The previous update ended with v1.7.0 on August 21, 2026. From v1.7.0 on August 21 to v1.12.0 on September 17, SmartPerfetto shipped nine new versions and merged 141 non-merge commits across 28 calendar days.

The period added an arbitrary dual-Trace workspace, source analysis without a required prebuilt index, explicit Auto/Fast/Full modes, durable investigation checkpoints, and claim verification that reaches the Web UI, CLI, reports, and snapshots. Across these changes, a run now keeps the request, tool execution, evidence, claims, and delivery status available for inspection.

Project: github.com/Gracker/SmartPerfetto.

The previous update ended with v1.1.1 on July 17, 2026. By then, SmartPerfetto already had a dual-trace workspace, Quick Mode, private analysis context, a unified Agent runtime architecture, and dedicated Camera, Heap, and GPU analysis capabilities.

Over the next five weeks, from July 18 through August 21, the repository advanced to v1.7.0. New features continued to land, but much of the work shifted toward knowledge provenance, distribution quality, access isolation, result completeness, and reversible improvement.

SmartPerfetto is moving from “complete one analysis” toward “complete analyses reliably and traceably across more environments.”

This post uses the publicly released v1.7.0 source as its endpoint and reviews the major capabilities added after v1.1.1.

Project and previous post:

• SmartPerfetto: https://github.com/Gracker/SmartPerfetto
• Previous post: SmartPerfetto 2026-06-04 to 2026-07-17 Six-Week Update

This post reviews every major SmartPerfetto change since the previous three-week update (2026-06-04, v1.0.28), through 2026-07-17 (v1.1.1). By the end, you will understand the problem each feature solved over these six weeks, where its boundaries lie, and the full feature map of the current release. Project repository: github.com/Gracker/SmartPerfetto.

The previous SmartPerfetto update was published on May 17. At that point the project had already moved from “an AI Assistant inside Perfetto UI” to “a reusable trace analysis platform.” By June 4, the new work is concentrated in five areas: Smart Mode, selected-range quick analysis, CLI capture, stronger evidence rules for Power / ANR / Input / IO / Network, and four Agent runtimes.

This article is based on the SmartPerfetto main branch on June 4, 2026. The latest public release at this point is v1.0.28. The goal is simple: list what changed after May 17, explain how runtimes and evidence sources are handled, and show what information makes a useful bug report.

Project links:

• SmartPerfetto main repository: https://github.com/Gracker/SmartPerfetto
• Previous update: https://www.androidperformance.com/en/2026/05/17/SmartPerfetto-Two-Week-Update/

When I wrote the SmartPerfetto open-source introduction on April 29, the headline was still “put an AI Assistant that can run SQL, invoke Skills, and generate reports inside the Perfetto UI.” Two weeks later the repository has moved a long way. The feature surface expanded from single-trace Q&A to reusable analysis results, multi-trace comparison, dual Claude/OpenAI runtimes, SQL guardrails, evidence-source indexing, no-install packages, rendering-pipeline teaching, and a more complete Provider diagnostic flow.

This article is based on the SmartPerfetto repository state as of May 17, 2026, and adds a new feature description on top of the previous post. Readers should walk away knowing three things: what was added in the past two weeks, where the current full feature boundary sits, and what information a good bug report should include.

Project links:

• SmartPerfetto main repository: https://github.com/Gracker/SmartPerfetto
• Previous open-source introduction: https://www.androidperformance.com/en/2026/04/29/SmartPerfetto-Open-Source-Perfetto-AI-Assistant/

This article addresses a topic easily overshadowed by startup speed and smoothness: first-frame interaction response. It asks how long the screen takes to provide the first frame of feedback after a user action. Complete page loading and stability later in an animation require other metrics.

After tapping a button, how long until its pressed state appears? After tapping a WeChat conversation, how long until the first frame of the conversation page starts moving? After swiping a list, how long until its content follows the finger? These questions relate to slow startup and dropped frames, but need their own measurement definitions.

We use Perfetto to break down this interval: from InputReader read_time, through the App receiving the event, to an associated frame’s present. This gives a candidate internal delay, estimated_input_to_present_ms. Only after confirming the target layer and that the business state visibly changed on that frame should a report call it input_to_present_ms. Neither value covers touch IC latency, delays before driver reporting, display scanning, or panel response. By the end, you should be able to define a start, endpoint, capture configuration, SQL metrics, and supplementary App markers for a tap or scroll scenario.

The article examines response latency through four independent measurements—read/dispatch, handling, ACK→first frame, and scroll responsiveness. Each comes with capture configuration, the first tracks to inspect in the UI, and SQL. It then covers five common attribution paths and the respective boundaries of high-speed cameras and Perfetto.

For slow camera opens, audio underruns, a stalled WebView first screen, or dropped frames in Flutter and games, the evidence is scattered across the app, system_server, cameraserver/audioserver, HAL, SurfaceFlinger, and scheduling tracks. Simply inspecting a few more tracks in the UI can easily leave critical timestamps unnoticed.

Using Camera and Audio as examples, this article describes a reusable method for domain-specific analysis: capture the right data, identify the objects, calculate stages and blocking, and preserve timestamps in the report so that reviewers can return to the UI. Camera and Audio are examples; the core is a domain schema and collaboration with platform tracing. By the end, you should at least be able to break a slow camera open into reviewable stages and an audio underrun into cycle anomalies, rather than delivering only a table of the longest slices.

FrameTimeline marks a frame as late, yet the main thread, RenderThread, and SurfaceFlinger all seem to stay within their budgets. It is tempting to write “possibly a GPU bottleneck.” That is a risky claim: GPU frequency, GPU counters, battery current, and power rails are not tools for attributing an individual App frame to a root cause.

Parts 07 and 08 covered the rendering path from the App to SurfaceFlinger; Parts 06 and 09 covered refresh rates and CPU scheduling/frequency. This article adds an often-skipped step without repeating those topics: when a problem appears to have reached the hardware resource layer, how should GPU, devfreq, battery counters, and power rails enter the analysis of the same trace interval?

These counters are difficult because not every device provides them, and different GPU vendors use different counter names. A conclusion cannot simply say “a high GPU value means a GPU bottleneck.” FrameTimeline, RenderThread, SurfaceFlinger, the GPU timeline, frequency, and power data must be examined over the same interval.

Many performance problems cannot be reproduced with a single tap. Slow boot, intermittent jank, problems after turning the screen on or off, audio interruptions after several hours, periodic stutters in an automotive system, and performance degradation as temperature rises are all poor fits for a manually recorded 10-second trace.

This article covers long-running field traces: defining the observation window in advance, using triggers to preserve the incident, ensuring the file finishes writing safely, and determining whether the collected data is trustworthy. Boot tracing is covered below as a special observation window. Part 02 already covered basic interactive capture methods—the command line, official scripts, Developer options, and the web interface. Here, we focus on what changes in the field and during long-running recording.

The hardest part of a memory problem is that a rising number does not immediately tell you what is growing. dumpsys meminfo provides RSS/PSS, and a Java heap dump reveals object retention relationships. But for JNI/C++, CPU-side Skia/Bitmap allocations, and native wrappers in media players, you often also need to know which call stack issued the malloc/new request.

This article covers heapprofd. Its value is bringing native allocations, frees, and call stacks into a Perfetto trace, where you can inspect them alongside application events, thread scheduling, and Binder on the same timeline.

Use heapprofd after confirming the trend. I would split the growth between native heap, graphics buffers, and other mappings with meminfo or smaps before opening a flamegraph. Otherwise, it is easy to chase a malloc stack when the growth came from GraphicBuffer. This article does not address Java object references or graphics backing memory.

We will cover when to use heapprofd, how to capture a profile, how to read it in the UI and SQL, how to interpret sampling intervals and symbolization, and finally what to check before publishing a report.

A system trace can tell you when a thread ran, which CPU core it ran on, and which FrameTimeline frame missed its deadline. With Binder/ftrace/sched evidence enabled, it can also reconstruct Binder calls and clues about waiting. But it does not know which frame ID a player is decoding, which scene a game engine is loading, or how long an application task has spent in a queue.

This article answers one question: how do application semantics enter a trace? The system can tell you that the main thread was slow; Track Event tells you which frame was being decoded, which queue was blocked, and which request moved between threads. Combining the two lets you narrow “the main thread was slow by 18 ms” to a candidate texture-upload interval around frame 1082, then continue validating it with RenderThread/HWUI, GPU, and FrameTimeline evidence.

The article follows the practical integration order: decide whether instrumentation is needed and at which layer, define an application-phase dictionary and backend, then build the minimal integration, category controls, combined system capture, and cross-thread flows. Finally, control write volume and define the app-side field tracing protocol.

One of the most troublesome situations when opening a trace is finding that the file appears to contain data, but some of it was lost along the way. The UI still shows tracks, and SQL still returns rows, yet part of a thread’s state history is missing, process names do not line up, or some Track Events have disappeared. The further you analyze it, the more your conclusions start to resemble guesses.

SQL can only analyze evidence that still exists in the trace. I would not discard an entire trace just because it reports ftrace loss; first identify whether scheduling, app markers, or frame data were affected.

Part 02 covered capture, and Part 11 covered SQL queries. This article focuses on one question: where did the trace lose data, which conclusions does that affect, and how should the next capture configuration change?

We will follow the data from the kernel to the trace file, locating loss at each stage: ftrace, producer shared memory, the central buffer, incremental state, and flush. Each stage loses data differently and needs a different remedy. At the end, there is a template for explaining how much of a trace remains trustworthy in an analysis report.

Selecting a window in Perfetto UI, inspecting the main thread, RenderThread, and CPU states, then taking screenshots works well for one investigation. The harder question is whether another trace or device supports the same conclusion. I care more about being able to recompute that conclusion next time than about making one screenshot look convincing.

Part 11 covers PerfettoSQL and Trace Processor. The aim is straightforward: turn judgments made in the UI into queries that others can check, then use Python to run them across multiple traces. You do not need to become a SQL expert first. Treat a trace as a set of timestamped tables, and translate your visual assessment into a query you can run repeatedly.

The article follows one path: understand Trace Processor, express an assessment as a query over a defined time window, run it from the command line, process multiple traces with Python, and finally turn the results into stable metrics and Trace Summary output. Each step includes SQL you can reuse.

SmartPerfetto is now fully open source. Open the repository and you can see the runnable mainline project as it exists today: the Perfetto UI fork, the agentv3 backend, MCP tools, YAML Skills, scene strategies, scripts, and documentation. There is no private core module kept outside the repository, and this is not just a thin demo shell.

The project comes from a very concrete daily workflow: you have a trace in hand, Perfetto has already exposed the facts, but moving from facts to judgment still means jumping through tables, writing SQL, matching threads, checking FrameTimeline, finding the Binder peer, and then returning to the timeline to confirm everything again. SmartPerfetto tries to turn those repeated actions into tools, so performance engineers can spend more time on judgment.

It is still in development. I am releasing it now because trace analysis grows from real samples: real devices, real vendor differences, real product traces, and real PRs all change how Skills and strategies should be written. Waiting until every capability is stable before publishing it one way would miss the stage where samples matter most.

If you often open Perfetto to inspect scrolling jank, startup, ANR, Binder, CPU scheduling, or rendering pipelines, SmartPerfetto provides a Perfetto UI with an AI Assistant. After loading a trace, you ask questions in natural language. The backend queries trace_processor_shell, invokes YAML Skills, organizes evidence, and streams conclusions plus data tables back into the browser.

Project links:

• Main repository: https://github.com/Gracker/SmartPerfetto
• Perfetto UI fork: https://github.com/Gracker/perfetto

For normal trial use, you only need the main repository. Gracker/perfetto is the frontend fork used by the perfetto/ submodule. It mainly matters to developers who want to modify the AI Assistant plugin UI.

The previous two technical articles are better for readers who want the engineering details:

• From Trace to Insight: Harness Engineering in SmartPerfetto AI Agent: the architecture of the SmartPerfetto AI Agent and the Harness Engineering process.
• SmartPerfetto Architecture Q&A: 8 In-Depth Technical Questions: Q&A around the Agent, Workflow, and YAML Skills.

Those two articles go deep into the internal architecture. Once the source is public, readers usually care more about what is actually in the repository, whether it can run, and which parts are not stable yet. This article focuses on the open-source release itself: what is open, what works today, how the internal pieces are divided, how to run it locally, and where collaboration is most useful.

SmartPerfetto Architecture Q&A

This article collects technical questions received after publishing From Trace to Insight: Harness Engineering in SmartPerfetto AI Agent and discusses them in Q&A format.

From Trace to Insight: Harness Engineering in SmartPerfetto AI Agent

This article documents the key technical decisions made while building SmartPerfetto from scratch – why certain approaches were chosen over others, where pitfalls were encountered, and how the design was adjusted afterward.

The tenth article in the Perfetto series focuses on Binder, Android’s core Inter-Process Communication (IPC) mechanism. Binder carries most interactions between system services and apps, and is often where latency and jank originate. This article uses signals from linux.ftrace (binder tracepoints + sched), thread_state, and ART Java monitor contention (via atrace dalvik) to provide a practical workflow for diagnosing transaction latency, thread-pool pressure, and lock contention.

This is the ninth article in the Perfetto series, focusing on CPU information analysis in Perfetto. Perfetto provides far superior data visualization and analysis capabilities compared to Systrace. Understanding CPU-related information is the foundation for locating performance bottlenecks and analyzing power consumption issues.

The goal of this series is to examine the overall operation of the Android system from a brand new graphical perspective through the Perfetto tool, while also providing a new way to learn the Framework. Perhaps you’ve read many source code analysis articles but always feel confused by the complex call chains or can’t remember specific execution flows. Through Perfetto, by visualizing these processes, you may gain a deeper and more intuitive understanding of the system.

This is the eighth article in the Perfetto series, providing an in-depth introduction to the Vsync mechanism in Android and its representation in Perfetto. The article will analyze how the Android system performs frame rendering and composition based on Vsync signals from Perfetto’s perspective, covering core concepts such as Vsync, Vsync-app, Vsync-sf, and VsyncWorkDuration.

With the popularization of high refresh rate screens, understanding the Vsync mechanism has become increasingly important. This article uses 120Hz refresh rate as the main narrative thread to help developers understand the working principles of Vsync in modern Android devices, and how to observe and analyze Vsync-related performance issues in Perfetto.

Version note: Android 17 (API 37) is the current technical reference; Android 13 is mentioned where a major mechanism was introduced. Code snippets are abbreviated AOSP main excerpts, with ... omitting some branches. They are not verbatim Android 17 release source; verify against your target branch.

This is the seventh article in the Perfetto series, focusing on MainThread (UI Thread) and RenderThread, the two most critical threads in any Android application. This article will examine the workflow of MainThread and RenderThread from Perfetto’s perspective, covering topics such as jank, software rendering, and frame drop calculations.

As Google officially promotes Perfetto as the replacement for Systrace, Perfetto has become the mainstream choice in performance analysis. This article combines specific Perfetto trace information to help readers understand the complete workflow of MainThread and RenderThread, enabling you to:

• Accurately identify key trace tags: Understand the roles of critical threads like UI Thread and RenderThread
• Understand the complete frame rendering process: Every step from Vsync signal to screen display
• Locate performance bottlenecks: Quickly find the root cause of jank and performance issues through trace information

This is the sixth article in the Android Perfetto series, mainly introducing knowledge related to 120Hz refresh rate on Android devices. Nowadays, 120Hz has become standard configuration for flagship Android phones. This article will discuss the advantages and challenges brought by high refresh rates, and analyze the working principle of 120Hz from a system perspective.

Over the past few years, the refresh rate of mobile device screens has evolved from 60Hz to 90Hz, and then to the now common 120Hz. This improvement not only brings smoother visual experience, but also puts forward new requirements for system architecture and application development. Through the Perfetto tool, we can more intuitively understand the process and performance of frame rendering on high refresh rate devices.

This article introduces Choreographer, a class that App developers may not frequently encounter but is critically important in the Android Framework rendering pipeline. We will cover the background of its introduction, a brief overview, partial source code analysis, its interaction with MessageQueue, its application in APM (Application Performance Monitoring), and some optimization ideas for Choreographer by mobile phone manufacturers.

The introduction of Choreographer is mainly to cooperate with Vsync to provide a stable Message processing timing for upper-layer application rendering. When the Vsync signal arrives, the system controls the timing of each frame’s drawing operation by adjusting the Vsync signal cycle. Currently, the screen refresh rate of mainstream mobile phones has reached 120Hz, which means refreshing once every 8.3ms. The system adjusts the Vsync cycle accordingly to match the screen refresh frequency. When an app has pending frame callbacks, Choreographer requests Vsync and handles the callbacks when it arrives; not every Vsync causes the app to draw. Understanding Choreographer can also help application developers deeply understand the operating principle of each frame, and at the same time deepen their understanding of core components such as Message, Handler, Looper, MessageQueue, Input, Animation, Measure, Layout, and Draw. Many APM (Application Performance Monitoring) tools also utilize the combination mechanisms of Choreographer (via FrameCallback), FrameMetrics/gfxinfo framestats (internally backed by FrameInfo), MessageQueue (via IdleHandler), and Looper (via custom MessageLogging) for performance monitoring. After deeply understanding these mechanisms, developers can conduct performance optimization more specifically and form systematic optimization ideas.

This is the fourth article in the Perfetto series, explaining how to use trace_processor_shell to open large files exceeding 2GB locally. In actual problem analysis, we often encounter very large Trace files (greater than 2GB) that may fail to open in ui.perfetto.dev because of browser memory limits. The limit applies to memory used while parsing, not directly to the trace file’s size. In this case, we can use the official trace_processor_shell tool to open large files locally.

With Google announcing the deprecation of the Systrace tool and the release of Perfetto, Perfetto has basically replaced Systrace in my daily work. At the same time, major manufacturers like OPPO and Vivo have also switched from Systrace to Perfetto. Many friends who are new to Android performance optimization feel a headache when facing the dazzling interface and complex functions of Perfetto. They hope that I can present those previous Systrace articles using Perfetto.

This is the first article in the Android App ANR series, mainly analyzing the design philosophy of Android ANR from a system perspective. The series directory is as follows:

1. Android App ANR Series 1: Understanding Android ANR Design Philosophy
2. Android App ANR Series 2: ANR Analysis Routines and Key Log Introduction
3. Android App ANR Series 3: ANR Case Sharing

This is the third article in the Perfetto series. The first two articles introduced what Perfetto is and how to capture Perfetto Trace. This article simply introduces how to look at the complex Perfetto information after opening Perfetto Trace on the web side.

With Google announcing the deprecation of the Systrace tool and the release of Perfetto, Perfetto has basically replaced Systrace in my daily work. At the same time, major manufacturers like OPPO and Vivo have also switched from Systrace to Perfetto. Many friends who are new to Android performance optimization feel a headache when facing the dazzling interface and complex functions of Perfetto. They hope that I can present those previous Systrace articles using Perfetto.

The previous article Android Perfetto Series 1: Introduction to Perfetto introduced what Perfetto is. This article provides a brief introduction to Perfetto capture.

With Google announcing the deprecation of the Systrace tool and the release of Perfetto, Perfetto has basically replaced Systrace in my daily work. At the same time, major manufacturers like OPPO and Vivo have also switched from Systrace to Perfetto. Many friends who are new to Android performance optimization feel a headache when facing the dazzling interface and complex functions of Perfetto. They hope that I can present those previous Systrace articles using Perfetto.

This is the first article in the Perfetto series. It mainly provides a brief introduction to the Perfetto tool, including its history, development, and what Perfetto can do.

With Google announcing the deprecation of the Systrace tool and the release of Perfetto, Perfetto has basically replaced Systrace in my daily work. At the same time, major manufacturers like OPPO and Vivo have also switched from Systrace to Perfetto. Many friends who are new to Android performance optimization feel a headache when facing the dazzling interface and complex functions of Perfetto. They hope that I can present those previous Systrace articles using Perfetto.

With Google announcing the deprecation of Systrace in favor of Perfetto, Perfetto has essentially replaced Systrace in my daily workflow. Major manufacturers like OPPO and vivo have also transitioned to Perfetto. Many developers new to Android performance optimization find Perfetto’s complex interface and features overwhelming, which is why I’ve decided to re-present my previous Systrace articles using Perfetto.

This is the third article in the “Systrace Thread CPU State Analysis Tips” series. It focuses on the Sleep and Uninterruptible Sleep states in Systrace—their causes, troubleshooting, and optimization. These states are major performance inhibitors and are often difficult to diagnose without a systematic approach.

The goal of this series is to use Systrace to view the Android system from a different perspective and to learn the Framework through visualization. While reading Framework source code can be difficult to remember, seeing the flow in Systrace can lead to deeper understanding. You can find the complete Systrace Basics and Action Series here.

This is the first article in the “Systrace Thread CPU State Analysis Tips” series. It analyzes the causes of the “Runnable” state in Systrace and provides optimization strategies for when Runnable segments are excessively long.

The goal of this series is to use Systrace to view the Android system from a different perspective and to learn the Framework through visualization. While reading Framework source code can be difficult to remember, seeing the flow in Systrace can lead to deeper understanding. You can find the complete Systrace Basics and Action Series here.

2021 has passed. Taking advantage of the New Year holiday, I’d like to look back at the year. This will be a casual review—writing whatever comes to mind. 2021 for me was marked by becoming a father, changing jobs (including a boring period of working from home), and making new friends. Overall, it was a pretty good year.

However, in terms of personal growth, I feel like I might have stagnated or even regressed, which is a bit alarming. Learning is like rowing upstream; if you don’t move forward, you fall back. 2022 needs to be a year of deep cultivation. I hope to progress together with everyone reading this.

I’ve also tallied some data related to my technical sharing, shared my income from these platforms, and listed some hardware and software I recommend. Feel free to take a look.

Recently, I read a new book: Building Smooth Android Apps (JD link: https://item.jd.com/10035215362170.html). I bought it because of the title, and after reading it, I felt it was necessary to write an article so that colleagues who haven’t bought it yet can understand what it’s about.

My personal suggestion is: if you are an experienced developer, I don’t recommend buying it. This book doesn’t go into much depth on principles and doesn’t offer a comprehensive overview of Android smoothness. If you are a beginner, it’s decent for broadening your horizons and identifying gaps in your knowledge, but it’s still a bit lacking for a deep understanding of Android smoothness.

I say this because the book doesn’t focus much on performance or smoothness. It lacks deep theoretical parts. Instead, a large portion is dedicated to static code analysis, using Android Studio Profiler, App architecture, app stay-alive techniques, network performance optimization, APK size optimization, app power consumption, etc. These topics are covered briefly and at a shallow level.

When discussing Android performance, Jank, Responsiveness, and ANR are usually grouped together because their causes are similar. They are simply categorized based on severity: Jank, Slow Response, and ANR. We can define “Broad Jank” to include all three. If a user reports that a phone or App is “stuttering,” they are likely referring to Broad Jank, and we must identify which specific issue is occurring.

If it’s stuttering during animation or list scrolling, we define it as Narrow Jank (referred to as Jank). If it’s slow app startup, slow screen wake-up, or slow scene switching, we define it as Slow Responsiveness (referred to as Slow). If it’s an ANR, it’s an Application Not Responding issue. Each situation requires different analysis and resolution methods.

Furthermore, within Apps or manufacturers, Jank, Responsiveness, and ANR have individual metrics like Frame Drop Rate, Startup Speed, and ANR Rate. Mastering the analysis and optimization of these issues is crucial for developers.

This is the third article in the Responsiveness series, focusing on extended knowledge when using Systrace to analyze app responsiveness, including startup speed testing, log interpretation, state analysis, and third-party startup libraries.

When discussing Android performance, Jank, Responsiveness, and ANR are usually grouped together because their causes are similar. They are simply categorized based on severity: Jank, Slow Response, and ANR. We can define “Broad Jank” to include all three. If a user reports that a phone or App is “stuttering,” they are likely referring to Broad Jank, and we must identify which specific issue is occurring.

If it’s stuttering during animation or list scrolling, we define it as Narrow Jank (referred to as Jank). If it’s slow app startup, slow screen wake-up, or slow scene switching, we define it as Slow Responsiveness (referred to as Slow). If it’s an ANR, it’s an Application Not Responding issue. Each situation requires different analysis and resolution methods.

Furthermore, within Apps or manufacturers, Jank, Responsiveness, and ANR have individual metrics like Frame Drop Rate, Startup Speed, and ANR Rate. Mastering the analysis and optimization of these issues is crucial for developers.

This is the second article in the Responsiveness series, using Android App Cold Start as an example to explain how to use Systrace for analysis.

When discussing Android performance, Jank, Responsiveness, and ANR are usually grouped together because their causes are similar. They are simply categorized based on severity: Jank, Slow Response, and ANR. We can define “Broad Jank” to include all three. If a user reports that a phone or App is “stuttering,” they are likely referring to Broad Jank, and we must identify which specific issue is occurring.

If it’s stuttering during animation or list scrolling, we define it as Narrow Jank (referred to as Jank). If it’s slow app startup, slow screen wake-up, or slow scene switching, we define it as Slow Responsiveness (referred to as Slow). If it’s an ANR, it’s an Application Not Responding issue. Each situation requires different analysis and resolution methods.

Furthermore, within Apps or manufacturers, Jank, Responsiveness, and ANR have individual metrics like Frame Drop Rate, Startup Speed, and ANR Rate. Mastering the analysis and optimization of these issues is crucial for developers.

This is the first article in the Responsiveness series, focusing on theoretical knowledge, including an overview of performance engineering, key responsiveness concepts, and analysis methodologies.

Different people have different understandings of smoothness (jank/dropped frames) and different perceptions of jitter thresholds. Therefore, before starting this series, it is necessary to clarify the content to avoid misunderstandings. Here are some basic explanations:

1. For mobile users, jank encompasses many scenarios: dropped frames when scrolling lists, excessive white screen during app startup, slow screen wake-up when pressing the power button, unresponsive interface followed by a crash, no response when clicking an icon, incoherent window animations, lagging touch response, or stuttering when entering the desktop after a reboot. These scenarios differ slightly from what developers understand as “jank.” Developers categorize these more finely, which is a cognitive gap between developers and users that must be noted when handling feedback from users or testers.
2. For developers, the above scenarios fall into three major categories: Smoothness (dropped frames in lists, incoherent animations, stuttering desktop entry), Responsiveness (long startup white screens, slow screen wake-up, lagging touch), and Stability (unresponsive interface/crashes, no response to icon clicks). This classification is used because each category requires different analysis methods and steps. Quickly identifying the category is crucial.
3. Technically, Smoothness, Responsiveness, and Stability (ANR) all feel like “jank” to users because their underlying principles are identical: the main thread’s Message exceeds its processing deadline. They are simply categorized by different timeout thresholds. Understanding these problems requires knowledge of basic system operation mechanisms, which this article will introduce.
4. This series primarily analyzes smoothness-related issues. Responsiveness and stability will be covered in dedicated articles. Understanding smoothness first will make analyzing responsiveness and stability much easier.
5. This series focuses on using Systrace (Perfetto) for analysis. Systrace is our entry point because many factors affect smoothness—some within the app itself and others within the system. Systrace (Perfetto) provides a holistic view of the system’s operation during the problem, helping us initially pinpoint the root cause.

Different people have different understandings of smoothness (jank/dropped frames) and different perceptions of jitter thresholds. Therefore, before starting this series, it is necessary to clarify the content to avoid misunderstandings. Here are some basic explanations:

1. For mobile users, jank encompasses many scenarios: dropped frames when scrolling lists, excessive white screen during app startup, slow screen wake-up when pressing the power button, unresponsive interface followed by a crash, no response when clicking an icon, incoherent window animations, lagging touch response, or stuttering when entering the desktop after a reboot. These scenarios differ slightly from what developers understand as “jank.” Developers categorize these more finely, which is a cognitive gap between developers and users that must be noted when handling feedback from users or testers.
2. For developers, the above scenarios fall into three major categories: Smoothness (dropped frames in lists, incoherent animations, stuttering desktop entry), Responsiveness (long startup white screens, slow screen wake-up, lagging touch), and Stability (unresponsive interface/crashes, no response to icon clicks). This classification is used because each category requires different analysis methods and steps. Quickly identifying the category is crucial.
3. Technically, Smoothness, Responsiveness, and Stability (ANR) all feel like “jank” to users because their underlying principles are identical: the main thread’s Message exceeds its processing deadline. They are simply categorized by different timeout thresholds. Understanding these problems requires knowledge of basic system operation mechanisms, which this article will introduce.
4. This series primarily analyzes smoothness-related issues. Responsiveness and stability will be covered in dedicated articles. Understanding smoothness first will make analyzing responsiveness and stability much easier.
5. This series focuses on using Systrace (Perfetto) for analysis. Systrace is our entry point because many factors affect smoothness—some within the app itself and others within the system. Systrace (Perfetto) provides a holistic view of the system’s operation during the problem, helping us initially pinpoint the root cause.

Different people have different understandings of smoothness (jank/dropped frames) and different perceptions of jitter thresholds. Therefore, before starting this series, it is necessary to clarify the content to avoid misunderstandings and help everyone approach these articles with the right questions. Here are some basic explanations:

1. For mobile users, jank encompasses many scenarios: dropped frames when scrolling lists, excessive white screen during app startup, slow screen wake-up when pressing the power button, unresponsive interface followed by a crash, no response when clicking an icon, incoherent window animations, lagging touch response, or stuttering when entering the desktop after a reboot. These scenarios differ slightly from what developers understand as “jank.” Developers categorize these more finely, which is a cognitive gap between developers and users that must be noted when handling feedback from users or testers.
2. For developers, the above scenarios fall into three major categories: Smoothness (dropped frames in lists, incoherent animations, stuttering desktop entry), Responsiveness (long startup white screens, slow screen wake-up, lagging touch), and Stability (unresponsive interface/crashes, no response to icon clicks). This classification is used because each category requires different analysis methods and steps. Quickly identifying the category is crucial.
3. Technically, Smoothness, Responsiveness, and Stability (ANR) all feel like “jank” to users because their underlying principles are identical: the main thread’s Message exceeds its processing deadline. They are simply categorized by different timeout thresholds. Understanding these problems requires knowledge of basic system operation mechanisms, which this article will introduce.
4. This series primarily analyzes smoothness-related issues. Responsiveness and stability will be covered in dedicated articles. Understanding smoothness first will make analyzing responsiveness and stability much easier.
5. This series focuses on using Systrace (Perfetto) for analysis. Systrace is our entry point because many factors affect smoothness—some within the app itself and others within the system. Systrace (Perfetto) provides a holistic view of the system’s operation during the problem, helping us initially pinpoint the root cause.

The content of this blog mainly focuses on Android development and optimization-related topics, including the use of performance tools, Android App optimization knowledge, Android Framework explanations, and performance theory. Here is an organized directory for your reference. You can choose the parts you are interested in. This directory includes not only blog content but also some of my answers on Zhihu or the Knowledge Planet - The Performance. This directory lists my original blog posts. Additionally, I have collected some excellent articles in Must-Knows for Android Performance Optimization, which I update periodically.

This is the first article in the Systrace series, primarily providing a brief introduction to Systrace, its basic usage, how to interpret Systrace traces, and how to analyze phenomena in Systrace in conjunction with other tools.

The purpose of this series is to view the overall operation of the Android system from a different perspective using Systrace, while also providing an alternative angle for learning the Framework. Perhaps you’ve read many articles about the Framework but can never remember the code, or you’re unclear about the execution flow. Maybe from Systrace’s graphical perspective, you can gain a deeper understanding.

As Systrace becomes increasingly feature-rich, combined with Android version iterations, the previous Systrace series tutorials have become somewhat outdated. Additionally, as my own skills have improved, I’ve been able to extract more information from Systrace, which has been very helpful in solving various performance issues. I need to document these skills to enhance my summarization and organization abilities, and if it helps those who read these articles, that would be excellent.

The purpose of this series is to view the overall operation of the Android system from a different perspective using Systrace, while also providing an alternative angle for learning the Framework. Perhaps you’ve read many articles about the Framework but can never remember the code, or you’re unclear about the execution flow. Maybe from Systrace’s graphical perspective, you can gain a deeper understanding.