Chapter 5: Developing a MapReduce Application

Easy Level Questions

These questions aim to assess the students’ ability to recall fundamental facts and basic concepts related to developing MapReduce applications.

1.     What is the primary purpose of the Configuration API in a Hadoop MapReduce application? [CO5]

2.     Name two essential steps involved in setting up a development environment for writing MapReduce jobs. [CO5]

3.     How can you monitor the progress and status of a running MapReduce job in a Hadoop cluster? [CO5]

4.     What information can be typically found in Hadoop logs when debugging a MapReduce job? [CO5]

Moderate Level Questions

These questions require students to apply their knowledge, interpret information, and make connections between different MapReduce application development concepts.

5.     Explain the importance of writing and testing unit tests for individual MapReduce components (e.g., Mapper, Reducer) before deploying to a cluster. [CO5]

6.     Describe the process of retrieving the final results of a completed MapReduce job from HDFS. [CO3, CO5]

7.     Discuss a scenario where remote debugging would be a more suitable approach than analyzing local logs for troubleshooting a MapReduce job. [CO5]

8.     How does profiling tasks help in tuning a MapReduce job for better performance? Provide a specific example. [CO5]

Difficult Level Questions

These questions challenge students to synthesize information, critically evaluate concepts, and design solutions, demonstrating a deeper understanding of MapReduce application development.

9.     Analyze the role of the MapReduce Web UI in managing and debugging complex workflows. What key metrics and visualizations does it offer for job analysis? [CO5]

10.                        A MapReduce job is consistently taking an unacceptably long time to complete on a production cluster. Propose a systematic approach to tune this job, detailing the steps you would take, from initial diagnosis using logs and profiling to potential code or configuration adjustments. [CO5]

11.                        Design a high-level MapReduce workflow for processing a large dataset that involves multiple interdependent MapReduce jobs. Explain how the output of one job would become the input for the next, and discuss how such workflows are typically managed. [CO5]

12.                        You have developed a MapReduce application that runs perfectly on a small development cluster but fails intermittently on a large production cluster. Discuss potential reasons for this disparity, focusing on issues related to configuration, resource allocation, and distributed environment challenges, and suggest debugging strategies. [CO5]