How to Add Amazon EMR MapReduce/Hive/Spark Steps with Inline Shell Script in the Arguments

How to Add Amazon EMR MapReduce/Hive/Spark Steps with Inline Shell Script in the Arguments

Keywords: Amazon EMR, MapReduce, Hive, Spark, Inline Shell Script, Steps, Data Science, Big Data, AWS, Cloud Computing, Shell Scripting, EMR Steps.

As data scientists and software engineers, we often deal with large volumes of data that require distributed processing. Amazon EMR (Elastic MapReduce) is a cloud-based, big data platform that allows users to process large datasets efficiently using popular frameworks such as MapReduce, Hive, and Spark. In this post, we’ll explore how to add Amazon EMR MapReduce/Hive/Spark steps with an inline shell script in the arguments.

What is Amazon EMR?

Amazon EMR is a web service that enables businesses, researchers, data analysts, and developers to easily and cost-effectively process vast amounts of data. It utilizes a hosted Hadoop framework running on the web-scale infrastructure of Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3).

Adding Steps to an Amazon EMR Cluster

Steps are the means by which you add Spark, Hive, or MapReduce jobs to your Amazon EMR cluster. Steps can be added when the cluster is launched or after the cluster is running.

Let’s now dive into how to add MapReduce/Hive/Spark steps with inline shell script in the arguments.

Adding MapReduce/Hive/Spark Steps with Inline Shell Script

In order to add a step with an inline shell script in the arguments, we’ll use the AWS CLI (Command Line Interface) to interface with the EMR service.

First, ensure that you have the AWS CLI installed and configured with your AWS credentials.

Here’s an example of how you might add a step to a running cluster using the add-steps command:

aws emr add-steps --cluster-id j-2AXXXXXXGAPLF --steps Type=CUSTOM_JAR,Name="Custom JAR Step",ActionOnFailure=CONTINUE,Jar=s3://mybucket/mytest.jar,Args=["arg1","arg2","arg3"]

In this command, --cluster-id is the ID of your running EMR cluster. The --steps attribute defines the step to be added. In this case, we’re adding a CUSTOM_JAR step. Here, s3://mybucket/mytest.jar is the path to your JAR file in S3, and arg1, arg2, and arg3 are your inline arguments.

To run a shell script as a step, you can use a script-runner provided by AWS:

aws emr add-steps --cluster-id j-2AXXXXXXGAPLF --steps Type=CUSTOM_JAR,Name="Custom Shell Script",ActionOnFailure=CONTINUE,Jar=s3://region.elasticmapreduce/libs/script-runner/script-runner.jar,Args=["s3://mybucket/myscript.sh","arg1","arg2"]

In this command, s3://region.elasticmapreduce/libs/script-runner/script-runner.jar is the path to the script-runner JAR provided by AWS, and s3://mybucket/myscript.sh is the path to your shell script in S3.

To add a Spark or Hive job, replace the CUSTOM_JAR type with Spark or Hive and provide the appropriate script file and arguments.

Conclusion

Amazon EMR is a powerful tool for processing large volumes of data. By understanding how to add MapReduce/Hive/Spark steps with inline shell scripts, you can make your processing workflows more flexible and adaptable to changing needs. Just remember to ensure that your scripts are well-tested and robust, as errors in your scripts can lead to failures in your steps and potentially in your entire EMR cluster. Happy data processing!

About Saturn Cloud

Saturn Cloud is your all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, and more. Join today and get 150 hours of free compute per month.