[Solved] You want to optimize a MapReduce job that processes large input splits and has many small map... Which combination of parameters would best reduce map output spills and improve shuffle performance? | Hadoop

Hadoop - Performance Tuning

You want to optimize a MapReduce job that processes large input splits and has many small map outputs. Which combination of parameters would best reduce map output spills and improve shuffle performance?

AIncrease mapreduce.job.reduces and decrease mapreduce.task.io.sort.factor

BIncrease mapreduce.task.io.sort.mb and increase mapreduce.reduce.shuffle.parallelcopies

CDecrease mapreduce.task.io.sort.mb and decrease mapreduce.reduce.shuffle.parallelcopies

DDecrease mapreduce.job.maps and increase mapreduce.task.io.sort.factor

Step-by-Step Solution

Solution:

Step 1: Reduce map output spills
Increasing mapreduce.task.io.sort.mb enlarges the sort buffer, reducing spills.
Step 2: Improve shuffle speed
Increasing mapreduce.reduce.shuffle.parallelcopies allows more parallel fetches, speeding shuffle.
Final Answer:
Increase mapreduce.task.io.sort.mb and increase mapreduce.reduce.shuffle.parallelcopies -> Option B
Quick Check:
Big sort buffer + more shuffle threads = better performance [OK]

Quick Trick: Bigger sort buffer and more shuffle threads boost performance [OK]

Common Mistakes:

Decreasing buffer size which increases spills
Reducing shuffle parallel copies which slows shuffle
Changing number of maps instead of tuning buffers

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More Hadoop Quizzes

Which combination of parameters would best reduce map output spills and improve shuffle performance?

Step 1: Reduce map output spills

Step 2: Improve shuffle speed

Final Answer:

Quick Check:

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