1.初始化

构造函数

1.可以看出默认的初始容量是16；
2.默认的平衡因子是0.75f


//默认初始容量时16
private static final int DEFAULT_CAPACITY = 16;

//平衡因子默认是0.75
private static final float LOAD_FACTOR = 0.75f;
//最大容量  1073741824
private static final int MAXIMUM_CAPACITY = 1 << 30;
//设置控制容量sizeCtl
public ConcurrentHashMap(int initialCapacity,
                         float loadFactor, int concurrencyLevel) {
    if (!(loadFactor > 0.0f) || initialCapacity < 0 || concurrencyLevel <= 0)
        throw new IllegalArgumentException();
    if (initialCapacity < concurrencyLevel)   // Use at least as many bins
        initialCapacity = concurrencyLevel;   // as estimated threads
    long size = (long)(1.0 + (long)initialCapacity / loadFactor);
    int cap = (size >= (long)MAXIMUM_CAPACITY) ?
        MAXIMUM_CAPACITY : tableSizeFor((int)size);
    this.sizeCtl = cap;
}

值的增删改查操作

值的存储过程

先看一下代码：

static final int TREEIFY_THRESHOLD = 8;

final V putVal(K key, V value, boolean onlyIfAbsent) {
    if (key == null || value == null) throw new NullPointerException();
    int hash = spread(key.hashCode()); //获取hash值
    int binCount = 0;
    for (Node<K,V>[] tab = table;;) {//如果初始化，则通过循环保证数据插入
        Node<K,V> f; int n, i, fh;
        if (tab == null || (n = tab.length) == 0)//如果存储数据的Node数组为空，初始化数组
            tab = initTable();
        else if ((f = tabAt(tab, i = (n - 1) & hash)) == null) {//当hash定位为空时，则通过CAS插入数据
            if (casTabAt(tab, i, null,
                         new Node<K,V>(hash, key, value, null)))
                break;                   // no lock when adding to empty bin
        }
        else if ((fh = f.hash) == MOVED)//Node数组进行扩容，原数组的数据进行转移
            tab = helpTransfer(tab, f);
        else {
            V oldVal = null;
            synchronized (f) {
                if (tabAt(tab, i) == f) { 
                    if (fh >= 0) {//如果hash相同，并且不是红黑树节点时
                        binCount = 1;
                        for (Node<K,V> e = f;; ++binCount) {//如果是链表的节点会一直循环
                            K ek;
                            if (e.hash == hash &&
                                ((ek = e.key) == key ||
                                 (ek != null && key.equals(ek)))) {//如果节点的key相同，直接替换
                                oldVal = e.val;
                                if (!onlyIfAbsent)
                                    e.val = value;
                                break;
                            }
                            Node<K,V> pred = e;
                            if ((e = e.next) == null) {//如果key不相同，则形成单向链表
                                pred.next = new Node<K,V>(hash, key,
                                                          value, null);
                                break;
                            }
                        }
                    }
                    else if (f instanceof TreeBin) {//判断节点是否是红黑树
                        Node<K,V> p;
                        binCount = 2;
                        if ((p = ((TreeBin<K,V>)f).putTreeVal(hash, key,
                                                       value)) != null) {//如果是红黑树，保存进数据返回红黑树的替换节点，如果是新插入节点则返回null
                            oldVal = p.val;
                            if (!onlyIfAbsent)
                                p.val = value;
                        }
                    }
                }
            }
            if (binCount != 0) {
                if (binCount >= TREEIFY_THRESHOLD)//如果链表长度超过8，则把链表变成红黑树
                    treeifyBin(tab, i);
                if (oldVal != null)
                    return oldVal;
                break;
            }
        }
    }
    addCount(1L, binCount);
    return null;
}

执行逻辑：如下图所示

红黑树的插入，查找，删除操作：

可以查看我的另一篇博客，这个是根据ConcurrentHashMap中红黑树的代码进行分析的。

博客地址：https://www.cnblogs.com/perferect/p/13569671.html

ConcurrentHashMap的数据存储结构：

经过上面的代码，我们可以分析出来，ConcurrentHashMap的数据结构是，数组+链表+红黑树的结构。

当链表节点个数超过8个时，才能判断是否要生成红黑树
当Node数组长度超过64，链表才能生成红黑树，否则重新resizeNode数组

值的查找

public V get(Object key) {
        Node<K,V>[] tab; Node<K,V> e, p; int n, eh; K ek;
        int h = spread(key.hashCode());
        if ((tab = table) != null && (n = tab.length) > 0 &&
            (e = tabAt(tab, (n - 1) & h)) != null) {
            if ((eh = e.hash) == h) {//如果在数组中key相等，返回查找值
                if ((ek = e.key) == key || (ek != null && key.equals(ek)))
                    return e.val;
            }
            else if (eh < 0)//查找红黑树中的节点
                return (p = e.find(h, key)) != null ? p.val : null;
            while ((e = e.next) != null) {//查找链表中对应的值
                if (e.hash == h &&
                    ((ek = e.key) == key || (ek != null && key.equals(ek))))
                    return e.val;
            }
        }
        return null;
    }

值的删除操作

先看一下代码：

逻辑和插入节点有点类似，看一下代码就能理解

public V remove(Object key) {
        return replaceNode(key, null, null);
    }
 final V replaceNode(Object key, V value, Object cv) {
        int hash = spread(key.hashCode());//获取hash
        for (Node<K,V>[] tab = table;;) {
            Node<K,V> f; int n, i, fh;
            if (tab == null || (n = tab.length) == 0 ||
                (f = tabAt(tab, i = (n - 1) & hash)) == null)//如果Node数组长度为0，或者hash节点为空，直接结束
                break;
            else if ((fh = f.hash) == MOVED)//如果Node数组在扩容中先进行扩容
                tab = helpTransfer(tab, f);
            else {
                V oldVal = null;
                boolean validated = false;
                synchronized (f) {
                    if (tabAt(tab, i) == f) {//hash节点没变
                        if (fh >= 0) {//是数组或链表结构时
                            validated = true;
                            for (Node<K,V> e = f, pred = null;;) {
                                K ek;
                                if (e.hash == hash &&
                                    ((ek = e.key) == key ||
                                     (ek != null && key.equals(ek)))) {// key值相同时
                                    V ev = e.val;
                                    if (cv == null || cv == ev ||
                                        (ev != null && cv.equals(ev))) {
                                        oldVal = ev;
                                        if (value != null)
                                            e.val = value;
                                        else if (pred != null)//逐个遍历e开头的链表
                                            pred.next = e.next;
                                        else
                                            setTabAt(tab, i, e.next);//替换链表e,为e的next，如果为数组的话，next为null
                                    }
                                    break;
                                }
                                pred = e;
                                if ((e = e.next) == null)//如果是链表结构，移动节点
                                    break;
                            }
                        }
                        else if (f instanceof TreeBin) {//如果是红黑树
                            validated = true;
                            TreeBin<K,V> t = (TreeBin<K,V>)f;
                            TreeNode<K,V> r, p;
                            if ((r = t.root) != null &&
                                (p = r.findTreeNode(hash, key, null)) != null) {//判断红黑树是否存在并且有这个节点
                                V pv = p.val;
                                if (cv == null || cv == pv ||
                                    (pv != null && cv.equals(pv))) {
                                    oldVal = pv;
                                    if (value != null)
                                        p.val = value;
                                    else if (t.removeTreeNode(p))//移除红黑树中的节点
                                        setTabAt(tab, i, untreeify(t.first));
                                }
                            }
                        }
                    }
                }
                if (validated) {
                    if (oldVal != null) {
                        if (value == null)
                            addCount(-1L, -1);
                        return oldVal;
                    }
                    break;
                }
            }
        }
        return null;
    }