Need to Know Your Spark Version? Here’s How to Find It

Apache Spark is a powerful distributed processing system used for big data workloads. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general computation graphs for data analysis. Knowing how to check the version of Spark you are working with is important, especially when integrating with different components, dealing with compatibility issues, or following documentation and tutorials which are version-specific. This guide will cover all the aspects of how to check your Spark version using Scala, with code snippets and outputs included where applicable.

Understanding Spark Components

Before we dive into the specifics of how to check the Spark version, let’s understand the Spark ecosystem components which are versioned: the Spark Core, Spark SQL, Spark Streaming, MLlib (for machine learning), and GraphX. Each of these may have minor differences in API between versions, so confirming your Spark version is crucial when working with these components.

Finding Spark Version from the Spark Shell

The Spark shell is an interactive environment for running Spark commands. You can start the Spark shell by simply running the ‘spark-shell’ command in your terminal. Once Spark shell has started, it will load the necessary environment, and you can then check the Spark version by accessing the version information of the SparkSession or SparkContext object.

Using SparkSession

SparkSession is a unified entry point introduced in Spark 2.0 and above, and it encapsulates both SQLContext and HiveContext. To check the Spark version through SparkSession, use the following code snippet:

val sparkVersion = spark.version
println(s"The version of Spark is $sparkVersion")

When the above code is executed in the Spark shell, a possible output would look like this:

The version of Spark is 3.1.1

Using SparkContext

In Spark versions prior to 2.0, or if you need to use SparkContext for some legacy reason, you can retrieve the version number as follows:

val sc = new org.apache.spark.SparkContext
val sparkVersion = sc.version
println(s"The version of Spark is $sparkVersion")

The corresponding output in the console will be similar to this:

The version of Spark is 3.1.1

Checking Spark Version with SBT or Maven Dependency

If you are working on a Spark project using build tools like SBT (Simple Build Tool) or Maven, you can find out the Spark version by looking at the project’s build file, typically either `build.sbt` for SBT or `pom.xml` for Maven.

Using build.sbt

In the build.sbt file, locate the library dependencies section and look for the Spark dependency:

libraryDependencies += "org.apache.spark" %% "spark-core" % "3.1.1"

Here, “3.1.1” signifies the version of Spark that your project depends on. Ensure that all Spark-related dependencies are using the same version to avoid compatibility issues.

Using pom.xml (Maven)

For Maven, inspect the section of your `pom.xml` file:

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.12</artifactId>
    <version>3.1.1</version>
</dependency>

Similar to the SBT file, this XML snippet indicates that the project is using Spark version “3.1.1”.

Programmatically Checking Spark Version

Within a Scala application, you can programmatically check the Spark version by instantiating a SparkContext or SparkSession and reading its ‘version’ property. This is useful for logging, debugging, or dynamically adjusting to different version features.

Example in a Scala Application

In a Scala application, you can include the following code to output the Spark version:

import org.apache.spark.sql.SparkSession

object SparkVersionCheck extends App {
    val spark = SparkSession.builder()
                            .appName("Spark Version Check")
                            .getOrCreate()

    val sparkVersion = spark.version
    println(s"The version of Spark is $sparkVersion")

    spark.stop()
}

Upon running your Scala application, the console output will display the Spark version being used by the application:

The version of Spark is 3.1.1

Using Spark REST API

Apache Spark also provides a REST API that can be used to gather information about your Spark application, including the version. This is particularly useful in a clustered environment. You can issue an HTTP request to the Spark master or worker URL, typically on port 8080, and parse the returned JSON information for the version detail:

curl http://:8080/json/

The JSON response will contain a field named “spark” with the version information. This field can be parsed using any JSON parsing library or even command-line tools like `jq` if you’re scripting the check.

Accessing Spark Version on Cloud and Managed Services

Many cloud providers and managed services like AWS EMR, Databricks, or Google Cloud Dataproc have built-in mechanisms to check the Spark version directly from their interfaces. Consult the documentation of the service you are using for specific instructions.

Conclusion

Knowing the version of Apache Spark you are working with is fundamental to ensuring compatibility and best practices in your Spark applications. Whether you access this information via the Spark shell, programmatically in Scala, through your build files, or by leveraging the Spark REST API, you now have all the necessary methods at your disposal to check the Spark version effectively. Always remember to test your code with the specific version of Spark your deployment environment uses, as different versions may have significantly different behaviors and features.

About Editorial Team

Our Editorial Team is made up of tech enthusiasts who are highly skilled in Apache Spark, PySpark, and Machine Learning. They are also proficient in Python, Pandas, R, Hive, PostgreSQL, Snowflake, and Databricks. They aren't just experts; they are passionate teachers. They are dedicated to making complex data concepts easy to understand through engaging and simple tutorials with examples.