Collectors Utility: groupingBy(), partitioningBy(), joining(), toList()0%

Parallel Streams and the Fork/Join Framework

Collectors Utility: groupingBy(), partitioningBy(), joining(), toList()

Advanced16 min read•Updated: 2025-01-15

📊 Collectors Utility: groupingBy(), partitioningBy(), joining(), toList()

Introduction

The java.util.stream.Collectors utility class is the powerhouse of the Stream API. It implements complex aggregation, partitioning, string joining, and multi-level data restructuring with minimal declarative code.

1. `Collectors.joining()`

Concatenates String representations with optional delimiters, prefixes, and suffixes:

1List<String> tags = List.of("Java", "Spring Boot", "Docker", "Microservices");
2 
3String formatted = tags.stream()
4                       .collect(Collectors.joining(", ", "[", "]"));
5System.out.println(formatted); 
6// Output: [Java, Spring Boot, Docker, Microservices]

2. `Collectors.partitioningBy()`

Partitions elements into two groups (true and false) using a Predicate:

1record Employee(String name, String department, double salary) {}
2 
3List<Employee> staff = List.of(
4    new Employee("Aarav", "IT", 85000),
5    new Employee("Neha", "HR", 45000),
6    new Employee("Rohit", "IT", 92000),
7    new Employee("Pooja", "Finance", 60000)
8);
9 
10// Partition into High Earners (salary >= 70000) and Standard
11Map<Boolean, List<Employee>> partitioned = staff.stream()
12    .collect(Collectors.partitioningBy(e -> e.salary() >= 70000));
13 
14System.out.println("High Earners: " + partitioned.get(true));
15System.out.println("Others: " + partitioned.get(false));

3. `Collectors.groupingBy()` (SQL-like GROUP BY)

Groups stream elements by a classification function into a Map<Key, List<T>>:

1// Group employees by department
2Map<String, List<Employee>> byDept = staff.stream()
3    .collect(Collectors.groupingBy(Employee::department));
4 
5// IT => [Aarav, Rohit], HR => [Neha], Finance => [Pooja]

Downstream Collectors (Calculating Averages or Counts):

1// Average salary per department
2Map<String, Double> avgSalaryByDept = staff.stream()
3    .collect(Collectors.groupingBy(
4        Employee::department,
5        Collectors.averagingDouble(Employee::salary)
6    ));

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Parallel Streams and the Fork/Join Framework

Collectors Utility: groupingBy(), partitioningBy(), joining(), toList()

Advanced16 min read•Updated: 2025-01-15

Study Materials

📊 Collectors Utility: groupingBy(), partitioningBy(), joining(), toList()

Introduction

1. `Collectors.joining()`

Concatenates String representations with optional delimiters, prefixes, and suffixes:

1List<String> tags = List.of("Java", "Spring Boot", "Docker", "Microservices");
2 
3String formatted = tags.stream()
4                       .collect(Collectors.joining(", ", "[", "]"));
5System.out.println(formatted); 
6// Output: [Java, Spring Boot, Docker, Microservices]

2. `Collectors.partitioningBy()`

Partitions elements into two groups (true and false) using a Predicate:

1record Employee(String name, String department, double salary) {}
2 
3List<Employee> staff = List.of(
4    new Employee("Aarav", "IT", 85000),
5    new Employee("Neha", "HR", 45000),
6    new Employee("Rohit", "IT", 92000),
7    new Employee("Pooja", "Finance", 60000)
8);
9 
10// Partition into High Earners (salary >= 70000) and Standard
11Map<Boolean, List<Employee>> partitioned = staff.stream()
12    .collect(Collectors.partitioningBy(e -> e.salary() >= 70000));
13 
14System.out.println("High Earners: " + partitioned.get(true));
15System.out.println("Others: " + partitioned.get(false));

3. `Collectors.groupingBy()` (SQL-like GROUP BY)

Groups stream elements by a classification function into a Map<Key, List<T>>:

1// Group employees by department
2Map<String, List<Employee>> byDept = staff.stream()
3    .collect(Collectors.groupingBy(Employee::department));
4 
5// IT => [Aarav, Rohit], HR => [Neha], Finance => [Pooja]

Downstream Collectors (Calculating Averages or Counts):

1// Average salary per department
2Map<String, Double> avgSalaryByDept = staff.stream()
3    .collect(Collectors.groupingBy(
4        Employee::department,
5        Collectors.averagingDouble(Employee::salary)
6    ));

Next Lesson

Parallel Streams and the Fork/Join Framework

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

Related Lessons

Previous Lesson	Next Lesson
Terminal Operations: collect(), forEach(), reduce(), count(), anyMatch()	Parallel Streams and the Fork/Join Framework

Practice Quiz

Test your understanding of this lesson with 1 questions. Each question has one correct answer.

Previous Next

📊 Collectors Utility: groupingBy(), partitioningBy(), joining(), toList()

Introduction

1. Collectors.joining()

2. Collectors.partitioningBy()

3. Collectors.groupingBy() (SQL-like GROUP BY)

Downstream Collectors (Calculating Averages or Counts):

Parallel Streams and the Fork/Join Framework

Related Lessons

Practice Quiz

Loading Page Content...

📊 Collectors Utility: groupingBy(), partitioningBy(), joining(), toList()

Introduction

1. Collectors.joining()

2. Collectors.partitioningBy()

3. Collectors.groupingBy() (SQL-like GROUP BY)

Downstream Collectors (Calculating Averages or Counts):

Parallel Streams and the Fork/Join Framework

Related Lessons

Practice Quiz

1. `Collectors.joining()`

2. `Collectors.partitioningBy()`

3. `Collectors.groupingBy()` (SQL-like GROUP BY)

1. `Collectors.joining()`

2. `Collectors.partitioningBy()`

3. `Collectors.groupingBy()` (SQL-like GROUP BY)