🔗 What are Parallel Streams?
Parallel Streams are a feature of the Java Stream API (introduced in Java 8) that allows you to process a collection of data in parallel — meaning the work is split into multiple threads that run concurrently.
In a normal stream (sequential stream), data processing happens in a single thread, step by step. In a parallel stream, Java breaks the data into chunks and processes each chunk in a separate thread (using the common ForkJoinPool under the hood).
🛠️ Example
Sequential Stream (single thread)
List<String> names = List.of("Alice", "Bob", "Charlie", "David");
names.stream()
.map(String::toUpperCase)
.forEach(System.out::println);
Parallel Stream (multi-threaded)
names.parallelStream()
.map(String::toUpperCase)
.forEach(System.out::println); // order might be different!💡 Key Features of Parallel Streams
| Feature | Explanation |
|---|---|
| Automatic parallelism | No need to manually create threads — Java does it for you. |
| Divide and Conquer | Collection is split into smaller parts and processed in parallel. |
| Uses ForkJoinPool | Specifically, the common pool (shared across your app). |
| Functional style | You write high-level transformations (map, filter, reduce), not low-level thread management. |
| Order not guaranteed | Parallel streams may process elements out of order (unless you use forEachOrdered). |
⚠️ When to Use Parallel Streams (and when to avoid them)
| Case | Recommendation |
|---|---|
| Large collections (thousands or millions of items) | ✅ Parallel stream is worth considering. |
| Small collections (few items) | ❌ Parallel stream overhead > benefit. Use sequential stream. |
| CPU-intensive operations (like complex calculations) | ✅ Parallel streams can speed things up. |
| IO-bound operations (e.g., reading files) | ❌ Parallel stream won’t help much (threads just wait). |
| Running inside a server application (like Spring) | ⚠️ Be careful — you might accidentally block the shared ForkJoinPool. |
🔥 Summary
Parallel streams are a convenient way to process large data collections in parallel, leveraging multiple CPU cores without manually writing thread code. But they are not magic — they have overhead and aren’t always faster than regular (sequential) streams, especially for small data sets.