Universal-Image-Loader的内存缓存策略
1. 只使用的是强引用缓存
- LruMemoryCache（这个类就是这个开源框架默认的内存缓存类，缓存的是bitmap的强引用）
2.使用强引用和弱引用相结合的缓存有
- UsingFreqLimitedMemoryCache（如果缓存的图片总量超过限定值，先删除使用频率最小的bitmap）
- LRULimitedMemoryCache（这个也是使用的lru算法，和LruMemoryCache不同的是，他缓存的是bitmap的弱引用）
- FIFOLimitedMemoryCache（先进先出的缓存策略，当超过设定值，先删除最先加入缓存的bitmap）
- LargestLimitedMemoryCache(当超过缓存限定值，先删除最大的bitmap对象)
- LimitedAgeMemoryCache（当 bitmap加入缓存中的时间超过我们设定的值，将其删除）
3.只使用弱引用缓存
- WeakMemoryCache（这个类缓存bitmap的总大小没有限制，唯一不足的地方就是不稳定，缓存的图片容易被回收掉）
继承关系：
public class UsingFreqLimitedMemoryCache extends LimitedMemoryCache public abstract class LimitedMemoryCache extends BaseMemoryCache public abstract class BaseMemoryCache implements MemoryCache public interface MemoryCache extends MemoryCacheAware<String, Bitmap> @Deprecated public interface MemoryCacheAware<K, V>
1、先来看MemoryCacheAware：
/* Interface for memory cache*/
@Deprecated
public interface MemoryCacheAware<K, V> {
/*Puts value into cache by key
* @return true - if value was put into cache successfully;false - if value was not put into cache
*/
boolean put(K key, V value);
/** Returns value by key. If there is no value for key then null will be returned. */
V get(K key);
/** Removes item by key */
V remove(K key);
/** Returns all keys of cache */
Collection<K> keys();
/** Remove all items from cache */
void clear();
}
2、现在一般使用MemoryCache替代MemoryCacheAware：
MemoryCacheAware源码：
public interface MemoryCache extends MemoryCacheAware<String, Bitmap> {
}
3、BaseMemoryCache 源码：
/*为memory cache提供一些基本功能；提供object的引用（非强引用）存储*/
public abstract class BaseMemoryCache implements MemoryCache {
/** 存储objects的非强引用，Collections.synchronizedMap保证线程安全*/
private final Map<String, Reference<Bitmap>> softMap = Collections.synchronizedMap(new HashMap<String, Reference<Bitmap>>());
@Override
public Bitmap get(String key) {
Bitmap result = null;
Reference<Bitmap> reference = softMap.get(key);
if (reference != null) {
result = reference.get();
}
return result;
}
@Override
public boolean put(String key, Bitmap value) {
softMap.put(key, createReference(value));
return true;
}
@Override
public Bitmap remove(String key) {
Reference<Bitmap> bmpRef = softMap.remove(key);
return bmpRef == null ? null : bmpRef.get();
}
@Override
public Collection<String> keys() {
synchronized (softMap) {
return new HashSet<String>(softMap.keySet());
}
}
@Override
public void clear() {
softMap.clear();
}
/** Creates {@linkplain Reference not strong} reference of value */
protected abstract Reference<Bitmap> createReference(Bitmap value);
}
4、LimitedMemoryCache源码：
/**
* 限定的Cache.提供Object的存储。所有存储的bitmap的总内存大小不超过限定值
* 注：该cache使用强引用和弱引用来存储Bitmaps;
* 强引用——对于限额内的bitmaps
* 弱应用——对于其他的Bitmaps
*/
public abstract class LimitedMemoryCache extends BaseMemoryCache {
private static final int MAX_NORMAL_CACHE_SIZE_IN_MB = 16;
private static final int MAX_NORMAL_CACHE_SIZE = MAX_NORMAL_CACHE_SIZE_IN_MB * 1024 * 1024;
private final int sizeLimit;
/*AtomicInteger，一个提供原子操作的Integer的类,使得操作线程安全*/
private final AtomicInteger cacheSize;
/**
* 包含存储objects的强引用。每个object都添加到最尾端；如果hard cache的大超过了限定值，首端的object将会被删除
*（但它依然存在在softMap中，而且可以随时被GC回收）
// 返回一个synchronizes封装的线程安全的List
public static <T> List<T> synchronizedList(List<T> list)*/
private final List<Bitmap> hardCache = Collections.synchronizedList(new LinkedList<Bitmap>());
/**构造函数：sizeLimit单位bytes */
public LimitedMemoryCache(int sizeLimit) {
this.sizeLimit = sizeLimit;
cacheSize = new AtomicInteger();
/**检测避免sizeLimit值设置过大 */
if (sizeLimit > MAX_NORMAL_CACHE_SIZE) {
L.w("You set too large memory cache size (more than %1$d Mb)", MAX_NORMAL_CACHE_SIZE_IN_MB);
}
}
/**操作成功返回true，操作失败返回false
先尝试将Bitmap添加至hard cache，再将其添加至soft cache*/
@Override
public boolean put(String key, Bitmap value) {
boolean putSuccessfully = false;
int valueSize = getSize(value); //抽象函数，返回Bitmap的大小值
int sizeLimit = getSizeLimit(); //返回sizeLimit
int curCacheSize = cacheSize.get();//返回cacheSize的当前值
/**如果添加的bitmap的size大于sizeLimit，则直接不将其添加至hard cache*/
if (valueSize < sizeLimit) {
/**判断新添加的Object的valueSize加上当前cache中已有object的curCacheSize超过限定值，则会删除适当Bitmap*/
while (curCacheSize + valueSize > sizeLimit) {
Bitmap removedValue = removeNext(); //abstract函数,返回需要删除的下一个Bitmap
if (hardCache.remove(removedValue)) {
curCacheSize = cacheSize.addAndGet(-getSize(removedValue));
}
}
hardCache.add(value); //添加到LinkedList<Bitmap>尾部
cacheSize.addAndGet(valueSize);//即cacheSize+valueSize
putSuccessfully = true;
}
// Add value to soft cache
super.put(key, value);
return putSuccessfully;
}
@Override
public Bitmap remove(String key) {
Bitmap value = super.get(key);
if (value != null) {
if (hardCache.remove(value)) {
cacheSize.addAndGet(-getSize(value));
}
}
return super.remove(key);
}
@Override
publicvoid clear() {
hardCache.clear();
cacheSize.set(0);
super.clear();
}
protected int getSizeLimit() {
return sizeLimit;
}
protected abstract int getSize(Bitmap value);
protected abstract Bitmap removeNext();
}
5、UsingFreqLimitedMemoryCache源码：（关键在于get中更新使用频率）
/**
* Limited {@link Bitmap bitmap} cache. Provides {@link Bitmap bitmaps} storing. Size of all stored bitmaps will not to
* exceed size limit. When cache reaches limit size then the bitmap which used the least frequently is deleted from
* cache（删除最少使用频率的bitmap）
* NOTE:使用强引用和弱引用；
* 强引用：for limited count of Bitmaps (depends on cache size)
* 弱应用：for all other cached Bitmaps
*/
public class UsingFreqLimitedMemoryCache extends LimitedMemoryCache {
/**
* Contains strong references to stored objects (keys) and last object usage date (in milliseconds). If hard cache
* size will exceed limit then object with the least frequently usage is deleted (but it continue exist at
* {@link #softMap} and can be collected by GC at any time)
*/
private final Map<Bitmap, Integer> usingCounts = Collections.synchronizedMap(new HashMap<Bitmap, Integer>());
public UsingFreqLimitedMemoryCache(int sizeLimit) {
super(sizeLimit);
}
@Override
public boolean put(String key, Bitmap value) {
if (super.put(key, value)) {
usingCounts.put(value, 0);
return true;
} else {
return false;
}
}
@Override
public Bitmap get(String key) {
Bitmap value = super.get(key);
// 如果value存在，则usageCount+ 1;
if (value != null) {
Integer usageCount = usingCounts.get(value);
if (usageCount != null) {
// 更新value对应的usageCount的值
usingCounts.put(value, usageCount + 1);
}
}
return value;
}
@Override
public Bitmap remove(String key) {
Bitmap value = super.get(key);
if (value != null) {
usingCounts.remove(value);
}
return super.remove(key);
}
@Override
public void clear() {
usingCounts.clear();
super.clear();
}
@Override
protected int getSize(Bitmap value) {
return value.getRowBytes() * value.getHeight();
}
@Override
protected Bitmap removeNext() {
Integer minUsageCount = null;
Bitmap leastUsedValue = null;
Set<Entry<Bitmap, Integer>> entries = usingCounts.entrySet();
synchronized (usingCounts) {
for (Entry<Bitmap, Integer> entry : entries) {
//初始化leastUsedValue
if (leastUsedValue == null) {
leastUsedValue = entry.getKey();
minUsageCount = entry.getValue();
} else {
//简单的比较得到最小值
Integer lastValueUsage = entry.getValue();
if (lastValueUsage < minUsageCount) {
minUsageCount = lastValueUsage;
leastUsedValue = entry.getKey();
}
}
}
}
usingCounts.remove(leastUsedValue);
return leastUsedValue;
}
@Override
protected Reference<Bitmap> createReference(Bitmap value) {
return new WeakReference<Bitmap>(value);
}
}
时间： 2024-12-17 03:54:27