ThreadLocal 源码

Posted 2020-12-23 codingbug

ThreadLocal 源码分析

* ThreadLocal的内存泄露问题，使用完后，执行remove操作

* 在开放定址算法中，线性探测法是散列解决冲突的一种方法，当hash一个关键字的时候，发现没有冲突，
就保存关键字，如果有冲突，就探测冲突地址的下一个地址，如此循环，知道有空地址为止，从而解决冲突

package java.lang;

public class ThreadLocal<T> {

    // 每当创建ThreadLocal是此值增加0x61c88647，
    // 是为了能让哈希码均匀的分布在2的n次方的数组里
    private final int threadLocalHashCode = nextHashCode();

    private static AtomicInteger nextHashCode =
        new AtomicInteger();

    private static final int HASH_INCREMENT = 0x61c88647;

    private static int nextHashCode() {
        return nextHashCode.getAndAdd(HASH_INCREMENT);
    }

    // 初始值为null
    protected T initialValue() {
        return null;
    }

    // 用java8的语法包装了下，有参构造器
    public static <S> ThreadLocal<S> withInitial(Supplier<? extends S> supplier) {
        return new SuppliedThreadLocal<>(supplier);
    }
    // 无参构造器
    public ThreadLocal() {
    }

    // 取值
    public T get() {
        // 获取当前线程
        Thread t = Thread.currentThread();
        // 获取ThreadLocal的内部map，ThreadLocal的值是存在内部map中的
        ThreadLocalMap map = getMap(t);
        if (map != null) {
            // ThreadLocalMap内部是一个entry[]数组
            ThreadLocalMap.Entry e = map.getEntry(this);
            if (e != null) {
                @SuppressWarnings("unchecked")
                T result = (T)e.value;
                return result;
            }
        }
        // 如果没有map把当前线程对象加入map中
        return setInitialValue();
    }

    
    private T setInitialValue() {
        T value = initialValue();
        Thread t = Thread.currentThread();
        ThreadLocalMap map = getMap(t);
        if (map != null)
            map.set(this, value);
        else
            // 没有map创建一个map
            createMap(t, value);
        return value;
    }

    // 往ThreadLocal里加值
    public void set(T value) {
        Thread t = Thread.currentThread();
        ThreadLocalMap map = getMap(t);
        if (map != null)
            map.set(this, value);
        else
            createMap(t, value);
    }

    
     public void remove() {
         ThreadLocalMap m = getMap(Thread.currentThread());
         if (m != null)
             m.remove(this);
     }

    // 此方法在InheritableThreadLocal有被重写
    ThreadLocalMap getMap(Thread t) {
        return t.threadLocals;
    }

    void createMap(Thread t, T firstValue) {
        t.threadLocals = new ThreadLocalMap(this, firstValue);
    }

    static ThreadLocalMap createInheritedMap(ThreadLocalMap parentMap) {
        return new ThreadLocalMap(parentMap);
    }
    
    T childValue(T parentValue) {
        throw new UnsupportedOperationException();
    }

    static final class SuppliedThreadLocal<T> extends ThreadLocal<T> {

        private final Supplier<? extends T> supplier;

        SuppliedThreadLocal(Supplier<? extends T> supplier) {
            this.supplier = Objects.requireNonNull(supplier);
        }

        @Override
        protected T initialValue() {
            return supplier.get();
        }
    }

    static class ThreadLocalMap {

        static class Entry extends WeakReference<ThreadLocal<?>> {
           
            Object value;
            // k是一个ThreadLocal对象
            Entry(ThreadLocal<?> k, Object v) {
                super(k);
                value = v;
            }
        }

        // 初始容量16，必须是2的n次方
        private static final int INITIAL_CAPACITY = 16;

        // Entry数组，大小必须是2的n次方
        private Entry[] table;

        // entry在数组中的下标
        private int size = 0;

        // 扩容时的阈值
        private int threshold; // Default to 0

        // 扩容时的阈值设为总长度的2/3
        private void setThreshold(int len) {
            threshold = len * 2 / 3;
        }

        // 下一个索引
        private static int nextIndex(int i, int len) {
            return ((i + 1 < len) ? i + 1 : 0);
        }

        // 上一个索引
        private static int prevIndex(int i, int len) {
            return ((i - 1 >= 0) ? i - 1 : len - 1);
        }

        // cap是2^n，所以cap-1的二进制就是低位连续的n个1，
        // threadLocalHashCode & (INITIAL_CAPACITY - 1)的值就是threadLocalHashCode的低n位
        ThreadLocalMap(ThreadLocal<?> firstKey, Object firstValue) {
            table = new Entry[INITIAL_CAPACITY];
            // 通过散列哈希确认存放的小标，此散列能保存map的值均匀的分布在2^n的数组里
            // 因此数组的cap也必须是2^n
            int i = firstKey.threadLocalHashCode & (INITIAL_CAPACITY - 1);
            table[i] = new Entry(firstKey, firstValue);
            size = 1;
            setThreshold(INITIAL_CAPACITY);
        }

        // 从父ThreadMap中copy一份到子map
        private ThreadLocalMap(ThreadLocalMap parentMap) {
            Entry[] parentTable = parentMap.table;
            int len = parentTable.length;
            setThreshold(len);
            table = new Entry[len];

            for (int j = 0; j < len; j++) {
                Entry e = parentTable[j];
                if (e != null) {
                    @SuppressWarnings("unchecked")
                    ThreadLocal<Object> key = (ThreadLocal<Object>) e.get();
                    if (key != null) {
                        Object value = key.childValue(e.value);
                        Entry c = new Entry(key, value);
                        int h = key.threadLocalHashCode & (len - 1);
                        while (table[h] != null)
                            h = nextIndex(h, len);
                        table[h] = c;
                        size++;
                    }
                }
            }
        }

        // 取值
        private Entry getEntry(ThreadLocal<?> key) {
            // 首先通过散列确定值在table中的下标
            int i = key.threadLocalHashCode & (table.length - 1);
            Entry e = table[i];
            if (e != null && e.get() == key)
                return e;
            else
                // 异常情况，通过entry没获取到值
                return getEntryAfterMiss(key, i, e);
        }

        // Miss entry单独处理
        private Entry getEntryAfterMiss(ThreadLocal<?> key, int i, Entry e) {
            Entry[] tab = table;
            int len = tab.length;

            // entry不为null
            while (e != null) {
                ThreadLocal<?> k = e.get();
                // 存在
                if (k == key)
                    return e;
                // 说明当前threadLocal已被回收，对应的entry应该被清除
                if (k == null)
                    // 具体清除逻辑
                    expungeStaleEntry(i);
                else
                    // 开放寻址法，也叫线性探测，闭散列，entry[]逻辑上是一个环形
                    // 找到下一个数组索引取出entry循环遍历
                    i = nextIndex(i, len);
                e = tab[i];
            }
            // entry都为null了，当然值为null
            return null;
        }

        // 设值
        private void set(ThreadLocal<?> key, Object value) {

            

            Entry[] tab = table;
            int len = tab.length;
            int i = key.threadLocalHashCode & (len-1);
            // 获取当前entry
            for (Entry e = tab[i];
                 e != null;
                 // 不为null继续获取下个entry
                 e = tab[i = nextIndex(i, len)]) {
                ThreadLocal<?> k = e.get();
                // 如果已有key并和要设置的key相等，覆盖value退出
                if (k == key) {
                    e.value = value;
                    return;
                }
                // 如果当前坐标系的k为null，说明该ThreadLocal已被回收，调用替换方法
                if (k == null) {
                    replaceStaleEntry(key, value, i);
                    return;
                }
            }

            tab[i] = new Entry(key, value);
            int sz = ++size;
            // 如果没有清除数据但是size大于阈值，通过重新hash、扩容来清除非法数据
            if (!cleanSomeSlots(i, sz) && sz >= threshold)
                rehash();
        }

        // 删除值
        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) {
                    // 清除软引用
                    e.clear();
                    // 清除entry
                    expungeStaleEntry(i);
                    return;
                }
            }
        }

        // 
        private void replaceStaleEntry(ThreadLocal<?> key, Object value,
                                       int staleSlot) {
            Entry[] tab = table;
            int len = tab.length;
            Entry e;

            // 向前遍历，如果已被gc回收，slotToExpunge标记为当前位置
            int slotToExpunge = staleSlot;
            for (int i = prevIndex(staleSlot, len);
                 (e = tab[i]) != null;
                 i = prevIndex(i, len))
                if (e.get() == null)
                    slotToExpunge = i;

            // 向后遍历查找是否已有要存的这个元素
            for (int i = nextIndex(staleSlot, len);
                 (e = tab[i]) != null;
                 i = nextIndex(i, len)) {
                ThreadLocal<?> k = e.get();

                // 如果有，替换value，更新下标
                if (k == key) {
                    e.value = value;

                    tab[i] = tab[staleSlot];
                    tab[staleSlot] = e;

                    if (slotToExpunge == staleSlot)
                        slotToExpunge = i;
                    // 向前继续清除null元素
                    cleanSomeSlots(expungeStaleEntry(slotToExpunge), len);
                    return;
                }

                // k为null，说明当前位置就是需要开始清理的位置
                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);

            // 不等，说明有还有被gc过的元素，继续清除
            if (slotToExpunge != staleSlot)
                cleanSomeSlots(expungeStaleEntry(slotToExpunge), len);
        }

        // 通过下标清理
        private int expungeStaleEntry(int staleSlot) {
            Entry[] tab = table;
            int len = tab.length;

            // 当前下标的entry value置空，entry指控，size -1
            tab[staleSlot].value = null;
            tab[staleSlot] = null;
            size--;

            // 重新hash
            Entry e;
            int i;
            for (i = nextIndex(staleSlot, len);
                 (e = tab[i]) != null;
                 i = nextIndex(i, len)) {
                ThreadLocal<?> k = e.get();
                if (k == null) {
                    // 为null的置空同上
                    e.value = null;
                    tab[i] = null;
                    size--;
                } else {
                    int h = k.threadLocalHashCode & (len - 1);
                    // 如果h不是当前位置i，则往后遍历找到空节点，把当前entry索引过去
                    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;
        }

        // 从i开始往后清理无效entry，n为扫描次数
        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];
                if (e != null && e.get() == null) {
                    // 发现无效数据就扩大清理范围
                    n = len;
                    removed = true;
                    i = expungeStaleEntry(i);
                }
            } while ( (n >>>= 1) != 0);
            return removed;
        }

        // 重新hash散列
        private void rehash() {
            // 先清理一遍无效数据
            expungeStaleEntries();

            // threshold = 2/3 len, - threshold/4 = len/2
            if (size >= threshold - threshold / 4)
                // 所以是超过len/2，就扩容
                resize();
        }

        // 扩容，双倍
        private void resize() {
            Entry[] oldTab = table;
            int oldLen = oldTab.length;
            // 双倍
            int newLen = oldLen * 2;
            Entry[] newTab = new Entry[newLen];
            int count = 0;

            for (int j = 0; j < oldLen; ++j) {
                Entry e = oldTab[j];
                if (e != null) {
                    ThreadLocal<?> k = e.get();
                    if (k == null) {
                        // 二次清理
                        e.value = null; // Help the GC
                    } else {
                        int h = k.threadLocalHashCode & (newLen - 1);
                        // 如果新表不为空，往后寻址为空的插入
                        while (newTab[h] != null)
                            h = nextIndex(h, newLen);
                        newTab[h] = e;
                        count++;
                    }
                }
            }
            // 设置阈值为之前的两倍
            setThreshold(newLen);
            size = count;
            table = newTab;
        }

        // 全局清除无效数据
        private void expungeStaleEntries() {
            Entry[] tab = table;
            int len = tab.length;
            for (int j = 0; j < len; j++) {
                Entry e = tab[j];
                // 满足条件
                if (e != null && e.get() == null)
                    expungeStaleEntry(j);
            }
        }
    }
}