distinct是对对象去重，所以流中的对象必须重写equals和hashCode方法。
例子：
List<User> users = new ArrayList<>();
users.add(new User("张三",30));
users.add(new User("李四",39));
users.add(new User("王五",20));
users.add(new User("王五",20));
System.out.println(users);
List<User> collect = users.stream().distinct().collect(Collectors.toList());
System.out.println(collect);输出：
源码分析
ReferencePipeline#distinct()
public final Stream<P_OUT> distinct() {
return DistinctOps.makeRef(this);
}看看DistinctOps.makeRef。
DistinctOps#makeRef
static <T> ReferencePipeline<T, T> makeRef(AbstractPipeline<?, T, ?> upstream) {
return new ReferencePipeline.StatefulOp<T, T>(upstream, StreamShape.REFERENCE,
StreamOpFlag.IS_DISTINCT | StreamOpFlag.NOT_SIZED) {
<P_IN> Node<T> reduce(PipelineHelper<T> helper, Spliterator<P_IN> spliterator) {
// If the stream is SORTED then it should also be ORDERED so the following will also
// preserve the sort order
TerminalOp<T, LinkedHashSet<T>> reduceOp
= ReduceOps.<T, LinkedHashSet<T>>makeRef(LinkedHashSet::new, LinkedHashSet::add,
LinkedHashSet::addAll);
return Nodes.node(reduceOp.evaluateParallel(helper, spliterator));
}
@Override
<P_IN> Node<T> opEvaluateParallel(PipelineHelper<T> helper,
Spliterator<P_IN> spliterator,
IntFunction<T[]> generator) {
if (StreamOpFlag.DISTINCT.isKnown(helper.getStreamAndOpFlags())) {
// No-op
return helper.evaluate(spliterator, false, generator);
}
else if (StreamOpFlag.ORDERED.isKnown(helper.getStreamAndOpFlags())) {
return reduce(helper, spliterator);
}
else {
// Holder of null state since ConcurrentHashMap does not support null values
AtomicBoolean seenNull = new AtomicBoolean(false);
ConcurrentHashMap<T, Boolean> map = new ConcurrentHashMap<>();
TerminalOp<T, Void> forEachOp = ForEachOps.makeRef(t -> {
if (t == null)
seenNull.set(true);
else
map.putIfAbsent(t, Boolean.TRUE);
}, false);
forEachOp.evaluateParallel(helper, spliterator);
// If null has been seen then copy the key set into a HashSet that supports null values
// and add null
Set<T> keys = map.keySet();
if (seenNull.get()) {
// TODO Implement a more efficient set-union view, rather than copying
keys = new HashSet<>(keys);
keys.add(null);
}
return Nodes.node(keys);
}
}
@Override
<P_IN> Spliterator<T> opEvaluateParallelLazy(PipelineHelper<T> helper, Spliterator<P_IN> spliterator) {
if (StreamOpFlag.DISTINCT.isKnown(helper.getStreamAndOpFlags())) {
// No-op
return helper.wrapSpliterator(spliterator);
}
else if (StreamOpFlag.ORDERED.isKnown(helper.getStreamAndOpFlags())) {
// Not lazy, barrier required to preserve order
return reduce(helper, spliterator).spliterator();
}
else {
// Lazy
return new StreamSpliterators.DistinctSpliterator<>(helper.wrapSpliterator(spliterator));
}
}
@Override
Sink<T> opWrapSink(int flags, Sink<T> sink) {
Objects.requireNonNull(sink);
if (StreamOpFlag.DISTINCT.isKnown(flags)) {
return sink;
} else if (StreamOpFlag.SORTED.isKnown(flags)) {
return new Sink.ChainedReference<T, T>(sink) {
boolean seenNull;
T lastSeen;
@Override
public void begin(long size) {
seenNull = false;
lastSeen = null;
downstream.begin(-1);
}
@Override
public void end() {
seenNull = false;
lastSeen = null;
downstream.end();
}
@Override
public void accept(T t) {
if (t == null) {
if (!seenNull) {
seenNull = true;
downstream.accept(lastSeen = null);
}
} else if (lastSeen == null || !t.equals(lastSeen)) {
downstream.accept(lastSeen = t);
}
}
};
} else {
return new Sink.ChainedReference<T, T>(sink) {
Set<T> seen;
@Override
public void begin(long size) {
seen = new HashSet<>();
downstream.begin(-1);
}
@Override
public void end() {
seen = null;
downstream.end();
}
@Override
public void accept(T t) {
if (!seen.contains(t)) {
seen.add(t);
downstream.accept(t);
}
}
};
}
}
};
}关注ReferencePipeline.StatefulOp里的opWrapSink方法。
ReferencePipeline#StatefulOp#opWrapSink
Sink<T> opWrapSink(int flags, Sink<T> sink) {
Objects.requireNonNull(sink);
if (StreamOpFlag.DISTINCT.isKnown(flags)) {
return sink;
} else if (StreamOpFlag.SORTED.isKnown(flags)) {
return new Sink.ChainedReference<T, T>(sink) {
boolean seenNull;
T lastSeen;
@Override
public void begin(long size) {
seenNull = false;
lastSeen = null;
downstream.begin(-1);
}
@Override
public void end() {
seenNull = false;
lastSeen = null;
downstream.end();
}
@Override
public void accept(T t) {
if (t == null) {
if (!seenNull) {
seenNull = true;
downstream.accept(lastSeen = null);
}
} else if (lastSeen == null || !t.equals(lastSeen)) {
downstream.accept(lastSeen = t);
}
}
};
} else {
return new Sink.ChainedReference<T, T>(sink) {
Set<T> seen;
@Override
public void begin(long size) {
seen = new HashSet<>();
downstream.begin(-1);
}
@Override
public void end() {
seen = null;
downstream.end();
}
@Override
public void accept(T t) {
if (!seen.contains(t)) {
seen.add(t);
downstream.accept(t);
}
}
};
}
}根据流的特征进行不同处理。如果Stream有DISTINCT特征，表示已经去重了，直接返回。如果Stream有SORTED特征，表示Stream按照相遇顺序遵循可比较元素的自然排序顺序的特征值，即流已经排序了，只需保存上一次的元素和对null处理即可。从上面代码的中间的if代码块的accept方法可知，保存了上一次的元素，这次进行处理时，进行null值判断和与上一次元素对比即可，用seenNull字段只对第一个遇到的null进行处理。如果Stream没有DISTINCT和SORTED特征，则走上面代码的else代码块。用HashSet进行去重。
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