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Grain
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Grain vs Python: What are the differences?

Grain: A strongly-typed functional programming language. Grain is a strongly-typed functional programming language built for the modern web. Unlike other languages used on the web today (like TypeScript or Elm), Grain doesn’t compile into JavaScript. Grain complies all the way down to WebAssembly, and is supported by a tiny JavaScript runtime to give Grain access to web features that WebAssembly doesn’t yet support; Python: 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.

Grain and Python can be categorized as "Languages" tools.

Grain and Python are both open source tools. Python with 25.3K GitHub stars and 10.5K forks on GitHub appears to be more popular than Grain with 1.21K GitHub stars and 21 GitHub forks.

What is Grain?

Grain is a strongly-typed functional programming language built for the modern web. Unlike other languages used on the web today (like TypeScript or Elm), Grain doesn’t compile into JavaScript. Grain complies all the way down to WebAssembly, and is supported by a tiny JavaScript runtime to give Grain access to web features that WebAssembly doesn’t yet support.

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.
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Why do developers choose Grain?
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          What are some alternatives to Grain and Python?
          PHP
          Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.
          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.
          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!
          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.
          ASP.NET
          .NET is a developer platform made up of tools, programming languages, and libraries for building many different types of applications.
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          Decisions about Grain and Python
          Conor Myhrvold
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 16 upvotes · 728.7K 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

          See more
          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|

          See more
          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 · 287.7K views
          atStitch FixStitch Fix
          Amazon EC2 Container Service
          Amazon EC2 Container Service
          Docker
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          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

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          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 · 30.2K 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 · 172.1K 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 .

          See more
          Interest over time
          Reviews of Grain and Python
          No reviews found
          How developers use Grain and Python
          Avatar of Exchange rates API
          Exchange rates API uses PythonPython

          Beautiful is better than ugly.

          Explicit is better than implicit.

          Simple is better than complex.

          Complex is better than complicated.

          Flat is better than nested.

          Sparse is better than dense.

          Readability counts.

          Special cases aren't special enough to break the rules.

          Although practicality beats purity.

          Errors should never pass silently.

          Unless explicitly silenced.

          In the face of ambiguity, refuse the temptation to guess.

          There should be one-- and preferably only one --obvious way to do it.

          Although that way may not be obvious at first unless you're Dutch.

          Now is better than never.

          Although never is often better than right now.

          If the implementation is hard to explain, it's a bad idea.

          If the implementation is easy to explain, it may be a good idea.

          Namespaces are one honking great idea -- let's do more of those!

          Avatar of Web Dreams
          Web Dreams uses PythonPython

          To me, this is by far the best programming language. Why? Because it’s the only language that really got me going after trying to get into programming with Java for a while. Python is powerful, easy to learn, and gets you to unsderstand other languages more once you understand it. Did I state I love the python language? Well, I do..

          Avatar of ttandon
          ttandon uses PythonPython

          Backend server for analysis of image samples from iPhone microscope lens. Chose this because of familiarity. The number one thing that I've learned at hackathons is that work exclusively with what you're 100% comfortable with. I use Python extensively at my day job at Wit.ai, so it was the obvious choice for the bulk of my coding.

          Avatar of papaver
          papaver uses PythonPython

          been a pythoner for around 7 years, maybe longer. quite adept at it, and love using the higher constructs like decorators. was my goto scripting language until i fell in love with clojure. python's also the goto for most vfx studios and great for the machine learning. numpy and pyqt for the win.

          Avatar of Blood Bot
          Blood Bot uses PythonPython

          Large swaths of resources built for python to achieve natural language processing. (We are in the process of deprecating the services written in python and porting them over to Javascript and node)

          How much does Grain cost?
          How much does Python cost?
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