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1. Overview

In this in-depth tutorial, we will go through the practical usage of Java 8 Streams from creation to parallel execution.

To understand this material, readers need to have a basic knowledge of Java 8 (lambda expressions, Optional, method references) and of the Stream API. If you aren’t familiar with these topics, please take a look at our previous articles – New Features in Java 8 and Introduction to Java 8 Streams.

2. Stream Creation

There are many ways to create a stream instance of different sources. Once created, the instance will not modify its source, therefore allowing the creation of multiple instances from a single source.

2.1. Empty Stream

The empty() method should be used in case of a creation of an empty stream:

Stream<String> streamEmpty = Stream.empty();

Its often the case that the empty() method is used upon creation to avoid returning null for streams with no element:

public Stream<String> streamOf(List<String> list) {
    return list == null || list.isEmpty() ? Stream.empty() : list.stream();

2.2. Stream of Collection

Stream can also be created of any type of Collection (Collection, List, Set):

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

2.3. Stream of Array

Array can also be a source of a Stream:

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

They can also be created out of an existing array or of a part of an array:

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

2.4. Stream.builder()

When builder is used the desired type should be additionally specified in the right part of the statement, otherwise the build() method will create an instance of the Stream<Object>:

Stream<String> streamBuilder =

2.5. Stream.generate()

The generate() method accepts a Supplier<T> for element generation. As the resulting stream is infinite, developer should specify the desired size or the generate() method will work until it reaches the memory limit:

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

The code above creates a sequence of ten strings with the value – “element”.

2.6. Stream.iterate()

Another way of creating an infinite stream is by using the iterate() method:

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

The first element of the resulting stream is a first parameter of the iterate() method. For creating every following element the specified function is applied to the previous element. In the example above the second element will be 42.

2.7. Stream of Primitives

Java 8 offers a possibility to create streams out of three primitive types: int, long and double. As Stream<T> is a generic interface and there is no way to use primitives as a type parameter with generics, three new special interfaces were created: IntStream, LongStream, DoubleStream.

Using the new interfaces alleviates unnecessary auto-boxing allows increased productivity:

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

The range(int startInclusive, int endExclusive) method creates an ordered stream from the first parameter to the second parameter. It increments the value of subsequent elements with the step equal to 1. The result doesn’t include the last parameter, it is just an upper bound of the sequence.

The rangeClosed(int startInclusive, int endInclusive) method does the same with only one difference – the second element is included. These two methods can be used to generate any of the three types of streams of primitives.

Since Java 8 the Random class provides a wide range of methods for generation streams of primitives. For example, the following code creates a DoubleStream, which has three elements:

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

2.8. Stream of String

String can also be used as a source for creating a stream.

With the help of the chars() method of the String class. Since there is no interface CharStream in JDK, the IntStream is used to represent a stream of chars instead.

IntStream streamOfChars = "abc".chars();

The following example breaks a String into sub-strings according to specified RegEx:

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

2.9. Stream of File

Java NIO class Files allows to generate a Stream<String> of a text file through the lines() method. Every line of the text becomes an element of the stream:

Path path = Paths.get("C:\\file.txt");
Stream<String> streamOfStrings = Files.lines(path);
Stream<String> 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<String> stream = 
  Stream.of("a", "b", "c").filter(element -> element.contains("b"));
Optional<String> anyElement = stream.findAny();

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

Optional<String> 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<String> elements =
  Stream.of("a", "b", "c").filter(element -> element.contains("b"))
Optional<String> anyElement = elements.stream().findAny();
Optional<String> 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<String> 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<String> with a sub-string of first few chars. This will be done by chaining the skip() and the map() methods:

Stream<String> 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<String> 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() {

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

List<String> list = Arrays.asList(“abc1”, “abc2”, “abc3”);
counter = 0;
Stream<String> stream = list.stream().filter(element -> {
    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<String> stream = list.stream().filter(element -> {
    log.info("filter() was called");
    return element.contains("2");
}).map(element -> {
    log.info("map() was called");
    return element.toUpperCase();

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 -> {
    return element.substring(0, 3);

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 -> {
    return element.substring(0, 3);

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<Product> 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<String> collectorCollection = 

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()

Processing the sum of all numeric elements of the stream:

int summingPrice = productList.stream()

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()

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<Integer, List<Product>> collectorMapOfLists = productList.stream()

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<Boolean, List<Product>> mapPartioned = productList.stream()
  .collect(Collectors.partitioningBy(element -> element.getPrice() > 15));

Pushing the collector to perform additional transformation:

Set<Product> unmodifiableSet = productList.stream()

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<Product, ?, LinkedList<Product>> toLinkedList =
  Collector.of(LinkedList::new, LinkedList::add, 
    (first, second) -> { 
       return first; 

LinkedList<Product> linkedListOfPersons =

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

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<Product> 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.

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();s


The Stream API is a powerful but simple to understand set of tools for processing sequence of elements. It allows us to reduce a huge amount of boilerplate code, create more readable programs and improve app’s productivity when used properly.

In most of the code samples shown in this article streams were left unconsumed (we didn’t apply the close() method or a terminal operation). In a real app, don’t leave an instantiated streams unconsumed as that will lead to memory leaks.

The complete code samples that accompany the article are available over on GitHub.

I just announced the new Spring 5 modules in REST With Spring:


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shivam purwar
shivam purwar

String [] col={“Name”,”id”,”tra_id”,”tra_amt”};//containg all column name
String[] values={“ss”,”123″,”a123″,”450.00″};//containg column related value
How can I map these value in map using Parallel Streams in java8?
After mapping using straming my map should contain format value.
Here key will be col value and value will be values value.
Map should contain below value after straming.


Grzegorz Piwowarek
Grzegorz Piwowarek

There is no easy way of achieving this in Java 8 because Stream API does not have a “zip” method implemented which would be super useful in this case but it’s doable. Keep in mind that it does not matter if you use parrallel streams or not. The end result will be the same. In Java 8, you need to create a Stream of integers and then start mapping your values manually: String[] col={"Name","id","tra_id","tra_amt"}; String[] values={"ss","123","a123","450.00"}; Map result = IntStream .range(0, col.length) .boxed() .collect(Collectors.toMap(i -> col[i], i -> values[i])); System.out.println(result); Let me know if this what you were looking for

shivam purwar
shivam purwar

Thanks Grzegorz,
I am thinking If I will use parrallel Stream then performance little bit fast.
Suppose I Have 10 million record in file and I want to process this one 10-15 minuts.
Then what should I use?
Currently I am using Bufferedreader and simple looping process then it take 25-30 minuts to process record.
Please tell me if any other way to which I can increase performance.

Grzegorz Piwowarek
Grzegorz Piwowarek

Well, parallel streams are hard to control and to predict what actually happens under the hood. In this case, we can only experiment and see. If you want to read more about reading huge files, check this write-up http://www.baeldung.com/java-read-lines-large-file