You have a Hadoop job processing large files that runs slowly and sometimes fails. Which combined tuning approach best improves performance and stability?

hard📝 Application Q8 of 15

Hadoop - Performance Tuning

AKeep default split size, increase number of reducers to max, and reduce map memory

BDecrease input split size, lower map memory, and disable reducers

CSet input split size to minimum, disable speculative execution, and reduce reducer count

DIncrease input split size, raise map memory, and set appropriate reducer count

Step-by-Step Solution

Solution:

Step 1: Analyze tuning for large files
Larger splits reduce overhead; raising map memory prevents failures; proper reducer count balances workload.
Step 2: Evaluate options for performance and stability
Increase input split size, raise map memory, and set appropriate reducer count combines these best practices to improve speed and avoid failures.
Final Answer:
Increase input split size, raise map memory, and set appropriate reducer count -> Option D
Quick Check:
Combined tuning = Better speed and stability [OK]

Quick Trick: Tune splits, memory, reducers together for best results [OK]

Common Mistakes:

Lowering memory causes failures
Too many reducers increase overhead
Disabling reducers breaks job logic

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More Hadoop Quizzes

You have a Hadoop job processing large files that runs slowly and sometimes fails. Which combined tuning approach best improves performance and stability?

Step 1: Analyze tuning for large files

Step 2: Evaluate options for performance and stability

Final Answer:

Quick Check:

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