Java 8 stream map function example

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Java 8 stream map function example

The addition of the current was one of the main features added to Java 8. This in-depth tutorial is an introduction to the many functionalities supported by flows, focusing on simple and practical examples. To understand this material, you need to have a basic knowledge and working java 8 (lambda expressions, optional, method references). Introduction First of all, Java 8 streams should not be confused with Java I/O flows (e.g. FileInputStream, etc); these have very little to do with each other. In a few collaborations, flows are wrappers around a data source, allowing us to operate with that data source and make mass processing convenient and fast. A stream does not store data and, in this sense, is not a data structure. It also never modifies the underlying data source. This functionality ? java.util.stream ? supports functional style operations in element flows, such as map-reduce transformations into collections. Let's immerse yourself in some simple examples of creating and using streams ? before we get into terminology and basics. Java Stream CreationLet gets for the first time a flow from an existing matrix: private static employee[] arrayOfEmps = { new Employee(1, Jeff Bezos, 100000.0), new Employee(2, Bill Gates, 200000.0), new Employee(3, Mark Zuckerberg, 300000.0) }; Stream.of(arrayOfEmps); We can also get a flow from an existing list: static list<Employee> private empList = Arrays.asList(arrayOfEmps); empList.stream(); Note that Java 8 has added a new stream() method to the Collection interface. And we can create a flow from individual objects using Stream.of():Stream.of(arrayOfEmps[0], arrayOfEmps[1], arrayOfEmps[2]);O simply using Stream.builder():Stream.Builder<Employee> empStreamBuilder = Stream.builder(); empStreamBuilder.accept(arrayOfEmps[0]); empStreamBuilder.accept(arrayOfEmps[1]); empStreamBuilder.accept(arrayOfEmps[2]); Stream<Employee> empStream = empStreamBuilder.build(); There are also other ways to get a stream, some of which we will see in the following sections. Java Stream OperationsLet now see some common uses and operations that we can perform and with the help of current support in language.forEachforEach() is a simpler and more common operation; it runs over the elements of the sequence, calling the function supplied in each element. The method is so common that @Test Iterable, Map etc:@Test public void whenIncrementSalaryForEachEmployee_thenApplyNewSalary() { empList.stream().forEach(e -> e.salaryIncrement(10.0)); contains(hasProperty(salary, equalTo(110000.0)), hasProperty(salary, equalTo(220000.0)), hasProperty(salary, equalTo(330000.0)) )); } This will effectively name the salaryIncrement() in each element of empList.forEach() is a terminal operation, which means that, once the operation has been carried out, that the current pipe is consumed, and can no longer be used. We'll talk more about terminal operations in the following section.mapmap() produces a new flow after </Employee> </Employee> </Employee > </Employee> function for each element in the original sequence. The new sequence could be of different type. The following example converts the Integer flow to the Employees flow:@Test public vacuum whenMapIdToEmployees_thenGetEmployeeStream() { Enter[] empIds = { 1, 2, 3 }; List<Employee> Employee = Stream.of(empIds) .map(employeeRepository::findById) .collect(Collectors.toList()); assertEquals(employees.size(), empIds.length); } Here, we get an Integer flow of employee ids from an array. Each Integer is passed to the employeeRepository::findById() function ? which returns the corresponding employee object; this effectively forms an employee stream.collect We have seen how collect() works in the example above; its one of the common ways to remove things from the stream once they are done with all processing:@Test empty public whenCollectStreamToList_thenGetList() { List<Employee> employees = empList.stream().collect(Collectors.toList()); assertEquals(empList, employees); } collect() performs mutable folding operations (repackageing elements to some data structures and applying some additional logic, concatenating them, etc.) on data elements maintained in the sequence instance. The strategy for this operation is provided through the implementation of the collector interface. In the example above, we used the toList collector to collect all stream elements in a list instance.filterNext, let's take a look at the filter(); this produces a new flow that contains elements of the original sequence that pass a given test (specified by a predicate). Let's take a look at how it works:@Test public vacuum whenFilterEmployees_thenGetFilteredStream() { Enter[] empIds = { 1, 2, 3, 4 }; List<Employee> employee = Stream.of(empIds) .map(employeeRepository::findById) .filter(e -> e != null) .filter(e -> e.getSalary() > 200000) .collect(Collectors.toList()); assertEquals(Arrays.asList(arrayOfEmps[2]), employees); In the example above, we first filter out null references for invalid employee IDs, and then reapply a filter to keep only employees with salaries above a certain threshold.findFirstfindFirst() returns an Optional for the first entry in the sequence; the optional can, of course, be empty:@Test public vacuum whenFindFirst_thenGetFirstEmployeeInStream() { Enter[] empIds = { 1, 2, 3, 4 }; Employee of the worker = Stream.of(empIds) .map(employeeRepository::findById) .filter(e -> e != null) .filter(e -> e.getSalary() > 100000) .findFirst() .orElse(null); assertEquals(employee.getSalary(), new Double(200000)); }Here the first employee with salary over 100,000 is returned. If this employee does not exist, then it is returned null.aArrayWe have seen how we used collect() to obtain data from the sequence. If we need to get an array out of sequence, we can simply use toArray(): @Test public void { Employee[] employees = empList.stream().toArray(Employee[]::new); assertThat(empList.toArray(), equalTo(employees)); } L'empleat de sintaxi[]::new crea una matriu buida de</Employee> </Employee> </Employee> </Employee> ? which is then filled with stream.flatMapA sequence elements can contain complex data structures such as Stream <> <String>>. In cases like this, flatMap() helps us flatten the data structure to simplify new operations:@Test public void whenFlatMapEmployeeNames_thenGetNameStream() { List <> <String>> namesNested = Arrays.asList(Arrays.asList(Jeff, Bezos), Arrays.asList(Bill, Gates), Arrays.asList(Mark, Zuckerberg)); Names<String> listflatStream = namesNested.stream() .flatMap(Collection::stream) .collect(Collectors.toList()); assertEquals(namesFlatStream.size(), namesNested.size() * 2); } See how we were able to convert <> <String>Stream > to a more<String> simple sequence: using the API() API.peek We saw forEach() before this section, which is a terminal operation. However, sometimes we need to perform multiple operations on each element of the sequence before any terminal.peek() operation is applied can be useful in situations like this. In few collaborations, it performs the specified operation on each element of the sequence and returns a new flow that can be used even more. peek() is an intermediate operation: @Test public void whenIncrementSalaryUsingPeek_thenApplyNewSalary() { Employee[] arrayOfEmps = { new Employee(1, Jeff Bezos, 100000.0), new Employee(2, Bill Gates, 200000.0), new Employee(3, Mark Zuckerberg, 300000.0) }; List<Employee> empList = Arrays.asList(arrayOfEmps); empList.stream() .peek(e -> e.salaryIncrement(10.0)) .peek(System.out::p rintln) .collect(Collectors.toList()); assertThat(empList, contains(hasProperty(salary, equalTo(110000.0)), hasProperty(salary, equalTo(220000.0)), hasProperty(salary, equalTo(330000.0)) )); } Here, the first look () is used to increase each employee's salary. The second look () is used to print employees. Finally, collect() is used as a terminal operation. Types of methods and pipelinesAs we have been discussing, java flow operations are divided into intermediate operations and terminals. Intermediate operations, such as filter() return a new script in which additional processing can be performed. Terminal operations, such as forEach(), mark the flow as consumed, after which the point can no longer be used. A current pipe consists of a current source, followed by zero or more intermediate operations, and a terminal operation. Here is a sample flow pipeline, where empList is the source, filter() is the intermediate operation and the count is the terminal operation: @Test public vacuum whenStreamCount_thenGetElementCount() { EmpCount = empList.stream() .filter(e -> e.getSalary() > 200000) .count(); assertEquals(empCount, Long new(1)); } Some operations are considered short circuit operations. Short-circuit operations allow calculations in infinite flows to be completed in finite time: @Test vacuum whenLimitInfiniteStream_thenGetFiniteElements() {<Integer> Stream infiniteStream = Stream.iterate(2, i -> i * 2); Llista<Integer> de recopilaci? = infiniteStream .skip(3)</Integer> </Integer> </Employee> </String> </String> </String> </String> </String> </String> .collect(Collectors.toList()); assertEquals(collect, Arrays.asList(16, 32, 64, 128, 256)); }Here, we use short circuit () operations to omit the first 3 elements, and limit() to limit to 5 elements of the infinite flow generated using iterate(). We'll talk more about infinite flows later. Lazy evaluation One of the most important features of java streams is that they allow for significant optimizations through lazy assessments. Computing on source data is only performed when the terminal operation begins, and source items are only consumed as needed. All intermediate operations are lazy, so they don't run until a processing result is needed. For example, consider the findFirst() example we saw before. How many times is the map() operation performed here? 4 times, since the input array contains 4 elements?@Test public vacuum whenFindFirst_thenGetFirstEmployeeInStream() { Enter[] empIds = { 1, 2, 3, 4 }; Employee of the worker = Stream.of(empIds) .map(employeeRepository::findById) .filter(e -> e != null) .filter(e -> e.getSalary() > 100000) .findFirst() .orElse(null); assertEquals(employee.getSalary(), new Double(200000)); }Stream performs the map and two filter operations, one item at a time. First performs all operations on iD 1. Since the salary of ID 1 is not greater than 100000, the procedure is passed to the next item. ID 2 satisfies the two filter predicates and therefore the sequence evaluates the terminal operation findFirst() and returns the result. Operations are not performed on iD 3 and 4.Processing streams allows you to avoid examining all data when this is not necessary. This behavior becomes even more important when the input flow is infinite and not just very pare sequence operations based on deLet startup with the sorted operation () ? this sorts the flow elements based on the past comparator we pass to it. For example, we may sort employees based on their names:@Test public emptiness whenSortStream_thenGetSortedStream() { List<Employee> employee = empList.stream() .sorted((e1, e2) -> e1.getName().compareTo(e2.getName())) .collect(Collectors.toList()); assertEquals(employees.get(0).getName(), Bill Gates); assertEquals(employees.get(1).getName(), Jeff Bezos); assertEquals(employees.getName(), MarkZuckerberg); Note that short circuit will not be applied for sorting (). This means that, in the example above, even if we had used findFirst() after the sorted(), the classification of all items is done before applying findFirst(). This happens because the operation cannot know which is the first element until the entire sequence is sorted.min and maxAs the name suggests, min() and max() return the minimum and maximum element in the stream respectively, based on a comparator. They return an Optional since a result may or may not (for example, due to filtering): @Test empty public whenFindMin_thenGetMinElementFromStream() { Employee firstEmp = empList.stream() </Employee> </Employee> e2) -> e1.getId() - e2.getId()) .orElseThrow(NoSuchElementException::new); assertEquals(firstEmp.getId(), new Enter(1)); }We can also avoid setting comparison logic using paring():@Test public void whenFindMax_thenGetMaxElementFromStream() { Employee maxSalEmp = empList.stream() .max(paring(Employee:::getSalary)) .orElseThrow() .orElseThrow() NoSuchElementException::new); assertEquals(maxSalEmp.getSalary(), new Double(300000.0)); } distinctdistinct() takes no arguments and returns the different elements of the sequence, removing duplicates. Use the equals() method of the elements to decide whether two elements are equal or not:@Test public vacuum whenApplyDistinct_thenRemoveDuplicatesFromStream() { List<Integer> intList = Arrays.asList(2, 5, 3, 2, 4, 3); List<Integer> distinctIntList = intList.stream().distinct().collect(Collectors.toList()); assertEquals(distinctIntList, Arrays.asList(2, 5, 3, 4)); } allMatch, anyMatch and noneMatchThese operations take a predicate and return a Boolean. Short circuit is applied and processing stops as soon as the answer is determined:@Test public void whenApplyMatch_thenReturnBoolean() { List<Integer> intList = Arrays.asList(2, 4, 5, 6, 8); boolean allEven = intList.stream().allMatch(and -> and %2 == 0); boolean oneEven = intList.stream().anyMatch(i -> and %2 == 0); boolean noneMultiple D'ree = intList.stream().noneMatch(and -> and %3 == 0); assertEquals(allEven, false); assertEquals(oneEven, true); assertEquals(noneMultipleOfThree, false); } allMatch() checks whether the predicate is true for all elements of the sequence. Here, it returns false as soon as you encounter 5, which is not divisible by 2.anyMatch() checks whether the predicate is true for any element in the stream. Here, again short circuit is applied and true is returned immediately after the first element.noneMatch() checks if there are no elements matching the predicate. Here, it simply returns false as soon as you encounter 6, which is divisible by 3.Java Stream SpecialsDes from what we have discussed so far, Stream is a flow of object references. However, there are also the IntStream, LongStream, and DoubleStream ? which are primitive specialties for int, long and double respectively. These are very convenient when it comes to a lot of numerical primitives. These specialized streams do not extend current, but extend BaseStream to the top of which Stream is also built. As a result, not all operations supported by Stream are present in these streaming implementations. For example, standard min() and max() take a comparator, while specialized flows do not. CreationThe most common way to create an IntStream is to name mapToInt() in an existing flow:@Test public vacuum whenFindMaxOnIntStream_thenGetMaxInteger() { Enter latestEmpId = empList.stream() .mapToInt(Employee::getId) .max() assertEquals(latestEmpId, new Integer(3)} Aqu? comencem amb un</Integer> </Integer> </Integer> </Integer> and get an IntStream by supplying the employee::getId to mapToInt. Finally, we call max() which returns the highest integer. We may also use IntStream.of() to create IntStream:IntStream.of(1, 2, 3);o IntStream.range():IntStream.range(10, 20)which creates IntStream from numbers 10 to 19.An important distinction to consider before moving on to the following topic: Stream.of(1, 2, 3)This returns a flow<Integer> and not IntStream.Similarly, using map() instead of mapToInt() returns a Stream and<Integer> not an IntStream.:empList.stream().map(Employee::getId); Specialized operationsSpecialized provide additional operations compared to the standard current, which are quite convenient when it comes to numbers. For example sum(), average(), range() etc:@Test public void whenApplySumOnIntStream_thenGetSum() { Double avgSal = empList.stream() .mapToDouble(Employee::getSalary) .average() .orElseThrow(NoSuchElementException::new); assertEquals(avgSal, new Double(200000)); } Reduction operations A reduction operation (also called sheet) takes a sequence of input elements and combines them into a single summary result by repeatedly applying a combined operation. We have already seen few reduction operations such as findFirst(), min() and max(). We saw the general purpose operation reduce() in action.reduceThe most common form of reduce() is:T reduce(T identity, BinaryOperator<T> accumulator)where identity is the starting value and accumulator is the binary operation we repeated apply. For example:@Test public vacuum whenApplyReduceOnStream_thenGetValue() { Double sumSal = empList.stream() .map(Employee::getSalary) .reduce(0.0, Double::sum); assertEquals(sumSal, new Double(600000)); } Here, we start with the initial value of 0 and repeat apply Double::sum() to elements in the sequence. We have effectively implemented DoubleStream.sum() by applying reduce() to Stream.Advanced collectWe we have already seen how we used Collectors.toList() to remove the list from the sequence. Let's now see few more ways to collect elements of public vacuum stream.joining@Test whenCollectByJoining_thenGetJoinedString() { String empNames = empList.stream() .map(Employee::getName) .collect(Collectors.joining(, )) .toString(); assertEquals(empNames, Jeff Bezos, Bill Gates, Mark Zuckerberg); } Collectors.joining() will insert the delimiter between the two string elements in the sequence. Use a java.util.StringJoiner internally to perform the joining operation.toSetEm we can also use toSet() to obtain a set of current elements:@Test public vacuum whenCollectBySet_thenGetSet() { Set<String> empNames = empList.stream() .map(Employee::getName) .collect(Collectors.toSet()); assertEquals(empNames.size(), 3); } toCollectionSeem use Collectors.toCollection() to extract items in any other collection by going through a<Collection>Provider. We can also use a constructora per al prove?dor: @Test buit p?blic whenToVectorCollection_thenGetVector() { Vector<String> empNames =</String> </Collection> </String> </T> </Integer> </Integer> </Integer> .map(Employee::getName) .collect(Collectors.toCollection(Vector::new)); assertEquals(empNames.size(), 3); }Here, an empty collection is created internally, and its add() method is named in each element of the stream.summarizingDoublesummarizingDouble() is another interesting collector - it applies a dual production mapping function to each input element and returns a special class that contains statistical information for the resulting values: @Test public vacuum whenApplySummarizing_thenGetBasicStats() { DoubleS statistics statistics statistics = empList.stream() .collect(Collectors.summarizingDouble(Employee::getSalary)); assertEquals(stats.getCount(), 3); assertEquals(stats.getSum(), 600000.0, assertEquals(stats.getSum(), 600000.0, 0); assertEquals(stats.getMin(), 100000.0, 0); assertEquals(stats.getMax(), 300000.0, 0); assertEquals(stats.getAverage(), 200000.0, 0); } See how we can analyze each employee's salary and get statistical information about this data, such as min, max, average etc.summaryStatistics() can be used to generate similar results when we are using one of the specialized streams:@Test public vacuum whenApplySummaryStatistics_thenGetBasicStats() { DoubleSummaryStatistics stats = empList.stream() .mapToDouble(Employee::getSalary) .. summaryStatistics(); assertEquals(stats.getCount(), 3); assertEquals(stats.getSum(), 600000.0, 0); assertEquals(stats.getMin(), 100000.0, 0); assertEquals(stats.getMax(), 300000.0, 0); assertEquals(stats.getAverage(), 200000.0, 0); } partitioningByWe can partition a stream in two - based on whether the elements satisfy certain criteria or not. We divide our list of numerical data, into even and ods:@Test public void whenStreamPartition_thenGetMap() { List<Integer> intList = Arrays.asList(2, 4, 5, 6, 8); Map <Boolean,></Boolean,> <Integer>> isEven = intList.stream().collect(Collectors.partitioningBy(i -> and %2 == 0)); assertEquals(isEven.get(true).size(), 4); assertEquals(isEven.get(false).size(), 1); } Here, the flow is partitioned on a map, with pairs and probabilities stored as true and false keys.agrupaci?BygroupingBy() offers advanced partitions - where we can partition the flow into more than two groups. It takes a rating function as its parameter. This rating function applies to each element in the sequence. The value returned by the function is used as the map key we get from the GroupingFor collector:@Test public vacuum whenStreamGroupingBy_thenGetMap() { Map <Character,></Character,> <Employee>> groupByAlphabet = empList.stream().collect(Collectors.groupingBy(e -> new Character(e.getName().charAt(0)));assertEquals() groupByAlphabet.get('B').get(0).getName(), Bill Gates); assertEquals(groupByAlbetpha.get('J').get(0).get(get).get(0).get Name(), Jeff Bezos); assertEquals(groupByAlphabet.get('M').get(0).getName(), Mark Zuckerberg); } In this quick example, we have grouped employees according to the of its name.mappinggroupingBy() discussed in the previous section, groups elements of the sequence with the use of a map. However,</Employee> </Integer> </Integer> </Integer> they may need to group data into a different type of item type. Here's how we can do that; we can use mapping() that can actually adapt the collector to a different type : using a mapping function:@Test public void whenStreamMapping_thenGetMap() { Map <Character,></Character,> <Integer>> idGroupedByAlbetpha = empList.stream().collect( Collectors.groupingBy(e -> new Character(e.getName().charAt(0)), Collectors.mapping(Employee::get, Collectors.toList()));assertEquals(idGroupedByAlphabet.get('B').get(0), new Integer(0). assertEquals(idGroupedByAlphabet .get('J').get(0), new Integer(1)); assertEquals(idGroupedByAlphabet.get('M').get(0), new In Integer(1)); assertEquals(idGroupedByAlphabet.get('M').get(0), new Integer(3)); } Here mapping() maps the Employee power element in only the employee id ? which is an Integer ? using the getId() mapping function. These ids are still grouped according to the initial character of the employee's name.reducingreducing() is similar to reduce() ? which we have explored before. It simply returns a collector who makes a reduction of his input elements:@Test public vacuum whenStreamReducing_thenGetValue() { Double percentage = 10.0; Double salIncrOverhead = empList.stream().collect(Collectors.reducing(0.0, e -> e.getSalary() * percentage / 100, (s1, s2) -> s1 + s2)); assertEquals(salIncrOverhead, 60000.0, 0); }Here the reduction() gets the wage increase for each employee and returns the sum.reducing() is most useful when used in a reduction of multiple levels, downstream of the groupby() or partitioningBy(). To perform a simple reduction in a sequence, use reduce() instead. For example, let's see how we can use reduction() with GroupingBy():@Test public void whenStreamGroupingAndReducing_thenGetMap() { Comparator<Employee> byNameLength = paring(Employee::getName); Map <Character,></Character,> <Employee>> longerNameByAlphabet = empList.stream().collect(Collectors.groupingBy(e -> new Character(e.getName().charAt(0)), Collectors.reducing(BinaryOperator.maxBy(byNameLength)));assertEquals(longestNameByAlphabet); .get('B').get(getName)), Bill assertEquals(longestNameByAlphabet.get('J').get().getName(), Jeff Bezos); assertEquals(longestNameByAlphabet.get('M').get().getName(), Mark Zuckerberg); } Here we group employees according to the initial character of their name. Within each group, we find the employee with the longest name. Parallel currents Using the support for parallel currents, we can perform parallel current operations without having to write any boiler plate code; we just need to designate the current as parallel:@Test public void whenParallelStream_thenPerformOperationsInParallel() { Employee[] arrayOfEmps = { new Employee(1, Jeff Bezos, 100000.0), new Employee(2, Bill Gates, 200000.0), new Employee(3, Mark Zuckerberg, 300000.0) };

List<Employee> empList = empList.stream().parallel().forEach(e -> e.salaryIncrement(10.0)); assertThat(empList, contains( hasProperty(salari, equalTo(110000.0)), hasProperty(salari, equalTo(220000.0)),</Employee> </Employee> </Employee> </Integer> </Integer> equalTo(330000.0)) )); }Here salaryIncrement() would run in parallel on multiple elements of the sequence, simply by adding the parallel syntax(). This functionality can of course be fine-tuned and configured even more, if you need more control over the performance features of the operation. As is the case with multith way code writing, we need to be aware of few things while using parallel flows: We need to make sure the code is thread secure. Special care should be taken if the operations performed in parallel modify the shared data. We should not use parallel flows if the order in which operations are performed or the command returned in the output flow matters. For example, operations such as findFirst() can generate the different result in case of parallel flows. In addition, we need to make sure it's worth making the code run in parallel. Understand the performance features of the particular operation, but also the system as a whole ? it is naturally very important here. Infinite StreamsSometimes, we may want to perform operations while the items are still generated. We may not know in advance how many items we will need. Unlike the use of the list or map, where all items are already populated, we can use infinite flows, also called dead-end flows. There are two ways to generate infinite flows: generate Provide a provider to generate() that is called each time new flow items have to be generated:@Test public void whenGenerateStream_thenGetInfiniteStream() { Stream.generate(Math::random) .limit(5) .forEach(System.out::p rintln); } Here, we pass Math::random() as Provider, which returns the next random number. With infinite flows, we need to provide a condition to finally finish processing. A common way to do this is to use limit(). In the example above, we limit the sequence to 5 random numbers and print them as they are generated. Note that the past provider for generating() might be noticeable and this sequence may not produce the same result when used in parallel.iterateiterate() takes two parameters: an initial value, called a seed element and a function that generates the next item using the previous value. iterate(), by design, is state and therefore may not be useful in parallel currents:@Test public vacuum whenIterateStream_thenGetInfiniteStream() { Stream<Integer> evenNumStream = Stream.iterate(2, and -> and *2); List<Integer> collect = evenNumStream .limit(5) .collect(Collectors.toList()); assertEquals(collect, Arrays.asList(2, 4, 8, 16, 32)); } Here, we pass 2 as the value of the seed, which becomes the first element of our current. This value is passed as input to the lambda, which returns 4. This value, in turn, is passed as input into the next iteration. This continues until we generate the number of specified by limit() that acts as an end condition. OperationsLet file see how we could use the script in file operations. File Type Operation@Test Vacuum</Integer> </Integer> </Integer > launches IOException { String[] words = { hello, redo, world, level }; try (PrintWriter pw = new PrintWriter(Files.newBufferedWriter(Paths.get(fileName)))) { Stream.of(words).forEach(pw::p rintln); } } } } Here we use forEach() to write each element of the sequence to the file by calling PrintWriter.println(). Read File Privacy List<String> getPalindrome(Stream<String> stream, int length) { return stream.filter(s -> s.length() == length) .filter(s -> pareToIgnoreCase(new StringBuilder(s).reverse().toString().)) == 0) .collect(Collectors.toList()); } @Test public void whenFileToStream_thenGetStream() launches IOException { List<String> str = getPalindrome(Files.lines(Paths.get(fileName)), 5); assertThat(str, contains(redo, level)); Here Files.lines() returns the lines of the file as a flow that is consumed by getPalindrome() for more processing.getPalindrome() works in the current, completely unaware of how the flow was generated. This also increases code reuse and simplifies unit testing. Java Streams Improvements In Java 9Java 8 brought java streams to the world. However, the following version of the language also contributed to the feature. Therefore, we will now give a brief overview of the improvements that Java 9 brought to the streams API. Let's do it.takeWhileThe takeWhile method is one of the new additions to the Streams API. It does what its name implies: it takes (elements of a stream) while a given condition is true. By the time the condition becomes false, it exits and returns a new flow with only items that matched the predicate. In other words, it's like a filter with a condition. Let's see a quick example. Stream.iterate(1, and -> i + 1) .takeWhile(n -> n <= 10)= .map(x= -=>x * x) .forEach(System.out::p rintln); In the above code we get an infinite flow and then use the takeWhile method to select numbers that are less than or equal to 10. After that, we calculate their square cases and print them. You might wonder what the difference is between takeWhile and filter. After all, you can achieve the same result with the following code: Stream.iterate(1, and -> and +1) .filter(x -> <= 10)= .map(x= -=>x * x) .forEach(System.out::p rintln); Well, in this particular scenario, both methods achieve the same result, but that's not always the case. We illustrate the difference with another example: Stream.of(1,2,3,4,5,6,7,8,9,0,9,8,7,6,5,4,3,2,1,0) .takeWhile(x -> x <= 5)= .foreach(system.out::p rintln);= stream.of(1,2,3,4,5,6,7,8,9,0,9,9,8,7,6,5,4,3,2,1,0)= .filter(x= -=>x <= 5) .forEach(System.out::p rintln); Here, we have two identical streams, which we filter using takeWhile and filter, respectively. So, what's the difference? If you run the code above you'll see that the first version prints out:1 2 3 4 5while the version with filter results in1 2 3 4 5 0 5 4 3 2 1 0As you see, filter() applies the predicate throughout the whole sequence. On the other hand, takeWhile stops evaluating as soon as it 5)= .foreach(system.out::println);here,= we= have= two= identical= streams,= which= we= filter= using= takewhile= and= filter,= respectively.= so,= what's= the= difference?= if= you= run= the= code= above= you'll= see= that= the= first= version= prints= out:1= 2= 3= 4= 5while= the= version= with= filter= results= in1= 2= 3= 4= 5= 0= 5= 4= 3= 2= 1= 0as= you= can= see,= filter()= applies= the= predicate= throughout= the= whole= sequence.= on= the= other= hand,= takewhile= stops= evaluating= as= soon= as= it=></= 5) .forEach(System.out::println); Here, we have two identical streams, which we filter using takeWhile and filter, respectively. So, what's the difference? If you run the code above you'll see that the first version prints out:1 2 3 4 5while the version with filter results in1 2 3 4 5 0 5 4 3 2 1 0As you can see, filter() applies the predicate throughout the whole sequence. On the other hand, takeWhile stops evaluating as soon as it > </=> </=> </=> </String> </String> </String> </String> la primera ocurr?ncia on la condici? ?s false.dropWhileThe dropWhile method fa gaireb? el mateix que fa el takewhile per? al rev?s. Conf?s? ?s senzill: mentre pren mentre la seva condici? ?s certa, cau elements mentre la condici? ?s certa. ?s a dir: el m?tode anterior utilitza el predicat (la condici?) per seleccionar els elements a conservar en la nova seq??ncia que retorna. Aquest m?tode fa el contrari, utilitzant la condici? per seleccionar els elements que no s'han d'incloure a la seq??ncia resultant. Vem un exemple:Stream.of(1,2,3,4,5,6,7,8,9,0,9,8,7,6,5,4,3,2,1,0) .dropWhile(x -> x <= 5)= .foreach(system.out::println);this= is= the= same= as= the= previous= example,= the= only= difference= being= that= we're= using= dropwhile= instead= of= takewhile.= that= is= to= say,= we're= now= dropping= elements= that= are= less= than= or= equals= to= five.= the= resulting= items= are:6= 7= 8= 9= 0= 9= 8= 7= 6= 5= 4= 3= 2= 1= 0as= you= can= see ,= there= are= numbers= less= than= or= equals= to= five= in= the= latter= half= of= the= sequence.= why?= it's= simple:= they= came= after= the= first= element= which= failed= to= match= the= predicate,= so= the= method= stopped= dropping= at= that= point.iteratewe've= already= mentioned= the= original= iterate()= method= that= was= introduced= in= the= 8th= version= of= java.= java= 9= brings= an= override= of= the= method.= so ,= what's= the= difference?as= you've= learned,= the= original= incarnation= of= the= method= had= two= arguments:= the= initializer= (a.k.a.= the= seed)= and= the= function= that= generates= the= next= value.= the= problem= with= the= method= is= that= it= didn't= include= a= way= for= the= loop= to= quit.= that's= great= when= you're= trying= to= create= infinite= streams,= but= that's= not= always= the= case.in= java= 9= we= have= the= new= version= of= iterate() ,= which= adds= a= new= parameter,= which= is= a= predicate= used= to= decide= when= the= loop= should= terminate.= as= long= as= the= condition= remains= true,= we= keep= going.consider= the= following= example:stream.= iterate(1,= i= -=> < 256,= i= -=> i * 2) .forEach(System.out::p rintln); El codi anterior imprimeix els poders de dos, sempre que siguin menys de 256. Podr?em dir que el nou m?tode iterate() ?s un reempla?ament per al bon-vell per a la declaraci?. De fet, el codi anterior equival al seg?ent extracte:for (int i = 1; resultat i < 256;= i*=2) {= system.out.println(i);= }ofnullablethe= last= item= in= this= list= of= additions= to= the= stream= apis= is= a= powerful= way= not= only= to= avoid= the= dreaded= null= pointer= exception= but= also= to= write= cleaner= code.= hopefully,= it's= very= straightforward.= check= out= the= following=> <Integer>= n?mero != null ? Stream.of(n?mero) : Stream.empty(); Suposem que el n?mero fa refer?ncia a algun enter obtingut a trav?s de la interf?cie d'usuari, la xarxa, el sistema de fitxers o una altra font externa no fiable. Per tant, podria ser We did not want to create a flow with a null element; which could result in a null pointer exception at some point. To prevent us from checking if it is null and return an empty sequence. The example above is a blunt, sure example. In real life, the code in similar scenarios could really become</Integer> </=> </=> very fast. We could use ofNullable() instead:Result<Integer> of sequence = Stream.ofNullable(number); The new method returns optional gaps in it receives runtime errors in scenarios that would normally cause one, as in the following example: Whole number = null; Result<Integer> sequence = Stream.ofNullable(number); result.map(x -> x * x).forEach(System.out::p rintln);%MCEPASTEBIN%Java Streams: What are the next steps? In this article, we focused on the details of the new stream functionality in Java 8. We saw several supported operations and how lambdas and pipes can be used to write concise code. We also saw some features of currents such as lazy evaluation, parallel and infinite currents. You will find the sources of the examples on GitHub.Now, what should you do next? Well, there's a lot to explore on your journey to be a better Java developer, so here are some suggestions. To begin with, you can continue exploring the concepts you've seen today with a look at the reactive paradigm, made possible by concepts very similar to those discussed here. Also, stay in touch with the Stackify blog. We are always publishing articles that may be of interest to you. You may need to learn more about the main java frameworks or how to properly manage exceptions in the language. In today's article, we covered an important feature that was introduced with Java 8. The language has come a long way since then and you may want to check out the latest developments. Finally, to be a great developer you can't overlook performance. We have messages covering from Java performance tuning tips to the main tools that you should check, and much more in between. And speaking of tools, you may want to take a look at the free profiler for Stackify, Prefix. With Prefix, you can monitor both the Windows desktop and web applications, review their performance, find hidden exceptions, and resolve errors before you reach production. In addition to Java, Prefix is also available for C#/.NET. Download and try this today. Today. </Integer></Integer>

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