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![How to create streams ?
● Streams from values:
Stream<String> stream = Stream.of("Hello");
● Empty streams : Stream.empty();
● Streams from functions:
Stream.iterate(1L, n -> n + 1);
Stream.generate(Math::random);
●
Streams from arrays: Arrays.stream(new int[]{1, 2, 3});
● Streams from collections: list().stream()
● Streams from files: Files.lines(path)
● Streams from other sources:
Pattern.compile(",").splitAsStream(str);](https://image.slidesharecdn.com/functional-programming-with-streams-v2-200728203729/85/Functional-programming-with-streams-6-320.jpg)
![How to create streams ?
● Streams from values:
Stream<String> stream = Stream.of("Hello");
● Empty streams : Stream.empty();
● Streams from functions:
Stream.iterate(1L, n -> n + 1);
Stream.generate(Math::random);
●
Streams from arrays: Arrays.stream(new int[]{1, 2, 3});
● Streams from collections: list().stream()
● Streams from files: Files.lines(path)
● Streams from other sources:
Pattern.compile(",").splitAsStream(str);](https://image.slidesharecdn.com/functional-programming-with-streams-v2-200728203729/75/Functional-programming-with-streams-6-2048.jpg)
Uploaded byRiadh MNASRI
Functional programming with streams
This document discusses functional programming with streams in Java. It defines streams as sequences of data elements that support sequential and parallel aggregate operations. It outlines different stream operations like intermediate and terminal operations. It also describes how to create streams from various sources like collections, arrays and files. Additionally, it explains how to apply common stream operations like mapping, filtering, matching, grouping and partitioning data.
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Functional programming with streams
- 1. Functional Programming withStreams Prepared by: Riadh MNASRI
- 2. Who am I? ● Riadh MNASRI ● Senior Java Developer ● Senior Kotlin Developer ● Interested By : - TDD, BDD, DDD, … - Clean Code - Functional Programming ● Twitter : @riadhmnasri
- 3. Agenda ● Whats Streamsare ? ● Stream operations ● How to create streams ● Applying different operations with streams ● Collecting data with streams ● Grouping ● Partitioning ● Finding and matching ● Conclusion ● References
- 4. What Streams are? ● A stream is a sequence of data elements supporting sequential and parallel aggregate operations. ● Consumes elements from its data source ● Focus on aggregagte computations ● Have no storage ● Can represent a sequence of infinite elements ● Support functional programming ● Stream are not reusable
- 5. Stream operations ● Intermediateoperations: lazy operations ● Terminal operations: eager operations
- 6. How to createstreams ? ● Streams from values: Stream<String> stream = Stream.of("Hello"); ● Empty streams : Stream.empty(); ● Streams from functions: Stream.iterate(1L, n -> n + 1); Stream.generate(Math::random); ● Streams from arrays: Arrays.stream(new int[]{1, 2, 3}); ● Streams from collections: list().stream() ● Streams from files: Files.lines(path) ● Streams from other sources: Pattern.compile(",").splitAsStream(str);
- 7. Applying different operationswith streams ● Debuging stream: peek() ● Applying foreach: forEach(Consumer<? super T> action) ● Applying map (one to one mapping): <R> Stream<R> map(Function<? super T,? extends R> mapper) ● Flatenning (one to many mapping): flatMap(n -> Stream.of(n, n * n)) ● Filtering: filter(p -> p.getIncome() > 5000.0) ● Reducing: reduce(0.0, Double::sum);
- 8. Collecting data withstreams ● <R> R collect(Supplier<R> supplier, BiConsumer<R,? super T> accumulator, BiConsumer<R,R> combiner) Example: collect(ArrayList::new, ArrayList::add, ArrayList::addAll) ● <R,A> R collect(Collector<? super T,A,R> collector) Example: collect(Collectors.toList());
- 9. Grouping ● In themost generic version, the groupingBy() method takes two parameters: - A classifier that is a function to generate the keys in the map. - A collector that performs a reduction operation on the values associated with each key: collect(Collectors.groupingBy(Person::getGen der, Collectors.counting()));
- 10. Partitioning ● Partitioning datais a special case of grouping data ● Grouping data is based on the keys returned from a function ● Partitioning groups data into two groups: - For one group a condition is true - For the other, the same condition is false ● The partitioning condition is specified using a Predicate: books .stream() .collect(Collectors.partitioningBy(Book::isProgramming));
- 11. Finding and matching ●boolean allMatch(Predicate<? super T> predicate) ● boolean anyMatch(Predicate<? super T> predicate) ● boolean noneMatch(Predicate<? super T> predicate) ● Optional<T> findAny() ● Optional<T> findFirst()
- 12.
- 13. Conclusion ● A streamis a sequence of data elements supporting sequential and parallel aggregate operations. ● Streams are connected through operations forming a pipeline. ● Streams support two types of operations: intermediate and terminal operations. ● Streams allows making aggregation operations on collections in functional programming style
- 14. References ● Book -Beginning Java 8 langugage features ● https://stackify.com/streams-guide-java-8/ ● Examples are available in my personal github : https://github.com/riadh-mnasri/bbl-java-stream-api