LRUCache原理分析
2024-08-25 06:50:37
一.注释
LRUCache的原理，基本都在注释里面描述清楚了。
/**
* A cache that holds strong references to a limited number of values. Each time
* a value is accessed, it is moved to the head of a queue. When a value is
* added to a full cache, the value at the end of that queue is evicted and may
* become eligible for garbage collection.
*
* <p>If your cached values hold resources that need to be explicitly released,
* override {@link #entryRemoved}.
*
* <p>If a cache miss should be computed on demand for the corresponding keys,
* override {@link #create}. This simplifies the calling code, allowing it to
* assume a value will always be returned, even when there's a cache miss.
*
* <p>By default, the cache size is measured in the number of entries. Override
* {@link #sizeOf} to size the cache in different units. For example, this cache
* is limited to 4MiB of bitmaps:
* <pre> {@code
* int cacheSize = 4 * 1024 * 1024; // 4MiB
* LruCache<String, Bitmap> bitmapCache = new LruCache<String, Bitmap>(cacheSize) {
* protected int sizeOf(String key, Bitmap value) {
* return value.getByteCount();
* }
* }}</pre>
*
* <p>This class is thread-safe. Perform multiple cache operations atomically by
* synchronizing on the cache: <pre> {@code
* synchronized (cache) {
* if (cache.get(key) == null) {
* cache.put(key, value);
* }
* }}</pre>
*
* <p>This class does not allow null to be used as a key or value. A return
* value of null from {@link #get}, {@link #put} or {@link #remove} is
* unambiguous: the key was not in the cache.
*
* <p>This class appeared in Android 3.1 (Honeycomb MR1); it's available as part
* of <a href="http://developer.android.com/sdk/compatibility-library.html">Android's
* Support Package</a> for earlier releases.
*/
1.每次一个元素被访问，它会被move到队列的head位置。当某个元素加入到已经full的池里面，最久未使用的一个元素会被delete。
2.如果元素需要做特殊的释放操作，请重载entryRemoved
3.如果某个key值，没有对应的初始值，可以通过create方法提供默认值。
4.创建一个LRUCache需要复写sizeof方法。
5.这个类是线程安全的。
二：源代码分析：
1.变量
private final LinkedHashMap<K, V> map;
/** Size of this cache in units. Not necessarily the number of elements. */
private int size; //count
private int maxSize; //容量
private int putCount;
private int createCount;
private int evictionCount; //delete count
private int hitCount; //命中 count
private int missCount; //未命中 count
2.构造函数
/**
* @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<K, V>(0, 0.75f, true);
}
这段代码最关键的是，new了一个LinkedHashMap，这个hashmap是可以根据访问先后次序，来控制顺序的。
LinkedHashMap：第一个参数表示初始数据是0个。
0.75f 是经典的加载因子的数值。
第三个参数true表示，以访问的先后顺序来排序。也就是做到了，容量满了以后，删除最久未访问的数据。
3.resize
/**
* 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);
}
maxsize就是容量，也就是说，队列的最大长度，当full的时候，最后一个会被delete。
resize 就是调用trimToSize
4.trimToSize
/**
* 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<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);
}
}
最外面的是while true 死循环。 然后开始判读size的值。1）获取最后的一个entry，拿到后，getkey & value。
2）map remove掉它。3）然后是获取这个entry占用的大小（不一定是1）。size 减去这个值。 4）evictionCount++
5）entryRemoved 之前说过，就是可以
5.get
/**
* 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
/**
* 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;
}
}map.put(key, mapValue);
} else {
size += safeSizeOf(key, createdValue);
}
} if (mapValue != null) {
entryRemoved(false, key, createdValue, mapValue);
return mapValue;
} else {
trimToSize(maxSize);
return createdValue;
}
}
1）从map中获取元素，2）没有的话，就创建一个 3）由于存在多线程问题，可能已经好了一个。所以要删除最新创建的这个，或者增加size的值
4）如果create的值不需要，就释放create的值，or trimToSize
6.put
/**
* Caches {@code value} for {@code key}. The value is moved to the head of
* the queue.
*
* @return the previous value mapped by {@code key}.
*/
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;
}
map 存在一个多线程 create，put的问题。所以在put的时候，可能由其他线程已经存在了oldvalue值。所以根据
previous = map.put(key, value);
这个特性，判断previous的值，来确认是否是重复put的问题。这里关键是牵涉到size的大小问题。
其他方法，注释已经写的很清楚，不难理解。
总结：
- LruCache 封装了 LinkedHashMap，提供了 LRU 缓存的功能；
- LruCache 通过 trimToSize 方法自动删除最近最少访问的键值对；
- LruCache 不允许空键值；
- LruCache 线程安全；
- LruCache 的源码在不同版本中不一样，需要区分
- 继承 LruCache 时，必须要复写 sizeOf 方法，用于计算每个条目的大小。
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