PySpark vs Spark-Scala
What is Big Data?
Data is defined as a factual information (such as measurement or statistics) used as a basis for reasoning, discussion, or calculation [1].
The data that is so large, complex or fast and it is impossible to process using traditional methods is termed as Big Data.
What is Apache Hadoop?
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures [2].
What is Apache Spark?
Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters [3].
PySpark vs Spark-Scala the Real Debate
When it comes to write a spark code, there is a lot of confusion among the developers. Apache Spark code can be written in Java, Scala, Python as well as R APIs. Out of these, Scala and Python are the most popular ones.
Spark lets its users write their code and run jobs on massive datasets and both Python and Scala are great options for the same.
Choosing the right language is not a trivial task because it becomes hard to switch once you develop core libraries using on language. Moreover, misconceptions like “Python is slower than Scala” are misleading and makes the choice of language more difficult.
Let’s discuss few differences between PySpark and Spark-Scala.
Datasets vs DataFrames
The main difference between DataFrames and Datasets is that datasets can only be implemented in languages that are type safe at compile time. Both Java and Scala are compile time type safe and as a result they support datasets. Whereas, Python and R are not compile time type safe hence they support DataFrames.
Performance Comparison
It is noticed that Scala definitely offers better performance than Python but it is not always the 10 time faster. As the number of cores increases, the performance advantage of Scala gradually decreases.
Scala is faster than Python due to its static type language. Spark is native in Scala, hence making writing Spark jobs in Scala the native way.
PySpark is converted to Spark SQL and then executed on a Java Virtual Machine (JVM) cluster. It is not a traditional Python execution environment.
UDFs
UDF stands for user defined functions. Both Python and Scala allow for UDFs when Spark native functions are not sufficient.
Type Safety
Scala is static-typed, while Python is a dynamically typed language. Scala offers safety benefits that are useful in the big data space. Due to its nature, it is more suitable for projects dealing with high volumes of data.
Learning the Language
When it comes to learning the language for coding, Python has an upper hand. Scala has a difficult syntax as compared to Python hence developers starting out might find it easier to write a Python code as compared to Scala code.
Conclusion
Spark is one of the most used frameworks and Scala and Python are both great for most of the workflows.
PySpark is more popular because Python is a easy to learn language and it has a large data community. It is well supported and is a great choice for most of the organizations.
Scala is powerful language that offer developer friendly features that might go missing in Python. It offers a lot of advance programming features. It is also great for lower level Spark programming.
The best language for building Spark jobs will eventually depend on a particular team of a particular organization. Once core libraries are developed in one language, to avoid any rework development will be done using the chosen language.
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