preface

When we operate on set data, we usually iterate through for or iterator, which is not very nice. Java provides the concept of Stream, which allows us to treat collection data as individual elements, and provides a multithreaded schema

The creation of a flow
Various data operations for streams
The termination operation of a stream
Aggregate processing of streams
Concurrent streams are used in conjunction with CompletableFuture

Pay attention to the public account, communicate together, wechat search: sneak forward

1 The construction of stream

The built-in constructor for stream

public static<T> Stream<T> iterate(final T seed, final UnaryOperator<T> f)
public static <T> Stream<T> concat(Stream<? extends T> a, Stream<? extends T> b)
public static<T> Builder<T> builder(a)
public static<T> Stream<T> of(T t)
public static<T> Stream<T> empty(a)
public static<T> Stream<T> generate(Supplier<T> s)
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The stream function declared by Collection

default Stream<E> stream(a)
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Collection declares a stream conversion function, which means that any Collection subclass has an official method that we implement to convert Collection to stream
For example, List to Stream

public static void main(String[] args){
    List<String> demo =  Arrays.asList("a"."b"."c");
    long count = demo.stream().peek(System.out::println).count();
    System.out.println(count);
}
-------result--------
a
b
c
3
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Stream defines the operation methods on elements

Filter filter

Stream<T> filter(Predicate<? super T> predicate)
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Predicate is a functional interface that can be replaced directly with lambda; If you have complex filtering logic, use the OR, and, negate methods
The sample

List<String> demo = Arrays.asList("a"."b"."c");
Predicate<String> f1 = item -> item.equals("a");
Predicate<String> f2 = item -> item.equals("b");
demo.stream().filter(f1.or(f2)).forEach(System.out::println);
-------result--------
a
b
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Mapping to Map

<R> Stream<R> map(Function<? super T, ? extends R> mapper)
IntStream mapToInt(ToIntFunction<? super T> mapper);
LongStream mapToLong(ToLongFunction<? super T> mapper);
DoubleStream mapToDouble(ToDoubleFunction<? super T> mapper);
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The sample

static class User{
    public User(Integer id){this.id = id; }
    Integer id; public Integer getId(a) {  returnid; }}public static void main(String[] args) {
    List<User> demo = Arrays.asList(new User(1), new User(2), new User(3));
    // Convert User to Integer(id)
    demo.stream().map(User::getId).forEach(System.out::println);
}
-------result--------
1
2
3
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Data processing PEEK

Stream<T> peek(Consumer<? super T> action);
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The difference with map is that there is no return value
The sample

static class User{
    public User(Integer id){this.id = id; }
    Integer id;
    public Integer getId(a) {  return id; }
    public void setId(Integer id) {  this.id = id; }}public static void main(String[] args) {
    List<User> demo = Arrays.asList(new User(1), new User(2), new User(3));
    // id squared, User converted to Integer(id)
    demo.stream().peek(user -> user.setId(user.id * user.id)).map(User::getId).forEach(System.out::println);
}
-------result--------
1
4
9
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Maps flatten flatmaps

<R> Stream<R> flatMap(Function<? super T, ? extends Stream<? extends R>> mapper);
IntStream flatMapToInt(Function<? super T, ? extends IntStream> mapper);
LongStream flatMapToLong(Function<? super T, ? extends LongStream> mapper);
DoubleStream flatMapToDouble(Function<? super T, ? extends DoubleStream> mapper);
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FlatMap: flatMap a Stream with element type T into a Stream with element type T
The sample

public static void main(String[] args) {
    List<Stream<Integer>> demo = Arrays.asList(Stream.of(5), Stream.of(2), Stream.of(1));
    demo.stream().flatMap(Function.identity()).forEach(System.out::println);
}
-------result--------
5
2
1
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To heavy distinct

Stream<T> distinct(a);
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The sample

List<Integer> demo = Arrays.asList(1.1.2);
demo.stream().distinct().forEach(System.out::println);
-------result--------
1
2
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Sorting sorted

Stream<T> sorted(a);
Stream<T> sorted(Comparator<? super T> comparator);
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The sample

List<Integer> demo = Arrays.asList(5.1.2);
// Default ascending order
demo.stream().sorted().forEach(System.out::println);
/ / descendingComparator<Integer> comparator = Comparator.<Integer, Integer>comparing(item -> item).reversed(); demo.stream().sorted(comparator).forEach(System.out::println); ------- Default ascending result--------1
2
5-- -- -- -- -- -- -- descending result -- -- -- -- -- -- -- --5
2
1
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Number limit limit and skip

// Truncate the first maxSize element
Stream<T> limit(long maxSize);
// Skip the first n streams
Stream<T> skip(long n);
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The sample

List<Integer> demo = Arrays.asList(1.2.3.4.5.6);
// Skip the first two, then limit the intercepts to two
demo.stream().skip(2).limit(2).forEach(System.out::println);
-------result--------
3
4
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New operations provided by JDK9

Different from filter, takeWhile takes the element that meets the condition until it does not. DropWhile discards elements that meet the criteria until they do not

default Stream<T> takeWhile(Predicate<? super T> predicate);
default Stream<T> dropWhile(Predicate<? super T> predicate);
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3 Stream termination action

Traverse the consumption

// Iterate over the consumption
void forEach(Consumer<? super T> action);
// Sequential traversal consumption, the difference between forEach and forEach is that forEachOrdered is executed in multi-threaded parallelStream, and its order is not out of order
void forEachOrdered(Consumer<? super T> action);
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The sample

List<Integer> demo = Arrays.asList(1.2.3);
demo.parallelStream().forEach(System.out::println);
demo.parallelStream().forEachOrdered(System.out::println);
-------forEach result--------
2
3
1
-------forEachOrdered result--------
1
2
3
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Get array results

// Transfer to an Object array
Object[] toArray();
// Stream into A[] array, specifying type A
<A> A[] toArray(IntFunction<A[]> generator)
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The sample

List<String> demo = Arrays.asList("1"."2"."3");
//<A> A[] toArray(IntFunction<A[]> generator)
String[] data = demo.stream().toArray(String[]::new);
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Maximum minimum value

// Get the minimum value
Optional<T> min(Comparator<? super T> comparator)
// Get the maximum value
Optional<T> max(Comparator<? super T> comparator)
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The sample

List<Integer> demo = Arrays.asList(1.2.3);
Optional<Integer> min = demo.stream().min(Comparator.comparing(item->item));
Optional<Integer> max = demo.stream().max(Comparator.comparing(item->item));
System.out.println(min.get()+"-"+max.get());
-------result--------
1-3
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To find the matching

// Any one matches
boolean anyMatch(Predicate<? super T> predicate)
// All matches
boolean allMatch(Predicate<? super T> predicate)
/ / don't match
boolean noneMatch(Predicate<? super T> predicate)
// find the first one
Optional<T> findFirst(a);
// Any one of them
Optional<T> findAny(a);
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Reduction to merge

// Pair up
Optional<T> reduce(BinaryOperator<T> accumulator)
// Pairwise merge, with initial value
T reduce(T identity, BinaryOperator<T> accumulator)
// Convert the element type first and then merge it with the initial value
<U> U reduce(U identity, BiFunction<U, ? super T, U> accumulator, BinaryOperator<U> combiner)
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The sample

List<Integer> demo = Arrays.asList(1.2.3.4.5.6.7.8);
// Numbers are converted to strings and then concatenated with "-"
String data = demo.stream().reduce("0", (u, t) -> u + "-" + t, (s1, s2) -> s1 + "-" + s2);
System.out.println(data);
-------result--------
0-1-2-3-4-5-6-7-8
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Counting elements

long count(a)
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The sample

List<Integer> demo = Arrays.asList(1.2.3.4.5.6);
System.out.println(demo.stream().count());
-------result--------
6
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Polymerization treatment of convection

/** * supplier: accumulator: element consumer (process and add R) * combiner: how to combiner */
<R> R collect(Supplier<R> supplier, BiConsumer<R, ? super T> accumulator, BiConsumer<R, R> combiner);
/** * Collector is a collector class composed of Supplier, Accumulator, Combiner, Finisher, and Characteristics. It can provide built-in aggregation classes or methods */
<R, A> R collect(Collector<? super T, A, R> collector);
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For an example, see below

4 Collector(aggregation) Tool class collection

Interface Collector and implementation class CollectorImpl

// Return the producer of the value type
Supplier<A> supplier(a);
// Stream element consumers
BiConsumer<A, T> accumulator(a);
// Return value consolidator (multiple thread operations generate multiple return values and need to merge)
BinaryOperator<A> combiner(a);
// Return value converter (last step, actual return result, generally return as is)
Function<A, R> finisher(a);
// Stream properties
Set<Characteristics> characteristics(a);

public static<T, A, R> Collector<T, A, R> of(Supplier supplier, BiConsumer
        
          accumulator, BinaryOperator
        ,> combiner, Function
        
          finisher, Characteristics... characteristics)
        ,>
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Stream aggregation converts to List, Set

// Convert to List
public static<T> Collector<T, ? , List<T>> toList()// Convert to Set
public static<T> Collector<T, ? , Set<T>> toSet()Copy the code

The sample

List<Integer> demo = Arrays.asList(1.2.3);
List<Integer> col = demo.stream().collect(Collectors.toList());
Set<Integer> set = demo.stream().collect(Collectors.toSet());
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Flow aggregation is converted to a Map

// Convert to Map
public static<T, K, U> Collector<T, ? , Map<K,U>> toMap( Function<?super T, ? extends K> keyMapper,
    Function<? super T, ? extends U> valueMapper)
/** * mergeFunction: */
public static<T, K, U> Collector<T, ? , Map<K,U>> toMap( Function<?super T, ? extends K> keyMapper,
	Function<? super T, ? extends U> valueMapper,
    BinaryOperator<U> mergeFunction)
/** * mergeFunction: how to combine the same key * mapSupplier: returns the producer of the Map */
public static<T, K, U, M extends Map<K, U>> Collector<T, ? , M> toMap( Function<?super T, ? extends K> keyMapper,
	Function<? super T, ? extends U> valueMapper,
	BinaryOperator<U> mergeFunction,
    Supplier<M> mapSupplier)
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If there are elements with the same key, an error is reported; Or use groupBy
The sample

List<User> demo = Arrays.asList(new User(1), new User(2), new User(3));
Map<Integer,User> map = demo.stream().collect(Collectors.toMap(User::getId,item->item));
System.out.println(map);
-------result-------
{1=TestS$User@7b23ec81, 2=TestS$User@6acbcfc0, 3=TestS$User@5f184fc6}
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String stream aggregation concatenation

// Concatenate multiple strings into a single string
public staticCollector<CharSequence, ? , String> joining();// Concatenate multiple strings into a single string (specifying delimiters)
public staticCollector<CharSequence, ? , String> joining(CharSequence delimiter)Copy the code

The sample

List<String> demo = Arrays.asList("c"."s"."c"."w"."Sneak along.");
String name = demo.stream().collect(Collectors.joining("-")); System.out.println(name); -------result------- C-S-C-W - stealth forwardCopy the code

The map handles the reaggregation flow

It is equivalent to map and collect

/** * mapper: indicates a mapping processor * downstream: indicates that the mapping process needs to be aggregated again */
public static<T, U, A, R> Collector<T, ? , R> mapping(Function<?super T, ? extends U> mapper, 
		Collector<? super U, A, R> downstream);
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The sample

List<String> demo = Arrays.asList("1"."2"."3");
List<Integer> data = demo.stream().collect(Collectors.mapping(Integer::valueOf, Collectors.toList()));
System.out.println(data);
-------result-------
[1.2.3]
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After aggregation, the results are transformed

/** * Downstream: aggregation process * finisher: result conversion process */
public static<T,A,R,RR> Collector<T,A,RR> collectingAndThen(Collector
       
         downstream, Function
        
          finisher)
        ,>
       ,a,r>; 
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The sample

List<Integer> demo = Arrays.asList(1.2.3.4.5.6);
// Aggregate into a List, and finally extract the size of the array as the return value
Integer size = demo.stream().collect(Collectors.collectingAndThen(Collectors.toList(), List::size));
System.out.println(size);
---------result----------
6
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Stream grouping (Map is HashMap)

/** * classifier specifies an attribute of type T as the Key. * after grouping, use List as the container for each flow. */
public static<T, K> Collector<T, ? , Map<K, List<T>>> groupingBy( Function<?super T, ? extends K> classifier);           
/** * downstream: downstream of each group of streams */
public static<T, K, A, D> Collector<T, ? , Map<K, D>> groupingBy( Function<?super T, ? extends K> classifier， 
		Collector<? super T, A, D> downstream)
/** * downstream: the aggregation processor for each group of streams */
public static<T, K, D, A, M extends Map<K, D>> Collector<T, ? , M> groupingBy( Function<?super T, ? extends K> classifier,
		Supplier<M> mapFactory,
		Collector<? super T, A, D> downstream)
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The sample

public static void main(String[] args) throws Exception {
    List<Integer> demo = Stream.iterate(0, item -> item + 1)
            .limit(15)
            .collect(Collectors.toList());
    // Divide into three groups, and each group of elements is converted to String
    Map<Integer, List<String>> map = demo.stream()
            .collect(Collectors.groupingBy(item -> item % 3,
                    HashMap::new,
                    Collectors.mapping(String::valueOf, Collectors.toList())));
    System.out.println(map);
}
---------result----------    
{0= [0.3.6.9.12].1= [1.4.7.10.13].2= [2.5.8.11.14]}    
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Stream grouping (ConcurrentHashMap is used for grouping)

/** * classifier: classifier; After grouping, use List as a container for each stream */
public static<T, K> Collector<T, ? , ConcurrentMap<K, List<T>>> groupingByConcurrent( Function<?super T, ? extends K> classifier);
/** * downstream: downstream: aggregation of streams */
public static<T, K, A, D> Collector<T, ? , ConcurrentMap<K, D>> groupingByConcurrent( Function<?super T, ? extends K> classifier, Collector<? super T, A, D> downstream)
/** * classifier: classifier * mapFactory: the production factory of the return value type Map (a subclass of ConcurrentMap) * Downstream: the aggregation processor of the flow */
public static<T, K, A, D, M extends ConcurrentMap<K, D>> Collector<T, ? , M> groupingByConcurrent( Function<?super T, ? extends K> classifier, 
		Supplier<M> mapFactory,
		Collector<? super T, A, D> downstream);
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Use the same as groupingBy

Split split, one into two (equivalent to special groupingBy)

public static<T> Collector<T, ? , Map<Boolean, List<T>>> partitioningBy( Predicate<?super T> predicate)
/** * Predicate: binary * Downstream: aggregate processor for streams */
public static<T, D, A> Collector<T, ? , Map<Boolean, D>> partitioningBy( Predicate<?super T> predicate, Collector<? super T, A, D> downstream)
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The sample

List<Integer> demo = Arrays.asList(1.2.3.4.5.6);
// Odd and even groups
Map<Boolean, List<Integer>> map = demo.stream()
	.collect(Collectors.partitioningBy(item -> item % 2= =0));
System.out.println(map);
---------result----------
{false= [1.3.5].true= [2.4.6]}
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Average polymerization

// Return type Double
public static<T> Collector<T, ? , Double> averagingDouble(ToDoubleFunction<?super T> mapper)
// Return type Long
public static<T> Collector<T, ? , Double> averagingLong(ToLongFunction<?super T> mapper)
// Returns an Int
public static<T> Collector<T, ? , Double> averagingInt(ToIntFunction<?super T> mapper)
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The sample

List<Integer> demo = Arrays.asList(1.2.5);
Double data = demo.stream().collect(Collectors.averagingInt(Integer::intValue));
System.out.println(data);
---------result----------
2.6666666666666665
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Stream aggregation finds Max and min values

/ / the minimum
public static<T> Collector<T, ? , Optional<T>> minBy(Comparator<?super T> comparator) 
/ / Max
public static<T> Collector<T, ? , Optional<T>> maxBy(Comparator<?super T> comparator)    
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The sample

List<Integer> demo = Arrays.asList(1.2.5);
Optional<Integer> min = demo.stream().collect(Collectors.minBy(Comparator.comparing(item -> item)));
Optional<Integer> max = demo.stream().collect(Collectors.maxBy(Comparator.comparing(item -> item)));
System.out.println(min.get()+"-"+max.get());
---------result----------
1-5
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Aggregate computes statistical results

You can get the total number of elements, the cumulative sum of elements, the minimum, the maximum, the average

// Returns an Int
public static<T> Collector<T, ? , IntSummaryStatistics> summarizingInt( ToIntFunction<?super T> mapper)
// Return type Double
public static<T> Collector<T, ? , DoubleSummaryStatistics> summarizingDouble( ToDoubleFunction<?super T> mapper)
// Return type Long
public static<T> Collector<T, ? , LongSummaryStatistics> summarizingLong( ToLongFunction<?super T> mapper)        
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The sample

List<Integer> demo = Arrays.asList(1.2.5);
IntSummaryStatistics data = demo.stream().collect(Collectors.summarizingInt(Integer::intValue));
System.out.println(data);
---------result----------
IntSummaryStatistics{count=3, sum=8, min=1, average=2.666667, max=5}
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New aggregation methods provided by JDK12

// Streams are aggregated by downstream1 and downstream2 respectively, and the two aggregated results are combined
public static<T, R1, R2, R> Collector<T, ? , R> teeing( Collector<?superT, ? , R1> downstream1, Collector<?superT, ? , R2> downstream2, BiFunction<?super R1, ? super R2, R> merger) 
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Use of concurrent paralleStream

Use with CompletableFuture and thread pools
The sample

public static void main(String[] args)  throws Exception{
    List<Integer> demo = Stream.iterate(0, item -> item + 1)
            .limit(5)
            .collect(Collectors.toList());
    / / sample 1
    Stopwatch stopwatch = Stopwatch.createStarted(Ticker.systemTicker());
    demo.stream().forEach(item -> {
        try {
            Thread.sleep(500);
            System.out.println(Example 1 - ""+Thread.currentThread().getName());
        } catch (Exception e) { }
    });
    System.out.println(Example 1 - ""+stopwatch.stop().elapsed(TimeUnit.MILLISECONDS));

    // For example 2, note that ForkJoinPool and parallelStream are required to use executor specified threads. Otherwise, use the default ForkJoinPool.commonPool().
    ExecutorService executor = new ForkJoinPool(10);
    stopwatch.reset(); stopwatch.start();
    CompletableFuture.runAsync(() -> demo.parallelStream().forEach(item -> {
        try {
            Thread.sleep(1000);
            System.out.println("Sample 2 -" + Thread.currentThread().getName());
        } catch (Exception e) { }
    }), executor).join();
    System.out.println("Sample 2 -"+stopwatch.stop().elapsed(TimeUnit.MILLISECONDS));
    / / sample 3
    stopwatch.reset(); stopwatch.start();
    demo.parallelStream().forEach(item -> {
        try {
            Thread.sleep(1000);
            System.out.println("Example 3 -"+Thread.currentThread().getName());
        } catch (Exception e) { }
    });
    System.out.println("Example 3 -"+stopwatch.stop().elapsed(TimeUnit.MILLISECONDS));
    executor.shutdown();

}
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——————-result————————–

The sample1- the main sample1- the main sample1- the main sample1- the main sample1- the main sample1-2501The sample2-ForkJoinPool-1-worker-19The sample2-ForkJoinPool-1-worker-9The sample2-ForkJoinPool-1-worker-5The sample2-ForkJoinPool-1-worker-27The sample2-ForkJoinPool-1-worker-23The sample2-1004The sample3- the main sample3-ForkJoinPool.commonPool-worker-5The sample3-ForkJoinPool.commonPool-worker-7The sample3-ForkJoinPool.commonPool-worker-9The sample3-ForkJoinPool.commonPool-worker-3The sample3-1001
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ParallelStream’s methods do run using multiple threads, and thread pools can be specified, but custom threads must be of type ForkJoinPool, otherwise ForkJoinPool.commonPool() is used by default

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Basic: java. Stream functions, elegant data flow operations

preface

Pay attention to the public account, communicate together, wechat search: sneak forward

1 The construction of stream

The built-in constructor for stream

The stream function declared by Collection

Stream defines the operation methods on elements

Filter filter

Mapping to Map

Data processing PEEK

Maps flatten flatmaps

To heavy distinct

Sorting sorted

Number limit limit and skip

New operations provided by JDK9

3 Stream termination action

Traverse the consumption

Get array results

Maximum minimum value

To find the matching

Reduction to merge

Counting elements

Polymerization treatment of convection

4 Collector(aggregation) Tool class collection

Interface Collector and implementation class CollectorImpl

Stream aggregation converts to List, Set

Flow aggregation is converted to a Map

String stream aggregation concatenation

The map handles the reaggregation flow

After aggregation, the results are transformed

Stream grouping (Map is HashMap)

Stream grouping (ConcurrentHashMap is used for grouping)

Split split, one into two (equivalent to special groupingBy)

Average polymerization

Stream aggregation finds Max and min values

Aggregate computes statistical results

New aggregation methods provided by JDK12

Use of concurrent paralleStream

Basic: java. Stream functions, elegant data flow operations

preface

Pay attention to the public account, communicate together, wechat search: sneak forward

1 The construction of stream

The built-in constructor for stream

The stream function declared by Collection

Stream defines the operation methods on elements

Filter filter

Mapping to Map

Data processing PEEK

Maps flatten flatmaps

To heavy distinct

Sorting sorted

Number limit limit and skip

New operations provided by JDK9

3 Stream termination action

Traverse the consumption

Get array results

Maximum minimum value

To find the matching

Reduction to merge

Counting elements

Polymerization treatment of convection

4 Collector(aggregation) Tool class collection

Interface Collector and implementation class CollectorImpl

Stream aggregation converts to List, Set

Flow aggregation is converted to a Map

String stream aggregation concatenation

The map handles the reaggregation flow

After aggregation, the results are transformed

Stream grouping (Map is HashMap)

Stream grouping (ConcurrentHashMap is used for grouping)

Split split, one into two (equivalent to special groupingBy)

Average polymerization

Stream aggregation finds Max and min values

Aggregate computes statistical results

New aggregation methods provided by JDK12

Use of concurrent paralleStream

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