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Why is tuning the number of mappers important in Hadoop jobs?

easy📝 Conceptual Q1 of 15
Hadoop - Performance Tuning
Why is tuning the number of mappers important in Hadoop jobs?
AIt disables data replication for faster processing
BIt increases the size of input files automatically
CIt balances workload and prevents resource overuse
DIt reduces the number of output files generated
Step-by-Step Solution
Solution:

  1. Step 1: Understand the role of mappers in Hadoop

    Mappers process input data in parallel. Too many or too few mappers can cause inefficiency.

  2. Step 2: Analyze the effect of tuning mapper count

    Proper tuning balances workload across nodes, preventing resource overuse and slowdowns.

  3. Final Answer:

    It balances workload and prevents resource overuse -> Option C

  4. Quick Check:

    Mapper tuning = Balanced workload [OK]
Quick Trick: Balance mappers to avoid overload or idle resources [OK]
Common Mistakes:
  • Assuming more mappers always speed up jobs
  • Ignoring cluster resource limits
  • Confusing mappers with reducers

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