When we want to use external dependencies in the PySpark code, we have two options. We can either pass them as jar files or Python scripts.
Table of Contents
In this article, I will show how to do that when running a PySpark job using AWS EMR. The jar and Python files will be stored on S3 in a location accessible from the EMR cluster (remember to set the permissions).
First, we have to add the --jars and --py-files parameters to the spark-submit command while starting a new PySpark job:
spark-submit --deploy-mode cluster \
--jars s3://some_bucket/java_code.jar \
--py-files s3://some_bucket/python_code.py \
s3://some_bucket/pyspark_job.py
In the pyspark_job.py file, I can import the code from the jar file just like any other dependency.
import python_code.something
Get Weekly AI Implementation Insights
Join engineering leaders who receive my analysis of common AI production failures and how to prevent them. No fluff, just actionable techniques.