图片加载框架Glide在我们实战中，运用的很频繁，但是glide为我们做的太多了，以至于我们忽略了处理图片很多基础知识点，本文基于Glide 4.80全方面的分析Glide为我们做的事情，在分析之前还是老规矩，花如此多事件分析的图片加载框架能带给我们什么？
1.Glide的内存缓存仅仅只有Lrucache么？
2.Glide的磁盘缓存默认是开的还是要手动设置的，还有很多人设置
RequestOptions().diskCacheStrategy(DiskCacheStrategy.RESOURCE);
这里面的DiskCacheStrategy.RESOURCE 和DiskCacheStrategy.DATA的区别是啥，默认又是哪个呢？
3，在Glide的源码中会看到BitmapPool也是一个Lru,他的作用是什么？
4.很多人在用Glide时，获取到一个Bitmap或者Drawable后防止oom还会用BitmapFactory.Options去压缩图片，如图所示
GlideRequest.into(new Target
() {
@Override
public void onResourceReady(@NonNull Drawable resource, @Nullable Transition transition) {
//很多人拿到此bitmap还会去压缩图片
}
真的有必要么？
接下来带着问题去分析源码吧
当调用Glide.with时，间接会调用getRetriever在调用with方法
@NonNull
private static RequestManagerRetriever getRetriever(@Nullable Context context) {
// Context could be null for other reasons (ie the user passes in null), but in practice it will
// only occur due to errors with the Fragment lifecycle.
Preconditions.checkNotNull(
context,
"You cannot start a load on a not yet attached View or a Fragment where getActivity() "
+ "returns null (which usually occurs when getActivity() is called before the Fragment "
+ "is attached or after the Fragment is destroyed).");
return Glide.get(context).getRequestManagerRetriever();
}
来看下getRequestManagerRetriever的配置
@NonNull
public static Glide get(@NonNull Context context) {
if (glide == null) {
GeneratedAppGlideModule annotationGeneratedModule =
getAnnotationGeneratedGlideModules(context.getApplicationContext());
synchronized (Glide.class) {
if (glide == null) {
checkAndInitializeGlide(context, annotationGeneratedModule);
}
}
}
return glide;
}
这里annotationGeneratedModule其实就是注解处理器，用过Glide的人都知道,再添加了注解和注解解析器的依赖后
compile 'com.github.bumptech.glide:annotations:4.8.0'
annotationProcessor 'com.github.bumptech.glide:compiler:4.8.0'
来看一下
/** Ensures that Glide's generated API is created for the Gallery sample. */
@GlideModule
public final class GalleryModule extends AppGlideModule {
// Intentionally empty.
@Override
public void applyOptions(@NonNull Context context, @NonNull GlideBuilder builder) {
super.applyOptions(context, builder);
}
}
然后builder里有各种参数，比如在Glide中如果你设置了
设置磁盘缓存，默认的磁盘路径和大小是image_manager_disk_cache 和 250M左右，可以重新实现这个接口，修改目录和大小。
甚至可以更改图片的下载方式
@GlideModule
public final class OkHttpLibraryGlideModule extends LibraryGlideModule {
@Override
public void registerComponents(
@NonNull Context context, @NonNull Glide glide, @NonNull Registry registry) {
registry.replace(GlideUrl.class, InputStream.class, new OkHttpUrlLoader.Factory());
}
}
用okhttp3加载
接下来checkAndInitializeGlide会间接调用initializeGlide
@GuardedBy("Glide.class")
@SuppressWarnings("deprecation")
private static void initializeGlide(
@NonNull Context context,
@NonNull GlideBuilder builder,
@Nullable GeneratedAppGlideModule annotationGeneratedModule) {
Context applicationContext = context.getApplicationContext();
List manifestModules = Collections.emptyList();
if (annotationGeneratedModule == null || annotationGeneratedModule.isManifestParsingEnabled()) {
manifestModules = new ManifestParser(applicationContext).parse();
}
if (annotationGeneratedModule != null
&& !annotationGeneratedModule.getExcludedModuleClasses().isEmpty()) {
Set> excludedModuleClasses = annotationGeneratedModule.getExcludedModuleClasses();
Iterator iterator = manifestModules.iterator();
while (iterator.hasNext()) {
com.bumptech.glide.module.GlideModule current = iterator.next();
if (!excludedModuleClasses.contains(current.getClass())) {
continue;
}
if (Log.isLoggable(TAG, Log.DEBUG)) {
Log.d(TAG, "AppGlideModule excludes manifest GlideModule: " + current);
}
iterator.remove();
}
}
if (Log.isLoggable(TAG, Log.DEBUG)) {
for (com.bumptech.glide.module.GlideModule glideModule : manifestModules) {
Log.d(TAG, "Discovered GlideModule from manifest: " + glideModule.getClass());
}
}
RequestManagerRetriever.RequestManagerFactory factory =
annotationGeneratedModule != null
? annotationGeneratedModule.getRequestManagerFactory()
: null;
builder.setRequestManagerFactory(factory);
for (com.bumptech.glide.module.GlideModule module : manifestModules) {
module.applyOptions(applicationContext, builder);
}
if (annotationGeneratedModule != null) {
annotationGeneratedModule.applyOptions(applicationContext, builder);
}
Glide glide = builder.build(applicationContext);
for (com.bumptech.glide.module.GlideModule module : manifestModules) {
try {
module.registerComponents(applicationContext, glide, glide.registry);
} catch (AbstractMethodError e) {
throw new IllegalStateException(
"Attempting to register a Glide v3 module. If you see this, you or one of your"
+ " dependencies may be including Glide v3 even though you're using Glide v4."
+ " You'll need to find and remove (or update) the offending dependency."
+ " The v3 module name is: "
+ module.getClass().getName(),
e);
}
}
if (annotationGeneratedModule != null) {
annotationGeneratedModule.registerComponents(applicationContext, glide, glide.registry);
}
applicationContext.registerComponentCallbacks(glide);
Glide.glide = glide;
}
其他的都是一些自定义的流程，重点看下这句话
Glide glide = builder.build(applicationContext);
会间接调用如下方法
@NonNull
Glide build(@NonNull Context context) {
if (sourceExecutor == null) {
sourceExecutor = GlideExecutor.newSourceExecutor();
}
if (diskCacheExecutor == null) {
diskCacheExecutor = GlideExecutor.newDiskCacheExecutor();
}
if (animationExecutor == null) {
animationExecutor = GlideExecutor.newAnimationExecutor();
}
if (memorySizeCalculator == null) {
memorySizeCalculator = new MemorySizeCalculator.Builder(context).build();
}
if (connectivityMonitorFactory == null) {
connectivityMonitorFactory = new DefaultConnectivityMonitorFactory();
}
if (bitmapPool == null) {
int size = memorySizeCalculator.getBitmapPoolSize();
if (size > 0) {
bitmapPool = new LruBitmapPool(size);
} else {
bitmapPool = new BitmapPoolAdapter();
}
}
if (arrayPool == null) {
arrayPool = new LruArrayPool(memorySizeCalculator.getArrayPoolSizeInBytes());
}
if (memoryCache == null) {
memoryCache = new LruResourceCache(memorySizeCalculator.getMemoryCacheSize());
}
if (diskCacheFactory == null) {
diskCacheFactory = new InternalCacheDiskCacheFactory(context);
}
if (engine == null) {
engine =
new Engine(
memoryCache,
diskCacheFactory,
diskCacheExecutor,
sourceExecutor,
GlideExecutor.newUnlimitedSourceExecutor(),
animationExecutor,
isActiveResourceRetentionAllowed);
}
if (defaultRequestListeners == null) {
defaultRequestListeners = Collections.emptyList();
} else {
defaultRequestListeners = Collections.unmodifiableList(defaultRequestListeners);
}
RequestManagerRetriever requestManagerRetriever =
new RequestManagerRetriever(requestManagerFactory);
return new Glide(
context,
engine,
memoryCache,
bitmapPool,
arrayPool,
requestManagerRetriever,
connectivityMonitorFactory,
logLevel,
defaultRequestOptionsFactory,
defaultTransitionOptions,
defaultRequestListeners,
isLoggingRequestOriginsEnabled,
isImageDecoderEnabledForBitmaps);
}
我们可以看到sourceExecutor就是下载图片的线程，diskCacheExecutor是执行硬盘缓存逻辑的线程
animationExecutor是执行动画逻辑的线程
里面分别开了线程池来实现
memorySizeCalculator里面封装了一些信息如下
Calculation complete, Calculated memory cache size: 15.34 MB, pool size: 7.67 MB, byte array size: 4.19 MB, memory class limited? false, max size: 161 MB, memoryClass: 384, isLowMemoryDevice: false
表示内存缓存，图片的poolsize，这其实和bitmapPool有关，bitmapPool其实是压缩过程中为了防止过多的开辟空间在bitmapfactory的属性中有个inBitmap属性，存放的就是bitmap，其实就是这个pool了
里面也是由lru管理的
arraypool就后面可以知道其实就是对于byte[]的管理，简单来说就是对于输出流的优化
memoryClass 可用最大的内存
算法其实和屏幕 宽高在乘以一些系数有关具体可以看MemorySizeCalculator类
接下来就是初始化内存缓存和硬盘缓存了
然后把所有的对象放到engin中
接下来创建RequestManagerRetriever对象简单来说是管理fragment的对象都知道Glide的with方法要传当前对象的参数，不然很有可能图片的释放不及时，从而造成内存泄漏，那他是怎么根据这个context的逻辑找到是fragment还是activity的呢，就是这个类的用途所在，里面也关联了Lifecycle，简单来说就是能实时知道页面的状态，从而更好的管理，很多文章都做了实现原理的分析，本文就不深入了
接下来把engine和RequestManagerRetriever封装到Glide中
在Glide初始化的时候还创建了很多的解析器封装到了registry中
比如要把要下载的urI转成nputStream，InputStream解析成bitmap等，都是在此解析器中返回一个RequestManager对象
现在此方法就熟悉很多了
@NonNull
public static RequestManager with(@NonNull Activity activity) {
return getRetriever(activity).get(activity);
}
在看下load方法，间接调用
@SuppressWarnings("deprecation")
public class GlideRequests extends RequestManager {
public GlideRequests(@NonNull Glide glide, @NonNull Lifecycle lifecycle,
@NonNull RequestManagerTreeNode treeNode, @NonNull Context context) {
super(glide, lifecycle, treeNode, context);
}
@Override
@CheckResult
@NonNull
public GlideRequest as(@NonNull Class resourceClass) {
return new GlideRequest<>(glide, this, resourceClass, context);
}
可以看到最终返回的是GlideRequest对象，里面封装了Gilde,RequestManager，图片显示的的过渡参数，好，基本的分析完了
接下来分析最重要的into方法
private > Y into(
@NonNull Y target,
@Nullable RequestListener targetListener,
BaseRequestOptions options,
Executor callbackExecutor) {
Preconditions.checkNotNull(target);
if (!isModelSet) {
throw new IllegalArgumentException("You must call #load() before calling #into()");
}
Request request = buildRequest(target, targetListener, options, callbackExecutor);
Request previous = target.getRequest();
if (request.isEquivalentTo(previous)
&& !isSkipMemoryCacheWithCompletePreviousRequest(options, previous)) {
// If the request is completed, beginning again will ensure the result is re-delivered,
// triggering RequestListeners and Targets. If the request is failed, beginning again will
// restart the request, giving it another chance to complete. If the request is already
// running, we can let it continue running without interruption.
if (!Preconditions.checkNotNull(previous).isRunning()) {
// Use the previous request rather than the new one to allow for optimizations like skipping
// setting placeholders, tracking and un-tracking Targets, and obtaining View dimensions
// that are done in the individual Request.
previous.begin();
}
return target;
}
requestManager.clear(target);
target.setRequest(request);
requestManager.track(target, request);
return target;
}
可以看到这里返回了一个request的接口对象，有三个实现类，分别是singleRequest,errorRequest和thumbnailRequest
这里主要看的是singleRequest，如果有用到图片加载缩略图的话，可以到thumbnailRequest去看
在singleRequest中
@SuppressWarnings("GuardedBy")
private SingleRequest(
Context context,
GlideContext glideContext,
@NonNull Object requestLock,
@Nullable Object model,
Class transcodeClass,
BaseRequestOptions requestOptions,
int overrideWidth,
int overrideHeight,
Priority priority,
Target target,
@Nullable RequestListener targetListener,
@Nullable List> requestListeners,
RequestCoordinator requestCoordinator,
Engine engine,
TransitionFactory animationFactory,
可以看到封装了包括GlideContext,宽高，图片质量，requestOptions（请求配置），回调的线程callbackExecutor等一系列的参数，然后调用begin方法进行请求或者取缓存内容
/** Starts tracking the given request. */
public void runRequest(@NonNull Request request) {
requests.add(request);
if (!isPaused) {
request.begin();
} else {
request.clear();
if (Log.isLoggable(TAG, Log.VERBOSE)) {
Log.v(TAG, "Paused, delaying request");
}
pendingRequests.add(request);
}
}
看下begin方法
@Override
public void begin() {
synchronized (requestLock) {
assertNotCallingCallbacks();
stateVerifier.throwIfRecycled();
startTime = LogTime.getLogTime();
if (model == null) {
if (Util.isValidDimensions(overrideWidth, overrideHeight)) {
width = overrideWidth;
height = overrideHeight;
}
// Only log at more verbose log levels if the user has set a fallback drawable, because
// fallback Drawables indicate the user expects null models occasionally.
int logLevel = getFallbackDrawable() == null ? Log.WARN : Log.DEBUG;
onLoadFailed(new GlideException("Received null model"), logLevel);
return;
}
if (status == Status.RUNNING) {
throw new IllegalArgumentException("Cannot restart a running request");
}
// If we're restarted after we're complete (usually via something like a notifyDataSetChanged
// that starts an identical request into the same Target or View), we can simply use the
// resource and size we retrieved the last time around and skip obtaining a new size, starting
// a new load etc. This does mean that users who want to restart a load because they expect
// that the view size has changed will need to explicitly clear the View or Target before
// starting the new load.
if (status == Status.COMPLETE) {
onResourceReady(resource, DataSource.MEMORY_CACHE);
return;
}
// Restarts for requests that are neither complete nor running can be treated as new requests
// and can run again from the beginning.
status = Status.WAITING_FOR_SIZE;
if (Util.isValidDimensions(overrideWidth, overrideHeight)) {
onSizeReady(overrideWidth, overrideHeight);
} else {
target.getSize(this);
}
if ((status == Status.RUNNING || status == Status.WAITING_FOR_SIZE)
&& canNotifyStatusChanged()) {
target.onLoadStarted(getPlaceholderDrawable());
}
if (IS_VERBOSE_LOGGABLE) {
logV("finished run method in " + LogTime.getElapsedMillis(startTime));
}
}
}
这里看到如果宽高都是无效的话，就会获取加载图片的宽高，那宽高合时加载完呢，其实在onPreDraw回调中会获得此view的宽高，然后调用onSizeReady，如果有效的就直接调用
可以看到在viewTarget里
private void notifyCbs(int width, int height) {
// One or more callbacks may trigger the removal of one or more additional callbacks, so we
// need a copy of the list to avoid a concurrent modification exception. One place this
// happens is when a full request completes from the in memory cache while its thumbnail is
// still being loaded asynchronously. See #2237.
for (SizeReadyCallback cb : new ArrayList<>(cbs)) {
cb.onSizeReady(width, height);
}
}
调用了onSizeReady方法，来看下实现
public void onSizeReady(int width, int height) {
stateVerifier.throwIfRecycled();
synchronized (requestLock) {
if (IS_VERBOSE_LOGGABLE) {
logV("Got onSizeReady in " + LogTime.getElapsedMillis(startTime));
}
if (status != Status.WAITING_FOR_SIZE) {
return;
}
status = Status.RUNNING;
float sizeMultiplier = requestOptions.getSizeMultiplier();
this.width = maybeApplySizeMultiplier(width, sizeMultiplier);
this.height = maybeApplySizeMultiplier(height, sizeMultiplier);
if (IS_VERBOSE_LOGGABLE) {
logV("finished setup for calling load in " + LogTime.getElapsedMillis(startTime));
}
loadStatus =
engine.load(
glideContext,
model,
requestOptions.getSignature(),
this.width,
this.height,
requestOptions.getResourceClass(),
transcodeClass,
priority,
requestOptions.getDiskCacheStrategy(),
requestOptions.getTransformations(),
requestOptions.isTransformationRequired(),
requestOptions.isScaleOnlyOrNoTransform(),
requestOptions.getOptions(),
requestOptions.isMemoryCacheable(),
requestOptions.getUseUnlimitedSourceGeneratorsPool(),
requestOptions.getUseAnimationPool(),
requestOptions.getOnlyRetrieveFromCache(),
this,
callbackExecutor);
在这里的重点方法就是load方法了
public LoadStatus load(
GlideContext glideContext,
Object model,
Key signature,
int width,
int height,
Class resourceClass,
Class transcodeClass,
Priority priority,
DiskCacheStrategy diskCacheStrategy,
Map, Transformation> transformations,
boolean isTransformationRequired,
boolean isScaleOnlyOrNoTransform,
Options options,
boolean isMemoryCacheable,
boolean useUnlimitedSourceExecutorPool,
boolean useAnimationPool,
boolean onlyRetrieveFromCache,
ResourceCallback cb,
Executor callbackExecutor) {
long startTime = VERBOSE_IS_LOGGABLE ? LogTime.getLogTime() : 0;
EngineKey key =
keyFactory.buildKey(
model,
signature,
width,
height,
transformations,
resourceClass,
transcodeClass,
options);
EngineResource memoryResource;
synchronized (this) {
memoryResource = loadFromMemory(key, isMemoryCacheable, startTime);
if (memoryResource == null) {
return waitForExistingOrStartNewJob(
glideContext,
model,
signature,
width,
height,
resourceClass,
transcodeClass,
priority,
diskCacheStrategy,
transformations,
isTransformationRequired,
isScaleOnlyOrNoTransform,
options,
isMemoryCacheable,
useUnlimitedSourceExecutorPool,
useAnimationPool,
onlyRetrieveFromCache,
cb,
callbackExecutor,
key,
startTime);
}
}
// Avoid calling back while holding the engine lock, doing so makes it easier for callers to
// deadlock.
cb.onResourceReady(memoryResource, DataSource.MEMORY_CACHE);
return null;
}
可以看到Glide是缓存的key是基于url，签名，宽，高，以及配置的参数等，然后从先从缓存中取数据
Glide内存缓存实现
@Nullable
private EngineResource loadFromMemory(
EngineKey key, boolean isMemoryCacheable, long startTime) {
if (!isMemoryCacheable) {
return null;
}
EngineResource active = loadFromActiveResources(key);
if (active != null) {
Log.i("Glide","from ActiveResourcesCache");
if (VERBOSE_IS_LOGGABLE) {
logWithTimeAndKey("Loaded resource from active resources", startTime, key);
}
return active;
}
EngineResource cached = loadFromCache(key);
if (cached != null) {
Log.i("Glide","from loadFromCache");
if (VERBOSE_IS_LOGGABLE) {
logWithTimeAndKey("Loaded resource from cache", startTime, key);
}
return cached;
}
return null;
}
这里先从ActiveResources中也就是map中取
final Map activeEngineResources = new HashMap<>();
也就是一个弱引用集合中取，取不到在从MemoryCache中取
private EngineResource loadFromCache(Key key) {
EngineResource cached = getEngineResourceFromCache(key);
if (cached != null) {
cached.acquire();
activeResources.activate(key, cached);
}
return cached;
}
...
private EngineResource getEngineResourceFromCache(Key key) {
Resource cached = cache.remove(key);
final EngineResource result;
if (cached == null) {
result = null;
} else if (cached instanceof EngineResource) {
// Save an object allocation if we've cached an EngineResource (the typical case).
result = (EngineResource) cached;
} else {
result =
new EngineResource<>(
cached, /*isMemoryCacheable=*/ true, /*isRecyclable=*/ true, key, /*listener=*/ this);
}
return result;
}
我们可以看到一旦从memory中取到key，就会去把这个key对应的resource放到activeResources中
这样做的好处是啥？据我猜测，无非就是想缓解memorycache的压力，因为linkedHashMap的效率肯定都没有map快的
如果没有内存缓存的话，就调用waitForExistingOrStartNewJob方法
private LoadStatus waitForExistingOrStartNewJob(
GlideContext glideContext,
Object model,
Key signature,
int width,
int height,
Class resourceClass,
Class transcodeClass,
Priority priority,
DiskCacheStrategy diskCacheStrategy,
Map, Transformation> transformations,
boolean isTransformationRequired,
boolean isScaleOnlyOrNoTransform,
Options options,
boolean isMemoryCacheable,
boolean useUnlimitedSourceExecutorPool,
boolean useAnimationPool,
boolean onlyRetrieveFromCache,
ResourceCallback cb,
Executor callbackExecutor,
EngineKey key,
long startTime) {
EngineJob current = jobs.get(key, onlyRetrieveFromCache);
if (current != null) {
current.addCallback(cb, callbackExecutor);
if (VERBOSE_IS_LOGGABLE) {
logWithTimeAndKey("Added to existing load", startTime, key);
}
return new LoadStatus(cb, current);
}
EngineJob engineJob =
engineJobFactory.build(
key,
isMemoryCacheable,
useUnlimitedSourceExecutorPool,
useAnimationPool,
onlyRetrieveFromCache);
DecodeJob decodeJob =
decodeJobFactory.build(
glideContext,
model,
key,
signature,
width,
height,
resourceClass,
transcodeClass,
priority,
diskCacheStrategy,
transformations,
isTransformationRequired,
isScaleOnlyOrNoTransform,
onlyRetrieveFromCache,
options,
engineJob);
jobs.put(key, engineJob);
engineJob.addCallback(cb, callbackExecutor);
engineJob.start(decodeJob);
if (VERBOSE_IS_LOGGABLE) {
logWithTimeAndKey("Started new load", startTime, key);
}
return new LoadStatus(cb, engineJob);
}
这里enginJob是为了过滤decodeJob的重复请求的，并开启decodeJob中的线程池可以看到在start方法中
public synchronized void start(DecodeJob decodeJob) {
this.decodeJob = decodeJob;
GlideExecutor executor =
decodeJob.willDecodeFromCache() ? diskCacheExecutor : getActiveSourceExecutor();
executor.execute(decodeJob);
}
这里的判断很明显，是判断此次要下载的是否要从硬盘缓存取，如果是的话就开启硬盘缓存的线程池，不是的话就开启网络的线程池
boolean willDecodeFromCache() {
Stage firstStage = getNextStage(Stage.INITIALIZE);
return firstStage == Stage.RESOURCE_CACHE || firstStage == Stage.DATA_CACHE;
}
...
private Stage getNextStage(Stage current) {
switch (current) {
case INITIALIZE:
return diskCacheStrategy.decodeCachedResource()
? Stage.RESOURCE_CACHE
: getNextStage(Stage.RESOURCE_CACHE);
case RESOURCE_CACHE:
return diskCacheStrategy.decodeCachedData()
? Stage.DATA_CACHE
: getNextStage(Stage.DATA_CACHE);
case DATA_CACHE:
// Skip loading from source if the user opted to only retrieve the resource from cache.
return onlyRetrieveFromCache ? Stage.FINISHED : Stage.SOURCE;
case SOURCE:
case FINISHED:
return Stage.FINISHED;
default:
throw new IllegalArgumentException("Unrecognized stage: " + current);
}
}
所以只要decodeCachedResource和decodeCachedData返回的值有一个是true的话就会开启硬盘缓存的线程池，而代码里几种硬盘缓存的策略中AUTOMATIC，RESOURCE，DATA，NONE，ALL中只有NONE两个都返回false也印证了这一点
从硬盘取缓存
@Override
public void run() {
// This should be much more fine grained, but since Java's thread pool implementation silently
// swallows all otherwise fatal exceptions, this will at least make it obvious to developers
// that something is failing.
GlideTrace.beginSectionFormat("DecodeJob#run(model=%s)", model);
// Methods in the try statement can invalidate currentFetcher, so set a local variable here to
// ensure that the fetcher is cleaned up either way.
DataFetcher localFetcher = currentFetcher;
try {
if (isCancelled) {
notifyFailed();
return;
}
runWrapped();
} catch (Throwable t) {
// Catch Throwable and not Exception to handle OOMs. Throwables are swallowed by our
// usage of .submit() in GlideExecutor so we're not silently hiding crashes by doing this. We
// are however ensuring that our callbacks are always notified when a load fails. Without this
// notification, uncaught throwables never notify the corresponding callbacks, which can cause
// loads to silently hang forever, a case that's especially bad for users using Futures on
// background threads.
if (Log.isLoggable(TAG, Log.DEBUG)) {
Log.d(TAG, "DecodeJob threw unexpectedly"
+ ", isCancelled: " + isCancelled
+ ", stage: " + stage, t);
}
// When we're encoding we've already notified our callback and it isn't safe to do so again.
if (stage != Stage.ENCODE) {
throwables.add(t);
notifyFailed();
}
if (!isCancelled) {
throw t;
}
} finally {
// Keeping track of the fetcher here and calling cleanup is excessively paranoid, we call
// close in all cases anyway.
if (localFetcher != null) {
localFetcher.cleanup();
}
GlideTrace.endSection();
}
}
会开启线程池走到run方法中，间接会到runWrapped中，这里可能会有点绕，它的核心是找到合适的缓存机制进行加载图片
首先当diskCacheStrategy.decodeCachedResource()返回true的话首先会新建对象ResourceCacheGenerator,然后调用它的startNext方法去取缓存的图片，来看下其实现
//ResourceCacheGenerator
public boolean startNext() {
List sourceIds = helper.getCacheKeys();
if (sourceIds.isEmpty()) {
return false;
}
List> resourceClasses = helper.getRegisteredResourceClasses();
if (resourceClasses.isEmpty()) {
if (File.class.equals(helper.getTranscodeClass())) {
return false;
}
// TODO(b/73882030): This case gets triggered when it shouldn't. With this assertion it causes
// all loads to fail. Without this assertion it causes loads to miss the disk cache
// unnecessarily
// throw new IllegalStateException(
// "Failed to find any load path from " + helper.getModelClass() + " to "
// + helper.getTranscodeClass());
}
while (modelLoaders == null || !hasNextModelLoader()) {
resourceClassIndex++;
if (resourceClassIndex >= resourceClasses.size()) {
sourceIdIndex++;
if (sourceIdIndex >= sourceIds.size()) {
return false;
}
resourceClassIndex = 0;
}
Key sourceId = sourceIds.get(sourceIdIndex);
Class resourceClass = resourceClasses.get(resourceClassIndex);
Transformation transformation = helper.getTransformation(resourceClass);
// PMD.AvoidInstantiatingObjectsInLoops Each iteration is comparatively expensive anyway,
// we only run until the first one succeeds, the loop runs for only a limited
// number of iterations on the order of 10-20 in the worst case.
currentKey =
new ResourceCacheKey(// NOPMD AvoidInstantiatingObjectsInLoops
helper.getArrayPool(),
sourceId,
helper.getSignature(),
helper.getWidth(),
helper.getHeight(),
transformation,
resourceClass,
helper.getOptions());
cacheFile = helper.getDiskCache().get(currentKey);
if (cacheFile != null) {
sourceKey = sourceId;
modelLoaders = helper.getModelLoaders(cacheFile);
modelLoaderIndex = 0;
}
}
loadData = null;
boolean started = false;
while (!started && hasNextModelLoader()) {
ModelLoader modelLoader = modelLoaders.get(modelLoaderIndex++);
loadData = modelLoader.buildLoadData(cacheFile,
helper.getWidth(), helper.getHeight(), helper.getOptions());
if (loadData != null && helper.hasLoadPath(loadData.fetcher.getDataClass())) {
started = true;
loadData.fetcher.loadData(helper.getPriority(), this);
}
}
return started;
}
可以看到代码很简单，由于初始化注册了资源解析器，也就是resourceClasses集合，分别是drawable,bitmap, gif
然后从把key经过宽和高，解析器类型的合成，合称为currentKey，然后再去DiskLruCacheWrapper取值
@Override
public File get(Key key) {
String safeKey = safeKeyGenerator.getSafeKey(key);
if (Log.isLoggable(TAG, Log.VERBOSE)) {
Log.v(TAG, "Get: Obtained: " + safeKey + " for for Key: " + key);
}
File result = null;
try {
// It is possible that the there will be a put in between these two gets. If so that shouldn't
// be a problem because we will always put the same value at the same key so our input streams
// will still represent the same data.
final DiskLruCache.Value value = getDiskCache().get(safeKey);
if (value != null) {
result = value.getFile(0);
}
} catch (IOException e) {
if (Log.isLoggable(TAG, Log.WARN)) {
Log.w(TAG, "Unable to get from disk cache", e);
}
}
return result;
}
可以看到磁盘缓存把key做了md5的加密，然后从DiskLruCache取，当然经过debug分析是没取到，因为存的时候不再soure这个分之存的，后面会分析，当在soure没取到时，会去data的分之中取，代码如下
//DataCacheGenerator
@Override
public boolean startNext() {
while (modelLoaders == null || !hasNextModelLoader()) {
sourceIdIndex++;
if (sourceIdIndex >= cacheKeys.size()) {
return false;
}
Key sourceId = cacheKeys.get(sourceIdIndex);
// PMD.AvoidInstantiatingObjectsInLoops The loop iterates a limited number of times
// and the actions it performs are much more expensive than a single allocation.
@SuppressWarnings("PMD.AvoidInstantiatingObjectsInLoops")
Key originalKey = new DataCacheKey(sourceId, helper.getSignature());
cacheFile = helper.getDiskCache().get(originalKey);
if (cacheFile != null) {
this.sourceKey = sourceId;
modelLoaders = helper.getModelLoaders(cacheFile);
modelLoaderIndex = 0;
}
}
loadData = null;
boolean started = false;
while (!started && hasNextModelLoader()) {
ModelLoader modelLoader = modelLoaders.get(modelLoaderIndex++);
loadData =
modelLoader.buildLoadData(cacheFile, helper.getWidth(), helper.getHeight(),
helper.getOptions());
if (loadData != null && helper.hasLoadPath(loadData.fetcher.getDataClass())) {
started = true;
loadData.fetcher.loadData(helper.getPriority(), this);
}
}
return started;
}
可以说代码基本一样的，除了key不同，并没有像source一样区分宽高等信息，因为data保存的是原数据，自然不用区分，既然拿到了key对应的file
@Override
public void loadData(@NonNull Priority priority,
@NonNull DataCallback callback) {
ByteBuffer result;
try {
result = ByteBufferUtil.fromFile(file);
} catch (IOException e) {
if (Log.isLoggable(TAG, Log.DEBUG)) {
Log.d(TAG, "Failed to obtain ByteBuffer for file", e);
}
callback.onLoadFailed(e);
return;
}
callback.onDataReady(result);
}
这里拿到了对应的file文件，通过ByteBufferUtil解析成了ByteBuffer流，间接调用onDataFetcherReady方法
@Override
public void onDataFetcherReady(Key sourceKey, Object data, DataFetcher fetcher,
DataSource dataSource, Key attemptedKey) {
this.currentSourceKey = sourceKey;
this.currentData = data;
this.currentFetcher = fetcher;
this.currentDataSource = dataSource;
this.currentAttemptingKey = attemptedKey;
if (Thread.currentThread() != currentThread) {
runReason = RunReason.DECODE_DATA;
callback.reschedule(this);
} else {
GlideTrace.beginSection("DecodeJob.decodeFromRetrievedData");
try {
decodeFromRetrievedData();
} finally {
GlideTrace.endSection();
}
}
}
关于Glide拿到流后具体的优化的操作下篇具体分析，不然太长了.