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链接:https://mp.weixin.qq.com/s/cPLkefpEb3w12-uoiqzTig
作者:连凌能

Android上图片加载的解决方案有多种,但是官方认可的是Glide。Glide提供简洁易用的api,整个框架也方便扩展,比如可以替换网络请求库,同时也提供了完备的缓存机制,应用层不需要自己去管理图片的缓存与获取,框架会分成内存缓存,文件缓存和远程缓存。本文不会从简单的使用着手,会把重点放在缓存机制的分析上。

一、综述

开始之前,关于Glide缓存请先思考几个问题:

  • Glide有几级缓存?

  • Glide内存缓存之间是什么关系?

  • Glide本地文件IO和网络请求是一个线程吗?如果不是,怎么实现线程切换?

  • Glide网络请求回来后数据直接返回给用户还是先存再返回?

加载开始入口从Engine.load()开始,先看下对这个方法的注释,

  • 会先检查(Active Resources),如果有就直接返回,Active Resources没有被引用的资源会放入Memory Cache,如果Active Resources没有,会往下走。

  • 检查Memory Cache中是否有需要的资源,如果有就返回,Memory Cache中没有就继续往下走。

  • 检查当前在运行中的job中是否有改资源的下载,有就在现有的job中直接添加callback返回,不重复下载,当然前提是计算得到的key是一致的,如果还是没有,就会构造一个新的job开始新的工作。

* Starts a load for the given arguments.
*
* <p>Must be called on the main thread.
*
* <p>The flow for any request is as follows:
* <ul>
* <li>Check the current set of actively used resources, return the active resource if
* present, and move any newly inactive resources into the memory cache.</li>
* <li>Check the memory cache and provide the cached resource if present.</li>
* <li>Check the current set of in progress loads and add the cb to the in progress load if
* one is present.</li>
* <li>Start a new load.</li>
* </ul>

ok, find the source code.

二、内存缓存

public <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) {
Util.assertMainThread();
long startTime = VERBOSE_IS_LOGGABLE ? LogTime.getLogTime() : 0; EngineKey key = keyFactory.buildKey(model, signature, width, height, transformations,
resourceClass, transcodeClass, options); // focus 1
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;
}
// focus 2
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;
} // focus 3
EngineJob<?> current = jobs.get(key, onlyRetrieveFromCache);
if (current != null) {
current.addCallback(cb);
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);
// focus 4
engineJob.start(decodeJob); if (VERBOSE_IS_LOGGABLE) {
logWithTimeAndKey("Started new load", startTime, key);
}
return new LoadStatus(cb, engineJob);
}

先看到 focus 1,这一步会从 ActiveResources 中加载资源,首先判断是否使用内存缓存,否的话返回null;否则到 ActiveResources 中取数据:

// Engine.java
@Nullable
private EngineResource<?> loadFromActiveResources(Key key, boolean isMemoryCacheable) {
if (!isMemoryCacheable) {
return null;
}
EngineResource<?> active = activeResources.get(key);
if (active != null) {
active.acquire();
} return active;
}

接下来看下ActiveResources, 其实是用过弱引用保存使用过的资源。

final class ActiveResources {

  ...
private final Handler mainHandler = new Handler(Looper.getMainLooper(), new Callback() {
@Override
public boolean handleMessage(Message msg) {
if (msg.what == MSG_CLEAN_REF) {
cleanupActiveReference((ResourceWeakReference) msg.obj);
return true;
}
return false;
}
});
@VisibleForTesting
final Map<Key, ResourceWeakReference> activeEngineResources = new HashMap<>(); ...
}

成功取到数据后回调类型也是内存缓存:


EngineResource<?> cached = loadFromCache(key, isMemoryCacheable);
if (cached != null) {
cb.onResourceReady(cached, DataSource.MEMORY_CACHE);
return null;
}

接着回到Engine.load()中继续看到focus 2,如果在cache中找到就是remove掉,然后返回EngineResource,其中需要EngineResource进行acquire一下,这个后面再看,然后会把资源移到ActiveResources中,也就是上面提到的缓存:

// Engine.java
private final MemoryCache cache; 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, true /*isMemoryCacheable*/, true /*isRecyclable*/);
}
return result;
}

其中cache是MemoryCache接口的实现,如果没设置,默认在build的时候是LruResourceCache, 也就是熟悉的LRU Cache:

// GlideBuilder.java
if (memoryCache == null) {
memoryCache = new LruResourceCache(memorySizeCalculator.getMemoryCacheSize());
}

再看下EngineResource,主要是对资源增加了引用计数的功能:

// EngineResource.java
private final boolean isCacheable;
private final boolean isRecyclable;
private ResourceListener listener;
private Key key;
private int acquired;
private boolean isRecycled;
private final Resource<Z> resource; interface ResourceListener {
void onResourceReleased(Key key, EngineResource<?> resource);
} EngineResource(Resource<Z> toWrap, boolean isCacheable, boolean isRecyclable) {
resource = Preconditions.checkNotNull(toWrap);
this.isCacheable = isCacheable;
this.isRecyclable = isRecyclable;
} void setResourceListener(Key key, ResourceListener listener) {
this.key = key;
this.listener = listener;
} Resource<Z> getResource() {
return resource;
} boolean isCacheable() {
return isCacheable;
} @NonNull
@Override
public Class<Z> getResourceClass() {
return resource.getResourceClass();
} @NonNull
@Override
public Z get() {
return resource.get();
} @Override
public int getSize() {
return resource.getSize();
} @Override
public void recycle() {
if (acquired > 0) {
throw new IllegalStateException("Cannot recycle a resource while it is still acquired");
}
if (isRecycled) {
throw new IllegalStateException("Cannot recycle a resource that has already been recycled");
}
isRecycled = true;
if (isRecyclable) {
resource.recycle();
}
} void acquire() {
if (isRecycled) {
throw new IllegalStateException("Cannot acquire a recycled resource");
}
if (!Looper.getMainLooper().equals(Looper.myLooper())) {
throw new IllegalThreadStateException("Must call acquire on the main thread");
}
++acquired;
} void release() {
if (acquired <= 0) {
throw new IllegalStateException("Cannot release a recycled or not yet acquired resource");
}
if (!Looper.getMainLooper().equals(Looper.myLooper())) {
throw new IllegalThreadStateException("Must call release on the main thread");
}
if (--acquired == 0) {
listener.onResourceReleased(key, this);
}
}

在release后会判断引用计数是否为0,如果是0就会回调onResourceReleased,在这里就是Engine,然后会把资源从ActiveResources中移除,资源默认是可缓存的,因此会把资源放到LruCache中。

// Engine.java
@Override
public void onResourceReleased(Key cacheKey, EngineResource<?> resource) {
Util.assertMainThread();
activeResources.deactivate(cacheKey);
if (resource.isCacheable()) {
cache.put(cacheKey, resource);
} else {
resourceRecycler.recycle(resource);
}
} // ActiveResources.java
void activate(Key key, EngineResource<?> resource) {
ResourceWeakReference toPut =
new ResourceWeakReference(
key,
resource,
getReferenceQueue(),
isActiveResourceRetentionAllowed); ResourceWeakReference removed = activeEngineResources.put(key, toPut);
if (removed != null) {
removed.reset();
}
} void deactivate(Key key) {
ResourceWeakReference removed = activeEngineResources.remove(key);
if (removed != null) {
removed.reset();
}
}

如果是回收呢,看看上面的EngineResource,如果引用计数为0并且还没与回收,就会调用真正的Resource.recycle(),看其中的一个BitmapResource是怎么回收的,就是放到Bitmap池中,也是用的LRU Cache,这个和今天的主题不相关,就不继续往下拓展。

// BitmapResource.java
@Override
public void recycle() {
bitmapPool.put(bitmap);
}

思路再拉到Engine.load()的流程中,接下来该看focus 3,这里再贴一下代码,如果job已经在运行了,那么直接添加一个回调后返回LoadStatus,这个可以允许用户取消任务:

// Engine.java
EngineJob<?> current = jobs.get(key, onlyRetrieveFromCache);
if (current != null) {
current.addCallback(cb);
if (VERBOSE_IS_LOGGABLE) {
logWithTimeAndKey("Added to existing load", startTime, key);
}
return new LoadStatus(cb, current);
} // LoadStatus
public static class LoadStatus {
private final EngineJob<?> engineJob;
private final ResourceCallback cb; LoadStatus(ResourceCallback cb, EngineJob<?> engineJob) {
this.cb = cb;
this.engineJob = engineJob;
} public void cancel() {
engineJob.removeCallback(cb);
}
}

接着往下看到focus 4, 到这里就需要创建后台任务去拉取磁盘文件或者发起网络请求。

三、磁盘缓存

// Engine.java
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);
engineJob.start(decodeJob);
return new LoadStatus(cb, engineJob);

先构造两个job,一个是EngineJob,另外一个DecodeJob,其中DecodeJob会根据需要解码的资源来源分成下面几个阶段:

// DecodeJob.java
/**
* Where we're trying to decode data from.
*/
private enum Stage {
/** The initial stage. */
INITIALIZE,
/** Decode from a cached resource. */
RESOURCE_CACHE,
/** Decode from cached source data. */
DATA_CACHE,
/** Decode from retrieved source. */
SOURCE,
/** Encoding transformed resources after a successful load. */
ENCODE,
/** No more viable stages. */
FINISHED,
}

在构造DecodeJob时会把状态置为INITIALIZE。

构造完两个 Job 后会调用 EngineJob.start(DecodeJob),首先会调用getNextStage来确定下一个阶段,这里面跟DiskCacheStrategy这个传入的磁盘缓存策略有关。

磁盘策略有下面几种:

  • **ALL: **缓存原始数据和转换后的数据

  • **NONE: **不缓存

  • **DATA: **原始数据,未经过解码或者转换

  • **RESOURCE: **缓存经过解码的数据

  • **AUTOMATIC(默认):**根据`EncodeStrategy`和`DataSource`等条件自动选择合适的缓存方

默认的AUTOMATIC方式是允许解码缓存的RESOURCE:

public static final DiskCacheStrategy AUTOMATIC = new DiskCacheStrategy() {
@Override
public boolean isDataCacheable(DataSource dataSource) {
return dataSource == DataSource.REMOTE;
} @Override
public boolean isResourceCacheable(boolean isFromAlternateCacheKey, DataSource dataSource,
EncodeStrategy encodeStrategy) {
return ((isFromAlternateCacheKey && dataSource == DataSource.DATA_DISK_CACHE)
|| dataSource == DataSource.LOCAL)
&& encodeStrategy == EncodeStrategy.TRANSFORMED;
} @Override
public boolean decodeCachedResource() {
return true;
} @Override
public boolean decodeCachedData() {
return true;
}
};

所以在 getNextStage 会先返回Stage.RESOURCE_CACHE,然后在start中会返回diskCacheExecutor,然后开始执行DecodeJob:

// EngineJob.java
public void start(DecodeJob<R> decodeJob) {
this.decodeJob = decodeJob;
GlideExecutor executor = decodeJob.willDecodeFromCache()
? diskCacheExecutor
: getActiveSourceExecutor();
executor.execute(decodeJob);
} // DecodeJob.java
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);
}
}

DecodeJob会回调run()开始执行, run()中调用runWrapped执行工作,这里runReason还是RunReason.INITIALIZE ,根据前面的分析指导这里会获得一个ResourceCacheGenerator,然后调用runGenerators:

// DecodeJob.java
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);
}
}

在 runGenerators 中,会调用 startNext,目前currentGenerator是ResourceCacheGenerator, 那么就是调用它的startNext方法:

// DecodeJob.java
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();
}
}

看下ResourceCacheGenerator.startNext(), 这里面就是重点逻辑了,首先从Registry中获取支持资源类型的ModelLoader(其中ModelLoader是在构造Glide的时候传进去), 然后从ModelLoader中构造LoadData,接着就能拿到DataFetcher,(关于ModelLoader/LoadData/DataFetcher之间的关系不在本次范围内,后面有机会再另写)通过它的loadData方法加载数据:

@Override
public boolean startNext() {
List<Key> sourceIds = helper.getCacheKeys();
if (sourceIds.isEmpty()) {
return false;
}
List<Class<?>> resourceClasses = helper.getRegisteredResourceClasses();
if (resourceClasses.isEmpty()) {
if (File.class.equals(helper.getTranscodeClass())) {
return false;
}
}
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);
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<File, ?> 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;
}

如果在Resource中找不到需要的资源,那么startNext就会返回false,在runGenerators中就会进入循环体内:

  • 接着会重复上面执行getNextStage,由于现在Stage已经是RESOURCE_CACHE,所以接下来会返回DataCacheGenerator,执行逻辑和上面的ResourceCacheGenerator是一样的,如果还是没有找到需要的,进入循环体内。

  • 此时getNextStage会根据用于是否设置只从磁盘中获取资源,如果是就会通知失败,回调onLoadFailed;如果不是就设置当前Stage为Stage.SOURCE,接着往下走。

  • 状态就会进入循环内部的if条件逻辑里面,调用reschedule。

  • 在reschedule把runReason设置成SWITCH_TO_SOURCE_SERVICE,然后通过callback回调。

  • DecodeJob中的callback是EngineJob传递过来的,所以现在返回到EngineJob。

  • 在EngineJob中通过getActiveSourceExecutor切换到网络线程池中,执行DecodeJob,下面就准备开始发起网络请求。

四、网络缓存

在Stage.SOURCE阶段,通过getNextGenerator返回的是SourceGenerator,所以目前的currentGenerator就是它。

流程还是一样的,SourceGenerator还是调用startNext方法,获取到对应的DataFetcher,这里其实是HttpUrlFetcher,发起网络请求。

// DecodeJob.java
private void runGenerators() {
...
while (!isCancelled && currentGenerator != null
&& !(isStarted = currentGenerator.startNext())) {
stage = getNextStage(stage);
currentGenerator = getNextGenerator(); if (stage == Stage.SOURCE) {
reschedule();
return;
}
}
...
} @Override
public void reschedule() {
runReason = RunReason.SWITCH_TO_SOURCE_SERVICE;
callback.reschedule(this);
} // EngineJob.java
@Override
public void reschedule(DecodeJob<?> job) {
getActiveSourceExecutor().execute(job);
}

先缓一缓,本文其实到了上面已经可以结束了,Glide涉及到的五级缓存都已经涉及到了,是真的就可以结束了吗?不是的,网络请求回来和缓存还有关系吗?接着看到HttpUrlFetcher,下载成功后回调onDataReady,其中callback是SourceGenerator:

// HttpUrlFetcher.java
@Override
public void loadData(@NonNull Priority priority,
@NonNull DataCallback<? super InputStream> callback) {
long startTime = LogTime.getLogTime();
try {
InputStream result = loadDataWithRedirects(glideUrl.toURL(), 0, null, glideUrl.getHeaders());
callback.onDataReady(result);
} catch (IOException e) {
if (Log.isLoggable(TAG, Log.DEBUG)) {
Log.d(TAG, "Failed to load data for url", e);
}
callback.onLoadFailed(e);
} finally {
if (Log.isLoggable(TAG, Log.VERBOSE)) {
Log.v(TAG, "Finished http url fetcher fetch in " + LogTime.getElapsedMillis(startTime));
}
}
} // EngineJob.java
@Override
public void reschedule(DecodeJob<?> job) {
getActiveSourceExecutor().execute(job);
}

正常情况会进入if判断逻辑里面,赋值dataToCache,然后回调cb.reschedule,而cb就是DecodeJob构造SourceGenerator的时候传入,cb是DecodeJob。

// SourceGenerator.java
@Override
public void onDataReady(Object data) {
DiskCacheStrategy diskCacheStrategy = helper.getDiskCacheStrategy();
if (data != null && diskCacheStrategy.isDataCacheable(loadData.fetcher.getDataSource())) {
dataToCache = data;
cb.reschedule();
} else {
cb.onDataFetcherReady(loadData.sourceKey, data, loadData.fetcher,
loadData.fetcher.getDataSource(), originalKey);
}
}

DecodeJob在reschedule回调EngineJob,最后还是回到SourceGenerator中的startNext()逻辑。

// DecodeJob.java
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);
}
} @Override
public void reschedule() {
runReason = RunReason.SWITCH_TO_SOURCE_SERVICE;
callback.reschedule(this);
}

和第一次进来的逻辑不一样,现在dataToCache != null,进入第一个if逻辑。

在逻辑里面调用cacheData,逻辑很明显,保持数据到本地,然后会构造一个DataCacheGenerator。

而DataCacheGenerator前面已经分析过了,就是用来加载本地原始数据的,这回会加载成功,返回true。

// SourceGenerator.java
@Override
public boolean startNext() {
if (dataToCache != null) {
Object data = dataToCache;
dataToCache = null;
cacheData(data);
} if (sourceCacheGenerator != null && sourceCacheGenerator.startNext()) {
return true;
}
...
} private void cacheData(Object dataToCache) {
long startTime = LogTime.getLogTime();
try {
Encoder<Object> encoder = helper.getSourceEncoder(dataToCache);
DataCacheWriter<Object> writer =
new DataCacheWriter<>(encoder, dataToCache, helper.getOptions());
originalKey = new DataCacheKey(loadData.sourceKey, helper.getSignature());
helper.getDiskCache().put(originalKey, writer);
} finally {
loadData.fetcher.cleanup();
} sourceCacheGenerator =
new DataCacheGenerator(Collections.singletonList(loadData.sourceKey), helper, this);
}

接下来就是一系列的回调了:

DataCacheGenerator的startNext逻辑里面会给DataFetcher传递自身作为callback,在加载本地数据成功后回调onDataReady。

// DataCacheGenerator
@Override
public boolean startNext() {
... loadData = null;
boolean started = false;
while (!started && hasNextModelLoader()) {
...
if (loadData != null && helper.hasLoadPath(loadData.fetcher.getDataClass())) {
started = true;
loadData.fetcher.loadData(helper.getPriority(), this);
}
}
return started;
} @Override
public void onDataReady(Object data) {
cb.onDataFetcherReady(sourceKey, data, loadData.fetcher, DataSource.DATA_DISK_CACHE, sourceKey);
}

而cb现在是SourceGenerator传递过来,SourceGenerator再回调它自己的cb,是DecodeJob在构造它的时候传过来。

// SourceGenerator.java
@Override
public void onDataFetcherReady(Key sourceKey, Object data, DataFetcher<?> fetcher,
DataSource dataSource, Key attemptedKey) {
cb.onDataFetcherReady(sourceKey, data, fetcher, loadData.fetcher.getDataSource(), sourceKey);
} // DecodeJob.java
@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 {
try {
decodeFromRetrievedData();
} finally {
GlideTrace.endSection();
}
}
}

在上面SourceGenerator把DecodeJob切换到ActiveSourceExecutor线程中执行,还记得一开始DecodeJob是在哪启动的吗?在EngineJob中启动,然后是把DecodeJob放到diskCacheExecutor中执行。

// EngineJob.java
public void start(DecodeJob<R> decodeJob) {
this.decodeJob = decodeJob;
GlideExecutor executor = decodeJob.willDecodeFromCache()
? diskCacheExecutor
: getActiveSourceExecutor();
executor.execute(decodeJob);
}

所以上面在DecodeJob的onDataFetcherReady会走到第一个if逻辑里面,然后赋值runReason = RunReason.DECODE_DATA,再一次回调Engine.reschedule,将工作线程切换到ActiveSourceExecutor。

// Engine.java
@Override
public void reschedule(DecodeJob<?> job) {
// Even if the job is cancelled here, it still needs to be scheduled so that it can clean itself
// up.
getActiveSourceExecutor().execute(job);
} //

然后还是走到DecodeJob, 现在会进入DECODE_DATA分支,在这里面会调用ResourceDecoder把数据解码:

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);
}
}

解码成功后调用notifyComplete(result, dataSource);

private void notifyComplete(Resource<R> resource, DataSource dataSource) {
setNotifiedOrThrow();
callback.onResourceReady(resource, dataSource);
}

五、总结

现在回答一下开头的几个问题。

1、有几级缓存?五级,分别是什么?

  • 活动资源 (Active Resources)

  • 内存缓存 (Memory Cache)

  • 资源类型(Resource Disk Cache)

  • 原始数据 (Data Disk Cache)

  • 网络缓存

2、Glide内存缓存之间是什么关系?

专门画了一幅图表明这个关系,言简意赅。

3、Glide本地文件IO和网络请求是一个线程吗?

明显不是,本地IO通过diskCacheExecutor,而网络IO通过ActiveSourceExecutor

4、Glide网络请求回来后数据直接返回给用户还是先存再返回?

不是直接返回给用户,会在SourceGenerator中构造一个DataCacheGenerator来取数据。

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