One of the most important decisions for Big data learners or beginners is choosing the best programming language for big data manipulation and analysis. Understanding business problems and choosing the right model is not enough, but implementing them perfectly is equally important and choosing the right language (or languages) for solving the problem goes a long way. Click here to learn more about sys.argv command line argument in Python.
If you search top and highly effective programming languages for Big Data on Google, you will find the following top 4 programming languages:
Java is one of the oldest languages of all 4 programming languages listed here. Traditional Frameworks of Big data like Apache Hadoop and all the tools within its ecosystem are Java-based, and hence using java opens up the possibility of utilizing a large ecosystem of tools in the big data world. There are several features/advantages due to which Java is favorite for Big data developers and tool creators:
- Java is a platform-agnostic language, and hence it can run on almost any system. Java is portable due to something called Java Virtual Machine – JVM. JVM is a foundation of Hadoop ecosystem tools like Map Reduce, Storm, Spark, etc. These tools are written in Java and run on JVM.
- Java provides various communities support like GitHub and stack overflow etc.
- Java is scalable, backward-compatible, stable, and production-ready language. Also, supports a large variety of tried and tested libraries.
- It is a statically typed language (We will see details of this functionality in later sections, in comparison with others)
Java is mostly the choice for most big data projects, but for the Spark framework, one has to ponder whether Java would be the best fit.
One major drawback of Java is its verbosity. One has to write long code (number of lines of code) to achieve simple functionality in Java.
Java does not support Read-Evaluate-Print-Loop (REPL), which is a major deal-breaker when choosing a programming language for big data processing.
A beautiful crossover between object-oriented and functional programming languages is Scala. Scala is a highly Scalable Language. Scala was invented by the German Computer Scientist Martin Odersky, and the first version was launched in the year 2003. Scala is comparatively new to the programming scene but quickly became popular. Above are a few quotes from bigger names in the industry for Scala. From the Spark context, many experts prefer Scala over other programming languages, as Spark is written in Scala. Scala is the native language of Spark. It means any new API always first be available in Scala.
Scala is a hybrid functional programming language because It has both object-oriented and functional programming features. As an OO Programming Language, it considers every value as an object, and all OOPS concepts apply. As a functional programming language, it defines and supports functions. All operations are done as functions. No variable stands by itself. Scala is a machine-compiled language.
Scala and Java are popular programming languages that run over JVM. JVM makes these languages framework friendly. One can say Scala is an advanced level of Java.
Features/Advantages of Scala:
- It’s a general-purpose object-oriented language with functional language properties too. It’s less verbose than Java.
- It can work with JVM and hence is portable.
- It can support Java APIs comfortably.
- It's fast and robust in the Spark context as its Spark native.
- It is a statically typed language.
- Scala supports Read-Evaluate-Print-Loop (REPL)
Drawbacks / Downsides of Scala:
- Scala is complex to learn due to the functional nature of the language.
- Steep learning curve.
- Lack of matured machine learning languages.
Guido van Rossum originally conceptualized the python in the late 1980s. Initially, it was designed to respond to the ABC programming language and later gained popularity as a functional language in a big data world. Python was declared as one of the fastest-growing programming languages in 2018 as per the recently held Stack Overflow Developer survey. It is a simple, open-source, general-purpose language and is very easy to learn.
Many data analysis, manipulation, machine learning, and deep learning libraries are written in Python, and hence it has gained popularity in the big data ecosystem. It’s a very user-friendly language, and it is its biggest advantage. Python is one of the de-facto languages of Data Science. It has a rich set of libraries, utilities, ready-to-use features, and support for a number of mature machine learning, big data processing, and visualization libraries.
Advantages of Python:
- It is interpreted language (i.e. support to REPL, Read, Evaluate, Print, Loop.) If you type a command into a command-line interpreter and it responds immediately. Java lacks this feature.
- Easy to learn, easy debugging, fewer lines of code.
- It is dynamically typed. i.e. can dynamically define variable types. i.e. Python as a language is type-safe.
- Python is platform agnostic and scalable.
- Python is slow. Big data professionals find projects built in Java / Scala are faster and more robust than the ones with python. Whilst using user-defined functions or third-party libraries in Python with Spark, processing would be slower as increased processing is involved as Python does not have equivalent Java/Scala native language API for these functionalities.
- Python does not support heavy-weight processing fork() using uWSGI, but it does not support true multithreading.
R is the language of statistics. R is a language and environment for statistical computing and graphics. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is named partly after the first names of the first two R authors and partly as a play on the name of S*. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000. R is fondly called the language of statisticians. It’s popular for research, plotting, and data analysis. With RStudio, it makes a killer statistic, plotting, and data analytics application.
R is majorly used for building data models to be used for data analysis.
Advantages/Features of R:
- Strong statistical modeling and visualization capabilities.
- Support for ‘data science’ related work.
- It can be integrated with Apache, Hadoop, and Spark easily.
Drawbacks/Disadvantages of R:
- R is not a general-purpose language.
- The code written in R cannot be directly deployed into production. It needs conversion into Java or Python.
- Not as fast as Java / Scala.
S is a statistical programming language developed primarily by John Chambers, and R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme.
Every framework is implemented in the underlying programming language for its implementation. Ex Zend uses PHP, Panda Framework uses python similarly, the Hadoop framework uses Java and Spark uses Scala.
However, Spark officially supports Java, Scala, Python, and R, all 4 languages. If one browses through Apache Spark’s official website documentation, he/she will find many other languages the open-source community utilizes for Spark implementation.
When any developer wants to start learning Spark, the first question he asks is, out of these pools of languages, which one to use and which to master? Solution Architects would have a tough time choosing the right language for the spark framework and Organizations will always be wondering which skill sets are relevant to my problem if one doesn’t have the right knowledge about these languages in the context of Spark.
Scala Vs Python Vs R Vs Java: Detailed Comparision
With the introduction of these 4 languages, let’s now compare these languages for the Spark framework:
|Performance||Faster||Faster (about 10x faster than Python)||Slower||Slower|
|Learning Curve||Easier than Java|
Tougher than Python
|Steep learning curve than Java & Python||Easiest||Moderate|
|User Groups||Web/Hadoop programmers||Big Data Programmers||Beginners & Data Engineers||Data Scientists/ Statisticians|
|Usage||Web development and Hadoop Native||Spark Native||Data Engineering/ Machine Learning/ Data Visualization||Visualization/ Data Analysis/ Statistics use cases|
|Type of Language||Object-Oriented, General Purpose||Object-Oriented & Functional General Purpose||General Purpose||Specifically for Data Scientists.|
Needs conversion into Scala/Python before productizing
|Concurrency||Support Concurrency||Support Concurrency||Does not Support Concurrency||NA|
|Ease of Use||Verbose||Lesser Verbose than Scala||Least Verbose||NA|
|Type Safety||Statically typed||Statically typed (except for Spark 2.0 Data frames)||Dynamically Typed||Dynamically Typed|
|Interpreted Language (REPL)||No||No||Yes||Yes|
|Maturated machine learning libraries availability/ Support||Limited||Limited||Excellent||Excellent|
|Web Notebooks Support||Java Kernel in Jupyter Notebook||Apache Zeppelin Notebook Support||Jupyter Notebook Support||R Notebook|
These languages can be categorized into 2 buckets basis high-level spark architecture support broadly:
- JVM Languages: Java and Scala
- Non-JVM Languages: Python and R
Due to these categorizations, performance may vary. Let’s understand architecture in a little depth to understand the performance implications of using these languages. This would also help us to understand the question of when to use which language.
Spark Framework High-level architecture
An application written in any one of the languages is submitted on the driver node, and further driver node distributes the workload by dividing the execution on multiple worker nodes.
JVM compatible Application Execution Flow
Consider the applications written are JVM compatible (Java/Scala). Now, Spark is also written in native JVM-compatible Scala language, hence there is no explicit conversion required at any point in time to execute JVM-compatible applications on Spark. Also, this makes the native language applications faster to perform on the Spark framework.
There are multiple scenarios for Python/R written applications:
Python/R driver talks to JVM driver by socket-based API. Both the driver processes are invoked on the driver node when the application language is non-JVM. Scenario 1: Applications for which Equivalent Java/Scala Driver API exists - This scenario executes the same way as JVM-compatible applications by invoking Java API on the driver node itself. The cost for inter-process communication through sockets is negligible, and hence performance is comparable. This is with the assumption that processed data over worker nodes are not to be sent back to the Driver again.
Scenario 1(b): If the assumption taken is void in scenario 1 i.e. processed data on worker nodes is to be sent back to the driver, then significant overhead and serialization is required. This adds to processing time, and hence performance in this scenario deteriorates.
Scenario 2: Applications for which Equivalent Java/Scala Driver API do not exist – Ex. UDF (User-defined functions) / Third-party python libraries. In such cases, Java API doesn’t exist; hence, additional executor sessions are initiated on the worker node, and python API is serialized on the worker node and executed. This python worker processes in addition to JVM, and coordination between them is overhead. Processes also compete for resources which adds to memory contention.
In addition, if the data is to send back to the driver node, then processing takes a lot of time, and the problem scales up as volume increases hence performance is a bigger problem.
As we have seen a performance, Let’s see the tabular comparison between these languages.
Which language is better for Spark and Why?
With the info we gathered for the languages, let's move to the main question i.e. which language to choose for Spark?
My answer is not a straightforward single language for this question. I will state my point of view on choosing the proper language:
- If you are a beginner and want to choose a language from learning Spark's perspective.
- If you are an organization/self-employed or looking to answer a question for a solution from a project perspective.
I. If you are a beginner:
- If you are a beginner and have no prior education in a programming language, then Python is the language for you, as it’s easy to pick up. Simple to understand and very user-friendly. It would prove a good starting point for building Spark knowledge further. Also, If you are looking to get into roles like ‘data engineering’, knowledge of Python along with supported libraries will go a long way.
- If you are a beginner but have education in programming languages, then you may find Java very familiar and easy to build upon prior knowledge. After all, it is the grapevine of all languages.
- If you are a hardcore big data programmer and love exploring complexities, Scala is the choice for you. It’s complex, but experts say that once you love Scala, you will prefer it over other languages anytime.
- If you are a data scientist or statistician and looking to work with Spark, R is the language for you. R is more science-oriented than Python.
II. If you are an organization/looking for a choice of language for implementations:
You need to answer the following important questions before choosing the language:
- Skills and Proficiency: Which skill sets and proficiency over the language do you already have with you/in your team?
- Design goals and availability of features/ Capability of language: Which libraries give you better support for the type of problem(s) you are trying to solve?
- Performance implications
Details of these are explained below:
1. Skillset: This is very straightforward. Whichever is an available skill set within a team, go with that to solve your problem after evaluating the answers to the other two questions.
If you are self-employed, the one you have proficiency in is the most likely suitable choice of language.
2. Library Support:
The Following gives high-level capabilities of languages:
- R: Good for research, plotting, and data analysis.
- Python: Good for small- or medium-scale projects to build models and analyze data, especially for fast start-ups or small teams.
- Scala/Java: Good for robust programming with many developers and teams; it has fewer machine learning utilities than Python and R, but it makes up for it with increased code maintenance. I think Scala/Java can be used for larger robust projects to ease maintenance. Also, If one wants the app to scale quickly and needs it to be robust, Scala is the choice.
Python and R: Python is a more universal language than R, but R is more science-oriented. Broadly, one can say Python can be implemented for Data engineering use cases and R for Data science-oriented use cases. On the other hand, if you discover these two languages have about the same library support you need, then pick the one whose syntax you prefer. You may find that you need both, depending on the situation.
3. Performance: As seen earlier in the article, Scala/ Java is about 10x faster than Python/R as they are JVM-supported languages. However, if you are writing Python/R applications wisely (like without using UDFs/ Not sending data back to the Driver etc), they can perform equally well.
For learning, depending on your prior knowledge, Python is the easiest of all to pick up.
For implementations, the choice is in your hands which language to choose for implementations but let me tell you one secret or a tip; you don’t have to stick to one language until you finish your project. You can divide your problem into small buckets and utilize the best language to solve the problem. This way, you can balance optimum performance, availability, proficiency in a skill, and the sub-problem at hand.
Do let us know how your experience was in learning the language comparisons and the language you think is better for Spark. Moreover, which one do you think is “the one for you”? Through comments below.