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What is a common consequence of not tuning memory settings in Hadoop jobs?

easy📝 Conceptual Q2 of 15
Hadoop - Performance Tuning
What is a common consequence of not tuning memory settings in Hadoop jobs?
AJobs may fail due to out-of-memory errors
BInput data size automatically decreases
COutput files become corrupted
DData replication factor increases
Step-by-Step Solution
Solution:

  1. Step 1: Identify the role of memory in Hadoop tasks

    Memory settings control how much RAM each task can use during execution.

  2. Step 2: Understand the impact of insufficient memory

    If memory is too low, tasks can run out of memory and fail, causing job failure.

  3. Final Answer:

    Jobs may fail due to out-of-memory errors -> Option A

  4. Quick Check:

    Memory tuning = Prevent job failure [OK]
Quick Trick: Set memory high enough to avoid task crashes [OK]
Common Mistakes:
  • Thinking memory affects input size
  • Believing output corruption is memory-related
  • Confusing replication with memory settings

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