Java Streams API Cheat Sheet

Covers creating streams, intermediate operations like filter and map, terminal operations and collectors, and safe use of parallel streams.

2 PagesIntermediateApr 2, 2026

Creating Streams

Common ways to obtain a Stream from collections and ranges.

java

Stream<String> s1 = Stream.of("a", "b", "c");Stream<Integer> s2 = List.of(1, 2, 3).stream();IntStream s3 = IntStream.range(0, 5);       // 0..4IntStream s4 = IntStream.rangeClosed(1, 5); // 1..5Stream<Integer> infinite = Stream.iterate(1, n -> n * 2).limit(5); // 1,2,4,8,16

Intermediate Operations

Lazily chained transformations that build a stream pipeline.

java

List<String> names = List.of("Alice", "Bob", "Charlie", "Dave");List<String> result = names.stream()    .filter(n -> n.length() > 3)     // keep matching elements    .map(String::toUpperCase)         // transform each element    .sorted()                         // natural ordering    .distinct()                       // remove duplicates    .limit(2)                         // take first 2    .collect(Collectors.toList());// flatMap - flattens nested streamsList<List<Integer>> nested = List.of(List.of(1, 2), List.of(3, 4));List<Integer> flat = nested.stream()    .flatMap(List::stream)    .collect(Collectors.toList()); // [1, 2, 3, 4]

Terminal Operations & Collectors

Operations that trigger the pipeline and produce a final result.

java

long count = names.stream().filter(n -> n.startsWith("A")).count();boolean any = names.stream().anyMatch(n -> n.equals("Bob"));Optional<String> first = names.stream().findFirst();int total = List.of(1, 2, 3).stream().reduce(0, Integer::sum); // reduce with identityMap<Integer, List<String>> byLength = names.stream()    .collect(Collectors.groupingBy(String::length));String joined = names.stream().collect(Collectors.joining(", ", "[", "]"));double avg = names.stream().collect(Collectors.averagingInt(String::length));

Parallel Streams

Run a stream pipeline across multiple threads for large datasets.

java

long total = List.of(1, 2, 3, 4, 5).parallelStream()    .mapToLong(Integer::longValue)    .sum();// Use only for CPU-bound, stateless, independent operations on large datasetsIntStream.rangeClosed(1, 1_000_000)    .parallel()    .filter(n -> n % 7 == 0)    .count();

Stream Fundamentals

Behaviors that trip people up the first time they use streams.

Stream is lazy- Intermediate operations (filter, map) build a pipeline but don't execute until a terminal operation is called
Streams are single-use- Calling a terminal operation consumes the stream; reusing it throws IllegalStateException
map vs flatMap- map transforms 1-to-1; flatMap transforms 1-to-many and flattens the result
Collectors.toList()/toSet()/toMap()- Common terminal collectors for materializing results
Method references- String::toUpperCase, System.out::println shorthand for simple lambdas
Primitive streams- IntStream/LongStream/DoubleStream avoid boxing overhead for numeric data

Pro Tip

Avoid mutating shared external state (like adding to an outside List) inside stream lambdas - it breaks under parallelStream() and defeats the purpose of functional-style pipelines; use collect() or reduce() instead.

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