To add JAR files to a Spark job using `spark-submit`, you can use the `–jars` option. This is useful when you have external dependencies that need to be available to your Spark job. Below are detailed explanations and examples:
Using the –jars Option
When you need to include additional JAR files in your Spark job, you use the `–jars` option followed by a comma-separated list of paths to the JAR files. These JAR files will be added to the classpath of the executor nodes.
Here is the general syntax:
spark-submit --jars path_to_jar1,path_to_jar2,... your_spark_application
Example with PySpark
Consider you have an external JAR file located at `/path/to/external-lib.jar` and you have a simple PySpark job `my_spark_job.py`:
spark-submit --jars /path/to/external-lib.jar my_spark_job.py
Example with Scala
For a Scala-based Spark application, the process is similar. Suppose your Scala application JAR is named `my_scala_spark_app.jar`:
spark-submit --jars /path/to/external-lib.jar --class com.example.MySparkApp my_scala_spark_app.jar
Example with Multiple JARs
If you have multiple JAR files to include in your Spark job, separate them with commas:
spark-submit --jars /path/to/external-lib1.jar,/path/to/external-lib2.jar my_spark_job.py
Verifying JAR Inclusion
You can verify that the JAR files are included by checking the logs of your Spark job. The logs should show that the JAR files have been added to the classpath of each executor.
Conclusion
Using the `–jars` option is a straightforward way to include external dependencies in your Spark job. Just make sure to specify the exact paths to the JAR files, and they will be added to the classpath of the executor nodes, making the classes and resources available during runtime.