ThreadLocal 是怎么实现的？
ThreadLocal 大家都很熟悉了，那么他是如何工作的呢？
下面按照我们平时的使用顺序，来扒一扒他的实现机制（注意源码只提供了必要内容）
代码版本 jdk8
- ThreadLocal()
- set() 划重点 大家要认真看帖
- get()
- remove()
ThreadLocal
/**
* Creates a thread local variable.
* @see #withInitial(java.util.function.Supplier)
*/
public ThreadLocal() {
}
诶？ 什么都没有！
惊喜不惊喜？ 意外不意外？
set()方法
核心方法ThreadLocal的主要逻辑都在这里了
public void set(T value) {
// 拿到当前线程
Thread t = Thread.currentThread();
ThreadLocalMap map = getMap(t);
if (map != null)
// 直觉告诉我这里不简单
map.set(this, value);
else
// 直觉告诉我这里不简单
createMap(t, value);
}
ThreadLocalMap getMap(Thread t) {
return t.threadLocals;
}
里面有几个个点内容可能比较多我们按顺序来
- ThreadLocal.ThreadLocalMap
- ThreadLocal.createMap()
- ThreadLocal.ThreadLocalMap.set()
ThreadLocal.ThreadLocalMap
Thread.threadLocals 是线程内部成员变量，ThreadLocal的内部类ThreadLocalMap,是一个map容器。
void createMap(Thread t, T firstValue) {
t.threadLocals = new ThreadLocalMap(this, firstValue);
}
static class ThreadLocalMap {
// 内部数组初始长度
private static final int INITIAL_CAPACITY = 16;
// 又是一个内部类，用于存储具体数据
static class Entry extends WeakReference<ThreadLocal<?>> {
/** The value associated with this ThreadLocal. */
Object value;
/**
*
*
*/
Entry(ThreadLocal<?> k, Object v) {
super(k);
value = v;
}
}
// 数组用于存储ThreadLocal 和 value的对应关系
private Entry[] table;
// 数组table 扩容的 阈值
private int threshold; // Default to 0
ThreadLocalMap(ThreadLocal<?> firstKey, Object firstValue) {
table = new Entry[INITIAL_CAPACITY];
int i = firstKey.threadLocalHashCode & (INITIAL_CAPACITY - 1);
table[i] = new Entry(firstKey, firstValue);
size = 1;
setThreshold(INITIAL_CAPACITY);
}
// 设置数组扩容阈值，初始化和扩容后都会调用这个方法
private void setThreshold(int len) {
threshold = len * 2 / 3;
}
}
ThreadLocalMap.Entry
这个类使用了 WeakReference（弱引用） 来存储 key(ThreadLocal)
因为弱引用不会阻止垃圾回，避免了因为线程类对ThreadLocal的引用导致线程存活的情况下GC无法回收ThreadLocal。
这样就会对线程池这种线程复用机制造成困扰，不当的操作更容易造成内存溢出。
弱引用解决了垃圾回收的问题，但带来了另一个问题，jdk开发者们需要维护数组中的过期的key，我们后面会看到这部分代码
ThreadLocal.ThreadLocalMap.set()
// 注意这里只写了有关 ThreadLocal.ThreadLocalMap.set() 的源码 不是全部源码
static class ThreadLocalMap {
// 看过hashmap等 map类实现的都知道，看map容器先看hash
private final int threadLocalHashCode = nextHashCode();
/**
* BigDecimal goldenRatioResult = BigDecimal.valueOf(Math.pow(2, 32)).divide(new BigDecimal("1.618"), 0, ROUND_HALF_UP);
* int hashIncrenment = ~goldenRatioResult.intValue() + 0b1; // 等效于 Math.abs() 结果是 1640531527 也就是十六进制的 0x61c88647
* 1.618 是 1:0.618，是神奇的黄金分割数。
* HASH_INCREMENT是根据黄金分隔数计算出来的一个值，使threadLocalHashCode的值之间被HASH_INCREMEN分隔
* 旨在用这样的hash值生成更均匀的数组下标， 并减少冲突概率
* 有一篇帖子是专门讲 为什么用0x61c88647这个数的，我就不献丑了文底贴链接
*
*
* 看不懂？没关系只需要记住用这个数作为间隔,生成的hash值计算出来的数组下标更均， 并且冲突几率小
* 后面称这个数为魔数
*/
private static final int HASH_INCREMENT = 0x61c88647;
/**
* 以HASH_INCREMENT 递增生成hash
*/
private static int nextHashCode() {
return nextHashCode.getAndAdd(HASH_INCREMENT);
}
private void set(ThreadLocal<?> key, Object value) {
Entry[] tab = table;
// len 数组的长度被控制为2的整数倍， len-1 的二进制为 1111111**11，这种有固定规律的格式，
// 方便通过位运算生成数组下标时排除自身因素造成的冲突
int len = tab.length;
// 通过hash值位运算计算出 数组下标
int i = key.threadLocalHashCode & (len-1);
// 这里与hashmap不同, 因为ThreadLocal 的特性在合理的架构设计下是不会大规模使用的。
// 又因为有魔数HASH_INCREMENT = 0x61c88647; 作为分隔。
// 所以hash取下标的操作 发生冲突的可能性很小，且分布有一定间隔，所以这里干脆用循环查找可用节点的方式解决冲突
for (Entry e = tab[i];
e != null;
e = tab[i = nextIndex(i, len)]) {
// 循环检查
ThreadLocal<?> k = e.get();
if (k == key) {
// 节点为当前 ThreadLocal 直接替换value
e.value = value;
return;
}
if (k == null) {
// 过期的数据， 因为key使用的弱引用gc回收之后就是空值， 需要维护清理
replaceStaleEntry(key, value, i);
return;
}
}
// e 等于空直接使用
tab[i] = new Entry(key, value);
int sz = ++size;
if (!cleanSomeSlots(i, sz) && sz >= threshold)
// 没有成功删除任何过时的节点, 并且当前集合内有效数据长度达到扩容阈值 去扩容
rehash();
}
}
这里又出现两个函数
- replaceStaleEntry 清理过期数据
- cleanSomeSlots 也是清理过期数据， 并返回是否成功清理了一个以上空间出来
- rehash 刷新加扩容
至此set函数有营养的部分已经结束，replaceStaleEntry cleanSomeSlots 等函数都是为了清理数据重排数组而封装出来的。
为什么要清理重排数组？ 因为上面说了 数组的item是Entry它使用弱引用存储key
后面贴一下相关源码不在详细介绍
/**
* Replace a stale entry encountered during a set operation
* with an entry for the specified key. The value passed in
* the value parameter is stored in the entry, whether or not
* an entry already exists for the specified key.
*
* As a side effect, this method expunges all stale entries in the
* "run" containing the stale entry. (A run is a sequence of entries
* between two null slots.)
*
* @param key the key
* @param value the value to be associated with key
* @param staleSlot index of the first stale entry encountered while
* searching for key.
*/
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).
// 找到最靠前的过期数据一次性清理干净
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) {
// 发现了当前ThreadLocal 存储在了其他位置 下面进行校准替换
// 一定是有过期数据没有清理造成的
e.value = value;
tab[i] = tab[staleSlot];
tab[staleSlot] = e;
// Start expunge at preceding stale entry if it exists
if (slotToExpunge == staleSlot)
// 与备份位置相同 即没有找到过期的节点
// 即从当前位置开始清理
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);
}
/**
* Heuristically scan some cells looking for stale entries.
* This is invoked when either a new element is added, or
* another stale one has been expunged. It performs a
* logarithmic number of scans, as a balance between no
* scanning (fast but retains garbage) and a number of scans
* proportional to number of elements, that would find all
* garbage but would cause some insertions to take O(n) time.
*
* @param i a position known NOT to hold a stale entry. The
* scan starts at the element after i.
*
* @param n scan control: {@code log2(n)} cells are scanned,
* unless a stale entry is found, in which case
* {@code log2(table.length)-1} additional cells are scanned.
* When called from insertions, this parameter is the number
* of elements, but when from replaceStaleEntry, it is the
* table length. (Note: all this could be changed to be either
* more or less aggressive by weighting n instead of just
* using straight log n. But this version is simple, fast, and
* seems to work well.)
*
* @return true if any stale entries have been removed.
*/
private boolean cleanSomeSlots(int i, int n) {
// 试探性的扫描一些元素，对过期节点进行清理
boolean removed = false;
Entry[] tab = table;
int len = tab.length;
do {
i = nextIndex(i, len);
Entry e = tab[i];
if (e != null && e.get() == null) {
n = len;
removed = true;
i = expungeStaleEntry(i);
}
} while ( (n >>>= 1) != 0);
return removed;
}
/**
* Expunge a stale entry by rehashing any possibly colliding entries
* lying between staleSlot and the next null slot. This also expunges
* any other stale entries encountered before the trailing null. See
* Knuth, Section 6.4
*
* @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).
*/
private int expungeStaleEntry(int staleSlot) {
Entry[] tab = table;
int len = tab.length;
// expunge entry at staleSlot
tab[staleSlot].value = null;
tab[staleSlot] = null;
size--;
// Rehash until we encounter null 刷新条目
Entry e;
int i;
for (i = nextIndex(staleSlot, len);
(e = tab[i]) != null;
i = nextIndex(i, len)) {
ThreadLocal<?> k = e.get();
if (k == null) {
e.value = null;
tab[i] = null;
size--;
} else {
int h = k.threadLocalHashCode & (len - 1);
if (h != i) {
tab[i] = null;
// 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;
}
/**
* Increment i modulo len.
*/
private static int nextIndex(int i, int len) {
return ((i + 1 < len) ? i + 1 : 0);
}
/**
* Decrement i modulo len.
*/
private static int prevIndex(int i, int len) {
return ((i - 1 >= 0) ? i - 1 : len - 1);
}
get 方法比较简单
public T get() {
Thread t = Thread.currentThread();
ThreadLocalMap map = getMap(t);
if (map != null) {
ThreadLocalMap.Entry e = map.getEntry(this);
if (e != null) {
@SuppressWarnings("unchecked")
T result = (T)e.value;
return result;
}
}
return setInitialValue();
}
// ThreadLocalMap map 没初始化 就先进行初始化
// 初始化结束不过瘾， 就先value设置个 null. （真皮）
private T setInitialValue() {
T value = initialValue();
Thread t = Thread.currentThread();
ThreadLocalMap map = getMap(t);
if (map != null)
map.set(this, value);
else
createMap(t, value);
return value;
}
protected T initialValue() {
return null;
}
// map.getEntry 在这里
private Entry getEntry(ThreadLocal<?> key) {
// hsah值计算下标 上面已经见识过了
int i = key.threadLocalHashCode & (table.length - 1);
Entry e = table[i];
if (e != null && e.get() == key)
// 是我的 value 直接返回
return e;
else
// 遇到了冲突 调用封装方法查找
return getEntryAfterMiss(key, i, e);
}
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)
// 找到了对应节点
return e;
if (k == null)
// 清理过期节点
expungeStaleEntry(i);
else
i = nextIndex(i, len);
e = tab[i];
}
return null;
}
好了 我们看到 get方法也会map的进行初始化，遇到hash碰撞就去循环递增知道遇到null（代表没找到value），或者找到存储着相同的key的item
remove 更简单
public void remove() {
ThreadLocalMap m = getMap(Thread.currentThread());
if (m != null)
m.remove(this);
}
/**
* Remove the entry for key.
*/
private void remove(ThreadLocal<?> key) {
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)]) {
if (e.get() == key) {
// 清除弱引用
e.clear();
// 清除节点
expungeStaleEntry(i);
return;
}
}
}
结尾
本文很多内容需要稍微了解map结构的原理才比较好理解，包括散列表 hash碰撞 位运算 二进制。迷糊的朋友们不要紧关注我本周内会出下一篇对hashMap 源码进行分析,其中会详细讲解hashmap的原理
( ⊙ o ⊙ )啊！
