Modernizing Apache Spark Applications With GenAI: Migrating From Java to Scala

If you're working on big data projects using Spark, you've likely come across discussions within your team about Java vs. Scala vs. Python, along with comparisons in terms of implementation, API support, and feasibility. These technologies are typically chosen on a case-by-case basis depending on the specific use case.

For example, data engineering teams often prefer to use Scala over Java because of:

• Native language advantage (Spark itself is written in Scala)
• Better compatibility with Spark's core APIs
• Early access to the latest Spark features
• More idiomatic patterns
• Functional programming features
• Less boilerplate code—typically 20–30% less compared to Java—which boosts productivity and speeds up development cycles
• Stronger type safety and compile-time checks
• Performance benefits

On the other hand, data science teams tend to lean toward Python because of its extensive support for machine learning libraries. However, PySpark (Python for Spark) comes with some translation overhead. That is, when you write Spark code in Scala or Java, it executes natively within the Spark engine. In contrast, PySpark runs Python code that must communicate with the JVM through a process called Py4J, introducing serialization and inter-process communication delays. This can raise performance concerns in PySpark.

The goal of this post is to provide a well-rounded comparison between Java and Scala from a data engineering perspective, and to show how generative AI code assistants can help modernize Java codebases to Scala, without discounting the strengths of either language.

In a Nutshell, What to Choose?

Both Java and Scala will do the job for you. Java is object-oriented programming language, whereas Scala is a functional language with object-oriented concepts. 

With the new Generative AI services/tools such as Amazon Q Developer, ChatGPT,  GitHub Copilot, and Google's Gemini code assist etc., developers/software engineers can save a significant amount of coding time (more than 50%) and boost productivity. These GenAI tools are integrated with popular IDEs (Integrated Development Environments) and will be handy to the developers with natural language capabilities. So, considering these GenAI tools makes the job easy.

Keep an eye on API support in terms of understanding the limitations and seamless implementation. For instance, Enterprise applications were mainly developed on Java and have vast API availability, whereas Scala is often used for more distributed high-compute applications like Apache Spark.

Migration from Java to Scala, or vice versa, is generally unnecessary unless faced with specific challenges such as a talent scarcity or corporate-wide technology alignment initiatives

Microservice architecture, which has become a de facto standard nowadays, can be implemented in both Java and Scala. Also, other languages like GO etc.

Perspective Decision

Developer perspective 

The decision will be guided by programming expertise, comfort level, and years of experience.

Project Management perspective: 

From the project management perspective, you don’t want to go with all the technical jargon. Instead, you want to quickly absorb the comprehensive comparison. below is a quick comparison in a nutshell.

• Learning curve: Learning Scala isn’t easy, especially for developers new to the language — but as the saying goes, "Once a programmer, always a programmer." There may be a steep learning curve, but it's definitely manageable with persistence.
• Project timelines: Java is more verbose in terms of lines of code, while Scala leverages type inference and functional constructs. As a result, code implementation may progress faster in Java, especially for teams already familiar with its syntax.
• Business Continuity Plan (BCP) and resource availability in market: Java’s been around since 1996, while Scala came along later in 2004. That means it’s usually easier to find experienced Java developers, especially when you need to plan for things like business continuity, than it is to find people with strong Scala skills. 
• Development team: Traditionally, development teams would stick to a single programming language for a given project. But with modern development trends, it’s becoming more common for developers to be familiar with, and even use, multiple languages during implementation. The great thing is, Java and Scala can interoperate seamlessly, allowing teams to combine the strengths of both in a single project.

Java and Scala Comparison

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showImages(images) 

} else if (editProfile) { showImages(images) 

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public class HelloJava 

{

 public static void main(String[] args) 

{ 

System.out.println(“Hello World !!!”) } 

}

Object HelloScala 

{ 

def main(args : Array[String]):Unit { println(“HelloWorld!!!”) 

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class Complex(val real: Int, val image: Int) {

  def +(that: Complex): Complex = new Complex(this.real + that.real, this.image + that.image)

  override def toString: String = s"$real + ${image}i"

}


val c1 = new Complex(2, 3)

val c2 = new Complex(1, 4)

println(c1 + c2) // Output: 3 + 7i

How GenAI Code Assistants Help Modernize Spark Application From Java to Scala

Generative AI services/tools helps to boost productivity: Adopting and using new GenAI tools such as Amazon Q Developer, ChatGPT,  GitHub Copilot, and Google's Gemini code assist, etc.

• Use Natural Language (aka NLP features) 
• Learning curve or transition phase can be minimized and improve the coding standards.
• These tools also help to do the coding documentation, code reviews and unit test cases. 
• Saving coding time and boost productivity
• Multi language support. like Java, Scala, SQL etc. More information can be found here. 

Modernizing Spark and Java Applications

In this section, we'll explore how Amazon Q (Generative AI Code Assistant) can support the modernization of Java to Scala, as well as the upgrade of Spark versions.

1. Install Amazon Q in your IDE. Please refer these installation instructions. The following shows the Amazon Q plugin enabled Visual Studio IDE 

2. Code Generation: In the following screenshot, i am showing that a sample Java Apache Spark code is generated using a prompt, "show apache spark code to read a dataset from s3 file names sales_dataset.csv and marketing_compaign_dataset.csv and perform join operation in java" you can create file in your repo instead of showing or pick your own spark code. 

Prompt: "Convert this java code to Scala"

3. Now that we've seen the high-level code conversion, let's take a closer look at the file level. For this example, I'll be using a Spark Example repo featuring the classic WordCount program written in Java. 

4. Let’s convert the Java code to Scala. In the example below, the red boxes highlight the prompt I used: “Convert JavaWordCount.java program to Scala”, along with Amazon Q’s response. Amazon Q not only converted the Java code to Scala but also created a new Scala file named WordCount.scala in the same repository directory. 

5. Next, let’s click on the WordCount.scala file to view the code generated by Amazon Q. As shown in the image below (highlighted in green), a new window opens displaying the WordCount.scala file, along with a tag indicating it was “Generated by Amazon Q.” 

When upgrading from an older version of Spark to a newer one, it’s important to be aware of potential breaking changes that could affect your existing code. For example, in Spark 2.4, the DATE_ADD(a, b) function allowed the second argument b to be a decimal. But starting with Spark 3.5, this argument must be an integer — decimal values are no longer supported. In the next section, we’ll walk through how to migrate a Spark 2.4 project to Spark 3.5.

Prompt: "Create a spark 2.4 version code with DATE_ADD(a,b) function previously allowed b to be decimal"

Prompt: "now modernize this code to spark 3.4"

Note: Although edge cases will invariably require manual attention, a GenAI assistant offers a powerful advantage by tackling the bulk of the Spark code conversion, potentially transforming a traditionally challenging migration into a far more efficient undertaking.

Disclaimer