java集合之ConcurrentHashMap详解
ConcurrentHashMap详解
JDK7
Segment
在jdk8之前concurrentHashMap使用该对象进行分段加锁，降低了锁的粒度，使得并发效率提高，Segment本身也相当于一个HashMap，Segment包含一个HashEntry数组，数组中每个HashEntry既是一个键值对，又是一个链表的头结点
get方法
- 根据key做hash运算，得到hash值
- 通过hash值，定位到对应的segment对象
- 再次通过hash值，定位到segment当中数组的具体位置
put方法
- 根据key做hash运算，得到hash值
- 通过hash值，定位到对应的segment对象
- 获取可重入锁
- 再次通过hash值，定位到segment当中数组的具体位置
- 插入或覆盖hashEntry对象
- 释放锁
但是使用这种方式实现需要进行两次hash操作，第一次hash操作找到对应的segment，第二次hash操作定位到元素所在链表的头部
JDK8
在jdk8的时候参考了HashMap的设计，采用了数组+链表+红黑树的方式，内部大量采用CAS操作，舍弃了分段锁的思想
CAS
CAS是compare and swap的缩写，即我们所说的比较交换，CAS属于乐观锁。
CAS包含三个操作数，---内存中的值(V)，预期原值(A)，新值(B) 如果内存中的值和A的值一样，就可以将内存中的值更新为B。CAS通过无限循环来获取数据，一直到V和A一致为止
乐观锁
乐观锁会很乐观的认为不会出现并发问题，所以采用无锁的机制来进行处理，比如通过给记录加version来获取数据，性能比悲观锁要高
悲观锁
悲观锁会很悲观的认为肯定会出现并发问题，所以会将资源锁住，该资源只能有一个线程进行操作，只有前一个获得锁的线程释放锁之后，下一个线程才可以访问
源码分析
重要变量
// 表示整个hash表，初始化阶段是在第一次插入的时候，容量总是2的次幂
transient volatile Node[] table;
// 下一个使用的表 只有在扩容的时候非空，其他情况都是null
private transient volatile Node[] nextTable;
/**
* Base counter value, used mainly when there is no contention,
* but also as a fallback during table initialization
* races. Updated via CAS.
*/
private transient volatile long baseCount;
// 用于初始化和扩容控制
// 0：默认值
// -1:正在初始化
// 大于0：为hash表的阈值
// 小于-1:有多个线程在进行扩容 该值为 -(1+正在扩容的线程数)
private transient volatile int sizeCtl;
/**
* The next table index (plus one) to split while resizing.
*/
private transient volatile int transferIndex;
/**
* Spinlock (locked via CAS) used when resizing and/or creating CounterCells.
*/
private transient volatile int cellsBusy;
/**
* Table of counter cells. When non-null, size is a power of 2.
*/
private transient volatile CounterCell[] counterCells;
// views
private transient KeySetView keySet;
private transient ValuesView values;
private transient EntrySetView entrySet;
构造函数
/**
* Creates a new, empty map with the default initial table size (16).
*/
public ConcurrentHashMap() {
}
/**
* Creates a new, empty map with an initial table size
* accommodating the specified number of elements without the need
* to dynamically resize.
*
* @param initialCapacity The implementation performs internal
* sizing to accommodate this many elements.
* @throws IllegalArgumentException if the initial capacity of
* elements is negative
*/
public ConcurrentHashMap(int initialCapacity) {
if (initialCapacity < 0)
throw new IllegalArgumentException();
int cap = ((initialCapacity >= (MAXIMUM_CAPACITY >>> 1)) ?
MAXIMUM_CAPACITY :
tableSizeFor(initialCapacity + (initialCapacity >>> 1) + 1));
this.sizeCtl = cap;
}
/**
* Creates a new map with the same mappings as the given map.
*
* @param m the map
*/
public ConcurrentHashMap(Map m) {
this.sizeCtl = DEFAULT_CAPACITY;
putAll(m);
}
/**
* Creates a new, empty map with an initial table size based on
* the given number of elements ({@code initialCapacity}) and
* initial table density ({@code loadFactor}).
*
* @param initialCapacity the initial capacity. The implementation
* performs internal sizing to accommodate this many elements,
* given the specified load factor.
* @param loadFactor the load factor (table density) for
* establishing the initial table size
* @throws IllegalArgumentException if the initial capacity of
* elements is negative or the load factor is nonpositive
*
* @since 1.6
*/
public ConcurrentHashMap(int initialCapacity, float loadFactor) {
this(initialCapacity, loadFactor, 1);
}
/**
* Creates a new, empty map with an initial table size based on
* the given number of elements ({@code initialCapacity}), table
* density ({@code loadFactor}), and number of concurrently
* updating threads ({@code concurrencyLevel}).
*
* @param initialCapacity the initial capacity. The implementation
* performs internal sizing to accommodate this many elements,
* given the specified load factor.
* @param loadFactor the load factor (table density) for
* establishing the initial table size
* @param concurrencyLevel the estimated number of concurrently
* updating threads. The implementation may use this value as
* a sizing hint.
* @throws IllegalArgumentException if the initial capacity is
* negative or the load factor or concurrencyLevel are
* nonpositive
*/
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;
}
重要方法
put方法
ConcurrentHashMap是如何保证在插入的时候线程安全的呢
public V put(K key, V value) {
return putVal(key, value, false);
}
final V putVal(K key, V value, boolean onlyIfAbsent) {
// ConcurrentHashMap不允许key和value为null
if (key == null || value == null) throw new NullPointerException();
// 计算hash值
int hash = spread(key.hashCode());
int binCount = 0;
for (Node[] tab = table;;) {
Node f; int n, i, fh;
// tab为null，哈希表还没有初始化，进行初始化哈希表
if (tab == null || (n = tab.length) == 0)
tab = initTable();
// 该索引位置为null，表示还没有元素
else if ((f = tabAt(tab, i = (n - 1) & hash)) == null) {
// 使用CAS的方式添加节点
if (casTabAt(tab, i, null,
new Node(hash, key, value, null)))
break; // no lock when adding to empty bin
}
// 节点的hash值为-1，表示该哈希表正在扩容
else if ((fh = f.hash) == MOVED)
tab = helpTransfer(tab, f);
else {
V oldVal = null;
// 对头节点加锁
synchronized (f) {
// 再次判断一下该节点是否为目标索引位置的头节点，防止期间被修改
if (tabAt(tab, i) == f) {
// 表示是普通的链表
if (fh >= 0) {
binCount = 1;
for (Node e = f;; ++binCount) {
K ek;
if (e.hash == hash &&
((ek = e.key) == key ||
(ek != null && key.equals(ek)))) {
oldVal = e.val;
if (!onlyIfAbsent)
e.val = value;
break;
}
Node pred = e;
if ((e = e.next) == null) {
pred.next = new Node(hash, key,
value, null);
break;
}
}
}
// 红黑树 TreeBin的hash值为TREEBIN，是-2
else if (f instanceof TreeBin) {
Node p;
binCount = 2;
if ((p = ((TreeBin)f).putTreeVal(hash, key,
value)) != null) {
oldVal = p.val;
if (!onlyIfAbsent)
p.val = value;
}
}
}
}
// 可以看一下上述的赋值流程
// 默认初始值是0
// 链表时为1 在遍历时进行累加，直到找到所要添加的位置为止
// 红黑树时为2
if (binCount != 0) {
// 链表的长度是否达到8 达到8转为红黑树
if (binCount >= TREEIFY_THRESHOLD)
treeifyBin(tab, i);
// oldVal不为null，表示只是对key的值进行的修改，没有添加元素，直接返回即可
if (oldVal != null)
return oldVal;
break;
}
}
}
//
addCount(1L, binCount);
return null;
}
哈希函数根据hashCode计算出哈希值，这里的hash值与HashMap的计算方式稍微有点不同，在低十六位异或高十六位之后还需要与HASH_BITS在进行与运算，HASH_BITS的值是0x7fffffff，转为二进制是31个1，进行与运算是为了保证得到的hash值为正数。
ConcurrentHashMap中hash值为负数包含有其他含义，-1表示为ForwardingNode节点，-2表示为TreeBin节点
static final int spread(int h) {
// (h ^ (h >>> 16)与hashMap相同
// HASH_BITS进行与运算
return (h ^ (h >>> 16)) & HASH_BITS;
}
初始化hash表的操作
private final Node[] initTable() {
Node[] tab; int sc;
// hash表为null时才需要进行初始化
while ((tab = table) == null || tab.length == 0) {
// sizeCtl小于0表示有其他线程在进行初始化操作了
if ((sc = sizeCtl) < 0)
Thread.yield(); // lost initialization race; just spin
// 将SIZECTL设为-1，表示该线程要开始初始化表了
else if (U.compareAndSwapInt(this, SIZECTL, sc, -1)) {
try {
if ((tab = table) == null || tab.length == 0) {
int n = (sc > 0) ? sc : DEFAULT_CAPACITY;
@SuppressWarnings("unchecked")
Node[] nt = (Node[])new Node[n];
table = tab = nt;
// n右移两位 表示1/4n n-1/4n为3/4n 即为n*0.75
sc = n - (n >>> 2);
}
} finally {
sizeCtl = sc;
}
break;
}
}
return tab;
}
private final void addCount(long x, int check) {
CounterCell[] as; long b, s;
if ((as = counterCells) != null ||
!U.compareAndSwapLong(this, BASECOUNT, b = baseCount, s = b + x)) {
CounterCell a; long v; int m;
boolean uncontended = true;
if (as == null || (m = as.length - 1) < 0 ||
(a = as[ThreadLocalRandom.getProbe() & m]) == null ||
!(uncontended =
U.compareAndSwapLong(a, CELLVALUE, v = a.value, v + x))) {
fullAddCount(x, uncontended);
return;
}
if (check <= 1)
return;
s = sumCount();
}
if (check >= 0) {
Node[] tab, nt; int n, sc;
while (s >= (long)(sc = sizeCtl) && (tab = table) != null &&
(n = tab.length) < MAXIMUM_CAPACITY) {
int rs = resizeStamp(n);
if (sc < 0) {
if ((sc >>> RESIZE_STAMP_SHIFT) != rs || sc == rs + 1 ||
sc == rs + MAX_RESIZERS || (nt = nextTable) == null ||
transferIndex <= 0)
break;
if (U.compareAndSwapInt(this, SIZECTL, sc, sc + 1))
transfer(tab, nt);
}
else if (U.compareAndSwapInt(this, SIZECTL, sc,
(rs << RESIZE_STAMP_SHIFT) + 2))
transfer(tab, null);
s = sumCount();
}
}
}
computeIfAbsent和putIfAbsent方法
ConcurrentHashMap有两个比较特殊的方法，这两个方法要是可以好好地利用起来，那就爽歪歪了
- 当Key存在的时候，如果Value获取比较昂贵的话，putIfAbsent就白白浪费时间在获取这个昂贵的Value上（这个点特别注意）
- Key不存在的时候，putIfAbsent返回null，小心空指针，而computeIfAbsent返回计算后的值
- 当Key不存在的时候，putIfAbsent允许put null进去，而computeIfAbsent不能，之后进行containsKey查询是有区别的
https://zhhll.icu/2020/java基础/集合/8.ConcurrentHashMap详解/
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