Python vs ReasonML

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Python
Python

29.4K
22.6K
+ 1
5.9K
ReasonML
ReasonML

23
11
+ 1
2
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Python vs ReasonML: What are the differences?

Developers describe Python as "A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java". 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. On the other hand, ReasonML is detailed as "A friendly programming language for JavaScript and OCaml". It lets you write simple, fast and quality type safe code while leveraging both the JavaScript & OCaml ecosystems.It is powerful, safe type inference means you rarely have to annotate types, but everything gets checked for you.

Python and ReasonML belong to "Languages" category of the tech stack.

Python and ReasonML are both open source tools. It seems that Python with 25.9K GitHub stars and 11K forks on GitHub has more adoption than ReasonML with 7.92K GitHub stars and 374 GitHub forks.

According to the StackShare community, Python has a broader approval, being mentioned in 3814 company stacks & 19518 developers stacks; compared to ReasonML, which is listed in 8 company stacks and 7 developer stacks.

What is 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.

What is ReasonML?

It lets you write simple, fast and quality type safe code while leveraging both the JavaScript & OCaml ecosystems.It is powerful, safe type inference means you rarely have to annotate types, but everything gets checked for you.
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      What are some alternatives to Python and ReasonML?
      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
      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 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 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
      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.
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      Decisions about Python and ReasonML
      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 16 upvotes · 727.4K views
      atUber TechnologiesUber Technologies
      Apache Spark
      Apache Spark
      C#
      C#
      OpenShift
      OpenShift
      JavaScript
      JavaScript
      Kubernetes
      Kubernetes
      C++
      C++
      Go
      Go
      Node.js
      Node.js
      Java
      Java
      Python
      Python
      Jaeger
      Jaeger

      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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      Amazon ElastiCache
      Amazon ElastiCache
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      AWS Elastic Load Balancing (ELB)
      AWS Elastic Load Balancing (ELB)
      Memcached
      Memcached
      Redis
      Redis
      Python
      Python
      AWS Lambda
      AWS Lambda
      Amazon RDS
      Amazon RDS
      Microsoft SQL Server
      Microsoft SQL Server
      MariaDB
      MariaDB
      Amazon RDS for PostgreSQL
      Amazon RDS for PostgreSQL
      Rails
      Rails
      Ruby
      Ruby
      Heroku
      Heroku
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk

      We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

      We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

      In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

      Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

      See more
      StackShare Editors
      StackShare Editors
      Kubernetes
      Kubernetes
      Go
      Go
      Python
      Python

      Following its migration from vanilla instances with autoscaling groups to Kubernetes, Postmates began facing challenges while “migrating workloads that needed to scale up very quickly.”

      The built-in Horizontal Pod Autoscaler (HPA) automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization. But the challenges for Postmates is that there’s no way to configure the scale velocity of one particular cluster with an HPA.

      For Postmates, which runs at least three different types of applications with distinct performance and scaling characteristics, this proved problematic.

      To overcome these challenges, the team created and open sourced the Configurable Horizontal Pod Autoscaler, which allows for fine-grained tuning on a per-HPA object basis. The result is that “you can configure critical services to scale down very slowly, while every other service could be configured to scale down instantly to reduce costs.”

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      Hampton Catlin
      Hampton Catlin
      VP of Engineering at Rent The Runway · | 6 upvotes · 8.3K views
      atRent the RunwayRent the Runway
      Java
      Java
      Python
      Python
      Ruby
      Ruby

      At our company, and I've noticed a lot of other ones... application developers and dev-ops people tend to use Ruby and our statisticians and data scientists love Python . Like most companies, our stack is kind of split that way. Ruby is used as glue in most of our production systems ( Java being the main backend language), and then all of our data scientists and their various pipelines tend towards Python

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      Ajit Parthan
      Ajit Parthan
      CTO at Shaw Academy · | 3 upvotes · 5.2K views
      atShaw AcademyShaw Academy
      Python
      Python
      PHP
      PHP
      #Etl

      Multiple systems means there is a requirement to cart data across them.

      Started off with Talend scripts. This was great as what we initially had were PHP/Python script - allowed for a more systematic approach to ETL.

      But ended up with a massive repository of scripts, complex crontab entries and regular failures due to memory issues.

      Using Stitch or similar services is a better approach: - no need to worry about the infrastructure needed for the ETL processes - a more formal mapping of data from source to destination as opposed to script developer doing his/her voodoo magic - lot of common sources and destination integrations are already builtin and out of the box

      etl @{etlasaservice}|topic:1323|

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      SVN (Subversion)
      SVN (Subversion)
      Git
      Git
      JSON
      JSON
      XML
      XML
      Python
      Python
      PHP
      PHP
      Java
      Java
      Swift
      Swift
      JavaScript
      JavaScript
      Linux
      Linux
      GitHub
      GitHub
      Visual Studio Code
      Visual Studio Code

      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)

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      Ajit Parthan
      Ajit Parthan
      CTO at Shaw Academy · | 1 upvotes · 4K views
      atShaw AcademyShaw Academy
      Python
      Python
      PHP
      PHP

      Multiple systems means there is a requirement to cart data across them.

      Started off with Talend scripts. This was great as what we initially had were PHP/Python script - allowed for a more systematic approach to ETL.

      But ended up with a massive repository of scripts, complex crontab entries and regular failures due to memory issues.

      Using Stitch or similar services is a better approach: - no need to worry about the infrastructure needed for the ETL processes - a more formal mapping of data from source to destination as opposed to script developer doing his/her voodoo magic - lot of common sources and destination integrations are already builtin and out of the box

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      Eric Colson
      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 286.4K views
      atStitch FixStitch Fix
      Amazon EC2 Container Service
      Amazon EC2 Container Service
      Docker
      Docker
      PyTorch
      PyTorch
      R
      R
      Python
      Python
      Presto
      Presto
      Apache Spark
      Apache Spark
      Amazon S3
      Amazon S3
      PostgreSQL
      PostgreSQL
      Kafka
      Kafka
      #AWS
      #Etl
      #ML
      #DataScience
      #DataStack
      #Data

      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

      See more
      Python
      Python
      Django
      Django
      JavaScript
      JavaScript
      Node.js
      Node.js

      Django or NodeJS? Hi, I’m thinking about which software I should use for my web-app. What about Node.js or Django for the back-end? I want to create an online preparation course for the final school exams in my country. At the beginning for maths. The course should contain tutorials and a lot of exercises of different types. E.g. multiple choice, user text/number input and drawing tasks. The exercises should change (different levels) with the learning progress. Wrong questions should asked again with different numbers. I also want a score system and statistics. So far, I have got only limited web development skills. (some HTML, CSS, Bootstrap and Wordpress). I don’t know JavaScript or Python.

      Possible pros for Python / Django: - easy syntax, easier to learn for me as a beginner - fast development, earlier release - libraries for mathematical and scientific computation

      Possible pros for JavaScript / Node.js: - great performance, better choice for real time applications: user should get the answer for a question quickly

      Which software would you use in my case? Are my arguments for Python/NodeJS right? Which kind of database would you use?

      Thank you for your answer!

      Node.js JavaScript Django Python

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      Git
      Git
      Docker
      Docker
      NATS
      NATS
      JavaScript
      JavaScript
      TypeScript
      TypeScript
      PostgreSQL
      PostgreSQL
      Python
      Python
      Go
      Go

      Go is a high performance language with simple syntax / semantics. Although it is not as expressive as some other languages, it's still a great language for backend development.

      Python is expressive and battery-included, and pre-installed in most linux distros, making it a great language for scripting.

      PostgreSQL: Rock-solid RDBMS with NoSQL support.

      TypeScript saves you from all nonsense semantics of JavaScript , LOL.

      NATS: fast message queue and easy to deploy / maintain.

      Docker makes deployment painless.

      Git essential tool for collaboration and source management.

      See more
      Omar Melendrez
      Omar Melendrez
      Front-end developer · | 3 upvotes · 4.2K views
      Python
      Python
      C#
      C#
      Node.js
      Node.js
      React
      React
      Vue.js
      Vue.js
      #Fullstack
      #Vscode

      I'm #Fullstack here and work with Vue.js, React and Node.js in some projects but also C# for other clients. Also started learning Python. And all this with just one tool!: #Vscode I have used Atom and Sublime Text in the past and they are very good too, but for me now is just vscode. I think the combination of vscode with the free available extensions that the community is creating makes a powerful tool and that's why vscode became the most popular IDE for software development. You can match it to your own needs in a couple of minutes. Did I mention you can style it your way? Amazing tool!

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      Tom Klein
      Tom Klein
      CEO at Gentlent · | 4 upvotes · 30.1K views
      atGentlentGentlent
      Python
      Python
      Electron
      Electron
      Socket.IO
      Socket.IO
      Google Compute Engine
      Google Compute Engine
      TypeScript
      TypeScript
      ES6
      ES6
      Ubuntu
      Ubuntu
      PostgreSQL
      PostgreSQL
      React
      React
      nginx
      nginx
      Sass
      Sass
      HTML5
      HTML5
      PHP
      PHP
      Node.js
      Node.js
      JavaScript
      JavaScript

      Our most used programming languages are JavaScript / Node.js for it's lightweight and fast use, PHP because everyone knows it, HTML5 because you can't live without it and Sass to write great CSS. Occasionally, we use nginx as a web server and proxy, React for our UX, PostgreSQL as fast relational database, Ubuntu as server OS, ES6 and TypeScript for Node, Google Compute Engine for our infrastructure, and Socket.IO and Electron for specific use cases. We also use Python for some of our backends.

      See more
      Praveen Mooli
      Praveen Mooli
      Technical Leader at Taylor and Francis · | 11 upvotes · 170.9K views
      MongoDB Atlas
      MongoDB Atlas
      Amazon S3
      Amazon S3
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon RDS
      Amazon RDS
      Serverless
      Serverless
      Docker
      Docker
      Terraform
      Terraform
      Travis CI
      Travis CI
      GitHub
      GitHub
      RxJS
      RxJS
      Angular 2
      Angular 2
      AWS Lambda
      AWS Lambda
      Amazon SQS
      Amazon SQS
      Amazon SNS
      Amazon SNS
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose
      Amazon Kinesis
      Amazon Kinesis
      Flask
      Flask
      Python
      Python
      ExpressJS
      ExpressJS
      Node.js
      Node.js
      Spring Boot
      Spring Boot
      Java
      Java
      #Backend
      #Microservices
      #Eventsourcingframework
      #Webapps
      #Devops
      #Data

      We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

      To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

      To build #Webapps we decided to use Angular 2 with RxJS

      #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

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      PubNub
      PubNub
      asyncio
      asyncio
      JavaScript
      JavaScript
      Python
      Python

      I love Python and JavaScript . You can do the same JavaScript async operations in Python by using asyncio. This is particularly useful when you need to do socket programming in Python. With streaming sockets, data can be sent or received at any time. In case your Python program is in the middle of executing some code, other threads can handle the new socket data. Libraries like asyncio implement multiple threads, so your Python program can work in an asynchronous fashion. PubNub makes bi-directional data streaming between devices even easier.

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      Helio Junior
      Helio Junior
      CSS 3
      CSS 3
      JavaScript
      JavaScript
      Python
      Python
      #DataScience
      #UXdesign
      #NodeJS
      #Electron

      Python is a excellent tool for #DataScience , but up to now is very poor in #uxdesign . To do some design I'm using JavaScript and #nodejs , #electron stack. The possibility of use CSS 3 to draw interfaces is very awesome and fast. Unfortunatelly Python don't have (yet) a good way to make a #UXdesign .

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