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关于Android内存缓存LruCache的使用及其源码解析

作者:_小马快跑_

LruCache作为内存缓存,使用强引用方式缓存有限个数据,当缓存的某个数据被访问时,它就会被移动到队列的头部,本文详细介绍了关于Android内存缓存LruCache的使用及其源码解析,需要的朋友可以参考下

整体介绍

LruCache 作为内存缓存,使用强引用方式缓存有限个数据,当缓存的某个数据被访问时,它就会被移动到队列的头部,当一个新数据要添加到LruCache而此时缓存大小要满时,队尾的数据就有可能会被垃圾回收器(GC)回收掉,LruCache使用的LRU(Least Recently Used)算法,即:把最近最少使用的数据从队列中移除,把内存分配给最新进入的数据。

 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();
     }
 }

LruCache 这个类是线程安全的。自动地执行多个缓存操作通过synchronized 同步缓存:

 synchronized (cache) {
   if (cache.get(key) == null) {
       cache.put(key, value);
   }
 }

LruCache不允许 null 作为一个 key 或 valueget(K)、put(K,V),remove(K) 中如果key 或 value 为 null,都会抛出异常:throw new NullPointerException("key == null || value == null")

常用API

方法备注
void resize(int maxSize)更新存储大小
V put(K key, V value)存数据,返回之前key对应的value,如果没有,返回null
V get(K key)取出key对应的缓存数据
V remove(K key)移除key对应的value
void evictAll()清空缓存数据
Map<K, V> snapshot()复制一份缓存并返回,顺序从最近最少访问到最多访问排序

使用示例

 private Bitmap bitmap;
 private String STRING_KEY = "data_string";
 private LruCache<String, Bitmap> lruCache;
 private static final int CACHE_SIZE = 10 * 1024 * 1024;//10M
 lruCache = new LruCache<String, Bitmap>(CACHE_SIZE) {
    @Override
    protected void entryRemoved(boolean evicted, String key, Bitmap oldValue, Bitmap newValue) {
       super.entryRemoved(evicted, key, oldValue, newValue);
    }
    @Override
    protected int sizeOf(String key, Bitmap value) {
        //这里返回的大小用单位kb来表示的
        return value.getByteCount() / 1024;
    }
};

最大缓存设置为10M,key是String类型,value设置的是Bitmap

  if (lruCache!=null){
     lruCache.put(STRING_KEY, bitmap);
   }
 if (lruCache != null) {
     bitmap = lruCache.get(STRING_KEY);
  }

源码分析

定义变量、构造器初始化

    //LruCache.java
    private final LinkedHashMap<K, V> map;//使用LinkedHashMap来存储操作数据
    /** Size of this cache in units. Not necessarily the number of elements. */
    private int size;//缓存数据大小,不一定等于元素的个数
    private int maxSize;//最大缓存大小
    private int putCount;// 成功添加数据的次数
    private int createCount;//手动创建缓存数据的次数
    private int evictionCount;//成功回收数据的次数
    private int hitCount;//查找数据时命中次数
    private int missCount;//查找数据时未命中次数
    /**
     * @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);
    }

构造函数中初始化了 LinkedHashMap,参数maxSize指定了缓存的最大大小。

修改缓存大小

/**
 * 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);
}

resize(int maxSize) 用来更新大小,先是加同步锁更新maxSize的值,接着调用了trimToSize()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!");
            }
            //如果已经小于最大缓存,则无需接着往下执行了
            if (size <= maxSize) {
                break;
            }
            //拿到最近最少使用的那条数据
            Map.Entry<K, V> toEvict = map.eldest();
            if (toEvict == null) {
                break;
            }
            key = toEvict.getKey();
            value = toEvict.getValue();
            //从LinkedHashMap移除这条最少使用的数据
            map.remove(key);
            //缓存大小size减去移除数据的大小,如果没有覆写sizeOf,则减去的值是1
            size -= safeSizeOf(key, value);
            evictionCount++;
        }
        entryRemoved(true, key, value, null);
    }
}
private int safeSizeOf(K key, V value) {
   int result = sizeOf(key, value);
    if (result < 0) {
        throw new IllegalStateException("Negative size: " + key + "=" + value);
    }
    return result;
}
/**
 * Returns the size of the entry for {@code key} and {@code value} in
 * user-defined units.  The default implementation returns 1 so that size
 * is the number of entries and max size is the maximum number of entries.
 *
 * <p>An entry's size must not change while it is in the cache.
 */
 protected int sizeOf(K key, V value) {
     return 1;
 }
protected void entryRemoved(boolean evicted, K key, V oldValue, V newValue) {}

trimToSize() 循环移除最近最少使用的数据直到剩余缓存数据的大小等于小于最大缓存大小。 注:我们看到sizeOf()entryRemoved()都是protected来修饰的,即可以被覆写,如果sizeOf()没有被覆写,那么变量size 代表的是缓存数据的数量,maxSize代表的是最大数量,如果覆写sizeOf(),如:

 @Override
  protected int sizeOf(String key, BitmapDrawable value) {
      return value.getBitmap().getByteCount() / 1024;
  }

此时size 代表的是缓存数据的大小maxSize代表的是最大缓存大小

存数据

/**
 * 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) {
    //key或value为空直接抛异常
    if (key == null || value == null) {
        throw new NullPointerException("key == null || value == null");
    }
    V previous;
    synchronized (this) {
        //添加数据次数+1
        putCount++;
         //缓存数据的大小增加
        size += safeSizeOf(key, value);
         //添加缓存数据,添加之前如果key值对应的value不为空,则newValue会覆盖oldValue,并返回oldValue;
         //如果key值对应的value为空,则返回Null
        previous = map.put(key, value);
        if (previous != null) {
           //之前的oldValue不为空,则在缓存size中减去oldValue
            size -= safeSizeOf(key, previous);
        }
    }
    if (previous != null) {
        entryRemoved(false, key, previous, value);
    }
    //重新检查缓存大小
    trimToSize(maxSize);
    return previous;
}

调用 LinkedHashMapput(key, value) 添加缓存数据后,在添加之前如果 key 值对应的value 不为空,则 newValue 会覆盖 oldValue ,并返回 oldValue;如果 key 值对应的 value 为空,则返回 null,接着根据返回值来重新设置缓存 size 和最大缓存 maxSize 的大小。

取数据

 /**
  * 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) {
         //通过key取数据
         mapValue = map.get(key);
         if (mapValue != null) {
             //如果取到了数据,命中次数+1
             hitCount++;
             return mapValue;
         }
         //没有取到数据,未命中此时+1
         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.
      */
     //如果没有覆写create(),默认create()方法返回的null
     V createdValue = create(key);    
     if (createdValue == null) {
         return null;
     }
     //如果覆写了create(),即根据key值手动创造了value,则继续往下执行
     synchronized (this) {
          //创造数据次数+1
         createCount++;
         //尝试将数据添加到缓存中
         mapValue = map.put(key, createdValue);
         if (mapValue != null) {
             // There was a conflict so undo that last put
             //mapValue不为空,说明之前的key对应的是有数据的,那么就跟我们手动创建的数据冲突了,
             //所以执行撤消操作,重新把mapValue添加到缓存中,用mapValue去覆盖createdValue
             map.put(key, mapValue);
         } else {
             //如果mapValue为空,说明之前的key值对应的value确实为空,我们手动添加createdValue后,
             //需要重新计算缓存size的大小
             size += safeSizeOf(key, createdValue);
         }
     }
     if (mapValue != null) {
         entryRemoved(false, key, createdValue, mapValue);
         return mapValue;
     } else {
         trimToSize(maxSize);
         return createdValue;
     }
 }
 protected V create(K key) {
      return null;
  }

取数据的流程大致是这样:

移除数据

 /**
  * Removes the entry for {@code key} if it exists.
  *
  * @return the previous value mapped by {@code key}.
  */
 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;
 }

调用 map.remove(key) 通过 key 值删除缓存中 key 对应的value,然后重新计算缓存大小,并返回删除的value

其他一些方法:

      //清空缓存
     public final void evictAll() {
          trimToSize(-1); // -1 will evict 0-sized elements
      }
     public synchronized final int size() {
         return size;
     }
     public synchronized final int maxSize() {
          return maxSize;
     }
     public synchronized final int hitCount() {
         return hitCount;
     }
     public synchronized final int missCount() {
         return missCount;
     }
    public synchronized final int createCount() {
         return createCount;
     }
     public synchronized final int putCount() {
         return putCount;
     }
     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.
      */
      //复制一份缓存,顺序从最近最少访问到最多访问排序
     public synchronized final Map<K, V> snapshot() {
         return new LinkedHashMap<K, V>(map);
     }

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