Databricks: Call Scala From Python

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Databricks: Call Scala From Python

Hey everyone, and welcome back to the blog! Today, we're diving into something super cool that can really level up your data engineering game in Databricks: calling Scala functions directly from your Python code. Yeah, you heard that right, guys! We're bridging the gap between these two powerful languages within the Databricks environment, and it's not as scary as it sounds. In fact, it's pretty darn straightforward once you get the hang of it. So, buckle up, grab your favorite IDE (or just stick with the Databricks notebook, no judgment here!), and let's explore how we can make Python and Scala play nicely together.

Why would you even want to do this, you ask? That's a fair question! You might have existing Scala libraries or codebases that you want to leverage without rewriting them entirely in Python. Maybe there are specific performance-critical operations that are best handled by Scala. Or perhaps your team has a mix of Python and Scala expertise, and you want to utilize both skill sets effectively. Whatever your reason, Databricks provides a seamless way to integrate these languages. It's all about maximizing efficiency and getting the most out of your existing assets. Think of it as having the best of both worlds – the ease of use and extensive libraries of Python, combined with the raw power and performance often associated with Scala for certain tasks.

The Magic Behind the Scenes: Spark's JVM Interoperability

The real MVP here is Apache Spark itself. Databricks is built on top of Spark, and Spark, being a JVM-based engine, inherently supports interoperability between JVM languages like Scala and Java. Python, on the other hand, interacts with Spark through PySpark, which acts as a bridge. When you call Scala code from Python, PySpark facilitates this communication by leveraging Spark's JVM capabilities. It's like having a translator that allows Python and Scala to understand each other's commands and data. This JVM interoperability is what makes all of this possible, enabling you to seamlessly invoke Scala methods and classes right from your Python scripts. We'll be using Py4J, a library that comes bundled with PySpark, which is the unsung hero enabling this cross-language communication. It essentially allows Python to dynamically access and control Java objects in the JVM, which is where our Scala code resides.

Setting the Stage: What You'll Need

Before we jump into the code, let's make sure you're all set up. You'll need:

  1. A Databricks Workspace: Obviously! This is where the magic happens.
  2. A Cluster: Make sure you have a cluster running. It doesn't need to be a massive one for testing, but it needs to be up and running.
  3. Basic Knowledge of Python and Scala: You don't need to be a guru in both, but understanding the fundamentals will make this a breeze.
  4. Your Scala Code: This is the code you want to call from Python. It can be a simple function, a class with methods, or even a whole library.

Once you have these prerequisites covered, we're ready to roll up our sleeves and get our hands dirty with some code. We'll start with a simple example to illustrate the concept, and then we can explore more complex scenarios. Don't worry if you're not a Scala expert; the examples will be easy to follow. The goal is to demonstrate the mechanism, not to become Scala prodigies overnight!

Your First Scala Function in Databricks

Alright, let's get our hands dirty with some actual code, guys! The first step is to create a simple Scala function that we can later call from our Python script. For this example, let's create a Scala object with a method that takes two integers and returns their sum. This is as basic as it gets, but it perfectly illustrates the concept. We'll define this directly within a Databricks notebook using the Scala magic command, %%scala. This is super convenient because it allows you to run Scala code cells right alongside your Python cells.

// Define a Scala object with a method
object MyScalaUtils {
  def addNumbers(a: Int, b: Int): Int = {
    a + b
  }
  def greet(name: String): String = {
    s"Hello, $name from Scala!"
  }
}

See how easy that was? We defined an object named MyScalaUtils and within it, a def (which stands for definition) for a function called addNumbers. This function takes two Int (integer) arguments, a and b, and simply returns their sum. We also added a greet function for a bit more variety, which takes a String and returns a greeting. This object and its methods are now available within the Spark JVM context of your Databricks cluster. Think of this object as a container for your utility functions. You can put as many functions as you need inside it, making it a neat way to organize your reusable Scala code. We're keeping it simple for now, but you can imagine packing much more complex logic in here, like data transformations or complex calculations, that you might want to trigger from Python.

Making Your Scala Code Accessible

When you run the %%scala cell, Databricks registers this Scala code within the JVM that your Spark driver and executors are running on. This is crucial because Python, through PySpark, will be interacting with this JVM. The object MyScalaUtils and its methods addNumbers and greet are now part of the JVM's classpath, making them discoverable. If you were writing a standalone Scala application, you'd typically compile this into a JAR file and include it in your project's dependencies. In Databricks notebooks, the %%scala magic command handles this registration for you dynamically. It's like println("Hello, World!") in Scala; it just works! The key takeaway here is that the code defined in the %%scala cell is now part of the runtime environment that your Python code will access. It's not isolated; it's integrated.

Now, let's switch gears and head back to Python to see how we can actually call these functions. This is where the spark object, which is automatically available in Databricks notebooks, comes into play. PySpark provides a way to interact with the JVM through the spark context, specifically using the spark.sparkContext._jvm attribute. This attribute gives you access to the JVM, allowing you to instantiate Scala objects and call their methods.

Calling Scala from Python: The PySpark Way

Now for the exciting part, guys – making the call! We've got our Scala code defined and ready to go. Let's fire up a Python cell in the same Databricks notebook and see how we can invoke MyScalaUtils.addNumbers and MyScalaUtils.greet. The core component we'll use is spark.sparkContext._jvm. This is your gateway to the Java Virtual Machine (JVM) where our Scala code is running. It acts as a proxy, allowing Python to interact with Scala objects and methods seamlessly. It’s quite powerful and opens up a world of possibilities for language integration.

First, we need to get a reference to our Scala object. Since MyScalaUtils is an object (a singleton instance in Scala), we can access it directly via the JVM.

# Get a reference to the Scala object
scala_utils = spark.sparkContext._jvm.MyScalaUtils

This line of Python code tells Spark to go into the JVM and find the object named MyScalaUtils. Once scala_utils is assigned, it acts like a Python proxy for your Scala object. You can now call its methods as if they were regular Python methods.

Let's test the addNumbers function. We'll pass in two integers, say 10 and 20, and print the result.

# Call the addNumbers method from Scala
result_sum = scala_utils.addNumbers(10, 20)
print(f"The sum from Scala is: {result_sum}")

And just like that, result_sum will hold the value 30. How cool is that? You're executing Scala code directly from your Python script! The types are automatically handled for you by PySpark. Python integers are passed as JVM integers, and the integer result is returned back to Python. It's a smooth, almost invisible translation.

Now, let's try the greet function to see how string handling works:

# Call the greet method from Scala
name = "Databricks User"
greeting = scala_utils.greet(name)
print(greeting)

This will output: Hello, Databricks User from Scala!. Again, the Python string "Databricks User" is seamlessly passed to the Scala method, and the returned Scala string is correctly interpreted by Python. This demonstrates the robustness of the JVM interoperability for different data types.

Understanding the _jvm Object

The spark.sparkContext._jvm object is your primary tool for interacting with Scala/Java code. It's a gateway that allows you to:

  • Access Scala Objects (Singletons): As we saw with MyScalaUtils, you can access singleton objects defined in Scala directly.
  • Instantiate Scala Classes: If you had a Scala class instead of an object, you could instantiate it like this (assuming a class MyScalaClass with a constructor):
    # Example for a Scala class (not used in this tutorial)
# scala_class_instance = spark.sparkContext._jvm.MyScalaClass(arg1, arg2)
  • Call Methods: Once you have a reference to an object or an instance of a class, you can call its methods.
  • Handle Data Types: PySpark does a remarkable job of converting common data types between Python and the JVM (e.g., Python int to Java int, Python str to Java String, Python list to Java ArrayList, etc.).

It's important to note that spark is an instance of SparkSession that's automatically created and available in Databricks notebooks. If you were running this in a standalone PySpark application, you would need to create your SparkSession first.

Working with More Complex Data Types

Okay, so adding numbers and printing greetings is cool, but real-world applications often involve more complex data types, like lists, dictionaries (maps in Scala), and DataFrames. Let's see how PySpark handles these when calling Scala functions. We'll expand our MyScalaUtils object to include a function that processes a list of numbers.

First, let's add a new function to our MyScalaUtils object in a %%scala cell. This function will take a Scala Seq (sequence) of integers and return their sum.

// Add a new function to MyScalaUtils object
object MyScalaUtils {
  def addNumbers(a: Int, b: Int): Int = {
    a + b
  }
  def greet(name: String): String = {
    s"Hello, $name from Scala!"
  }
  // New function to sum a list of integers
  def sumList(numbers: Seq[Int]): Int = {
    numbers.sum
  }
}

Now, back in our Python cell, we can call this sumList function. When you pass a Python list to a Scala method expecting a Seq[Int], PySpark automatically converts it. Let's try it:

# Get the reference to the Scala object again (if you restarted the kernel or are in a new cell)
scala_utils = spark.sparkContext._jvm.MyScalaUtils

# Define a Python list
my_python_list = [1, 2, 3, 4, 5]

# Call the sumList method from Scala, passing the Python list
result_list_sum = scala_utils.sumList(my_python_list)
print(f"The sum of the list from Scala is: {result_list_sum}")

This should output: The sum of the list from Scala is: 15. PySpark handles the conversion from Python list to Scala Seq[Int] seamlessly. This is a huge time-saver, as you don't need to manually convert data structures.

Handling Scala Maps (Python Dictionaries)

Let's add another function to our Scala object that works with a map. This function will take a Map[String, Int] and return the sum of its values.

// Add another function to MyScalaUtils object
object MyScalaUtils {
  def addNumbers(a: Int, b: Int): Int = {
    a + b
  }
  def greet(name: String): String = {
    s"Hello, $name from Scala!"
  }
  def sumList(numbers: Seq[Int]): Int = {
    numbers.sum
  }
  // New function to sum values in a Map
  def sumMapValues(data: Map[String, Int]): Int = {
    data.values.sum
  }
}

Now, let's call this sumMapValues function from Python. PySpark will convert a Python dictionary (dict) into a Scala Map. Note that for Scala Map[String, Int], the keys must be strings and the values integers in Python.

# Get the reference to the Scala object
scala_utils = spark.sparkContext._jvm.MyScalaUtils

# Define a Python dictionary
my_python_dict = {"apple": 10, "banana": 20, "cherry": 30}

# Call the sumMapValues method from Scala, passing the Python dictionary
result_dict_sum = scala_utils.sumMapValues(my_python_dict)
print(f"The sum of map values from Scala is: {result_dict_sum}")

This will output: The sum of map values from Scala is: 60. Again, the type conversion is handled automatically. This makes integrating complex data structures between Python and Scala remarkably smooth.

Working with Spark DataFrames

This is where things get really interesting. You can pass Spark DataFrames created in Python (PySpark DataFrames) to Scala functions that expect Spark DataFrames. The underlying data is shared, and PySpark ensures the reference is correctly passed. Let's say you have a Scala function that performs some DataFrame operation.

For instance, imagine a Scala function that filters a DataFrame based on a column value. You'd define it in Scala like this:

import org.apache.spark.sql.DataFrame

object DataFrameUtils {
  def filterByValue(df: DataFrame, columnName: String, value: Int): DataFrame = {
    df.filter(df(columnName) === value)
  }
}

And here's how you'd call it from Python:

from pyspark.sql import SparkSession

# Assume 'spark' is your SparkSession
# Create a sample DataFrame in Python
data = [("Alice", 1), ("Bob", 2), ("Charlie", 1)]
columns = ["name", "id"]
df = spark.createDataFrame(data, columns)

# Get the reference to the Scala DataFrameUtils object
df_utils = spark.sparkContext._jvm.DataFrameUtils

# Call the Scala function, passing the PySpark DataFrame
filtered_df = df_utils.filterByValue(ndf._jdf, "id", 1)

# Show the result (which is a PySpark DataFrame)
filtered_df.show()

Important Note: When passing a PySpark DataFrame to a Scala function that expects a org.apache.spark.sql.DataFrame, you need to access the underlying Java DataFrame using the _jdf attribute of the PySpark DataFrame. So, instead of passing ndf, you pass ndf._jdf. This is because PySpark DataFrames have a Python wrapper around the actual JVM DataFrame object.

This allows you to write sophisticated DataFrame transformations or UDFs (User Defined Functions) in Scala for performance gains and then seamlessly integrate them into your Python data pipelines. The output would be:

+-----+--+
| name|id|
+-----+--+
|Alice| 1|
|Charlie| 1|
+-----+--+

This capability is incredibly powerful for teams that want to leverage both Python's ease of use and Scala's performance optimizations for data manipulation.

Packaging Scala Code as a JAR

While using %%scala cells is fantastic for quick tests and simple functions, for more complex or reusable Scala code, it's best practice to package your Scala code into a JAR file. This makes your code modular, versionable, and easier to manage, especially in production environments. Here’s a general overview of how you’d do this:

  1. Write your Scala code: This could be in a standard Scala project (e.g., using sbt or Maven).
  2. Compile and package: Build your project to create a JAR file. Ensure your code is in an accessible format, like public methods within objects or classes.
  3. Upload the JAR to Databricks: You can upload JARs to your Databricks workspace via the Data tab or by referencing them in your cluster configuration.
  4. Attach the JAR to your cluster: When configuring your cluster, under the Libraries tab, you can add your uploaded JAR file. This makes the code within the JAR available to all notebooks running on that cluster.

Once the JAR is attached to your cluster, your Scala code becomes available in the JVM, just like it was with the %%scala magic command. You can then access your objects and methods from Python using spark.sparkContext._jvm as demonstrated before.

For example, if your JAR contained an object com.mycompany.MyScalaLib.DataProcessor with a method process(df: DataFrame): DataFrame, you would access it in Python like this:

# Assuming the JAR is attached to the cluster
scala_processor = spark.sparkContext._jvm.com.mycompany.MyScalaLib.DataProcessor

# Example usage with a DataFrame 'df'
# processed_df = scala_processor.process(df._jdf)

Using JARs is the professional way to manage shared Scala libraries in Databricks, ensuring consistency and maintainability across your projects. It separates your reusable logic from your notebook scripts, making your workflow cleaner and more robust.

Best Practices and Considerations

While calling Scala from Python in Databricks is powerful, there are a few things to keep in mind to ensure a smooth experience:

  • Performance: While Scala can offer performance benefits, excessive inter-process communication (Python -> JVM -> Python) can introduce overhead. For simple operations, Python might be just as fast. Use Scala for genuinely computationally intensive tasks or when leveraging existing Scala libraries.
  • Error Handling: Errors originating in Scala will be thrown as Py4JJavaError exceptions in Python. Make sure to wrap your calls in try-except blocks and inspect the error messages carefully, as they often contain detailed stack traces from the JVM.
  • Data Serialization: PySpark handles most common data types, but complex custom objects might require explicit serialization/deserialization logic or use of Spark's built-in serializers.
  • Classpath Issues: Ensure that any required Scala libraries or your custom JARs are correctly attached to the cluster. If your Scala code depends on other external libraries, they need to be available in the JVM environment.
  • Readability: Keep your code organized. Use %%scala for quick experiments and package more robust, reusable logic into JARs. Document your Scala functions well, as you'll be calling them from a different language.
  • Databricks Runtime Version: Be aware that the Java/JVM version used by Databricks Runtime can influence compatibility. Generally, Databricks Runtime handles this well, but it's something to consider for very specific or older libraries.

Final Thoughts

And there you have it, folks! You've learned how to bridge the gap between Python and Scala in Databricks, enabling you to call Scala functions directly from your Python code. We covered creating simple Scala functions, passing various data types, and even touched upon using JARs for production code. This interoperability is a killer feature of Databricks that allows you to harness the strengths of both languages. It’s all about flexibility and making the most of the tools available. So go forth, experiment, and integrate your Scala brilliance into your Python workflows! Happy coding, everyone!