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Which Hadoop Component Handles Job Scheduling and Resources?
YARN manages Hadoop job scheduling and resource allocation via ResourceManager and schedulers, supporting multi-tenant analytics like Pig/Hive complaint analysis—essential for certification exam mastery.
Question
Which Hadoop component manages job scheduling and resource allocation?
A. Oozie
B. YARN
C. Hive
D. HDFS
Answer
B. YARN
Explanation
YARN (Yet Another Resource Negotiator) manages job scheduling and resource allocation in Hadoop by decoupling resource management from job execution through its ResourceManager, which oversees cluster-wide CPU, memory, and container distribution via pluggable schedulers like Capacity Scheduler or Fair Scheduler, while per-application ApplicationMasters negotiate specific resources from NodeManagers for tasks such as Pig ETL jobs or Hive queries in the Customer Complaint Analysis project. This architecture supports multi-tenancy by allocating dynamic containers for MapReduce, Spark, or other frameworks running concurrently, enforcing queue-based priorities, handling failures through restarts, and optimizing data locality to minimize network overhead—enabling efficient execution of large-scale batch analytics across thousands of nodes without the limitations of the older JobTracker in Hadoop 1.x.