DEA-C01 Data Operations and Support Practice Question
A company runs an Amazon EMR cluster with Spark jobs. One job fails with 'Container killed by YARN for exceeding memory limits'. The data engineer has already increased the executor memory. What is the NEXT best step to resolve the issue?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the YARN container memory allocation (yarn.nodemanager.resource.memory-mb).
Increasing the yarn.nodemanager.resource.memory-mb allows YARN to allocate larger containers, preventing the kill. Option A: Setting spark.executor.memoryOverhead increases off-heap memory but may still exceed YARN limits if they are not also raised. Option C: Reducing partitions decreases parallelism and may reduce memory per executor, but does not address the root cause of insufficient container memory. Option D: Increasing driver memory is not relevant because the issue is with executors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set spark.executor.memoryOverhead to a higher value.
Why it's wrong here
Memory overhead is part of executor memory, but the main limit is YARN's allocation.
- ✓
Increase the YARN container memory allocation (yarn.nodemanager.resource.memory-mb).
Why this is correct
This allows larger containers, preventing YARN from killing them.
- ✗
Decrease the number of Spark partitions.
Why it's wrong here
Fewer partitions may reduce memory per task but does not increase container limits.
- ✗
Increase the driver memory.
Why it's wrong here
Driver memory is separate; the container killed is likely an executor.
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