Android Studio 3.0: Android Profiler

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WOJTEK KALICINSKI: Android Studio 3.0 introduces an integrated view for providing the memory, CPU, and network request characteristics of your app. It’s a complete overhaul of the previous tools found under the Android Monitor tab, which we’ve now renamed to Android Profiler. If you’re profiling with an API 26, so Android O or above, device or emulator, you’re all set. You can profile any APK, regardless of the build system it was created with. If you connect the Profiler to a device with a lower API level, you might see this warning. Because we need to instrument your code to take full advantage of the Profiler on older devices, you should use Android Studio to build and deploy your app.

The main view of the Profiler presents a unified timeline showing you the user charts for CPU, memory, and network in real time. At the top, you can also see important app events such as user input, activity state transitions, and device rotations, giving you more context for the data below. Clicking on any of the three charts takes you to one of the detailed views. Let’s check out the CPU Profiler first. The UI shows a live CPU utilization graph, and the list of all threads in your app and their states, such as when they were active or waiting for I/O. You can get a detailed trace of methods executed in a given period by pressing the record button. One thing to note is the trace type. Sampled has a smaller overhead, but is not as accurate, meaning it could miss the execution of a very short-lived method. Instrumented means that the Profiler will record every method, enter, and exit, accurately. On pre-Android O devices, there’s a limit to how much data can be recorded. So be aware that the instrumented trace capture will reach that limit faster. If you want to increase the limit or change the sampling rate, you can do that in the Edit Configuration window here.

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After you’ve finished the capture, select the thread you’re interested in. The Top Down view shows a tree of methods and their callees, aggregating trace information for identical methods that share the same call stack, similar to the flame chart. The Bottom Up tab displays a list of method calls where expanding a method’s node displays its callers. You can see a graphical representation on the call chart, which shows time spent in each call on the horizontal axis. The Memory Profiler shows a real-time chart of memory used by your app attributed to various buckets such as Java, native, graphics, et cetera. You can see any garbage collection events on the timeline, and you can force one using the button on the top left. You can dump the heap at any moment and examine instance properties of objects. The References tab can help you figure out memory leaks by listing all references, pointing to the object being examined. There’s one more feature that is very useful for tracking memory use over time– allocation tracking. If you’re profiling against a device running API 26 or higher, you can simply highlight any part of the timeline, and you’ll get a list of all objects allocated and deallocated within that period, including the call stack of the allocation. On API 25 and lower, you will need to explicitly start and stop recording allocations using the record button, similar to how the CPU method tracing works. Another difference is that on lower API versions, the call stacks are, by default, capped to a depth of 16 frames. There is a system property you can set prior to running your app to temporarily enable deeper stack traces. Finally, let’s look at the Network Profiler. The line labeled Radio shows a high-level overview of the networking state of the device. By looking at it, you can tell how often the device is waking up the radio into a high-powered state, which might suggest you’re not batching your network requests correctly and wasting battery life. The chart below shows actual network connection activity, including bytes sent and received. Whenever advanced profiling is switched on or you’re running on a compatible API 26 device, you can highlight any slice of time and inspect all network calls made, including actual HTTP request and response data and the call stack where the call happened. We even show previews for images and enable syntax highlighting for XML and JSON. If you’ve ever used anything like Chrome’s DevTools Network tab, this view should look very familiar. It’s great for debugging your app’s HTTP server interactions and data consumption. The HTTP call inspection is currently enabled for the standard HttpURLConnection class and the OkHttp client but should also work with other libraries built on top of these APIs. So that’s it for the three new Profilers. We are shipping the first version in Android Studio 3.0 and will be continuing to add new features. I hope they’ll prove useful in debugging performance issues in your apps. And if there are any features you’d like to see, let us know on the issue tracker. Thanks for watching. [MUSIC PLAYING]


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