Java 优先队列 PriorityQueue PriorityBlockingQueue 源码分析

Yano_nankai

发布于 2019-02-25 16:25:47

9670

发布于 2019-02-25 16:25:47

基本使用

@Test
public void testPriorityQueue() throws InterruptedException {
    PriorityQueue priorityQueue = new PriorityQueue(Lists.newArrayList(5, 4, 2, 1, 3));
    System.out.println(priorityQueue);
    System.out.println(priorityQueue.poll());
    System.out.println(priorityQueue.poll());

    PriorityBlockingQueue<Integer> blockingQueue = new PriorityBlockingQueue<>();
    blockingQueue.add(5);
    System.out.println(blockingQueue.poll());
    System.out.println(blockingQueue.take());
}

输出

[1, 3, 2, 4, 5]
1
2
5
(阻塞)

PriorityQueue

成员变量

/**
 * Priority queue represented as a balanced binary heap: the two
 * children of queue[n] are queue[2*n+1] and queue[2*(n+1)].  The
 * priority queue is ordered by comparator, or by the elements'
 * natural ordering, if comparator is null: For each node n in the
 * heap and each descendant d of n, n <= d.  The element with the
 * lowest value is in queue[0], assuming the queue is nonempty.
 */
transient Object[] queue; // non-private to simplify nested class access

/**
 * The number of elements in the priority queue.
 */
private int size = 0;

/**
 * The comparator, or null if priority queue uses elements'
 * natural ordering.
 */
private final Comparator<? super E> comparator;

/**
 * The number of times this priority queue has been
 * <i>structurally modified</i>.  See AbstractList for gory details.
 */
transient int modCount = 0; // non-private to simplify nested class access

通过数组实现一个堆，元素在queue数组中并不是完全有序的，仅堆顶元素最大或最小。

基本方法

public E poll() {
    if (size == 0)
        return null;
    int s = --size;
    modCount++;
    E result = (E) queue[0];
    E x = (E) queue[s];
    queue[s] = null;
    if (s != 0)
        siftDown(0, x);
    return result;
}

/**
 * Inserts item x at position k, maintaining heap invariant by
 * demoting x down the tree repeatedly until it is less than or
 * equal to its children or is a leaf.
 *
 * @param k the position to fill
 * @param x the item to insert
 */
private void siftDown(int k, E x) {
    if (comparator != null)
        siftDownUsingComparator(k, x);
    else
        siftDownComparable(k, x);
}

@SuppressWarnings("unchecked")
private void siftDownComparable(int k, E x) {
    Comparable<? super E> key = (Comparable<? super E>)x;
    int half = size >>> 1;        // loop while a non-leaf
    while (k < half) {
        int child = (k << 1) + 1; // assume left child is least
        Object c = queue[child];
        int right = child + 1;
        if (right < size &&
            ((Comparable<? super E>) c).compareTo((E) queue[right]) > 0)
            c = queue[child = right];
        if (key.compareTo((E) c) <= 0)
            break;
        queue[k] = c;
        k = child;
    }
    queue[k] = key;
}

以poll方法为例，实际上是获取堆顶元素，然后调整堆。

调整堆的方法（以大顶堆为例）：

判断是否传入comparator，有则按照comparator排序，否则按照自然顺序排序
取节点左右孩子节点最大值，与父亲节点交换

扩容方法

/**
 * Increases the capacity of the array.
 *
 * @param minCapacity the desired minimum capacity
 */
private void grow(int minCapacity) {
    int oldCapacity = queue.length;
    // Double size if small; else grow by 50%
    int newCapacity = oldCapacity + ((oldCapacity < 64) ?
                                     (oldCapacity + 2) :
                                     (oldCapacity >> 1));
    // overflow-conscious code
    if (newCapacity - MAX_ARRAY_SIZE > 0)
        newCapacity = hugeCapacity(minCapacity);
    queue = Arrays.copyOf(queue, newCapacity);
}

private static int hugeCapacity(int minCapacity) {
    if (minCapacity < 0) // overflow
        throw new OutOfMemoryError();
    return (minCapacity > MAX_ARRAY_SIZE) ?
        Integer.MAX_VALUE :
        MAX_ARRAY_SIZE;
}

小容量扩容1倍
大容量扩容0.5倍
快溢出时调整为Integer.MAX_VALUE - 8 或 Integer.MAX_VALUE

是否线程安全

非线程安全

PriorityBlockingQueue

其实现基本与PriorityQueue一致，不过PriorityBlockingQueue是线程安全的，并且实现了BlockingQueue接口，在队列为空时take会阻塞。

/**
 * Priority queue represented as a balanced binary heap: the two
 * children of queue[n] are queue[2*n+1] and queue[2*(n+1)].  The
 * priority queue is ordered by comparator, or by the elements'
 * natural ordering, if comparator is null: For each node n in the
 * heap and each descendant d of n, n <= d.  The element with the
 * lowest value is in queue[0], assuming the queue is nonempty.
 */
private transient Object[] queue;

/**
 * The number of elements in the priority queue.
 */
private transient int size;

/**
 * The comparator, or null if priority queue uses elements'
 * natural ordering.
 */
private transient Comparator<? super E> comparator;

/**
 * Lock used for all public operations
 */
private final ReentrantLock lock;

/**
 * Condition for blocking when empty
 */
private final Condition notEmpty;

/**
 * Spinlock for allocation, acquired via CAS.
 */
private transient volatile int allocationSpinLock;

/**
 * A plain PriorityQueue used only for serialization,
 * to maintain compatibility with previous versions
 * of this class. Non-null only during serialization/deserialization.
 */
private PriorityQueue<E> q;

和PriorityQueue的区别：增加了

重入锁ReentrantLock
Condition，用于队列空情况下的阻塞
allocationSpinLock，通过CAS手段对queue扩容

private void tryGrow(Object[] array, int oldCap) {
    lock.unlock(); // must release and then re-acquire main lock
    Object[] newArray = null;
    if (allocationSpinLock == 0 &&
        UNSAFE.compareAndSwapInt(this, allocationSpinLockOffset,
                                 0, 1)) {
        try {
            int newCap = oldCap + ((oldCap < 64) ?
                                   (oldCap + 2) : // grow faster if small
                                   (oldCap >> 1));
            if (newCap - MAX_ARRAY_SIZE > 0) {    // possible overflow
                int minCap = oldCap + 1;
                if (minCap < 0 || minCap > MAX_ARRAY_SIZE)
                    throw new OutOfMemoryError();
                newCap = MAX_ARRAY_SIZE;
            }
            if (newCap > oldCap && queue == array)
                newArray = new Object[newCap];
        } finally {
            allocationSpinLock = 0;
        }
    }
    if (newArray == null) // back off if another thread is allocating
        Thread.yield();
    lock.lock();
    if (newArray != null && queue == array) {
        queue = newArray;
        System.arraycopy(array, 0, newArray, 0, oldCap);
    }
}

可以看到与PriorityQueue的扩容函数很像，不同点：

调用函数时必须持有锁
使用CAS方法进行扩容，在allocationSpinLock为0，并且CAS将其置为1时，线程才能够对数组进行扩容。如果多个线程并发扩容，其余线程会调用Thread.yield()方法。

为什么这样实现PriorityBlockingQueue扩容？

因为PriorityBlockingQueue内部使用的ReentrantLock重入锁，同一个线程多次调用add函数，可能恰好同时调用了tryGrow函数。此时通过重入锁是无法加锁的，仅能通过Synchronized或CAS方式控制并发。

allocationSpinLock是transient的，因为序列化时并不需要此参数；同时又是volatile的，因为可能有多个线程同时调用。

private transient volatile int allocationSpinLock;

UNSAFE.compareAndSwapInt

// Unsafe mechanics
private static final sun.misc.Unsafe UNSAFE;
private static final long allocationSpinLockOffset;
static {
    try {
        UNSAFE = sun.misc.Unsafe.getUnsafe();
        Class<?> k = PriorityBlockingQueue.class;
        allocationSpinLockOffset = UNSAFE.objectFieldOffset
            (k.getDeclaredField("allocationSpinLock"));
    } catch (Exception e) {
        throw new Error(e);
    }
}

调用方法

UNSAFE.compareAndSwapInt(this, allocationSpinLockOffset, 0, 1)

allocationSpinLockOffset是allocationSpinLock变量在PriorityBlockingQueue类中的偏移量。

那么使用allocationSpinLockOffset有什么好处呢？它和直接修改allocationSpinLock变量有什么区别？

获取该字段在类中的内存偏移量，直接将内存中的值改为新值。直接修改allocationSpinLock并不是CAS。JDK 1.8代码如下：

public final int getAndAddInt(Object var1, long var2, int var4) {
    int var5;
    do {
        var5 = this.getIntVolatile(var1, var2);
    } while(!this.compareAndSwapInt(var1, var2, var5, var5 + var4));

    return var5;
}

在AtomicInteger类中的调用如下，getAndAddInt方法由具体类的实现方法，抽取到了UNSAFE类中：

public final int getAndDecrement() {
    return unsafe.getAndAddInt(this, valueOffset, -1);
}

对比 PriorityQueue 和 PriorityBlockingQueue

PriorityQueue是非线程安全的，PriorityBlockingQueue是线程安全的
PriorityBlockingQueue使用重入锁，每一个操作都需要加锁
PriorityBlockingQueue扩容时使用了CAS操作
两者都使用了堆，算法原理相同
PriorityBlockingQueue可以在queue为空时阻塞take操作

JDK实现堆的方法

/**
 * Establishes the heap invariant (described above) in the entire tree,
 * assuming nothing about the order of the elements prior to the call.
 */
@SuppressWarnings("unchecked")
private void heapify() {
    for (int i = (size >>> 1) - 1; i >= 0; i--)
        siftDown(i, (E) queue[i]);
}

private void siftDown(int k, E x) {
    if (comparator != null)
        siftDownUsingComparator(k, x);
    else
        siftDownComparable(k, x);
}

@SuppressWarnings("unchecked")
private void siftDownComparable(int k, E x) {
    Comparable<? super E> key = (Comparable<? super E>)x;
    int half = size >>> 1;        // loop while a non-leaf
    while (k < half) {
        int child = (k << 1) + 1; // assume left child is least
        Object c = queue[child];
        int right = child + 1;
        if (right < size &&
            ((Comparable<? super E>) c).compareTo((E) queue[right]) > 0)
            c = queue[child = right];
        if (key.compareTo((E) c) <= 0)
            break;
        queue[k] = c;
        k = child;
    }
    queue[k] = key;
}

public boolean offer(E e) {
    if (e == null)
        throw new NullPointerException();
    modCount++;
    int i = size;
    if (i >= queue.length)
        grow(i + 1);
    size = i + 1;
    if (i == 0)
        queue[0] = e;
    else
        siftUp(i, e);
    return true;
}

private void siftUp(int k, E x) {
    if (comparator != null)
        siftUpUsingComparator(k, x);
    else
        siftUpComparable(k, x);
}

@SuppressWarnings("unchecked")
private void siftUpComparable(int k, E x) {
    Comparable<? super E> key = (Comparable<? super E>) x;
    while (k > 0) {
        int parent = (k - 1) >>> 1;
        Object e = queue[parent];
        if (key.compareTo((E) e) >= 0)
            break;
        queue[k] = e;
        k = parent;
    }
    queue[k] = key;
}

本文参与腾讯云自媒体分享计划，分享自作者个人站点/博客。

原始发表：2019.01.04 ，如有侵权请联系 cloudcommunity@tencent.com 删除

jdk

编程算法

本文分享自作者个人站点/博客前往查看

如有侵权，请联系 cloudcommunity@tencent.com 删除。

本文参与腾讯云自媒体分享计划，欢迎热爱写作的你一起参与！

jdk

编程算法

登录后参与评论

0 条评论

热度