详解使用LruCache来解决图片OOM的问题

LruCache 什么是LruCache？ LruCache实现原理是什么？

这两个问题其实可以作为一个问题来回答，知道了什么是 LruCache，就自然而然的知道 LruCache 的实现原理；Lru的全称是Least Recently Used ，近期最少使用的！所以我们可以推断出 LruCache 的实现原理：把近期最少使用的数据从缓存中移除，保留使用最频繁的数据，那具体代码要怎么实现呢，我们进入到源码中看看。 LruCache源码分析

public class LruCache<K, V> { //缓存 map 集合，为什么要用LinkedHashMap //因为没错取了缓存值之后，都要进行排序，以确保 //下次移除的是最少使用的值 private final LinkedHashMap<K, V> map; //当前缓存的值 private int size; //最大值 private int maxSize; //添加到缓存中的个数 private int putCount; //创建的个数 private int createCount; //被移除的个数 private int evictionCount; //命中个数 private int hitCount; //丢失个数 private int missCount; //实例化 Lru，需要传入缓存的最大值 //这个最大值可以是个数，比如对象的个数，也可以是内存的大小 //比如，最大内存只能缓存5兆 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); } //重置最大缓存的值 public void resize(int maxSize) { if (maxSize <= 0) { throw new IllegalArgumentException("maxSize <= 0"); } synchronized (this) { this.maxSize = maxSize; } trimToSize(maxSize); } //通过 key 获取缓存值 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++; } //如果没有，用户可以去创建 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 { //缓存的大小改变 size += safeSizeOf(key, createdValue); } } //这里没有移除，只是改变了位置 if (mapValue != null) { entryRemoved(false, key, createdValue, mapValue); return mapValue; } else { //判断缓存是否越界 trimToSize(maxSize); return createdValue; } } //添加缓存，跟上面这个方法的 create 之后的代码一样的 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; } //检测缓存是否越界 private 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!"); } //如果没有，则返回 if (size <= maxSize) { break; } //以下代码表示已经超出了最大范围 Map.Entry<K, V> toEvict = null; for (Map.Entry<K, V> entry : map.entrySet()) { toEvict = entry; } if (toEvict == null) { break; } //移除最后一个，也就是最少使用的缓存 key = toEvict.getKey(); value = toEvict.getValue(); map.remove(key); size -= safeSizeOf(key, value); evictionCount++; } entryRemoved(true, key, value, null); } } //手动移除，用户调用 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; } //这里用户可以重写它，实现数据和内存回收操作 protected void entryRemoved(boolean evicted, K key, V oldValue, V newValue) {} protected V create(K key) { return 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; } //这个方法要特别注意，跟我们实例化 LruCache 的 maxSize 要呼应，怎么做到呼应呢，比如 maxSize 的大小为缓存的个数，这里就是 return 1就 ok，如果是内存的大小，如果5M，这个就不能是个数 了，这是应该是每个缓存 value 的 size 大小，如果是 Bitmap，这应该是 bitmap.getByteCount(); protected int sizeOf(K key, V value) { return 1; } //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; } public synchronized final Map<K, V> snapshot() { return new LinkedHashMap<K, V>(map); } }

当我们项目中要去从网络下载图片的时候，我们并不会直接去从网络下载，我们会先从内存缓存中去查找是否有该图片，如果没有就去文件缓存中查找是否有该图片，如果还没有，我们就从网络下载图片。本博文就是从LruCache下手讲如何做内存缓存，内存缓存的查找策略是：先从强引用缓存中查找，如果没有再从软引用缓存中查找，如果在软引用缓存中找到了，就把它移入强引用缓存；如果强引用缓存满了，就会根据Lru算法把某些图片移入软引用缓存，如果软引用缓存也满了，最早的软引用就会被删除。这里，我有必要说明下几个概念：强引用、软引用、弱引用。 强引用：就是直接引用一个对象，一般的对象引用均是强引用 软引用：引用一个对象，当内存不足并且除了我们的引用之外没有其他地方引用此对象的情况 下，该对象会被gc回收 弱引用：引用一个对象，当除了我们的引用之外没有其他地方引用此对象的情况下，只要gc被调用，它就会被回收（请注意它和软引用的区别)

import android.graphics.Bitmap; import android.support.v4.util.LruCache; import com.shanpiao.common.logger.Logger; import java.lang.ref.SoftReference; import java.util.LinkedHashMap; /** * Created by Army on 2017/3/9 */ public class ImageMemoryCache { /** * 从内存读取数据速度是最快的，为了更大限度使用内存，这里使用了两层缓存。 * 强引用缓存不会轻易被回收，用来保存常用数据，不常用的转入软引用缓存。 */ private static final String TAG = "ImageMemoryCache"; private static LruCache<String, Bitmap> mLruCache; // 强引用缓存 private static LinkedHashMap<String, SoftReference<Bitmap>> mSoftCache; // 软引用缓存 private static final int LRU_CACHE_SIZE = 4 * 1024 * 1024; // 强引用缓存容量：4MB private static final int SOFT_CACHE_NUM = 20; // 软引用缓存个数 // 在这里分别初始化强引用缓存和弱引用缓存 public ImageMemoryCache() { mLruCache = new LruCache<String, Bitmap>(LRU_CACHE_SIZE) { @Override // sizeOf返回为单个hashmap value的大小 protected int sizeOf(String key, Bitmap value) { if (value != null) return value.getRowBytes() * value.getHeight(); else return 0; } @Override protected void entryRemoved(boolean evicted, String key, Bitmap oldValue, Bitmap newValue) { if (oldValue != null) { // 强引用缓存容量满的时候，会根据LRU算法把最近没有被使用的图片转入此软引用缓存 Logger.d(TAG, "LruCache is full,move to SoftRefernceCache"); mSoftCache.put(key, new SoftReference<Bitmap>(oldValue)); } } }; mSoftCache = new LinkedHashMap<String, SoftReference<Bitmap>>( SOFT_CACHE_NUM, 0.75f, true) { private static final long serialVersionUID = 1L; /** * 当软引用数量大于20的时候，最旧的软引用将会被从链式哈希表中移出 */ @Override protected boolean removeEldestEntry( Entry<String, SoftReference<Bitmap>> eldest) { if (size() > SOFT_CACHE_NUM) { Logger.d(TAG, "should remove the eldest from SoftReference"); return true; } return false; } }; } /** * 从缓存中获取图片 */ public Bitmap getBitmapFromMemory(String url) { Bitmap bitmap; // 先从强引用缓存中获取 synchronized (mLruCache) { bitmap = mLruCache.get(url); if (bitmap != null) { // 如果找到的话，把元素移到LinkedHashMap的最前面，从而保证在LRU算法中是最后被删除 mLruCache.remove(url); mLruCache.put(url, bitmap); Logger.d(TAG, "get bmp from LruCache,url=" + url); return bitmap; } } // 如果强引用缓存中找不到，到软引用缓存中找，找到后就把它从软引用中移到强引用缓存中 synchronized (mSoftCache) { SoftReference<Bitmap> bitmapReference = mSoftCache.get(url); if (bitmapReference != null) { bitmap = bitmapReference.get(); if (bitmap != null) { // 将图片移回LruCache mLruCache.put(url, bitmap); mSoftCache.remove(url); Logger.d(TAG, "get bmp from SoftReferenceCache, url=" + url); return bitmap; } else { mSoftCache.remove(url); } } } return null; } /** * 添加图片到缓存 */ public void addBitmapToMemory(String url, Bitmap bitmap) { if (bitmap != null) { synchronized (mLruCache) { mLruCache.put(url, bitmap); } } } public void clearCache() { mSoftCache.clear(); } }

LruCache的源码供大家参考：

/* * Copyright (C) 2011 The Android Open Source Project * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ /** * Cache保存一个强引用来限制内容数量，每当Item被访问的时候，此Item就会移动到队列的头部。 * 当cache已满的时候加入新的item时，在队列尾部的item会被回收。 * * 如果你cache的某个值需要明确释放，重写entryRemoved() * * 如果key相对应的item丢掉啦，重写create().这简化了调用代码，即使丢失了也总会返回。 * * 默认cache大小是测量的item的数量，重写sizeof计算不同item的大小。 * * <pre> {@code * 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(); * } * }}</pre> * * <p>This class is thread-safe. Perform multiple cache operations atomically by * synchronizing on the cache: <pre> {@code * synchronized (cache) { * if (cache.get(key) == null) { * cache.put(key, value); * } * }}</pre> * * 不允许key或者value为null * 当get（），put（），remove（）返回值为null时，key相应的项不在cache中 */ 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;//已经存储的大小 private int maxSize;//规定的最大存储空间 private int putCount;//put的次数 private int createCount;//create的次数 private int evictionCount; //回收的次数 private int hitCount;//命中的次数 private int missCount;//丢失的次数 /** * @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); } /** *通过key返回相应的item，或者创建返回相应的item。相应的item会移动到队列的头部， * 如果item的value没有被cache或者不能被创建，则返回null。 */ 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; } } /** * 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; } /** * @param maxSize the maximum size of the cache before returning. May be -1 * to evict even 0-sized elements. */ private 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(); */ //modify by echy Iterator<Entry<K, V>> iter = map.entrySet().iterator(); Map.Entry<K, V> toEvict = null; while (iter.hasNext()) { toEvict = (Entry<K, V>) iter.next(); break; } if (toEvict == null) { break; } key = toEvict.getKey(); value = toEvict.getValue(); map.remove(key); size -= safeSizeOf(key, value); evictionCount++; } entryRemoved(true, key, value, null); } } /** * 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; } /** * Called for entries that have been evicted or removed. This method is * invoked when a value is evicted to make space, removed by a call to * {@link #remove}, or replaced by a call to {@link #put}. The default * implementation does nothing. * * <p>The method is called without synchronization: other threads may * access the cache while this method is executing. * * @param evicted true if the entry is being removed to make space, false * if the removal was caused by a {@link #put} or {@link #remove}. * @param newValue the new value for {@code key}, if it exists. If non-null, * this removal was caused by a {@link #put}. Otherwise it was caused by * an eviction or a {@link #remove}. */ protected void entryRemoved(boolean evicted, K key, V oldValue, V newValue) {} /** * Called after a cache miss to compute a value for the corresponding key. * Returns the computed value or null if no value can be computed. The * default implementation returns null. * * <p>The method is called without synchronization: other threads may * access the cache while this method is executing. * * <p>If a value for {@code key} exists in the cache when this method * returns, the created value will be released with {@link #entryRemoved} * and discarded. This can occur when multiple threads request the same key * at the same time (causing multiple values to be created), or when one * thread calls {@link #put} while another is creating a value for the same * key. */ protected V create(K key) { return 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; } /** * Clear the cache, calling {@link #entryRemoved} on each removed entry. */ public final void evictAll() { trimToSize(-1); // -1 will evict 0-sized elements } /** * For caches that do not override {@link #sizeOf}, this returns the number * of entries in the cache. For all other caches, this returns the sum of * the sizes of the entries in this cache. */ public synchronized final int size() { return size; } /** * For caches that do not override {@link #sizeOf}, this returns the maximum * number of entries in the cache. For all other caches, this returns the * maximum sum of the sizes of the entries in this cache. */ public synchronized final int maxSize() { return maxSize; } /** * Returns the number of times {@link #get} returned a value that was * already present in the cache. */ public synchronized final int hitCount() { return hitCount; } /** * Returns the number of times {@link #get} returned null or required a new * value to be created. */ public synchronized final int missCount() { return missCount; } /** * Returns the number of times {@link #create(Object)} returned a value. */ public synchronized final int createCount() { return createCount; } /** * Returns the number of times {@link #put} was called. */ public synchronized final int putCount() { return putCount; } /** * Returns the number of values that have been evicted. */ 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. */ public synchronized final Map<K, V> snapshot() { return new LinkedHashMap<K, V>(map); } @Override public synchronized final String toString() { int accesses = hitCount + missCount; int hitPercent = accesses != 0 ? (100 * hitCount / accesses) : 0; return String.format("LruCache[maxSize=%d,hits=%d,misses=%d,hitRate=%d%%]", maxSize, hitCount, missCount, hitPercent); } }