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 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
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package java8.util.stream;

import java.lang.reflect.Method;
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.EnumSet;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;

import java8.util.DoubleSummaryStatistics;
import java8.util.IntSummaryStatistics;
import java8.util.Lists;
import java8.util.LongSummaryStatistics;
import java8.util.Maps;
import java8.util.Objects;
import java8.util.Optional;
import java8.util.Sets;
import java8.util.StringJoiner;
import java8.util.concurrent.ConcurrentMaps;
import java8.util.function.BiConsumer;
import java8.util.function.BiFunction;
import java8.util.function.BinaryOperator;
import java8.util.function.BinaryOperators;
import java8.util.function.Consumer;
import java8.util.function.Function;
import java8.util.function.Functions;
import java8.util.function.Predicate;
import java8.util.function.Supplier;
import java8.util.function.ToDoubleFunction;
import java8.util.function.ToIntFunction;
import java8.util.function.ToLongFunction;
import java8.util.stream.Collector.Characteristics;

/**
 * Implementations of {@link Collector} that implement various useful reduction
 * operations, such as accumulating elements into collections, summarizing
 * elements according to various criteria, etc.
 *
 * 

The following are examples of using the predefined collectors to perform * common mutable reduction tasks: * *

{@code
 *     // Accumulate names into a List
 *     List list = people.stream().map(Person::getName).collect(Collectors.toList());
 *
 *     // Accumulate names into a TreeSet
 *     Set 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> byDept
 *         = employees.stream()
 *                    .collect(Collectors.groupingBy(Employee::getDepartment));
 *
 *     // Compute sum of salaries by department
 *     Map totalByDept
 *         = employees.stream()
 *                    .collect(Collectors.groupingBy(Employee::getDepartment,
 *                                                   Collectors.summingInt(Employee::getSalary)));
 *
 *     // Partition students into passing and failing
 *     Map> passingFailing =
 *         students.stream()
 *                 .collect(Collectors.partitioningBy(s -> s.getGrade() >= PASS_THRESHOLD));
 *
 * }
* * @since 1.8 */ public final class Collectors { static final Set CH_CONCURRENT_ID = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT, Collector.Characteristics.UNORDERED, Collector.Characteristics.IDENTITY_FINISH)); static final Set CH_CONCURRENT_NOID = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT, Collector.Characteristics.UNORDERED)); static final Set CH_ID = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH)); static final Set CH_UNORDERED_ID = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.UNORDERED, Collector.Characteristics.IDENTITY_FINISH)); static final Set CH_NOID = Collections.emptySet(); static final Set CH_UNORDERED_NOID = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.UNORDERED)); private Collectors() { } static final Supplier DBL_SUM_STATS = DoubleSummaryStatistics::new; static final Supplier INT_SUM_STATS = IntSummaryStatistics::new; static final Supplier LNG_SUM_STATS = LongSummaryStatistics::new; private static Supplier> hashMapNew() { return HashMap::new; } private static Supplier> concHashMapNew() { return ConcurrentHashMap::new; } @SuppressWarnings("unchecked") private static Supplier> concHashMapNew2() { return (Supplier>) (Supplier) concHashMapNew(); } private static Supplier> arrayListNew() { return ArrayList::new; } private static Supplier> hashSetNew() { return HashSet::new; } @SuppressWarnings("unchecked") private static final Function, Map> UNMOD_MAP_FINISHER = map -> Maps .ofEntries(map.entrySet().toArray(new Map.Entry[0])); @SuppressWarnings("unchecked") private static final Function, Map> unmodMapFinisher() { return (Function, Map>) (Function) UNMOD_MAP_FINISHER; } private static final BiConsumer, ?> LIST_ADD = List::add; private static final BiConsumer, ?> SET_ADD = Set::add; @SuppressWarnings("unchecked") private static final BiConsumer, T> listAdd() { return (BiConsumer, T>) (BiConsumer) LIST_ADD; } @SuppressWarnings("unchecked") private static final BiConsumer, T> setAdd() { return (BiConsumer, T>) (BiConsumer) SET_ADD; } private static final Method LIST_FROM_TRUSTED_ARRAY; private static final Method LIST_FROM_TRUSTED_ARRAY_NULLS_ALLOWED; static { try { Class clazz = Class.forName("java8.util.ImmutableCollections"); LIST_FROM_TRUSTED_ARRAY = clazz.getDeclaredMethod("listFromTrustedArray", Object[].class); LIST_FROM_TRUSTED_ARRAY_NULLS_ALLOWED = clazz.getDeclaredMethod("listFromTrustedArrayNullsAllowed", Object[].class); LIST_FROM_TRUSTED_ARRAY.setAccessible(true); LIST_FROM_TRUSTED_ARRAY_NULLS_ALLOWED.setAccessible(true); } catch (Exception e) { throw new Error(e); } } @SuppressWarnings("unchecked") static List listFromTrustedArray(Object[] array) { try { return (List) LIST_FROM_TRUSTED_ARRAY.invoke(null, (Object) array); } catch (Exception e) { throw new Error(e); } } @SuppressWarnings("unchecked") static List listFromTrustedArrayNullsAllowed(Object[] array) { try { return (List) LIST_FROM_TRUSTED_ARRAY_NULLS_ALLOWED.invoke(null, (Object) array); } catch (Exception e) { throw new Error(e); } } /** * Returns a new {@code Collector} described by the given {@code supplier}, * {@code accumulator}, and {@code combiner} functions. The resulting * {@code Collector} has the {@code Collector.Characteristics.IDENTITY_FINISH} * characteristic. * * @param supplier The supplier function for the new collector * @param accumulator The accumulator function for the new collector * @param combiner The combiner function for the new collector * @param characteristics The collector characteristics for the new * collector * @param The type of input elements for the new collector * @param The type of intermediate accumulation result, and final result, * for the new collector * @throws NullPointerException if any argument is null * @return the new {@code Collector} */ public static Collector of(Supplier supplier, BiConsumer accumulator, BinaryOperator combiner, Characteristics... characteristics) { Objects.requireNonNull(supplier); Objects.requireNonNull(accumulator); Objects.requireNonNull(combiner); Objects.requireNonNull(characteristics); Set cs = (characteristics.length == 0) ? Collectors.CH_ID : Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH, characteristics)); return new Collectors.CollectorImpl<>(supplier, accumulator, combiner, cs); } /** * Returns a new {@code Collector} described by the given {@code supplier}, * {@code accumulator}, {@code combiner}, and {@code finisher} functions. * * @param supplier The supplier function for the new collector * @param accumulator The accumulator function for the new collector * @param combiner The combiner function for the new collector * @param finisher The finisher function for the new collector * @param characteristics The collector characteristics for the new * collector * @param The type of input elements for the new collector * @param The intermediate accumulation type of the new collector * @param The final result type of the new collector * @throws NullPointerException if any argument is null * @return the new {@code Collector} */ public static Collector of(Supplier supplier, BiConsumer accumulator, BinaryOperator combiner, Function finisher, Characteristics... characteristics) { Objects.requireNonNull(supplier); Objects.requireNonNull(accumulator); Objects.requireNonNull(combiner); Objects.requireNonNull(finisher); Objects.requireNonNull(characteristics); Set cs = Collectors.CH_NOID; if (characteristics.length > 0) { cs = EnumSet.noneOf(Characteristics.class); Collections.addAll(cs, characteristics); cs = Collections.unmodifiableSet(cs); } return new Collectors.CollectorImpl<>(supplier, accumulator, combiner, finisher, cs); } /** * Construct an {@code IllegalStateException} with appropriate message. * * @param k the duplicate key * @param u 1st value to be accumulated/merged * @param v 2nd value to be accumulated/merged */ private static IllegalStateException duplicateKeyException( Object k, Object u, Object v) { return new IllegalStateException(String.format( "Duplicate key %s (attempted merging values %s and %s)", k, u, v)); } /** * {@code BinaryOperator} that merges the contents of its right * argument into its left argument, throwing {@code IllegalStateException} * if duplicate keys are encountered. * * @param type of the map keys * @param type of the map values * @param type of the map * @return a merge function for two maps */ private static > BinaryOperator uniqKeysMapMerger() { return (m1, m2) -> { for (Map.Entry e : m2.entrySet()) { K k = e.getKey(); V v = Objects.requireNonNull(e.getValue()); V u = null; if (m1 instanceof ConcurrentMap) { u = ((ConcurrentMap) m1).putIfAbsent(k, v); } else { u = Maps.putIfAbsent(m1, k, v); } if (u != null) { throw duplicateKeyException(k, u, v); } } return m1; }; } /** * {@code BiConsumer} that accumulates (key, value) pairs * extracted from elements into the map, throwing {@code IllegalStateException} * if duplicate keys are encountered. * * @param keyMapper a function that maps an element into a key * @param valueMapper a function that maps an element into a value * @param type of elements * @param type of map keys * @param type of map values * @return an accumulating consumer */ private static BiConsumer, T> uniqKeysMapAccumulator(Function keyMapper, Function valueMapper) { return (map, element) -> { K k = keyMapper.apply(element); V v = Objects.requireNonNull(valueMapper.apply(element)); V u = null; if (map instanceof ConcurrentMap) { u = ((ConcurrentMap) map).putIfAbsent(k, v); } else { u = Maps.putIfAbsent(map, k, v); } if (u != null) { throw duplicateKeyException(k, u, v); } }; } /** * Simple implementation class for {@code Collector}. * * @param the type of elements to be collected * @param the type of the result */ static class CollectorImpl implements Collector { private final Supplier supplier; private final BiConsumer accumulator; private final BinaryOperator combiner; private final Function finisher; private final Set characteristics; CollectorImpl(Supplier supplier, BiConsumer accumulator, BinaryOperator combiner, Function finisher, Set characteristics) { this.supplier = supplier; this.accumulator = accumulator; this.combiner = combiner; this.finisher = finisher; this.characteristics = characteristics; } @SuppressWarnings("unchecked") CollectorImpl(Supplier supplier, BiConsumer accumulator, BinaryOperator combiner, Set characteristics) { this(supplier, accumulator, combiner, a -> (R)a/*castingIdentity()*/, characteristics); } @Override public BiConsumer accumulator() { return accumulator; } @Override public Supplier supplier() { return supplier; } @Override public BinaryOperator combiner() { return combiner; } @Override public Function finisher() { return finisher; } @Override public Set 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 the type of the input elements * @param the type of the resulting {@code Collection} * @param collectionFactory a supplier providing a new empty {@code Collection} * into which the results will be inserted * @return a {@code Collector} which collects all the input elements into a * {@code Collection}, in encounter order */ public static > Collector 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 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 Collector> toList() { return new CollectorImpl<>(arrayListNew(), listAdd(), (left, right) -> { left.addAll(right); return left; }, CH_ID); } /** * Returns a {@code Collector} that accumulates the input elements into an * {@link Lists#of(Object[]) unmodifiable List} in encounter * order. The returned Collector disallows null values and will throw * {@code NullPointerException} if it is presented with a null value. * * @param the type of the input elements * @return a {@code Collector} which collects all the input elements into an * unmodifiable List, in encounter order * @since 10 */ public static Collector> toUnmodifiableList() { return new CollectorImpl<>(arrayListNew(), listAdd(), (left, right) -> { left.addAll(right); return left; }, list -> { if (list.getClass() == ArrayList.class) { // ensure it's trusted return listFromTrustedArray(list.toArray()); } else { throw new IllegalArgumentException(); } }, CH_NOID); } /** * 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)}. * *

This is an {@link Collector.Characteristics#UNORDERED unordered} * Collector. * * @param the type of the input elements * @return a {@code Collector} which collects all the input elements into a * {@code Set} */ public static Collector> toSet() { return new CollectorImpl<>(hashSetNew(), setAdd(), (left, right) -> { if (left.size() < right.size()) { right.addAll(left); return right; } else { left.addAll(right); return left; } }, CH_UNORDERED_ID); } /** * Returns a {@code Collector} that accumulates the input elements into an * {@link Sets#of(Object[]) unmodifiable Set}. The returned * Collector disallows null values and will throw {@code NullPointerException} * if it is presented with a null value. If the input contains duplicate elements, * an arbitrary element of the duplicates is preserved. * *

This is an {@link Collector.Characteristics#UNORDERED unordered} * Collector. * * @param the type of the input elements * @return a {@code Collector} which collects all the input elements into an * unmodifiable Set * @since 10 */ @SuppressWarnings("unchecked") public static Collector> toUnmodifiableSet() { return new CollectorImpl<>(hashSetNew(), setAdd(), (left, right) -> { if (left.size() < right.size()) { right.addAll(left); return right; } else { left.addAll(right); return left; } }, set -> (Set) Sets.of(set.toArray()), CH_UNORDERED_NOID); } /** * 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 joining() { return new CollectorImpl( 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 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 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} that merges the contents of its right * argument into its left argument, using the provided merge function to * handle duplicate keys. * * @param type of the map keys * @param type of the map values * @param type of the map * @param mergeFunction A merge function suitable for * {@link Maps#merge(Map, Object, Object, BiFunction)} * @return a merge function for two maps */ private static > BinaryOperator mapMerger(BinaryOperator mergeFunction) { return (m1, m2) -> { for (Map.Entry e : m2.entrySet()) { Maps.merge(m1, e.getKey(), e.getValue(), mergeFunction); } return m1; }; } private static > BinaryOperator mapMergerConcurrent(BinaryOperator mergeFunction) { return (m1, m2) -> { for (Map.Entry e : m2.entrySet()) { ConcurrentMaps.merge(m1, 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. * *

API Note:
* 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: *

{@code
     *     Map> lastNamesByCity
     *         = people.stream().collect(groupingBy(Person::getCity,
     *                                              mapping(Person::getLastName, toSet())));
     * }
* * @param the type of the input elements * @param type of elements accepted by downstream collector * @param intermediate accumulation type of the downstream collector * @param 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 Collector mapping(Function mapper, Collector downstream) { BiConsumer 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} accepting elements of type {@code U} to one * accepting elements of type {@code T} by applying a flat mapping function * to each input element before accumulation. The flat mapping function * maps an input element to a {@link Stream stream} covering zero or more * output elements that are then accumulated downstream. Each mapped stream * is {@link java8.util.stream.BaseStream#close() closed} after its contents * have been placed downstream. (If a mapped stream is {@code null} * an empty stream is used, instead.) * *

API Note:
* The {@code flatMapping()} 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 Order}, to accumulate the set of line items for each customer: *

{@code
     *     Map> itemsByCustomerName
     *         = orders.stream().collect(groupingBy(Order::getCustomerName,
     *                                              flatMapping(order -> order.getLineItems().stream(), toSet())));
     * }
* * @param the type of the input elements * @param type of elements accepted by downstream collector * @param
intermediate accumulation type of the downstream collector * @param result type of collector * @param mapper a function to be applied to the input elements, which * returns a stream of results * @param downstream a collector which will receive the elements of the * stream returned by mapper * @return a collector which applies the mapping function to the input * elements and provides the flat mapped results to the downstream collector * @since 9 */ public static Collector flatMapping(Function> mapper, Collector downstream) { BiConsumer downstreamAccumulator = downstream.accumulator(); return new CollectorImpl<>(downstream.supplier(), (r, t) -> { Stream result = null; try { result = mapper.apply(t); if (result != null) { result.sequential().forEach(u -> downstreamAccumulator.accept(r, u)); } } finally { if (result != null) { try { result.close(); } catch (Exception ignore) { } } } }, downstream.combiner(), downstream.finisher(), downstream.characteristics()); } /** * Adapts a {@code Collector} to one accepting elements of the same type * {@code T} by applying the predicate to each input element and only * accumulating if the predicate returns {@code true}. * *

API Note:
* The {@code filtering()} 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 Employee}, to accumulate the employees in each department that have a * salary above a certain threshold: *

{@code
     *     Map> wellPaidEmployeesByDepartment
     *         = employees.stream().collect(groupingBy(Employee::getDepartment,
     *                                              filtering(e -> e.getSalary() > 2000, toSet())));
     * }
* A filtering collector differs from a stream's {@code filter()} operation. * In this example, suppose there are no employees whose salary is above the * threshold in some department. Using a filtering collector as shown above * would result in a mapping from that department to an empty {@code Set}. * If a stream {@code filter()} operation were done instead, there would be * no mapping for that department at all. * * @param the type of the input elements * @param
intermediate accumulation type of the downstream collector * @param result type of collector * @param predicate a predicate to be applied to the input elements * @param downstream a collector which will accept values that match the * predicate * @return a collector which applies the predicate to the input elements * and provides matching elements to the downstream collector * @since 9 */ public static Collector filtering(Predicate predicate, Collector downstream) { BiConsumer downstreamAccumulator = downstream.accumulator(); return new CollectorImpl<>(downstream.supplier(), (r, t) -> { if (predicate.test(t)) { downstreamAccumulator.accept(r, 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: *
{@code
     *     List list
     *         = people.stream().collect(collectingAndThen(toList(), Collections::unmodifiableList));
     * }
* * @param the type of the input elements * @param
intermediate accumulation type of the downstream collector * @param result type of the downstream collector * @param 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 Collector collectingAndThen(Collector downstream, Function finisher) { Set 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(), Functions.andThen(downstream.finisher(), 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. * *

Implementation Requirements:
* This produces a result equivalent to: *

{@code
     *     reducing(0L, e -> 1L, Long::sum)
     * }
* * @param the type of the input elements * @return a {@code Collector} that counts the input elements */ public static Collector counting() { return summingLong(e -> 1L); } /** * Returns a {@code Collector} that produces the minimal element according * to a given {@code Comparator}, described as an {@code Optional}. * *

Implementation Requirements:
* This produces a result equivalent to: *

{@code
     *     reducing(BinaryOperator.minBy(comparator))
     * }
* * @param 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 Collector> minBy(Comparator comparator) { return reducing(BinaryOperators.minBy(comparator)); } /** * Returns a {@code Collector} that produces the maximal element according * to a given {@code Comparator}, described as an {@code Optional}. * *

Implementation Requirements:
* This produces a result equivalent to: *

{@code
     *     reducing(BinaryOperator.maxBy(comparator))
     * }
* * @param 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 Collector> maxBy(Comparator comparator) { return reducing(BinaryOperators.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 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 Collector summingInt(ToIntFunction 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 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 Collector summingLong(ToLongFunction 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. * *

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 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 Collector summingDouble(ToDoubleFunction mapper) { /* * In the arrays allocated for the collect operation, index 0 * holds the high-order bits of the running sum, index 1 holds * the (negative) 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) -> { double val = mapper.applyAsDouble(t); sumWithCompensation(a, val); a[2] += val;}, (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], * negative 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 the type of the input elements * @param mapper a function extracting the property to be averaged * @return a {@code Collector} that produces the arithmetic mean of a * derived property */ public static Collector averagingInt(ToIntFunction 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 the type of the input elements * @param mapper a function extracting the property to be averaged * @return a {@code Collector} that produces the arithmetic mean of a * derived property */ public static Collector averagingLong(ToLongFunction 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. * *

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}. * *

Implementation Note:
The {@code double} format can represent all * consecutive integers in the range -253 to * 253. If the pipeline has more than 253 * values, the divisor in the average computation will saturate at * 253, leading to additional numerical errors. * * @param the type of the input elements * @param mapper a function extracting the property to be averaged * @return a {@code Collector} that produces the arithmetic mean of a * derived property */ public static Collector averagingDouble(ToDoubleFunction mapper) { /* * In the arrays allocated for the collect operation, index 0 * holds the high-order bits of the running sum, index 1 holds * the (negative) 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) -> { double val = mapper.applyAsDouble(t); sumWithCompensation(a, val); a[2]++; a[3] += val; }, (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. * *

API Note:
* 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 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} 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 Collector reducing(T identity, BinaryOperator 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 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}. * *

API Note:
* 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. * *

For example, given a stream of {@code Person}, to calculate tallest * person in each city: *

{@code
     *     Comparator byHeight = Comparator.comparing(Person::getHeight);
     *     Map> tallestByCity
     *         = people.stream().collect(groupingBy(Person::getCity, reducing(BinaryOperator.maxBy(byHeight))));
     * }
* * @param element type for the input and output of the reduction * @param op a {@code BinaryOperator} 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 Collector> reducing(BinaryOperator op) { class OptionalBox implements Consumer { 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>( 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. * *

API Note:
* 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. * *

For example, given a stream of {@code Person}, to calculate the longest * last name of residents in each city: *

{@code
     *     Comparator byLength = Comparator.comparing(String::length);
     *     Map longestLastNameByCity
     *         = people.stream().collect(groupingBy(Person::getCity,
     *                                              reducing("", Person::getLastName, BinaryOperator.maxBy(byLength))));
     * }
* * @param the type of the input elements * @param 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} used to reduce the mapped values * @return a {@code Collector} implementing the map-reduce operation * * @see #reducing(Object, BinaryOperator) * @see #reducing(BinaryOperator) */ public static Collector reducing(U identity, Function mapper, BinaryOperator 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}. * *

The classification function maps elements to some key type {@code K}. * The collector produces a {@code Map>} 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. * *

There are no guarantees on the type, mutability, serializability, or * thread-safety of the {@code Map} or {@code List} objects returned. *

Implementation Requirements:
* This produces a result similar to: *

{@code
     *     groupingBy(classifier, toList())
     * }
* *

Implementation Note:
* 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 the type of the input elements * @param 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 Collector>> groupingBy(Function 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}. * *

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}. * *

There are no guarantees on the type, mutability, * serializability, or thread-safety of the {@code Map} returned. * *

For example, to compute the set of last names of people in each city: *

{@code
     *     Map> namesByCity
     *         = people.stream().collect(groupingBy(Person::getCity,
     *                                              mapping(Person::getLastName, toSet())));
     * }
* *

Implementation Note:
* 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 the type of the input elements * @param the type of the keys * @param
the intermediate accumulation type of the downstream collector * @param 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 Collector> groupingBy(Function classifier, Collector downstream) { return groupingBy(classifier, hashMapNew(), 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. * *

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}. * *

For example, to compute the set of last names of people in each city, * where the city names are sorted: *

{@code
     *     Map> namesByCity
     *         = people.stream().collect(groupingBy(Person::getCity, TreeMap::new,
     *                                              mapping(Person::getLastName, toSet())));
     * }
* *

Implementation Note:
* 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 the type of the input elements * @param the type of the keys * @param
the intermediate accumulation type of the downstream collector * @param the result type of the downstream reduction * @param 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 supplier providing a new empty {@code Map} * into which the results will be inserted * @return a {@code Collector} implementing the cascaded group-by operation * * @see #groupingBy(Function, Collector) * @see #groupingBy(Function) * @see #groupingByConcurrent(Function, Supplier, Collector) */ public static > Collector groupingBy(Function classifier, Supplier mapFactory, Collector downstream) { Supplier downstreamSupplier = downstream.supplier(); BiConsumer downstreamAccumulator = downstream.accumulator(); BiConsumer, T> accumulator = (m, t) -> { K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key"); A container = Maps.computeIfAbsent(m, key, k -> downstreamSupplier.get()); downstreamAccumulator.accept(container, t); }; BinaryOperator> merger = Collectors.>mapMerger(downstream.combiner()); @SuppressWarnings("unchecked") Supplier> mangledFactory = (Supplier>) mapFactory; if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) { return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_ID); } else { @SuppressWarnings("unchecked") Function downstreamFinisher = (Function) downstream.finisher(); Function, M> finisher = intermediate -> { Maps.replaceAll(intermediate, (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. * *

This is a {@link Collector.Characteristics#CONCURRENT concurrent} and * {@link Collector.Characteristics#UNORDERED unordered} Collector. * *

The classification function maps elements to some key type {@code K}. * The collector produces a {@code ConcurrentMap>} 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. * *

There are no guarantees on the type, mutability, or serializability * of the {@code ConcurrentMap} or {@code List} objects returned, or of the * thread-safety of the {@code List} objects returned. *

Implementation Requirements:
* This produces a result similar to: *

{@code
     *     groupingByConcurrent(classifier, toList())
     * }
* * @param the type of the input elements * @param 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 Collector>> groupingByConcurrent(Function classifier) { return groupingByConcurrent(classifier, concHashMapNew(), 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}. * *

This is a {@link Collector.Characteristics#CONCURRENT concurrent} and * {@link Collector.Characteristics#UNORDERED unordered} Collector. * *

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 ConcurrentMap}. * *

There are no guarantees on the type, mutability, or serializability * of the {@code ConcurrentMap} returned. * *

For example, to compute the set of last names of people in each city, * where the city names are sorted: *

{@code
     *     ConcurrentMap> namesByCity
     *         = people.stream().collect(groupingByConcurrent(Person::getCity,
     *                                                        mapping(Person::getLastName, toSet())));
     * }
* * @param the type of the input elements * @param the type of the keys * @param
the intermediate accumulation type of the downstream collector * @param 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 Collector> groupingByConcurrent(Function classifier, Collector downstream) { return groupingByConcurrent(classifier, concHashMapNew(), 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. * *

This is a {@link Collector.Characteristics#CONCURRENT concurrent} and * {@link Collector.Characteristics#UNORDERED unordered} Collector. * *

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 ConcurrentMap}. * *

For example, to compute the set of last names of people in each city, * where the city names are sorted: *

{@code
     *     ConcurrentMap> namesByCity
     *         = people.stream().collect(groupingByConcurrent(Person::getCity, ConcurrentSkipListMap::new,
     *                                                        mapping(Person::getLastName, toSet())));
     * }
* * * @param the type of the input elements * @param the type of the keys * @param
the intermediate accumulation type of the downstream collector * @param the result type of the downstream reduction * @param 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 supplier providing a new empty {@code ConcurrentMap} * into which the results will be inserted * @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 > Collector groupingByConcurrent(Function classifier, Supplier mapFactory, Collector downstream) { Supplier downstreamSupplier = downstream.supplier(); BiConsumer downstreamAccumulator = downstream.accumulator(); BinaryOperator> merger = Collectors.>mapMergerConcurrent(downstream.combiner()); @SuppressWarnings("unchecked") Supplier> mangledFactory = (Supplier>) mapFactory; BiConsumer, T> 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 = ConcurrentMaps.computeIfAbsent(m, 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 = ConcurrentMaps.computeIfAbsent(m, 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 downstreamFinisher = (Function) downstream.finisher(); Function, M> finisher = intermediate -> { ConcurrentMaps.replaceAll(intermediate, (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>}. * * The returned {@code Map} always contains mappings for both * {@code false} and {@code true} keys. * There are no guarantees on the type, mutability, * serializability, or thread-safety of the {@code Map} or {@code List} * returned. * *

API Note:
* If a partition has no elements, its value in the result Map will be * an empty List. * * @param 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 Collector>> 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} whose values are the result of the downstream * reduction. * *

* The returned {@code Map} always contains mappings for both * {@code false} and {@code true} keys. * There are no guarantees on the type, mutability, * serializability, or thread-safety of the {@code Map} returned. * *

API Note:
* If a partition has no elements, its value in the result Map will be * obtained by calling the downstream collector's supplier function and then * applying the finisher function. * * @param the type of the input elements * @param
the intermediate accumulation type of the downstream collector * @param 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 Collector> partitioningBy(Predicate predicate, Collector downstream) { BiConsumer downstreamAccumulator = downstream.accumulator(); BiConsumer, T> accumulator = (result, t) -> downstreamAccumulator.accept(predicate.test(t) ? result.forTrue : result.forFalse, t); BinaryOperator op = downstream.combiner(); BinaryOperator> merger = (left, right) -> new Partition<>(op.apply(left.forTrue, right.forTrue), op.apply(left.forFalse, right.forFalse)); Supplier> 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, Map> 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. * *

If the mapped keys contain duplicates (according to * {@link Object#equals(Object)}), an {@code IllegalStateException} is * thrown when the collection operation is performed. If the mapped keys * might have duplicates, use {@link #toMap(Function, Function, BinaryOperator)} * instead. * *

There are no guarantees on the type, mutability, serializability, * or thread-safety of the {@code Map} returned. * *

API Note:
* It is common for either the key or the value to be the input elements. * In this case, the utility method * {@link java8.util.function.Functions#identity()} may be helpful. * For example, the following produces a {@code Map} mapping * students to their grade point average: *

{@code
     *     Map studentToGPA =
     *         students.stream().collect(toMap(Functions.identity(),
     *                                         student -> computeGPA(student)));
     * }
* And the following produces a {@code Map} mapping a unique identifier to * students: *
{@code
     *     Map studentIdToStudent =
     *         students.stream().collect(toMap(Student::getId,
     *                                         Functions.identity()));
     * }
* *

Implementation Note:
* 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 the type of the input elements * @param the output type of the key mapping function * @param 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 Collector> toMap(Function keyMapper, Function valueMapper) { return new CollectorImpl<>(hashMapNew(), uniqKeysMapAccumulator((Function) keyMapper, (Function) valueMapper), uniqKeysMapMerger(), CH_ID); } /** * Returns a {@code Collector} that accumulates the input elements into an * {@link Maps#ofEntries(java.util.Map.Entry[]) unmodifiable Map}, * whose keys and values are the result of applying the provided * mapping functions to the input elements. * *

If the mapped keys contain duplicates (according to * {@link Object#equals(Object)}), an {@code IllegalStateException} is * thrown when the collection operation is performed. If the mapped keys * might have duplicates, use {@link #toUnmodifiableMap(Function, Function, BinaryOperator)} * to handle merging of the values. * *

The returned Collector disallows null keys and values. If either mapping function * returns null, {@code NullPointerException} will be thrown. * * @param the type of the input elements * @param the output type of the key mapping function * @param the output type of the value mapping function * @param keyMapper a mapping function to produce keys, must be non-null * @param valueMapper a mapping function to produce values, must be non-null * @return a {@code Collector} which collects elements into an unmodifiable Map * whose keys and values are the result of applying mapping functions to * the input elements * @throws NullPointerException if either keyMapper or valueMapper is null * * @see #toUnmodifiableMap(Function, Function, BinaryOperator) * @since 10 */ public static Collector> toUnmodifiableMap(Function keyMapper, Function valueMapper) { Objects.requireNonNull(keyMapper, "keyMapper"); Objects.requireNonNull(valueMapper, "valueMapper"); return collectingAndThen( toMap(keyMapper, valueMapper), unmodMapFinisher()); } /** * 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. * *

If the mapped keys contain 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. * *

There are no guarantees on the type, mutability, serializability, * or thread-safety of the {@code Map} returned. * *

API Note:
* 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 deal with these collisions, and produce a * {@code Map} mapping names to a concatenated list of addresses: *

{@code
     *     Map phoneBook =
     *         people.stream().collect(toMap(Person::getName,
     *                                       Person::getAddress,
     *                                       (s, a) -> s + ", " + a));
     * }
* *

Implementation Note:
* 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 the type of the input elements * @param the output type of the key mapping function * @param 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 Maps#merge(Map, 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 Collector> toMap(Function keyMapper, Function valueMapper, BinaryOperator mergeFunction) { return toMap(keyMapper, valueMapper, mergeFunction, hashMapNew()); } /** * Returns a {@code Collector} that accumulates the input elements into an * {@link Maps#ofEntries(java.util.Map.Entry[]) unmodifiable Map}, * whose keys and values are the result of applying the provided * mapping functions to the input elements. * *

If the mapped * keys contain 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 returned Collector disallows null keys and values. If either mapping function * returns null, {@code NullPointerException} will be thrown. * * @param the type of the input elements * @param the output type of the key mapping function * @param the output type of the value mapping function * @param keyMapper a mapping function to produce keys, must be non-null * @param valueMapper a mapping function to produce values, must be non-null * @param mergeFunction a merge function, used to resolve collisions between * values associated with the same key, as supplied * to {@link Maps#merge(Map, Object, Object, BiFunction)}, * must be non-null * @return a {@code Collector} which collects elements into an unmodifiable 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 * @throws NullPointerException if the keyMapper, valueMapper, or mergeFunction is null * * @see #toUnmodifiableMap(Function, Function) * @since 10 */ public static Collector> toUnmodifiableMap(Function keyMapper, Function valueMapper, BinaryOperator mergeFunction) { Objects.requireNonNull(keyMapper, "keyMapper"); Objects.requireNonNull(valueMapper, "valueMapper"); Objects.requireNonNull(mergeFunction, "mergeFunction"); return collectingAndThen( toMap(keyMapper, valueMapper, mergeFunction, hashMapNew()), Collectors.unmodMapFinisher()); } /** * 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. * *

If the mapped keys contain 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. * *

Implementation Note:
* 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 the type of the input elements * @param the output type of the key mapping function * @param the output type of the value mapping function * @param 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 Maps#merge(Map, Object, Object, BiFunction)} * @param mapFactory a supplier providing 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 > Collector toMap(Function keyMapper, Function valueMapper, BinaryOperator mergeFunction, Supplier mapFactory) { BiConsumer accumulator = (map, element) -> Maps.merge(map, keyMapper.apply(element), valueMapper.apply(element), mergeFunction); return new CollectorImpl<>(mapFactory, 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. * *

If the mapped keys contain 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. * *

There are no guarantees on the type, mutability, or serializability * of the {@code ConcurrentMap} returned. * *

API Note:
* It is common for either the key or the value to be the input elements. * In this case, the utility method * {@link java8.util.function.Functions#identity()} may be helpful. * For example, the following produces a {@code ConcurrentMap} mapping * students to their grade point average: *

{@code
     *     ConcurrentMap studentToGPA =
     *         students.stream().collect(toConcurrentMap(Functions.identity(),
     *                                                   student -> computeGPA(student)));
     * }
* And the following produces a {@code ConcurrentMap} mapping a * unique identifier to students: *
{@code
     *     ConcurrentMap studentIdToStudent =
     *         students.stream().collect(toConcurrentMap(Student::getId,
     *                                                   Functions.identity()));
     * }
* *

This is a {@link Collector.Characteristics#CONCURRENT concurrent} and * {@link Collector.Characteristics#UNORDERED unordered} Collector. * * @param the type of the input elements * @param the output type of the key mapping function * @param 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 Collector> toConcurrentMap(Function keyMapper, Function valueMapper) { return new CollectorImpl<>(concHashMapNew2(), uniqKeysMapAccumulator((Function) keyMapper, (Function) valueMapper), uniqKeysMapMerger(), CH_CONCURRENT_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. * *

If the mapped keys contain 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. * *

There are no guarantees on the type, mutability, or serializability * of the {@code ConcurrentMap} returned. * *

API Note:
* 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 deal with these collisions, and produce a * {@code ConcurrentMap} mapping names to a concatenated list of addresses: *

{@code
     *     ConcurrentMap phoneBook =
     *         people.stream().collect(toConcurrentMap(Person::getName,
     *                                                 Person::getAddress,
     *                                                 (s, a) -> s + ", " + a));
     * }
* *

This is a {@link Collector.Characteristics#CONCURRENT concurrent} and * {@link Collector.Characteristics#UNORDERED unordered} Collector. * * @param the type of the input elements * @param the output type of the key mapping function * @param 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 ConcurrentMaps#merge(ConcurrentMap, 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 Collector> toConcurrentMap(Function keyMapper, Function valueMapper, BinaryOperator mergeFunction) { return toConcurrentMap(keyMapper, valueMapper, mergeFunction, concHashMapNew()); } /** * 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. * *

If the mapped keys contain 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. * *

This is a {@link Collector.Characteristics#CONCURRENT concurrent} and * {@link Collector.Characteristics#UNORDERED unordered} Collector. * * @param the type of the input elements * @param the output type of the key mapping function * @param the output type of the value mapping function * @param 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 ConcurrentMaps#merge(ConcurrentMap, Object, Object, BiFunction)} * @param mapFactory a supplier providing a new empty {@code ConcurrentMap} * 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 > Collector toConcurrentMap(Function keyMapper, Function valueMapper, BinaryOperator mergeFunction, Supplier mapFactory) { BiConsumer accumulator = (map, element) -> ConcurrentMaps.merge(map, keyMapper.apply(element), valueMapper.apply(element), mergeFunction); return new CollectorImpl<>(mapFactory, accumulator, mapMergerConcurrent(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 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 Collector summarizingInt(ToIntFunction mapper) { return new CollectorImpl( INT_SUM_STATS, (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 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 Collector summarizingLong(ToLongFunction mapper) { return new CollectorImpl( LNG_SUM_STATS, (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 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 Collector summarizingDouble(ToDoubleFunction mapper) { return new CollectorImpl( DBL_SUM_STATS, (r, t) -> r.accept(mapper.applyAsDouble(t)), (l, r) -> { l.combine(r); return l; }, CH_ID); } /** * Returns a {@code Collector} that is a composite of two downstream collectors. * Every element passed to the resulting collector is processed by both downstream * collectors, then their results are merged using the specified merge function * into the final result. * *

The resulting collector functions do the following: * *

    *
  • supplier: creates a result container that contains result containers * obtained by calling each collector's supplier *
  • accumulator: calls each collector's accumulator with its result container * and the input element *
  • combiner: calls each collector's combiner with two result containers *
  • finisher: calls each collector's finisher with its result container, * then calls the supplied merger and returns its result. *
* *

The resulting collector is {@link Collector.Characteristics#UNORDERED} if both downstream * collectors are unordered and {@link Collector.Characteristics#CONCURRENT} if both downstream * collectors are concurrent. * * @param the type of the input elements * @param the result type of the first collector * @param the result type of the second collector * @param the final result type * @param downstream1 the first downstream collector * @param downstream2 the second downstream collector * @param merger the function which merges two results into the single one * @return a {@code Collector} which aggregates the results of two supplied collectors. * @since 12 */ public static Collector teeing(Collector downstream1, Collector downstream2, BiFunction merger) { return teeing0(downstream1, downstream2, merger); } private static Collector teeing0(Collector downstream1, Collector downstream2, BiFunction merger) { Objects.requireNonNull(downstream1, "downstream1"); Objects.requireNonNull(downstream2, "downstream2"); Objects.requireNonNull(merger, "merger"); Supplier c1Supplier = Objects.requireNonNull(downstream1.supplier(), "downstream1 supplier"); Supplier c2Supplier = Objects.requireNonNull(downstream2.supplier(), "downstream2 supplier"); BiConsumer c1Accumulator = Objects.requireNonNull(downstream1.accumulator(), "downstream1 accumulator"); BiConsumer c2Accumulator = Objects.requireNonNull(downstream2.accumulator(), "downstream2 accumulator"); BinaryOperator c1Combiner = Objects.requireNonNull(downstream1.combiner(), "downstream1 combiner"); BinaryOperator c2Combiner = Objects.requireNonNull(downstream2.combiner(), "downstream2 combiner"); Function c1Finisher = Objects.requireNonNull(downstream1.finisher(), "downstream1 finisher"); Function c2Finisher = Objects.requireNonNull(downstream2.finisher(), "downstream2 finisher"); Set characteristics; Set c1Characteristics = downstream1.characteristics(); Set c2Characteristics = downstream2.characteristics(); if (CH_ID.containsAll(c1Characteristics) || CH_ID.containsAll(c2Characteristics)) { characteristics = CH_NOID; } else { EnumSet c = EnumSet.noneOf(Collector.Characteristics.class); c.addAll(c1Characteristics); c.retainAll(c2Characteristics); c.remove(Collector.Characteristics.IDENTITY_FINISH); characteristics = Collections.unmodifiableSet(c); } class PairBox { A1 left = c1Supplier.get(); A2 right = c2Supplier.get(); void add(T t) { c1Accumulator.accept(left, t); c2Accumulator.accept(right, t); } PairBox combine(PairBox other) { left = c1Combiner.apply(left, other.left); right = c2Combiner.apply(right, other.right); return this; } R get() { R1 r1 = c1Finisher.apply(left); R2 r2 = c2Finisher.apply(right); return merger.apply(r1, r2); } } return new CollectorImpl<>(PairBox::new, PairBox::add, PairBox::combine, PairBox::get, characteristics); } /** * Implementation class used by partitioningBy. */ private static final class Partition extends AbstractMap implements Map { final T forTrue; final T forFalse; Partition(T forTrue, T forFalse) { this.forTrue = forTrue; this.forFalse = forFalse; } @Override public Set> entrySet() { return new AbstractSet>() { @Override public Iterator> iterator() { Map.Entry falseEntry = new SimpleImmutableEntry<>(false, forFalse); Map.Entry trueEntry = new SimpleImmutableEntry<>(true, forTrue); return Arrays.asList(falseEntry, trueEntry).iterator(); } @Override public int size() { return 2; } }; } @Override public boolean containsKey(Object key) { return key instanceof Boolean; } @Override public T get(Object key) { return (key instanceof Boolean) ? (((Boolean) key).booleanValue() ? forTrue : forFalse) : null; } @Override public int size() { return 2; } @Override public boolean isEmpty() { return false; } } }