Tutorial Java 8 Stream API

1. Ikhtisar

Dalam tutorial mendalam ini, kita akan membahas penggunaan praktis Java 8 Stream mulai dari pembuatan hingga eksekusi paralel.

Untuk memahami materi ini, pembaca harus memiliki pengetahuan dasar tentang Java 8 (ekspresi lambda, Opsional, referensi metode) dan API Stream. Jika Anda tidak terbiasa dengan topik ini, lihat artikel kami sebelumnya - Fitur Baru di Java 8 dan Pengantar Aliran Java 8.

2. Pembuatan Aliran

Ada banyak cara untuk membuat contoh aliran dari sumber yang berbeda. Setelah dibuat, instance tidak akan mengubah sumbernya, sehingga memungkinkan pembuatan beberapa instance dari satu sumber.

2.1. Aliran Kosong

Metode empty () harus digunakan dalam kasus pembuatan aliran kosong:

Stream streamEmpty = Stream.empty();

Seringkali metode empty () digunakan saat pembuatan untuk menghindari pengembalian null untuk aliran tanpa elemen:

public Stream streamOf(List list)  return list == null 

2.2. Aliran Koleksi

Aliran juga dapat dibuat dari semua jenis Koleksi ( Koleksi, Daftar, Set ):

Collection collection = Arrays.asList("a", "b", "c"); Stream streamOfCollection = collection.stream();

2.3. Aliran Array

Array juga bisa menjadi sumber Stream:

Stream streamOfArray = Stream.of("a", "b", "c");

Mereka juga dapat dibuat dari larik yang sudah ada atau dari bagian larik:

String[] arr = new String[]{"a", "b", "c"}; Stream streamOfArrayFull = Arrays.stream(arr); Stream streamOfArrayPart = Arrays.stream(arr, 1, 3);

2.4. Stream.builder ()

Ketika builder digunakan , jenis yang diinginkan juga harus ditentukan di bagian kanan pernyataan, jika tidak metode build () akan membuat instance Stream:

Stream streamBuilder = Stream.builder().add("a").add("b").add("c").build();

2.5. Stream.generate ()

Metode generate () menerima Pemasok untuk pembuatan elemen. Karena aliran yang dihasilkan tidak terbatas, pengembang harus menentukan ukuran yang diinginkan atau metode generate () akan bekerja hingga mencapai batas memori:

Stream streamGenerated = Stream.generate(() -> "element").limit(10);

Kode di atas membuat urutan sepuluh string dengan nilai - "elemen" .

2.6. Stream.iterate ()

Cara lain untuk membuat aliran tak terbatas adalah dengan menggunakan metode iterate () :

Stream streamIterated = Stream.iterate(40, n -> n + 2).limit(20);

Elemen pertama dari aliran yang dihasilkan adalah parameter pertama dari metode iterate () . Untuk membuat setiap elemen berikut, fungsi yang ditentukan diterapkan ke elemen sebelumnya. Dalam contoh di atas, elemen kedua adalah 42.

2.7. Aliran Primitif

Java 8 menawarkan kemungkinan untuk membuat aliran dari tiga tipe primitif: int, long dan double. Karena Stream adalah antarmuka generik dan tidak ada cara untuk menggunakan primitif sebagai parameter tipe dengan generik, tiga antarmuka khusus baru dibuat: IntStream, LongStream, DoubleStream.

Menggunakan antarmuka baru mengurangi auto-boxing yang tidak perlu memungkinkan peningkatan produktivitas:

IntStream intStream = IntStream.range(1, 3); LongStream longStream = LongStream.rangeClosed(1, 3);

Metode range (int startInclusive, int endExclusive) membuat aliran berurutan dari parameter pertama ke parameter kedua. Ini menambah nilai elemen berikutnya dengan langkah sama dengan 1. Hasilnya tidak termasuk parameter terakhir, itu hanya batas atas dari urutan.

Metode rangeClosed (int startInclusive, int endInclusive) melakukan hal yang sama hanya dengan satu perbedaan - elemen kedua disertakan. Kedua metode ini dapat digunakan untuk menghasilkan salah satu dari tiga jenis aliran primitif.

Sejak Java 8, kelas Random menyediakan berbagai metode untuk aliran generasi primitif. Misalnya, kode berikut membuat DoubleStream, yang memiliki tiga elemen:

Random random = new Random(); DoubleStream doubleStream = random.doubles(3);

2.8. Aliran String

String juga dapat digunakan sebagai sumber untuk membuat aliran.

Dengan bantuan metode chars () dari kelas String . Karena tidak ada antarmuka CharStream di JDK, IntStream digunakan untuk mewakili aliran karakter.

IntStream streamOfChars = "abc".chars();

Contoh berikut memecah String menjadi sub-string sesuai dengan RegEx yang ditentukan :

Stream streamOfString = Pattern.compile(", ").splitAsStream("a, b, c");

2.9. Aliran File

File kelas Java NIO memungkinkan untuk menghasilkan aliran file teks melalui metode lines () . Setiap baris teks menjadi elemen aliran:

Path path = Paths.get("C:\\file.txt"); Stream streamOfStrings = Files.lines(path); Stream streamWithCharset = Files.lines(path, Charset.forName("UTF-8"));

The Charset can be specified as an argument of the lines() method.

3. Referencing a Stream

It is possible to instantiate a stream and to have an accessible reference to it as long as only intermediate operations were called. Executing a terminal operation makes a stream inaccessible.

To demonstrate this we will forget for a while that the best practice is to chain sequence of operation. Besides its unnecessary verbosity, technically the following code is valid:

Stream stream = Stream.of("a", "b", "c").filter(element -> element.contains("b")); Optional anyElement = stream.findAny();

But an attempt to reuse the same reference after calling the terminal operation will trigger the IllegalStateException:

Optional firstElement = stream.findFirst();

As the IllegalStateException is a RuntimeException, a compiler will not signalize about a problem. So, it is very important to remember that Java 8 streams can't be reused.

This kind of behavior is logical because streams were designed to provide an ability to apply a finite sequence of operations to the source of elements in a functional style, but not to store elements.

So, to make previous code work properly some changes should be done:

List elements = Stream.of("a", "b", "c").filter(element -> element.contains("b")) .collect(Collectors.toList()); Optional anyElement = elements.stream().findAny(); Optional firstElement = elements.stream().findFirst();

4. Stream Pipeline

To perform a sequence of operations over the elements of the data source and aggregate their results, three parts are needed – the source, intermediate operation(s) and a terminal operation.

Intermediate operations return a new modified stream. For example, to create a new stream of the existing one without few elements the skip() method should be used:

Stream onceModifiedStream = Stream.of("abcd", "bbcd", "cbcd").skip(1);

If more than one modification is needed, intermediate operations can be chained. Assume that we also need to substitute every element of current Stream with a sub-string of first few chars. This will be done by chaining the skip() and the map() methods:

Stream twiceModifiedStream = stream.skip(1).map(element -> element.substring(0, 3));

As you can see, the map() method takes a lambda expression as a parameter. If you want to learn more about lambdas take a look at our tutorial Lambda Expressions and Functional Interfaces: Tips and Best Practices.

A stream by itself is worthless, the real thing a user is interested in is a result of the terminal operation, which can be a value of some type or an action applied to every element of the stream. Only one terminal operation can be used per stream.

The right and most convenient way to use streams are by a stream pipeline, which is a chain of stream source, intermediate operations, and a terminal operation. For example:

List list = Arrays.asList("abc1", "abc2", "abc3"); long size = list.stream().skip(1) .map(element -> element.substring(0, 3)).sorted().count();

5. Lazy Invocation

Intermediate operations are lazy. This means that they will be invoked only if it is necessary for the terminal operation execution.

To demonstrate this, imagine that we have method wasCalled(), which increments an inner counter every time it was called:

private long counter; private void wasCalled() { counter++; }

Let's call method wasCalled() from operation filter():

List list = Arrays.asList(“abc1”, “abc2”, “abc3”); counter = 0; Stream stream = list.stream().filter(element -> { wasCalled(); return element.contains("2"); });

As we have a source of three elements we can assume that method filter() will be called three times and the value of the counter variable will be 3. But running this code doesn't change counter at all, it is still zero, so, the filter() method wasn't called even once. The reason why – is missing of the terminal operation.

Let's rewrite this code a little bit by adding a map() operation and a terminal operation – findFirst(). We will also add an ability to track an order of method calls with a help of logging:

Optional stream = list.stream().filter(element -> { log.info("filter() was called"); return element.contains("2"); }).map(element -> { log.info("map() was called"); return element.toUpperCase(); }).findFirst();

Resulting log shows that the filter() method was called twice and the map() method just once. It is so because the pipeline executes vertically. In our example the first element of the stream didn't satisfy filter's predicate, then the filter() method was invoked for the second element, which passed the filter. Without calling the filter() for third element we went down through pipeline to the map() method.

The findFirst() operation satisfies by just one element. So, in this particular example the lazy invocation allowed to avoid two method calls – one for the filter() and one for the map().

6. Order of Execution

From the performance point of view, the right order is one of the most important aspects of chaining operations in the stream pipeline:

long size = list.stream().map(element -> { wasCalled(); return element.substring(0, 3); }).skip(2).count();

Execution of this code will increase the value of the counter by three. This means that the map() method of the stream was called three times. But the value of the size is one. So, resulting stream has just one element and we executed the expensive map() operations for no reason twice out of three times.

If we change the order of the skip() and the map() methods, the counter will increase only by one. So, the method map() will be called just once:

long size = list.stream().skip(2).map(element -> { wasCalled(); return element.substring(0, 3); }).count();

This brings us up to the rule: intermediate operations which reduce the size of the stream should be placed before operations which are applying to each element. So, keep such methods as skip(), filter(), distinct() at the top of your stream pipeline.

7. Stream Reduction

The API has many terminal operations which aggregate a stream to a type or to a primitive, for example, count(), max(), min(), sum(), but these operations work according to the predefined implementation. And what if a developer needs to customize a Stream's reduction mechanism? There are two methods which allow to do this – the reduce()and the collect() methods.

7.1. The reduce() Method

There are three variations of this method, which differ by their signatures and returning types. They can have the following parameters:

identity – the initial value for an accumulator or a default value if a stream is empty and there is nothing to accumulate;

accumulator – a function which specifies a logic of aggregation of elements. As accumulator creates a new value for every step of reducing, the quantity of new values equals to the stream's size and only the last value is useful. This is not very good for the performance.

combiner – a function which aggregates results of the accumulator. Combiner is called only in a parallel mode to reduce results of accumulators from different threads.

So, let's look at these three methods in action:

OptionalInt reduced = IntStream.range(1, 4).reduce((a, b) -> a + b);

reduced = 6 (1 + 2 + 3)

int reducedTwoParams = IntStream.range(1, 4).reduce(10, (a, b) -> a + b);

reducedTwoParams = 16 (10 + 1 + 2 + 3)

int reducedParams = Stream.of(1, 2, 3) .reduce(10, (a, b) -> a + b, (a, b) -> { log.info("combiner was called"); return a + b; });

The result will be the same as in the previous example (16) and there will be no login which means, that combiner wasn't called. To make a combiner work, a stream should be parallel:

int reducedParallel = Arrays.asList(1, 2, 3).parallelStream() .reduce(10, (a, b) -> a + b, (a, b) -> { log.info("combiner was called"); return a + b; });

The result here is different (36) and the combiner was called twice. Here the reduction works by the following algorithm: accumulator ran three times by adding every element of the stream to identity to every element of the stream. These actions are being done in parallel. As a result, they have (10 + 1 = 11; 10 + 2 = 12; 10 + 3 = 13;). Now combiner can merge these three results. It needs two iterations for that (12 + 13 = 25; 25 + 11 = 36).

7.2. The collect() Method

Reduction of a stream can also be executed by another terminal operation – the collect() method. It accepts an argument of the type Collector, which specifies the mechanism of reduction. There are already created predefined collectors for most common operations. They can be accessed with the help of the Collectors type.

In this section we will use the following List as a source for all streams:

List productList = Arrays.asList(new Product(23, "potatoes"), new Product(14, "orange"), new Product(13, "lemon"), new Product(23, "bread"), new Product(13, "sugar"));

Converting a stream to the Collection (Collection, List or Set):

List collectorCollection = productList.stream().map(Product::getName).collect(Collectors.toList());

Reducing to String:

String listToString = productList.stream().map(Product::getName) .collect(Collectors.joining(", ", "[", "]"));

The joiner() method can have from one to three parameters (delimiter, prefix, suffix). The handiest thing about using joiner() – developer doesn't need to check if the stream reaches its end to apply the suffix and not to apply a delimiter. Collector will take care of that.

Processing the average value of all numeric elements of the stream:

double averagePrice = productList.stream() .collect(Collectors.averagingInt(Product::getPrice));

Processing the sum of all numeric elements of the stream:

int summingPrice = productList.stream() .collect(Collectors.summingInt(Product::getPrice));

Methods averagingXX(), summingXX() and summarizingXX() can work as with primitives (int, long, double) as with their wrapper classes (Integer, Long, Double). One more powerful feature of these methods is providing the mapping. So, developer doesn't need to use an additional map() operation before the collect() method.

Collecting statistical information about stream’s elements:

IntSummaryStatistics statistics = productList.stream() .collect(Collectors.summarizingInt(Product::getPrice));

By using the resulting instance of type IntSummaryStatistics developer can create a statistical report by applying toString() method. The result will be a String common to this one “IntSummaryStatistics{count=5, sum=86, min=13, average=17,200000, max=23}”.

It is also easy to extract from this object separate values for count, sum, min, average by applying methods getCount(), getSum(), getMin(), getAverage(), getMax(). All these values can be extracted from a single pipeline.

Grouping of stream’s elements according to the specified function:

Map
    
      collectorMapOfLists = productList.stream() .collect(Collectors.groupingBy(Product::getPrice));
    

In the example above the stream was reduced to the Map which groups all products by their price.

Dividing stream’s elements into groups according to some predicate:

Map
    
      mapPartioned = productList.stream() .collect(Collectors.partitioningBy(element -> element.getPrice() > 15));
    

Pushing the collector to perform additional transformation:

Set unmodifiableSet = productList.stream() .collect(Collectors.collectingAndThen(Collectors.toSet(), Collections::unmodifiableSet));

In this particular case, the collector has converted a stream to a Set and then created the unmodifiable Set out of it.

Custom collector:

If for some reason, a custom collector should be created, the most easier and the less verbose way of doing so – is to use the method of() of the type Collector.

Collector
    
      toLinkedList = Collector.of(LinkedList::new, LinkedList::add, (first, second) -> { first.addAll(second); return first; }); LinkedList linkedListOfPersons = productList.stream().collect(toLinkedList);
    

In this example, an instance of the Collector got reduced to the LinkedList.

Parallel Streams

Before Java 8, parallelization was complex. Emerging of the ExecutorService and the ForkJoin simplified developer’s life a little bit, but they still should keep in mind how to create a specific executor, how to run it and so on. Java 8 introduced a way of accomplishing parallelism in a functional style.

The API allows creating parallel streams, which perform operations in a parallel mode. When the source of a stream is a Collection or an array it can be achieved with the help of the parallelStream() method:

Stream streamOfCollection = productList.parallelStream(); boolean isParallel = streamOfCollection.isParallel(); boolean bigPrice = streamOfCollection .map(product -> product.getPrice() * 12) .anyMatch(price -> price > 200);

If the source of stream is something different than a Collection or an array, the parallel() method should be used:

IntStream intStreamParallel = IntStream.range(1, 150).parallel(); boolean isParallel = intStreamParallel.isParallel();

Under the hood, Stream API automatically uses the ForkJoin framework to execute operations in parallel. By default, the common thread pool will be used and there is no way (at least for now) to assign some custom thread pool to it. This can be overcome by using a custom set of parallel collectors.

When using streams in parallel mode, avoid blocking operations and use parallel mode when tasks need the similar amount of time to execute (if one task lasts much longer than the other, it can slow down the complete app’s workflow).

The stream in parallel mode can be converted back to the sequential mode by using the sequential() method:

IntStream intStreamSequential = intStreamParallel.sequential(); boolean isParallel = intStreamSequential.isParallel();

Conclusions

Stream API adalah seperangkat alat yang ampuh namun sederhana untuk dipahami untuk memproses urutan elemen. Ini memungkinkan kami untuk mengurangi sejumlah besar kode boilerplate, membuat program yang lebih mudah dibaca, dan meningkatkan produktivitas aplikasi jika digunakan dengan benar.

Di sebagian besar contoh kode yang ditampilkan dalam artikel ini, aliran dibiarkan tidak dikonsumsi (kami tidak menerapkan metode close () atau operasi terminal). Dalam aplikasi nyata, jangan biarkan aliran yang dibuat tidak digunakan karena akan menyebabkan kebocoran memori.

Contoh kode lengkap yang menyertai artikel tersedia di GitHub.