以應用程式為導向的剖析

本頁面說明如何使用 ProfilingManager API 記錄系統追蹤記錄。

ProfilingManager 也可以記錄其他設定檔類型。這項程序與記錄系統追蹤記錄類似,但每種追蹤記錄都使用不同的建構工具。支援的設定檔和建構工具如下:

新增依附元件

如要獲得最佳 ProfilingManager API 體驗,請將下列 Jetpack 程式庫新增至 build.gradle.kts 檔案。

Kotlin

   dependencies {
       implementation("androidx.tracing:tracing-ktx:2.0.2")
       implementation("androidx.core:core:1.19.0")
   }
   

Groovy

   dependencies {
       implementation 'androidx.tracing:tracing:2.0.2'
       implementation 'androidx.core:core:1.19.0'
   }
   

錄製系統追蹤記錄

新增必要依附元件後,請使用下列程式碼記錄系統追蹤記錄。這個範例說明如何從可組合函式啟動剖析工作階段,同時安全地管理主執行緒以外的繁重作業。

Kotlin

@RequiresApi(Build.VERSION_CODES.VANILLA_ICE_CREAM)
@Composable
fun ProfiledScreen(modifier: Modifier = Modifier) {
    // Use the application context: requestProfiling resolves the ProfilingManager
    // system service from it, so there's no reason to hand it a short-lived Activity.
    val appContext = LocalContext.current.applicationContext
    val scope = rememberCoroutineScope()

    Button(
        onClick = {
            // Run the orchestration off the main thread. Profiling a heavy operation
            // on the UI thread would freeze the UI (ANR) and distort the very metrics
            // you're trying to capture.
            //
            // Note: this scope is tied to composition. If the user leaves this screen
            // mid-session, the coroutine is cancelled and stopSignal.cancel() might not
            // run, but setDurationMs() acts as a safety net and ends the trace.
            scope.launch(Dispatchers.Default) {
                val callbackExecutor = Dispatchers.IO.asExecutor()
                val resultCallback = Consumer<ProfilingResult> { profilingResult ->
                    if (profilingResult.errorCode == ProfilingResult.ERROR_NONE) {
                        Log.d("ProfileTest", "Result file: ${profilingResult.resultFilePath}")
                    } else {
                        // errorMessage explains the failure (e.g., rate limiting); keep it.
                        Log.e(
                            "ProfileTest",
                            "Profiling failed errorCode=${profilingResult.errorCode} " +
                                "errorMessage=${profilingResult.errorMessage}"
                        )
                    }
                }

                val stopSignal = CancellationSignal()
                val requestBuilder = SystemTraceRequestBuilder().apply {
                    setCancellationSignal(stopSignal)
                    setTag("FOO") // Caller-supplied tag for identification.
                    setDurationMs(60000) // Hard cap: ends the session if cancel() never fires.
                    setBufferFillPolicy(BufferFillPolicy.RING_BUFFER)
                    setBufferSizeKb(32768)
                }

                // 1. Start the session. This is asynchronous system IPC. The tracing
                //    engine takes a moment to start and allocate buffers.
                requestProfiling(appContext, requestBuilder.build(), callbackExecutor, resultCallback)

                // 2. The API exposes no "profiling started" signal, so pad with a short,
                //    best-effort delay before running the code you care about. This is
                //    approximate. Increase it on slower or heavily loaded devices.
                delay(STARTUP_PADDING_MS)

                // 3. The session is already recording every thread in your app. This slice
                //    doesn't scope what's captured. It just labels this region of the
                //    timeline so heavyOperation() is easier to find. trace { } closes the
                //    section even if the block throws.

                trace("MyApp:HeavyOperation") {
                    heavyOperation()
                }

                // 4. Stop recording. Until this fires or the setDurationMs() cap is
                //    reached (whichever comes first), the session keeps capturing app-wide
                //    activity.

                stopSignal.cancel()
            }
        }
    ) {
        Text("Run & Profile Heavy Operation")
    }
}

// Best-effort wait for the system trace engine to initialize before profiling.
// There is no deterministic start callback; tune this for your target devices.
private const val STARTUP_PADDING_MS = 100L

fun heavyOperation() {
    // Background computations to profile.
}

Java

void heavyOperation() {
  // Computations you want to profile
}

void sampleRecordSystemTrace() {
  Executor mainExecutor = Executors.newSingleThreadExecutor();
  Consumer<ProfilingResult> resultCallback =
      new Consumer<ProfilingResult>() {
        @Override
        public void accept(ProfilingResult profilingResult) {
          if (profilingResult.getErrorCode() == ProfilingResult.ERROR_NONE) {
            Log.d(
                "ProfileTest",
                "Received profiling result file=" + profilingResult.getResultFilePath());
            setupProfileUploadWorker(profilingResult.getResultFilePath());
          } else {
            Log.e(
                "ProfileTest",
                "Profiling failed errorcode="

                    + profilingResult.getErrorCode()
                    + " errormsg="
                    + profilingResult.getErrorMessage());
          }
        }
      };
  CancellationSignal stopSignal = new CancellationSignal();

  SystemTraceRequestBuilder requestBuilder = new SystemTraceRequestBuilder();
  requestBuilder.setCancellationSignal(stopSignal);
  requestBuilder.setTag("FOO");
  requestBuilder.setDurationMs(60000);
  requestBuilder.setBufferFillPolicy(BufferFillPolicy.RING_BUFFER);
  requestBuilder.setBufferSizeKb(32768);
  Profiling.requestProfiling(getApplicationContext(), requestBuilder.build(), mainExecutor,
      resultCallback);

  // Wait some time for profiling to start.

  Trace.beginSection("MyApp:HeavyOperation");
  heavyOperation();
  Trace.endSection();

  // Once the interesting code section is profiled, stop profile
  stopSignal.cancel();
}

程式碼範例會執行下列步驟,設定及管理剖析工作階段:

  1. 設定執行器。建立 Executor,定義接收剖析結果的執行緒。剖析作業會在背景執行。如果您稍後要在回呼中新增更多處理作業,使用非 UI 執行緒執行器有助於避免發生應用程式無回應 (ANR) 錯誤。

  2. 處理剖析結果。建立 Consumer<ProfilingResult> 物件。 系統會使用這個物件,將 ProfilingManager 的剖析結果傳回應用程式。

  3. 建立剖析要求。建立 SystemTraceRequestBuilder 來設定剖析工作階段。這個建構工具可讓您自訂 ProfilingManager 追蹤設定。您可以選擇是否要自訂建構工具,如果沒有,系統會使用預設設定。

    • 定義標記。使用 setTag() 為追蹤名稱新增標記。這個標記有助於識別追蹤記錄。
    • 選用:設定時間長度。使用 setDurationMs() 指定要分析的毫秒數。舉例來說,60000 會設定 60 秒的追蹤記錄。如果未在指定時間內觸發 CancellationSignal,追蹤作業會在時間到期後自動結束。
    • 選擇緩衝區政策。使用 setBufferFillPolicy() 定義追蹤資料的儲存方式。BufferFillPolicy.RING_BUFFER 表示緩衝區已滿時,新資料會覆寫最舊的資料,持續記錄近期活動。
    • 設定緩衝區空間。使用 setBufferSizeKb() 指定追蹤記錄的緩衝區空間,藉此控制輸出追蹤記錄檔案的大小。
  4. 選用:管理工作階段生命週期。建立 CancellationSignal。 您可以隨時停止剖析工作階段,精確控制工作階段長度。

  5. 開始檢測並接收結果。撥打 requestProfiling() 時,ProfilingManager 會在背景啟動剖析工作階段。完成剖析後,系統會將 ProfilingResult 傳送至您的 resultCallback#accept 方法。如果剖析作業順利完成,ProfilingResult 會透過 ProfilingResult#getResultFilePath 提供追蹤記錄在裝置上的儲存路徑。您可以透過程式輔助方式取得這個檔案,也可以在電腦上執行 adb pull <trace_path> 進行本機剖析。

  6. 新增自訂追蹤點。您可以在應用程式的程式碼中新增自訂追蹤點。在先前的程式碼範例中,trace("MyApp:HeavyOperation") { ... } 區塊會在產生的設定檔中建立自訂切片。