Scala makes it easy for developers to dig deep into the Spark source code to access and implement all the new features of the framework. Choosing a programming language for Apache Spark is a subjective matter, as the reasons why a particular data scientist or data analyst prefers Python or Scala for Apache Spark do not always apply to others. However, when there is significant processing logic, performance is an important factor and Scala definitely offers better performance than Python, for programming against Spark. In summary, Scala is my first choice of programming language for Spark projects and I will consider Python when the use case fits.
Before choosing a language to program with Apache Spark it is necessary for developers to learn Scala and Python to become familiar with their features. Scala was developed to allow common programming patterns to be expressed in a concise, type-safe format. Many organisations favour the speed and simplicity of Spark, which supports many application programming interfaces (APIs) available from languages such as Java, R, Python and Scala. You can use Scala' s basic programming features with the IntelliJ IDE and get useful features such as type hints and compile-time checks for free.
Scala is definitely the better choice for Spark Streaming feature because Python Spark support is not advanced and mature like Scala. Let's explore some important factors to consider before deciding on Scala vs Python as the primary programming language for Apache Spark. Using Scala for Spark provides access to the latest features of the Spark framework, as they are first available in Scala and then ported to Python. Scala is also ideal for low-level Spark programming and for easy navigation directly to the underlying source code.
Scala is a powerful programming language that offers easy-to-develop features that are not available in Python. Learning Scala enriches a programmer's knowledge of several novel abstractions in the type system, novel functional programming features and immutable data. Refactoring code from a statically typed language like Scala is much easier and hassle-free than refactoring code from a dynamic language like Python. Scala allows developers to write efficient, readable and maintainable services without hanging the program code in an unreadable web of call-backs.
Performance is mediocre when Python programming code is used to make calls to Spark libraries, but if there is a lot of processing involved, the Python code becomes much slower than the equivalent Scala code.
