Specialized StreamsIntStream | for int elements | DoubleStream | for double elements | LongStream | for long elements |
It has better performance to use these specialized streams when using numeric data types, because there is no boxing/unboxing Suppress elementslimit | .limit(5)
will limit the result to the first 5 elements
| skip | .skip(5)
will skip the first 5 elements
| filter | .filter(e -> e.getSalary() > 200000)
will keep the elements that satisfy the given predicate. In this case, all elements that have salary above 200000
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Comparing elementsdistinct | .distinct()
will compare the elements in the stream using equals() and eliminate duplicates
| sorted | .sorted((e1, e2) -> e1.getName().compareTo(e2.getName()))
will sort the elements with the given comparator. Elements must be Comparable.
| min | Similar to sorted, but it will find the min element according to the given comparator | max | Similar to sorted, but it will find the max element according to the given comparator |
Apply a function to each elementmap | .map(employeeRepository::findById)
will apply the given function and substitute the elements in the stream for new elements. In this case, it received a stream of employee IDs and returned a stream of Employee objects
| mapToDouble
mapToInt
mapToLong | similar to map, but the function converts the element to the specified primitive type, resulting in a specialized stream IntStream, DoubleStream or LongStream | flatMap | similar to map, but the number of elements resulting may be different. It's normally used to convert a List of List into a single list with all the elements | peek | .peek(e -> e.salaryIncrement(10.0))
will apply the give function to all elements in the list, but doesn't substitute the elements in the list
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| | Reduce elements to single valuereduce | .reduce(0.0, Double::sum)
will return a single value. It starts with the identity value (0.0) and applies the given function to each element in the array. In this case it's summing all elements, one by one
| allMatch | .allMatch(i -> i % 2 == 0);
will check if all elements match the given condition. If so, returns true, else returns false
| anyMatch | .anyMatch(i -> i % 2 == 0);
will check if one of the elements match the given condition. If so, returns true, else returns false
| noneMatch | .noneMatch(i -> i % 2 == 0);
will check if no elements match the given condition. If so, returns true, else returns false
| findFirst | .findFirst()
will return an Optional with the first element in the stream
| forEach | forEach(e -> e.salaryIncrement(10.0))
will apply the given function to each element in the stream, but it's a terminal operation and returns void
| count | .count()
outputs the number of elements in the stream
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Collect elementstoList | collect(Collectors.toList())
gather all elements in the stream into a List
| toSet | collect(Collectors.toSet())
gather all elements in the stream into a Set
| toCollection | collect(Collectors.toCollection(Vector::new))
gather all elements in the list in an arbitrary Collection
| joining | collect(Collectors.joining(", ")).toString()
will join String elements with the given separator and return the aggregated String
| summarizingDouble | summaryStatistics | partitioningBy | .collect(Collectors.partitioningBy(s -> s.getGrade() >= PASS_THRESHOLD))
will partition the data into 2 categories based on the given condition. The result will be a Map<Boolean, List<Student>>
| groupingBy | .collect(Collectors.groupingBy(Employee::getDepartment));
will group the elements into categories based on the function. The result will be a Map<Department, List<Employee>>
| mapping | mapping(Person::getLastName, toSet())
it receives a function to be applied to all elements and way of collecting downstream the elements. In this case, it will get the last name of all persons and add them to a set
| reducing |
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