Java 8 Stream API Example Tutorial

Stream API Overview

Before we look into Java 8 Stream API Examples, let’s see why it was required. Suppose we want to iterate over a list of integers and find out sum of all the integers greater than 10.

Prior to Java 8, the approach to do it would be:

private static int sumIterator(List<Integer> list) {

Iterator<Integer> it = list.iterator();

int sum = 0;

while (it.hasNext()) {

int num = it.next();

if (num > 10) {

sum += num;

}

return sum;

}

There are three major problems with the above approach:

We just want to know the sum of integers but we would also have to provide how the iteration will take place, this is also called external iteration because client program is handling the algorithm to iterate over the list.
The program is sequential in nature, there is no way we can do this in parallel easily.
There is a lot of code to do even a simple task.

To overcome all the above shortcomings, Java 8 introduces Stream API. We can use Stream API to implement internal iteration, that is better because java framework is in control of the iteration.

Internal iteration provides several features such as sequential and parallel execution, filtering based on the given criteria, mapping etc.

Most of the Stream API method arguments are functional interfaces, so lambda expressions work very well with them. Let’s see how can we write above logic in a single line statement.

private static int sumStream(List<Integer> list) {

return list.stream().filter(i -> i > 10).mapToInt(i -> i).sum();

}

Notice that above program utilizes java framework iteration strategy, filtering and mapping methods and would increase efficiency.

First of all we will look into the core concepts of Stream API and then we will go through some examples for understanding most commonly used methods.

Collections and Streams

A collection is an in-memory data structure to hold values and before we start using collection, all the values should have been populated. Whereas a Stream is a data structure that is computed on-demand.

Stream doesn’t store data, it operates on the source data structure (collection and array) and produce pipelined data that we can use and perform specific operations. Such as we can create a stream from the list and filter it based on a condition.

Stream operations use functional interfaces, that makes it a very good fit for functional programming using lambda expressions. As you can see in the above example that using lambda expressions make our code readable and short.

Stream internal iteration principle helps in achieving lazy-seeking in some of the stream operations. For example filtering, mapping, or duplicate removal can be implemented lazily, allowing higher performance and scope for optimization.

Streams are consumable, so there is no way to create a reference to stream for future usage. Since the data is on-demand, it’s not possible to reuse the same stream multiple times.

Stream support sequential as well as parallel processing, parallel processing can be very helpful in achieving high performance for large collections.

All the Stream API interfaces and classes are in the java.util.stream package. Since we can use primitive data types such as int, long in the collections using auto-boxing and these operations could take a lot of time, there are specific classes for these – IntStream, LongStream and DoubleStream.

Commonly used Functional Interfaces in Stream

Some of the commonly used functional interfaces in the Stream API methods are:

Function and BiFunction: Function represents a function that takes one type of argument and returns another type of argument. Function<t, r=""> is the generic form where T is the type of the input to the function and R is the type of the result of the function. For handling primitive types, there are specific Function interfaces – ToIntFunction, ToLongFunction, ToDoubleFunction, ToIntBiFunction, ToLongBiFunction,ToDoubleBiFunction, LongToIntFunction, LongToDoubleFunction, IntToLongFunction,IntToDoubleFunction etc.

Some of the Stream methods where Function or it’s primitive specialization is used are:
- <R> Stream<R> map(Function<? super T, ? extends R> mapper)
- IntStream mapToInt(ToIntFunction<? super T> mapper) – similarly for long and double returning primitive specific stream.
- IntStream flatMapToInt(Function<? super T, ? extends IntStream> mapper) – similarly for long and double
- <A> A[] toArray(IntFunction<A[]> generator)
- <U> U reduce(U identity, BiFunction<U, ? super T, U> accumulator, BinaryOperator<U> combiner)

Predicate and BiPredicate: It represents a predicate against which elements of the stream are tested. This is used to filter elements from the stream. Just like Function, there are primitive specific interfaces for int, long and double.

Some of the Stream methods where Predicate or BiPredicate specializations are used are:
- Stream<T> filter(Predicate<? super T> predicate)
- boolean anyMatch(Predicate<? super T> predicate)
- boolean allMatch(Predicate<? super T> predicate)
- boolean noneMatch(Predicate<? super T> predicate)

Consumer and BiConsumer: It represents an operation that accepts a single input argument and returns no result. It can be used to perform some action on all the elements of the stream.

Some of the Stream methods where Consumer, BiConsumer or it’s primitive specialization interfaces are used are:
- Stream<T> peek(Consumer<? super T> action)
- void forEach(Consumer<? super T> action)
- void forEachOrdered(Consumer<? super T> action)

Supplier: Supplier represent an operation through which we can generate new values in the stream. Some of the methods in Stream that takes Supplier argument are:
- public static<T> Stream<T> generate(Supplier<T> s)
- <R> R collect(Supplier<R> supplier,BiConsumer<R, ? super T> accumulator,BiConsumer<R, R> combiner)

java.util.Optional

Optional is a container object which may or may not contain a non-null value. If a value is present,isPresent() will return true and get() will return the value. Stream terminal operations return Optional object. Some of these methods are:

Optional<T> reduce(BinaryOperator<T> accumulator)
Optional<T> min(Comparator<? super T> comparator)
Optional<T> max(Comparator<? super T> comparator)
Optional<T> findFirst()
Optional<T> findAny()

java.util.Spliterator

For supporting parallel execution in Stream API, Spliterator interface is used. Spliterator trySplitmethod returns a new Spliterator that manages a subset of the elements of the original Spliterator.

Intermediate and Terminal Operations

Stream API operations that returns a new Stream are called intermediate operations. Most of the times, these operations are lazy in nature, so they start producing new stream elements and send it to the next operation. Intermediate operations are never the final result producing operations. Commonly used intermediate operations are filter and map.

Stream API operations that returns a result or produce a side effect. Once the terminal method is called on a stream, it consumes the stream and after that we can’t use stream. Terminal operations are eager in nature i.e they process all the elements in the stream before returning the result. Commonly used terminal methods are forEach, toArray, min, max, findFirst, anyMatch, allMatch etc. You can identify terminal methods from the return type, they will never return a Stream.

Short Circuiting Operations

An intermediate operation is called short circuiting, if it may produce finite stream for an infinite stream. For example limit() and skip() are two short circuiting intermediate operations.

A terminal operation is called short circuiting, if it may terminate in finite time for infinite stream. For example anyMatch, allMatch, noneMatch, findFirst and findAny are short circuiting terminal operations.

Java Stream Examples

I have covered almost all the important parts of the Java Stream API. It’s exciting to use this new API features and let’s see it in action with some examples.

Creating Streams

There are several ways through which we can create a stream from array and collections. Let’s look into these with simple examples.

We can use Stream.of() to create a stream from similar type of data. For example, we can create Stream of integers from a group of int or Integer objects.

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Stream<Integer> stream = Stream.of(1,2,3,4);

We can use Stream.of() with an array of Objects to return the stream. Note that it doesn’t support autoboxing, so we can’t pass primitive type array.

Stream<Integer> stream = Stream.of(new Integer[]{1,2,3,4});

//works fine

Stream<Integer> stream1 = Stream.of(new int[]{1,2,3,4});

//Compile time error, Type mismatch: cannot convert from Stream<int[]> to Stream<Integer>

We can use Collection stream() to create sequential stream and parallelStream() to create parallel stream.

List<Integer> myList = new ArrayList<>();

for(int i=0; i<100; i++) myList.add(i);

//sequential stream

Stream<Integer> sequentialStream = myList.stream();

//parallel stream

Stream<Integer> parallelStream = myList.parallelStream();

We can use Stream.generate() and Stream.iterate() methods to create Stream.

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Stream<String> stream1 = Stream.generate(() -> {return "abc";});

Stream<String> stream2 = Stream.iterate("abc", (i) -> i);
Using Arrays.stream() and String.chars() methods.

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LongStream is = Arrays.stream(new long[]{1,2,3,4});

IntStream is2 = "abc".chars();

Converting Stream to Collection or Array

There are several ways through which we can get a Collection or Array from a Stream.

We can use Stream collect() method to get List, Map or Set from stream.

Stream<Integer> intStream = Stream.of(1,2,3,4);

List<Integer> intList = intStream.collect(Collectors.toList());

System.out.println(intList); //prints [1, 2, 3, 4]

intStream = Stream.of(1,2,3,4); //stream is closed, so we need to create it again

Map<Integer,Integer> intMap = intStream.collect(Collectors.toMap(i -> i, i -> i+10));

System.out.println(intMap); //prints {1=11, 2=12, 3=13, 4=14}

We can use stream toArray() method to create an array from the stream.

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Stream<Integer> intStream = Stream.of(1,2,3,4);

Integer[] intArray = intStream.toArray(Integer[]::new);

System.out.println(Arrays.toString(intArray)); //prints [1, 2, 3, 4]

Stream Intermediate Operations

Let’s look into commonly used Stream intermediate operations example.

Stream filter() example: We can use filter() method to test stream elements for a condition and generate filtered list.

List<Integer> myList = new ArrayList<>();

for(int i=0; i<100; i++) myList.add(i);

Stream<Integer> sequentialStream = myList.stream();

Stream<Integer> highNums = sequentialStream.filter(p -> p > 90); //filter numbers greater than 90

System.out.print("High Nums greater than 90=");

highNums.forEach(p -> System.out.print(p+" "));

//prints "High Nums greater than 90=91 92 93 94 95 96 97 98 99 "

Stream map() example: We can use map() to apply functions to an stream. Let’s see how we can use it to apply upper case function to a list of Strings.

Stream<String> names = Stream.of("aBc", "d", "ef");

System.out.println(names.map(s -> {

return s.toUpperCase();

}).collect(Collectors.toList()));

//prints [ABC, D, EF]

Stream sorted() example: We can use sorted() to sort the stream elements by passing Comparator argument.

Stream<String> names2 = Stream.of("aBc", "d", "ef", "123456");

List<String> reverseSorted = names2.sorted(Comparator.reverseOrder()).collect(Collectors.toList());

System.out.println(reverseSorted); // [ef, d, aBc, 123456]

Stream<String> names3 = Stream.of("aBc", "d", "ef", "123456");

List<String> naturalSorted = names3.sorted().collect(Collectors.toList());

System.out.println(naturalSorted); //[123456, aBc, d, ef]

Stream flatMap() example: We can use flatMap() to create a stream from the stream of list. Let’s see a simple example to clear this doubt.

Stream<List<String>> namesOriginalList = Stream.of(

Arrays.asList("Pankaj"),

Arrays.asList("David", "Lisa"),

Arrays.asList("Amit"));

//flat the stream from List<String> to String stream

Stream<String> flatStream = namesOriginalList

.flatMap(strList -> strList.stream());

flatStream.forEach(System.out::println);

Stream Terminal Operations

Let’s look at some of the terminal operations example.

Stream reduce() example: We can use reduce() to perform a reduction on the elements of the stream, using an associative accumulation function, and return an Optional. Let’s see how we can use it multiply the integers in a stream.

Stream<Integer> numbers = Stream.of(1,2,3,4,5);

Optional<Integer> intOptional = numbers.reduce((i,j) -> {return i*j;});

if(intOptional.isPresent()) System.out.println("Multiplication = "+intOptional.get()); //120

Stream count() example: We can use this terminal operation to count the number of items in the stream.

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Stream<Integer> numbers1 = Stream.of(1,2,3,4,5);

System.out.println("Number of elements in stream="+numbers1.count()); //5
Stream forEach() example: This can be used for iterating over the stream. We can use this in place of iterator. Let’s see how to use it for printing all the elements of the stream.

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Stream<Integer> numbers2 = Stream.of(1,2,3,4,5);

numbers2.forEach(i -> System.out.print(i+",")); //1,2,3,4,5,

Stream match() examples: Let’s see some of the examples for matching methods in Stream API.

Stream<Integer> numbers3 = Stream.of(1,2,3,4,5);

System.out.println("Stream contains 4? "+numbers3.anyMatch(i -> i==4));

//Stream contains 4? true

Stream<Integer> numbers4 = Stream.of(1,2,3,4,5);

System.out.println("Stream contains all elements less than 10? "+numbers4.allMatch(i -> i<10));

//Stream contains all elements less than 10? true

Stream<Integer> numbers5 = Stream.of(1,2,3,4,5);

System.out.println("Stream doesn't contain 10? "+numbers5.noneMatch(i -> i==10));

//Stream doesn't contain 10? true

Stream findFirst() example: This is a short circuiting terminal operation, let’s see how we can use it to find the first string from a stream starting with D.

Stream<String> names4 = Stream.of("Pankaj","Amit","David", "Lisa");

Optional<String> firstNameWithD = names4.filter(i -> i.startsWith("D")).findFirst();

if(firstNameWithD.isPresent()){

System.out.println("First Name starting with D="+firstNameWithD.get()); //David

}

Java Stream API Limitations

Stream API brings a lot of new stuffs to work with list and arrays, but it has some limitations too.

Stateless lambda expressions: If you are using parallel stream and lambda expressions are stateful, it can result in random responses. Let’s see it with a simple program.

StatefulParallelStream.java
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29	`package` `com.journaldev.java8.stream;` `import` `java.util.ArrayList;` `import` `java.util.Arrays;` `import` `java.util.List;` `import` `java.util.stream.Stream;` `public` `class` `StatefulParallelStream {` `public` `static` `void` `main(String[] args) {` `List<Integer> ss = Arrays.asList(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15);` `List<Integer> result =` `new` `ArrayList<Integer>();` `Stream<Integer> stream = ss.parallelStream();` `stream.map(s -> {` `synchronized` `(result) {` `if` `(result.size() <` `10) {` `result.add(s);` `}` `}` `return` `s;` `}).forEach( e -> {});` `System.out.println(result);` `}` `}`

If we run above program, you will get different results because it depends on the way stream is getting iterated and we don’t have any order defined for parallel processing. If we use sequential stream, then this problem will not arise.

Once a Stream is consumed, it can’t be used later on. As you can see in above examples that every time I am creating a stream.
There are a lot of methods in Stream API and the most confusing part is the overloaded methods. It makes the learning curve time taking.

That’s all for Stream API in Java. I am looking forward to use this feature and make the code readable with better performance through parallel processing.