Alternatives to Python logo

Alternatives to Python

Java, R Language, JavaScript, Scala, and Anaconda are the most popular alternatives and competitors to Python.
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What is Python and what are its top alternatives?

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.
Python is a tool in the Languages category of a tech stack.
Python is an open source tool with 41.5K GitHub stars and 21K GitHub forks. Here’s a link to Python's open source repository on GitHub

Top Alternatives to Python

  • Java

    Java

    Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere! ...

  • R Language

    R Language

    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. ...

  • 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. ...

  • Scala

    Scala

    Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them. ...

  • Anaconda

    Anaconda

    A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. ...

  • Perl

    Perl

    Perl is a general-purpose programming language originally developed for text manipulation and now used for a wide range of tasks including system administration, web development, network programming, GUI development, and more. ...

  • PHP

    PHP

    Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world. ...

  • Ruby

    Ruby

    Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming. ...

Python alternatives & related posts

Java logo

Java

91.6K
68.3K
3.6K
A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
91.6K
68.3K
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3.6K
PROS OF JAVA
  • 582
    Great libraries
  • 439
    Widely used
  • 398
    Excellent tooling
  • 385
    Huge amount of documentation available
  • 330
    Large pool of developers available
  • 203
    Open source
  • 199
    Excellent performance
  • 154
    Great development
  • 148
    Vast array of 3rd party libraries
  • 147
    Used for android
  • 59
    Compiled Language
  • 49
    Used for Web
  • 46
    Managed memory
  • 44
    Native threads
  • 44
    High Performance
  • 41
    Statically typed
  • 35
    Easy to read
  • 33
    Great Community
  • 29
    Reliable platform
  • 24
    JVM compatibility
  • 24
    Sturdy garbage collection
  • 21
    Cross Platform Enterprise Integration
  • 20
    Universal platform
  • 20
    Good amount of APIs
  • 18
    Great Support
  • 13
    Great ecosystem
  • 11
    Lots of boilerplate
  • 11
    Backward compatible
  • 10
    Everywhere
  • 9
    Excellent SDK - JDK
  • 7
    Static typing
  • 6
    Long term language
  • 6
    Better than Ruby
  • 6
    Mature language thus stable systems
  • 6
    Portability
  • 6
    Cross-platform
  • 6
    It's Java
  • 5
    Vast Collections Library
  • 5
    Used for Android development
  • 5
    Clojure
  • 4
    Most developers favorite
  • 4
    Old tech
  • 3
    Best martial for design
  • 3
    Great Structure
  • 3
    Testable
  • 3
    Javadoc
  • 3
    Stable platform, which many new languages depend on
  • 2
    History
CONS OF JAVA
  • 30
    Verbosity
  • 25
    NullpointerException
  • 16
    Overcomplexity is praised in community culture
  • 14
    Nightmare to Write
  • 11
    Boiler plate code
  • 8
    Classpath hell prior to Java 9
  • 6
    No REPL
  • 4
    No property
  • 2
    Floating-point errors
  • 2
    There is not optional parameter
  • 2
    Code are too long
  • 2
    Non-intuitive generic implementation
  • 1
    Returning Wildcard Types
  • 1
    Java's too statically, stronglly, and strictly typed
  • 1
    Terrbible compared to Python/Batch Perormence

related Java posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M 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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Kamil Kowalski
Lead Architect at Fresha · | 27 upvotes · 1.2M views

When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.

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R Language logo

R Language

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382
A language and environment for statistical computing and graphics
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PROS OF R LANGUAGE
  • 79
    Data analysis
  • 61
    Graphics and data visualization
  • 52
    Free
  • 41
    Great community
  • 37
    Flexible statistical analysis toolkit
  • 26
    Access to powerful, cutting-edge analytics
  • 25
    Easy packages setup
  • 18
    Interactive
  • 11
    R Studio IDE
  • 9
    Hacky
  • 5
    Preferred Medium
  • 5
    Shiny interactive plots
  • 5
    Shiny apps
  • 4
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
CONS OF R LANGUAGE
  • 4
    Very messy syntax
  • 3
    Tables must fit in RAM
  • 2
    No push command for vectors/lists
  • 2
    Messy syntax for string concatenation
  • 2
    Arrays indices start with 1
  • 1
    Messy character encoding
  • 0
    Poor syntax for classes
  • 0
    Messy syntax for array/vector combination

related R Language posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.1M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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Maged Maged Rafaat Kamal
Shared insights
on
PythonPythonR LanguageR Language

I am currently trying to learn R Language for machine learning, I already have a good knowledge of Python. What resources would you recommend to learn from as a beginner in R?

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JavaScript logo

JavaScript

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Lightweight, interpreted, object-oriented language with first-class functions
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PROS OF JAVASCRIPT
  • 1.6K
    Can be used on frontend/backend
  • 1.5K
    It's everywhere
  • 1.1K
    Lots of great frameworks
  • 887
    Fast
  • 736
    Light weight
  • 416
    Flexible
  • 383
    You can't get a device today that doesn't run js
  • 284
    Non-blocking i/o
  • 233
    Ubiquitousness
  • 188
    Expressive
  • 51
    Extended functionality to web pages
  • 44
    Relatively easy language
  • 42
    Executed on the client side
  • 26
    Relatively fast to the end user
  • 22
    Pure Javascript
  • 17
    Functional programming
  • 11
    Async
  • 8
    Setup is easy
  • 7
    Because I love functions
  • 7
    JavaScript is the New PHP
  • 7
    Like it or not, JS is part of the web standard
  • 7
    Its everywhere
  • 7
    Full-stack
  • 6
    Expansive community
  • 6
    Future Language of The Web
  • 6
    Can be used in backend, frontend and DB
  • 5
    Evolution of C
  • 5
    Everyone use it
  • 5
    Love-hate relationship
  • 5
    Easy to hire developers
  • 5
    Supports lambdas and closures
  • 5
    Agile, packages simple to use
  • 5
    Popularized Class-Less Architecture & Lambdas
  • 5
    For the good parts
  • 4
    Everywhere
  • 4
    Hard not to use
  • 4
    Promise relationship
  • 4
    Scope manipulation
  • 4
    It's fun
  • 4
    Client processing
  • 4
    Nice
  • 4
    Easy to make something
  • 4
    Can be used on frontend/backend/Mobile/create PRO Ui
  • 4
    Easy
  • 4
    Photoshop has 3 JS runtimes built in
  • 4
    Can be used both as frontend and backend as well
  • 4
    Most Popular Language in the World
  • 4
    It let's me use Babel & Typescript
  • 4
    What to add
  • 4
    Clojurescript
  • 4
    Function expressions are useful for callbacks
  • 4
    No need to use PHP
  • 4
    Its fun and fast
  • 4
    Powerful
  • 4
    Versitile
  • 4
    Client side JS uses the visitors CPU to save Server Res
  • 4
    1.6K Can be used on frontend/backend
  • 4
    Stockholm Syndrome
  • 3
    Because it is so simple and lightweight
  • 3
    Only Programming language on browser
  • 2
    JavaScript j.s
  • 2
    Acoperișul 0757604335
CONS OF JAVASCRIPT
  • 21
    A constant moving target, too much churn
  • 20
    Horribly inconsistent
  • 14
    Javascript is the New PHP
  • 8
    No ability to monitor memory utilitization
  • 6
    Shows Zero output in case of ANY error
  • 5
    Can be ugly
  • 4
    Thinks strange results are better than errors
  • 2
    No GitHub
  • 1
    Slow

related JavaScript posts

Zach Holman

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

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

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

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

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M 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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Scala logo

Scala

7.6K
5.7K
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A pure-bred object-oriented language that runs on the JVM
7.6K
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PROS OF SCALA
  • 188
    Static typing
  • 179
    Pattern-matching
  • 177
    Jvm
  • 172
    Scala is fun
  • 138
    Types
  • 95
    Concurrency
  • 88
    Actor library
  • 86
    Solve functional problems
  • 83
    Open source
  • 80
    Solve concurrency in a safer way
  • 44
    Functional
  • 23
    Generics
  • 23
    Fast
  • 18
    It makes me a better engineer
  • 17
    Syntactic sugar
  • 13
    Scalable
  • 10
    First-class functions
  • 10
    Type safety
  • 9
    Interactive REPL
  • 8
    Expressive
  • 7
    SBT
  • 6
    Implicit parameters
  • 6
    Case classes
  • 4
    Used by Twitter
  • 4
    JVM, OOP and Functional programming, and static typing
  • 4
    Rapid and Safe Development using Functional Programming
  • 4
    Object-oriented
  • 3
    Functional Proframming
  • 2
    Spark
  • 2
    Beautiful Code
  • 2
    Safety
  • 2
    Growing Community
  • 1
    DSL
  • 1
    Rich Static Types System and great Concurrency support
  • 1
    Naturally enforce high code quality
  • 1
    Akka Streams
  • 1
    Akka
  • 1
    Reactive Streams
  • 1
    Easy embedded DSLs
  • 1
    Mill build tool
  • 0
    Freedom to choose the right tools for a job
CONS OF SCALA
  • 11
    Slow compilation time
  • 6
    Multiple ropes and styles to hang your self
  • 4
    Too few developers available
  • 3
    Complicated subtyping
  • 2
    My coworkers using scala are racist against other stuff

related Scala posts

Shared insights
on
JavaJavaScalaScalaApache SparkApache Spark

I am new to Apache Spark and Scala both. I am basically a Java developer and have around 10 years of experience in Java.

I wish to work on some Machine learning or AI tech stacks. Please assist me in the tech stack and help make a clear Road Map. Any feedback is welcome.

Technologies apart from Scala and Spark are also welcome. Please note that the tools should be relevant to Machine Learning or Artificial Intelligence.

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Marc Bollinger
Infra & Data Eng Manager at Thumbtack · | 5 upvotes · 465.3K views

Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.

We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.

We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.

To find out more, read our 2017 engineering blog post about the migration!

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Anaconda logo

Anaconda

350
381
0
The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
350
381
+ 1
0
PROS OF ANACONDA
    Be the first to leave a pro
    CONS OF ANACONDA
      Be the first to leave a con

      related Anaconda posts

      Shared insights
      on
      JavaJavaAnacondaAnacondaPythonPython

      I am going to learn machine learning and self host an online IDE, the tool that i may use is Python, Anaconda, various python library and etc. which tools should i go for? this may include Java development, web development. Now i have 1 more candidate which are visual studio code online (code server). i will host on google cloud

      See more
      Guillaume Simler

      Jupyter Anaconda Pandas IPython

      A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

      The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

      See more
      Perl logo

      Perl

      1.8K
      782
      514
      Highly capable, feature-rich programming language with over 26 years of development
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      782
      + 1
      514
      PROS OF PERL
      • 68
        Lots of libraries
      • 62
        Open source
      • 58
        Text processing
      • 50
        Powerful
      • 45
        Unix-style
      • 44
        Regex
      • 35
        Stable
      • 28
        Concise syntax
      • 27
        Hackerish
      • 19
        Easy to use
      • 10
        Swiss army chainsaw
      • 10
        CPAN
      • 9
        Code Less Do More
      • 7
        Freedom
      • 6
        All purpose
      • 5
        Familiar
      • 4
        Readability
      • 4
        Community
      • 3
        It's the best one-off task language
      • 3
        Modular
      • 3
        Object-Oriented
      • 2
        Postmodern
      • 2
        Smart (does alot for you)
      • 2
        Good man pages
      • 2
        For a man
      • 2
        Many ways to do it
      • 1
        Auto case variables
      • 1
        Hashes
      • 1
        Multi-threaded support
      • 1
        Single Source Library (CPAN)
      CONS OF PERL
      • 3
        Messy $/@/% syntax
      • 3
        No exception handling
      • 2
        No OS threads
      • 2
        Bad OO support
      • 2
        "1;"
      • 1
        Copy-on-create for interpreter-based threads
      • 1
        Barewords
      • 1
        Errors/warnings are ignored by default
      • 1
        Variables are global by default

      related Perl posts

      Seth Ammons
      Principal Software Developer at SendGrid · | 10 upvotes · 47.1K views
      Shared insights
      on
      GoGoDockerDockerPerlPerl
      at

      In addition to our fancy Docker setup, we have captured and sanitized production logs for the behavior of our legacy Perl MTA, and we can test that the log output from the new Go version behaves the same way as the old version. These tests are set up to allow us to switch between the legacy and new version of the MTA and ensure that both systems behave in a legacy-compatible way. Not only can we ensure that we operate against a variety of issues we've seen over time from inboxes, but we know that the newest version of our MTA continues to cover all the same expected behaviors of the legacy version. #CodeCollaborationVersionControl #ContinuousIntegration

      See more
      PHP logo

      PHP

      112.5K
      56.5K
      4.5K
      A popular general-purpose scripting language that is especially suited to web development
      112.5K
      56.5K
      + 1
      4.5K
      PROS OF PHP
      • 942
        Large community
      • 806
        Open source
      • 759
        Easy deployment
      • 482
        Great frameworks
      • 385
        The best glue on the web
      • 234
        Continual improvements
      • 181
        Good old web
      • 142
        Web foundation
      • 133
        Community packages
      • 124
        Tool support
      • 33
        Used by wordpress
      • 31
        Excellent documentation
      • 26
        Used by Facebook
      • 23
        Because of Symfony
      • 19
        Dynamic Language
      • 15
        Cheap hosting
      • 14
        Awesome Language and easy to implement
      • 14
        Very powerful web language
      • 13
        Fast development
      • 11
        Easy to learn
      • 11
        Composer
      • 10
        Flexibility, syntax, extensibility
      • 10
        Because of Laravel
      • 8
        Easiest deployment
      • 7
        Readable Code
      • 7
        Short development lead times
      • 7
        Worst popularity quality ratio
      • 7
        Fastestest Time to Version 1.0 Deployments
      • 6
        Faster then ever
      • 6
        Fast
      • 6
        Most of the web uses it
      • 5
        Open source and large community
      • 5
        Simple, flexible yet Scalable
      • 4
        Has the best ecommerce(Magento,Prestashop,Opencart,etc)
      • 4
        Large community, easy setup, easy deployment, framework
      • 4
        Open source and great framework
      • 4
        Easy to use and learn
      • 4
        Cheap to own
      • 4
        Easy to learn, a big community, lot of frameworks
      • 4
        Is like one zip of air
      • 4
        I have no choice :(
      • 3
        Great developer experience
      • 2
        Hard not to use
      • 2
        FFI
      • 2
        Interpreted at the run time
      • 2
        Great flexibility. From fast prototyping to large apps
      • 2
        Used by STOMT
      • 2
        Fault tolerance
      • 2
        Safe the planet
      • 2
        Walk away
      CONS OF PHP
      • 20
        So easy to learn, good practices are hard to find
      • 16
        Inconsistent API
      • 8
        Fragmented community
      • 5
        Not secure
      • 2
        No routing system
      • 1
        Hard to debug
      • 1
        Old

      related PHP posts

      Nick Rockwell
      SVP, Engineering at Fastly · | 44 upvotes · 1.7M views

      When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

      So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

      React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

      Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

      See more
      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 25 upvotes · 2.1M views

      Our whole Node.js backend stack consists of the following tools:

      • Lerna as a tool for multi package and multi repository management
      • npm as package manager
      • NestJS as Node.js framework
      • TypeScript as programming language
      • ExpressJS as web server
      • Swagger UI for visualizing and interacting with the API’s resources
      • Postman as a tool for API development
      • TypeORM as object relational mapping layer
      • JSON Web Token for access token management

      The main reason we have chosen Node.js over PHP is related to the following artifacts:

      • Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
      • Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
      • A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
      • Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.
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      Ruby

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      A dynamic, interpreted, open source programming language with a focus on simplicity and productivity
      25.5K
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      PROS OF RUBY
      • 600
        Programme friendly
      • 533
        Quick to develop
      • 488
        Great community
      • 467
        Productivity
      • 430
        Simplicity
      • 272
        Open source
      • 234
        Meta-programming
      • 202
        Powerful
      • 157
        Blocks
      • 138
        Powerful one-liners
      • 66
        Flexible
      • 56
        Easy to learn
      • 48
        Easy to start
      • 40
        Maintainability
      • 36
        Lambdas
      • 30
        Procs
      • 19
        Fun to write
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        Diverse web frameworks
      • 11
        Reads like English
      • 9
        Rails
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        Makes me smarter and happier
      • 7
        Elegant syntax
      • 6
        Very Dynamic
      • 5
        Programmer happiness
      • 5
        Matz
      • 4
        Generally fun but makes you wanna cry sometimes
      • 4
        Fun and useful
      • 3
        Friendly
      • 3
        Object Oriented
      • 3
        There are so many ways to make it do what you want
      • 2
        Easy packaging and modules
      • 2
        Primitive types can be tampered with
      • 2
        Elegant code
      CONS OF RUBY
      • 7
        Memory hog
      • 7
        Really slow if you're not really careful
      • 3
        Nested Blocks can make code unreadable
      • 2
        Encouraging imperative programming
      • 1
        Ambiguous Syntax, such as function parentheses

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      When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.

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      Jonathan Pugh
      Software Engineer / Project Manager / Technical Architect · | 25 upvotes · 1.6M 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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