Functional Programming With Streams

Functional Programming With Streams

Transcript

1. Functional Programming with Streams 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 Streams are ? • 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 • Intermediate operations: lazy operations • Terminal operations: eager operations
6. 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);
7. Applying different operations with 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 with streams • <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 the most 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 data is 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. Live coding
13. Conclusion • A stream is 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