大多数用户感知到的卡顿等性能问题主要原因都是因为渲染性能。
Android 系统每隔大概16.6毫秒（1000ms / 60）发出VSYNC信号，触发对UI进行渲染，如果每次渲染成功，这样就能够达到画面所需要的的60fps ,要做到实现60fps,就意味着程序渲染工作需要在16ms之内完成。
认识卡顿现象
12fps： 类似于手动快速翻书的帧率；
24fps：人肉眼感知的连续线的运动效果；通常是电影胶卷使用的帧率
60fps：是人眼与大脑之间的协作无感知的画面更新
CPU: 负责measure、layout、record、execute的计算操作
GPU:负责将资源组件拆分到不同的像素上显示；
display: 负责将Frame buffer中的数据显示出来
当系统每隔16ms发出VSYNC时，如果CPU ,GPU都已经完成了相关的操作，那么display绘制就会很顺畅，但是如果CPU/GPU还在生产帧数据，从帧缓存中读取出来的数据就是之前的，这样在两个刷新周期之间显示了同一帧的数据，这就是我们通常说的发生了丢帧现象
监控卡顿的方法：
如果需要准确分析卡顿发生在哪一个函数，资源占用情况如何等，我们介绍两种比较主流的监控方案：
1. 利用主线程的Looper打印日志 (BlockCanary的原理)
先来看看Looper.java的源码
//此接口的实现类，系统给我们提供了一个LogPrinter
public interface Printer {
/**
* Write a line of text to the output. There is no need to terminate
* the given string with a newline.
*/
void println(String x);
}
。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。
/**
* Control logging of messages as they are processed by this Looper. If
* enabled, a log message will be written to printer
* at the beginning and ending of each message dispatch, identifying the
* target Handler and message contents.
*
* @param printer A Printer object that will receive log messages, or
* null to disable message logging.
*/
public void setMessageLogging(@Nullable Printer printer) {
mLogging = printer;
}
。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。
/**
* Run the message queue in this thread. Be sure to call
* {@link #quit()} to end the loop.
*/
public static void loop() {
final Looper me = myLooper();
if (me == null) {
throw new RuntimeException("No Looper; Looper.prepare() wasn't called on this thread.");
}
final MessageQueue queue = me.mQueue;
// Make sure the identity of this thread is that of the local process,
// and keep track of what that identity token actually is.
Binder.clearCallingIdentity();
final long ident = Binder.clearCallingIdentity();
// Allow overriding a threshold with a system prop. e.g.
// adb shell 'setprop log.looper.1000.main.slow 1 && stop && start'
final int thresholdOverride =
SystemProperties.getInt("log.looper."
+ Process.myUid() + "."
+ Thread.currentThread().getName()
+ ".slow", 0);
boolean slowDeliveryDetected = false;
for (;;) {
Message msg = queue.next(); // might block
if (msg == null) {
// No message indicates that the message queue is quitting.
return;
}
//此处如果logging不为空，那么他会调用Printer的println()打印一些信息，而Printer是一个接口，就需要我们自己去实现这个接口，并完成我们自己的Println()函数的方法体，打印我们自己需要的信息
// This must be in a local variable, in case a UI event sets the logger
final Printer logging = me.mLogging;
if (logging != null) {
logging.println(">>>>> Dispatching to " + msg.target + " " +
msg.callback + ": " + msg.what);
}
final long traceTag = me.mTraceTag;
long slowDispatchThresholdMs = me.mSlowDispatchThresholdMs;
long slowDeliveryThresholdMs = me.mSlowDeliveryThresholdMs;
if (thresholdOverride > 0) {
slowDispatchThresholdMs = thresholdOverride;
slowDeliveryThresholdMs = thresholdOverride;
}
final boolean logSlowDelivery = (slowDeliveryThresholdMs > 0) && (msg.when > 0);
final boolean logSlowDispatch = (slowDispatchThresholdMs > 0);
final boolean needStartTime = logSlowDelivery || logSlowDispatch;
final boolean needEndTime = logSlowDispatch;
if (traceTag != 0 && Trace.isTagEnabled(traceTag)) {
Trace.traceBegin(traceTag, msg.target.getTraceName(msg));
}
final long dispatchStart = needStartTime ? SystemClock.uptimeMillis() : 0;
final long dispatchEnd;
try {
msg.target.dispatchMessage(msg);
dispatchEnd = needEndTime ? SystemClock.uptimeMillis() : 0;
} finally {
if (traceTag != 0) {
Trace.traceEnd(traceTag);
}
}
if (logSlowDelivery) {
if (slowDeliveryDetected) {
if ((dispatchStart - msg.when) <= 10) {
Slog.w(TAG, "Drained");
slowDeliveryDetected = false;
}
} else {
if (showSlowLog(slowDeliveryThresholdMs, msg.when, dispatchStart, "delivery",
msg)) {
// Once we write a slow delivery log, suppress until the queue drains.
slowDeliveryDetected = true;
}
}
}
if (logSlowDispatch) {
showSlowLog(slowDispatchThresholdMs, dispatchStart, dispatchEnd, "dispatch", msg);
}
//此处打印结束
if (logging != null) {
logging.println("<<<<< Finished to " + msg.target + " " + msg.callback);
}
// Make sure that during the course of dispatching the
// identity of the thread wasn't corrupted.
final long newIdent = Binder.clearCallingIdentity();
if (ident != newIdent) {
Log.wtf(TAG, "Thread identity changed from 0x"
+ Long.toHexString(ident) + " to 0x"
+ Long.toHexString(newIdent) + " while dispatching to "
+ msg.target.getClass().getName() + " "
+ msg.callback + " what=" + msg.what);
}
msg.recycleUnchecked();
}
}
如果我们想用主线程的Looper来打印相关的日志信息，那么我们就需要给Looper实例提供一个Printer的实例，而Printer是一个接口，那么我们就来实现一个Printer的实现类：LogMonitor.java
public class LogMonitor implements Printer {
private StackSampler mStackSampler;
private boolean mPrintingStarted = false;
private long mStartTimestamp;
// 卡顿阈值
private long mBlockThresholdMillis = 3000;
//采样频率
private long mSampleInterval = 1000;
private Handler mLogHandler;
public LogMonitor() {
mStackSampler = new StackSampler(mSampleInterval);
HandlerThread handlerThread = new HandlerThread("block-canary-io");
handlerThread.start();
mLogHandler = new Handler(handlerThread.getLooper());
}
@Override
public void println(String x) {
//从if到else会执行 dispatchMessage，如果执行耗时超过阈值，输出卡顿信息
if (!mPrintingStarted) {
//记录开始时间
mStartTimestamp = System.currentTimeMillis();
mPrintingStarted = true;
mStackSampler.startDump();
} else {
final long endTime = System.currentTimeMillis();
mPrintingStarted = false;
//出现卡顿
if (isBlock(endTime)) {
notifyBlockEvent(endTime);
}
mStackSampler.stopDump();
}
}
private void notifyBlockEvent(final long endTime) {
mLogHandler.post(new Runnable() {
@Override
public void run() {
//获得卡顿时 主线程堆栈
List stacks = mStackSampler.getStacks(mStartTimestamp, endTime);
for (String stack : stacks) {
Log.e("block-canary", stack);
}
}
});
}
private boolean isBlock(long endTime) {
return endTime - mStartTimestamp > mBlockThresholdMillis;
}
}
再来一个堆栈信息采集器：
public class StackSampler {
public static final String SEPARATOR = "\r\n";
public static final SimpleDateFormat TIME_FORMATTER =
new SimpleDateFormat("MM-dd HH:mm:ss.SSS");
private Handler mHandler;
private Map mStackMap = new LinkedHashMap<>();
private int mMaxCount = 100;
private long mSampleInterval;
//是否需要采样
protected AtomicBoolean mShouldSample = new AtomicBoolean(false);
public StackSampler(long sampleInterval) {
mSampleInterval = sampleInterval;
HandlerThread handlerThread = new HandlerThread("block-canary-sampler");
handlerThread.start();
mHandler = new Handler(handlerThread.getLooper());
}
/**
* 开始采样 执行堆栈
*/
public void startDump() {
//避免重复开始
if (mShouldSample.get()) {
return;
}
mShouldSample.set(true);
mHandler.removeCallbacks(mRunnable);
mHandler.postDelayed(mRunnable, mSampleInterval);
}
public void stopDump() {
if (!mShouldSample.get()) {
return;
}
mShouldSample.set(false);
mHandler.removeCallbacks(mRunnable);
}
public List getStacks(long startTime, long endTime) {
ArrayList result = new ArrayList<>();
synchronized (mStackMap) {
for (Long entryTime : mStackMap.keySet()) {
if (startTime < entryTime && entryTime < endTime) {
result.add(TIME_FORMATTER.format(entryTime)
+ SEPARATOR
+ SEPARATOR
+ mStackMap.get(entryTime));
}
}
}
return result;
}
private Runnable mRunnable = new Runnable() {
@Override
public void run() {
StringBuilder sb = new StringBuilder();
StackTraceElement[] stackTrace = Looper.getMainLooper().getThread().getStackTrace();
for (StackTraceElement s : stackTrace) {
sb.append(s.toString()).append("\n");
}
synchronized (mStackMap) {
//最多保存100条堆栈信息
if (mStackMap.size() == mMaxCount) {
mStackMap.remove(mStackMap.keySet().iterator().next());
}
mStackMap.put(System.currentTimeMillis(), sb.toString());
}
if (mShouldSample.get()) {
mHandler.postDelayed(mRunnable, mSampleInterval);
}
}
};
}
再来一个静态方法调用使用：
public class BlockCanary {
public static void install() {
LogMonitor logMonitor = new LogMonitor();
Looper.getMainLooper().setMessageLogging(logMonitor);
}
}
来看一下BlockCanary的工作流程图
采集的信息截图，当然你也可以修改成保存到文件中去：
2. 使用Choreographer.FrameCallback监控
当每一帧被渲染时会触发此接口的回调，并带着底层VSYNC信息到达的时间戳。
/**
* Implement this interface to receive a callback when a new display frame is
* being rendered. The callback is invoked on the {@link Looper} thread to
* which the {@link Choreographer} is attached.
*/
public interface FrameCallback {
/**
* Called when a new display frame is being rendered.
*
* This method provides the time in nanoseconds when the frame started being rendered.
* The frame time provides a stable time base for synchronizing animations
* and drawing. It should be used instead of {@link SystemClock#uptimeMillis()}
* or {@link System#nanoTime()} for animations and drawing in the UI. Using the frame
* time helps to reduce inter-frame jitter because the frame time is fixed at the time
* the frame was scheduled to start, regardless of when the animations or drawing
* callback actually runs. All callbacks that run as part of rendering a frame will
* observe the same frame time so using the frame time also helps to synchronize effects
* that are performed by different callbacks.
*
* Please note that the framework already takes care to process animations and
* drawing using the frame time as a stable time base. Most applications should
* not need to use the frame time information directly.
*
*
* @param frameTimeNanos The time in nanoseconds when the frame started being rendered,
* in the {@link System#nanoTime()} timebase. Divide this value by {@code 1000000}
* to convert it to the {@link SystemClock#uptimeMillis()} time base.
*/
public void doFrame(long frameTimeNanos);
}
所以我们就自定义一个该接口的实现：
public class ChoreographerHelper {
public static void start() {
if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.JELLY_BEAN) {
Choreographer.getInstance().postFrameCallback(new Choreographer.FrameCallback() {
long lastFrameTimeNanos = 0;
@Override
public void doFrame(long frameTimeNanos) {
//上次回调时间
if (lastFrameTimeNanos == 0) {
lastFrameTimeNanos = frameTimeNanos;
Choreographer.getInstance().postFrameCallback(this);
return;
}
long diff = (frameTimeNanos - lastFrameTimeNanos) / 1_000_000;
if (diff > 16.6f) {
//掉帧数
int droppedCount = (int) (diff / 16.6);
}
lastFrameTimeNanos = frameTimeNanos;
Choreographer.getInstance().postFrameCallback(this);
}
});
}
}
}
上面的实现可以帮助我们获取实时的帧数和掉帧数，如果掉的比较多时（有可能卡顿），我们可以用第一种监控方式里面的堆栈信息采集器来进行采集，同时也可以输出采集的信息。
以上两种监控卡顿的方式仅供参考，感谢腾讯-享学课堂的老师的资料，
常见卡顿的处理：
-
嵌套太深，过于复杂的布局
系统对于视图的绘制过程包括measure ,layout ,draw三个过程，如果嵌套的太深，自然对每个视图都进行三步测绘过程就会需要更多的时间来完成，这样就会造成卡顿等现象。
解决方案：
参考《Android性能优化之布局优化》
-
过度的绘制（OverDraw）
如果在屏幕上的某一个像素点有多次绘制，就是过度绘制了，较为常见的就是重复的绘制背景绘制一些不可见的UI元素。
我们可以在我们设备的“系统设置”->"开发者选项"->"调试GPU过度绘制"中开启调试，此时你的设备界面可能出现五种颜色标识：
原色： 没有过度绘制
蓝色： 1次过度绘制
绿色： 2次过度绘制
粉色： 3次过度绘制
红色： 大于等于4次绘制
解决方案：
1）移除一些不需要的背景
如果有些子视图有背景，而且会覆盖父视图时，那么主视图的背景就不必要设置，系统对于没有背景的是不会直接渲染内容，这样就可以提高渲染的性能。
2）使视图层级结构扁平化
优化布局的层次层次来减少重叠的视图
3) 降低透明度
系统对于不透明的view ,之需要渲染一次就可以显示出来，而如果设置了透明度，则至少需要渲染两次（因为系统要先知道他的下层元素是什么，然后再结合上层的view进行混色处理）。
透明的动画，淡入淡出和阴影效果等都是和透明度相关的，这样就会造成过度绘制。
-
异步加载布局
LayoutInflater 在加载XML布局的过程中会在主线程使用IO读取XML文件进行解析，再跟进解析的结果利用反射创建布局中的View/ViewGroup对象。这个过程会虽然布局的复杂度上升，耗时自然也会随之增大。
AsyncLayoutInflater 内部是一个线程来进行递归遍历xml文件的节点，然后全部解析完成后将结果通过callback回调到主线程。
implementation "androidx.asynclayoutinflater:asynclayoutinflater:1.0.0"
public class MainActivity extends AppCompatActivity {
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
new AsyncLayoutInflater(this).inflate(R.layout.activity_main, null, new AsyncLayoutInflater.OnInflateFinishedListener() {
@Override
public void onInflateFinished(@NonNull View view, int resid, @Nullable ViewGroup parent) {
setContentView(view);
}
});
}
AsyncLayoutInflater的使用局限性：
1) 不支持包含Fragment的layout
2) 不支持设置LayoutInflater.Factory 或LayoutInflator.Factory2
3) 需要一个线程安全的generateLayoutParams 的parent
4) 如果无法异步构造的布局，则会自动退回到UI主线程上
5) 需要构建的view中不能直接使用Handler或者调用Looper.myLooper(),因为异步线程默认情况下是没有调用Looper.prepare();