EMR Update Hadoop jar files

Yes, you can add the same version of the Parquet jars to both Spark and Hive classpaths in Amazon EMR to ensure consistency when reading and writing data in Parquet format. Here's how you can add the jars:

1. Identify the Jars:

   • First, identify the specific version of the Parquet jars that you want to use with both Spark and Hive. Ensure that these jars are compatible with the versions of Spark and Hive installed on your EMR cluster.

2. Upload Jars to S3:

   • Upload the Parquet jars to an Amazon S3 bucket. Make sure the bucket is in the same AWS region as your EMR cluster.

3. Create a Bootstrap Action:

   • In the EMR console, navigate to the cluster you want to modify.
   • Click on "Create cluster" if you are creating a new cluster, or select an existing cluster and click "Modify."
   • In the "Software configuration" section, click on "Edit software settings."
   • Scroll down to the "Bootstrap actions" section and click "Add bootstrap action."
   • Choose "Custom action" and click "Configure and add."
   • In the "Script location" field, specify the S3 path to a script that will download and add the Parquet jars to the classpath. This script can be a shell script or Python script.
   • Save the bootstrap action configuration.

4. Create and Configure the Script:

   • Write a script that downloads the Parquet jars from the S3 bucket and adds them to the classpath of both Spark and Hive.
   • The script should be executable and stored in the specified S3 location.

5. Run the Cluster:

   • Start or modify the EMR cluster as you normally would.
   • During cluster startup, the bootstrap action script will execute, downloading the Parquet jars and adding them to the classpath.

6. Verify Configuration:

   • Once the cluster is running, you can verify that the Parquet jars are added to the classpath of Spark and Hive.
   • Connect to the cluster via SSH and check the classpath configuration for Spark and Hive.