Overview

Android profiling captures detailed data about your application’s performance during launch, helping you identify slow methods and optimize startup time. Android profiling is built on top of the ProfilingManager Android API and samples the device’s CPU to collect method call stacks from the application’s process.

Prerequisites

• Your Android application must use the Datadog Android SDK version 3.6.0+.
• RUM without Limits must be enabled in your organization.

Setup

1. Set up Mobile RUM for Android.
2. Initialize the RUM SDK and configure the applicationLaunchSampleRate, which determines the percentage of application launches that are profiled (for example, 15% means profiling runs on 15 out of 100 launches).

  class SampleApplication : Application() {
      override fun onCreate() {
          super.onCreate()
          val configuration = Configuration.Builder(
              clientToken = "<CLIENT_TOKEN>",
              env = "<ENV_NAME>",
              variant = "<APP_VARIANT_NAME>"
          ).build()

          Datadog.initialize(this, configuration, trackingConsent)

          // Enable RUM (required for Profiling)
          val rumConfig = RumConfiguration.Builder(applicationId)
              .build()
          Rum.enable(rumConfig)

          // Enable Profiling
          val profilingConfig = ProfilingConfiguration.Builder()
            .setApplicationLaunchSampleRate(15) // default is 15%
            .build()

          Profiling.enable(profilingConfig)
      }
  }

The ProfilingManager API also supports disabling rate limiting during debug builds.

Explore profiling data

During the time to initial display

Android application launch profiling data is attached to the time to initial display vital event in a RUM session. You can access the time to initial display from the session side panel, view side panel, or directly from the time to initial display vital side panel.

Use the flame graph to identify which methods consume the most CPU time during launch, the thread timeline to see parallel execution patterns, and the call graph to trace method dependencies. You can also download the profiling data for external analysis or deeper investigation.

Further Reading

Additional helpful documentation, links, and articles:

Real User MonitoringBLOG Start monitoring Android applicationsDOCUMENTATION APM and Distributed TracingDOCUMENTATION