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Perl vs Python: What are the differences?

What is Perl? Highly capable, feature-rich programming language with over 26 years of development. 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.

What is 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.

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

"Lots of libraries", "Open source" and "Text processing" are the key factors why developers consider Perl; whereas "Great libraries", "Readable code" and "Beautiful code" are the primary reasons why Python is favored.

Perl and Python are both open source tools. It seems that Python with 25.3K GitHub stars and 10.5K forks on GitHub has more adoption than Perl with 435 GitHub stars and 152 GitHub forks.

According to the StackShare community, Python has a broader approval, being mentioned in 2826 company stacks & 3632 developers stacks; compared to Perl, which is listed in 133 company stacks and 64 developer stacks.

What is 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.

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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What are some alternatives to Perl and Python?
PHP
Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.
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.
C
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!
PowerShell
A command-line shell and scripting language built on .NET. Helps system administrators and power-users rapidly automate tasks that manage operating systems (Linux, macOS, and Windows) and processes.
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Decisions about Perl and Python
Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 16 upvotes · 731.3K 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

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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

See more
Eric Colson
Eric Colson
Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 289.8K 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

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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.

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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 · 30.3K 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.

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Praveen Mooli
Praveen Mooli
Technical Leader at Taylor and Francis · | 11 upvotes · 174K 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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Interest over time
Reviews of Perl and Python
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How developers use Perl 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)

Avatar of Perljobs.Ru
Perljobs.Ru uses PerlPerl

The whole backend part (deployment and other scripts, business logic, web interface) is written in Perl.

Весь бэкенд (скрипты деплоя и прочие, бизнес-логика, веб-интерфейс) написан на Perl.

Avatar of John Galbraith
John Galbraith uses PerlPerl

I use Perl to rip through log files and compare them to some signature files I have created. When I get a match, it adds the bad guy to the list of shame in MySQL.

Avatar of Alexander Karelas
Alexander Karelas uses PerlPerl

A very expressive language, lets you say the same thing in many different ways

Avatar of rapt.fm
rapt.fm uses PerlPerl

We use perl with rex to control our distributed systems.

Avatar of ssshake
ssshake uses PerlPerl

I use perl on some legacy applications.

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