Java 8 Stream API Tutorial: 20 Practical Examples for Filtering, Mapping, Reducing, and More

Java 8 introduced the Stream API and Lambda expressions, offering a concise and functional way to process collections and arrays.

A stream treats the source data as a flow, allowing intermediate operations (e.g., filter , map , flatMap , sorted , distinct , limit , skip ) and terminal operations (e.g., forEach , findFirst , findAny , anyMatch , count , reduce , collect , joining ).

Streams can be created from collections using collection.stream() , from arrays with Arrays.stream(array) , or via static factory methods such as Stream.of(...) , Stream.iterate(seed, f) , and Stream.generate(supplier) .

The tutorial provides twenty practical examples, including:

Filtering employees with salary greater than 8000 and collecting the results.

Sorting employees by salary descending and age ascending.

Finding maximum, minimum, sum, and count values in numeric collections.

Mapping strings to uppercase and adding a constant to integer elements.

Using flatMap to split and merge string arrays.

Reducing a list of integers to sum, product, and maximum.

Collecting stream results into List , Set , and Map structures.

Performing statistical aggregations such as counting, averaging, summing, and summarizing employee salaries.

Partitioning and grouping employees by salary thresholds, gender, and region.

Joining element strings with custom delimiters.

Combining streams, removing duplicates, limiting, and skipping elements.

Key code snippets:

List<Person> personList = new ArrayList<>();
personList.add(new Person("Tom", 8900, 23, "male", "New York"));
List<String> highEarners = personList.stream()
    .filter(p -> p.getSalary() > 8000)
    .map(Person::getName)
    .collect(Collectors.toList());

List<Integer> numbers = Arrays.asList(1,3,5,7,9,11);
int sum = numbers.stream().reduce(Integer::sum).orElse(0);

Map<String, List<Person>> byGender = personList.stream()
    .collect(Collectors.groupingBy(Person::getSex));

These examples illustrate how Stream operations simplify data processing, reduce boilerplate code, and improve readability compared to traditional iterative approaches.