Why is it important to set both mapreduce.reduce.memory.mb and mapreduce.reduce.java.opts consistently when tuning reduce task memory?

hard📝 Conceptual Q10 of 15

Hadoop - Performance Tuning

Why is it important to set both mapreduce.reduce.memory.mb and mapreduce.reduce.java.opts consistently when tuning reduce task memory?

ABecause container memory and JVM heap size must align to prevent OutOfMemory errors

BBecause reduce tasks run faster with mismatched memory settings

CBecause map tasks depend on reduce memory settings

DBecause shuffle parallel copies depend on reduce memory

Step-by-Step Solution

Solution:

Step 1: Understand container vs JVM heap memory
mapreduce.reduce.memory.mb sets container memory; java.opts sets JVM heap size inside container.
Step 2: Importance of consistent settings
If JVM heap (-Xmx) is larger than container memory, or container memory is too small, OutOfMemory errors occur.
Final Answer:
Because container memory and JVM heap size must align to prevent OutOfMemory errors -> Option A
Quick Check:
Align container and JVM heap memory to avoid reduce task failures [OK]

Quick Trick: Match JVM heap (-Xmx) with container memory for stability [OK]

Common Mistakes:

Master "Performance Tuning" in Hadoop

9 interactive learning modes - each teaches the same concept differently

Want More Practice?

15+ quiz questions · All difficulty levels · Free

More Hadoop Quizzes