Apache Spark is a high-speed cluster computing technology, that accelerates the Hadoop computational software process and was introduced by Apache Software Foundation. Apache Spark enhances the speed and supports multiple programming languages such as - Scala, Python, Java and R. All these 4 APIs possess their own special features and are predominant for programming in Spark.
But data scientists usually prefer to learn Python and Scala for Spark, as Java does not support Read-Evaluate-Print-Loop, and R is not a general purpose language. Both Python and Scala are easy to program and help data experts get productive fast. Choosing a programming language for Apache Spark depends on the type of application to be developed.
Scala vs Python for Spark
Both are Object Oriented plus functional and have the same syntax and passionate support communities. Below a list of Scala Python comparison helps you choose the best programming language based on your requirements.
Scala vs Python Performance
Scala is a trending programming language in Big Data. It runs 10 times faster than Python, as it uses Java Virtual Machine in runtime. Python is highly productive and a very simple language to learn. Whereas, Scala, due to its high-level functional features requires more thinking and abstraction. But once you get familiar with Scala, your productivity will dramatically boost.
Both are good in their specifications, but if you are working with simple intuitive logic then Python does the job greatly. And if you are developing something more complex, then go for Scala.
Refactoring the Code Safely
There is a need to refactor the code continuously when programming with Apache Spark. Scala is a statically typed language and Python is a dynamically typed language. Refactoring the program code of a statically typed language is much easier and hassle-free than refactoring the code of dynamic language. Many a time, developers face difficulties after modifying the program code of python. This is because it creates more bugs than fixing the older ones. So, it is better to choose Scala which is a compiled language.
Scala, Python Integration
The diverse and complex infrastructure of Big Data systems requests a programming language, that has the power to integrate across several services and databases. Scala, with the Play framework, has the ability to integrate easily with various concurrency primitives like Akka’s actors in the Big Data ecosystem, as it offers many reactive cores and asynchronous libraries. Scala allows developers to write maintainable, readable and efficient services. Python, using uWSGI, supports heavyweight process forking but does not support true multithreading.
Both Python and Scala are equally powerful languages in the context of Spark. So the desired functionality can be achieved either by using Python or Scala. But when compared to Scala, Python is very easy to understand. Python is less prolix, that helps developers to write code easily in Python for Spark.
Scala vs Python for Machine Learning
Python language is recommended if you are implementing Machine Learning algorithms like Graphx or GraphFrames or MLlib and data science technologies. MLlib only contains parallel Machine Learning algorithms, that are appropriate to run on a bunch of distributed data set. Developers with a good command over Python can build ML application without SPARK MLLIB. But if you are designing ML models, then Scala is the best choice because any new addition of ML algorithms will be implemented first in Scala and then Python. Scala is preferred for implementing data engineering technologies.
Python is slower but very easy to use, while Scala is fastest and moderately easy to use. Scala provides access to the latest features of the Spark, as Apache Spark is written in Scala. Language choice for programming in Apache Spark depends on the features that best fit the project needs, as each one has its own pros and cons. So, it is necessary for developers to learn both Scala and Python before choosing a programming language.
Also Read: 7 Useful Machine Learning Packages in R