}
if (decodeFormat == null) {
decodeFormat = DecodeFormat.DEFAULT;
}
return new Glide(engine, memoryCache, bitmapPool, context, decodeFormat);
}
}
这里也就是构建Glide对象的地方了。那么观察第22行,你会发现这里new出了一个LruResourceCache,并把它赋值到了memoryCache这个对象上面。你没有猜错,这个就是Glide实现内存缓存所使用的LruCache对象了。不过我这里并不打算展开来讲LruCache算法的具体实现,如果你感兴趣的话可以自己研究一下它的源码。
现在创建好了LruResourceCache对象只能说是把准备工作做好了,接下来我们就一步步研究Glide中的内存缓存到底是如何实现的。
刚才在Engine的load()方法中我们已经看到了生成缓存Key的代码,而内存缓存的代码其实也是在这里实现的,那么我们重新来看一下Engine类load()方法的完整源码:
public class Engine implements EngineJobListener,
MemoryCache.ResourceRemovedListener,
EngineResource.ResourceListener {
…
public <T, Z, R> LoadStatus load(Key signature, int width, int height, DataFetcher<T> fetcher,
DataLoadProvider<T, Z> loadProvider, Transformation<Z> transformation, ResourceTranscoder<Z, R> transcoder,
Priority priority, boolean isMemoryCacheable, DiskCacheStrategy diskCacheStrategy, ResourceCallback cb) {
Util.assertMainThread();
long startTime = LogTime.getLogTime();
final String id = fetcher.getId();
EngineKey key = keyFactory.buildKey(id, signature, width, height, loadProvider.getCacheDecoder(),
loadProvider.getSourceDecoder(), transformation, loadProvider.getEncoder(),
transcoder, loadProvider.getSourceEncoder());
EngineResource<?> cached = loadFromCache(key, isMemoryCacheable);
if (cached != null) {
cb.onResourceReady(cached);
if (Log.isLoggable(TAG, Log.VERBOSE)) {
logWithTimeAndKey("Loaded resource from cache", startTime, key);
}
return null;
}
EngineResource<?> active = loadFromActiveResources(key, isMemoryCacheable);
if (active != null) {
cb.onResourceReady(active);
if (Log.isLoggable(TAG, Log.VERBOSE)) {
logWithTimeAndKey("Loaded resource from active resources", startTime, key);
}
return null;
}
EngineJob current = jobs.get(key);
if (current != null) {
current.addCallback(cb);
if (Log.isLoggable(TAG, Log.VERBOSE)) {
logWithTimeAndKey("Added to existing load", startTime, key);
}
return new LoadStatus(cb, current);
}
EngineJob engineJob = engineJobFactory.build(key, isMemoryCacheable);
DecodeJob<T, Z, R> decodeJob = new DecodeJob<T, Z, R>(key, width, height, fetcher, loadProvider, transformation,
transcoder, diskCacheProvider, diskCacheStrategy, priority);
EngineRunnable runnable = new EngineRunnable(engineJob, decodeJob, priority);
jobs.put(key, engineJob);
engineJob.addCallback(cb);
engineJob.start(runnable);
if (Log.isLoggable(TAG, Log.VERBOSE)) {
logWithTimeAndKey("Started new load", startTime, key);
}
return new LoadStatus(cb, engineJob);
}
...
}
可以看到,这里在第17行调用了loadFromCache()方法来获取缓存图片,如果获取到就直接调用cb.onResourceReady()方法进行回调。如果没有获取到,则会在第26行调用loadFromActiveResources()方法来获取缓存图片,获取到的话也直接进行回调。只有在两个方法都没有获取到缓存的情况下,才会继续向下执行,从而开启线程来加载图片。
也就是说,Glide的图片加载过程中会调用两个方法来获取内存缓存,loadFromCache()和loadFromActiveResources()。这两个方法中一个使用的就是LruCache算法,另一个使用的就是弱引用。我们来看一下它们的源码:
public class Engine implements EngineJobListener,
MemoryCache.ResourceRemovedListener,
EngineResource.ResourceListener {
private final MemoryCache cache;
private final Map<Key, WeakReference<EngineResource<?>>> activeResources;
...
private EngineResource<?> loadFromCache(Key key, boolean isMemoryCacheable) {
if (!isMemoryCacheable)