LeakCanary 与 鹅场Matrix ResourceCanary对比分析

 

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LeakCanary是Square公司基于MAT开源的一个内存泄漏检测神器，在发生内存泄漏的时候LeakCanary会自动显示泄漏信息，现在更新了好几个版本，用kotlin语言重新实现了一遍；鹅场APM性能监控框架也集成了内存泄露模块 ResourcePlugin ，这里就两者进行对比。

 

1、组件启动

LeakCanary自动注册启动

原理：专门定制了一个ContentProvider,来注册启动LeakCanary

实现如下：

/**
 * Content providers are loaded before the application class is created. [LeakSentryInstaller] is
 * used to install [leaksentry.LeakSentry] on application start.
 */
internal class LeakSentryInstaller : ContentProvider() {

  override fun onCreate(): Boolean {
    CanaryLog.logger = DefaultCanaryLog()
    val application = context!!.applicationContext as Application
    InternalLeakSentry.install(application)
    return true
  }
  
  ...
}

 

 ResourcePlugin 需要手动启动

public class MatrixApplication extends Application {
    ...
    @Override
    public void onCreate() {
        super.onCreate();
        ...
        ResourcePlugin resPlugin = null;
        if (matrixEnable) {
           resPlugin = new ResourcePlugin(new ResourceConfig.Builder()
                    .dynamicConfig(dynamicConfig)
                    .setDumpHprof(false)
                    .setDetectDebuger(true)     //only set true when in sample, not in your app
                    .build())
            //resource
            builder.plugin(resPlugin );
            ResourcePlugin.activityLeakFixer(this);

           ...
        }

        Matrix.init(builder.build());
        if(resPlugin != null){
            resPlugin.start(); 
        }

    }
  
}

 

 

2、watch范围和自动watch的对象

 

 LeakCanary RefWatcher可以watch任何对象(包括Activity、Fragment、Fragment.View)

class RefWatcher{
    fun watch(watchedInstance: Any) {...}
    fun watch( watchedInstance: Any,name: String) {...}
}

 

支持自动watch Activity、Fragment、Fragment.View对象

1.自动watcher Activity

internal class ActivityDestroyWatcher {
private val lifecycleCallbacks =
    object : Application.ActivityLifecycleCallbacks by noOpDelegate() {
      override fun onActivityDestroyed(activity: Activity) {
        if (configProvider().watchActivities) {
            refWatcher.watch(activity)
        }
      }
    }

  companion object {
    fun install(... ) {
      val activityDestroyWatcher =
        ActivityDestroyWatcher(refWatcher, configProvider) 
application.registerActivityLifecycleCallbacks(activityDestroyWatcher.lifecycleCallbacks)
    }
  }
}

ActivityDestroyWatcher.install在LeakSentryInstaller.onCreate间接调用，注册ActivityLifecycleCallbacks 监听Activity的生命周期，从而实现自动watch Activity对象。

 

2.自动watch Fragment、Fragment.View

//子类有
//SupportFragmentDestroyWatcher
//AndroidOFragmentDestroyWatcher
internal interface FragmentDestroyWatcher {

  fun watchFragments(activity: Activity)

  companion object {
    ...
    fun install(... ) {
    
     ...
      application.registerActivityLifecycleCallbacks(object : Application.ActivityLifecycleCallbacks by noOpDelegate() {
        override fun onActivityCreated( activity: Activity,
savedInstanceState: Bundle? ) {
          for (watcher in fragmentDestroyWatchers) {
            watcher.watchFragments(activity)
          }
        }
      })
    }
 
  }
}

 

FragmentDestroyWatcher .install在LeakSentryInstaller.onCreate间接调用，注册ActivityLifecycleCallbacks 监听Activity的生命周期函数onCreate,然后对activity.fragmentManager注册FragmentLifecycleCallbacks监听Fragment的周期函数，从而实现自动watch Fragment、Fragment.View如下：

internal class XXXFragmentDestroyWatcher(...) : FragmentDestroyWatcher {

  private val fragmentLifecycleCallbacks = object : FragmentManager.FragmentLifecycleCallbacks() {

    override fun onFragmentViewDestroyed(
      fm: FragmentManager,
      fragment: Fragment
    ) {
      val view = fragment.view
      if (view != null && configProvider().watchFragmentViews) {
         //watcher view
         refWatcher.watch(view)
      }
    }

    override fun onFragmentDestroyed(
      fm: FragmentManager,
      fragment: Fragment
    ) {
      if (configProvider().watchFragments) {
        //watcher fragment
        refWatcher.watch(fragment)
      }
    }
  }
  

  //AndroidOFragmentDestroyWatcher
  override fun watchFragments(activity: Activity) {
    val fragmentManager = activity.fragmentManager
    fragmentManager.registerFragmentLifecycleCallbacks(fragmentLifecycleCallbacks, true)
  }
  
 //SupportFragmentDestroyWatcher
  override fun watchFragments(activity: Activity) {
    if (activity is FragmentActivity) {
      val supportFragmentManager = activity.supportFragmentManager
      supportFragmentManager.registerFragmentLifecycleCallbacks(fragmentLifecycleCallbacks, true)
    }
  }
}

从源码上可以看出，貌似只自动watch 以及Fragment，嵌套的Fragment就不行了，如果是watch其他对象（包括子Fragment），则需要手动调用 RefWatcher.watch方法。

 

Replugin 只有一个ActivityRefWatcher,只支持watcher Activity,也是通过注册ActivityLifecycleCallbacks 监听Activity的生命周期，从而实现自动watcher Activity对象。

 

public class ActivityRefWatcher extends FilePublisher implements Watcher {
     @Override
    public void start() {
        stopDetect();
        final Application app = mResourcePlugin.getApplication();
        if (app != null) {
            app.registerActivityLifecycleCallbacks(mRemovedActivityMonitor);
            //轮询检测是否发生溢出
            scheduleDetectProcedure();
           
        }
    }

private final Application.ActivityLifecycleCallbacks mRemovedActivityMonitor = new ActivityLifeCycleCallbacksAdapter() {
 
    @Override
    public void onActivityDestroyed(Activity activity) {
        //push mDestroyedActivityInfos集合中，通过轮询检测对mDestroyedActivityInfos进行处理
        pushDestroyedActivityInfo(activity);
        synchronized (mDestroyedActivityInfos) {
            mDestroyedActivityInfos.notifyAll();
        }
    }
};
 

 

3、检测泄露实现

1.检测线程

  LeakCanay检测实现，旧版本是在一个HandlerThread 轮询检测,现在发生改变，先在主线程中触发检测，由RefWatcher.watch主动触发，对activity，Fragment，Fragment.view的检测，即由生命周期触发，然后在 非主线程中进行真正的check。

 

现在主线中被动触发检测依据如下：

class RefWatcher{
   
 fun watch( watchedInstance: Any,name: String) {
    ...
    watchedInstances[key] = reference
    checkRetainedExecutor.execute {
      moveToRetained(key)
    }
   }
}


internal object InternalLeakSentry {

  ...
  private val checkRetainedExecutor = Executor {
　　//主线程handler
    mainHandler.postDelayed(it, LeakSentry.config.watchDurationMillis)
  }
  val refWatcher = RefWatcher(
      clock = clock,
      checkRetainedExecutor = checkRetainedExecutor,
      onInstanceRetained = { listener.onReferenceRetained() },
      isEnabled = { LeakSentry.config.enabled }
  )
...
}

 

从moveToRetained调用，最终辗转到HeapDumpTrigger的方法scheduleRetainedInstanceCheck方法，然后在非主线中进行真正check，代码如下：

 

internal class HeapDumpTrigger() {
 private fun scheduleRetainedInstanceCheck(reason: String) {
    if (checkScheduled) {
      CanaryLog.d("Already scheduled retained check, ignoring ($reason)")
      return
    }
    checkScheduled = true
    //非主线程hanlder
    backgroundHandler.post {
      checkScheduled = false
      checkRetainedInstances(reason)
    }
  }
...
}

 

 

ResourcePlugin参考LeakCanary旧版本，采用线程轮询检测，依据如下：

 



//ActivityRefWatcher.start
private void scheduleDetectProcedure() {


    //检测轮询 mScanDestroyedActivitiesTask execute函数一直返回RetryableTask.Status.RETRY
    　　mDetectExecutor.executeInBackground(mScanDestroyedActivitiesTask);
}



class RetryableTaskExecutor{
     private void postToBackgroundWithDelay(final RetryableTask task, final int failedAttempts) {
         //非主线程 handler
         mBackgroundHandler.postDelayed(new Runnable() {
            @Override
            public void run() {
                RetryableTask.Status status = task.execute();
                if (status == RetryableTask.Status.RETRY) {
                    postToBackgroundWithDelay(task, failedAttempts + 1);
                }
            }
        }, mDelayMillis);
    }
}




 


 2、检测泄露逻辑实现


   LeakCanay Check检测


  原理：VM会将可回收的对象加入 WeakReference 关联的 ReferenceQueue 


   1）根据retainedReferenceCount > 0，触发一次gc请求，再次获取retainedReferenceCount 




 var retainedReferenceCount = refWatcher.retainedInstanceCount

    if (retainedReferenceCount > 0) {
      gcTrigger.runGc()
      retainedReferenceCount = refWatcher.retainedInstanceCount
    }




 


   2）判断retainedReferenceCount  是否大于retainedVisibleThreshold（默认为5），小于则跳过接下来的检测




if (checkRetainedCount(retainedReferenceCount, config.retainedVisibleThreshold)) return




 


  3）根据dumpHeapWhenDebugging开关和是否在Debug调试，如果配置开关开启且在调试，则延时轮询等待，调试结束




if (!config.dumpHeapWhenDebugging && DebuggerControl.isDebuggerAttached) {
      showRetainedCountWithDebuggerAttached(retainedReferenceCount)
      scheduleRetainedInstanceCheck("debugger was attached", WAIT_FOR_DEBUG_MILLIS）
      return
    }




 


  4）dump Hprof文件




 val heapDumpFile = heapDumper.dumpHeap()
 if (heapDumpFile == null) {
    showRetainedCountWithHeapDumpFailed(retainedReferenceCount)
    return
}




 


  5）开启HeapAnalyzerService进行Hprof分析


 


在旧版本中，在个别系统上可能存在误报，原因大致如下：


    

  • 

    VM 并没有提供强制触发 GC 的 API ，通过 System.gc()或 Runtime.getRuntime().gc()只能“建议”系统进行 GC ，如果系统忽略了我们的 GC 请求，可回收的对象就不会被加入 ReferenceQueue
    

    • 

  • 

    将可回收对象加入 ReferenceQueue 需要等待一段时间，LeakCanary 采用延时 100ms 的做法加以规避，但似乎并不绝对管用
    

    • 

  • 

    监测逻辑是异步的，如果判断 Activity 是否可回收时某个 Activity 正好还被某个方法的局部变量持有，就会引起误判
    

    • 

  • 

    若反复进入泄漏的 Activity ，LeakCanary 会重复提示该 Activity 已泄漏
    

    • 



现在这个2.0-alpha-2版本也没有进行排重，当然这个也不好说，假如一个Activity有多处泄露，且泄露原因不同，排重 就会导致漏报。


 


ResourcePlugin Check检测


 原理：直接通过WeakReference.get()来判断对象是否已被回收，避免因延迟导致误判


1）判断当前mDestroyedActivityInfos是否空，为空的话，就没必要泄露，因为是轮询，所以要防止CPU空转，浪费电




// If destroyed activity list is empty, just wait to save power.
while (mDestroyedActivityInfos.isEmpty()) {
    synchronized (mDestroyedActivityInfos) {
        try {
               mDestroyedActivityInfos.wait();
        } catch (Throwable ignored) {
           // Ignored.
        }
    }
}




 


2）根据配置开关和是否在Debug调试，如果配置开关开启且在调试，跳过此次check,等待下次轮询，调试结束




// Fake leaks will be generated when debugger is attached.
if (Debug.isDebuggerConnected() && !mResourcePlugin.getConfig().getDetectDebugger()) {
        MatrixLog.w(TAG, "debugger is connected, to avoid fake result, detection was delayed.");
        return Status.RETRY;
}




 


3）增加一个一定能被回收的“哨兵”对象，用来确认系统确实进行了GC，没有进行GC,则跳过此次check,等待下次轮询




final WeakReference<Object> sentinelRef = new WeakReference<>(new Object());
triggerGc();
if (sentinelRef.get() != null) {
   // System ignored our gc request, we will retry later.
   MatrixLog.d(TAG, "system ignore our gc request, wait for next detection.");
   return Status.RETRY;
}




 


4）对已判断为泄漏的Activity，记录其类名，避免重复提示该Activity已泄漏，有效期一天




final DestroyedActivityInfo destroyedActivityInfo = infoIt.next();
if (isPublished(destroyedActivityInfo.mActivityName)) {
    MatrixLog.v(TAG, "activity with key [%s] was already published.", destroyedActivityInfo.mActivityName);
    infoIt.remove();
    continue;
}




前面已经提过排重还是有缺陷的，比如一个Activity有多处泄露，且泄露原因不同，排重 就会导致漏报


 


5）若发现某个Activity无法被回收，再重复判断3次，且要求从该Activity被记录起有2个以上的Activity被创建才认为是泄漏，以防在判断时该Activity被局部变量持有导致误判




++destroyedActivityInfo.mDetectedCount;
long createdActivityCountFromDestroy = mCurrentCreatedActivityCount.get() - destroyedActivityInfo.mLastCreatedActivityCount;
if (destroyedActivityInfo.mDetectedCount < mMaxRedetectTimes
                    || (createdActivityCountFromDestroy < CREATED_ACTIVITY_COUNT_THRESHOLD && !mResourcePlugin.getConfig().getDetectDebugger())) {
    // Although the sentinel tell us the activity should have been recycled,
    // system may still ignore it, so try again until we reach max retry times.
   continue;
}




 


6.根据是否设置了mHeapDumper(即配置快关)，若设置了，进行dumpHeap,然后开启服务CanaryWorkerService，进行shrinkHprofAndReport，否则进行简单的onDetectIssue




if (mHeapDumper != null) {
    final File hprofFile = mHeapDumper.dumpHeap();
    if (hprofFile != null) {
        markPublished(destroyedActivityInfo.mActivityName);
        final HeapDump heapDump = new HeapDump(hprofFile, destroyedActivityInfo.mKey, destroyedActivityInfo.mActivityName);
        mHeapDumpHandler.process(heapDump);
        infoIt.remove();
    } else {
         infoIt.remove();
     }
} else {
                   
       markPublished(destroyedActivityInfo.mActivityName);
       if (mResourcePlugin != null) {
             ...           
             mResourcePlugin.onDetectIssue(new Issue(resultJson));
                  
       }
}




 


 


4、Hprof裁剪和分析（暂时不详细分析）


 LeakCanary没有对Hprof文件进行shrink裁剪,使用haha进行解析，分析出其泄露对象的GC Root引用链，把检测和分析都放在客户端。


 


ResourcePlugin只有检测和Hprof文件shrink功能,不支持在客户端Hprof文件，需要利用其分析库源码打成jar单独Hprof对进行分析,在分析过程中也可以把找出冗余Bitmap的GC ROOT链。


裁剪Hprof文件源码见：HprofBufferShrinker().shrink


冗余Bitmap分析器：DuplicatedBitmapAnalyzer


Activity泄露分析器：ActivityLeakAnalyzer


 


Hprof 文件的大小一般约为 Dump 时的内存占用大小，Dump 出来的 Hprof 大则一百多M,，如果不做任何处理直接将此 Hprof 文件上传到服务端，一方面会消耗大量带宽资源，另一方面服务端将 Hprof 文件长期存档时也会占用服务器的存储空间。通过分析 Hprof 文件格式可知，Hprof 文件中 buffer 区存放了所有对象的数据，包括字符串数据、所有的数组等，而我们的分析过程却只需要用到部分字符串数据和 Bitmap 的 buffer 数组，其余的 buffer 数据都可以直接剔除，这样处理之后的 Hprof 文件通常能比原始文件小 1/10 以上。


 


LeakCanary 中的引用链查找算法都是针对单个目标设计的，ResourceCanary 中查找冗余 Bitmap 时可能找到多个结果，如果分别对每个结果中的 Bitmap 对象调用该算法，在访问引用关系图中的节点时会遇到非常多的重复访问的节点，降低了查找效率。ResourcePlugin 修改了 LeakCanary 的引用链查找算法，使其在一次调用中能同时查找多个目标到 GC Root 的最短引用链。


 


总结 




 


 


参考资料：


Matrix ResourceCanary -- Activity 泄漏及Bitmap冗余检测


 


 


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            posted on 
2019-07-06 01:35 
toney.wu 
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