性能测试
ThreadLocal一般在多线程环境用来保存当前线程的数据。用户可以很方便地使用,并且不关心、不感知多线程的问题。下面我会用两个场景来展示多线程的问题:
- 多个线程同时操作一个ThreadLocal
- 一个线程操作多个ThreadLocal
1. 多个线程同时操作一个ThreadLocal
测试代码分别用于ThreadLocal和FastThreadLocal。 代码如下:
package com.github.shoothzj.demo.netty; import lombok.extern.slf4j.Slf4j; import org.junit.Test; import java.util.concurrent.CountDownLatch; /** * @author hezhangjian */ @Slf4j public class ThreadLocalTest { @Test public void testThreadLocal() throws Exception { CountDownLatch cdl = new CountDownLatch(10000); ThreadLocal
threadLocal = new ThreadLocal (); long starTime = System.currentTimeMillis(); for (int i = 0; i < 10000; i++) { new Thread(new Runnable() { @Override public void run() { threadLocal.set(Thread.currentThread().getName()); for (int k = 0; k < 100000; k++) { threadLocal.get(); } cdl.countDown(); } }, "Thread" + (i + 1)).start(); } cdl.await(); System.out.println(System.currentTimeMillis() - starTime + "ms"); } }
上述的代码创建了一万个线程,并将线程名设置在ThreadLocal中,随后获取这个值十万次,然后通过CountDownLoatch计算总耗时。运行这个程序大概耗时1000ms。
接下来,测试FastThreadLocal,代码基本上相似:
package com.github.shoothzj.demo.netty; import io.netty.util.concurrent.FastThreadLocal; import io.netty.util.concurrent.FastThreadLocalThread; import lombok.extern.slf4j.Slf4j; import org.junit.Test; import java.util.concurrent.CountDownLatch; /** * @author hezhangjian */ @Slf4j public class FastThreadLocalTest { @Test public void testFastThreadLocal() throws Exception { CountDownLatch cdl = new CountDownLatch(10000); FastThreadLocal
threadLocal = new FastThreadLocal (); long starTime = System.currentTimeMillis(); for (int i = 0; i < 10000; i++) { new FastThreadLocalThread(new Runnable() { @Override public void run() { threadLocal.set(Thread.currentThread().getName()); for (int k = 0; k < 100000; k++) { threadLocal.get(); } cdl.countDown(); } }, "Thread" + (i + 1)).start(); } cdl.await(); System.out.println(System.currentTimeMillis() - starTime); } }
跑完之后,用时还是差不多1000ms。这证明了两者在这个场景下没有什么差别
2. 单个线程操作多个ThreadLocal
先看ThreadLocal的:
package com.github.shoothzj.demo.netty; import lombok.extern.slf4j.Slf4j; import org.junit.Test; import java.util.concurrent.CountDownLatch; /** * @author hezhangjian */ @Slf4j public class ThreadLocalSingleThreadTest { @Test public void testThreadLocal() throws Exception { CountDownLatch cdl = new CountDownLatch(1); int size = 10000; ThreadLocal
tls[] = new ThreadLocal[size]; for (int i = 0; i < size; i++) { tls[i] = new ThreadLocal (); } new Thread(new Runnable() { @Override public void run() { long starTime = System.currentTimeMillis(); for (int i = 0; i < size; i++) { tls[i].set("value" + i); } for (int i = 0; i < size; i++) { for (int k = 0; k < 100000; k++) { tls[i].get(); } } System.out.println(System.currentTimeMillis() - starTime + "ms"); cdl.countDown(); } }).start(); cdl.await(); } }
上述的代码创建了一万个ThreadLocal,然后设置一个值,随后获取十万次数值,大概耗时2000ms
接下来我们测试FastThreadLocal
public static void test1() { int size = 10000; FastThreadLocal
tls[] = new FastThreadLocal[size]; for (int i = 0; i < size; i++) { tls[i] = new FastThreadLocal (); } new FastThreadLocalThread(new Runnable() { @Override public void run() { long starTime = System.currentTimeMillis(); for (int i = 0; i < size; i++) { tls[i].set("value" + i); } for (int i = 0; i < size; i++) { for (int k = 0; k < 100000; k++) { tls[i].get(); } } System.out.println(System.currentTimeMillis() - starTime + "ms"); } }).start(); }
运行结果大概只有30ms; 可以发现存在了数量级的差距。接下来重点分析ThreadLocal的机制和FastThreadLocal为什么比ThreadLocal快
ThreadLocal机制
我们经常会使用到set和get方法,我们分别查看一下源代码:
public void set(T value) { Thread t = Thread.currentThread(); ThreadLocalMap map = getMap(t); if (map != null) map.set(this, value); else createMap(t, value); } ThreadLocalMap getMap(Thread t) { return t.threadLocals; }
首先,获取当前的线程,然后获取存储在当前线程中的ThreadLocal变量。变量其实是一个ThreadLocalMap。最后,查看ThreadLocalMap是否为空,如果为空,则创建一个新的空Map,如果key不为空,则以ThreadLocal为key,存储这个数据
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(); }
一般来说,ThreadLocal Map使用数组来存储数据,类似于HashMap。 每个ThreadLocal在初始化时都会分配一个threadLocal HashCode,然后按照数组的长度执行模块化操作,因此会发生哈希冲突。 在HashMap中,使用数组+链表来处理冲突,而在ThreadLocal Map中,也是一样的。 Next索引用于执行遍历操作,这显然具有较差的性能。 让我们再次看一下get方法。
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(); }
同样,首先获取当前线程,然后获取当前线程中的ThreadLocal映射,然后以当前ThreadLocal作为键来获取ThreadLocal映射中的值:
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); } 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; }
在相同的设置模式下,数组下标通过模块化获取来获取,否则,如果没有冲突,将遍历数据,因此可以通过分析大致了解以下问题:
- ThreadLocal Map是存储在Thread下的,ThreadLocal是键,因此多个线程在同一个ThreadLocal上进行操作实际上是在每个ThreadLocal Map线程中插入的一条记录,没有冲突问题;
- ThreadLocalMap在解决冲突时会通过遍历极大地影响性能。
- FastThreadLocal通过其他方式解决冲突以优化性能
让我们继续看看FastThreadLocal如何实现性能优化
译者说:为什么set的时候不适用fastPath(),因为往往大家使用完ThreadLocal都会remove,这个时候,经常是createEntry,而非updateEntry
为什么Netty的FastThreadLocal这么快
Netty分别提供了两类FastThreadLocal和FastThreadLocalThread。 FastThreadLocalThread继承自Thread。 以下也是常用的set和get方法的源代码分析:
public final void set(V value) { if (value != InternalThreadLocalMap.UNSET) { set(InternalThreadLocalMap.get(), value); } else { remove(); } } public final void set(InternalThreadLocalMap threadLocalMap, V value) { if (value != InternalThreadLocalMap.UNSET) { if (threadLocalMap.setIndexedVariable(index, value)) { addToVariablesToRemove(threadLocalMap, this); } } else { remove(threadLocalMap); } }
首先,将值确定为Internal ThreadLocalMap。 UNSET,然后内部ThreadLocalMap也用于存储数据:
public static InternalThreadLocalMap get() { Thread thread = Thread.currentThread(); if (thread instanceof FastThreadLocalThread) { return fastGet((FastThreadLocalThread) thread); } else { return slowGet(); } } private static InternalThreadLocalMap fastGet(FastThreadLocalThread thread) { InternalThreadLocalMap threadLocalMap = thread.threadLocalMap(); if (threadLocalMap == null) { thread.setThreadLocalMap(threadLocalMap = new InternalThreadLocalMap()); } return threadLocalMap; }
可以发现内部ThreadLocal映射也存储在FastThreadLocalThread中。 不同之处在于,它直接使用FastThreadLocal的index属性,而不是使用ThreadLocal的相应哈希值对位置进行建模。 实例化时初始化索引:
private final int index; public FastThreadLocal() { index = InternalThreadLocalMap.nextVariableIndex(); }
Then enter the nextVariableIndex method:
static final AtomicInteger nextIndex = new AtomicInteger(); public static int nextVariableIndex() { int index = nextIndex.getAndIncrement(); if (index < 0) { nextIndex.decrementAndGet(); throw new IllegalStateException("too many thread-local indexed variables"); } return index; }
内部ThreadLocal映射中有一个静态nextIndex对象,用于生成数组下标,因为它是静态的,所以每个FastThreadLocal生成的索引都是连续的。 让我们看看如何在内部ThreadLocal映射中设置索引变量:
public boolean setIndexedVariable(int index, Object value) { Object[] lookup = indexedVariables; if (index < lookup.length) { Object oldValue = lookup[index]; lookup[index] = value; return oldValue == UNSET; } else { expandIndexedVariableTableAndSet(index, value); return true; } }
索引变量是存储值s的对象数组; 直接使用index作为数组下标进行存储; 如果index大于数组的长度,则将其展开; get方法通过FastThreadLocal中的索引快速读取:
public final V get(InternalThreadLocalMap threadLocalMap) { Object v = threadLocalMap.indexedVariable(index); if (v != InternalThreadLocalMap.UNSET) { return (V) v; } return initialize(threadLocalMap); } public Object indexedVariable(int index) { Object[] lookup = indexedVariables; return index < lookup.length? lookup[index] : UNSET; }
通过下标直接阅读非常快,这是牺牲空间换来的速度
总结
通过以上分析,我们可以知道,当有很多ThreadLocal读写操作时,我们可能会遇到性能问题; 另外,FastThreadLocal实现了O(1)通过空间读取数据的时间; 还有一个问题,为什么不直接使用HashMap(数组+黑红树林)代替ThreadLocalMap。
原文地址
https://programmer.ink/think/why-netty-s-fastthreadlocal-is-fast.html