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per K> cmp = comparator;
outer: for (;;) {
for (Node<K,V> b = findPredecessor(key, cmp), n = b.next;;) { //查找前继节点
if (n != null) { //查找到前继节点
Object v; int c;
Node<K,V> f = n.next; //获取后继节点的后继节点
if (n != b.next) //发生竞争，两次节点获取不一致，并发导致
break;
if ((v = n.value) == null) { // 节点已经被删除
n.helpDelete(b, f);
break;
}
if (b.value == null || v == n)
break;
if ((c = cpr(cmp, key, n.key)) > 0) { //进行下一轮查找，比当前key大
b = n;
n = f;
continue;
}
if (c == 0) { //相等时直接cas修改值
if (onlyIfAbsent || n.casValue(v, value)) {
@SuppressWarnings("unchecked") V vv = (V)v;
return vv;
}
break; // restart if lost race to replace value
}
// else c < 0; fall through
}
z = new Node<K,V>(key, value, n); //9. n.key > key > b.key
if (!b.casNext(n, z)) //cas修改值
break; // restart if lost race to append to b
break outer;
}
}
int rnd = ThreadLocalRandom.nextSecondarySeed(); //获取随机数
if ((rnd & 0x80000001) == 0) { // test highest and lowest bits
int level = 1, max;
while (((rnd >>>= 1) & 1) != 0) // 获取跳表层级
++level;
Index<K,V> idx = null;
HeadIndex<K,V> h = head;
if (level <= (max = h.level)) { //如果获取的调表层级小于等于当前最大层级，则直接添加，并将它们组成一个上下的链表
for (int i = 1; i <= level; ++i)
idx = new Index<K,V>(z, idx, null);
}
else { // try to grow by one level //否则增加一层level，在这里体现为Index<K,V>数组
level = max + 1; // hold in array and later pick the one to use
@SuppressWarnings("unchecked")Index<K,V>[] idxs =
(Index<K,V>[])new Index<?,?>[level+1];
for (int i = 1; i <= level; ++i)
idxs[i] = idx = new Index<K,V>(z, idx, null);
for (;;) {
h = head;
int oldLevel = h.level;
if (level <= oldLevel) // lost race to add level
break;
HeadIndex<K,V> newh = h;
Node<K,V> oldbase = h.node;
for (int j = oldLevel+1; j <= level; ++j) //新添加的level层的具体数据
newh = new HeadIndex<K,V>(oldbase, newh, idxs[j], j);
if (casHead(h, newh)) {
h = newh;
idx = idxs[level = oldLevel];
break;
}
}
}
// 逐层插入数据过程
splice: for (int insertionLevel = level;;) {
int j = h.level;
for (Index<K,V> q = h, r = q.right, t = idx;;) {
if (q == null || t == null)
break splice;
if (r != null) {
Node<K,V> n = r.node;
// compare before deletion check avoids needing recheck
int c = cpr(cmp, key, n.key);
if (n.value == null) {
if (!q.unlink(r))
break;
r = q.right;
continue;
}
if (c > 0) {
q = r;
r = r.right;
continue;
}
}
if (j == insertionLevel) {
if (!q.link(r, t))
break; // restart
if (t.node.value == null) {
findNode(key);
break splice;
}
if (--insertionLevel == 0)
break splice;
}
if (--j >= insertionLevel && j < level)
t = t.down;
q = q.down;
r = q.right;
}
}
}
return null;
}
这里的插入方法很复杂，可以分为3大步来理解：第一步获取前继节点后通过CAS来插入节点；第二步对level层数进行判断，如果大于最大层数，则插入一层；第三步插入对应层的数据。整个插入过程全部通过CAS自旋的方式保证并发情况下的数据正确性。
总结
JDK中提供了丰富的并发容器供我们使用，文章中介绍的也并不全面，重点是要通过了解各种并发容器的原理，明白他们各自独特的使用场景。这里简单做个总结：当并发读远多于修改的场景下需要使用List和Set时，可以考虑使用CopyOnWriteArrayList和CopyOnWriteArraySet；当需要并发使用<Key, Value>键值对存取数据时，可以使用ConcurrentHashMap；当要保证并发<Key, Value>键值对有序时可以使用ConcurrentSkipListMap。
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