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zwllxs:
是你自己编码不规范,和lombok没一点关系,model中,类 ...
lombok生成getter、setter的小陷阱 -
kailee:
博主分析了大半天的没用的。。。吓得我以为啥陷阱一般boolea ...
lombok生成getter、setter的小陷阱 -
maoweiwer:
这代码编译能过?new ServletInputStream( ...
ServletRequest中getReader()和getInputStream()只能调用一次的解决办法 -
xczzmn:
将字段类型boolean换成Boolean就可以了
lombok生成getter、setter的小陷阱 -
xugangwen:
和lombok没关系
lombok生成getter、setter的小陷阱
最近碰到一个使用ThreadLocal时因为未调用remove()而险些引起内存溢出的问题,所以看了下ThreadLocal的源码,结合线程池原理做一个简单的分析,确认是否最终会导致内存溢出。
既然是因为没调用remove()方法而险些导致内存溢出,那首先看下remove()方法中做了什么。
从remove()的实现来看就是一个map.remove()的调用。既然不调用map.remove()可能会引起内存溢出的话,就需要看看ThreadLocalMap的实现了。
首先从声明上来看,ThreadLocalMap并不是一个java.util.Map接口的实现,但是从Entry的实现和整个ThreadLocalMap的实现来看却实现了一个Map的功能,并且从具体的方法的实现上来看,整个ThreadLocalMap实现了一个HashMap的功能,对比HashMap的实现就能看出。
但是,值得注意的是ThreadLocalMap并没有put(K key, V value)方法,而是set(ThreadLocal key, Object value),从这里可以看出,ThreadLocalMap并不是想象那样以Thread为key,而是以ThreadLocal为key。
了解了ThreadLocalMap的实现,也知道ThreadLocal.remove()其实就是ThreadLocalMap.remove(),那么再看看ThreadLocal的set(T value)方法,看看value是如何存储的。
可以看到,set(T value)方法为每个Thread对象都创建了一个ThreadLocalMap,并且将value放入ThreadLocalMap中,ThreadLocalMap作为Thread对象的成员变量保存。那么可以用下图来表示ThreadLocal在存储value时的关系。
所以当ThreadLocal作为单例时,每个Thread对应的ThreadLocalMap中只会有一个键值对。那么如果不调用remove()会怎么样呢?
假设一种场景,使用线程池,线程池中有200个线程,并且这些线程都不会释放,ThreadLocal做单例使用。那么最多也就会产生200个ThreadLocalMap,而每个ThreadLocalMap中只有一个键值对,那最多也就是200个键值对存在。
但是线程池并不是固定一个线程数不改变,下面贴一段tomcat的线程池配置
可以看到线程池其实有线程最小值和最大值的,并且有超时时间,所以当线程空闲时间超时后,线程会被销毁。那么当线程销毁时,线程所持有的ThreadLocalMap也会失去引用,并且由于ThreadLocalMap中的Entry是WeakReference,所以当YGC时,被销毁的Thread所对应的value也会被回收掉,所以即使不调用remove()方法,也不会引起内存溢出。
既然是因为没调用remove()方法而险些导致内存溢出,那首先看下remove()方法中做了什么。
public void remove() { ThreadLocalMap m = getMap(Thread.currentThread()); if (m != null) m.remove(this); }
从remove()的实现来看就是一个map.remove()的调用。既然不调用map.remove()可能会引起内存溢出的话,就需要看看ThreadLocalMap的实现了。
/** * ThreadLocalMap is a customized hash map suitable only for * maintaining thread local values. No operations are exported * outside of the ThreadLocal class. The class is package private to * allow declaration of fields in class Thread. To help deal with * very large and long-lived usages, the hash table entries use * WeakReferences for keys. However, since reference queues are not * used, stale entries are guaranteed to be removed only when * the table starts running out of space. */ static class ThreadLocalMap { /** * The entries in this hash map extend WeakReference, using * its main ref field as the key (which is always a * ThreadLocal object). Note that null keys (i.e. entry.get() * == null) mean that the key is no longer referenced, so the * entry can be expunged from table. Such entries are referred to * as "stale entries" in the code that follows. */ 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 /** * Set the resize threshold to maintain at worst a 2/3 load factor. */ private void setThreshold(int len) { threshold = len * 2 / 3; } /** * 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); } /** * Construct a new map initially containing (firstKey, firstValue). * ThreadLocalMaps are constructed lazily, so we only create * one when we have at least one entry to put in it. */ 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); } /** * Construct a new map including all Inheritable ThreadLocals * from given parent map. Called only by createInheritedMap. * * @param parentMap the map associated with parent thread. */ 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) { ThreadLocal key = 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++; } } } } /** * Get the entry associated with key. This method * itself handles only the fast path: a direct hit of existing * key. It otherwise relays to getEntryAfterMiss. This is * designed to maximize performance for direct hits, in part * by making this method readily inlinable. * * @param key the thread local object * @return the entry associated with key, or null if no such */ 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); } /** * Version of getEntry method for use when key is not found in * its direct hash slot. * * @param key the thread local object * @param i the table index for key's hash code * @param e the entry at table[i] * @return the entry associated with key, or null if no such */ 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; } /** * Set the value associated with key. * * @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(); } /** * 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) { e.clear(); expungeStaleEntry(i); return; } } } /** * 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). 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); } /** * 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 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; } /** * 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: <tt>log2(n)</tt> cells are scanned, * unless a stale entry is found, in which case * <tt>log2(table.length)-1</tt> 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) { 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; } /** * Re-pack and/or re-size the table. First scan the entire * table removing stale entries. If this doesn't sufficiently * shrink the size of the table, double the table size. */ private void rehash() { expungeStaleEntries(); // Use lower threshold for doubling to avoid hysteresis if (size >= threshold - threshold / 4) resize(); } /** * Double the capacity of the table. */ private void resize() { Entry[] oldTab = table; int oldLen = oldTab.length; 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) { e.value = null; // Help the GC } else { int h = k.threadLocalHashCode & (newLen - 1); while (newTab[h] != null) h = nextIndex(h, newLen); newTab[h] = e; count++; } } } setThreshold(newLen); size = count; table = newTab; } /** * Expunge all stale entries in the table. */ private void expungeStaleEntries() { Entry[] tab = table; int len = tab.length; for (int j = 0; j < len; j++) { Entry e = tab[j]; if (e != null && e.get() == null) expungeStaleEntry(j); } } }
首先从声明上来看,ThreadLocalMap并不是一个java.util.Map接口的实现,但是从Entry的实现和整个ThreadLocalMap的实现来看却实现了一个Map的功能,并且从具体的方法的实现上来看,整个ThreadLocalMap实现了一个HashMap的功能,对比HashMap的实现就能看出。
但是,值得注意的是ThreadLocalMap并没有put(K key, V value)方法,而是set(ThreadLocal key, Object value),从这里可以看出,ThreadLocalMap并不是想象那样以Thread为key,而是以ThreadLocal为key。
了解了ThreadLocalMap的实现,也知道ThreadLocal.remove()其实就是ThreadLocalMap.remove(),那么再看看ThreadLocal的set(T value)方法,看看value是如何存储的。
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; } void createMap(Thread t, T firstValue) { t.threadLocals = new ThreadLocalMap(this, firstValue); }
可以看到,set(T value)方法为每个Thread对象都创建了一个ThreadLocalMap,并且将value放入ThreadLocalMap中,ThreadLocalMap作为Thread对象的成员变量保存。那么可以用下图来表示ThreadLocal在存储value时的关系。
所以当ThreadLocal作为单例时,每个Thread对应的ThreadLocalMap中只会有一个键值对。那么如果不调用remove()会怎么样呢?
假设一种场景,使用线程池,线程池中有200个线程,并且这些线程都不会释放,ThreadLocal做单例使用。那么最多也就会产生200个ThreadLocalMap,而每个ThreadLocalMap中只有一个键值对,那最多也就是200个键值对存在。
但是线程池并不是固定一个线程数不改变,下面贴一段tomcat的线程池配置
<Connector executor="tomcatThreadPool" port="8080" protocol="HTTP/1.1" connectionTimeout="60000" keepAliveTimeout="30000" minProcessors="5" maxProcessors="75" maxKeepAliveRequests="150" redirectPort="8443" URIEncoding="UTF-8" acceptCount="1000" disableUploadTimeout="true"/>
可以看到线程池其实有线程最小值和最大值的,并且有超时时间,所以当线程空闲时间超时后,线程会被销毁。那么当线程销毁时,线程所持有的ThreadLocalMap也会失去引用,并且由于ThreadLocalMap中的Entry是WeakReference,所以当YGC时,被销毁的Thread所对应的value也会被回收掉,所以即使不调用remove()方法,也不会引起内存溢出。
评论
1 楼
zhuyucheng123
2014-06-11
对问题的分析很精彩,但是我想问问,像后面配置文件这样解释的话,那么内存是否泄露是不是要依赖与第三方线程池呢?如果第三方线程池刚好没有所谓的“超时时间”的话,就会发生内存泄露了,对吗?
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