PriorityQueue特点-基于源码分析

朱雀 2024-04-18 18:32 59阅读 0赞

愉快地聊一聊PriorityQueue的特点吧~（以下都是基于jdk1.8）

PriorityQueue的继承树如下图：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0x1eWFuYw_size_16_color_FFFFFF_t_70]

（1）底层使用可变数组Object\[ \] queue，数组容量按需增长

（2）它是一个比较标准的队列，不是绝对标准，因为它不是严格的先进先出，内部按队列元素的大小进行了重新排序（定制排序、自然排序），所以要放入集合中的元素必须实现Comparable接口。

（3）不能存储null

（4）默认数组初始长度是11，也可以指定初始容量（也是实际会分配的容量）

（5）线程不安全。可以使用java.util.concurrent.PriorityBlockingQueue（线程安全）

（6）当遍历一个 PriorityQueue 时，没有任何顺序保证

（7）通过判断如果需要扩容，先扩容，再插入。【数组容量满了之后才会触发扩容】

（8）二叉堆【根元素最小】

（9）默认数组最大长度是MAX\_ARRAY\_SIZE = Integer.MAX\_VALUE - 8

这里只取部分源码进行分析：扩容，以及二叉堆的调整。

（1）扩容，以offer为例：

//添加元素
    	public boolean offer(E e) { 
            if (e == null)
                throw new NullPointerException();
            //记录二叉堆被调整的次数
            modCount++;
            //size是数组元素的个数
            int i = size;
            //如果当前数组元素个数>=数组容量，则进行扩容
            if (i >= queue.length)
            	//扩容，扩容的最小容量是当前数组元素个数加1
                grow(i + 1);
            size = i + 1;
            if (i == 0)
                queue[0] = e;
            else
            	//从下往上调整二叉堆
                siftUp(i, e);
            return true;
        }

再看具体的扩容方法：

/** * 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%
            //数组现有容量小于64时，扩容的容量就是现有容量的2倍，再加2
            //否则，扩容的容量就是现有数组容量+现有数组容量/2
            int newCapacity = oldCapacity + ((oldCapacity < 64) ?
                                             (oldCapacity + 2) :
                                             (oldCapacity >> 1));
            // overflow-conscious code
            //扩容容量是否超过数组默认最大长度
            if (newCapacity - MAX_ARRAY_SIZE > 0)
                newCapacity = hugeCapacity(minCapacity);
            //指向扩容后的新数组
            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;
        }

再看Arrays类下的静态方法Arrays.copyOf方法：

//Cloning
    	/** * Copies the specified array, truncating or padding with nulls (if necessary) * so the copy has the specified length. For all indices that are * valid in both the original array and the copy, the two arrays will * contain identical values. For any indices that are valid in the * copy but not the original, the copy will contain <tt>null</tt>. * Such indices will exist if and only if the specified length * is greater than that of the original array. * The resulting array is of exactly the same class as the original array. * * @param <T> the class of the objects in the array * @param original the array to be copied * @param newLength the length of the copy to be returned * @return a copy of the original array, truncated or padded with nulls * to obtain the specified length * @throws NegativeArraySizeException if <tt>newLength</tt> is negative * @throws NullPointerException if <tt>original</tt> is null * @since 1.6 */
        @SuppressWarnings("unchecked")
        public static <T> T[] copyOf(T[] original, int newLength) { 
            return (T[]) copyOf(original, newLength, original.getClass());
        }
        
    	/** * Copies the specified array, truncating or padding with nulls (if necessary) * so the copy has the specified length. For all indices that are * valid in both the original array and the copy, the two arrays will * contain identical values. For any indices that are valid in the * copy but not the original, the copy will contain <tt>null</tt>. * Such indices will exist if and only if the specified length * is greater than that of the original array. * The resulting array is of the class <tt>newType</tt>. * * @param <U> the class of the objects in the original array * @param <T> the class of the objects in the returned array * @param original the array to be copied * @param newLength the length of the copy to be returned * @param newType the class of the copy to be returned * @return a copy of the original array, truncated or padded with nulls * to obtain the specified length * @throws NegativeArraySizeException if <tt>newLength</tt> is negative * @throws NullPointerException if <tt>original</tt> is null * @throws ArrayStoreException if an element copied from * <tt>original</tt> is not of a runtime type that can be stored in * an array of class <tt>newType</tt> * @since 1.6 */
        public static <T,U> T[] copyOf(U[] original, int newLength, Class<? extends T[]> newType) { 
            @SuppressWarnings("unchecked")
            T[] copy = ((Object)newType == (Object)Object[].class)
                ? (T[]) new Object[newLength]
                : (T[]) Array.newInstance(newType.getComponentType(), newLength);
            System.arraycopy(original, 0, copy, 0,
                             Math.min(original.length, newLength));
            return copy;
        }

（2）二叉堆的调整：

siftUp：自下往上调整

/** * 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)
            	//定制排序
                siftUpUsingComparator(k, x);
            else
            	//自然排序
                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) { 
        	//找到二叉堆中的索引k的父节点，从此处开始，拿待插入元素与其比较，
        	//如果待插入元素大于父节点元素的值，则在k索引处插入元素。
        	//否则，父节点值下移，待插入元素继续与上层父节点比较。
            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;
        }

siftDown：自上往下调整

@SuppressWarnings("unchecked")
        public E poll() { 
            if (size == 0)
                return null;
            int s = --size;
            modCount++;
            //取出堆顶元素，为堆中最后一个元素（变为待插入元素）寻找插入位置，进行二叉堆的调整
            E result = (E) queue[0];
            E x = (E) queue[s];
            queue[s] = null;
            if (s != 0)
            	//调整二叉堆
                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)
            	//定制排序
                siftDownUsingComparator(k, x);
            else
            	//自然排序
                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;
            //自上往下调整，找到根节点的左孩子child和右孩子right，
            //比较得到左右孩子中较小的一个。
            //待插入元素与较小孩子元素进行比较，
            //如果待插入元素大于较小的孩子元素，则较小孩子上移，继续循环此过程。
            //否则，k处即为待插入位置。
            while (k < half) { 
                int child = (k << 1) + 1;
                Object c = queue[child];
                int right = child + 1;
                if (right < size &&
                    comparator.compare((E) c, (E) queue[right]) > 0)
                    c = queue[child = right];
                if (comparator.compare(x, (E) c) <= 0)
                    break;
                queue[k] = c;
                k = child;
            }
            queue[k] = x;
        }

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0x1eWFuYw_size_16_color_FFFFFF_t_70]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/3880fc1fea7047c49eb87ddd3cd6103c.png