Picking a programming language for Apache Spark depends largely on what programming language you like – Scala or python. Based on the use cases, data scientists decide which language will be perfect for Apache Spark programming.
While it is useful to learn both Scala for Spark and Python for Spark, here are some checklists to tick off before diving into the main question: Why Pyspark is taking over Scala?
Apache Spark can be termed as Hadoop’s faster counterpart. It’s API is meant for data processing and analysis in multiple programming languages like Java, Python, and Scala. Since we are here to understand how Python is overstepping Scala, we will negate the discussions about Java for this time. Also, Java is too verbose and doesn’t support REPL (Read-Evaluate-Print-Loop). This is a major problem with Java while choosing a language for big data processing.
Scala and Python for Apache Spark
Both these programming languages are easy and offer a lot of productivity to programmers. Most data scientists opt to learn both these languages for Apache Spark. However, you will hear a majority of data scientists picking Scala over Python for Apache Spark. The major reason for this is that Scala offers more speed. It happens to be ten times faster than Python. Few more reasons are:
Scala helps handle the complicated and diverse infrastructure of big data systems. Such complex systems demand powerful language, and Scala is perfect for a programmer looking to write efficient lines of codes.
Despite being statically typed language, Scala does help in pinpointing time errors.
Scala is fast and powerful, but there are many complexities with Scala. As a result, when a direct comparison is drawn between Pyspark and Scala, python for Apache Spark might take the winning cup.
Why is Pyspark taking over Scala?
Python for Apache Spark is pretty easy to learn and use. However, this not the only reason why Pyspark is a better choice than Scala. There’s more.
Python API for Spark may be slower on the cluster, but at the end, data scientists can do a lot more with it as compared to Scala. The complexity of Scala is absent. The interface is simple and comprehensive.
Talking about the readability of code, maintenance and familiarity with Python API for Apache Spark is far better than Scala.
Python comes with several libraries related to machine learning and natural language processing. This aids in data analysis and also has statistics that are much mature and time-tested. For instance, numpy, pandas, scikit-learn, seaborn and matplotlib.
Note: Most data scientists use a hybrid approach where they use the best of both the APIs.
Lastly, Scala community often turns out to be lot less helpful to programmers. This makes Python a much valuable learning. If you have enough experience with any statically typed programming language like Java, you can stop worrying about not using Scala altogether.
The highlight: Scala lacks data science libraries
One of the major reasons why Python is preferred over Scala for Apache Spark is that the later lacks proper data science libraries and tools unlike Python. There’s lack of visualization, not up to the mark local data transformations, and Scala doesn’t have good local tools as well. Also, there are easier ways to call R directly from Python, making Python a better choice over Scala.
Ease of learning the languages: Python over Scala
Big data scientists need to be very cautious while learning Scala, thanks to the multiple syntactic sugars. It might sometimes become a crazy deal for programmers to learn Scala, as Scala has fewer libraries and communities aren’t that helpful. Scala is a sophisticated coding language, which means that programmers need to pay a lot of attention towards the readability of the code.
All these end up making Scala a difficult language to grasp, especially for beginners or inexperienced programmers starting off with big data.
Unless you are all game for highly complex data analysis, Python serves well for simple to moderately complicated data analysis. Even then, it complicated things sound like a challenge to you; you can always do with a final Python layer with Scala.
6 Comments
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The fact that Scala is harder to learn is not an excuse not to use it. The time and effort invested in learning Scala are well worth the results. Scala is not only faster, but it is a much stronger language in terms of features. Python may be useful for small test run by data scientists, but it is a long way from being good enough for production code running on real big data. It is also not true that Scala has no machine learning libraries: there is mllib for example, and if you are a real programmer you can easily use Scala to write your own machine learning code. With regards to using Spark – Scala is definitely the first choice, as Spark was written in Scala so the integration is far better than with Python.
nice comment, fair enough
The post speaks of return on investment of time.
If someone has following types of jobs :
(i) write scalable applications on Bigdata
(ii) wants to dig internals of spark
(iii) wants to build tools and products to support the big data system
Then Scala is absolutely necessary.
However, if for needs like:
(i) a person is basically a machine learning engineering or data scientist who primarily works on training ML models in tensorflow/pytorch/keras and has to do limited data wrangling to prepare feature data.
(ii) person who need to use spark sparingly and is usually a Data Engineer working in lots of other technologies related to data like (airflow, superset etc..) which requires to use python, then learning scala would be more expensive then spending time in technologies that is core to job’s requirement.
It really depends basically learning scala is not must to use Spark, else the support for other languages like python,Java and now R would not have been made available for spark.
Thank you for sharing a good information