Scala Vs Python for Spark

Scala Vs Python for Spark. Which is best?

Today in this blog we discuss on, which is most preferable language for spark. Spark job are commonly written in Scala, python, java and R. Selection of language for the spark job plays a important role, based on the use cases and specific kind of application to be developed - data experts decides to choose which language suits better for programming. We will eliminate the R language and java from the comparison list as it does not support Read-Evaluate-Print_loop (REPL) which is a major part in choosing the programming language for big data analysis and processing. Most preferable language used by many MNC and data experts are Scala and python.

Scala or Python:

If you are a beginner or looking for an opportunity to step in-to the Big data domain, then first step is to choose the language. Let's explore some important factors to choose the preferred programming language based on the efficiency of the solution to the big data and have a look into advantage of both Scala and Python before deciding which is best programming language

Performance - Efficiency:

Both Scala as well as Python are easy to code programming language. Spark itself a framework built completely using Scala. With this point Scala takes a lead in race and it has a capability to server Spark application to run 10 times faster than application code that runs with help of Python.

Reason behind the performance is coding in Python will require the framework to convert it as JVM process first before starting the execution which makes it to lag behind in terms of execution time. Usage of python with spark results in performance overhead than Scala but the performance of python with spark increases with increase in number of cores. As the number of cores increases, the performance advantage of Scala decreases. However, when there is a significant logic to be processed, Scala definitely outperforms than python, for programming against spark.

Performance - Concurrency:

Scala allows developers to write efficient, readable and maintainable service with making changes to the program code into and unreadable cobweb of call backs. Python supports heavy-weighted process by fork-lifting the uwsgi, but the fact is that python does not support true multi-threading. When using python against spark, irrespective of threads the process has- only one CPU core is active at the time of processing. But the downfall is that whatever new code is to be deployed, more processes need to restart and it also requires additional memory overhead. Scala on the other-hand , is more efficient and easy to work with in these aspects

ML & AI Libraries:

Developers should always be conscious about the evolving API's, framework changes and latest libraries and keep themselves updated to achieve their requirement in a matter of time also with reduced number of lines of code. Even though Scala outperforms python against spark, Scala lags behind in providing libraries for machine learning. Python has a vast range of libraries for AI and ML. Moreover for using GraphX, GraphFrames and plots, python is preferred. Its visualization libraries complement pyspark as neither spark nor Scala have anything great than this.

Having said that, Scala does not have sufficient Data science tools and libraries like python for machine learning and NLP. SparkMlib - the machine learning library also has only fewer ML algo's, but they are ideal for big data processing.

Scala or Python - Final strike:

Processing speed of Scala is far faster, while Python is slightly lags behind in processing speed but contains many supporting libraries with it to make the coding simple and easy. Latest features in spark will first be available in scala then ported to python as spark itself is written in scala. Also, using python might increase the probability of issues and bugs because the translation between two different language is difficult.

Language to choose purely depends on the features that our project demands and the problem that we need to solve, as each one has its own advantage and dis-advantage over one another. Before choosing a language to start out developing, developers should study the features available in both scala and python. Hence, by doing so, its much easier to form a choice on when to use PySpark and when to use spark with scala.

We would wish to hear from you, in your opinion which is that the best suitable for programming in spark. Please do mention your language choice within the comment below.

Happy Learning!!!

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