Module 10: Parallel Streams & Performance

Phase

Phase 3 – Advanced Concepts

Target Audience

• Java developers using Streams in production
• Developers concerned about performance
• Anyone preparing for senior-level Java interviews

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1. Objective of This Module

By the end of this module, you will:

• Understand how parallel streams work internally
• Know when to use and when to avoid parallel streams
• Understand the role of ForkJoinPool
• Write performance-aware Java 8 code

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2. What Are Parallel Streams?

A Parallel Stream splits stream elements into multiple chunks and processes them concurrently using multiple threads.

list.parallelStream()
    .filter(n -> n > 10)
    .map(n -> n * 2)
    .toList();

Parallel streams use the ForkJoinPool internally.

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3. Sequential vs Parallel Streams

Aspect	Sequential Stream	Parallel Stream
Execution	Single thread	Multiple threads
Order	Preserved	Not guaranteed
Performance	Predictable	Data & CPU dependent
Debugging	Easier	Harder

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4. ForkJoinPool Basics

• Parallel streams use ForkJoinPool.commonPool()
• Number of threads ≈ available CPU cores
• Shared across the JVM

ForkJoinPool.commonPool();

⚠️ Heavy usage can affect other parallel tasks.

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5. When Parallel Streams Work Well

✔ Large data sets
✔ CPU-intensive operations
✔ Stateless operations
✔ Independent processing

Example:

IntStream.range(1, 1_000_000)
         .parallel()
         .map(n -> n * n)
         .sum();

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6. When NOT to Use Parallel Streams

❌ Small collections
❌ IO-bound operations
❌ Operations with shared mutable state
❌ When order matters

Bad example:

list.parallelStream()
    .forEach(System.out::println);

Output order is unpredictable.

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7. Thread-Safety Concerns

Avoid modifying shared variables:

❌ Unsafe:

int sum = 0;
list.parallelStream().forEach(n -> sum += n);

✔ Safe:

int sum = list.parallelStream().mapToInt(Integer::intValue).sum();

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8. Performance Considerations

• Measure before optimizing
• Parallel streams have overhead
• Not always faster than sequential streams
• Use benchmarks (System.nanoTime, JMH)

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9. Real-World Guidelines

• Default to sequential streams
• Use parallel streams selectively
• Never use parallel streams blindly
• Test under real workloads

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10. Hands-On Exercises

Exercise 1

Compare execution time of sequential vs parallel stream.

Exercise 2

Identify thread-safety issues in parallel stream code.

Exercise 3

Refactor unsafe parallel code to safe alternatives.

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11. Common Mistakes

• Assuming parallel means faster
• Using parallel streams for database or API calls
• Ignoring shared thread pool effects

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12. Summary

• Parallel streams use ForkJoinPool
• Best for CPU-heavy workloads
• Must be used with caution

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13. What’s Next?

➡️ Phase 4 – Expert Level ➡️ Module 11: Java 8 Best Practices & Anti-Patterns

🎉 You’ve completed Phase 3 – Advanced Concepts