基于源码的Java集合框架学习⑪ IdentityHashMap

类 IdentityHashMap

此类利用哈希表实现 Map 接口，比较键（和值）时使用引用相等性代替对象相等性。换句话说，在 IdentityHashMap 中，当且仅当 (k1 == k2) 时，才认为两个键 k1 和 k2 相等（在正常 Map 实现（如 HashMap）中，当且仅当满足下列条件时才认为两个键 k1 和 k2 相等：(k1 == null ? k2 == null : e1.equals(e2))）。

此类不是 通用 Map 实现！此类实现 Map 接口时，它有意违反 Map 的常规协定，该协定在比较对象时强制使用 equals 方法。此类设计仅用于其中需要引用相等性语义的罕见情况。

此类的典型用法是拓扑保留对象图形转换，如序列化或深层复制。要执行这样的转换，程序必须维护用于跟踪所有已处理对象引用的“节点表”。节点表一定不等于不同对象，即使它们偶然相等也如此。此类的另一种典型用法是维护代理对象。例如，调试设施可能希望为正在调试程序中的每个对象维护代理对象。

此类提供所有的可选映射操作，并且允许 null 值和 null 键。此类对映射的顺序不提供任何保证；特别是不保证顺序随时间的推移保持不变。

此类提供基本操作（get 和 put）的稳定性能，假定系统标识了将桶间元素正确分开的哈希函数 (System.identityHashCode(Object))。

此类具有一个调整参数（影响性能但不影响语义）：expected maximum size。此参数是希望映射保持的键值映射关系最大数。在内部，此参数用于确定最初组成哈希表的桶数。未指定所期望的最大数量和桶数之间的确切关系。

如果映射的大小（键值映射关系数）已经超过期望的最大数量，则桶数会增加，增加桶数（“重新哈希”）可能相当昂贵，因此创建具有足够大的期望最大数量的标识哈希映射更合算。另一方面，对 collection 视图进行迭代所需的时间与哈希表中的桶数成正比，所以如果特别注重迭代性能或内存使用，则不宜将期望的最大数量设置得过高。

此实现不是同步的。

由所有此类的“collection 视图方法”所返回的迭代器都是快速失败。

实现注意事项：此为简单的线性探头 哈希表，如 Sedgewick 和 Knuth 原文示例中所述。该数组交替保持键和值（对于大型表来说，它比使用独立组保持键和值更具优势）。对于多数 JRE 实现和混合操作，此类比 HashMap（它使用链 而不使用线性探头）能产生更好的性能。

源码

成员以及构造方法：

    /**
     * The initial capacity used by the no-args constructor.
     * MUST be a power of two.  The value 32 corresponds to the
     * (specified) expected maximum size of 21, given a load factor
     * of 2/3.
     */
    private static final int DEFAULT_CAPACITY = 32;

    /**
     * The minimum capacity, used if a lower value is implicitly specified
     * by either of the constructors with arguments.  The value 4 corresponds
     * to an expected maximum size of 2, given a load factor of 2/3.
     * MUST be a power of two.
     */
    private static final int MINIMUM_CAPACITY = 4;

    /**
     * The maximum capacity, used if a higher value is implicitly specified
     * by either of the constructors with arguments.
     * MUST be a power of two <= 1<<29.
     */
    private static final int MAXIMUM_CAPACITY = 1 << 29;

    /**
     * Constructs a new, empty identity hash map with a default expected
     * maximum size (21).
     */
    public IdentityHashMap() {
        init(DEFAULT_CAPACITY);
    }

    /**
     * Constructs a new, empty map with the specified expected maximum size.
     * Putting more than the expected number of key-value mappings into
     * the map may cause the internal data structure to grow, which may be
     * somewhat time-consuming.
     *
     * @param expectedMaxSize the expected maximum size of the map
     * @throws IllegalArgumentException if expectedMaxSize is negative
     */
    public IdentityHashMap(int expectedMaxSize) {
        if (expectedMaxSize < 0)
            throw new IllegalArgumentException("expectedMaxSize is negative: "
                                               + expectedMaxSize);
        init(capacity(expectedMaxSize));
    }

    /**
     * Returns the appropriate capacity for the specified expected maximum
     * size.  Returns the smallest power of two between MINIMUM_CAPACITY
     * and MAXIMUM_CAPACITY, inclusive, that is greater than
     * (3 * expectedMaxSize)/2, if such a number exists.  Otherwise
     * returns MAXIMUM_CAPACITY.  If (3 * expectedMaxSize)/2 is negative, it
     * is assumed that overflow has occurred, and MAXIMUM_CAPACITY is returned.
     */
    private int capacity(int expectedMaxSize) {
        // Compute min capacity for expectedMaxSize given a load factor of 2/3
        int minCapacity = (3 * expectedMaxSize)/2;

        // Compute the appropriate capacity
        int result;
        if (minCapacity > MAXIMUM_CAPACITY || minCapacity < 0) {
            result = MAXIMUM_CAPACITY;
        } else {
            result = MINIMUM_CAPACITY;
            while (result < minCapacity)
                result <<= 1;
        }
        return result;
    }

    /**
     * Initializes object to be an empty map with the specified initial
     * capacity, which is assumed to be a power of two between
     * MINIMUM_CAPACITY and MAXIMUM_CAPACITY inclusive.
     */
    private void init(int initCapacity) {
        // assert (initCapacity & -initCapacity) == initCapacity; // power of 2
        // assert initCapacity >= MINIMUM_CAPACITY;
        // assert initCapacity <= MAXIMUM_CAPACITY;
	// 阈值为容量的2/3
        threshold = (initCapacity * 2)/3;
        // 内部数组的size为容量的2倍，因为IdentityHashMap将所有的key和value都存储到Object[]数组table中，
        // 并且key和value相邻存储,因此map中每一对键值对都要占用两个位置。
        // 数组第一个位置存储的是key，第二个位置存储的是value。因此奇数位置处存储的是key，偶数位置处存储的是value。
        table = new Object[2 * initCapacity];
    }

    /**
     * Constructs a new identity hash map containing the keys-value mappings
     * in the specified map.
     *
     * @param m the map whose mappings are to be placed into this map
     * @throws NullPointerException if the specified map is null
     */
    public IdentityHashMap(Map m) {
        // Allow for a bit of growth
        this((int) ((1 + m.size()) * 1.1));
        putAll(m);
    }

put方法：

    public V put(K key, V value) {
    	// 若key为null返回定义的nullkey
        Object k = maskNull(key);
        Object[] tab = table;
        int len = tab.length;
        // 获得位置
        int i = hash(k, len);

        Object item;
        while ( (item = tab[i]) != null) {
            if (item == k) {//这里使用==判断key是否是同一个key
            	i+1的位置上存着i位置上key的对应值
                V oldValue = (V) tab[i + 1];
                tab[i + 1] = value;
                return oldValue;
            }
            // 产生冲突，找到下一个为null的位置
            // 获取下一个键的位置 i+2
            i = nextKeyIndex(i, len);
        }
	// 数组中不存在这个键则新增
        modCount++;
        tab[i] = k;
        tab[i + 1] = value;
        // size+1后大于或等于阈值时扩容
        if (++size >= threshold)
            resize(len); // len == 2 * current capacity.
        return null;
    }
    /**
     * Value representing null keys inside tables.
     */
    private static final Object NULL_KEY = new Object();

    /**
     * Use NULL_KEY for key if it is null.
     */
    private static Object maskNull(Object key) {
        return (key == null ? NULL_KEY : key);
    }
    /**
     * Returns index for Object x.
     */
    private static int hash(Object x, int length) {
    	// 使用System.identityHashCode来确定对象的哈希码，该方法返回对象的地址。
        int h = System.identityHashCode(x);
        // Multiply by -127, and left-shift to use least bit as part of hash
        return ((h << 1) - (h << 8)) & (length - 1);
    }
        /**
     * Circularly traverses table of size len.
     */
    private static int nextKeyIndex(int i, int len) {
        return (i + 2 < len ? i + 2 : 0);
    }

扩容：

    private void resize(int newCapacity) {
        // assert (newCapacity & -newCapacity) == newCapacity; // power of 2
        int newLength = newCapacity * 2;

        Object[] oldTable = table;
        int oldLength = oldTable.length;
        if (oldLength == 2*MAXIMUM_CAPACITY) { // can't expand any further
             // 若当前数组容量已经是最大
            if (threshold == MAXIMUM_CAPACITY-1)
            	// 阈值最大，报错
                throw new IllegalStateException("Capacity exhausted.");
            // 扩大阈值
            threshold = MAXIMUM_CAPACITY-1;  // Gigantic map!
            return;
        }
        // 容量已经够用了 返回
        if (oldLength >= newLength)
            return;

        Object[] newTable = new Object[newLength];
        // newLength前边已经乘2
        threshold = newLength / 3;

        for (int j = 0; j < oldLength; j += 2) {
            Object key = oldTable[j];
            if (key != null) {
                Object value = oldTable[j+1];
                oldTable[j] = null;
                oldTable[j+1] = null;
                // 重新计算hash值
                int i = hash(key, newLength);
                while (newTable[i] != null)
                    i = nextKeyIndex(i, newLength);
                newTable[i] = key;
                newTable[i + 1] = value;
            }
        }
        table = newTable;
    }

get方法：

    public V get(Object key) {
    	// nullkey判断
        Object k = maskNull(key);
        Object[] tab = table;
        int len = tab.length;
        // 计算位置
        int i = hash(k, len);
        while (true) {
            // 一直到找到该key（key地址相等）或者不存在该key（位置为null）时返回。
            Object item = tab[i];
            if (item == k)
                return (V) tab[i + 1];
            if (item == null)
                return null;
            // put时发生冲突会放到之后第一个为null的位置上
            i = nextKeyIndex(i, len);
        }
    }

remove方法：

    public V remove(Object key) {
        Object k = maskNull(key);
        Object[] tab = table;
        int len = tab.length;
        int i = hash(k, len);

        while (true) {
            Object item = tab[i];
            if (item == k) {
                modCount++;
                size--;
                V oldValue = (V) tab[i + 1];
                tab[i + 1] = null;
                tab[i] = null;
                // 这里会把因为产生冲突而后移的元素的位置都重新调整
                closeDeletion(i);
                return oldValue;
            }
            if (item == null)
                return null;
            i = nextKeyIndex(i, len);
        }

    }
    private void closeDeletion(int d) {
        // Adapted from Knuth Section 6.4 Algorithm R
        Object[] tab = table;
        int len = tab.length;

        // Look for items to swap into newly vacated slot
        // starting at index immediately following deletion,
        // and continuing until a null slot is seen, indicating
        // the end of a run of possibly-colliding keys.
        Object item;
        for (int i = nextKeyIndex(d, len); (item = tab[i]) != null;
             i = nextKeyIndex(i, len) ) {
            // The following test triggers if the item at slot i (which
            // hashes to be at slot r) should take the spot vacated by d.
            // If so, we swap it in, and then continue with d now at the
            // newly vacated i.  This process will terminate when we hit
            // the null slot at the end of this run.
            // The test is messy because we are using a circular table.
            int r = hash(item, len);
            // 将冲突元素移动到前一个冲突元素的位置
            if ((i < r && (r <= d || d <= i)) || (r <= d && d <= i)) {
                tab[d] = item;
                tab[d + 1] = tab[i + 1];
                tab[i] = null;
                tab[i + 1] = null;
                d = i;
            }
        }
    }

与HashMap的比较

• 比较key时是“引用相等”。
• 支持null
• 所有的key和value都存储到Object[]数组table中，并且key和value相邻存储，当出现哈希冲突时，会往下遍历数组，直到找到一个空闲的位置。
• 加载因子为2/3