PySpark vs Scala

Overview

Scala

Stacks11.9K

Followers7.8K

Votes1.5K

GitHub Stars14.4K

Forks3.1K

PySpark

Stacks491

Followers295

Votes0

PySpark vs Scala: What are the differences?

Introduction

This Markdown code provides key differences between PySpark and Scala for website use. PySpark is the Python API for Apache Spark, while Scala is a language that can be used with Spark. Below are the key differences:

Language Compatibility: PySpark allows developers to write Spark applications using Python programming language, whereas Scala provides a native integration with Spark and is the primary language for writing Spark applications. This difference allows developers to choose the language they are most comfortable with for implementing Spark applications.
Performance: Scala offers better performance compared to PySpark. Due to its static typing and direct integration with Spark, Scala can optimize Spark operations and achieve faster execution times. On the other hand, PySpark being dynamically typed, has a slight performance overhead due to type-checking at runtime.
Ease of Use: PySpark is generally considered more user-friendly and easier to understand for beginners due to its Python syntax and wide range of libraries and packages available for data processing and analysis. Scala, while powerful, may have a steeper learning curve for developers who are not familiar with functional programming concepts.
Development Speed: PySpark often provides faster development speed in terms of writing and debugging code. Python's concise syntax and interactive mode make it easier to experiment and prototype Spark applications. Scala, being a statically-typed language, may require more code and time to write and debug compared to PySpark.
Integration with Python Ecosystem: PySpark has a strong integration with the Python ecosystem, allowing developers to leverage powerful libraries and frameworks like Pandas, NumPy, and Scikit-learn for data preprocessing, machine learning, and visualization. Scala, while having its own ecosystem, may not have the same level of maturity and variety of libraries available.
Data Type Handling: PySpark provides built-in support for dynamic data types and automatic inference of schema from data sources, making it easier to work with semi-structured or unstructured data. Scala, being statically typed, requires explicit declaration and handling of data types, which can be more efficient but also more restrictive in certain scenarios.

In summary, PySpark is more user-friendly and offers better integration with the Python ecosystem, while Scala provides better performance and is the preferred choice for developers with experience in functional programming and a need for faster execution times.

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Advice on Scala, PySpark

Jakub

Jan 2, 2020

Decided

We needed to incorporate Big Data Framework for data stream analysis, specifically Apache Spark / Apache Storm. The three options of languages were most suitable for the job - Python, Java, Scala.

The winner was Python for the top of the class, high-performance data analysis libraries (NumPy, Pandas) written in C, quick learning curve, quick prototyping allowance, and a great connection with other future tools for machine learning as Tensorflow.

The whole code was shorter & more readable which made it easier to develop and maintain.

290k views290k

Comments

zen

Sep 26, 2019

Needs advice

Finding the best server-side tool for building a personal information organizer that focuses on performance, simplicity, and scalability.

performance and scalability get a prototype going fast by keeping codebase simple find hosting that is affordable and scales well (Java/Scala-based ones might not be affordable)

306k views306k

Comments

Detailed Comparison

Scala	PySpark
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.	It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
Statistics
GitHub Stars 14.4K	GitHub Stars -
GitHub Forks 3.1K	GitHub Forks -
Stacks 11.9K	Stacks 491
Followers 7.8K	Followers 295
Votes 1.5K	Votes 0
Pros & Cons
Pros 188 Static typing 178 Pattern-matching 175 Jvm 172 Scala is fun 138 Types Cons 11 Slow compilation time 7 Multiple ropes and styles to hang your self 6 Too few developers available 4 Complicated subtyping 2 My coworkers using scala are racist against other stuff	No community feedback yet
Integrations
Java	No integrations available

What are some alternatives to Scala, PySpark?

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.

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.

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.

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!

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

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.

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

Swift

Writing code is interactive and fun, the syntax is concise yet expressive, and apps run lightning-fast. Swift is ready for your next iOS and OS X project — or for addition into your current app — because Swift code works side-by-side with Objective-C.

Related Comparisons

PySpark vs Scala: What are the differences?

Introduction

Language Compatibility: PySpark allows developers to write Spark applications using Python programming language, whereas Scala provides a native integration with Spark and is the primary language for writing Spark applications. This difference allows developers to choose the language they are most comfortable with for implementing Spark applications.
Performance: Scala offers better performance compared to PySpark. Due to its static typing and direct integration with Spark, Scala can optimize Spark operations and achieve faster execution times. On the other hand, PySpark being dynamically typed, has a slight performance overhead due to type-checking at runtime.
Ease of Use: PySpark is generally considered more user-friendly and easier to understand for beginners due to its Python syntax and wide range of libraries and packages available for data processing and analysis. Scala, while powerful, may have a steeper learning curve for developers who are not familiar with functional programming concepts.
Development Speed: PySpark often provides faster development speed in terms of writing and debugging code. Python's concise syntax and interactive mode make it easier to experiment and prototype Spark applications. Scala, being a statically-typed language, may require more code and time to write and debug compared to PySpark.
Integration with Python Ecosystem: PySpark has a strong integration with the Python ecosystem, allowing developers to leverage powerful libraries and frameworks like Pandas, NumPy, and Scikit-learn for data preprocessing, machine learning, and visualization. Scala, while having its own ecosystem, may not have the same level of maturity and variety of libraries available.
Data Type Handling: PySpark provides built-in support for dynamic data types and automatic inference of schema from data sources, making it easier to work with semi-structured or unstructured data. Scala, being statically typed, requires explicit declaration and handling of data types, which can be more efficient but also more restrictive in certain scenarios.

PySpark vs Scala

Overview

PySpark vs Scala: What are the differences?