Scala vs Python: Apache Spark -

Difference Between Python and Scala

Hello Friends, today Post about Scala vs Python for Apache Spark also we will know about Difference Between Python and Scala Which is the best choice.

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Scala vs Python: Apache Spark - Difference Between Python and Scala

Python (पाइथन) vs Scala (स्केला)

Python (पाइथन) is a high level, interpreted and general-purpose dynamic programming language (लैंग्वेज) that focuses on code (कोड) readability. Python (पाइथन) requires less typing, provides new libraries, fast prototyping, and several other new features (फीचर).

Scala (स्केला) is a high level language (लैंग्वेज).it is a purely object-oriented programming language (लैंग्वेज). The source code (कोड) of the Scala (स्केला) is designed in such a way that its compiler can interpret the Java (जावा) classes.

Why is Scala (स्केला) used?

What is Scala (स्केला) used for? A lot of things, ranging from machine learning to web apps. As a high-level general-purpose language (लैंग्वेज), Scala (स्केला) boasts an extensive range of possible applications. Scala (स्केला) allows developers (डेवलपर)s to make good use of standard JVM features (फीचर) and Java (जावा) libraries.

Is it worth learning Scala (स्केला) in 2020?

The number of great libraries and frameworks is amazing. Scala (स्केला) also supports new technologies (टेक्नोलॉजी) like Data Science (डाटा साइंस) or Blockchain with its amazing tools. Yet if you have your one favourite Java (जावा) library you can easily adapt it to Scala (स्केला)'s environment because Scala (स्केला) is running on JVM. Same like Java (जावा).

What is difference between Scala (स्केला) and Python (पाइथन)?

Python (पाइथन) is a high level, interpreted and general purpose dynamic programming language (लैंग्वेज) that focuses on code (कोड) readability. Python (पाइथन) requires less typing, provides new libraries, fast prototyping, and several other new features (फीचर). Scala (स्केला) is a high level language (लैंग्वेज).it is a purely object-oriented programming language (लैंग्वेज).

Is Scala (स्केला) better than Python (पाइथन)?

Comparison of Python vs Scala For Apache Spark

Apache Spark (स्पार्क) is one of the most popular frameworks for big data analysis. Spark (स्पार्क) is written in Scala (स्केला) as it can be quite fast because it's statically typed and it compiles in a known way to the JVM. Though Spark (स्पार्क) has API’s for Scala (स्केला), Python (पाइथन), Java (जावा), and R but the popularly used language (लैंग्वेज) are the former two. Java (जावा) does not support Read-Evaluate-Print-Loop, and R is not a general-purpose language (लैंग्वेज). The Data Science (डाटा साइंस) community is divided in two camps; one prefers Scala (स्केला) whereas the other preferring Python (पाइथन). Each has its pros and cons and the final choice should depend on the outcome application

Performance

Scala (स्केला) is frequently over 10 times faster than Python (पाइथन). Scala (स्केला) uses Java (जावा) Virtual Machine (JVM) during runtime which gives is some speed over Python (पाइथन) in most cases. Python (पाइथन) is dynamically typed and this reduces the speed. Compiled language (लैंग्वेज) are faster than interpreted. In case of Python (पाइथन), Spark (स्पार्क) libraries are called which require a lot of code (कोड) processing and hence slower performance. In this scenario Scala (स्केला) works well for limited cores. Moreover Scala (स्केला) is native for Hadoop (हदूप) as its based on JVM. Hadoop (हदूप) is important because Spark (स्पार्क) was made on the top of the Hadoop (हदूप)'s filesystem HDFS. Python (पाइथन) interacts with Hadoop (हदूप) services very badly, so developers (डेवलपर)s have to use 3rd party libraries (like Hadoop (हदूप)y). Scala (स्केला) interacts with Hadoop (हदूप) via native Hadoop (हदूप)'s API in Java (जावा). That's why it's very easy to write native Hadoop (हदूप) applications in Scala (स्केला).

Learning Curve

Both are functional and object oriented language (लैंग्वेज) which have similar syntax in addition to a thriving support communities. Scala (स्केला) may be a bit more complex to learn in comparison to Python (पाइथन) due to its high-level functional features (फीचर). Python (पाइथन) is preferable for simple intuitive logic whereas Scala (स्केला) is more useful for complex workflows. Python (पाइथन) has simple syntax and good standard libraries.

Concurrency

Scala (स्केला) has multiple standard libraries and cores which allows quick integration of the databases (डेटाबेस) in Big Data ecosystems. Scala (स्केला) allows writing of code (कोड) with multiple concurrency primitives whereas Python (पाइथन) doesn’t support concurrency or multithreading. Due to its concurrency feature, Scala (स्केला) allows better memory management and data processing. However Python (पाइथन) does support heavyweight process forking. Here, only one thread is active at a time. So whenever a new code (कोड) is deployed, more processes must be restarted which increases the memory overhead.

Usability

Both are expressive and we can achieve high functionality level with them. Python (पाइथन) is more user friendly and concise. Scala (स्केला) is always more powerful in terms of framework, libraries, implicit, macros etc. Scala (स्केला) works well within the MapReduce framework because of its functional nature. Many Scala (स्केला) data frameworks follow similar abstract data types that are consistent with Scala (स्केला)’s collection of APIs. developers (डेवलपर)s just need to learn the basic standard collections, which allow them to easily get acquainted with other libraries. Spark (स्पार्क) is written in Scala (स्केला) so knowing Scala (स्केला) will let you understand and modify what Spark (स्पार्क) does internally. Moreover many upcoming features (फीचर) will first have their APIs in Scala (स्केला) and Java (जावा) and the Python (पाइथन) APIs evolve in the later versions. But for NLP, Python (पाइथन) is preferred as Scala (स्केला) doesn’t have many tools for machine learning or NLP. Moreover for using GraphX, GraphFrames and MLLib, Python (पाइथन) is preferred. Python (पाइथन)’s visualization libraries complement PySpark (स्पार्क) as neither Spark (स्पार्क) nor Scala (स्केला) have anything comparable.

code (कोड) Restoration and safety

Scala (स्केला) is a statically typed language (लैंग्वेज) which allows us to find compile time errors. whereas Python (पाइथन) is a dynamically typed language (लैंग्वेज). Python (पाइथन) language (लैंग्वेज) is highly prone to bugs every time you make changes to the existing code (कोड). Hence refactoring the code (कोड) for Scala (स्केला) is easier than refactoring for Python (पाइथन).

Conclusion

Python (पाइथन) is slower but very easy to use, while Scala (स्केला) is fastest and moderately easy to use. Scala (स्केला) provides access to the latest features (फीचर) of the Spark (स्पार्क), as Apache Spark (स्पार्क) is written in Scala (स्केला). language (लैंग्वेज) choice for programming in Apache Spark (स्पार्क) depends on the features (फीचर) that best fit the project needs, as each one has its own pros and cons. Python (पाइथन) is more analytical oriented while Scala (स्केला) is more engineering-oriented but both are great language (लैंग्वेज) for building Data Science (डाटा साइंस) applications. Overall, Scala (स्केला) would be more beneficial in order to utilize the full potential of Spark (स्पार्क). The arcane syntax is worth learning if you really want to do out-of-the-box machine learning over Spark (स्पार्क).

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Scala vs Python: Apache Spark - Difference Between Python and Scala