Alternatives to YAML logo

Alternatives to YAML

JSON, RAML, Ansible, Docker Compose, and Python are the most popular alternatives and competitors to YAML.
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What is YAML and what are its top alternatives?

A human-readable data-serialization language. It is commonly used for configuration files, but could be used in many applications where data is being stored or transmitted.
YAML is a tool in the Languages category of a tech stack.

Top Alternatives to YAML

  • JSON
    JSON

    JavaScript Object Notation is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language. ...

  • RAML
    RAML

    RESTful API Modeling Language (RAML) makes it easy to manage the whole API lifecycle from design to sharing. It's concise - you only write what you need to define - and reusable. It is machine readable API design that is actually human friendly. ...

  • Ansible
    Ansible

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

  • 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. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • 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. ...

YAML alternatives & related posts

JSON logo

JSON

1.9K
1.6K
9
A lightweight data-interchange format
1.9K
1.6K
+ 1
9
PROS OF JSON
  • 5
    Simple
  • 4
    Widely supported
CONS OF JSON
    Be the first to leave a con

    related JSON posts

    Ali Soueidan
    Creative Web Developer at Ali Soueidan · | 18 upvotes · 1.2M views

    Application and Data: Since my personal website ( https://alisoueidan.com ) is a SPA I've chosen to use Vue.js, as a framework to create it. After a short skeptical phase I immediately felt in love with the single file component concept! I also used vuex for state management, which makes working with several components, which are communicating with each other even more fun and convenient to use. Of course, using Vue requires using JavaScript as well, since it is the basis of it.

    For markup and style, I used Pug and Sass, since they’re the perfect match to me. I love the clean and strict syntax of both of them and even more that their structure is almost similar. Also, both of them come with an expanded functionality such as mixins, loops and so on related to their “siblings” (HTML and CSS). Both of them require nesting and prevent untidy code, which can be a huge advantage when working in teams. I used JSON to store data (since the data quantity on my website is moderate) – JSON works also good in combo with Pug, using for loops, based on the JSON Objects for example.

    To send my contact form I used PHP, since sending emails using PHP is still relatively convenient, simple and easy done.

    DevOps: Of course, I used Git to do my version management (which I even do in smaller projects like my website just have an additional backup of my code). On top of that I used GitHub since it now supports private repository for free accounts (which I am using for my own). I use Babel to use ES6 functionality such as arrow functions and so on, and still don’t losing cross browser compatibility.

    Side note: I used npm for package management. 🎉

    *Business Tools: * I use Asana to organize my project. This is a big advantage to me, even if I work alone, since “private” projects can get interrupted for some time. By using Asana I still know (even after month of not touching a project) what I’ve done, on which task I was at last working on and what still is to do. Working in Teams (for enterprise I’d take on Jira instead) of course Asana is a Tool which I really love to use as well. All the graphics on my website are SVG which I have created with Adobe Illustrator and adjusted within the SVG code or by using JavaScript or CSS (SASS).

    See more

    I use Visual Studio Code because at this time is a mature software and I can do practically everything using it.

    • It's free and open source: The project is hosted on GitHub and it’s free to download, fork, modify and contribute to the project.

    • Multi-platform: You can download binaries for different platforms, included Windows (x64), MacOS and Linux (.rpm and .deb packages)

    • LightWeight: It runs smoothly in different devices. It has an average memory and CPU usage. Starts almost immediately and it’s very stable.

    • Extended language support: Supports by default the majority of the most used languages and syntax like JavaScript, HTML, C#, Swift, Java, PHP, Python and others. Also, VS Code supports different file types associated to projects like .ini, .properties, XML and JSON files.

    • Integrated tools: Includes an integrated terminal, debugger, problem list and console output inspector. The project navigator sidebar is simple and powerful: you can manage your files and folders with ease. The command palette helps you find commands by text. The search widget has a powerful auto-complete feature to search and find your files.

    • Extensible and configurable: There are many extensions available for every language supported, including syntax highlighters, IntelliSense and code completion, and debuggers. There are also extension to manage application configuration and architecture like Docker and Jenkins.

    • Integrated with Git: You can visually manage your project repositories, pull, commit and push your changes, and easy conflict resolution.( there is support for SVN (Subversion) users by plugin)

    See more
    RAML logo

    RAML

    127
    155
    39
    RESTful API Modeling Language (RAML) makes it easy to manage the whole API lifecycle from design to sharing
    127
    155
    + 1
    39
    PROS OF RAML
    • 15
      API Specification
    • 7
      Human Readable
    • 6
      API Documentation
    • 3
      Design Patterns & Code Reuse
    • 2
      API Modeling
    • 2
      Automatic Generation of Mule flow
    • 2
      Unit Testing
    • 1
      API Mocking
    • 1
      SDK Generation
    CONS OF RAML
      Be the first to leave a con

      related RAML posts

      Ansible logo

      Ansible

      18.9K
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      Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
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      PROS OF ANSIBLE
      • 284
        Agentless
      • 210
        Great configuration
      • 199
        Simple
      • 176
        Powerful
      • 155
        Easy to learn
      • 69
        Flexible
      • 55
        Doesn't get in the way of getting s--- done
      • 35
        Makes sense
      • 30
        Super efficient and flexible
      • 27
        Powerful
      • 11
        Dynamic Inventory
      • 9
        Backed by Red Hat
      • 7
        Works with AWS
      • 6
        Cloud Oriented
      • 6
        Easy to maintain
      • 4
        Vagrant provisioner
      • 4
        Simple and powerful
      • 4
        Multi language
      • 4
        Simple
      • 4
        Because SSH
      • 4
        Procedural or declarative, or both
      • 4
        Easy
      • 3
        Consistency
      • 2
        Well-documented
      • 2
        Masterless
      • 2
        Debugging is simple
      • 2
        Merge hash to get final configuration similar to hiera
      • 2
        Fast as hell
      • 1
        Manage any OS
      • 1
        Work on windows, but difficult to manage
      • 1
        Certified Content
      CONS OF ANSIBLE
      • 8
        Dangerous
      • 5
        Hard to install
      • 3
        Doesn't Run on Windows
      • 3
        Bloated
      • 3
        Backward compatibility
      • 2
        No immutable infrastructure

      related Ansible posts

      Tymoteusz Paul
      Devops guy at X20X Development LTD · | 23 upvotes · 8.7M 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
      Sebastian Gębski

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

      See more
      Docker Compose logo

      Docker Compose

      21.3K
      16.1K
      501
      Define and run multi-container applications with Docker
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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
      Python logo

      Python

      240.8K
      196.3K
      6.9K
      A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
      240.8K
      196.3K
      + 1
      6.9K
      PROS OF PYTHON
      • 1.2K
        Great libraries
      • 961
        Readable code
      • 846
        Beautiful code
      • 787
        Rapid development
      • 689
        Large community
      • 435
        Open source
      • 393
        Elegant
      • 282
        Great community
      • 272
        Object oriented
      • 220
        Dynamic typing
      • 77
        Great standard library
      • 59
        Very fast
      • 55
        Functional programming
      • 49
        Easy to learn
      • 45
        Scientific computing
      • 35
        Great documentation
      • 29
        Productivity
      • 28
        Easy to read
      • 28
        Matlab alternative
      • 23
        Simple is better than complex
      • 20
        It's the way I think
      • 19
        Imperative
      • 18
        Free
      • 18
        Very programmer and non-programmer friendly
      • 17
        Powerfull language
      • 17
        Machine learning support
      • 16
        Fast and simple
      • 14
        Scripting
      • 12
        Explicit is better than implicit
      • 11
        Ease of development
      • 10
        Clear and easy and powerfull
      • 9
        Unlimited power
      • 8
        It's lean and fun to code
      • 8
        Import antigravity
      • 7
        Print "life is short, use python"
      • 7
        Python has great libraries for data processing
      • 6
        Although practicality beats purity
      • 6
        Flat is better than nested
      • 6
        Great for tooling
      • 6
        Rapid Prototyping
      • 6
        Readability counts
      • 6
        High Documented language
      • 6
        I love snakes
      • 6
        Fast coding and good for competitions
      • 6
        There should be one-- and preferably only one --obvious
      • 6
        Now is better than never
      • 5
        Great for analytics
      • 5
        Lists, tuples, dictionaries
      • 4
        Easy to learn and use
      • 4
        Simple and easy to learn
      • 4
        Easy to setup and run smooth
      • 4
        Web scraping
      • 4
        CG industry needs
      • 4
        Socially engaged community
      • 4
        Complex is better than complicated
      • 4
        Multiple Inheritence
      • 4
        Beautiful is better than ugly
      • 4
        Plotting
      • 3
        If the implementation is hard to explain, it's a bad id
      • 3
        Special cases aren't special enough to break the rules
      • 3
        Pip install everything
      • 3
        List comprehensions
      • 3
        No cruft
      • 3
        Generators
      • 3
        Import this
      • 3
        It is Very easy , simple and will you be love programmi
      • 3
        Many types of collections
      • 3
        If the implementation is easy to explain, it may be a g
      • 2
        Batteries included
      • 2
        Should START with this but not STICK with This
      • 2
        Powerful language for AI
      • 2
        Can understand easily who are new to programming
      • 2
        Flexible and easy
      • 2
        Good for hacking
      • 2
        A-to-Z
      • 2
        Because of Netflix
      • 2
        Only one way to do it
      • 2
        Better outcome
      • 1
        Sexy af
      • 1
        Slow
      • 1
        Securit
      • 0
        Ni
      • 0
        Powerful
      CONS OF PYTHON
      • 53
        Still divided between python 2 and python 3
      • 28
        Performance impact
      • 26
        Poor syntax for anonymous functions
      • 22
        GIL
      • 19
        Package management is a mess
      • 14
        Too imperative-oriented
      • 12
        Hard to understand
      • 12
        Dynamic typing
      • 12
        Very slow
      • 8
        Indentations matter a lot
      • 8
        Not everything is expression
      • 7
        Incredibly slow
      • 7
        Explicit self parameter in methods
      • 6
        Requires C functions for dynamic modules
      • 6
        Poor DSL capabilities
      • 6
        No anonymous functions
      • 5
        Fake object-oriented programming
      • 5
        Threading
      • 5
        The "lisp style" whitespaces
      • 5
        Official documentation is unclear.
      • 5
        Hard to obfuscate
      • 5
        Circular import
      • 4
        Lack of Syntax Sugar leads to "the pyramid of doom"
      • 4
        The benevolent-dictator-for-life quit
      • 4
        Not suitable for autocomplete
      • 2
        Meta classes
      • 1
        Training wheels (forced indentation)

      related Python 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
      Nick Parsons
      Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.9M views

      Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

      We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

      We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

      Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

      #FrameworksFullStack #Languages

      See more
      JavaScript logo

      JavaScript

      352.7K
      268.4K
      8.1K
      Lightweight, interpreted, object-oriented language with first-class functions
      352.7K
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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.

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      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

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      Git

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      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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      GitHub

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        Easy source control and everything is backed up
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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 · 2.6M 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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