As you go more advanced in your use of Spark, you would need to configure Spark and utilize its APIs. For you to be able to make behind the scene changes in Spark, you would need the knowledge of Scala, Python or both Scala and Python to be able to achieve this. If you however decide to go for either of them, this article would try to discuss the advantages Scala has over Python and vice versa. The final choice of which of them to learn or learn first however ultimately lies with you as both of them can serve properly. None is however fully more convenient than the other. It all depends on the user and which of them he/she is more comfortable with. This chapter would thus discuss the features of programming generally and explain which of Python or Scala has an advantage in that area.
Features to Consider While Programming APIs in Spark
The major factors to consider while choosing the programming language to use for APIs in Spark include performance, learning curve, ease of use and libraries.
The speed of your final program is of importance to you and most especially the user of the program. In terms of speed, Scala is better as it is about 10 times faster than when Python in terms of execution. The effect might not be noticed if you just use your python codes to request Spark libraries. In the event however, when you want your python codes is involved in many processing tasks in the program, then you would have a slower result than Scala on the same machine. It is however, possible to use more machines to cover up the slow speed. This would however lead to more expenses. In terms of performance therefore, Scala has the advantage.
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For beginners who neither have knowledge about Scala nor Python, it is easier to learn Python than to learn Scala. While Scala is also friendly and easy to learn, learning Python is still easier than learning Scala. The difficult aspect of Scala is evident when you want to use objects-oriented concepts that are advanced. If you already have knowledge of Java, it would be very easy for you to also learn Python. Python also has a very large community that you could always get help from. This implies that Python also has more resources including tutorials and videos that you can easily have access to, to learn. Scala, however, is still a developing language itself and the Scala community is still coming up. Scala also has some sort of Syntactic sugars you have to be careful when learning as well as when using them. In terms of learning curve therefore, Python has the advantage.
Ease of Use
Spark was built using Scala. This makes it easier for you to use Scala with Spark compared with other languages including Python. Your knowledge of Scala would give you the advantage of knowing exactly how Spark works as well as the behind the scene workings of Spark. If you have no knowledge of Spark, there are possibilities that there are some tasks you might desire to achieve that you will never be able to achieve in Spark. A good example is that you need a new type of Resilient Distributed Dataset (RDD). Just like many open source documentation, the documentation of Spark is not perfect. So the real documentation of Spark is its code. The only way you would be able to understand this codes is if you have a good knowledge the programming language it was written in, which is Scala. Learning Scala will thus, give you the ability to be able to fully understand Spark and achieve anything in Spark. Without Scala however, there are some codes you would run and you would understand what is happening behind the scene while the code is running. In terms of ease of use therefore, Scala has the advantage.
Python has a larger number and better libraries in NL and ML than Spark even though the libraries are not big data oriented libraries. Spark’s ML library algorithms, even though they are few but are actually programmed for big data. Scala however does not have the quantity of Data Science tools and libraries that are available in Python. Neither Scala has good local data nor has it good visualization. Furthermore, Scala lacks quality local tools. These have made a lot of Spark user to be more comfortable with using the Python core parts of R. This gives them the ability to directly request R from Python. This part is thus seriously lacking in Python. Scala has few libraries, but they are lacking far behind in terms of quantity and quality especially when compared to Python. The large Python community is majorly responsible for a lot of these libraries and more are still being developed. Using Python would therefore give you access to a lot of libraries that you can use to make your job faster. For instance: the propriety offering of Databricks is based on Python instead of on Scala. In terms of libraries therefore, Python has the advantage.