【Java】concurrentHashMap

concurrentHashMap类引入了段的概念，读操作不需要上锁，写操作只需要获取相应的段的锁即可，而非锁定全部的数据。所以map里面是一个segment的数组，segment里面才是entry的数组。map的操作最终会定位到每一个具体的段，进行get和put操作。

private static int hash(int h) {
        // Spread bits to regularize both segment and index locations,
        // using variant of single-word Wang/Jenkins hash.
        h += (h <<  15) ^ 0xffffcd7d;
        h ^= (h >>> 10);
        h += (h <<   3);
        h ^= (h >>>  6);
        h += (h <<   2) + (h << 14);
        return h ^ (h >>> 16);
    }

    /**
     * Returns the segment that should be used for key with given hash
     * @param hash the hash code for the key
     * @return the segment
     */
    final Segment<K,V> segmentFor(int hash) {
        return segments[(hash >>> segmentShift) & segmentMask];
    }

也是从key的hashCode值计算得到hash值，然后从hash值利用移位和掩码得到segment的索引，委托给segment进行操作。

transient volatile int count;

        /**
         * Number of updates that alter the size of the table. This is
         * used during bulk-read methods to make sure they see a
         * consistent snapshot: If modCounts change during a traversal
         * of segments computing size or checking containsValue, then
         * we might have an inconsistent view of state so (usually)
         * must retry.
         */
        transient int modCount;

        /**
         * The table is rehashed when its size exceeds this threshold.
         * (The value of this field is always <tt>(int)(capacity *
         * loadFactor)</tt>.)
         */
        transient int threshold;

        /**
         * The per-segment table.
         */
        transient volatile HashEntry<K,V>[] table;

        /**
         * The load factor for the hash table.  Even though this value
         * is same for all segments, it is replicated to avoid needing
         * links to outer object.
         * @serial
         */
        final float loadFactor;

V get(Object key, int hash) {
            if (count != 0) { // read-volatile
                HashEntry<K,V> e = getFirst(hash);
                while (e != null) {
                    if (e.hash == hash && key.equals(e.key)) {
                        V v = e.value;
                        if (v != null)
                            return v;
                        return readValueUnderLock(e); // recheck
                    }
                    e = e.next;
                }
            }
            return null;
        }

V put(K key, int hash, V value, boolean onlyIfAbsent) {
            lock();
            try {
                int c = count;
                if (c++ > threshold) // ensure capacity
                    rehash();
                HashEntry<K,V>[] tab = table;
                int index = hash & (tab.length - 1);
                HashEntry<K,V> first = tab[index];
                HashEntry<K,V> e = first;
                while (e != null && (e.hash != hash || !key.equals(e.key)))
                    e = e.next;

                V oldValue;
                if (e != null) {
                    oldValue = e.value;
                    if (!onlyIfAbsent)
                        e.value = value;
                }
                else {
                    oldValue = null;
                    ++modCount;
                    tab[index] = new HashEntry<K,V>(key, hash, first, value);
                    count = c; // write-volatile
                }
                return oldValue;
            } finally {
                unlock();
            }
        }

所以，对mao中单个entry的操作实际上是定位到具体的段，然后委托给段处理的，每一段的内部可以决定是否上锁，锁粒度很小。具体的处理逻辑和hashmap差不多。

但是对于整个map的操作则需要所有段协同，比如size。但是这里也不是锁住整个map进行的。

public int size() {
        final Segment<K,V>[] segments = this.segments;
        long sum = 0;
        long check = 0;
        int[] mc = new int[segments.length];
        // Try a few times to get accurate count. On failure due to
        // continuous async changes in table, resort to locking.
        for (int k = 0; k < RETRIES_BEFORE_LOCK; ++k) {
            check = 0;
            sum = 0;
            int mcsum = 0;
            for (int i = 0; i < segments.length; ++i) {
                sum += segments[i].count;
                mcsum += mc[i] = segments[i].modCount;
            }
            if (mcsum != 0) {
                for (int i = 0; i < segments.length; ++i) {
                    check += segments[i].count;
                    if (mc[i] != segments[i].modCount) {
                        check = -1; // force retry
                        break;
                    }
                }
            }
            if (check == sum)
                break;
        }
        if (check != sum) { // Resort to locking all segments
            sum = 0;
            for (int i = 0; i < segments.length; ++i)
                segments[i].lock();
            for (int i = 0; i < segments.length; ++i)
                sum += segments[i].count;
            for (int i = 0; i < segments.length; ++i)
                segments[i].unlock();
        }
        if (sum > Integer.MAX_VALUE)
            return Integer.MAX_VALUE;
        else
            return (int)sum;
    }

size的思路是先不锁，计算每一个段的大小，累加，然后也存一下每一个段的modcount，然后再计算一次sum，同时比较每一个段的modcount和第一次是否相同，如果不同，说明这个段在两次计算之间发生了改变，需要重新进行这个过程。这就是for循环内部的过程。这个双重检验的过程会进行最多2次，由RETRIES_BEFORE_LOCK标示。然后退出这个循环。再检测一次sum和check是否相等，到这里可能是因为想等退出的也可能是重试次数用完到达的，所以要检测。若想等返回，否则说明一直有修改，需要上锁再计算，这就是整个size的操作。