Understanding Java Stream API: filter, map, flatMap, and Parallel Operations

Introduction to Java Stream API, explaining that streams provide a unified way to process collections and large data efficiently.

Demonstrates filter operation with example code showing how to filter a list of maps by non‑null IP values.

list.stream().filter(smap -> null != smap.get("ip") && !"".equals(smap.get("ip"))).forEach(imp -> { listipzone.add(wry.findIP(imp.get("ip").toString())); });

Shows the definition of Stream.filter(Predicate<? super T> predicate) and a simple example converting an array to a stream and filtering for the element "a".

Explains the map operation, its signature Stream<R> map(Function<? super T, ? extends R> mapper), and provides examples that convert integers to strings and extract the name field from a custom Emp object.

public static void main(String[] args) { Integer[] dd = {1,2,3}; Stream<Integer> stream = Arrays.stream(dd); stream.map(str -> Integer.toString(str)).forEach(System.out::println); List<Emp> list = Arrays.asList(new Emp("a"), new Emp("b"), new Emp("c")); list.stream().map(emp -> emp.getName()).forEach(System.out::println); }

Introduces flatMap, its signature, and a sample that splits strings into character arrays and then flattens them into a single stream.

String[] strs = {"aaa","bbb","ccc"}; Arrays.stream(strs).map(str -> str.split("")).flatMap(Arrays::stream).forEach(System.out::println);

Lists common ways to create streams (Stream.of, Arrays.stream, collection.stream) and how to convert streams back to arrays, lists, sets, strings, or maps using collectors.

Describes intermediate operations (map, filter, distinct, sorted, peek, limit, skip, parallel, etc.) and terminal operations (forEach, collect, reduce, min, max, count, match, findFirst, etc.).

Shows usage of limit and skip together with mapToInt and peek to process a list of integers.

List<Integer> personList2 = persons.stream().map(Person::getName).limit(10).skip(3).collect(Collectors.toList());

Explains parallel streams: calling parallel() on an existing stream or using parallelStream() on a collection to leverage multi‑core CPUs.

int sumSize = Stream.of("Apple","Banana","Orange","Pear").parallel().map(s -> s.length()).reduce(Integer::sum).get();