Imagine the first day of a new Apache Spark project. The project manager looks at the team and says: which one to choose, scala or python. So let’s start with “scala vs python for spark”.
You may wonder if this is a tricky question. What does the enterprise demand say? Is this like asking iOS or Android? Is there a right or wrong answer?
So we are here to inform and provide clarity. Today we’re looking at two popular programming languages, Scala and Python, and comparing them in the context of Apache Spark and Big Data in general.
First, let’s review this “scala vs python for spark” comparison.
What is Scala?
Scala, an acronym for “scalable language,” is a general-purpose, concise, high-level programming language that combines functional programming and object-oriented programming. It runs on JVM (Java Virtual Machine) and interoperates with existing Java code and libraries.
Many programmers find Scala code to be error-free, concise, and readable, making it simple to use for writing, compiling, debugging, and running programs, particularly compared to other languages.
What is Python?
Python developers define the language as “…an interpreted, object-oriented, a high-level programming language with dynamic semantics. Its high-level built-in data structures, combined with dynamic binding and dynamic typing, which makes it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components.”
Programmers like Python because of its relative simplicity, support of multiple packages and modules, and its interpreter and standard libraries are available for free.
What is Apache Spark?
Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers. It utilizes in-memory caching and optimized query execution for fast queries against data of any size. Simply put, Spark is a fast and general engine for large-scale data processing.
Spark is a general-purpose, cluster computing framework that rapidly performs processing tasks with extensive datasets. The framework can also distribute data processing tasks across many nodes, by itself or simultaneously with other distributed computing tools.
Many organizations favor Spark’s speed and simplicity, which supports many available application programming interfaces (APIs) from languages like Java, R, Python, and Scala.
What is Scala Used For?
Anything you use Java for, you can use Scala instead. It’s ideal for back-end code, scripts, software development, and web design. Programmers also tout Scala’s seamless integration of object-oriented features and functional languages as the perfect tool for parallel batch processing, data analysis using Spark, AWS Lambda expressions, and ad hoc scripting with REPL.
What is Python used for?
Python’s simplicity and simple to learn syntax make it the ideal choice for developing desktop graphical user interface (GUI) applications, web applications, and websites.
Furthermore, Python’s ecosystem is an ideal resource for machine learning and artificial intelligence (AI), two of today’s increasingly deployed technologies. Python’s syntax looks like the English language, creating a more comfortable and familiar environment for learning.
Why learn Scala for Spark?
Now that we have been introduced to the primary players, let’s discuss why Scala for Spark is a smart idea. We’ve seen earlier that Spark has a Scala API (one of many). So why would Scala stand out?
Here are five compelling reasons why you should learn Scala programming.
Spark is written in Scala
When you want to get the most out of a framework, you need to master its original language. Scala is not only Spark’s programming language, but it’s also scalable on JVM.
Scala is Less difficult and cluttered than Java
One complex line of Scala code replaces between 20 to 25 lines of Java code. Scala’s simplicity is a must for Big Data processors. As a bonus, there’s robust interoperability between Scala and Java code— Scala developers can also use their Scala code to access Java libraries directly.
Scala is Very Popular
Many big businesses and organizations use or have migrated to Scala. Additionally, Scala has a brighter future in many ways. For instance, and as more people become aware of its ease of scalability, even big financial institutions and investment banks are gradually turning to Scala to provide the low-latency solutions they require.
Parallelism and Concurrency
Scala’s design creates an environment well suited for both these computations types. Frameworks such as Akka, Lift and Play help programmers design better applications on JVM.
Why learn Python for Spark ?
If you are a beginner and have no prior education of programming language then Python is the language for you, as it’s easy to pick up. Simple to understand and very user-friendly. It would prove a good starting point for building Spark knowledge further. Also, If you are looking for getting into roles like ‘data engineering’.
Library support from python for small- or medium-scale projects to build models and analyze data, especially for fast start-ups or small teams.Python is preferred for implementing Machine Learning algorithms.
As seen earlier in the article, Scala/ Java is about 10x faster than Python/R as they are JVM-supported languages. However, if you are writing Python/R applications wisely (like without using UDFs/ Not sending data back to the Driver etc) they can perform equally well.
PySpark is a Python API for Spark released by the Apache Spark community to support Python with Spark. Using PySpark, one can easily integrate and work with RDDs in Python programming language too.
Readability, maintenance, and familiarity of code are better in Python API.
Overall, Which Language is Better?
The best way to answer the ” scala vs python for spark” question is by first comparing each language, broken down by features.
val demos = sc.textFile("/user/cloudera/sparkcourse/") demos. flatMap(line => line.split(" ")). map(word => (word, 1)). reduceByKey(_ + _)
demos = sc.textFile("/user/cloudera/spark-course/") demos. flatMap(lambda line: line.split()). map(lambda word: (word, 1)). reduceByKey(lambda a, b: a + b)
scala> var answer = "Forty two" answer: String = Forty two scala> answer = 42 <console>:12: error: type mismatch; found : Int(42) required: String answer = 42 ^
>>> answer = “Forty two” >>> answer 'Forty two' >>> type(answer) <type 'str'> >>> answer = 42 >>> answer 42 >>> type(answer) <type 'int'>
Scala clocks in at ten times faster than Python, thanks to the former’s static type language.
Scala handles concurrency and parallelism very well, while Python doesn’t support true multi-threading.
Static-typed variables can’t change. Scala is a static-typed language, while Python is a dynamically typed language. Type-safety makes Scala a better choice for high-volume projects because its static nature lends itself to faster bug and compile-time error detection.
The scalability of Python is lesser as compared to Scala. Scala is widely used in distributed system and reactive systems.
I hope you now have a better clarity about scala vs python for Apache Spark. Thankyou for reading this blog.