原文鏈接http://zhhll.icu/2020/12/14/java%E5%9F%BA%E7%A1%80/%E9%9B%86%E5%90%88/ConcurrentHashMap%E8%AF%A6%E8%A7%A3/
ConcurrentHashMap詳解
JDK7
Segment
在jdk8之前concurrentHashMap使用該物件進行分段加鎖，降低了鎖的粒度，使得并發效率提高，Segment本身也相當于一個HashMap，Segment包含一個HashEntry陣列，陣列中每個HashEntry既是一個鍵值對，又是一個鏈表的頭結點
get方法
- 根據key做hash運算，得到hash值
- 通過hash值，定位到對應的segment物件
- 再次通過hash值，定位到segment當中陣列的具體位置
put方法
- 根據key做hash運算，得到hash值
- 通過hash值，定位到對應的segment物件
- 獲取可重入鎖
- 再次通過hash值，定位到segment當中陣列的具體位置
- 插入或覆寫hashEntry物件
- 釋放鎖
但是使用這種方式實作需要進行兩次hash操作，第一次hash操作找到對應的segment，第二次hash操作定位到元素所在鏈表的頭部
JDK8
在jdk8的時候參考了HashMap的設計，采用了陣列+鏈表+紅黑樹的方式，內部大量采用CAS操作，舍棄了分段鎖的思想
CAS
CAS是compare and swap的縮寫，即我們所說的比較交換，CAS屬于樂觀鎖，
CAS包含三個運算元，---記憶體中的值(V)，預期原值(A)，新值(B) 如果記憶體中的值和A的值一樣，就可以將記憶體中的值更新為B，CAS通過無限回圈來獲取資料，一直到V和A一致為止
樂觀鎖
樂觀鎖會很樂觀的認為不會出現并發問題，所以采用無鎖的機制來進行處理，比如通過給記錄加version來獲取資料，性能比悲觀鎖要高
悲觀鎖
悲觀鎖會很悲觀的認為肯定會出現并發問題，所以會將資源鎖住，該資源只能有一個執行緒進行操作，只有前一個獲得鎖的執行緒釋放鎖之后，下一個執行緒才可以訪問
原始碼分析
重要變數
// 表示整個hash表，初始化階段是在第一次插入的時候，容量總是2的次冪
transient volatile Node<K,V>[] table;
// 下一個使用的表 只有在擴容的時候非空，其他情況都是null
private transient volatile Node<K,V>[] 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;
// 用于初始化和擴容控制
// 0：默認值
// -1:正在初始化
// 大于0：為hash表的閾值
// 小于-1:有多個執行緒在進行擴容 該值為 -(1+正在擴容的執行緒數)
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<K,V> keySet;
private transient ValuesView<K,V> values;
private transient EntrySetView<K,V> entrySet;
建構式
/**
* 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<? extends K, ? extends V> 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是如何保證在插入的時候執行緒安全的呢
public V put(K key, V value) {
return putVal(key, value, false);
}
final V putVal(K key, V value, boolean onlyIfAbsent) {
// ConcurrentHashMap不允許key和value為null
if (key == null || value =https://www.cnblogs.com/life-time/p/= null) throw new NullPointerException();
// 計算hash值
int hash = spread(key.hashCode());
int binCount = 0;
for (Node[] tab = table;;) {
Node f; int n, i, fh;
// tab為null，哈希表還沒有初始化，進行初始化哈希表
if (tab == null || (n = tab.length) == 0)
tab = initTable();
// 該索引位置為null，表示還沒有元素
else if ((f = tabAt(tab, i = (n - 1) & hash)) == null) {
// 使用CAS的方式添加節點
if (casTabAt(tab, i, null,
new Node(hash, key, value, null)))
break; // no lock when adding to empty bin
}
// 節點的hash值為-1，表示該哈希表正在擴容
else if ((fh = f.hash) == MOVED)
tab = helpTransfer(tab, f);
else {
V oldVal = null;
// 對頭節點加鎖
synchronized (f) {
// 再次判斷一下該節點是否為目標索引位置的頭節點，防止期間被修改
if (tabAt(tab, i) == f) {
// 表示是普通的鏈表
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;
}
}
}
// 紅黑樹 TreeBin的hash值為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;
}
}
}
}
// 可以看一下上述的賦值流程
// 默認初始值是0
// 鏈表時為1 在遍歷時進行累加，直到找到所要添加的位置為止
// 紅黑樹時為2
if (binCount != 0) {
// 鏈表的長度是否達到8 達到8轉為紅黑樹
if (binCount >= TREEIFY_THRESHOLD)
treeifyBin(tab, i);
// oldVal不為null，表示只是對key的值進行的修改，沒有添加元素，直接回傳即可
if (oldVal != null)
return oldVal;
break;
}
}
}
//
addCount(1L, binCount);
return null;
}
哈希函式根據hashCode計算出哈希值，這里的hash值與HashMap的計算方式稍微有點不同，在低十六位異或高十六位之后還需要與HASH_BITS在進行與運算，HASH_BITS的值是0x7fffffff，轉為二進制是31個1，進行與運算是為了保證得到的hash值為正數，
ConcurrentHashMap中hash值為負數包含有其他含義，-1表示為ForwardingNode節點，-2表示為TreeBin節點
static final int spread(int h) {
// (h ^ (h >>> 16)與hashMap相同
// HASH_BITS進行與運算
return (h ^ (h >>> 16)) & HASH_BITS;
}
初始化hash表的操作
private final Node<K,V>[] initTable() {
Node<K,V>[] tab; int sc;
// hash表為null時才需要進行初始化
while ((tab = table) == null || tab.length == 0) {
// sizeCtl小于0表示有其他執行緒在進行初始化操作了
if ((sc = sizeCtl) < 0)
Thread.yield(); // lost initialization race; just spin
// 將SIZECTL設為-1，表示該執行緒要開始初始化表了
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<K,V>[] nt = (Node<K,V>[])new Node<?,?>[n];
table = tab = nt;
// n右移兩位 表示1/4n n-1/4n為3/4n 即為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<K,V>[] 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();
}
}
}
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標籤：Java