ThreadLocalMap的源码分析
分析之前我们来看看ThreadLocalMap有哪些成员变量吧！
static class Entry extends WeakReference<ThreadLocal<?>> {
/** The value associated with this ThreadLocal. */
Object value;
Entry(ThreadLocal<?> k, Object v) {
super(k);
value = v;
}
}
/**
* The initial capacity -- MUST be a power of two.
*/
private static final int INITIAL_CAPACITY = 16;
/**
* The table, resized as necessary.
* table.length MUST always be a power of two.
*/
private Entry[] table;
/**
* The number of entries in the table. //
*/
private int size = 0;
/**
* The next size value at which to resize.
*/
private int threshold; // Default to 0
现在我们依次来分析下吧：
- Entry:我们可以看出Entry是继承WeakReference（弱引用），在jvm中引用分为四种：强引用，软引用，弱引用，虚引用。jvm 第一次GC首先回收的是弱引用。这样设计是方便回收。而Entry 类是一个key-value对，key就是ThreadLocal的对象。
- INITIAL_CAPACITY :table数组的初始化大小。这里必须是2的幂次方
- table:entry 数组；size：数组中真实数据的大小；
-threshold:下次需要扩容的阈值，默认 0ThreadLocalMap的有关操作
ThreadLocalMap的set操作：
/**
* Set the value associated with key. //设置和相关的值
*
* @param key the thread local object
* @param value the value to be set
*/
private void set(ThreadLocal<?> key, Object value) {
// We don't use a fast path as with get() because it is at
// least as common to use set() to create new entries as
// it is to replace existing ones, in which case, a fast
// path would fail more often than not.
Entry[] tab = table;
int len = tab.length;
int i = key.threadLocalHashCode & (len-1);
for (Entry e = tab[i];
e != null;
e = tab[i = nextIndex(i, len)]) {
ThreadLocal<?> k = e.get();
if (k == key) {
e.value = value;
return;
}
if (k == null) {
replaceStaleEntry(key, value, i);
return;
}
}
tab[i] = new Entry(key, value);
int sz = ++size;
if (!cleanSomeSlots(i, sz) && sz >= threshold)
rehash();
}步骤
- 利用 int i = key.threadLocalHashCode & (len-1); key的hashcode来得到key所在数组中的位置
- 从i到len遍历数组：如果key==k，设置值（原先有值会被覆盖）返回；如果k ==null则就replaceStaleEntry(key, value, i);清除该entry 返回；如果没有找到，就在数组下标为i的地方创建new Entry(key, value);数组大小加1，如果空间没有清除或者大小超过阈值就重新hash。
现在我们具体分析set方法中一些函数
private void replaceStaleEntry(ThreadLocal<?> key, Object value,
int staleSlot) {
Entry[] tab = table;
int len = tab.length;
Entry e;
// Back up to check for prior stale entry in current run.
// We clean out whole runs at a time to avoid continual
// incremental rehashing due to garbage collector freeing
// up refs in bunches (i.e., whenever the collector runs).
int slotToExpunge = staleSlot;
for (int i = prevIndex(staleSlot, len);
(e = tab[i]) != null;
i = prevIndex(i, len))
if (e.get() == null)
slotToExpunge = i;
// Find either the key or trailing null slot of run, whichever
// occurs first
for (int i = nextIndex(staleSlot, len);
(e = tab[i]) != null;
i = nextIndex(i, len)) {
ThreadLocal<?> k = e.get();
// If we find key, then we need to swap it
// with the stale entry to maintain hash table order.
// The newly stale slot, or any other stale slot
// encountered above it, can then be sent to expungeStaleEntry
// to remove or rehash all of the other entries in run.
if (k == key) {
e.value = value;
tab[i] = tab[staleSlot];
tab[staleSlot] = e;
// Start expunge at preceding stale entry if it exists
if (slotToExpunge == staleSlot)
slotToExpunge = i;
cleanSomeSlots(expungeStaleEntry(slotToExpunge), len);
return;
}
// If we didn't find stale entry on backward scan, the
// first stale entry seen while scanning for key is the
// first still present in the run.
if (k == null && slotToExpunge == staleSlot)
slotToExpunge = i;
}
// If key not found, put new entry in stale slot
tab[staleSlot].value = null;
tab[staleSlot] = new Entry(key, value);
// If there are any other stale entries in run, expunge them
if (slotToExpunge != staleSlot)
cleanSomeSlots(expungeStaleEntry(slotToExpunge), len);
}
从上处代码中我们不来看出里面中有个函数出现的次数最多：cleanSomeSlots(expungeStaleEntry(slotToExpunge), len); 所以我们先从这两个函数来分析：
expungeStaleEntry(slotToExpunge)
* @param staleSlot index of slot known to have null key
* @return the index of the next null slot after staleSlot
* (all between staleSlot and this slot will have been checked
* for expunging).
//staleSloat:数组中entry的key为空的位置
private int expungeStaleEntry(int staleSlot) {
Entry[] tab = table;
int len = tab.length;
// expunge entry at staleSlot //清除空的Entry
tab[staleSlot].value = null;
tab[staleSlot] = null;
size--; //大小减一
//我们来看下面这个for循环主要是干什么的
//从我们遇到为null的Entry的下一个位置开始，进行for循环。直到我们再次遇到entry为null时返回其位置。
// Rehash until we encounter null
Entry e;
int i;
for (i = nextIndex(staleSlot, len);
(e = tab[i]) != null;
i = nextIndex(i, len)) {
ThreadLocal<?> k = e.get();
//遍历中如果遇到可以清理的话就顺便清理
if (k == null) {
e.value = null;
tab[i] = null;
size--;
} else {
//遇到还没被回收的，rehash 找到新的为空的索引位置
int h = k.threadLocalHashCode & (len - 1);
if (h != i) {
//将原位置置 null
tab[i] = null;
//找到新的位置
// Unlike Knuth 6.4 Algorithm R, we must scan until
// null because multiple entries could have been stale.
while (tab[h] != null)
h = nextIndex(h, len);
tab[h] = e;
}
}
}
return i;
}boolean cleanSomeSlots(int i, int n)
//试探性地扫描一些单元格，寻找过时的条目。
private boolean cleanSomeSlots(int i, int n) {
boolean removed = false;
Entry[] tab = table;
int len = tab.length;
do {
i = nextIndex(i, len);
Entry e = tab[i];
//清理
if (e != null && e.get() == null) {
n = len;
removed = true;
i = expungeStaleEntry(i);
}
} while ( (n >>>= 1) != 0);
return removed;
}
Entry getEntry(ThreadLocal<?> key)
private Entry getEntry(ThreadLocal<?> key) {
int i = key.threadLocalHashCode & (table.length - 1);
Entry e = table[i];
if (e != null && e.get() == key)
return e;
else
return getEntryAfterMiss(key, i, e);
}
取值的代码非常清楚，尤其着重注意下getEntryAfterMiss(key, i, e);这是hash没命中时采用的。下面我们来看看getEntryAfterMiss(key, i, e);
getEntryAfterMiss(key, i, e);
//通过遍历得到其职，这种解决hash冲突的方法就是开发地址法
private Entry getEntryAfterMiss(ThreadLocal<?> key, int i, Entry e) {
Entry[] tab = table;
int len = tab.length;
while (e != null) {
ThreadLocal<?> k = e.get();
if (k == key)
return e;
if (k == null)
expungeStaleEntry(i);
else
i = nextIndex(i, len);
e = tab[i];
}
return null;
}