How to Check the Spark Version

We can check the Spark version using Spark Shell In this article, we will discuss how we can check the Spark version using spark-shell and spark-submit command on our local setup as well as on Cluster Setup in detail. So, let’s see how this can done.

Table of Content

How to Check Spark Version

If you are using Spark locally, then in order to find the version, you can follow these steps:

  1. Open your terminal.
  2. Start the Spark Shell by running: spark-shell
  3. After Spark Shell runs and is successfully booted up with the Spark shell, you will see the version stated at the top of the console. Or, you can get the version quickly by typing the following command: 
sc.version

This will display the version of Spark.

Using Spark-submit Command

Another method of finding the version of Spark would be to use the spark-submit command. This is very useful when you’re utilizing Spark in standalone mode or independent mode or if you have to find the version from outside the Spark shell environment.

  1. Open your terminal.
  2. Execute the following command:
    spark-submit –version

This will print out the version number of Apache Spark that is currently installed on your system.

How to Check Spark Version on Cluster

1. Using Spark UI

If you’re working with a Spark cluster, then Spark UI is the most ideal way to check the version. Here’s how you can do this:

  1. Open the Spark UI in your browser (typically at http://<driver-node>:4040).
  2. Navigate to the “Environment” tab.
  3. The Spark Version info is listed below “Spark Version” in the Spark Properties section.

2. Using YARN Command (for Hadoop clusters)

While  using Spark with YARN, you can check the Spark version by querying the YARN resource manager:

  1. Open your terminal and use this command
    yarn application -status <application-id>
  2. This will return the application’s status, including the Spark version used for that particular application.

Why is Spark Version Important?

If you are using Spark, then depending on the version some of the features or performance optimizations can differ. So, if you have a clear idea about the version that you are using then it would be easy to identify the error or make upgrades. Moreover, this also helps when you are using different libraries and APIs, as it can help you find the most compatible version available.

Common Issues When Checking Spark Version

While checking your version of Spark is pretty simple, you may encounter a few common issues:

  1. No Spark version shown: If you don’t see the Spark version, check whether Spark is properly installed and configured. If you are in a cluster, make sure you have access to the cluster nodes.
  2. Inconsistent versions: In a distributed setup, you may find different nodes of different versions of Spark. This can lead to compatibility issues. Always ensure consistent versions across all nodes in your cluster.

Conclusion

The initial step in managing a Spark installation is to check the version of Apache Spark installed. In a distributed cluster or when working locally, knowing the version is very important to ensure compatibility and further run the operations. Numerous methods exist to obtain the desired information depending on the environment that is being used, including the Spark shell, spark-submit, and Spark UI. If you want to learn more about these 

FAQs

Q1: Can I upgrade my Spark version easily?

Yes, upgrading Spark is fairly simple, especially if you’re upgrading using a local installation. In cluster environments, this requires coordination between the cluster manager YARN, for example, or Kubernetes the actual upgrade.

Q2: Will checking the version affect my running jobs?

No, it will not impact any running jobs when checking the Spark version by the method above because such actions are read-only operations.

Q3: What if I don’t have access to the command line?

If you do not have access to a terminal, you can check your version of Spark via the Spark UI or through your cluster management tool like YARN or Kubernetes.