最近挺有意思的，面试了好几个人后聊到了 LruCache缓存机制。
部分人还是对这个不太了解，今天就结合源码，简单的分享一下这个缓存机制的核心原理。
概念
缓存算法为LRU（Least Recently Used），最近最少使用算法。核心思想是当缓存满时，会优先淘汰那些近期最少使用的缓存对象。
LruCache
LruCache位于android.util.下，在support也有一份。
package android.util;
import java.util.LinkedHashMap;
import java.util.Map;
public class LruCache {
//核心就在这里了
private final LinkedHashMap map;
private int size;
private int maxSize;
private int putCount;
private int createCount;
private int evictionCount;
private int hitCount;
private int missCount;
/**
* @param maxSize for caches that do not override {@link #sizeOf}, this is
* the maximum number of entries in the cache. For all other caches,
* this is the maximum sum of the sizes of the entries in this cache.
*/
public LruCache(int maxSize) {
if (maxSize <= 0) {
throw new IllegalArgumentException("maxSize <= 0");
}
this.maxSize = maxSize;
this.map = new LinkedHashMap(0, 0.75f, true);
}
/**
* Sets the size of the cache.
*
* @param maxSize The new maximum size.
*/
public void resize(int maxSize) {
if (maxSize <= 0) {
throw new IllegalArgumentException("maxSize <= 0");
}
synchronized (this) {
this.maxSize = maxSize;
}
trimToSize(maxSize);
}
/**
* Returns the value for {@code key} if it exists in the cache or can be
* created by {@code #create}. If a value was returned, it is moved to the
* head of the queue. This returns null if a value is not cached and cannot
* be created.
*/
public final V get(K key) {
if (key == null) {
throw new NullPointerException("key == null");
}
V mapValue;
synchronized (this) {
mapValue = map.get(key);
if (mapValue != null) {
hitCount++;
return mapValue;
}
missCount++;
}
/*
* Attempt to create a value. This may take a long time, and the map
* may be different when create() returns. If a conflicting value was
* added to the map while create() was working, we leave that value in
* the map and release the created value.
*/
V createdValue = create(key);
if (createdValue == null) {
return null;
}
synchronized (this) {
createCount++;
mapValue = map.put(key, createdValue);
if (mapValue != null) {
// There was a conflict so undo that last put
map.put(key, mapValue);
} else {
size += safeSizeOf(key, createdValue);
}
}
if (mapValue != null) {
entryRemoved(false, key, createdValue, mapValue);
return mapValue;
} else {
trimToSize(maxSize);
return createdValue;
}
}
//...
}
核心就在于其内部实现了一个LinkedHashMap并且，其实现这个类的构造方法是
this.map = new LinkedHashMap(0, 0.75f, true);
LinkedHashMap
关于LinkedHashMap，先提两点：
1、LinkedHashMap可以认为是HashMap+LinkedList，即它既使用HashMap操作数据结构，又使用LinkedList维护插入元素的先后顺序。
2、LinkedHashMap的基本实现思想就是----多态。可以说，理解多态，再去理解LinkedHashMap原理会事半功倍；反之也是，对于LinkedHashMap原理的学习，也可以促进和加深对于多态的理解。
可以找到LinkedHashMap这个三个参数的构造方法
/**
* Constructs an empty LinkedHashMap instance with the
* specified initial capacity, load factor and ordering mode.
*
* @param initialCapacity the initial capacity
* @param loadFactor the load factor
* @param accessOrder the ordering mode - true for
* access-order, false for insertion-order
* @throws IllegalArgumentException if the initial capacity is negative
* or the load factor is nonpositive
*/
public LinkedHashMap(int initialCapacity,
float loadFactor,
boolean accessOrder) {
super(initialCapacity, loadFactor);
this.accessOrder = accessOrder;
}
其中accessOrder = true，这个参数具体有什么用？
核心原理
LinkedHashMap重写了父类HashMap的get方法，实际在调用父类getEntry()方法取得查找的元素后，再判断当排序模式accessOrder为true时（即按访问顺序排序），先将当前节点从链表中移除，然后再将当前节点插入到链表尾部。由于的链表的增加、删除操作是常量级的，故并不会带来性能的损失。
/**
* 通过key获取value，与HashMap的区别是：当LinkedHashMap按访问顺序排序的时候，会将访问的当前节点移到链表尾部(头结点的前一个节点)
*/
public V get(Object key) {
// 调用父类HashMap的getEntry()方法，取得要查找的元素。
Entry e = (Entry)getEntry(key);
if (e == null)
return null;
// 记录访问顺序。
e.recordAccess(this);
return e.value;
}
/**
* 在HashMap的put和get方法中，会调用该方法，在HashMap中该方法为空
* 在LinkedHashMap中，当按访问顺序排序时，该方法会将当前节点插入到链表尾部(头结点的前一个节点)，否则不做任何事
*/
void recordAccess(HashMap m) {
LinkedHashMap lm = (LinkedHashMap)m;
//当LinkedHashMap按访问排序时
if (lm.accessOrder) {
lm.modCount++;
//移除当前节点
remove();
//将当前节点插入到头结点前面
addBefore(lm.header);
}
}
/**
* 移除节点，并修改前后引用
*/
private void remove() {
before.after = after;
after.before = before;
}
private void addBefore(Entry existingEntry) {
after = existingEntry;
before = existingEntry.before;
before.after = this;
after.before = this;
}
我们可以看到看到每次recordAccess的时候做了两件事情：
- 把待移动的Entry的前后Entry相连
- 把待移动的Entry移动到尾部
小结
LruCache内部维护了一个有序的LinkedHashMap，当每次使用其实的对象的时候，就将这个对象移除，然后将这个对象移动到链表的尾部。当每次去 get 或者 put 的时候都会调用trimToSize(maxSize)这个方法。
/**
* Remove the eldest entries until the total of remaining entries is at or
* below the requested size.
*
* @param maxSize the maximum size of the cache before returning. May be -1
* to evict even 0-sized elements.
*/
public void trimToSize(int maxSize) {
while (true) {
K key;
V value;
synchronized (this) {
if (size < 0 || (map.isEmpty() && size != 0)) {
throw new IllegalStateException(getClass().getName()
+ ".sizeOf() is reporting inconsistent results!");
}
if (size <= maxSize) {
break;
}
Map.Entry toEvict = map.eldest();
if (toEvict == null) {
break;
}
key = toEvict.getKey();
value = toEvict.getValue();
map.remove(key);
size -= safeSizeOf(key, value);
evictionCount++;
}
entryRemoved(true, key, value, null);
}
}
里面有一个 while (true)的循环，如果内存不足的情况下回直接删除位于队首的数据。只有当前缓存容量小于maxSize的时候才break。
所以这样就达到了LRU 算法和 LruCache。