Apache Spark and Scala – Why Scala with Spark!

Apache Spark and Scala – Why Scala with Spark!

Apache Spark, The Fast and Reliable Big Data Processing Engine supports many different programming languages including Python, Java and Scala. The language that one should use in Spark has been a matter of discussion from some time now. However, as per some industry professionals, Scala has an edge over Java and Python.

Let us have a look at what makes Scala more feasible than the other two languages.
Scala vs. Java
Java is undoubtedly one of the most preferred programming languages. However, when we are talking about Big Data in Spark, Java can be a bit tedious to use. As compared to Scala, Java is a lot more verbose. For getting the same outcome, a lot more coding is required in Java as compared to Scala.

Yes, Lambda Expressions that are introduced in Java 8 does make job a little easier, but still it is not as precise as Scala. As many as 20 lines of Java can be easily replaced with a single line of Scala coding. Even though Scala is hard as compared to Java, but learning and using Scala will surely make programming easier for you.

Another big drawback of Java is that it does not support Read-Evaluate-Print Loop (REPL) shell. It allows the developers to easily access and explore their dataset as well as prototype the applications without getting involved in an entire cycle of development. It is a must-have feature if you are working on big data project in Spark.

Scala vs. Python
There are many similarities between Scala and Python, like succinct syntax, object oriented, active support communities, etc. But as far as the functionality is concerned, Scala does beat Python in many aspects.

For starters, Scala in general is faster than Python. If you have a self-written processing logic, Scala is sure to provide you with a better performance. The type interference mechanism of Scala makes it look like dynamically-typed language but in reality, it is very much static. This allows you to easily compile and catch the errors, which some of the developers still believe is the best way to catch errors.

Another important point is that Spark is created with Scala, thus, if you are proficient with Scala you can easily get into the source code if the results are not what you’ve expected. With the help of Scala, you also get to ability to get your hands on the latest features first as the features are first introduced on Scala and then on the other programming languages in Spark.

In Python’s defense, it does have very impressive libraries for machine learning but these libraries are generally considered good for data that completely fits into a single machine. If you are working on Spark, the MLlib is a better alternative to Python libraries for machine learning that are spread across multiple nodes. And most of the MLlib algorithms are initially implemented in Scala and then later ported to Python.

To summarize all of it, if you are working with Apache Spark & Scala, Scala should be the preferred programming language and Python can be used if it fits the requirements.

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