In Big Data Analysis,Apache Spark is one of the most popular framework .The Apache Spark is written in Scala .Apache Spark has API’s for Scala, Python, Java and R , So we can work with any of these, but the popularly used languages are the Scala and Python.
Java does not support Read-Evaluate-Print-Loop, and R is not a general purpose language.Hence , data science community is divided in two camps; one which prefers Scala whereas the other preferring Python.
Scala vs Python- Which one to choose for Spark Programming?
Choosing a programming language for Apache Spark is a subjective matter. It is useful for a data scientist to learn Scala, Python, R, and Java for programming in Spark and choose the preferred language based on factors like performance.
Scala and Python are both easy to program and help data experts get productivity fast.
Scala programming language is 10 times faster than Python for data analysis and processing due to JVM. The performance is average when Python programming code is used to make calls to Spark libraries. Python PyPy interpreter has an in-built JIT (Just-In-Time) compiler is very fast but it does not provide support for various Python C extensions. In such situations, the CPython interpreter with C extensions for libraries outperforms PyPy interpreter.
Scala is faster than Python when there are less number of cores. As the number of cores increases, the performance advantage of Scala starts to reduce.
When working with lot of cores, performance is not a major driving factor in choosing the programming language for Apache Spark. However, when there is significant processing logic, performance is a major factor and Scala definitely offers better performance than Python, for programming against Spark.
Moreover Scala is native for Hadoop as its based on JVM. Hadoop is important because Spark was made on the top of the Hadoop’s filesystem HDFS. Python interacts with Hadoop services very badly, so developers have to use 3rd party libraries (like hadoopy). Scala interacts with Hadoop via native Hadoop’s API in Java. That’s why it’s very easy to write native Hadoop applications in Scala.
Learning Scala enriches a programmer’s knowledge of various novel abstractions in the type system, novel functional programming features and immutable data.. Programmers might find the syntax of Scala for programming in Spark crazy hard at times. Few libraries, in Scala makes it difficult to define random symbolic operators that can be understood by inexperienced programmers. While using Scala, developers need to focus on the readability of the code. Scala is a sophisticated language with flexible syntax when compared to Java or Python.
Python is comparatively easier to learn for Java programmers because of its syntax and standard libraries. However, Python is not an ideal choice for highly concurrent and scalable systems.
The complex and diverse infrastructure of big data systems demands a programming language, that has the power to integrate across several databases and services.
Scala has multiple standard libraries and cores which allows quick integration of the databases in Big Data ecosystems. Scala allows writing of code with multiple concurrency primitives.
Python, to the contrary, does support heavyweight process forking using uwsgi but it does not support true multithreading. When using Python for Spark, irrespective of the number of threads the process has –only one CPU is active at a time for a Python process. This helps get around with one process per CPU core but the downfall to this is, that whenever a new code is to be deployed, more processes need to restart and it also requires additional memory overhead.
Due to its concurrency feature, Scala allows better memory management and data processing.
Both are expressive and we can achieve high functionality level with them. Python is more user friendly and concise. Scala is always more powerful in terms of framework, libraries, implicit, macros etc.
Scala works well within the MapReduce framework because of its functional nature. Many Scala data frameworks follow similar abstract data types that are consistent with Scala’s collection of APIs.
But for NLP, Python is preferred as Scala doesn’t have many tools for machine learning or NLP. Moreover for using GraphX, GraphFrames and MLLib, Python is preferred. Python’s visualization libraries complement Pyspark as neither Spark nor Scala have anything comparable.
When programming with Apache Spark, developers need to continuously re-factor the code based on changing requirements. Scala is a statically typed language though it appears like a dynamically typed language because of the classy type inference mechanism. Being a statically typed language –Scala still provides the compiler to catch compile time errors.
Python is an effective choice against Spark for smaller ad hoc experiments but it does not scale efficiently like the statically type language – Scala, for large software engineering efforts in production.
Scala programming language has several existential types, macros and implicits. The secret syntax of Scala might make it difficult to experiment with the advanced features which might be incomprehensible to the developers. However, the advantage of Scala comes with using these powerful features in important frameworks and libraries.
In Python, SparkMLib –the machine learning library has only fewer ML algorithms but they are ideal for big data processing. Scala lacks good visualization and local data transformations.
Scala is best pick for Spark Streaming feature because Python Spark streaming support is not advanced and mature like Scala.
The conclusion we can draw from above points is, 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 first analyze requirement of project needs, compare language features with those requirements and then decide “Scala or Python”.