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. ...

  • Node.js
    Node.js

    Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices. ...

  • HTML5
    HTML5

    HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997. ...

YAML alternatives & related posts

JSON logo

JSON

2K
9
A lightweight data-interchange format
2K
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
    39
    RESTful API Modeling Language (RAML) makes it easy to manage the whole API lifecycle from design to sharing
    127
    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

      19.2K
      1.3K
      Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine
      19.2K
      1.3K
      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 · 10.1M 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

      21.8K
      501
      Define and run multi-container applications with Docker
      21.8K
      501
      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 · 11.9M 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

      246.2K
      6.9K
      A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
      246.2K
      6.9K
      PROS OF PYTHON
      • 1.2K
        Great libraries
      • 964
        Readable code
      • 847
        Beautiful code
      • 788
        Rapid development
      • 691
        Large community
      • 438
        Open source
      • 393
        Elegant
      • 282
        Great community
      • 273
        Object oriented
      • 221
        Dynamic typing
      • 77
        Great standard library
      • 60
        Very fast
      • 55
        Functional programming
      • 51
        Easy to learn
      • 46
        Scientific computing
      • 35
        Great documentation
      • 29
        Productivity
      • 28
        Easy to read
      • 28
        Matlab alternative
      • 24
        Simple is better than complex
      • 20
        It's the way I think
      • 19
        Imperative
      • 18
        Very programmer and non-programmer friendly
      • 18
        Free
      • 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
        Import antigravity
      • 8
        It's lean and fun to code
      • 7
        Print "life is short, use python"
      • 7
        Python has great libraries for data processing
      • 6
        Rapid Prototyping
      • 6
        Readability counts
      • 6
        Now is better than never
      • 6
        Great for tooling
      • 6
        Flat is better than nested
      • 6
        Although practicality beats purity
      • 6
        I love snakes
      • 6
        High Documented language
      • 6
        There should be one-- and preferably only one --obvious
      • 6
        Fast coding and good for competitions
      • 5
        Web scraping
      • 5
        Lists, tuples, dictionaries
      • 5
        Great for analytics
      • 4
        Easy to setup and run smooth
      • 4
        Easy to learn and use
      • 4
        Plotting
      • 4
        Beautiful is better than ugly
      • 4
        Multiple Inheritence
      • 4
        Socially engaged community
      • 4
        Complex is better than complicated
      • 4
        CG industry needs
      • 4
        Simple and easy to learn
      • 3
        It is Very easy , simple and will you be love programmi
      • 3
        Flexible and easy
      • 3
        Many types of collections
      • 3
        If the implementation is easy to explain, it may be a g
      • 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
        Powerful language for AI
      • 2
        Can understand easily who are new to programming
      • 2
        Should START with this but not STICK with This
      • 2
        A-to-Z
      • 2
        Because of Netflix
      • 2
        Only one way to do it
      • 2
        Better outcome
      • 2
        Batteries included
      • 2
        Good for hacking
      • 2
        Securit
      • 1
        Procedural programming
      • 1
        Best friend for NLP
      • 1
        Slow
      • 1
        Automation friendly
      • 1
        Sexy af
      • 0
        Ni
      • 0
        Keep it simple
      • 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 · 13.1M views

      How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

      Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

      Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

      https://eng.uber.com/distributed-tracing/

      (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

      Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

      See more
      Nick Parsons
      Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 4.4M 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

      363.2K
      8.1K
      Lightweight, interpreted, object-oriented language with first-class functions
      363.2K
      8.1K
      PROS OF JAVASCRIPT
      • 1.7K
        Can be used on frontend/backend
      • 1.5K
        It's everywhere
      • 1.2K
        Lots of great frameworks
      • 898
        Fast
      • 746
        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
        Future Language of The Web
      • 12
        Setup is easy
      • 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
        Easy
      • 9
        Can be used in backend, frontend and DB
      • 9
        Expansive community
      • 9
        Everyone use it
      • 8
        Easy to hire developers
      • 8
        Most Popular Language in the World
      • 8
        For the good parts
      • 8
        Can be used both as frontend and backend as well
      • 8
        No need to use PHP
      • 8
        Powerful
      • 7
        Evolution of C
      • 7
        Its fun and fast
      • 7
        It's fun
      • 7
        Nice
      • 7
        Versitile
      • 7
        Hard not to use
      • 7
        Popularized Class-Less Architecture & Lambdas
      • 7
        Agile, packages simple to use
      • 7
        Supports lambdas and closures
      • 7
        Love-hate relationship
      • 7
        Photoshop has 3 JS runtimes built in
      • 6
        1.6K Can be used on frontend/backend
      • 6
        Client side JS uses the visitors CPU to save Server Res
      • 6
        It let's me use Babel & Typescript
      • 6
        Easy to make something
      • 6
        Can be used on frontend/backend/Mobile/create PRO Ui
      • 5
        Client processing
      • 5
        What to add
      • 5
        Everywhere
      • 5
        Scope manipulation
      • 5
        Function expressions are useful for callbacks
      • 5
        Stockholm Syndrome
      • 5
        Promise relationship
      • 5
        Clojurescript
      • 4
        Only Programming language on browser
      • 4
        Because it is so simple and lightweight
      • 1
        Easy to learn and test
      • 1
        Easy to understand
      • 1
        Not the best
      • 1
        Subskill #4
      • 1
        Hard to learn
      • 1
        Test2
      • 1
        Test
      • 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
      • 0
        HORRIBLE DOCUMENTS, faulty code, repo has bugs

      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 · 13.1M views

      How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

      Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

      Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

      https://eng.uber.com/distributed-tracing/

      (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

      Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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      Node.js

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      A platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications
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      PROS OF NODE.JS
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        Websockets
      • 84
        Node Modules
      • 69
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        Great modularity
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        Allows us to reuse code in the frontend
      • 42
        Easy to start
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        Great for Data Streaming
      • 32
        Realtime
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        Awesome
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        Non blocking IO
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        Can be used as a proxy
      • 17
        High performance, open source, scalable
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        Non-blocking and modular
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        Easy and Fun
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        Easy and powerful
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        Same lang as AngularJS
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        Fast
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        Great for webapps
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        Fast, simple code and async
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        React
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        Friendly
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        Control everything
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        Its amazingly fast and scalable
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        Easy to use and fast and goes well with JSONdb's
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        Scalable
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        Blazing fast
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        Scales, fast, simple, great community, npm, express
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      CONS OF NODE.JS
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        New framework every day
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        Very very Slow
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        Breaking updates
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      related Node.js posts

      Shared insights
      on
      Node.jsNode.jsGraphQLGraphQLMongoDBMongoDB

      I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

      For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

      1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

      2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

      3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

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      Anurag Maurya

      Needs advice on code coverage tool in Node.js/ExpressJS with External API Testing Framework

      Hello community,

      I have a web application with the backend developed using Node.js and Express.js. The backend server is in one directory, and I have a separate API testing framework, made using SuperTest, Mocha, and Chai, in another directory. The testing framework pings the API, retrieves responses, and performs validations.

      I'm currently looking for a code coverage tool that can accurately measure the code coverage of my backend code when triggered by the API testing framework. I've tried using Istanbul and NYC with instrumented code, but the results are not as expected.

      Could you please recommend a reliable code coverage tool or suggest an approach to effectively measure the code coverage of my Node.js/Express.js backend code in this setup?

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      HTML5 logo

      HTML5

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      5th major revision of the core language of the World Wide Web
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      PROS OF HTML5
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      CONS OF HTML5
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      Jan Vlnas
      Senior Software Engineer at Mews · | 26 upvotes · 432K views
      Shared insights
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      HTML5HTML5JavaScriptJavaScriptNext.jsNext.js

      Few years ago we were building a Next.js site with a few simple forms. This required handling forms validation and submission, but instead of picking some forms library, we went with plain JavaScript and constraint validation API in HTML5. This shaved off a few KBs of dependencies and gave us full control over the validation behavior and look. I describe this approach, with its pros and cons, in a blog post.

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      Jonathan Pugh
      Software Engineer / Project Manager / Technical Architect · | 25 upvotes · 3.1M views

      I needed to choose a full stack of tools for cross platform mobile application design & development. After much research and trying different tools, these are what I came up with that work for me today:

      For the client coding I chose Framework7 because of its performance, easy learning curve, and very well designed, beautiful UI widgets. I think it's perfect for solo development or small teams. I didn't like React Native. It felt heavy to me and rigid. Framework7 allows the use of #CSS3, which I think is the best technology to come out of the #WWW movement. No other tech has been able to allow designers and developers to develop such flexible, high performance, customisable user interface elements that are highly responsive and hardware accelerated before. Now #CSS3 includes variables and flexboxes it is truly a powerful language and there is no longer a need for preprocessors such as #SCSS / #Sass / #less. React Native contains a very limited interpretation of #CSS3 which I found very frustrating after using #CSS3 for some years already and knowing its powerful features. The other very nice feature of Framework7 is that you can even build for the browser if you want your app to be available for desktop web browsers. The latest release also includes the ability to build for #Electron so you can have MacOS, Windows and Linux desktop apps. This is not possible with React Native yet.

      Framework7 runs on top of Apache Cordova. Cordova and webviews have been slated as being slow in the past. Having a game developer background I found the tweeks to make it run as smooth as silk. One of those tweeks is to use WKWebView. Another important one was using srcset on images.

      I use #Template7 for the for the templating system which is a no-nonsense mobile-centric #HandleBars style extensible templating system. It's easy to write custom helpers for, is fast and has a small footprint. I'm not forced into a new paradigm or learning some new syntax. It operates with standard JavaScript, HTML5 and CSS 3. It's written by the developer of Framework7 and so dovetails with it as expected.

      I configured TypeScript to work with the latest version of Framework7. I consider TypeScript to be one of the best creations to come out of Microsoft in some time. They must have an amazing team working on it. It's very powerful and flexible. It helps you catch a lot of bugs and also provides code completion in supporting IDEs. So for my IDE I use Visual Studio Code which is a blazingly fast and silky smooth editor that integrates seamlessly with TypeScript for the ultimate type checking setup (both products are produced by Microsoft).

      I use Webpack and Babel to compile the JavaScript. TypeScript can compile to JavaScript directly but Babel offers a few more options and polyfills so you can use the latest (and even prerelease) JavaScript features today and compile to be backwards compatible with virtually any browser. My favorite recent addition is "optional chaining" which greatly simplifies and increases readability of a number of sections of my code dealing with getting and setting data in nested objects.

      I use some Ruby scripts to process images with ImageMagick and pngquant to optimise for size and even auto insert responsive image code into the HTML5. Ruby is the ultimate cross platform scripting language. Even as your scripts become large, Ruby allows you to refactor your code easily and make it Object Oriented if necessary. I find it the quickest and easiest way to maintain certain aspects of my build process.

      For the user interface design and prototyping I use Figma. Figma has an almost identical user interface to #Sketch but has the added advantage of being cross platform (MacOS and Windows). Its real-time collaboration features are outstanding and I use them a often as I work mostly on remote projects. Clients can collaborate in real-time and see changes I make as I make them. The clickable prototyping features in Figma are also very well designed and mean I can send clickable prototypes to clients to try user interface updates as they are made and get immediate feedback. I'm currently also evaluating the latest version of #AdobeXD as an alternative to Figma as it has the very cool auto-animate feature. It doesn't have real-time collaboration yet, but I heard it is proposed for 2019.

      For the UI icons I use Font Awesome Pro. They have the largest selection and best looking icons you can find on the internet with several variations in styles so you can find most of the icons you want for standard projects.

      For the backend I was using the #GraphCool Framework. As I later found out, #GraphQL still has some way to go in order to provide the full power of a mature graph query language so later in my project I ripped out #GraphCool and replaced it with CouchDB and Pouchdb. Primarily so I could provide good offline app support. CouchDB with Pouchdb is very flexible and efficient combination and overcomes some of the restrictions I found in #GraphQL and hence #GraphCool also. The most impressive and important feature of CouchDB is its replication. You can configure it in various ways for backups, fault tolerance, caching or conditional merging of databases. CouchDB and Pouchdb even supports storing, retrieving and serving binary or image data or other mime types. This removes a level of complexity usually present in database implementations where binary or image data is usually referenced through an #HTML5 link. With CouchDB and Pouchdb apps can operate offline and sync later, very efficiently, when the network connection is good.

      I use PhoneGap when testing the app. It auto-reloads your app when its code is changed and you can also install it on Android phones to preview your app instantly. iOS is a bit more tricky cause of Apple's policies so it's not available on the App Store, but you can build it and install it yourself to your device.

      So that's my latest mobile stack. What tools do you use? Have you tried these ones?

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