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What Happens Without Main-Class in Hadoop Project JAR Manifest?
Hadoop JAR manifests identify the main driver class for MapReduce job execution, preventing common errors and enabling easy deployment—critical for Hive & Pig certification projects analyzing customer complaints.
Question
Why is a JAR manifest necessary in Hadoop projects?
A. It stores SQL import scripts
B. It stores Hive query metadata
C. It compresses data for HDFS
D. It identifies the main class for execution
Answer
D. It identifies the main class for execution
Explanation
A JAR manifest is necessary in Hadoop projects because it specifies the Main-Class attribute pointing to the driver class with the main() method, allowing seamless execution of MapReduce jobs via “hadoop jar myproject.jar” commands without requiring users to manually specify the fully qualified class name on the command line. This metadata in META-INF/MANIFEST.MF ensures the Hadoop client (YARN ApplicationMaster) correctly locates and invokes the entry point responsible for configuring Job parameters like input/output paths, mapper/reducer classes, data formats, and resource allocations before submitting to the cluster. Without it, execution fails with “no main manifest attribute” errors, making proper manifest packaging essential for deploying self-contained analytics applications in Hive & Pig certification workflows like Customer Complaint Analysis.