图片缓存策略

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我是靠谱客的博主激情黄蜂，这篇文章主要介绍图片缓存策略，现在分享给大家，希望可以做个参考。

众所周知，基于客户端app的开发中都会涉及到大量的图片，包括在线或者本地内置的，而对于在线图片的读取如果都实施从网络上读，会造成大量流量的浪费并且交互非常糟糕。所以对于已经读取过的在线图片，需要在本地有一些缓存以便快速读取展现给用户，而本地缓存主要策略包括：
内存缓存+sd卡缓存双缓存机制：

内存缓存策略LruCache：Least Recently Used最近最少使用算法即会淘汰最近最少使用的数据,可以看看源码：

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public class LruCache<K, V> {
private final LinkedHashMap<K, V> map;
/** Size of this cache in units. Not necessarily the number of elements. */
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<K, V>(0, 0.75f, true);
}
...
}

可以看到LruCache的构造函数可以指定缓存的最大容量，并初始化了一个LinkedHashMap，也就是LruCache主要依赖LinkedHashMap实现的核心算法。

LruCache关键方法有：从缓存取数据get、向缓存存数据put、移除最近最少使用的数据trimToSize方法，下面依次看这三个方法

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/**
* 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;
}
}

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/**
* 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;
}
}

1、从map取数据 map存在对应的value则直接返回value

2、从map取数据 map不存在则去create一个对应的数据并put到map中,重新计算大小之后调用trimToSize方法，删除访问次数最少的元素

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/**
* 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;
}

将键值对放入map，重新计算大小之后调用trimToSize方法，删除访问次数最少的元素

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/**
* 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);
}
}

1、当前size小于maxSize 不做任何操作

2、当前size大于等于maxSize 取出最近最少使用的数据移除并调整size

sd卡缓存策略DiskLruCache：

源码参考：
https://android.googlesource.com/platform/libcore/+/jb-mr2-release/luni/src/main/java/libcore/io/DiskLruCache.java

源码详解：
http://blog.csdn.net/lmj623565791/article/details/47251585

DiskLruCache缓存框架中有一个关键的文件journal文件，这个文件会存储所有的读取操作记录

journal文件格式

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libcore.io.DiskLruCache
1
1
1
DIRTY c3bac86f2e7a291a1a200b853835b664
CLEAN c3bac86f2e7a291a1a200b853835b664 4698
READ c3bac86f2e7a291a1a200b853835b664
DIRTY c59f9eec4b616dc6682c7fa8bd1e061f
CLEAN c59f9eec4b616dc6682c7fa8bd1e061f 4698
READ c59f9eec4b616dc6682c7fa8bd1e061f
DIRTY be8bdac81c12a08e15988555d85dfd2b
CLEAN be8bdac81c12a08e15988555d85dfd2b 99
READ be8bdac81c12a08e15988555d85dfd2b
DIRTY 536788f4dbdffeecfbb8f350a941eea3
REMOVE 536788f4dbdffeecfbb8f350a941eea3