Module 6: Collectors & Data Aggregation

Target Audience

• Java developers using Streams in real applications
• Developers working with reporting and aggregation logic
• Anyone preparing for Java 8 interviews or backend work

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1. Objective of This Module

By the end of this module, you will:

• Understand what Collectors are
• Convert Streams into different data structures
• Perform grouping, partitioning, and aggregation
• Build real-world reporting logic using Java 8

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2. What Is a Collector?

A Collector:

• Is a terminal operation
• Accumulates Stream elements into a result
• Is commonly used with collect()

List<String> result =
    list.stream().collect(Collectors.toList());

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3. Common Collectors

toList()

List<String> names =
    employees.stream()
             .map(Employee::getName)
             .collect(Collectors.toList());

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toSet()

Set<String> uniqueDepartments =
    employees.stream()
             .map(Employee::getDepartment)
             .collect(Collectors.toSet());

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toMap()

Map<Integer, String> employeeMap =
    employees.stream()
             .collect(Collectors.toMap(
                 Employee::getId,
                 Employee::getName
             ));

⚠️ Keys must be unique unless a merge function is provided.

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4. groupingBy()

Used to group elements based on a condition.

Map<String, List<Employee>> byDepartment =
    employees.stream()
             .collect(Collectors.groupingBy(Employee::getDepartment));

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groupingBy with Downstream Collector

Map<String, Long> countByDepartment =
    employees.stream()
             .collect(Collectors.groupingBy(
                 Employee::getDepartment,
                 Collectors.counting()
             ));

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5. partitioningBy()

Used when the condition results in true / false.

Map<Boolean, List<Employee>> partitioned =
    employees.stream()
             .collect(Collectors.partitioningBy(
                 e -> e.getSalary() > 50000
             ));

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6. Joining Strings

String names =
    employees.stream()
             .map(Employee::getName)
             .collect(Collectors.joining(", "));

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7. Summarizing and Averaging

IntSummaryStatistics stats =
    employees.stream()
             .collect(Collectors.summarizingInt(Employee::getSalary));

Provides:

• count
• sum
• min
• max
• average

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8. Real-World Use Cases

• Reports by department
• User statistics dashboards
• Sales summaries
• Analytics data preparation

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9. Common Mistakes

• Overusing grouping when simple mapping is enough
• Creating complex collectors hurting readability
• Forgetting merge functions in toMap()

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10. Hands-On Exercises

Exercise 1

Group employees by department.

Exercise 2

Partition employees by active / inactive.

Exercise 3

Create a summary report of salaries.

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11. Summary

• Collectors turn Streams into results
• groupingBy and partitioningBy are powerful tools
• Downstream collectors unlock advanced aggregation

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12. What’s Next?

➡️ Module 7: Optional & Null Safety