Executing external JAR functions in the Spark Shell is a common requirement when you want to leverage pre-compiled code or libraries to solve data processing tasks. Below is a detailed guide on how to achieve this.
Steps to Execute External JAR Functions in Spark Shell
1. Compiling the External Code
First, you need to have your external code packaged as a JAR file. Suppose you have a simple Scala function in an external project:
// Filename: SimpleApp.scala
package com.example
object SimpleApp {
def addTwoNumbers(a: Int, b: Int): Int = {
a + b
}
}
Compile this Scala code and package it as a JAR using `sbt` (Scala Build Tool) or any other build tool.
The `build.sbt` file might look like:
// Filename: build.sbt
name := "SimpleApp"
version := "0.1"
scalaVersion := "2.12.8"
libraryDependencies += "org.apache.spark" %% "spark-core" % "2.4.0"
Run the following command to package the JAR:
sbt package
This will generate a JAR file typically located in the `target/scala-2.12/` directory, named `simpleapp_2.12-0.1.jar`.
2. Starting Spark Shell with JAR file
Once you have your JAR file, you can start the Spark Shell and include this JAR in the classpath:
spark-shell --jars /path/to/your/simpleapp_2.12-0.1.jar
3. Using the External Function within Spark Shell
Now, you can call the function from the SimpleApp object inside your Spark Shell.
// Import the necessary package
import com.example.SimpleApp
// Use the external function
val result = SimpleApp.addTwoNumbers(3, 5)
println(result)
The expected output should be as follows:
res0: Int = 8
Example: Using External JAR Function in PySpark Shell
1. Compiling the External Code
Let’s assume the same external Scala code and JAR file setup as described above.
2. Starting PySpark Shell with JAR file
Start the PySpark Shell and include the JAR:
pyspark --jars /path/to/your/simpleapp_2.12-0.1.jar
3. Using the External Function in PySpark
To use the function, you’ll need to employ Py4J, which is the default bridge between Python and Java in PySpark. Below is a demonstration:
# Import necessary modules
from py4j.java_gateway import java_import
# Create a reference to the current Spark context
sc = spark.sparkContext
# Import the external class into the JVM
java_import(sc._gateway.jvm, "com.example.SimpleApp")
# Use the external function
result = sc._gateway.jvm.com.example.SimpleApp.addTwoNumbers(3, 5)
print(result)
The expected output should be:
8
Conclusion
Executing external JAR functions in the Spark Shell can be a very useful approach to extend the functionalities of your Spark application. By following the above steps, you can easily integrate and execute external Scala functions both in Spark Shell and PySpark Shell.