基于LinkedHashMap实现LRU缓存调度算法原理及应用
在Android中实用LRU+软引用(弱引用)的方法来缓存图片,可以减少内存溢出的情况。
实现思路:
在把图片保存到LRU集合中的时候,同时保存在一个弱引用的集合之中,如果此元素被LRU算法删除,可能垃圾回收器还并没有回收,可以通过弱引用的集合获取到此引用。
public LinkedHashMap (int initialCapacity, float loadFactor, boolean accessOrder);
initialCapacity 初始容量
loadFactor 加载因子,一般是 0.75f
accessOrder false 基于插入顺序 true 基于访问顺序(get一个元素后,这个元素被加到最后,使用了LRU 最近最少被使用的调度算法)
当有新元素加入Map的时候会调用Entry的addEntry方法,会调用removeEldestEntry方法,这里就是实现LRU元素过期机制的地方,默认的情况下removeEldestEntry方法只返回false表示元素永远不过期。
如 boolean accessOrder = true;
Map<String, String> m = new LinkedHashMap<String, String>(20, .80f, accessOrder );
m.put("1", "my"));
m.put("2", "map"));
m.put("3", "test"));
m.get("1");
m.get("2");
Log.d("tag", m);
若 accessOrder == true; 输出 {3=test, 1=my, 2=map}
accessOrder == false; 输出 {1=my, 2=map,3=test}
LinkedHashMap已经为我们自己实现LRU算法提供了便利。
LinkedHashMap继承了HashMap底层是通过Hash表+单向链表实现Hash算法,内部自己维护了一套元素访问顺序的列表。
Java代码
/**
* The head of the doubly linked list.
*/
private transient Entry<K,V> header;
.....
/**
* LinkedHashMap entry.
*/
private static class Entry<K,V> extends HashMap.Entry<K,V> {
// These fields comprise the doubly linked list used for iteration.
Entry<K,V> before, after;
HashMap构造函数中回调了子类的init方法实现对元素初始化
Java代码
void init() {
header = new Entry<K,V>(-1, null, null, null);
header.before = header.after = header;
}
LinkedHashMap中有一个属性可以执行列表元素的排序算法
Java代码
/**
* The iteration ordering method for this linked hash map: <tt>true</tt>
* for access-order, <tt>false</tt> for insertion-order.
*
* @serial
*/
private final boolean accessOrder;
注释已经写的很明白,accessOrder为true使用访问顺序排序,false使用插入顺序排序那么在哪里可以设置这个值。
Java代码
/**
* Constructs an empty <tt>LinkedHashMap</tt> instance with the
* specified initial capacity, load factor and ordering mode.
*
* @param initialCapacity the initial capacity.
* @param loadFactor the load factor.
* @param accessOrder the ordering mode - <tt>true</tt> for
* access-order, <tt>false</tt> for insertion-order.
* @throws IllegalArgumentException if the initial capacity is negative
* or the load factor is nonpositive.
*/
public LinkedHashMap(int initialCapacity,
float loadFactor,
boolean accessOrder) {
super(initialCapacity, loadFactor);
this.accessOrder = accessOrder;
}
那么我们就行有访问顺序排序方式实现LRU,那么哪里LinkedHashMap是如何实现LRU的呢?
Java代码
//LinkedHashMap方法
public V get(Object key) {
Entry<K,V> e = (Entry<K,V>)getEntry(key);
if (e == null)
return null;
e.recordAccess(this);
return e.value;
}
//HashMap方法
public V put(K key, V value) {
if (key == null)
return putForNullKey(value);
int hash = hash(key.hashCode());
int i = indexFor(hash, table.length);
for (Entry<K,V> e = table[i]; e != null; e = e.next) {
Object k;
if (e.hash == hash && ((k = e.key) == key || key.equals(k))) {
V oldValue = e.value;
e.value = value;
e.recordAccess(this);
return oldValue;
}
}
modCount++;
addEntry(hash, key, value, i);
return null;
}
当调用get或者put方法的时候,如果K-V已经存在,会回调Entry.recordAccess()方法
我们再看一下LinkedHashMap的Entry实现
Java代码
/**
* This method is invoked by the superclass whenever the value
* of a pre-existing entry is read by Map.get or modified by Map.set.
* If the enclosing Map is access-ordered, it moves the entry
* to the end of the list; otherwise, it does nothing.
*/
void recordAccess(HashMap<K,V> m) {
LinkedHashMap<K,V> lm = (LinkedHashMap<K,V>)m;
if (lm.accessOrder) {
lm.modCount++;
remove();
addBefore(lm.header);
}
}
/**
* Remove this entry from the linked list.
*/
private void remove() {
before.after = after;
after.before = before;
}
/**
* Insert this entry before the specified existing entry in the list.
*/
private void addBefore(Entry<K,V> existingEntry) {
after = existingEntry;
before = existingEntry.before;
before.after = this;
after.before = this;
}
recordAccess方法会accessOrder为true会先调用remove清楚的当前首尾元素的指向关系,之后调用addBefore方法,将当前元素加入header之前。
当有新元素加入Map的时候会调用Entry的addEntry方法,会调用removeEldestEntry方法,这里就是实现LRU元素过期机制的地方,默认的情况下removeEldestEntry方法只返回false表示元素永远不过期。
Java代码
/**
* This override alters behavior of superclass put method. It causes newly
* allocated entry to get inserted at the end of the linked list and
* removes the eldest entry if appropriate.
*/
void addEntry(int hash, K key, V value, int bucketIndex) {
createEntry(hash, key, value, bucketIndex);
// Remove eldest entry if instructed, else grow capacity if appropriate
Entry<K,V> eldest = header.after;
if (removeEldestEntry(eldest)) {
removeEntryForKey(eldest.key);
} else {
if (size >= threshold)
resize(2 * table.length);
}
}
/**
* This override differs from addEntry in that it doesn't resize the
* table or remove the eldest entry.
*/
void createEntry(int hash, K key, V value, int bucketIndex) {
HashMap.Entry<K,V> old = table[bucketIndex];
Entry<K,V> e = new Entry<K,V>(hash, key, value, old);
table[bucketIndex] = e;
e.addBefore(header);
size++;
}
protected boolean removeEldestEntry(Map.Entry<K,V> eldest) {
return false;
}
基本的原理已经介绍完了,那基于LinkedHashMap我们看一下是该如何实现呢?
Java代码
public static class LRULinkedHashMap<K, V> extends LinkedHashMap<K, V> {
/** serialVersionUID */
private static final long serialVersionUID = -5933045562735378538L;
/** 最大数据存储容量 */
private static final int LRU_MAX_CAPACITY = 1024;
/** 存储数据容量 */
private int capacity;
/**
* 默认构造方法
*/
public LRULinkedHashMap() {
super();
}
/**
* 带参数构造方法
* @param initialCapacity 容量
* @param loadFactor 装载因子
* @param isLRU 是否使用lru算法,true:使用(按方案顺序排序);false:不使用(按存储顺序排序)
*/
public LRULinkedHashMap(int initialCapacity, float loadFactor, boolean isLRU) {
super(initialCapacity, loadFactor, true);
capacity = LRU_MAX_CAPACITY;
}
/**
* 带参数构造方法
* @param initialCapacity 容量
* @param loadFactor 装载因子
* @param isLRU 是否使用lru算法,true:使用(按方案顺序排序);false:不使用(按存储顺序排序)
* @param lruCapacity lru存储数据容量
*/
public LRULinkedHashMap(int initialCapacity, float loadFactor, boolean isLRU, int lruCapacity) {
super(initialCapacity, loadFactor, true);
this.capacity = lruCapacity;
}
/**
* @see java.util.LinkedHashMap#removeEldestEntry(java.util.Map.Entry)
*/
@Override
protected boolean removeEldestEntry(Entry<K, V> eldest) {
System.out.println(eldest.getKey() + "=" + eldest.getValue());
if(size() > capacity) {
return true;
}
return false;
}
}
测试代码:
Java代码
public static void main(String[] args) {
LinkedHashMap<String, String> map = new LRULinkedHashMap<String, String>(16, 0.75f, true);
map.put("a", "a"); //a a
map.put("b", "b"); //a a b
map.put("c", "c"); //a a b c
map.put("a", "a"); // b c a
map.put("d", "d"); //b b c a d
map.put("a", "a"); // b c d a
map.put("b", "b"); // c d a b
map.put("f", "f"); //c c d a b f
map.put("g", "g"); //c c d a b f g
map.get("d"); //c a b f g d
for (Entry<String, String> entry : map.entrySet()) {
System.out.print(entry.getValue() + ", ");
}
System.out.println();
map.get("a"); //c b f g d a
for (Entry<String, String> entry : map.entrySet()) {
System.out.print(entry.getValue() + ", ");
}
System.out.println();
map.get("c"); //b f g d a c
for (Entry<String, String> entry : map.entrySet()) {
System.out.print(entry.getValue() + ", ");
}
System.out.println();
map.get("b"); //f g d a c b
for (Entry<String, String> entry : map.entrySet()) {
System.out.print(entry.getValue() + ", ");
}
System.out.println();
map.put("h", "h"); //f f g d a c b h
for (Entry<String, String> entry : map.entrySet()) {
System.out.print(entry.getValue() + ", ");
}
System.out.println();
}
运行结果:
a=a
a=a
a=a
b=b
c=c
c=c
c, a, b, f, g, d,
c, b, f, g, d, a,
b, f, g, d, a, c,
f, g, d, a, c, b,
f=f
f, g, d, a, c, b, h,