Parallel Stream Pitfalls: Race Conditions, Prefetching & Memory Overruns

I used Java's parallelStream() for processing large lists. Unexpected behavior occurred—incorrect results or OutOfMemoryError. What went wrong?

Common problems with parallel streams include:

1. Race conditions when modifying shared state inside forEach (e.g. sum += value).
2. Premature prefetching by forEach or other terminal ops, buffering large datasets in memory—leading to OOM errors.
3. Loss of context, like missing transactional or thread-local data in parallel operations. Fixes: • Use proper stream reductions instead of side-effects: java Copy code int sum = list.parallelStream().reduce(0, Integer::sum); • Instead of forEach, use collect(...) or safe reduction collectors. • Avoid parallelism for IO-heavy tasks; use CompletableFuture or custom threading if needed. • Manage transaction context properly—possibly with ThreadLocal or custom ForkJoinPool.