Скачать или смотреть Solving the InvocationTargetException in Spark + BigQuery with Custom Shaded Jars

Discover how to fix the `InvocationTargetException: java.lang.VerifyError` issue when using Spark with BigQuery by utilizing custom shaded jars.
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Solving the InvocationTargetException in Spark + BigQuery with Custom Shaded Jars

When working with Apache Spark and Google BigQuery, developers occasionally encounter frustrating errors that halt their progress. One common error is the InvocationTargetException: java.lang.VerifyError: Bad type on operand stack. This error is not only confusing but can be quite daunting, especially when trying to determine its root cause. In this guide, we aim to demystify this error and provide you with practical solutions to get your Spark + BigQuery project running smoothly.

Understanding the Problem

The error often arises under various configurations—in this case, the user tried different combinations of:

Languages: Python and Scala

JDK Versions: JDK 8 and JDK 11

Spark Versions: Spark 3.1.2 and Spark 3.3.1

Despite these efforts, the same error persisted. This indicates that the issue likely lies not with the versions themselves but with the compatibility of certain libraries used within your Spark job.

The Error Message

When attempting to write a simple DataFrame to BigQuery, the following error was encountered:

[[See Video to Reveal this Text or Code Snippet]]

This raised a red flag between the integration of the required libraries to connect Spark with Google BigQuery. But fear not, as we will explore a solution to tackle this problem below.

Solution: Custom Shaded Jars

The main fix for the InvocationTargetException when using Spark with BigQuery is to utilize custom shaded jars. Here's a breakdown of what that means and how to implement this solution effectively.

Why Use Custom Shaded Jars?

Managed Spark environments like Databricks and Amazon EMR handle the complexities of integration through custom shaded jars. Shaded jars bundle dependencies together to avoid version conflicts between libraries, ensuring smoother execution of your Spark jobs. In local setups, manual handling is necessary to achieve the same effect.

Steps to Implement Custom Shaded Jars

Identify Required Dependencies:

Make sure you know the specific versions of the libraries you are using, including:

Google Cloud Spark Connector

Google Cloud GCS Connector

Any other libraries that may conflict

Create a Shaded Jar:

Use build tools like Maven or Gradle to create a shaded jar that includes all the dependencies your Spark job requires.

Modify Spark Configuration:

Adjust your Spark configuration to point to the newly created shaded jar. This often involves:

Updating SPARK_HOME to include your custom jar path when starting Spark.

Run Your Spark Job:

After configuring your Spark environment to include the shaded jar, attempt to rerun the operation that previously resulted in the error.

Sample Configuration

While the specifics may vary based on your environment, here's a short configuration guideline to assist you in the setup process. You might use a command like this to run your Spark shell:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Encountering the InvocationTargetException: java.lang.VerifyError when interfacing Spark with BigQuery can be an unnerving experience, but with the right approach, it can be resolved efficiently. By utilizing custom shaded jars, you can mitigate library conflicts and smooth out the integration process. As always, ensure to test your configurations extensively to safeguard against new issues.

By following the steps outlined above, you should be able to overcome this obstacle and continue your development work without further interruptions. Happy coding!