LruCache源码解析

最近被问到LruCache原理一直觉得很简单的东西猛然一想，卧槽忘了，赶紧翻开源码瞧瞧！

1、首先构造lrucache的时候会新建一个linkedHashMap来作为存储容器

 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);
    }

2、构造LinkedHashMap的时候传入了一个true作为accessOrder的值

 public LinkedHashMap(int initialCapacity,
                         float loadFactor,
                         boolean accessOrder) {
        super(initialCapacity, loadFactor);
        this.accessOrder = accessOrder;
    }

3、当LruCache调用get的时候会调用LinkedHashMap的方法

public final V get(K key) {
        if (key == null) {
            throw new NullPointerException("key == null");
        }

        V mapValue;
        synchronized (this) {
            mapValue = map.get(key);//这里会调用LinkedHashMap的get方法
            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;
        }
    }

4、这是LinkedHashMap的get方法

public V get(Object key) {
        Node<K,V> e;
        if ((e = getNode(key)) == null)
            return null;
        if (accessOrder)
            afterNodeAccess(e);
        return e.value;
    }

5、因为前面传入的accessOrder为true所以一定会调用afterNodeAccess方法

void afterNodeAccess(Node<K,V> e) { // move node to last
        LinkedHashMapEntry<K,V> last;
        if (accessOrder && (last = tail) != e) {
            LinkedHashMapEntry<K,V> p =
                (LinkedHashMapEntry<K,V>)e, b = p.before, a = p.after;
            p.after = null;
            if (b == null)
                head = a;
            else
                b.after = a;
            if (a != null)
                a.before = b;
            else
                last = b;
            if (last == null)
                head = p;
            else {
                p.before = last;
                last.after = p;
            }
            tail = p;
            ++modCount;
        }
    }

6、afterNodeAccess方法直接将被访问的元素放到了队列尾部并返回，然后lruCache进行重新trimtoSize的时候直接将移除的使map.eldest()返回的元素

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);
        }
    }

7、LinkedHashMap eldest()方法返回的直接是头节点，也就是最近使用的元素都会到LinkedHashMap的末尾节点，而头节点聚集的都是最久未使用元素

public Map.Entry<K, V> eldest() {
        return head;
    }

这就实现了缓存策略最近使用优先级最高