1Z0-829 Working with Streams and Lambda Expressions Practice Question
A team is implementing a parallel stream to process a large dataset. They notice that the operation is slower than expected. Which change is most likely to improve performance?
⚠ Common exam trap
It's easy for candidates to assume more threads (Option C) or switching to sequential (Option A) will fix performance, overlooking that the root cause is usually an inefficiently splittable source or non-associative/stateful operations, which are the key factors the exam tests for parallel stream optimization.
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
✓
Ensure the stream source is an ArrayList and the operation is stateless and associative.
Parallel stream performance depends on the stream source being efficiently splittable (like an ArrayList, which has O(1) random access and can be split evenly) and the operation being stateless and associative to avoid synchronization overhead and ensure correct results. A poorly splittable source (e.g., LinkedList) or stateful operations can cause contention and reduce parallelism gains.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a sequential stream.
Why it's wrong here
A is wrong: sequential may be slower for large data sets.
- ✗
Use a custom Collector instead of reduce.
Why it's wrong here
C is wrong: custom Collector does not inherently improve parallel performance.
- ✗
Increase the parallelism level by setting -Djava.util.concurrent.ForkJoinPool.common.parallelism=100.
Why it's wrong here
B is wrong: too many threads can cause overhead and contention.
- ✓
Ensure the stream source is an ArrayList and the operation is stateless and associative.
Why this is correct
D is correct: ArrayList splits well, and stateless/associative operations reduce merge cost.
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