理解LruCache
LRU(Least Recently Used)緩存算法，即為最近最少使用算法，它的核心思想是當(dāng)緩存滿時，會優(yōu)先淘汰那些近期最少使用的緩存對象。
LruCache是一個泛型類，它內(nèi)部采用了一個LinkedHashMap以強(qiáng)引用的方式存儲外界的緩存對象，其提供get和put方法來完成緩存的獲取和添加。
下面我們具體看下LruCache.java這個類：
public class LruCache<K, V> {
private final LinkedHashMap<K, V> map;
private int size; // 當(dāng)前大小
private int maxSize; // 最大容量
private int putCount; // put次數(shù)
private int createCount; // 創(chuàng)建次數(shù)
private int evictionCount; // 回收次數(shù)
private int hitCount; // 命中次數(shù)
private int missCount; // 未命中次數(shù)
......
}
可以看到LruCache主要維護(hù)一個LinkedHashMap，主要算法原理是把最近使用的對象引用存儲在 LinkedHashMap 中。
接下來看構(gòu)造函數(shù)：
public LruCache(int maxSize) {
if (maxSize <= 0) {
throw new IllegalArgumentException("maxSize <= 0");
}
this.maxSize = maxSize;
this.map = new LinkedHashMap<K, V>(0, 0.75f, true);
}
主要設(shè)置Cache最大值maxSize，初始化一個LinkedHashMap， accessOrder 設(shè)置為 true 來實(shí)現(xiàn) LRU，利用訪問順序而不是插入順序。
public void resize(int maxSize) {
if (maxSize <= 0) {
throw new IllegalArgumentException("maxSize <= 0");
}
synchronized (this) {
this.maxSize = maxSize;
}
trimToSize(maxSize);
}
重新設(shè)定Cache最大值
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;
}
}
get方法，如果Cache中存在該item，返回item；如果不存在，則創(chuàng)建item。
public final V put(K key, V value) {
if (key == null || value == null) {
throw new NullPointerException("key == null || value == null");
}
V previous;
synchronized (this) {
putCount++;
size += safeSizeOf(key, value);
previous = map.put(key, value);
if (previous != null) {
size -= safeSizeOf(key, previous);
}
}
if (previous != null) {
entryRemoved(false, key, previous, value);
}
trimToSize(maxSize);
return previous;
}
往Cache中put相應(yīng)item。
對于get和put的基本原理，這里引用一篇文章中的圖片說明圖片來源：
LruCache基本原理
這張圖很好地說明了最近最少的思想。
上面幾個方法中都提到了trimToSize：
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<K, V> 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);
}
}
重新計算Cache大小，將最久沒有用到的eldest刪除。
public final V remove(K key) {
if (key == null) {
throw new NullPointerException("key == null");
}
V previous;
synchronized (this) {
previous = map.remove(key);
if (previous != null) {
size -= safeSizeOf(key, previous);
}
}
if (previous != null) {
entryRemoved(false, key, previous, null);
}
return previous;
}
刪除key
protected void entryRemoved(boolean evicted, K key, V oldValue, V newValue) {}
當(dāng) item 被回收或者刪掉時調(diào)用。該方法當(dāng) value 被回收釋放存儲空間時被 remove 調(diào)用，或者替換 item 值時 put 調(diào)用，默認(rèn)實(shí)現(xiàn)什么都沒做，使用時可以根據(jù)需要重寫。
protected V create(K key) {
return null;
}
同樣，可以根據(jù)需要重寫。
private int safeSizeOf(K key, V value) {
int result = sizeOf(key, value);
if (result < 0) {
throw new IllegalStateException("Negative size: " + key + "=" + value);
}
return result;
}
protected int sizeOf(K key, V value) {
return 1;
}
返回一組key/value的entry大小，默認(rèn)為1
public final void evictAll() {
trimToSize(-1); // -1 will evict 0-sized elements
}
清空Cache，trimToSize(-1)。
/**
* For caches that do not override {@link #sizeOf}, this returns the number
* of entries in the cache. For all other caches, this returns the sum of
* the sizes of the entries in this cache.
*/
public synchronized final int size() {
return size;
}
/**
* For caches that do not override {@link #sizeOf}, this returns the maximum
* number of entries in the cache. For all other caches, this returns the
* maximum sum of the sizes of the entries in this cache.
*/
public synchronized final int maxSize() {
return maxSize;
}
/**
* Returns the number of times {@link #get} returned a value that was
* already present in the cache.
*/
public synchronized final int hitCount() {
return hitCount;
}
/**
* Returns the number of times {@link #get} returned null or required a new
* value to be created.
*/
public synchronized final int missCount() {
return missCount;
}
/**
* Returns the number of times {@link #create(Object)} returned a value.
*/
public synchronized final int createCount() {
return createCount;
}
/**
* Returns the number of times {@link #put} was called.
*/
public synchronized final int putCount() {
return putCount;
}
/**
* Returns the number of values that have been evicted.
*/
public synchronized final int evictionCount() {
return evictionCount;
}
/**
* Returns a copy of the current contents of the cache, ordered from least
* recently accessed to most recently accessed.
*/
public synchronized final Map<K, V> snapshot() {
return new LinkedHashMap<K, V>(map);
}
@Override public synchronized final String toString() {
int accesses = hitCount + missCount;
int hitPercent = accesses != 0 ? (100 * hitCount / accesses) : 0;
return String.format("LruCache[maxSize=%d,hits=%d,misses=%d,hitRate=%d%%]",
maxSize, hitCount, missCount, hitPercent);
}
其他一些方法。
