What Does Hadoop Driver File Configure in MapReduce Jobs?
Hadoop driver files configure job settings, mapper/reducer classes, and HDFS paths for MapReduce execution, enabling scalable big data processing—key for Hive & Pig certification’s Customer Complaint Analysis workflows.
Question
What does the driver file do in a Hadoop program?
A. Stores data schemas
B. Displays Hadoop job progress
C. Defines the mapper logic
D. Configures job settings, mapper/reducer classes, and output paths
Answer
D. Configures job settings, mapper/reducer classes, and output paths
Explanation
The driver file in a Hadoop program contains the main() method that serves as the client-side orchestrator for MapReduce job execution, configuring essential Job objects with parameters like input/output HDFS paths, mapper and reducer class references, input/output key-value format specifications (e.g., TextInputFormat, TextOutputFormat), number of reduce tasks, custom partitioners, combiners for partial aggregation, and runtime optimizations such as JVM reuse or compression codecs before submitting the job to the YARN ResourceManager for distributed cluster execution. This central configuration layer ensures seamless data flow from input splits processed independently by mappers, through the shuffle/sort phase exchanging intermediate results, to final reducer aggregation and output writing, while handling job monitoring, failure recovery via retries, and status callbacks—making it indispensable for packaging scalable analytics like complaint pattern analysis in Hive & Pig certification projects.