java 32 lines · 7 steps

Flattening nested data with Java streams

Four stream pipelines show how flatMap collapses nested collections before transforming and aggregating them.

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Intermediate streams flatmap collectors functional aggregation

1public List<String> collectTagsFromArticles(List<Article> articles) {
2    return articles.stream()
3        .flatMap(article -> article.getTags().stream())
4        .map(String::trim)
5        .filter(tag -> !tag.isEmpty())
6        .map(String::toLowerCase)
7        .distinct()
8        .sorted()
9        .collect(Collectors.toList());
10}
11 
12public Map<String, Long> countLineItemsByCategory(List<Order> orders) {
13    return orders.stream()
14        .flatMap(order -> order.getLineItems().stream())
15        .collect(Collectors.groupingBy(
16            item -> item.getProduct().getCategory(),
17            Collectors.counting()
18        ));
19}
20 
21public List<Coordinate> flattenGrid(List<List<Coordinate>> rows) {
22    return rows.stream()
23        .flatMap(List::stream)
24        .collect(Collectors.toList());
25}
26 
27public OptionalDouble averageScoreAcrossExams(List<Student> students) {
28    return students.stream()
29        .flatMapToInt(student -> student.getExams().stream()
30            .mapToInt(Exam::getScore))
31        .average();
32}

01 / 01

STEP 01

Walkthrough

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Three takeaways

1flatMap turns a stream of collections into one flat stream you can process uniformly.
2Collectors like groupingBy and counting fold a stream into a summarized data structure in one pass.
3Primitive stream variants such as flatMapToInt unlock numeric operations like average without boxing.

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