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PDE Practice Question: A Dataflow batch job frequently fails with…
A Dataflow batch job frequently fails with 'OutOfMemoryError'. Which THREE are common causes? (Choose 3)
⚠ Common exam trap
Google Cloud often tests the misconception that increasing parallelism (Option A) always reduces memory errors, but in Dataflow, hot keys cause memory issues regardless of worker count because the hot key's data is processed by a single worker.
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
✓
Inefficient GroupByKey with hot keys
A hot key in a GroupByKey operation causes all values for that key to be processed by a single worker, leading to memory exhaustion when the key's associated data exceeds the worker's memory capacity. This is a common cause of OutOfMemoryError in Dataflow batch jobs, as the SDK buffers all values for a key before emitting the result.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Too many parallel workers
Why it's wrong here
More workers distribute work and reduce memory per worker, not a direct cause of OOM.
- ✓
Inefficient GroupByKey with hot keys
Why this is correct
Hot keys cause all values to be processed by a single worker, leading to memory exhaustion.
- ✓
Too many side inputs
Why this is correct
Large or numerous side inputs can be broadcast to all workers, exhausting memory.
- ✓
Too large window accumulation in streaming mode
Why this is correct
Accumulating large windows (e.g., global window) can cause unbounded state growth, leading to OOM.
- ✗
Using Dataflow Shuffle
Why it's wrong here
Dataflow Shuffle offloads shuffle operations to a managed service, reducing memory usage on workers.
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