Module 5: Stream API – Practical Operations

Target Audience

• Java developers who understand Stream basics
• Developers working with real-world data
• Anyone using Java 8 in backend applications

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1. Objective of This Module

By the end of this module, you will:

• Use Stream operations to solve real problems
• Understand commonly used Stream methods
• Chain operations effectively
• Avoid common misuse of Streams

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2. Commonly Used Stream Operations

filter()

Used to include elements based on a condition.

List<String> result =
    names.stream()
         .filter(name -> name.startsWith("A"))
         .toList();

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map()

Transforms each element.

List<Integer> lengths =
    names.stream()
         .map(String::length)
         .toList();

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flatMap()

Flattens nested structures.

List<String> allSkills =
    employees.stream()
             .flatMap(emp -> emp.getSkills().stream())
             .toList();

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3. Sorting and Removing Duplicates

distinct()

list.stream().distinct().toList();

sorted()

list.stream().sorted().toList();

Custom sorting:

list.stream()
    .sorted(Comparator.comparing(Employee::getSalary))
    .toList();

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4. Limiting and Skipping Elements

list.stream().limit(5).toList();
list.stream().skip(5).toList();

Useful for pagination-like scenarios.

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5. Chaining Stream Operations

employees.stream()
         .filter(e -> e.getSalary() > 50000)
         .map(Employee::getName)
         .sorted()
         .toList();

✔ Reads like a pipeline
✔ Easy to reason about

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6. forEach – Use with Caution

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

⚠️ forEach is a terminal operation
⚠️ Avoid using it for business logic
⚠️ Prefer map, collect for transformations

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7. Real-World Examples

Example 1: Employee Names by Department

employees.stream()
         .filter(e -> e.getDepartment().equals("IT"))
         .map(Employee::getName)
         .toList();

Example 2: Flatten Nested Lists

orders.stream()
      .flatMap(order -> order.getItems().stream())
      .toList();

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8. Common Mistakes

• Using Streams for simple loops
• Over-chaining making code unreadable
• Using forEach for logic
• Modifying external variables inside streams

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9. Hands-On Exercises

Exercise 1

Filter employees earning more than a given salary.

Exercise 2

Convert a list of strings to uppercase using streams.

Exercise 3

Flatten a list of lists using flatMap.

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10. Summary

• Streams shine in data transformation
• Readability matters more than cleverness
• Practice real-world use cases

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11. What’s Next?

➡️ Module 6: Collectors & Data Aggregation