Python vs Spring Boot

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Python
Python

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32.8K
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6K
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Spring Boot

5.5K
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563
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Python vs Spring Boot: 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, Spring Boot is detailed as "Create Spring-powered, production-grade applications and services with absolute minimum fuss". Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can "just run". We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss. Most Spring Boot applications need very little Spring configuration.

Python belongs to "Languages" category of the tech stack, while Spring Boot can be primarily classified under "Frameworks (Full Stack)".

"Great libraries", "Readable code" and "Beautiful code" are the key factors why developers consider Python; whereas "Powerful and handy", "Easy setup" and "Java" are the primary reasons why Spring Boot is favored.

Python and Spring Boot are both open source tools. It seems that Spring Boot with 39.3K GitHub stars and 25.5K forks on GitHub has more adoption than Python with 25K GitHub stars and 10.3K GitHub forks.

reddit, Instacart, and Lyft are some of the popular companies that use Python, whereas Spring Boot is used by MIT, PedidosYa, and Intuit. Python has a broader approval, being mentioned in 2789 company stacks & 3500 developers stacks; compared to Spring Boot, which is listed in 326 company stacks and 585 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 Spring Boot?

Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can "just run". We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss. Most Spring Boot applications need very little Spring configuration.
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What are some alternatives to Python and Spring Boot?
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 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 Spring Boot
StackShare Editors
StackShare Editors
Python
Python
Go
Go
Kubernetes
Kubernetes

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 · | 9 upvotes · 109.4K views
atRent the RunwayRent the Runway
Ruby
Ruby
Python
Python
Java
Java

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 · 5.9K views
atShaw AcademyShaw Academy
PHP
PHP
Python
Python
#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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Visual Studio Code
Visual Studio Code
GitHub
GitHub
Linux
Linux
JavaScript
JavaScript
Swift
Swift
Java
Java
PHP
PHP
Python
Python
XML
XML
JSON
JSON
Git
Git
SVN (Subversion)
SVN (Subversion)

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 · 4.6K views
atShaw AcademyShaw Academy
PHP
PHP
Python
Python

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 · 621.6K views
atStitch FixStitch Fix
Kafka
Kafka
PostgreSQL
PostgreSQL
Amazon S3
Amazon S3
Apache Spark
Apache Spark
Presto
Presto
Python
Python
R Language
R Language
PyTorch
PyTorch
Docker
Docker
Amazon EC2 Container Service
Amazon EC2 Container Service
#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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Node.js
Node.js
JavaScript
JavaScript
Django
Django
Python
Python

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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Marwen Trabelsi
Marwen Trabelsi
Spring Boot
Spring Boot

I use Spring-Boot because it almost let you get things done quickly for a JVM-target project, with auto configuration components and dependency management starters. It is almost perfectly tailored for microservices applications development with a single unit deployment artifact (JAR) along with support for Service Registry and Discovery, Circuit Breaker pattern...

Any third-party library or any back-end service would perfectly integrate well since Spring offers integration support for most of mainstream services, let it be a RDBMS service, a NoSQL database, a Message Broker...

Coming to day-to-day development, Spring-Boot enjoys a great community so you can get support, direction, focused guidance from almost everywhere.

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

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.

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Omar Melendrez
Omar Melendrez
Front-end developer · | 4 upvotes · 4.4K views
Vue.js
Vue.js
React
React
Node.js
Node.js
C#
C#
Python
Python
#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 · 42.8K views
atGentlentGentlent
JavaScript
JavaScript
Node.js
Node.js
PHP
PHP
HTML5
HTML5
Sass
Sass
nginx
nginx
React
React
PostgreSQL
PostgreSQL
Ubuntu
Ubuntu
ES6
ES6
TypeScript
TypeScript
Google Compute Engine
Google Compute Engine
Socket.IO
Socket.IO
Electron
Electron
Python
Python

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.

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Praveen Mooli
Praveen Mooli
Engineering Manager at Taylor and Francis · | 12 upvotes · 547.9K views
MongoDB Atlas
MongoDB Atlas
Java
Java
Spring Boot
Spring Boot
Node.js
Node.js
ExpressJS
ExpressJS
Python
Python
Flask
Flask
Amazon Kinesis
Amazon Kinesis
Amazon Kinesis Firehose
Amazon Kinesis Firehose
Amazon SNS
Amazon SNS
Amazon SQS
Amazon SQS
AWS Lambda
AWS Lambda
Angular 2
Angular 2
RxJS
RxJS
GitHub
GitHub
Travis CI
Travis CI
Terraform
Terraform
Docker
Docker
Serverless
Serverless
Amazon RDS
Amazon RDS
Amazon DynamoDB
Amazon DynamoDB
Amazon S3
Amazon S3
#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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Python
Python
JavaScript
JavaScript
asyncio
asyncio
PubNub
PubNub

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
Python
Python
JavaScript
JavaScript
CSS 3
CSS 3
#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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Bryam Rodriguez
Bryam Rodriguez
JavaScript
JavaScript
Bit
Bit
Python
Python
Next.js
Next.js
React Native
React Native
Redis
Redis
MongoDB
MongoDB
PostgreSQL
PostgreSQL
RSpec
RSpec
react-testing-library
react-testing-library
Jest
Jest
Create React App
Create React App
Redux
Redux
React
React
Rails
Rails
Ruby
Ruby

I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

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Interest over time
Reviews of Python and Spring Boot
Review ofSpring BootSpring Boot

spring boot allow my team to start building web services quickly and package it in a stand alone application

How developers use Python and Spring Boot
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)

Avatar of Climate CoLab
Climate CoLab uses Spring BootSpring Boot

Spring-Boot allows us to create stand-alone web servers and helps us configure many of our dependencies with sane default, while maintaining flexibility where we need it.

Avatar of Emcee
Emcee uses Spring BootSpring Boot

Probably the best application framework in Java, by far. Time-proven, mature.

Avatar of p009922
p009922 uses Spring BootSpring Boot

light weight server approach for REST-services

Avatar of Project44
Project44 uses Spring BootSpring Boot

All services are spring-boot applications.

Avatar of Vaadin
Vaadin uses Spring BootSpring Boot

Solid base for Java-based web app backend

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