Innovation.
174.2K
140K
+ 1
3.9K
PROS OF DOCKER
  • 823
    Rapid integration and build up
  • 692
    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
    Fast and Portable
  • 6
    Easy setup
  • 5
    Because its fun
  • 4
    Makes shipping to production very simple
  • 3
    It's dope
  • 3
    Highly useful
  • 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
    HIgh Throughput
  • 2
    Very easy to setup integrate and build
  • 2
    Package the environment with the application
  • 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 · 11.1M 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 · 9.7M 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
Kubernetes logo

Kubernetes

59.8K
51.7K
681
Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
59.8K
51.7K
+ 1
681
PROS OF KUBERNETES
  • 166
    Leading docker container management solution
  • 129
    Simple and powerful
  • 107
    Open source
  • 76
    Backed by google
  • 58
    The right abstractions
  • 25
    Scale services
  • 20
    Replication controller
  • 11
    Permission managment
  • 9
    Supports autoscaling
  • 8
    Simple
  • 8
    Cheap
  • 6
    Self-healing
  • 5
    Open, powerful, stable
  • 5
    Reliable
  • 5
    No cloud platform lock-in
  • 5
    Promotes modern/good infrascture practice
  • 4
    Scalable
  • 4
    Quick cloud setup
  • 3
    Custom and extensibility
  • 3
    Captain of Container Ship
  • 3
    Cloud Agnostic
  • 3
    Backed by Red Hat
  • 3
    Runs on azure
  • 3
    A self healing environment with rich metadata
  • 2
    Everything of CaaS
  • 2
    Gke
  • 2
    Golang
  • 2
    Easy setup
  • 2
    Expandable
  • 2
    Sfg
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 · 12.6M 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
Yshay Yaacobi

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

See more
Docker Swarm logo

Docker Swarm

793
988
282
Native clustering for Docker. Turn a pool of Docker hosts into a single, virtual host.
793
988
+ 1
282
PROS OF DOCKER SWARM
  • 55
    Docker friendly
  • 46
    Easy to setup
  • 40
    Standard Docker API
  • 38
    Easy to use
  • 23
    Native
  • 22
    Free
  • 13
    Clustering made easy
  • 12
    Simple usage
  • 11
    Integral part of docker
  • 6
    Cross Platform
  • 5
    Labels and annotations
  • 5
    Performance
  • 3
    Easy Networking
  • 3
    Shallow learning curve
CONS OF DOCKER SWARM
  • 9
    Low adoption

related Docker Swarm posts

Yshay Yaacobi

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

See more
Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.1M 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
Elementor logo

Elementor

1.5K
186
1
Drag & Drop page builder for WordPress
1.5K
186
+ 1
1
PROS OF ELEMENTOR
  • 1
    Drag and drop interface
CONS OF ELEMENTOR
    Be the first to leave a con

    related Elementor posts

    Shared insights
    on
    ElementorElementorWordPressWordPress

    hello guys, I need your help. I created a website, I've been using Elementor forever, but yesterday I bought a template after I made the purchase I knew I made a mistake, cause the template was in HTML, can anyone please show me how to put this HTML template in my WordPress so it will be the face of my website, thank you in advance.

    See more
    Conductor logo

    Conductor

    65
    122
    0
    A microservices orchestration engine that runs in the cloud
    65
    122
    + 1
    0
    PROS OF CONDUCTOR
      Be the first to leave a pro
      CONS OF CONDUCTOR
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        Git logo

        Git

        297.1K
        178.4K
        6.6K
        Fast, scalable, distributed revision control system
        297.1K
        178.4K
        + 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
        • 8
          Worst documentation ever possibly made
        • 5
          Awful merge handling
        • 3
          Unexistent preventive security flows
        • 3
          Rebase hell
        • 2
          Ironically even die-hard supporters screw up badly
        • 2
          When --force is disabled, cannot rebase
        • 1
          Doesn't scale for big data

        related Git posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.1M 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 · 9.7M 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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