Apache Spark is a leading general data processing platform that runs programs 100 times faster in memory and 10 times faster on disk than the traditional choice for Big Data applications, Hadoop.
Spark is a project from Apache that the company likes to sell as a lightning fast cluster computing platform. A dilemma amongst the developers and users of the Spark platform is about the best programming language to be used for developing Apache Spark solutions. There are three languages that Apache Spark supports- Java, Python, and Scala.
Choosing a programming language out of the three is a subjective matter that depends on various factors, like the programmer’s comfort and skills, the project’s requirements, etc.
Why Leave out Java?
While Java has been programmer’s favorite language for decades now, it lags behind when delivering the value that Scala and Python do.
First, it is verbose as compared to Python and Scala. Second, while the latest versions of the Java programming language allowed lambda and Streaming APIs, they don’t even compare to what Scala offers.
Java also does not support REPL- the Read-Evaluate-Print loop interactive shell that is crucial for all developers who work on Big Data analytics and Data science.
Conclusively, any new features in Apache Spark will have their API released in Scala first and then in Java as Spark is itself implemented in Scala.
Comparing Scala and Python for Apache Spark
Let’s leave out Java and focus on the differentiating factors between Scala and Python for Apache Spark programs.
- Scala is ten times faster than Python for analyzing and processing data owing to the JVM. For the same tasks, Python poses a performance overhead on the system. But, the decision really depends on what you are trying to achieve through your system. When there are a lot of cores involved, performance can be neglected. However, when there is a high amount of processing logic involved, you might want to choose Scala over Python.
- Big Data systems need that the programming language used for development be integrated across databases and services. Scala wins here for the Play framework that offers asynchronous libraries and reactive cores that are easy to integrate. While Python supports heavyweight process forking, it does not support multithreading in its true essence.
- When talking about the ease of learning and the ease of use, Python gets the upper hand out of the two languages. Python is less verbose and more user-friendly than Scala.
- Python is often admired for its general-purpose usage and simple syntax. But, it lags behind Scala in all other factors.
- When talking about the agile methodology, it is important to change the requirements of the code as data explorations are performed at each level and iteration. Every time a refactoring is performed, there is an internal risk of breaking the logic and leaving out bugs. Since Scala is a compiled language, it gets the advantage over Python here.
- Python programming language brings many functionalities to the table in the form of out-of-the-box packages that implement most of the standard procedures and models that are conventionally adopted in the industry far and wide. While Scala lacks these features, it can always benefit from its compatibility to Java libraries. Another point to consider is that Python implementations lack scalability whereas, Scala implementations, though few, are production-ready and scalable.
These differences are sure to strike a chord with you and help you understand the subtle things that demarcate the territories of the three languages in Apache Spark solutions’ implementations.