Are You Ready for Apache Spark and Scala

While the big data experts are still unearthing new benefits of using Scala and Python on standard JVMs, the debate as to which language is better for Apache Spark is gaining momentum in the industry. While it is said that choosing between Scala and Python is a subjective matter, Scala does carry a certain advantage over not just Python, but R and Java as well.

To make things easier for you, we’ve tried to differentiate both Scala and Python on a number of factors, like Performance, Readiness and Type Safety.  

Performance

Scala is a lot faster than Python if you are analyzing and processing data. While the performance of Python is satisfactory when the language codes are used for calling the libraries of Spark, they are not as quick as Spark language codes when large amounts of processing are involved.

However, the performance of Scala takes a hit when there are more number of cores involved. But if you ask experts, performance doesn’t play a major role when they are working with a lot of cores. Performance does play an important role when a lot of processing logic is required and Scala is the language that offers a much better performance when significant amount of processing logic is involved.

Readiness

Big Data systems have a diverse and complex infrastructure and require a language that can seamlessly integrate across various services and databases. The Play framework of Scala allows it to offer a number of reactive cores and asynchronous libraries that can be easily integrated with the ecosystem of Big Data.

Python, on the other hand, also supports a number of process forking heavyweights with its uswgi but is unable to back true multithreading. Moreover, no matter how many threads the process has, one CPU is only active for one Python process. Thus, every single time a new code is required, Python will need additional overhead of memory and you will be required to restart more processes. This makes Scala a much better choice than Python for Apache Spark.

Type Safety

While working with Apache Spark, developers are required to constantly re-factor code whenever the requirements change. While Scala appears to be a dynamically types language due to its mechanism of type interface, it actually is, a statically typed language. As a result, it allows the compiler to easily catch errors of compile time.

Moreover, refactoring the code of statically typed Scala is a lot easier than the dynamically typed Python. Most of the developers would agree that Python often results in more errors when you try to fix the existing ones.

Scala is the language in which the framework for Apache Spark is written and this helps the Scala developers to easily reach and work on the source code, if at all, something is not working as expected. Python programming in Spark opens new doors to bugs and other problems as the translation that takes place between the languages is complex. Moreover, while using Scala, you will get your hands on all the new features of Apache Spark first as they are made available for Scala and later for Python.

Which side are you on in this Scala vs. Python debate? Do let us know through the comments section below.