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文章目录
  1. 结构
  2. heapify() –> siftDown() –> siftDownComparable() –> siftDownUsingComparator()
  3. 说点什么
  4. 推荐文章：

【java源码一带一路系列】之PriorityQueue

字数统计: 1.7k字   |   阅读时长: 8分钟
2017.06.23 Chen hongen 后端

按照下图的配方，走了一遍源码。
凑齐PriorityQueue就可以召唤神龙了。
Ler’s go go go!

clipboard.png
clipboard.png

结构

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/**
 * 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

没错这是个数组，为了更好的理解注释的含义，请看下面↓。

满二叉树：

所有的节点都有2个叶子节点，除了最后层叶子节点；

节点数n和深度d的关系 n=2^d-1；

第i层上的节点数为2^(i-1)；

第n个节点的父节点：n/2,左子节点：2n,右子节点：2n+1;(参考下图)

满二叉树与完全二叉树

完全二叉树：

有且仅有最底层叶子节点不完整就是完全二叉树。（例如：把15去掉）

最小堆：

父节点小于左右子节点的完全二叉树。

转数组：

用数组来存储二叉树后（参见下图）可得，根节点A[0]；左子节点a[2n+1];右子节点a[2(n+1)]，父节点a[(n-1)/2]。（n为数组下标，从0开始）

二叉树转数组

是的，优先队列的存储结构大概就是这样推演而来。

heapify() –> siftDown() –> siftDownComparable() –> siftDownUsingComparator()

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/**
 * 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]);
}
/**
 * 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;
}
@SuppressWarnings("unchecked")
private void siftDownUsingComparator(int k, E x) {
    int half = size >>> 1;
    while (k < half) {
        int child = (k << 1) + 1; //2n + 1,这里n是下标
        Object c = queue[child];
        int right = child + 1;
        if (right < size &&
            comparator.compare((E) c, (E) queue[right]) > 0) // 找出最小子节点
            c = queue[child = right];
        if (comparator.compare(x, (E) c) <= 0) // 父节点小则退出循环，否则进行替换
            break;
        queue[k] = c;
        k = child;
    }
    queue[k] = x;
}

这是任意数组最小堆化的过程。如果是一个合格的最小堆，那么所有的父节点都在数组前半部分，而通过父节点又能得到左右子节点。因此源码一上来就“size >>> 1”（相当于除以2），只需对前半部分进行循环处理，使得循环结束后所有父节点均大于左/右子节点。这里非根父节点会被多次比较到。heapify()后将得到上文所说的最小堆数组。

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@SuppressWarnings("unchecked")
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;
}

poll()的核心也是siftDown，而这里的“siftDown(0, x);”与之前的“siftDown(i, (E) queue[i]);”不同的是，下标0所对应的元素本非x。也就是说，这里进行了个转换：把最后queue[s]替换了queue[0]进行新的最小堆数组化。

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 /**
 * Removes a single instance of the specified element from this queue,
 * if it is present.  More formally, removes an element {@code e} such
 * that {@code o.equals(e)}, if this queue contains one or more such
 * elements.  Returns {@code true} if and only if this queue contained
 * the specified element (or equivalently, if this queue changed as a
 * result of the call).
 *
 * @param o element to be removed from this queue, if present
 * @return {@code true} if this queue changed as a result of the call
 */
public boolean remove(Object o) {
    int i = indexOf(o);
    if (i == -1)
        return false;
    else {
        removeAt(i);
        return true;
    }
}
/**
 * Removes the ith element from queue.
 *
 * Normally this method leaves the elements at up to i-1,
 * inclusive, untouched.  Under these circumstances, it returns
 * null.  Occasionally, in order to maintain the heap invariant,
 * it must swap a later element of the list with one earlier than
 * i.  Under these circumstances, this method returns the element
 * that was previously at the end of the list and is now at some
 * position before i. This fact is used by iterator.remove so as to
 * avoid missing traversing elements.
 */
@SuppressWarnings("unchecked")
private E removeAt(int i) {
    // assert i >= 0 && i < size;
    modCount++;
    int s = --size;
    if (s == i) // removed last element
        queue[i] = null;
    else {
        E moved = (E) queue[s];
        queue[s] = null;
        siftDown(i, moved); 
        if (queue[i] == moved) {
            siftUp(i, moved);
            if (queue[i] != moved)
                return moved;
        }
    }
    return null;
}

如你所见，remove()也用到了siftDown()（同时还有siftUp()，下面介绍）。这里 经过siftDown后，如果queue[i] == moved则表示queue[i]的左右子节点都大于moved，即保证了i节点子树是最小堆，但queue[i]的父节点是否小于moved却未知，故又进行了siftUp。（图片来自【2】）

image

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/**
 * Inserts item x at position k, maintaining heap invariant by
 * promoting x up the tree until it is greater than or equal to
 * its parent, or is the root.
 *
 * To simplify and speed up coercions and comparisons. the
 * Comparable and Comparator versions are separated into different
 * methods that are otherwise identical. (Similarly for siftDown.)
 *
 * @param k the position to fill
 * @param x the item to insert
 */
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;
}
@SuppressWarnings("unchecked")
private void siftUpUsingComparator(int k, E x) {
    while (k > 0) {
        int parent = (k - 1) >>> 1;
        Object e = queue[parent];
        if (comparator.compare(x, (E) e) >= 0)
            break;
        queue[k] = e;
        k = parent;
    }
    queue[k] = x;
}

与之相对的，还有名为siftUpComparable()/siftUpUsingComparator()的方法。在新增元素时被调用。新增元素放在下标为size的位置。这里的down与up指的是被比较对象x的去向。比较后x被赋值给子节点就是down，被赋值给父节点就是up。当然你来写的时候也可能新增时，从上到下循环遍历。

说点什么

PriorityQueue有序；不允许为null；非线程安全；（PriorityBlockingQueue线程安全）；没有介绍的地方大抵与其他集合框架相似，如扩容机制等。

优先队列每次出队的元素都是优先级最高（权值最小）的元素,通过比较（Comparator或元素本身自然排序）决定优先级。

记得常来复习啊~~~

更多有意思的内容，欢迎访问笔者小站： rebey.cn

推荐文章：

【1】【深入理解Java集合框架】深入理解Java PriorityQueue;

【2】java集合——Queue；

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