Using Python Mapper and Reducer Functions in EMR Cluster

Answer - A.

This is mentioned in the AWS Documentation.

########

Submit a Streaming Step.

This section covers the basics of submitting a Streaming step to a cluster.

A Streaming application reads input from standard input and then runs a script or executable (called a mapper) against each input.

The result from each of the inputs is saved locally, typically on a Hadoop Distributed File System (HDFS) partition.

After all the input is processed by the mapper, a second script or executable (called a reducer) processes the mapper results.

The results from the reducer are sent to standard output.

You can chain together a series of Streaming steps, where the output of one step becomes the input of another step.

The mapper and the reducer can each be referenced as a file or you can supply a Java class.

You can implement the mapper and reducer in any of the supported languages, including Ruby, Perl, Python, PHP, or Bash.

########

Option B is incorrect since this is used if you want a custom image for the nodes in your cluster.

Options C and D are incorrect since the ideal approach is to use the inbuilt step functionality.

For more information on creating a streaming step, please refer to the below URL.

https://docs.aws.amazon.com/emr/latest/ReleaseGuide/CLI_CreateStreaming.html

The correct answer is A. Create a step for the EMR Cluster.

Here's a detailed explanation:

Amazon Elastic MapReduce (EMR) is a managed Hadoop framework that allows you to process large amounts of data in a distributed and scalable manner. EMR provides a way to run data processing jobs on a cluster of Amazon EC2 instances, which are managed by EMR.

When you create an EMR cluster, you can specify the steps that you want the cluster to perform. A step is a unit of work that performs a specific action, such as processing data, copying data, or running a custom script.

In this case, the team has their own implementations of the Mapper and Reducer functions developed in Python. To use these functions in the EMR cluster, the team can create a custom step that runs the Python code. Here's how they can do it:

1. Create a new EMR cluster or use an existing one.
2. In the EMR console, click on "Add step".
3. In the "Add step" dialog box, specify the following:
   • Step type: Custom JAR
   • Name: A name for the step
   • JAR location: s3://<bucket>/<path>/script.py
   • Arguments: The arguments that the Python script expects (if any)
4. Click "Add" to create the step.

In the JAR location field, you can specify the location of the Python script. You must upload the script to an S3 bucket first, and then specify the S3 URL in the JAR location field.

When you run the step, EMR launches a new Amazon EC2 instance and runs the Python script on it. EMR also manages the cluster resources and scales up or down as needed to handle the workload.

Option B is not a good choice because it involves creating a custom Amazon Machine Image (AMI) for the cluster. This approach can be more time-consuming and difficult to manage compared to using a custom step.

Option C is also not a good choice because AWS Lambda is a serverless compute service that is optimized for short-lived, event-driven functions. It is not designed for long-running data processing jobs.

Option D is not a good choice because AWS Data Pipeline is a service that allows you to schedule and orchestrate data processing workflows. It is not designed to run custom scripts or code.