推荐阅读：
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 } ... } 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、watcher范围和自动wacherd的对象
class RefWatcher{ fun watch(watchedInstance: Any) {...} fun watch( watchedInstance: Any,name: String) {...} } 支持自动watcher 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watcher Activity对象。
2.自动watcher 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) } } }) } } } 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) } } } 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 { 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() {
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); } }
原理：VM会将可回收的对象加入 WeakReference 关联的 ReferenceQueue
var retainedReferenceCount = refWatcher.retainedInstanceCount if (retainedReferenceCount > 0) { gcTrigger.runGc() retainedReferenceCount = refWatcher.retainedInstanceCount } if (checkRetainedCount(retainedReferenceCount, config.retainedVisibleThreshold)) return
if (!config.dumpHeapWhenDebugging && DebuggerControl.isDebuggerAttached) { showRetainedCountWithDebuggerAttached(retainedReferenceCount) scheduleRetainedInstanceCheck("debugger was attached", WAIT_FOR_DEBUG_MILLIS） return } val heapDumpFile = heapDumper.dumpHeap() if (heapDumpFile == null) { showRetainedCountWithHeapDumpFailed(retainedReferenceCount) return} 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。
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，需要利用其提供的jar单独对进行分析Hprof,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。为此我们修改了 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。
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