NumPy vs Riot

Overview

NumPy

Stacks4.3K

Followers799

Votes15

GitHub Stars30.7K

Forks11.7K

Riot

Stacks116

Followers100

Votes68

GitHub Stars14.9K

Forks966

NumPy vs Riot: What are the differences?

Developers describe NumPy as "Fundamental package for scientific computing with Python". Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. On the other hand, Riot is detailed as "A React-like user interface micro-library". Riot brings custom tags to all browsers. Think React + Polymer but with enjoyable syntax and a small learning curve.

NumPy and Riot are primarily classified as "Data Science" and "Javascript UI Libraries" tools respectively.

Some of the features offered by NumPy are:

a powerful N-dimensional array object
sophisticated (broadcasting) functions
tools for integrating C/C++ and Fortran code

On the other hand, Riot provides the following key features:

Absolutely the smallest possible amount of DOM updates and reflows.
One way data flow: updates and unmounts are propagated downwards from parent to children.
Expressions are pre-compiled and cached for high performance.

NumPy and Riot are both open source tools. Riot with 13.7K GitHub stars and 1.02K forks on GitHub appears to be more popular than NumPy with 11.1K GitHub stars and 3.67K GitHub forks.

Instacart, Suggestic, and Twilio SendGrid are some of the popular companies that use NumPy, whereas Riot is used by BestFone 2.0, Walla!, and Thanx. NumPy has a broader approval, being mentioned in 63 company stacks & 34 developers stacks; compared to Riot, which is listed in 9 company stacks and 6 developer stacks.

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CLI (Node.js)

Manual

Advice on NumPy, Riot

Kerjohn

Oct 3, 2020

Decidedon

Redis

Python

PyTorch

Server side

We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

Client side

UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

Cache

Caching: We decided between @{Redis}|tool:1031| and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use @{Redis}|tool:1031| to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

Database

Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on @{MongoDB}|tool:1030| because it is lightweight and we can easily host the database with @{MongoDB Atlas}|tool:5739| . Everyone on our team also has experience working with @{MongoDB}|tool:1030|.

Infrastructure

Deployment: We decided to use @{Heroku}|tool:133| over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

Other Tools

Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

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Comments

Detailed Comparison

NumPy	Riot
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.	Riot brings custom tags to all browsers. Think React + Polymer but with enjoyable syntax and a small learning curve.
Powerful n-dimensional arrays; Numerical computing tools; Interoperable; Performant; Easy to use	Absolutely the smallest possible amount of DOM updates and reflows.;One way data flow: updates and unmounts are propagated downwards from parent to children.;Expressions are pre-compiled and cached for high performance.;Lifecycle events for more control.
Statistics
GitHub Stars 30.7K	GitHub Stars 14.9K
GitHub Forks 11.7K	GitHub Forks 966
Stacks 4.3K	Stacks 116
Followers 799	Followers 100
Votes 15	Votes 68
Pros & Cons
Pros 10 Great for data analysis 4 Faster than list	Pros 13 Its just easy... no training wheels needed 13 Light weight. Fast. Clear 11 Very simple, fast 9 Straightforward 6 Minimalistic Cons 1 Smaller community
Integrations
Python	No integrations available

What are some alternatives to NumPy, Riot?

jQuery

jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML.

AngularJS

AngularJS lets you write client-side web applications as if you had a smarter browser. It lets you use good old HTML (or HAML, Jade and friends!) as your template language and lets you extend HTML’s syntax to express your application’s components clearly and succinctly. It automatically synchronizes data from your UI (view) with your JavaScript objects (model) through 2-way data binding.

React

Lots of people use React as the V in MVC. Since React makes no assumptions about the rest of your technology stack, it's easy to try it out on a small feature in an existing project.

Vue.js

It is a library for building interactive web interfaces. It provides data-reactive components with a simple and flexible API.

jQuery UI

Whether you're building highly interactive web applications or you just need to add a date picker to a form control, jQuery UI is the perfect choice.

Svelte

If you've ever built a JavaScript application, the chances are you've encountered – or at least heard of – frameworks like React, Angular, Vue and Ractive. Like Svelte, these tools all share a goal of making it easy to build slick interactive user interfaces. Rather than interpreting your application code at run time, your app is converted into ideal JavaScript at build time. That means you don't pay the performance cost of the framework's abstractions, or incur a penalty when your app first loads.

Flux

Flux is the application architecture that Facebook uses for building client-side web applications. It complements React's composable view components by utilizing a unidirectional data flow. It's more of a pattern rather than a formal framework, and you can start using Flux immediately without a lot of new code.

Famo.us

Famo.us is a free and open source JavaScript platform for building mobile apps and desktop experiences. What makes Famo.us unique is its JavaScript rendering engine and 3D physics engine that gives developers the power and tools to build native quality apps and animations using pure JavaScript.

Marko

Marko is a really fast and lightweight HTML-based templating engine that compiles templates to readable Node.js-compatible JavaScript modules, and it works on the server and in the browser. It supports streaming, async rendering and custom tags.

Kendo UI

Fast, light, complete: 70+ jQuery-based UI widgets in one powerful toolset. AngularJS integration, Bootstrap support, mobile controls, offline data solution.

Related Comparisons

Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.