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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：
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：
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？
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。
