You set mapreduce.reduce.memory.mb=1024 but the reduce tasks fail with OutOfMemory errors. What is a likely cause?

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Hadoop - Performance Tuning

You set mapreduce.reduce.memory.mb=1024 but the reduce tasks fail with OutOfMemory errors. What is a likely cause?

AThe number of reduce tasks is too high

BThe JVM heap size (-Xmx) is not set or too low

CThe map tasks are using too much memory

DThe shuffle parallel copies parameter is too low

Step-by-Step Solution

Solution:

Step 1: Understand memory allocation for reduce tasks
mapreduce.reduce.memory.mb sets container memory, but JVM heap size must be set explicitly.
Step 2: Identify cause of OutOfMemory
If -Xmx is not set or too low, JVM heap is insufficient causing errors.
Final Answer:
The JVM heap size (-Xmx) is not set or too low -> Option B
Quick Check:
Set -Xmx in java.opts to avoid reduce OOM errors [OK]

Quick Trick: Always set JVM heap size (-Xmx) when adjusting container memory [OK]

Common Mistakes:

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