Java Stream

What is stream?

Stream is an abstraction of data operations. It takes input from the collections, Arrays of I/O channels.

From imperative to declarative

For example, given a list of people, find out the first 10 people with age less or equals to 18.

The following solution is the imperative approach:

public void imperativeApproach() throws IOException {
        List<Person> people = MockData.getPeople();

        List<Person> peopleAbove18 = new ArrayList<>();
        for (Person person : people) {
            if (person.getAge() <= 18) {
                peopleAbove18.add(person);
            }
        }

        for (Person person: peopleAbove18) {
            System.out.print(person);
        }
}

The following is the declare approach style:

public void declareApproach() throws IOException {
        List<Person> people = MockData.getPeople();
       people.stream()
                    // a lambda function as a filter
                  .filter(person -> person.getAge() <= 18)
                  .limit(10)
                  .collect(Collectors.toList())
                  .forEach(System.out::print);
}

Abstraction

We mentioned that stream is an abstraction to the data manipulation. It abstract them in the following way:

  • Concrete: can be the Sets, Lists, Maps, etc
  • Stream: can be filter, map, etc.
  • Concrete: collect the data to make it concrete again.

Intermediate and Terminate Operation

Java stream has different operation units:

  • Intermediate operators: map, filter, sorted
  • Terminators: collect, foreach, reduce

Each intermediate operation is lazily executed and return a stream, until a terminal operation is met.

Range

With IntStream.range(), you can create a stream with fixed set of elements, for example:

    public void rangeIteratingLists() throws Exception {
        List<Person> people = MockData.getPeople();

        // Use int stream to loop through the list and print the object.
        IntStream.range(0, 10).forEach(i -> System.out.println(people.get(i)));

        // If you want to use for the first elements
        people.stream().limit(10).forEach(System.out::println);
    }

You can also iterate the function for the given number times:

    public void intStreamIterate() throws Exception {
        // This is very much like the fold function on Kotlin,
        // that it keep iterating based on the iterator you provided.
        IntStream.iterate(0, operand -> operand + 1).limit(10).forEach(System.out::println);
    }

Max, Min and Comparators

Java stream provides built in Min/Max function that support customized comparators. For example:

    public void min() throws Exception {
        final List<Integer> numbers = ImmutableList.of(1, 2, 3, 100, 23, 93, 99);

        int min = numbers.stream().min(Comparator.naturalOrder()).get();

        System.out.println(min);
    }

Distinct

Sometimes, we would like to get the distinct elements from the stream, then we could use the distinct api of the stream

  public void distinct() throws Exception {
    final List<Integer> numbers = ImmutableList.of(1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 9, 9, 9);

    List<Integer> distinctNumbers = numbers.stream()
        .distinct()
        .collect(Collectors.toList());

  }

Filtering and Transformation

Stream filter api enables you to filter the content of the element, for example:

    public void understandingFilter() throws Exception {
        ImmutableList<Car> cars = MockData.getCars();

        // Predicate is an assertion that returns true or false
        final Predicate<Car> carPredicate = car -> car.getPrice() < 20000;

        List<Car> carsFiltered = cars.stream()
            .filter(carPredicate)
            .collect(Collectors.toList());

And map API enable you to transform the format of the element, for example, we could define a another object and transform the given stream to the targeted stream:

    public void ourFirstMapping() throws Exception {
        // transform from one data type to another
        List<Person> people = MockData.getPeople();

        people.stream().map(p -> {
            return new PersonDTO(p.getId(), p.getFirstName(), p.getAge());
        }).collect(Collectors.toList());

    }

Group Data

One common function in SQL queries are data grouping, for example:

SELECT COUNT(*), TYPE FROM JOB WHERE USER_ID = 123 GROUP BY TYPE

Java stream provides similar functionalities:

  public void groupingAndCounting() throws Exception {
    ArrayList<String> names = Lists
        .newArrayList(
            "John",
            "John",
            "Mariam",
            "Alex",
            "Mohammado",
            "Mohammado",
            "Vincent",
            "Alex",
            "Alex"
        );

    Map<String, Long> counting = names.stream()
        .collect(Collectors.groupingBy(Function.identity(), Collectors.counting()));

    counting.forEach((name, count) -> System.out.println(name + " > " + count));
  }

Reduce and Flatmap

Very similar to the Hadoop Map/Reduce job, where map take care of transformation of the data, while the reduce job collect the data and do the final computation. For example:

  public void reduce() throws Exception {
    Integer[] integers = {1, 2, 3, 4, 99, 100, 121, 1302, 199};

     // Compute the same of the elements, with the initial element as a
    int sum = Arrays.stream(integers).reduce(0, (a, b) -> a + b);
    System.out.println(sum);

    // use the function reference
    int sum2 = Arrays.stream(integers).reduce(0, Integer::sum);
    System.out.println(sum2);

  }

Flat map is different from the map function that it could flat the internal structure first.

For example:

List<List<String>> list = Arrays.asList(
  Arrays.asList("a"),
  Arrays.asList("b"));
System.out.println(list);

System.out.println(list
  .stream()
  .flatMap(Collection::stream)
  .collect(Collectors.toList()));

The result of the stream is a String List.

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