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Java example source code file (Collectors.java)
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The Collectors.java Java example source code
/*
* Copyright (c) 2012, 2013, Oracle and/or its affiliates. All rights reserved.
* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
*
* This code is free software; you can redistribute it and/or modify it
* under the terms of the GNU General Public License version 2 only, as
* published by the Free Software Foundation. Oracle designates this
* particular file as subject to the "Classpath" exception as provided
* by Oracle in the LICENSE file that accompanied this code.
*
* This code is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
* version 2 for more details (a copy is included in the LICENSE file that
* accompanied this code).
*
* You should have received a copy of the GNU General Public License version
* 2 along with this work; if not, write to the Free Software Foundation,
* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*
* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
* or visit www.oracle.com if you need additional information or have any
* questions.
*/
package java.util.stream;
import java.util.AbstractMap;
import java.util.AbstractSet;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;
import java.util.Comparator;
import java.util.DoubleSummaryStatistics;
import java.util.EnumSet;
import java.util.HashMap;
import java.util.HashSet;
import java.util.IntSummaryStatistics;
import java.util.Iterator;
import java.util.List;
import java.util.LongSummaryStatistics;
import java.util.Map;
import java.util.Objects;
import java.util.Optional;
import java.util.Set;
import java.util.StringJoiner;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import java.util.function.BiConsumer;
import java.util.function.BiFunction;
import java.util.function.BinaryOperator;
import java.util.function.Consumer;
import java.util.function.Function;
import java.util.function.Predicate;
import java.util.function.Supplier;
import java.util.function.ToDoubleFunction;
import java.util.function.ToIntFunction;
import java.util.function.ToLongFunction;
/**
* Implementations of {@link Collector} that implement various useful reduction
* operations, such as accumulating elements into collections, summarizing
* elements according to various criteria, etc.
*
* <p>The following are examples of using the predefined collectors to perform
* common mutable reduction tasks:
*
* <pre>{@code
* // Accumulate names into a List
* List<String> list = people.stream().map(Person::getName).collect(Collectors.toList());
*
* // Accumulate names into a TreeSet
* Set<String> set = people.stream().map(Person::getName).collect(Collectors.toCollection(TreeSet::new));
*
* // Convert elements to strings and concatenate them, separated by commas
* String joined = things.stream()
* .map(Object::toString)
* .collect(Collectors.joining(", "));
*
* // Compute sum of salaries of employee
* int total = employees.stream()
* .collect(Collectors.summingInt(Employee::getSalary)));
*
* // Group employees by department
* Map<Department, List byDept
* = employees.stream()
* .collect(Collectors.groupingBy(Employee::getDepartment));
*
* // Compute sum of salaries by department
* Map<Department, Integer> totalByDept
* = employees.stream()
* .collect(Collectors.groupingBy(Employee::getDepartment,
* Collectors.summingInt(Employee::getSalary)));
*
* // Partition students into passing and failing
* Map<Boolean, List passingFailing =
* students.stream()
* .collect(Collectors.partitioningBy(s -> s.getGrade() >= PASS_THRESHOLD));
*
* }</pre>
*
* @since 1.8
*/
public final class Collectors {
static final Set<Collector.Characteristics> CH_CONCURRENT_ID
= Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT,
Collector.Characteristics.UNORDERED,
Collector.Characteristics.IDENTITY_FINISH));
static final Set<Collector.Characteristics> CH_CONCURRENT_NOID
= Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT,
Collector.Characteristics.UNORDERED));
static final Set<Collector.Characteristics> CH_ID
= Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH));
static final Set<Collector.Characteristics> CH_UNORDERED_ID
= Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.UNORDERED,
Collector.Characteristics.IDENTITY_FINISH));
static final Set<Collector.Characteristics> CH_NOID = Collections.emptySet();
private Collectors() { }
/**
* Returns a merge function, suitable for use in
* {@link Map#merge(Object, Object, BiFunction) Map.merge()} or
* {@link #toMap(Function, Function, BinaryOperator) toMap()}, which always
* throws {@code IllegalStateException}. This can be used to enforce the
* assumption that the elements being collected are distinct.
*
* @param <T> the type of input arguments to the merge function
* @return a merge function which always throw {@code IllegalStateException}
*/
private static <T> BinaryOperator throwingMerger() {
return (u,v) -> { throw new IllegalStateException(String.format("Duplicate key %s", u)); };
}
@SuppressWarnings("unchecked")
private static <I, R> Function castingIdentity() {
return i -> (R) i;
}
/**
* Simple implementation class for {@code Collector}.
*
* @param <T> the type of elements to be collected
* @param <R> the type of the result
*/
static class CollectorImpl<T, A, R> implements Collector {
private final Supplier<A> supplier;
private final BiConsumer<A, T> accumulator;
private final BinaryOperator<A> combiner;
private final Function<A, R> finisher;
private final Set<Characteristics> characteristics;
CollectorImpl(Supplier<A> supplier,
BiConsumer<A, T> accumulator,
BinaryOperator<A> combiner,
Function<A,R> finisher,
Set<Characteristics> characteristics) {
this.supplier = supplier;
this.accumulator = accumulator;
this.combiner = combiner;
this.finisher = finisher;
this.characteristics = characteristics;
}
CollectorImpl(Supplier<A> supplier,
BiConsumer<A, T> accumulator,
BinaryOperator<A> combiner,
Set<Characteristics> characteristics) {
this(supplier, accumulator, combiner, castingIdentity(), characteristics);
}
@Override
public BiConsumer<A, T> accumulator() {
return accumulator;
}
@Override
public Supplier<A> supplier() {
return supplier;
}
@Override
public BinaryOperator<A> combiner() {
return combiner;
}
@Override
public Function<A, R> finisher() {
return finisher;
}
@Override
public Set<Characteristics> characteristics() {
return characteristics;
}
}
/**
* Returns a {@code Collector} that accumulates the input elements into a
* new {@code Collection}, in encounter order. The {@code Collection} is
* created by the provided factory.
*
* @param <T> the type of the input elements
* @param <C> the type of the resulting {@code Collection}
* @param collectionFactory a {@code Supplier} which returns a new, empty
* {@code Collection} of the appropriate type
* @return a {@code Collector} which collects all the input elements into a
* {@code Collection}, in encounter order
*/
public static <T, C extends Collection
Collector<T, ?, C> toCollection(Supplier collectionFactory) {
return new CollectorImpl<>(collectionFactory, Collection::add,
(r1, r2) -> { r1.addAll(r2); return r1; },
CH_ID);
}
/**
* Returns a {@code Collector} that accumulates the input elements into a
* new {@code List}. There are no guarantees on the type, mutability,
* serializability, or thread-safety of the {@code List} returned; if more
* control over the returned {@code List} is required, use {@link #toCollection(Supplier)}.
*
* @param <T> the type of the input elements
* @return a {@code Collector} which collects all the input elements into a
* {@code List}, in encounter order
*/
public static <T>
Collector<T, ?, List toList() {
return new CollectorImpl<>((Supplier>) ArrayList::new, List::add,
(left, right) -> { left.addAll(right); return left; },
CH_ID);
}
/**
* Returns a {@code Collector} that accumulates the input elements into a
* new {@code Set}. There are no guarantees on the type, mutability,
* serializability, or thread-safety of the {@code Set} returned; if more
* control over the returned {@code Set} is required, use
* {@link #toCollection(Supplier)}.
*
* <p>This is an {@link Collector.Characteristics#UNORDERED unordered}
* Collector.
*
* @param <T> the type of the input elements
* @return a {@code Collector} which collects all the input elements into a
* {@code Set}
*/
public static <T>
Collector<T, ?, Set toSet() {
return new CollectorImpl<>((Supplier>) HashSet::new, Set::add,
(left, right) -> { left.addAll(right); return left; },
CH_UNORDERED_ID);
}
/**
* Returns a {@code Collector} that concatenates the input elements into a
* {@code String}, in encounter order.
*
* @return a {@code Collector} that concatenates the input elements into a
* {@code String}, in encounter order
*/
public static Collector<CharSequence, ?, String> joining() {
return new CollectorImpl<CharSequence, StringBuilder, String>(
StringBuilder::new, StringBuilder::append,
(r1, r2) -> { r1.append(r2); return r1; },
StringBuilder::toString, CH_NOID);
}
/**
* Returns a {@code Collector} that concatenates the input elements,
* separated by the specified delimiter, in encounter order.
*
* @param delimiter the delimiter to be used between each element
* @return A {@code Collector} which concatenates CharSequence elements,
* separated by the specified delimiter, in encounter order
*/
public static Collector<CharSequence, ?, String> joining(CharSequence delimiter) {
return joining(delimiter, "", "");
}
/**
* Returns a {@code Collector} that concatenates the input elements,
* separated by the specified delimiter, with the specified prefix and
* suffix, in encounter order.
*
* @param delimiter the delimiter to be used between each element
* @param prefix the sequence of characters to be used at the beginning
* of the joined result
* @param suffix the sequence of characters to be used at the end
* of the joined result
* @return A {@code Collector} which concatenates CharSequence elements,
* separated by the specified delimiter, in encounter order
*/
public static Collector<CharSequence, ?, String> joining(CharSequence delimiter,
CharSequence prefix,
CharSequence suffix) {
return new CollectorImpl<>(
() -> new StringJoiner(delimiter, prefix, suffix),
StringJoiner::add, StringJoiner::merge,
StringJoiner::toString, CH_NOID);
}
/**
* {@code BinaryOperator<Map>} that merges the contents of its right
* argument into its left argument, using the provided merge function to
* handle duplicate keys.
*
* @param <K> type of the map keys
* @param <V> type of the map values
* @param <M> type of the map
* @param mergeFunction A merge function suitable for
* {@link Map#merge(Object, Object, BiFunction) Map.merge()}
* @return a merge function for two maps
*/
private static <K, V, M extends Map
BinaryOperator<M> mapMerger(BinaryOperator mergeFunction) {
return (m1, m2) -> {
for (Map.Entry<K,V> e : m2.entrySet())
m1.merge(e.getKey(), e.getValue(), mergeFunction);
return m1;
};
}
/**
* Adapts a {@code Collector} accepting elements of type {@code U} to one
* accepting elements of type {@code T} by applying a mapping function to
* each input element before accumulation.
*
* @apiNote
* The {@code mapping()} collectors are most useful when used in a
* multi-level reduction, such as downstream of a {@code groupingBy} or
* {@code partitioningBy}. For example, given a stream of
* {@code Person}, to accumulate the set of last names in each city:
* <pre>{@code
* Map<City, Set lastNamesByCity
* = people.stream().collect(groupingBy(Person::getCity,
* mapping(Person::getLastName, toSet())));
* }</pre>
*
* @param <T> the type of the input elements
* @param <U> type of elements accepted by downstream collector
* @param <A> intermediate accumulation type of the downstream collector
* @param <R> result type of collector
* @param mapper a function to be applied to the input elements
* @param downstream a collector which will accept mapped values
* @return a collector which applies the mapping function to the input
* elements and provides the mapped results to the downstream collector
*/
public static <T, U, A, R>
Collector<T, ?, R> mapping(Function mapper,
Collector<? super U, A, R> downstream) {
BiConsumer<A, ? super U> downstreamAccumulator = downstream.accumulator();
return new CollectorImpl<>(downstream.supplier(),
(r, t) -> downstreamAccumulator.accept(r, mapper.apply(t)),
downstream.combiner(), downstream.finisher(),
downstream.characteristics());
}
/**
* Adapts a {@code Collector} to perform an additional finishing
* transformation. For example, one could adapt the {@link #toList()}
* collector to always produce an immutable list with:
* <pre>{@code
* List<String> people
* = people.stream().collect(collectingAndThen(toList(), Collections::unmodifiableList));
* }</pre>
*
* @param <T> the type of the input elements
* @param <A> intermediate accumulation type of the downstream collector
* @param <R> result type of the downstream collector
* @param <RR> result type of the resulting collector
* @param downstream a collector
* @param finisher a function to be applied to the final result of the downstream collector
* @return a collector which performs the action of the downstream collector,
* followed by an additional finishing step
*/
public static<T,A,R,RR> Collector collectingAndThen(Collector downstream,
Function<R,RR> finisher) {
Set<Collector.Characteristics> characteristics = downstream.characteristics();
if (characteristics.contains(Collector.Characteristics.IDENTITY_FINISH)) {
if (characteristics.size() == 1)
characteristics = Collectors.CH_NOID;
else {
characteristics = EnumSet.copyOf(characteristics);
characteristics.remove(Collector.Characteristics.IDENTITY_FINISH);
characteristics = Collections.unmodifiableSet(characteristics);
}
}
return new CollectorImpl<>(downstream.supplier(),
downstream.accumulator(),
downstream.combiner(),
downstream.finisher().andThen(finisher),
characteristics);
}
/**
* Returns a {@code Collector} accepting elements of type {@code T} that
* counts the number of input elements. If no elements are present, the
* result is 0.
*
* @implSpec
* This produces a result equivalent to:
* <pre>{@code
* reducing(0L, e -> 1L, Long::sum)
* }</pre>
*
* @param <T> the type of the input elements
* @return a {@code Collector} that counts the input elements
*/
public static <T> Collector
counting() {
return reducing(0L, e -> 1L, Long::sum);
}
/**
* Returns a {@code Collector} that produces the minimal element according
* to a given {@code Comparator}, described as an {@code Optional<T>}.
*
* @implSpec
* This produces a result equivalent to:
* <pre>{@code
* reducing(BinaryOperator.minBy(comparator))
* }</pre>
*
* @param <T> the type of the input elements
* @param comparator a {@code Comparator} for comparing elements
* @return a {@code Collector} that produces the minimal value
*/
public static <T> Collector>
minBy(Comparator<? super T> comparator) {
return reducing(BinaryOperator.minBy(comparator));
}
/**
* Returns a {@code Collector} that produces the maximal element according
* to a given {@code Comparator}, described as an {@code Optional<T>}.
*
* @implSpec
* This produces a result equivalent to:
* <pre>{@code
* reducing(BinaryOperator.maxBy(comparator))
* }</pre>
*
* @param <T> the type of the input elements
* @param comparator a {@code Comparator} for comparing elements
* @return a {@code Collector} that produces the maximal value
*/
public static <T> Collector>
maxBy(Comparator<? super T> comparator) {
return reducing(BinaryOperator.maxBy(comparator));
}
/**
* Returns a {@code Collector} that produces the sum of a integer-valued
* function applied to the input elements. If no elements are present,
* the result is 0.
*
* @param <T> the type of the input elements
* @param mapper a function extracting the property to be summed
* @return a {@code Collector} that produces the sum of a derived property
*/
public static <T> Collector
summingInt(ToIntFunction<? super T> mapper) {
return new CollectorImpl<>(
() -> new int[1],
(a, t) -> { a[0] += mapper.applyAsInt(t); },
(a, b) -> { a[0] += b[0]; return a; },
a -> a[0], CH_NOID);
}
/**
* Returns a {@code Collector} that produces the sum of a long-valued
* function applied to the input elements. If no elements are present,
* the result is 0.
*
* @param <T> the type of the input elements
* @param mapper a function extracting the property to be summed
* @return a {@code Collector} that produces the sum of a derived property
*/
public static <T> Collector
summingLong(ToLongFunction<? super T> mapper) {
return new CollectorImpl<>(
() -> new long[1],
(a, t) -> { a[0] += mapper.applyAsLong(t); },
(a, b) -> { a[0] += b[0]; return a; },
a -> a[0], CH_NOID);
}
/**
* Returns a {@code Collector} that produces the sum of a double-valued
* function applied to the input elements. If no elements are present,
* the result is 0.
*
* <p>The sum returned can vary depending upon the order in which
* values are recorded, due to accumulated rounding error in
* addition of values of differing magnitudes. Values sorted by increasing
* absolute magnitude tend to yield more accurate results. If any recorded
* value is a {@code NaN} or the sum is at any point a {@code NaN} then the
* sum will be {@code NaN}.
*
* @param <T> the type of the input elements
* @param mapper a function extracting the property to be summed
* @return a {@code Collector} that produces the sum of a derived property
*/
public static <T> Collector
summingDouble(ToDoubleFunction<? super T> mapper) {
/*
* In the arrays allocated for the collect operation, index 0
* holds the high-order bits of the running sum, index 1 holds
* the low-order bits of the sum computed via compensated
* summation, and index 2 holds the simple sum used to compute
* the proper result if the stream contains infinite values of
* the same sign.
*/
return new CollectorImpl<>(
() -> new double[3],
(a, t) -> { sumWithCompensation(a, mapper.applyAsDouble(t));
a[2] += mapper.applyAsDouble(t);},
(a, b) -> { sumWithCompensation(a, b[0]);
a[2] += b[2];
return sumWithCompensation(a, b[1]); },
a -> computeFinalSum(a),
CH_NOID);
}
/**
* Incorporate a new double value using Kahan summation /
* compensation summation.
*
* High-order bits of the sum are in intermediateSum[0], low-order
* bits of the sum are in intermediateSum[1], any additional
* elements are application-specific.
*
* @param intermediateSum the high-order and low-order words of the intermediate sum
* @param value the name value to be included in the running sum
*/
static double[] sumWithCompensation(double[] intermediateSum, double value) {
double tmp = value - intermediateSum[1];
double sum = intermediateSum[0];
double velvel = sum + tmp; // Little wolf of rounding error
intermediateSum[1] = (velvel - sum) - tmp;
intermediateSum[0] = velvel;
return intermediateSum;
}
/**
* If the compensated sum is spuriously NaN from accumulating one
* or more same-signed infinite values, return the
* correctly-signed infinity stored in the simple sum.
*/
static double computeFinalSum(double[] summands) {
// Better error bounds to add both terms as the final sum
double tmp = summands[0] + summands[1];
double simpleSum = summands[summands.length - 1];
if (Double.isNaN(tmp) && Double.isInfinite(simpleSum))
return simpleSum;
else
return tmp;
}
/**
* Returns a {@code Collector} that produces the arithmetic mean of an integer-valued
* function applied to the input elements. If no elements are present,
* the result is 0.
*
* @param <T> the type of the input elements
* @param mapper a function extracting the property to be summed
* @return a {@code Collector} that produces the sum of a derived property
*/
public static <T> Collector
averagingInt(ToIntFunction<? super T> mapper) {
return new CollectorImpl<>(
() -> new long[2],
(a, t) -> { a[0] += mapper.applyAsInt(t); a[1]++; },
(a, b) -> { a[0] += b[0]; a[1] += b[1]; return a; },
a -> (a[1] == 0) ? 0.0d : (double) a[0] / a[1], CH_NOID);
}
/**
* Returns a {@code Collector} that produces the arithmetic mean of a long-valued
* function applied to the input elements. If no elements are present,
* the result is 0.
*
* @param <T> the type of the input elements
* @param mapper a function extracting the property to be summed
* @return a {@code Collector} that produces the sum of a derived property
*/
public static <T> Collector
averagingLong(ToLongFunction<? super T> mapper) {
return new CollectorImpl<>(
() -> new long[2],
(a, t) -> { a[0] += mapper.applyAsLong(t); a[1]++; },
(a, b) -> { a[0] += b[0]; a[1] += b[1]; return a; },
a -> (a[1] == 0) ? 0.0d : (double) a[0] / a[1], CH_NOID);
}
/**
* Returns a {@code Collector} that produces the arithmetic mean of a double-valued
* function applied to the input elements. If no elements are present,
* the result is 0.
*
* <p>The average returned can vary depending upon the order in which
* values are recorded, due to accumulated rounding error in
* addition of values of differing magnitudes. Values sorted by increasing
* absolute magnitude tend to yield more accurate results. If any recorded
* value is a {@code NaN} or the sum is at any point a {@code NaN} then the
* average will be {@code NaN}.
*
* @implNote The {@code double} format can represent all
* consecutive integers in the range -2<sup>53 to
* 2<sup>53. If the pipeline has more than 253
* values, the divisor in the average computation will saturate at
* 2<sup>53, leading to additional numerical errors.
*
* @param <T> the type of the input elements
* @param mapper a function extracting the property to be summed
* @return a {@code Collector} that produces the sum of a derived property
*/
public static <T> Collector
averagingDouble(ToDoubleFunction<? super T> mapper) {
/*
* In the arrays allocated for the collect operation, index 0
* holds the high-order bits of the running sum, index 1 holds
* the low-order bits of the sum computed via compensated
* summation, and index 2 holds the number of values seen.
*/
return new CollectorImpl<>(
() -> new double[4],
(a, t) -> { sumWithCompensation(a, mapper.applyAsDouble(t)); a[2]++; a[3]+= mapper.applyAsDouble(t);},
(a, b) -> { sumWithCompensation(a, b[0]); sumWithCompensation(a, b[1]); a[2] += b[2]; a[3] += b[3]; return a; },
a -> (a[2] == 0) ? 0.0d : (computeFinalSum(a) / a[2]),
CH_NOID);
}
/**
* Returns a {@code Collector} which performs a reduction of its
* input elements under a specified {@code BinaryOperator} using the
* provided identity.
*
* @apiNote
* The {@code reducing()} collectors are most useful when used in a
* multi-level reduction, downstream of {@code groupingBy} or
* {@code partitioningBy}. To perform a simple reduction on a stream,
* use {@link Stream#reduce(Object, BinaryOperator)}} instead.
*
* @param <T> element type for the input and output of the reduction
* @param identity the identity value for the reduction (also, the value
* that is returned when there are no input elements)
* @param op a {@code BinaryOperator<T>} used to reduce the input elements
* @return a {@code Collector} which implements the reduction operation
*
* @see #reducing(BinaryOperator)
* @see #reducing(Object, Function, BinaryOperator)
*/
public static <T> Collector
reducing(T identity, BinaryOperator<T> op) {
return new CollectorImpl<>(
boxSupplier(identity),
(a, t) -> { a[0] = op.apply(a[0], t); },
(a, b) -> { a[0] = op.apply(a[0], b[0]); return a; },
a -> a[0],
CH_NOID);
}
@SuppressWarnings("unchecked")
private static <T> Supplier boxSupplier(T identity) {
return () -> (T[]) new Object[] { identity };
}
/**
* Returns a {@code Collector} which performs a reduction of its
* input elements under a specified {@code BinaryOperator}. The result
* is described as an {@code Optional<T>}.
*
* @apiNote
* The {@code reducing()} collectors are most useful when used in a
* multi-level reduction, downstream of {@code groupingBy} or
* {@code partitioningBy}. To perform a simple reduction on a stream,
* use {@link Stream#reduce(BinaryOperator)} instead.
*
* <p>For example, given a stream of {@code Person}, to calculate tallest
* person in each city:
* <pre>{@code
* Comparator<Person> byHeight = Comparator.comparing(Person::getHeight);
* Map<City, Person> tallestByCity
* = people.stream().collect(groupingBy(Person::getCity, reducing(BinaryOperator.maxBy(byHeight))));
* }</pre>
*
* @param <T> element type for the input and output of the reduction
* @param op a {@code BinaryOperator<T>} used to reduce the input elements
* @return a {@code Collector} which implements the reduction operation
*
* @see #reducing(Object, BinaryOperator)
* @see #reducing(Object, Function, BinaryOperator)
*/
public static <T> Collector>
reducing(BinaryOperator<T> op) {
class OptionalBox implements Consumer<T> {
T value = null;
boolean present = false;
@Override
public void accept(T t) {
if (present) {
value = op.apply(value, t);
}
else {
value = t;
present = true;
}
}
}
return new CollectorImpl<T, OptionalBox, Optional(
OptionalBox::new, OptionalBox::accept,
(a, b) -> { if (b.present) a.accept(b.value); return a; },
a -> Optional.ofNullable(a.value), CH_NOID);
}
/**
* Returns a {@code Collector} which performs a reduction of its
* input elements under a specified mapping function and
* {@code BinaryOperator}. This is a generalization of
* {@link #reducing(Object, BinaryOperator)} which allows a transformation
* of the elements before reduction.
*
* @apiNote
* The {@code reducing()} collectors are most useful when used in a
* multi-level reduction, downstream of {@code groupingBy} or
* {@code partitioningBy}. To perform a simple map-reduce on a stream,
* use {@link Stream#map(Function)} and {@link Stream#reduce(Object, BinaryOperator)}
* instead.
*
* <p>For example, given a stream of {@code Person}, to calculate the longest
* last name of residents in each city:
* <pre>{@code
* Comparator<String> byLength = Comparator.comparing(String::length);
* Map<City, String> longestLastNameByCity
* = people.stream().collect(groupingBy(Person::getCity,
* reducing(Person::getLastName, BinaryOperator.maxBy(byLength))));
* }</pre>
*
* @param <T> the type of the input elements
* @param <U> the type of the mapped values
* @param identity the identity value for the reduction (also, the value
* that is returned when there are no input elements)
* @param mapper a mapping function to apply to each input value
* @param op a {@code BinaryOperator<U>} used to reduce the mapped values
* @return a {@code Collector} implementing the map-reduce operation
*
* @see #reducing(Object, BinaryOperator)
* @see #reducing(BinaryOperator)
*/
public static <T, U>
Collector<T, ?, U> reducing(U identity,
Function<? super T, ? extends U> mapper,
BinaryOperator<U> op) {
return new CollectorImpl<>(
boxSupplier(identity),
(a, t) -> { a[0] = op.apply(a[0], mapper.apply(t)); },
(a, b) -> { a[0] = op.apply(a[0], b[0]); return a; },
a -> a[0], CH_NOID);
}
/**
* Returns a {@code Collector} implementing a "group by" operation on
* input elements of type {@code T}, grouping elements according to a
* classification function, and returning the results in a {@code Map}.
*
* <p>The classification function maps elements to some key type {@code K}.
* The collector produces a {@code Map<K, List} whose keys are the
* values resulting from applying the classification function to the input
* elements, and whose corresponding values are {@code List}s containing the
* input elements which map to the associated key under the classification
* function.
*
* <p>There are no guarantees on the type, mutability, serializability, or
* thread-safety of the {@code Map} or {@code List} objects returned.
* @implSpec
* This produces a result similar to:
* <pre>{@code
* groupingBy(classifier, toList());
* }</pre>
*
* @implNote
* The returned {@code Collector} is not concurrent. For parallel stream
* pipelines, the {@code combiner} function operates by merging the keys
* from one map into another, which can be an expensive operation. If
* preservation of the order in which elements appear in the resulting {@code Map}
* collector is not required, using {@link #groupingByConcurrent(Function)}
* may offer better parallel performance.
*
* @param <T> the type of the input elements
* @param <K> the type of the keys
* @param classifier the classifier function mapping input elements to keys
* @return a {@code Collector} implementing the group-by operation
*
* @see #groupingBy(Function, Collector)
* @see #groupingBy(Function, Supplier, Collector)
* @see #groupingByConcurrent(Function)
*/
public static <T, K> Collector>>
groupingBy(Function<? super T, ? extends K> classifier) {
return groupingBy(classifier, toList());
}
/**
* Returns a {@code Collector} implementing a cascaded "group by" operation
* on input elements of type {@code T}, grouping elements according to a
* classification function, and then performing a reduction operation on
* the values associated with a given key using the specified downstream
* {@code Collector}.
*
* <p>The classification function maps elements to some key type {@code K}.
* The downstream collector operates on elements of type {@code T} and
* produces a result of type {@code D}. The resulting collector produces a
* {@code Map<K, D>}.
*
* <p>There are no guarantees on the type, mutability,
* serializability, or thread-safety of the {@code Map} returned.
*
* <p>For example, to compute the set of last names of people in each city:
* <pre>{@code
* Map<City, Set namesByCity
* = people.stream().collect(groupingBy(Person::getCity,
* mapping(Person::getLastName, toSet())));
* }</pre>
*
* @implNote
* The returned {@code Collector} is not concurrent. For parallel stream
* pipelines, the {@code combiner} function operates by merging the keys
* from one map into another, which can be an expensive operation. If
* preservation of the order in which elements are presented to the downstream
* collector is not required, using {@link #groupingByConcurrent(Function, Collector)}
* may offer better parallel performance.
*
* @param <T> the type of the input elements
* @param <K> the type of the keys
* @param <A> the intermediate accumulation type of the downstream collector
* @param <D> the result type of the downstream reduction
* @param classifier a classifier function mapping input elements to keys
* @param downstream a {@code Collector} implementing the downstream reduction
* @return a {@code Collector} implementing the cascaded group-by operation
* @see #groupingBy(Function)
*
* @see #groupingBy(Function, Supplier, Collector)
* @see #groupingByConcurrent(Function, Collector)
*/
public static <T, K, A, D>
Collector<T, ?, Map groupingBy(Function classifier,
Collector<? super T, A, D> downstream) {
return groupingBy(classifier, HashMap::new, downstream);
}
/**
* Returns a {@code Collector} implementing a cascaded "group by" operation
* on input elements of type {@code T}, grouping elements according to a
* classification function, and then performing a reduction operation on
* the values associated with a given key using the specified downstream
* {@code Collector}. The {@code Map} produced by the Collector is created
* with the supplied factory function.
*
* <p>The classification function maps elements to some key type {@code K}.
* The downstream collector operates on elements of type {@code T} and
* produces a result of type {@code D}. The resulting collector produces a
* {@code Map<K, D>}.
*
* <p>For example, to compute the set of last names of people in each city,
* where the city names are sorted:
* <pre>{@code
* Map<City, Set namesByCity
* = people.stream().collect(groupingBy(Person::getCity, TreeMap::new,
* mapping(Person::getLastName, toSet())));
* }</pre>
*
* @implNote
* The returned {@code Collector} is not concurrent. For parallel stream
* pipelines, the {@code combiner} function operates by merging the keys
* from one map into another, which can be an expensive operation. If
* preservation of the order in which elements are presented to the downstream
* collector is not required, using {@link #groupingByConcurrent(Function, Supplier, Collector)}
* may offer better parallel performance.
*
* @param <T> the type of the input elements
* @param <K> the type of the keys
* @param <A> the intermediate accumulation type of the downstream collector
* @param <D> the result type of the downstream reduction
* @param <M> the type of the resulting {@code Map}
* @param classifier a classifier function mapping input elements to keys
* @param downstream a {@code Collector} implementing the downstream reduction
* @param mapFactory a function which, when called, produces a new empty
* {@code Map} of the desired type
* @return a {@code Collector} implementing the cascaded group-by operation
*
* @see #groupingBy(Function, Collector)
* @see #groupingBy(Function)
* @see #groupingByConcurrent(Function, Supplier, Collector)
*/
public static <T, K, D, A, M extends Map
Collector<T, ?, M> groupingBy(Function classifier,
Supplier<M> mapFactory,
Collector<? super T, A, D> downstream) {
Supplier<A> downstreamSupplier = downstream.supplier();
BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
BiConsumer<Map accumulator = (m, t) -> {
K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
A container = m.computeIfAbsent(key, k -> downstreamSupplier.get());
downstreamAccumulator.accept(container, t);
};
BinaryOperator<Map merger = Collectors.>mapMerger(downstream.combiner());
@SuppressWarnings("unchecked")
Supplier<Map mangledFactory = (Supplier>) mapFactory;
if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_ID);
}
else {
@SuppressWarnings("unchecked")
Function<A, A> downstreamFinisher = (Function) downstream.finisher();
Function<Map finisher = intermediate -> {
intermediate.replaceAll((k, v) -> downstreamFinisher.apply(v));
@SuppressWarnings("unchecked")
M castResult = (M) intermediate;
return castResult;
};
return new CollectorImpl<>(mangledFactory, accumulator, merger, finisher, CH_NOID);
}
}
/**
* Returns a concurrent {@code Collector} implementing a "group by"
* operation on input elements of type {@code T}, grouping elements
* according to a classification function.
*
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
*
* <p>The classification function maps elements to some key type {@code K}.
* The collector produces a {@code ConcurrentMap<K, List} whose keys are the
* values resulting from applying the classification function to the input
* elements, and whose corresponding values are {@code List}s containing the
* input elements which map to the associated key under the classification
* function.
*
* <p>There are no guarantees on the type, mutability, or serializability
* of the {@code Map} or {@code List} objects returned, or of the
* thread-safety of the {@code List} objects returned.
* @implSpec
* This produces a result similar to:
* <pre>{@code
* groupingByConcurrent(classifier, toList());
* }</pre>
*
* @param <T> the type of the input elements
* @param <K> the type of the keys
* @param classifier a classifier function mapping input elements to keys
* @return a concurrent, unordered {@code Collector} implementing the group-by operation
*
* @see #groupingBy(Function)
* @see #groupingByConcurrent(Function, Collector)
* @see #groupingByConcurrent(Function, Supplier, Collector)
*/
public static <T, K>
Collector<T, ?, ConcurrentMap>
groupingByConcurrent(Function<? super T, ? extends K> classifier) {
return groupingByConcurrent(classifier, ConcurrentHashMap::new, toList());
}
/**
* Returns a concurrent {@code Collector} implementing a cascaded "group by"
* operation on input elements of type {@code T}, grouping elements
* according to a classification function, and then performing a reduction
* operation on the values associated with a given key using the specified
* downstream {@code Collector}.
*
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
*
* <p>The classification function maps elements to some key type {@code K}.
* The downstream collector operates on elements of type {@code T} and
* produces a result of type {@code D}. The resulting collector produces a
* {@code Map<K, D>}.
*
* <p>For example, to compute the set of last names of people in each city,
* where the city names are sorted:
* <pre>{@code
* ConcurrentMap<City, Set namesByCity
* = people.stream().collect(groupingByConcurrent(Person::getCity,
* mapping(Person::getLastName, toSet())));
* }</pre>
*
* @param <T> the type of the input elements
* @param <K> the type of the keys
* @param <A> the intermediate accumulation type of the downstream collector
* @param <D> the result type of the downstream reduction
* @param classifier a classifier function mapping input elements to keys
* @param downstream a {@code Collector} implementing the downstream reduction
* @return a concurrent, unordered {@code Collector} implementing the cascaded group-by operation
*
* @see #groupingBy(Function, Collector)
* @see #groupingByConcurrent(Function)
* @see #groupingByConcurrent(Function, Supplier, Collector)
*/
public static <T, K, A, D>
Collector<T, ?, ConcurrentMap groupingByConcurrent(Function classifier,
Collector<? super T, A, D> downstream) {
return groupingByConcurrent(classifier, ConcurrentHashMap::new, downstream);
}
/**
* Returns a concurrent {@code Collector} implementing a cascaded "group by"
* operation on input elements of type {@code T}, grouping elements
* according to a classification function, and then performing a reduction
* operation on the values associated with a given key using the specified
* downstream {@code Collector}. The {@code ConcurrentMap} produced by the
* Collector is created with the supplied factory function.
*
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
*
* <p>The classification function maps elements to some key type {@code K}.
* The downstream collector operates on elements of type {@code T} and
* produces a result of type {@code D}. The resulting collector produces a
* {@code Map<K, D>}.
*
* <p>For example, to compute the set of last names of people in each city,
* where the city names are sorted:
* <pre>{@code
* ConcurrentMap<City, Set namesByCity
* = people.stream().collect(groupingBy(Person::getCity, ConcurrentSkipListMap::new,
* mapping(Person::getLastName, toSet())));
* }</pre>
*
*
* @param <T> the type of the input elements
* @param <K> the type of the keys
* @param <A> the intermediate accumulation type of the downstream collector
* @param <D> the result type of the downstream reduction
* @param <M> the type of the resulting {@code ConcurrentMap}
* @param classifier a classifier function mapping input elements to keys
* @param downstream a {@code Collector} implementing the downstream reduction
* @param mapFactory a function which, when called, produces a new empty
* {@code ConcurrentMap} of the desired type
* @return a concurrent, unordered {@code Collector} implementing the cascaded group-by operation
*
* @see #groupingByConcurrent(Function)
* @see #groupingByConcurrent(Function, Collector)
* @see #groupingBy(Function, Supplier, Collector)
*/
public static <T, K, A, D, M extends ConcurrentMap
Collector<T, ?, M> groupingByConcurrent(Function classifier,
Supplier<M> mapFactory,
Collector<? super T, A, D> downstream) {
Supplier<A> downstreamSupplier = downstream.supplier();
BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
BinaryOperator<ConcurrentMap merger = Collectors.>mapMerger(downstream.combiner());
@SuppressWarnings("unchecked")
Supplier<ConcurrentMap mangledFactory = (Supplier>) mapFactory;
BiConsumer<ConcurrentMap accumulator;
if (downstream.characteristics().contains(Collector.Characteristics.CONCURRENT)) {
accumulator = (m, t) -> {
K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
A resultContainer = m.computeIfAbsent(key, k -> downstreamSupplier.get());
downstreamAccumulator.accept(resultContainer, t);
};
}
else {
accumulator = (m, t) -> {
K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
A resultContainer = m.computeIfAbsent(key, k -> downstreamSupplier.get());
synchronized (resultContainer) {
downstreamAccumulator.accept(resultContainer, t);
}
};
}
if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_CONCURRENT_ID);
}
else {
@SuppressWarnings("unchecked")
Function<A, A> downstreamFinisher = (Function) downstream.finisher();
Function<ConcurrentMap finisher = intermediate -> {
intermediate.replaceAll((k, v) -> downstreamFinisher.apply(v));
@SuppressWarnings("unchecked")
M castResult = (M) intermediate;
return castResult;
};
return new CollectorImpl<>(mangledFactory, accumulator, merger, finisher, CH_CONCURRENT_NOID);
}
}
/**
* Returns a {@code Collector} which partitions the input elements according
* to a {@code Predicate}, and organizes them into a
* {@code Map<Boolean, List}.
*
* There are no guarantees on the type, mutability,
* serializability, or thread-safety of the {@code Map} returned.
*
* @param <T> the type of the input elements
* @param predicate a predicate used for classifying input elements
* @return a {@code Collector} implementing the partitioning operation
*
* @see #partitioningBy(Predicate, Collector)
*/
public static <T>
Collector<T, ?, Map> partitioningBy(Predicate predicate) {
return partitioningBy(predicate, toList());
}
/**
* Returns a {@code Collector} which partitions the input elements according
* to a {@code Predicate}, reduces the values in each partition according to
* another {@code Collector}, and organizes them into a
* {@code Map<Boolean, D>} whose values are the result of the downstream
* reduction.
*
* <p>There are no guarantees on the type, mutability,
* serializability, or thread-safety of the {@code Map} returned.
*
* @param <T> the type of the input elements
* @param <A> the intermediate accumulation type of the downstream collector
* @param <D> the result type of the downstream reduction
* @param predicate a predicate used for classifying input elements
* @param downstream a {@code Collector} implementing the downstream
* reduction
* @return a {@code Collector} implementing the cascaded partitioning
* operation
*
* @see #partitioningBy(Predicate)
*/
public static <T, D, A>
Collector<T, ?, Map partitioningBy(Predicate predicate,
Collector<? super T, A, D> downstream) {
BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
BiConsumer<Partition accumulator = (result, t) ->
downstreamAccumulator.accept(predicate.test(t) ? result.forTrue : result.forFalse, t);
BinaryOperator<A> op = downstream.combiner();
BinaryOperator<Partition merger = (left, right) ->
new Partition<>(op.apply(left.forTrue, right.forTrue),
op.apply(left.forFalse, right.forFalse));
Supplier<Partition supplier = () ->
new Partition<>(downstream.supplier().get(),
downstream.supplier().get());
if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
return new CollectorImpl<>(supplier, accumulator, merger, CH_ID);
}
else {
Function<Partition> finisher = par ->
new Partition<>(downstream.finisher().apply(par.forTrue),
downstream.finisher().apply(par.forFalse));
return new CollectorImpl<>(supplier, accumulator, merger, finisher, CH_NOID);
}
}
/**
* Returns a {@code Collector} that accumulates elements into a
* {@code Map} whose keys and values are the result of applying the provided
* mapping functions to the input elements.
*
* <p>If the mapped keys contains duplicates (according to
* {@link Object#equals(Object)}), an {@code IllegalStateException} is
* thrown when the collection operation is performed. If the mapped keys
* may have duplicates, use {@link #toMap(Function, Function, BinaryOperator)}
* instead.
*
* @apiNote
* It is common for either the key or the value to be the input elements.
* In this case, the utility method
* {@link java.util.function.Function#identity()} may be helpful.
* For example, the following produces a {@code Map} mapping
* students to their grade point average:
* <pre>{@code
* Map<Student, Double> studentToGPA
* students.stream().collect(toMap(Functions.identity(),
* student -> computeGPA(student)));
* }</pre>
* And the following produces a {@code Map} mapping a unique identifier to
* students:
* <pre>{@code
* Map<String, Student> studentIdToStudent
* students.stream().collect(toMap(Student::getId,
* Functions.identity());
* }</pre>
*
* @implNote
* The returned {@code Collector} is not concurrent. For parallel stream
* pipelines, the {@code combiner} function operates by merging the keys
* from one map into another, which can be an expensive operation. If it is
* not required that results are inserted into the {@code Map} in encounter
* order, using {@link #toConcurrentMap(Function, Function)}
* may offer better parallel performance.
*
* @param <T> the type of the input elements
* @param <K> the output type of the key mapping function
* @param <U> the output type of the value mapping function
* @param keyMapper a mapping function to produce keys
* @param valueMapper a mapping function to produce values
* @return a {@code Collector} which collects elements into a {@code Map}
* whose keys and values are the result of applying mapping functions to
* the input elements
*
* @see #toMap(Function, Function, BinaryOperator)
* @see #toMap(Function, Function, BinaryOperator, Supplier)
* @see #toConcurrentMap(Function, Function)
*/
public static <T, K, U>
Collector<T, ?, Map toMap(Function keyMapper,
Function<? super T, ? extends U> valueMapper) {
return toMap(keyMapper, valueMapper, throwingMerger(), HashMap::new);
}
/**
* Returns a {@code Collector} that accumulates elements into a
* {@code Map} whose keys and values are the result of applying the provided
* mapping functions to the input elements.
*
* <p>If the mapped
* keys contains duplicates (according to {@link Object#equals(Object)}),
* the value mapping function is applied to each equal element, and the
* results are merged using the provided merging function.
*
* @apiNote
* There are multiple ways to deal with collisions between multiple elements
* mapping to the same key. The other forms of {@code toMap} simply use
* a merge function that throws unconditionally, but you can easily write
* more flexible merge policies. For example, if you have a stream
* of {@code Person}, and you want to produce a "phone book" mapping name to
* address, but it is possible that two persons have the same name, you can
* do as follows to gracefully deals with these collisions, and produce a
* {@code Map} mapping names to a concatenated list of addresses:
* <pre>{@code
* Map<String, String> phoneBook
* people.stream().collect(toMap(Person::getName,
* Person::getAddress,
* (s, a) -> s + ", " + a));
* }</pre>
*
* @implNote
* The returned {@code Collector} is not concurrent. For parallel stream
* pipelines, the {@code combiner} function operates by merging the keys
* from one map into another, which can be an expensive operation. If it is
* not required that results are merged into the {@code Map} in encounter
* order, using {@link #toConcurrentMap(Function, Function, BinaryOperator)}
* may offer better parallel performance.
*
* @param <T> the type of the input elements
* @param <K> the output type of the key mapping function
* @param <U> the output type of the value mapping function
* @param keyMapper a mapping function to produce keys
* @param valueMapper a mapping function to produce values
* @param mergeFunction a merge function, used to resolve collisions between
* values associated with the same key, as supplied
* to {@link Map#merge(Object, Object, BiFunction)}
* @return a {@code Collector} which collects elements into a {@code Map}
* whose keys are the result of applying a key mapping function to the input
* elements, and whose values are the result of applying a value mapping
* function to all input elements equal to the key and combining them
* using the merge function
*
* @see #toMap(Function, Function)
* @see #toMap(Function, Function, BinaryOperator, Supplier)
* @see #toConcurrentMap(Function, Function, BinaryOperator)
*/
public static <T, K, U>
Collector<T, ?, Map toMap(Function keyMapper,
Function<? super T, ? extends U> valueMapper,
BinaryOperator<U> mergeFunction) {
return toMap(keyMapper, valueMapper, mergeFunction, HashMap::new);
}
/**
* Returns a {@code Collector} that accumulates elements into a
* {@code Map} whose keys and values are the result of applying the provided
* mapping functions to the input elements.
*
* <p>If the mapped
* keys contains duplicates (according to {@link Object#equals(Object)}),
* the value mapping function is applied to each equal element, and the
* results are merged using the provided merging function. The {@code Map}
* is created by a provided supplier function.
*
* @implNote
* The returned {@code Collector} is not concurrent. For parallel stream
* pipelines, the {@code combiner} function operates by merging the keys
* from one map into another, which can be an expensive operation. If it is
* not required that results are merged into the {@code Map} in encounter
* order, using {@link #toConcurrentMap(Function, Function, BinaryOperator, Supplier)}
* may offer better parallel performance.
*
* @param <T> the type of the input elements
* @param <K> the output type of the key mapping function
* @param <U> the output type of the value mapping function
* @param <M> the type of the resulting {@code Map}
* @param keyMapper a mapping function to produce keys
* @param valueMapper a mapping function to produce values
* @param mergeFunction a merge function, used to resolve collisions between
* values associated with the same key, as supplied
* to {@link Map#merge(Object, Object, BiFunction)}
* @param mapSupplier a function which returns a new, empty {@code Map} into
* which the results will be inserted
* @return a {@code Collector} which collects elements into a {@code Map}
* whose keys are the result of applying a key mapping function to the input
* elements, and whose values are the result of applying a value mapping
* function to all input elements equal to the key and combining them
* using the merge function
*
* @see #toMap(Function, Function)
* @see #toMap(Function, Function, BinaryOperator)
* @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
*/
public static <T, K, U, M extends Map
Collector<T, ?, M> toMap(Function keyMapper,
Function<? super T, ? extends U> valueMapper,
BinaryOperator<U> mergeFunction,
Supplier<M> mapSupplier) {
BiConsumer<M, T> accumulator
= (map, element) -> map.merge(keyMapper.apply(element),
valueMapper.apply(element), mergeFunction);
return new CollectorImpl<>(mapSupplier, accumulator, mapMerger(mergeFunction), CH_ID);
}
/**
* Returns a concurrent {@code Collector} that accumulates elements into a
* {@code ConcurrentMap} whose keys and values are the result of applying
* the provided mapping functions to the input elements.
*
* <p>If the mapped keys contains duplicates (according to
* {@link Object#equals(Object)}), an {@code IllegalStateException} is
* thrown when the collection operation is performed. If the mapped keys
* may have duplicates, use
* {@link #toConcurrentMap(Function, Function, BinaryOperator)} instead.
*
* @apiNote
* It is common for either the key or the value to be the input elements.
* In this case, the utility method
* {@link java.util.function.Function#identity()} may be helpful.
* For example, the following produces a {@code Map} mapping
* students to their grade point average:
* <pre>{@code
* Map<Student, Double> studentToGPA
* students.stream().collect(toMap(Functions.identity(),
* student -> computeGPA(student)));
* }</pre>
* And the following produces a {@code Map} mapping a unique identifier to
* students:
* <pre>{@code
* Map<String, Student> studentIdToStudent
* students.stream().collect(toConcurrentMap(Student::getId,
* Functions.identity());
* }</pre>
*
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
*
* @param <T> the type of the input elements
* @param <K> the output type of the key mapping function
* @param <U> the output type of the value mapping function
* @param keyMapper the mapping function to produce keys
* @param valueMapper the mapping function to produce values
* @return a concurrent, unordered {@code Collector} which collects elements into a
* {@code ConcurrentMap} whose keys are the result of applying a key mapping
* function to the input elements, and whose values are the result of
* applying a value mapping function to the input elements
*
* @see #toMap(Function, Function)
* @see #toConcurrentMap(Function, Function, BinaryOperator)
* @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
*/
public static <T, K, U>
Collector<T, ?, ConcurrentMap toConcurrentMap(Function keyMapper,
Function<? super T, ? extends U> valueMapper) {
return toConcurrentMap(keyMapper, valueMapper, throwingMerger(), ConcurrentHashMap::new);
}
/**
* Returns a concurrent {@code Collector} that accumulates elements into a
* {@code ConcurrentMap} whose keys and values are the result of applying
* the provided mapping functions to the input elements.
*
* <p>If the mapped keys contains duplicates (according to {@link Object#equals(Object)}),
* the value mapping function is applied to each equal element, and the
* results are merged using the provided merging function.
*
* @apiNote
* There are multiple ways to deal with collisions between multiple elements
* mapping to the same key. The other forms of {@code toConcurrentMap} simply use
* a merge function that throws unconditionally, but you can easily write
* more flexible merge policies. For example, if you have a stream
* of {@code Person}, and you want to produce a "phone book" mapping name to
* address, but it is possible that two persons have the same name, you can
* do as follows to gracefully deals with these collisions, and produce a
* {@code Map} mapping names to a concatenated list of addresses:
* <pre>{@code
* Map<String, String> phoneBook
* people.stream().collect(toConcurrentMap(Person::getName,
* Person::getAddress,
* (s, a) -> s + ", " + a));
* }</pre>
*
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
*
* @param <T> the type of the input elements
* @param <K> the output type of the key mapping function
* @param <U> the output type of the value mapping function
* @param keyMapper a mapping function to produce keys
* @param valueMapper a mapping function to produce values
* @param mergeFunction a merge function, used to resolve collisions between
* values associated with the same key, as supplied
* to {@link Map#merge(Object, Object, BiFunction)}
* @return a concurrent, unordered {@code Collector} which collects elements into a
* {@code ConcurrentMap} whose keys are the result of applying a key mapping
* function to the input elements, and whose values are the result of
* applying a value mapping function to all input elements equal to the key
* and combining them using the merge function
*
* @see #toConcurrentMap(Function, Function)
* @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
* @see #toMap(Function, Function, BinaryOperator)
*/
public static <T, K, U>
Collector<T, ?, ConcurrentMap
toConcurrentMap(Function<? super T, ? extends K> keyMapper,
Function<? super T, ? extends U> valueMapper,
BinaryOperator<U> mergeFunction) {
return toConcurrentMap(keyMapper, valueMapper, mergeFunction, ConcurrentHashMap::new);
}
/**
* Returns a concurrent {@code Collector} that accumulates elements into a
* {@code ConcurrentMap} whose keys and values are the result of applying
* the provided mapping functions to the input elements.
*
* <p>If the mapped keys contains duplicates (according to {@link Object#equals(Object)}),
* the value mapping function is applied to each equal element, and the
* results are merged using the provided merging function. The
* {@code ConcurrentMap} is created by a provided supplier function.
*
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
*
* @param <T> the type of the input elements
* @param <K> the output type of the key mapping function
* @param <U> the output type of the value mapping function
* @param <M> the type of the resulting {@code ConcurrentMap}
* @param keyMapper a mapping function to produce keys
* @param valueMapper a mapping function to produce values
* @param mergeFunction a merge function, used to resolve collisions between
* values associated with the same key, as supplied
* to {@link Map#merge(Object, Object, BiFunction)}
* @param mapSupplier a function which returns a new, empty {@code Map} into
* which the results will be inserted
* @return a concurrent, unordered {@code Collector} which collects elements into a
* {@code ConcurrentMap} whose keys are the result of applying a key mapping
* function to the input elements, and whose values are the result of
* applying a value mapping function to all input elements equal to the key
* and combining them using the merge function
*
* @see #toConcurrentMap(Function, Function)
* @see #toConcurrentMap(Function, Function, BinaryOperator)
* @see #toMap(Function, Function, BinaryOperator, Supplier)
*/
public static <T, K, U, M extends ConcurrentMap
Collector<T, ?, M> toConcurrentMap(Function keyMapper,
Function<? super T, ? extends U> valueMapper,
BinaryOperator<U> mergeFunction,
Supplier<M> mapSupplier) {
BiConsumer<M, T> accumulator
= (map, element) -> map.merge(keyMapper.apply(element),
valueMapper.apply(element), mergeFunction);
return new CollectorImpl<>(mapSupplier, accumulator, mapMerger(mergeFunction), CH_CONCURRENT_ID);
}
/**
* Returns a {@code Collector} which applies an {@code int}-producing
* mapping function to each input element, and returns summary statistics
* for the resulting values.
*
* @param <T> the type of the input elements
* @param mapper a mapping function to apply to each element
* @return a {@code Collector} implementing the summary-statistics reduction
*
* @see #summarizingDouble(ToDoubleFunction)
* @see #summarizingLong(ToLongFunction)
*/
public static <T>
Collector<T, ?, IntSummaryStatistics> summarizingInt(ToIntFunction mapper) {
return new CollectorImpl<T, IntSummaryStatistics, IntSummaryStatistics>(
IntSummaryStatistics::new,
(r, t) -> r.accept(mapper.applyAsInt(t)),
(l, r) -> { l.combine(r); return l; }, CH_ID);
}
/**
* Returns a {@code Collector} which applies an {@code long}-producing
* mapping function to each input element, and returns summary statistics
* for the resulting values.
*
* @param <T> the type of the input elements
* @param mapper the mapping function to apply to each element
* @return a {@code Collector} implementing the summary-statistics reduction
*
* @see #summarizingDouble(ToDoubleFunction)
* @see #summarizingInt(ToIntFunction)
*/
public static <T>
Collector<T, ?, LongSummaryStatistics> summarizingLong(ToLongFunction mapper) {
return new CollectorImpl<T, LongSummaryStatistics, LongSummaryStatistics>(
LongSummaryStatistics::new,
(r, t) -> r.accept(mapper.applyAsLong(t)),
(l, r) -> { l.combine(r); return l; }, CH_ID);
}
/**
* Returns a {@code Collector} which applies an {@code double}-producing
* mapping function to each input element, and returns summary statistics
* for the resulting values.
*
* @param <T> the type of the input elements
* @param mapper a mapping function to apply to each element
* @return a {@code Collector} implementing the summary-statistics reduction
*
* @see #summarizingLong(ToLongFunction)
* @see #summarizingInt(ToIntFunction)
*/
public static <T>
Collector<T, ?, DoubleSummaryStatistics> summarizingDouble(ToDoubleFunction mapper) {
return new CollectorImpl<T, DoubleSummaryStatistics, DoubleSummaryStatistics>(
DoubleSummaryStatistics::new,
(r, t) -> r.accept(mapper.applyAsDouble(t)),
(l, r) -> { l.combine(r); return l; }, CH_ID);
}
/**
* Implementation class used by partitioningBy.
*/
private static final class Partition<T>
extends AbstractMap<Boolean, T>
implements Map<Boolean, T> {
final T forTrue;
final T forFalse;
Partition(T forTrue, T forFalse) {
this.forTrue = forTrue;
this.forFalse = forFalse;
}
@Override
public Set<Map.Entry entrySet() {
return new AbstractSet<Map.Entry() {
@Override
public Iterator<Map.Entry iterator() {
Map.Entry<Boolean, T> falseEntry = new SimpleImmutableEntry<>(false, forFalse);
Map.Entry<Boolean, T> trueEntry = new SimpleImmutableEntry<>(true, forTrue);
return Arrays.asList(falseEntry, trueEntry).iterator();
}
@Override
public int size() {
return 2;
}
};
}
}
}
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