Glide缓存流程

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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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正文完
缓存 网络请求
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2019-10-14
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