@TOC
Glide是一个快速高效的Android图片加载库,注重于平滑的滚动。Glide提供了易用的API,高性能、可扩展的图片解码管道(decode pipeline),以及自动的资源池技术。 Glide 支持拉取,解码和展示视频快照,图片,和GIF动画。Glide的Api是如此的灵活,开发者甚至可以插入和替换成自己喜爱的任何网络栈。默认情况下,Glide使用的是一个定制化的基于HttpUrlConnection的栈,但同时也提供了与Google Volley和Square OkHttp快速集成的工具库。 虽然Glide 的主要目标是让任何形式的图片列表的滚动尽可能地变得更快、更平滑,但实际上,Glide几乎能满足你对远程图片的拉取/缩放/显示的一切需求。
Glide.with(context)
.load(url)
.into(imageView);
@NonNull
public static RequestManager with(@NonNull Context context) {
return getRetriever(context).get(context);
}
with方法首先给我们返回了一个RequestManager,我们看下with方法,这里应该做了初始化的工作,因为Glide并不需要我们在Application初始化的时候手动去做初始化的工作。
@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();
}
/**
* Get the singleton.
*
* @return the singleton
*/
@NonNull
public static Glide get(@NonNull Context context) {
if (glide == null) {
synchronized (Glide.class) {
if (glide == null) {
checkAndInitializeGlide(context);
}
}
}
return glide;
}
果然如我们所料,这里初始化了Glide,是个double check的单例。checkAndInitializeGlide方法就不看了,和普通的初始化工作没什么两样。getRequestManagerRetriever()就是把Glide初始化过程中new出来的RequestManagerRetriever返回了,接着我们看下获取请求管理器的代码:
@NonNull
public RequestManager get(@NonNull Context context) {
if (context == null) {
throw new IllegalArgumentException("You cannot start a load on a null Context");
} else if (Util.isOnMainThread() && !(context instanceof Application)) {
if (context instanceof FragmentActivity) {
return get((FragmentActivity) context);
} else if (context instanceof Activity) {
return get((Activity) context);
} else if (context instanceof ContextWrapper) {
return get(((ContextWrapper) context).getBaseContext());
}
}
return getApplicationManager(context);
}
这里根据不同的context类型创建了不同的RequestManager,主要是用于区分不同容器的生命周期的管理,这里我们不细看,后面会有专门的章节分析Glide的生命周期管理,内存泄漏的控制。 到这里,我们的Glide的初始化工作就完成了,如果不是首次调用,都不需要初始化。接着我们看load方法,我们拿网络图片为例:
@NonNull
@CheckResult
@Override
public RequestBuilder<Drawable> load(@Nullable String string) {
return asDrawable().load(string);
}
@NonNull
@CheckResult
public RequestBuilder<Drawable> asDrawable() {
return as(Drawable.class);
}
@NonNull
@CheckResult
public <ResourceType> RequestBuilder<ResourceType> as(
@NonNull Class<ResourceType> resourceClass) {
return new RequestBuilder<>(glide, this, resourceClass, context);
}
这里默认情况下我们会创建一个ResourceType为Drawable类型的RequestBuilder,我们继续看ResourceBuilder的load方法:
@NonNull
@Override
@CheckResult
public RequestBuilder<TranscodeType> load(@Nullable String string) {
return loadGeneric(string);
}
@NonNull
private RequestBuilder<TranscodeType> loadGeneric(@Nullable Object model) {
this.model = model;
isModelSet = true;
return this;
}
到这里load方法也已经结束了,他的主要的工作就是创建一个RequestBuilder并把设置存起来。继续,来看into方法:
@NonNull
public ViewTarget<ImageView, TranscodeType> into(@NonNull ImageView view) {
Util.assertMainThread();
Preconditions.checkNotNull(view);
BaseRequestOptions<?> requestOptions = this;
if (!requestOptions.isTransformationSet()
&& requestOptions.isTransformationAllowed()
&& view.getScaleType() != null) {
// Clone in this method so that if we use this RequestBuilder to load into a View and then
// into a different target, we don't retain the transformation applied based on the previous
// View's scale type.
switch (view.getScaleType()) {
case CENTER_CROP:
requestOptions = requestOptions.clone().optionalCenterCrop();
break;
case CENTER_INSIDE:
requestOptions = requestOptions.clone().optionalCenterInside();
break;
case FIT_CENTER:
case FIT_START:
case FIT_END:
requestOptions = requestOptions.clone().optionalFitCenter();
break;
case FIT_XY:
requestOptions = requestOptions.clone().optionalCenterInside();
break;
case CENTER:
case MATRIX:
default:
// Do nothing.
}
}
return into(
glideContext.buildImageViewTarget(view, transcodeClass),
/*targetListener=*/ null,
requestOptions,
Executors.mainThreadExecutor());
}
前面一部分是缩放模式,我们看真正的into方法:
private <Y extends Target<TranscodeType>> Y into(
@NonNull Y target,
@Nullable RequestListener<TranscodeType> 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)) {
request.recycle();
// 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;
}
首先看下方法的四个参数:
- 第一个是Target,可以将资源加载到其中,并在加载期间通知相关的生命周期事件。这里帮我们把我们传入的ImageView做了一次wrap操作,包装成了一个Target;
- 第二个参数是资源加载的回调,有需要的话可以自己实现;
- 第三个参数是请求参数,也就是这里的RequestBuilder;
- 第四个参数是指定回调执行的线程,这里是在主线程上执行,也就是在主线程操作ui。
接着我们看方法实现:首先我们创建一个请求:
private Request buildRequest(
Target<TranscodeType> target,
@Nullable RequestListener<TranscodeType> targetListener,
BaseRequestOptions<?> requestOptions,
Executor callbackExecutor) {
return buildRequestRecursive(
target,
targetListener,
/*parentCoordinator=*/ null,
transitionOptions,
requestOptions.getPriority(),
requestOptions.getOverrideWidth(),
requestOptions.getOverrideHeight(),
requestOptions,
callbackExecutor);
}
////////////////////////////////////////////////////////////////////////////////////
private Request buildRequestRecursive(
Target<TranscodeType> target,
@Nullable RequestListener<TranscodeType> targetListener,
@Nullable RequestCoordinator parentCoordinator,
TransitionOptions<?, ? super TranscodeType> transitionOptions,
Priority priority,
int overrideWidth,
int overrideHeight,
BaseRequestOptions<?> requestOptions,
Executor callbackExecutor) {
// Build the ErrorRequestCoordinator first if necessary so we can update parentCoordinator.
ErrorRequestCoordinator errorRequestCoordinator = null;
if (errorBuilder != null) {
errorRequestCoordinator = new ErrorRequestCoordinator(parentCoordinator);
parentCoordinator = errorRequestCoordinator;
}
Request mainRequest =
buildThumbnailRequestRecursive(
target,
targetListener,
parentCoordinator,
transitionOptions,
priority,
overrideWidth,
overrideHeight,
requestOptions,
callbackExecutor);
if (errorRequestCoordinator == null) {
return mainRequest;
}
int errorOverrideWidth = errorBuilder.getOverrideWidth();
int errorOverrideHeight = errorBuilder.getOverrideHeight();
if (Util.isValidDimensions(overrideWidth, overrideHeight)
&& !errorBuilder.isValidOverride()) {
errorOverrideWidth = requestOptions.getOverrideWidth();
errorOverrideHeight = requestOptions.getOverrideHeight();
}
Request errorRequest =
errorBuilder.buildRequestRecursive(
target,
targetListener,
errorRequestCoordinator,
errorBuilder.transitionOptions,
errorBuilder.getPriority(),
errorOverrideWidth,
errorOverrideHeight,
errorBuilder,
callbackExecutor);
errorRequestCoordinator.setRequests(mainRequest, errorRequest);
return errorRequestCoordinator;
}
这里由于没有errorBuilder,所以我们就来看mainRequest(由于代码块太长,只截取默认情况下的逻辑分支):
return obtainRequest(
target,
targetListener,
requestOptions,
parentCoordinator,
transitionOptions,
priority,
overrideWidth,
overrideHeight,
callbackExecutor);
return SingleRequest.obtain(
context,
glideContext,
model,
transcodeClass,
requestOptions,
overrideWidth,
overrideHeight,
priority,
target,
targetListener,
requestListeners,
requestCoordinator,
glideContext.getEngine(),
transitionOptions.getTransitionFactory(),
callbackExecutor);
public static <R> SingleRequest<R> obtain(
Context context,
GlideContext glideContext,
Object model,
Class<R> transcodeClass,
BaseRequestOptions<?> requestOptions,
int overrideWidth,
int overrideHeight,
Priority priority,
Target<R> target,
RequestListener<R> targetListener,
@Nullable List<RequestListener<R>> requestListeners,
RequestCoordinator requestCoordinator,
Engine engine,
TransitionFactory<? super R> animationFactory,
Executor callbackExecutor) {
@SuppressWarnings("unchecked") SingleRequest<R> request =
(SingleRequest<R>) POOL.acquire();
if (request == null) {
request = new SingleRequest<>();
}
request.init(
context,
glideContext,
model,
transcodeClass,
requestOptions,
overrideWidth,
overrideHeight,
priority,
target,
targetListener,
requestListeners,
requestCoordinator,
engine,
animationFactory,
callbackExecutor);
return request;
}
我们看到在SingleRequest的obtain方法中,首先去POOL中取一个可用的,如果没有取到再去创建一个,之后将请求初始化,并返回,到这里我们创建请求就完成了。 接着target的getRequest方法返回的应该是之前设置的request,这里的判断应该是为了防止重复的请求,如果之前已经有了这个请求,则把这次的请求释放掉,再看之前的请求是否在执行,不在执行就开始执行。如果没有这个请求,调用requestManager的track方法:
synchronized void track(@NonNull Target<?> target, @NonNull Request request) {
targetTracker.track(target);
requestTracker.runRequest(request);
}
我们主要看请求:
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);
}
}
这里请求就开始执行了,我们看具体实现类SingleRequest的begin()方法:
@Override
public synchronized void begin() {
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));
}
}
这里绝大部分都是校验代码,关键在修改状态为Status.WAITING_FOR_SIZE后,这里如果校验尺寸是合法的,那么会执行onSizeReady(),我们进入方法看一看:
@Override
public synchronized void onSizeReady(int width, int height) {
stateVerifier.throwIfRecycled();
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);
// This is a hack that's only useful for testing right now where loads complete synchronously
// even though under any executor running on any thread but the main thread, the load would
// have completed asynchronously.
if (status != Status.RUNNING) {
loadStatus = null;
}
if (IS_VERBOSE_LOGGABLE) {
logV("finished onSizeReady in " + LogTime.getElapsedMillis(startTime));
}
}
这里就是engine真正的load方法了:
public synchronized <R> LoadStatus load(
GlideContext glideContext,
Object model,
Key signature,
int width,
int height,
Class<?> resourceClass,
Class<R> transcodeClass,
Priority priority,
DiskCacheStrategy diskCacheStrategy,
Map<Class<?>, 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<?> active = loadFromActiveResources(key, isMemoryCacheable);
if (active != null) {
cb.onResourceReady(active, DataSource.MEMORY_CACHE);
if (VERBOSE_IS_LOGGABLE) {
logWithTimeAndKey("Loaded resource from active resources", startTime, key);
}
return null;
}
EngineResource<?> cached = loadFromCache(key, isMemoryCacheable);
if (cached != null) {
cb.onResourceReady(cached, DataSource.MEMORY_CACHE);
if (VERBOSE_IS_LOGGABLE) {
logWithTimeAndKey("Loaded resource from cache", startTime, key);
}
return null;
}
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<R> engineJob =
engineJobFactory.build(
key,
isMemoryCacheable,
useUnlimitedSourceExecutorPool,
useAnimationPool,
onlyRetrieveFromCache);
DecodeJob<R> 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);
}
首先我们根据入参构建EngineKey,接着根据EngineKey去寻找ActiveResource,我们看下是如何寻找的:
@Nullable
private EngineResource<?> loadFromActiveResources(Key key, boolean isMemoryCacheable) {
if (!isMemoryCacheable) {
return null;
}
EngineResource<?> active = activeResources.get(key);
if (active != null) {
active.acquire();
}
return active;
}
这里的activeResource肯定是使用了内存缓存的,如果内存缓存配置为不可用,直接返空值,接着我们看下activeResource的get方法:
final Map<Key, ResourceWeakReference> activeEngineResources = new HashMap<>();
@Nullable
synchronized EngineResource<?> get(Key key) {
ResourceWeakReference activeRef = activeEngineResources.get(key);
if (activeRef == null) {
return null;
}
EngineResource<?> active = activeRef.get();
if (active == null) {
cleanupActiveReference(activeRef);
}
return active;
}
这边是把弱引用的EngineResource作为值存在Map中。 如果loadFromActiveResource没有找到资源,那么就调用loadFromCache:
private EngineResource<?> loadFromCache(Key key, boolean isMemoryCacheable) {
if (!isMemoryCacheable) {
return null;
}
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;
}
首先我们把key从MemoryCache中移除并返回这个key对应的Resource,这里的MemoryCache是一个借口,我们在这里的具体实现是LruResourceCache,也就是大名鼎鼎的LruCache:
if (memoryCache == null) {
memoryCache = new LruResourceCache(memorySizeCalculator.getMemoryCacheSize());
}
这里就不深入探究LruCache的实现原理了,有很多文章都有介绍,您也可以自己阅读源码研究。这里我们把从缓存获取到的资源返回并且加入ActiveResources。再接着,我们从Jobs中查找key对应的EngineJob,如果存在就去调用addCallback,这里似乎并没有做真正的工作。我们再往下看,我们创建了一个EngineJob和一个DecodeJob,把EngineJob加入了jobs中,再添加回调,然后开始解码工作,我们看下start(decodeJob)方法:
public synchronized void start(DecodeJob<R> decodeJob) {
this.decodeJob = decodeJob;
GlideExecutor executor = decodeJob.willDecodeFromCache()
? diskCacheExecutor
: getActiveSourceExecutor();
executor.execute(decodeJob);
}
这里我们可以看到,我们把decodeJob丢到了线程池中执行,我们看下decodeJob的run方法:
@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 (CallbackException e) {
// If a callback not controlled by Glide throws an exception, we should avoid the Glide
// specific debug logic below.
throw e;
} 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;
}
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();
}
}
private void runWrapped() {
switch (runReason) {
case INITIALIZE:
stage = getNextStage(Stage.INITIALIZE);
currentGenerator = getNextGenerator();
runGenerators();
break;
case SWITCH_TO_SOURCE_SERVICE:
runGenerators();
break;
case DECODE_DATA:
decodeFromRetrievedData();
break;
default:
throw new IllegalStateException("Unrecognized run reason: " + runReason);
}
}
private DataFetcherGenerator getNextGenerator() {
switch (stage) {
case RESOURCE_CACHE:
return new ResourceCacheGenerator(decodeHelper, this);
case DATA_CACHE:
return new DataCacheGenerator(decodeHelper, this);
case SOURCE:
return new SourceGenerator(decodeHelper, this);
case FINISHED:
return null;
default:
throw new IllegalStateException("Unrecognized stage: " + stage);
}
}
private void runGenerators() {
currentThread = Thread.currentThread();
startFetchTime = LogTime.getLogTime();
boolean isStarted = false;
while (!isCancelled && currentGenerator != null
&& !(isStarted = currentGenerator.startNext())) {
stage = getNextStage(stage);
currentGenerator = getNextGenerator();
if (stage == Stage.SOURCE) {
reschedule();
return;
}
}
// We've run out of stages and generators, give up.
if ((stage == Stage.FINISHED || isCancelled) && !isStarted) {
notifyFailed();
}
// Otherwise a generator started a new load and we expect to be called back in
// onDataFetcherReady.
}
这里的DataFetcher就是拉取数据的,是一个接口,我们可以自定义扩展它,因此可以实现自定义网络库的能力。Glide中也自带了很多的实现类用来获取不同环境下的资源,这里不再多做分析了。在runWrap方法中封装了decode的所有阶段和各个阶段执行的工作,最终把解码后的数据回调出去,最终把资源设置到封装了ImageView的DrawableImageViewTarget中
@Override
protected void setResource(@Nullable Drawable resource) {
view.setImageDrawable(resource);
}
结束语 Glide的基本执行流程就分析到这里了,可以看到和我们之前分析的RxJava一样,对于职责的分工非常明确,功能的抽象设计的非常赞,深深感受到了自己的差距还是很大,还需要好好修炼设计的内功鸭。