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What Role Do Driver Files Play in Hadoop MapReduce Job Execution?
Driver files in Hadoop applications manage MapReduce execution by configuring mappers, reducers, data types, HDFS paths, and job parameters for efficient big data processing—essential for Hive & Pig certification exam success.
Question
What is the purpose of defining driver files in Hadoop applications?
A. To control and configure execution flow between mapper and reducer
B. To execute SQL queries directly from Hadoop
C. To store user credentials securely in HDFS
D. To visualize the MapReduce output in dashboards
Answer
A. To control and configure execution flow between mapper and reducer
Explanation
Driver files in Hadoop applications contain the main() method that orchestrates the entire MapReduce job execution by configuring critical parameters such as input/output paths in HDFS, mapper and reducer class specifications, input/output key-value data types (e.g., Text, IntWritable), number of reducers, combiners, partitioners, and job-specific optimizations like compression or speculative execution. This driver class submits the configured Job object to the Hadoop cluster’s ResourceManager or JobTracker, monitors progress through callbacks, handles failures via retries, and determines overall job success based on completion status—ensuring seamless data flow from input splits processed by mappers, through shuffle/sort phase, to final aggregation by reducers without manual intervention.