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1 | | -努力编写中... |
| 1 | +HashMap 源码中主要了解其核心源码及实现逻辑。ConcurrentHashMap 就不再重复那些数据结构相关的内容咯,这里重点看一下它的并发安全实现。源码如下。 |
| 2 | + |
| 3 | +```java |
| 4 | +public class ConcurrentHashMap<K,V> extends AbstractMap<K,V> implements ConcurrentMap<K,V>, |
| 5 | + Serializable { |
| 6 | + |
| 7 | + /* --------- 常量及成员变量的设计 几乎与HashMap相差无几 -------- */ |
| 8 | + |
| 9 | + /** |
| 10 | + * 最大容量 |
| 11 | + */ |
| 12 | + private static final int MAXIMUM_CAPACITY = 1 << 30; |
| 13 | + |
| 14 | + /** |
| 15 | + * 默认初始容量 |
| 16 | + */ |
| 17 | + private static final int DEFAULT_CAPACITY = 16; |
| 18 | + |
| 19 | + /** |
| 20 | + * 单个数组最大容量 |
| 21 | + */ |
| 22 | + static final int MAX_ARRAY_SIZE = Integer.MAX_VALUE - 8; |
| 23 | + |
| 24 | + /** |
| 25 | + * 默认并发等级,也就分成多少个单独上锁的区域 |
| 26 | + */ |
| 27 | + private static final int DEFAULT_CONCURRENCY_LEVEL = 16; |
| 28 | + |
| 29 | + /** |
| 30 | + * 扩容因子 |
| 31 | + */ |
| 32 | + private static final float LOAD_FACTOR = 0.75f; |
| 33 | + |
| 34 | + /** |
| 35 | + * |
| 36 | + */ |
| 37 | + transient volatile Node<K,V>[] table; |
| 38 | + |
| 39 | + /** |
| 40 | + * |
| 41 | + */ |
| 42 | + private transient volatile Node<K,V>[] nextTable; |
| 43 | + |
| 44 | + /* --------- 系列构造方法,依然推荐在初始化时根据实际情况设置好初始容量 -------- */ |
| 45 | + public ConcurrentHashMap() { |
| 46 | + } |
| 47 | + |
| 48 | + public ConcurrentHashMap(int initialCapacity) { |
| 49 | + if (initialCapacity < 0) |
| 50 | + throw new IllegalArgumentException(); |
| 51 | + int cap = ((initialCapacity >= (MAXIMUM_CAPACITY >>> 1)) ? |
| 52 | + MAXIMUM_CAPACITY : |
| 53 | + tableSizeFor(initialCapacity + (initialCapacity >>> 1) + 1)); |
| 54 | + this.sizeCtl = cap; |
| 55 | + } |
| 56 | + |
| 57 | + public ConcurrentHashMap(Map<? extends K, ? extends V> m) { |
| 58 | + this.sizeCtl = DEFAULT_CAPACITY; |
| 59 | + putAll(m); |
| 60 | + } |
| 61 | + |
| 62 | + public ConcurrentHashMap(int initialCapacity, float loadFactor) { |
| 63 | + this(initialCapacity, loadFactor, 1); |
| 64 | + } |
| 65 | + |
| 66 | + public ConcurrentHashMap(int initialCapacity, |
| 67 | + float loadFactor, int concurrencyLevel) { |
| 68 | + if (!(loadFactor > 0.0f) || initialCapacity < 0 || concurrencyLevel <= 0) |
| 69 | + throw new IllegalArgumentException(); |
| 70 | + if (initialCapacity < concurrencyLevel) // Use at least as many bins |
| 71 | + initialCapacity = concurrencyLevel; // as estimated threads |
| 72 | + long size = (long)(1.0 + (long)initialCapacity / loadFactor); |
| 73 | + int cap = (size >= (long)MAXIMUM_CAPACITY) ? |
| 74 | + MAXIMUM_CAPACITY : tableSizeFor((int)size); |
| 75 | + this.sizeCtl = cap; |
| 76 | + } |
| 77 | + |
| 78 | + /** |
| 79 | + * ConcurrentHashMap 的核心就在于其put元素时 利用synchronized局部锁 和 |
| 80 | + * CAS乐观锁机制 大大提升了本集合的并发能力,比JDK7的分段锁性能更强 |
| 81 | + */ |
| 82 | + public V put(K key, V value) { |
| 83 | + return putVal(key, value, false); |
| 84 | + } |
| 85 | + |
| 86 | + /** |
| 87 | + * 当前指定数组位置无元素时,使用CAS操作 将 Node键值对 放入对应的数组下标。 |
| 88 | + * 出现hash冲突,则用synchronized局部锁锁住,若当前hash对应的节点是链表的头节点,遍历链表, |
| 89 | + * 若找到对应的node节点,则修改node节点的val,否则在链表末尾添加node节点;倘若当前节点是 |
| 90 | + * 红黑树的根节点,在树结构上遍历元素,更新或增加节点 |
| 91 | + */ |
| 92 | + final V putVal(K key, V value, boolean onlyIfAbsent) { |
| 93 | + if (key == null || value == null) throw new NullPointerException(); |
| 94 | + int hash = spread(key.hashCode()); |
| 95 | + int binCount = 0; |
| 96 | + for (Node<K,V>[] tab = table;;) { |
| 97 | + Node<K,V> f; int n, i, fh; |
| 98 | + if (tab == null || (n = tab.length) == 0) |
| 99 | + tab = initTable(); |
| 100 | + else if ((f = tabAt(tab, i = (n - 1) & hash)) == null) { |
| 101 | + // 注意!这是一个CAS的方法,将新节点放入指定位置,不用加锁阻塞线程 |
| 102 | + // 也能保证并发安全 |
| 103 | + if (casTabAt(tab, i, null, new Node<K,V>(hash, key, value, null))) |
| 104 | + break; // no lock when adding to empty bin |
| 105 | + } |
| 106 | + // 当前Map在扩容,先协助扩容,在更新值 |
| 107 | + else if ((fh = f.hash) == MOVED) |
| 108 | + tab = helpTransfer(tab, f); |
| 109 | + else { // hash冲突 |
| 110 | + V oldVal = null; |
| 111 | + // 局部锁,有效减少锁竞争的发生 |
| 112 | + synchronized (f) { // f 是 链表头节点/红黑树根节点 |
| 113 | + if (tabAt(tab, i) == f) { |
| 114 | + if (fh >= 0) { |
| 115 | + binCount = 1; |
| 116 | + for (Node<K,V> e = f;; ++binCount) { |
| 117 | + K ek; |
| 118 | + // 若节点已经存在,修改该节点的值 |
| 119 | + if (e.hash == hash && ((ek = e.key) == key || |
| 120 | + (ek != null && key.equals(ek)))) { |
| 121 | + oldVal = e.val; |
| 122 | + if (!onlyIfAbsent) |
| 123 | + e.val = value; |
| 124 | + break; |
| 125 | + } |
| 126 | + Node<K,V> pred = e; |
| 127 | + // 节点不存在,添加到链表末尾 |
| 128 | + if ((e = e.next) == null) { |
| 129 | + pred.next = new Node<K,V>(hash, key, |
| 130 | + value, null); |
| 131 | + break; |
| 132 | + } |
| 133 | + } |
| 134 | + } |
| 135 | + // 如果该节点是 红黑树节点 |
| 136 | + else if (f instanceof TreeBin) { |
| 137 | + Node<K,V> p; |
| 138 | + binCount = 2; |
| 139 | + if ((p = ((TreeBin<K,V>)f).putTreeVal(hash, key, |
| 140 | + value)) != null) { |
| 141 | + oldVal = p.val; |
| 142 | + if (!onlyIfAbsent) |
| 143 | + p.val = value; |
| 144 | + } |
| 145 | + } |
| 146 | + } |
| 147 | + } |
| 148 | + // 链表节点超过了8,链表转为红黑树 |
| 149 | + if (binCount != 0) { |
| 150 | + if (binCount >= TREEIFY_THRESHOLD) |
| 151 | + treeifyBin(tab, i); |
| 152 | + if (oldVal != null) |
| 153 | + return oldVal; |
| 154 | + break; |
| 155 | + } |
| 156 | + } |
| 157 | + } |
| 158 | + // 统计节点个数,检查是否需要resize |
| 159 | + addCount(1L, binCount); |
| 160 | + return null; |
| 161 | + } |
| 162 | +} |
| 163 | +``` |
| 164 | +**与JDK1.7在同步机制上的区别** 总结如下。JDK1.7 使用的是分段锁机制,其内部类Segment 继承了 ReentrantLock,将 容器内的数组划分成多段区域,每个区域对应一把锁,相比于HashTable确实提升了不少并发能力,但在数据量庞大的情况下,性能依然不容乐观,只能通过不断的增加锁来维持并发性能。而JDK1.8则使用了 CAS乐观锁 + synchronized局部锁 处理并发问题,锁粒度更细,即使数据量很大也能保证良好的并发性。 |
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