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What Role Does Configuration Object Play in Hadoop MapReduce Jobs?
Understand why Hadoop’s configuration object is vital for storing job parameters like input/output paths, mapper classes, and resource settings to ensure successful MapReduce execution on clusters.
Question
Why is a configuration object critical in Hadoop jobs?
A. To replicate HDFS blocks
B. To store job parameters like input/output paths
C. To run reducers without mappers
D. To partition keys across reducers
Answer
B. To store job parameters like input/output paths
Explanation
A configuration object is critical in Hadoop jobs because it serves as the central repository for all job-specific parameters, such as input and output paths, mapper/reducer classes, number of reduce tasks, data formats, and resource allocation settings like memory limits. Before submitting a job via Job.getInstance(conf), developers populate the Configuration instance with calls like FileInputFormat.setInputPaths(conf, inputPath) and job.setMapperClass(MyMapper.class), ensuring the YARN resource manager and tasks have all necessary details to execute correctly across the cluster. Without it, jobs cannot specify essential runtime behaviors, making the configuration object foundational for defining, customizing, and successfully running any MapReduce job, unlike unrelated functions like block replication or partitioning.