For those curious to learn about Java 8 Stream and its data processing and analysis capabilities, this is the right place to be. Java 8 Stream is a powerful feature that allows developers to process data concisely and efficiently. It has been a game-changer in Java programming since its release in 2014. Further, Java 8 Stream is becoming increasingly popular among developers due to its ease of use.
This article will dive deep into Java 8 Stream and explore its functionalities, use cases, and benefits. It will also compare Java 8 Stream to traditional ways of processing data, demonstrating to readers how to take full advantage of this powerful tool.
Right from beginners to experienced Java developers, this article has valuable insights for everyone.
Java Stream is a new feature introduced in Java 8 that provides a functional approach to processing data. It enables developers to perform various operations on a collection or array in a declarative way. This API is based on the principle of internal iteration, which means that the Java framework handles the iteration instead of the client program. Java Stream API is designed to provide sequential and parallel execution of operations and filtering based on the given criteria.
A collection is an in-memory data structure that holds values, whereas a Java Stream is a data structure that is computed on demand. Java Stream doesn't store data; it operates on the source data structure (collection and array) and produces pipelined data to perform specific operations.
Java Stream operations use functional interfaces, making them a good fit for functional programming using lambda expressions. The internal iteration principle of Java 8 Stream helps achieve 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.
A stream provides a set of elements of a specific type sequentially. A stream gets/computes elements on demand. It never stores the elements.
A stream takes Collections, Arrays, or I/O resources as input sources.
A Stream supports aggregate operations like filter, map, limit, reduce, etc.
Operations performed on a stream do not modify its source. For example, filtering a Stream obtained from a collection produces a new Stream without the filtered elements rather than removing elements from the source collection.
Streams evaluate code only when required.
Streams are consumable, so there is no way to create a reference to a stream for future usage. Since the data is on-demand, it's not possible to reuse the same stream multiple times.
The elements of a stream are only visited once during the life of a stream. Like an Iterator, a new stream must be generated to revisit the same elements of the source.
filter(Predicate<? super T> predicate)
This method returns a new stream containing only the elements of the original stream that match the given predicate.
map(Function<? super T,? extends R> mapper)
This method returns a new stream consisting of the results of applying the given function to the elements of the original stream.
flatMap(Function<? super T,? extends Stream<? extends R>> mapper)
This method returns a new stream consisting of the results of replacing each element of the original stream with the contents of a mapped stream produced by applying the provided mapping function to each element.
This method returns a new stream consisting of the distinct elements of the original stream (according to Object. equals(Object)).
This method returns a new stream consisting of the elements of the original stream, sorted according to their natural order.
peek(Consumer<? super T> action)
This method returns a new stream consisting of the elements of the original stream, additionally performing the provided action on each element as elements are consumed from the resulting stream.
This method returns a new stream consisting of the first maxSize elements of the original stream or all the elements of the stream if it contains fewer elements than maxSize.
This method returns a new stream consisting of all elements of the original stream except the first n elements.
forEach(Consumer<? super T> action)
This method performs the given action for each element of the stream.
reduce(T identity, BinaryOperator<T> accumulator)
This method returns the result of applying the accumulator to the identity value and each element of the stream, going left to right.
A stream can be created from a collection using the stream() method:
Elements in a stream can be filtered using the filter() method. The filter() method takes a Predicate that returns true or false for each element in the stream. The resulting stream contains only the elements for which the predicate returns true.
To transform elements in a stream, developers can use the map() method. The map() method takes a Function that takes an element from the stream and returns a transformed element.
Multiple stream operations can be combined using the fluent API. For example, developers can filter and map elements in a stream:
Developers can also reduce the elements in a stream to a single value using the reduce() method. The reduce() method takes a BinaryOperator that takes two elements from the stream and returns a single element. The reduce() method returns an Optional because the stream may be empty.
The elements in a stream can be collected into a collection using the collect() method. The collect() method takes a Collector that specifies how to collect the elements. Developers can use the Collectors class to create common collectors.
In this example, one can create a list of names and then use a stream to filter out only the names that start with the letter "S." Developers typically use the filter() method, which takes a Predicate as an argument, to perform the filtering. They then use the forEach() method to print out each filtered element.
To create a list of fruits, developers use a stream to convert each element to uppercase using the map() method, which takes a Function as an argument. We then use the forEach() method to print out each transformed element.
Here, one can create a list of numbers and use a stream to filter out duplicates using the distinct() method. They can then use the forEach() method to print out each distinct element.
In this example, developers create a list of numbers and use a stream to calculate the sum of all elements using the reduce() method. The first argument of the reduce() method is the identity value (in this case, 0), and the second argument is a BinaryOperator that is used to combine two elements. They then print out the sum of all elements.
Java 8 Stream uses a lazy approach, which makes it very efficient. Instead of processing all elements at once, it processes them one by one, only when required. Additionally, the Stream API provides support for parallel processing, which means that the processing of data can be divided into multiple threads, making it even faster.
Stream API provides various operations like filter, map, and reduce, which makes the code simple, easy to read, and less prone to errors.
Java 8 Stream allows developers to iterate through a collection of data without using external iterators, making the code concise and less prone to errors.
Java 8 Stream API is designed to work with functional programming concepts, which means that it supports operations like map, filter, and reduce, which are common in functional programming.
Collectors are used to combine the elements of a Stream into a collection or a single value. Java 8 Stream provides various built-in collectors like toList, toSet, and joining, which make it easy to collect the elements of a Stream.
FlatMap is an advanced operation that flattens a Stream of collections into a single Stream. It is useful when developers need to merge two or more collections and perform an operation on them.
Java 8 Stream API provides support for infinite Streams, which means that developers can create a Stream that generates an infinite number of values. This feature is useful when one needs to generate a large number of values or when one needs to simulate an infinite data source.
The Optional class is used to represent a value that may or may not be present. Java 8 Stream provides support for Optional, which means that developers can use it to perform operations on Streams that may or may not have values. Optional helps to avoid NullPointerExceptions and makes the code more robust.
With the ability to perform complex operations on large data sets in a concise and readable manner, Java 8 Stream has significantly improved the efficiency of Java programming. By leveraging the various methods provided by Java 8 Stream, developers can easily filter, transform and sort data while still maintaining the immutability of the original data. This feature has greatly simplified the code-writing process and made it easier for developers to focus on the logic of their programs rather than the intricacies of data manipulation.
As with any new technology, it's important to stay up to date with the latest developments and best practices. Our website offers a wealth of information on Java programming, including tutorials on Java 8 Stream and other important features.
For those looking to deepen their understanding of Java programming, Cogent University is a valuable resource that offers in-depth training and hands-on experience. visit Cogent University to know more about our bootcamps.
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