This is the first article in the series on using the MAT tool. This series consists of three articles, detailing how to use MAT to analyze memory issues, whether they are Java application memory issues or Android application memory issues:

1. Android Memory Optimization (1) - Getting Started with MAT
2. Android Memory Optimization (2) - Advanced MAT Usage
3. Android Memory Optimization (3) - Opening Original Bitmap Images in MAT

Introduction to MAT

What is MAT?

MAT (Memory Analyzer Tool), a memory analysis tool based on Eclipse, is a fast and feature-rich JAVA heap analysis tool. It helps us find memory leaks and reduce memory consumption. Using the memory analysis tool to analyze numerous objects, quickly calculate the size occupied by objects in memory, see who is preventing the garbage collector from reclaiming, and visually view the objects that may cause this result through reports.

Of course, MAT also has an independent version that doesn’t rely on Eclipse, but this version requires converting the file generated by DDMS before it can be opened in the standalone version of MAT when debugging Android memory. However, the Android SDK already provides this Tool, so it is also very convenient to use.

Introduction

This article is a translation of Android Performance Case Study Follow-up by the renowned Romain Guy. It explores several techniques, methodologies, and tools for Android performance optimization.

Translation

Two years ago, I published Android Performance Case Study to help Android developers understand the tools and techniques needed to identify, track, and optimize performance bottlenecks.

That article used the Twitter client Falcon Pro as a case study. Its developer, Joaquim Vergès, was kind enough to let me use his app as an example and quickly addressed the issues I found. Fast forward to recently: Joaquim was building Falcon Pro 3 from scratch. Before its release, he contacted me about a scrolling performance issue. Once again, I had to analyze it without access to the source code.

Here’s the final round-up of Android tips I’ve been posting.

I’ve officially run out of things to post. If I ever come across something new I’ll post it but it won’t be daily anymore. It’s been fun everyone!

Here’s the fourth round-up of Android tips I’ve been posting.

Here’s the third round-up of Android tips I’ve been posting.

Following my previous article, Using Android Studio to View Android Lollipop Source Code, we know that simply reading code has its limits—understanding can remain superficial and easily forgotten. In contrast, code you’ve personally written or modified tends to leave a much deeper impression, and the process becomes easier to grasp during the implementation phase. While studying the source code, being able to modify it, run it on a phone, and see the results firsthand significantly boosts both learning efficiency and enthusiasm. This article explains how to compile the Android Lollipop source code yourself and run it on a Nexus 5.

Why compile your own firmware instead of using Google’s factory images?
Google’s factory images are “User” builds, which are highly restrictive and prevent you from pushing files to the system at will. Compiling your own “Userdebug” build gives you the freedom and root access needed for deep development.

Note: This content is outdated. Please refer to the new Systrace Series Articles for updated information.

This is the first article in the Android Performance Optimization Tools series. This series mainly introduces the tools used during the Android performance optimization process, how to use these tools to discover problems, and how to solve them. In terms of performance optimization, Android provides many performance tools for everyone to use. Following our consistent “discover problem - solve problem” thinking, discovering the problem is the most important part. Trying to solve a problem without first identifying it properly often leads to half the effort for twice the result.

In this article, we’ll start with a brief introduction to the Systrace tool.

Introduction to Systrace

Systrace is a performance data sampling and analysis tool introduced in Android 4.1. It helps developers collect execution information from key Android subsystems (such as SurfaceFlinger, WindowManagerService, and other critical Framework modules, services, and the View system), allowing for a more intuitive analysis of system bottlenecks and performance improvements.

Systrace’s capabilities include tracking system I/O operations, kernel workqueues, CPU load, and the health of various Android subsystems. On the Android platform, it’s composed of three main parts:

• Kernel Space: Systrace leverages the ftrace feature in the Linux Kernel. Therefore, to use Systrace, the relevant ftrace modules in the kernel must be enabled.
• Data Collection: Android defines a Trace class that applications can use to output statistical information to ftrace. Additionally, the atrace program in Android reads statistical info from ftrace and passes it to data analysis tools.
• Data Analysis Tools: Android provides systrace.py (a Python script located in Android SDK directory/platform-tools/systrace that calls atrace internally) to configure data collection (such as tags, output filename, etc.), collect ftrace statistics, and generate a resulting HTML file for user viewing. Essentially, Systrace is a wrapper around the Linux Kernel’s ftrace. Applications need to utilize the Trace class provided by Android to use Systrace.

Official documentation and usage for Systrace can be found here: Systrace

Android Studio

As Google’s “own son,” the Nexus phone series receives special treatment that is obvious to everyone. After Android 5.0 was released, the Nexus 5 was updated to the latest system immediately. Similarly, Android Studio, as Google’s official IDE, is highly valued. I switched from Eclipse to Android Studio right from the start, upgrading from the initial beta versions all the way to the current 1.0 stable version (1.1 was released today, and I’ve already upgraded).

Introduction

The previous article covered the theory of overdraw and tools to detect it. While iOS users rely on Apple’s curation, Android users rely on developers’ discipline. Unfortunately, many market-leading Android apps still suffer from significant overdraw issues. As a developer, I want to see Android bridge and eventually surpass the experience gap with iOS.

This post walks through a practical overdraw optimization process. Since every app is different, these steps are a reference to help you start your own optimization journey.

If you missed the theory part, check it here: Android Performance Optimization: Overdraw (Part 1)

It’s been a while since my last update. After joining a new company, things have been busy, but I’ve been spending a lot of time researching Android performance. I’ve realized there’s so much I still don’t know, so I’m starting from the application level and working my way down. This series will document my learnings on Android performance optimization.

First, we’ll discuss GPU Overdraw, which is often the most direct point of contact for developers. This topic is split into two parts: Part 1 covers the theory and optimization suggestions, and Part 2 will walk through a practical optimization example.

What is Overdraw?

GPU Overdraw refers to the system drawing more than one layer on a single pixel during a frame. For example, if a TextView has a background color, the pixels displaying the text are drawn twice: once for the background and once for the characters. Overdraw inevitably impacts performance because memory bandwidth is finite. When overdraw exceeds the available bandwidth, the frame rate drops. Bandwidth limits vary significantly across different devices.

In Android development, using EditText is very common. However, sometimes EditText automatically grabs focus when entering a page, causing the soft keyboard to pop up immediately. While this is convenient in some cases, most of the time we prefer the keyboard to appear only when the user explicitly clicks on the EditText.

Here’s the second round-up of Android tips I’ve been posting.

With my recent project I’ve been posting one Android class/method a day. People have been asking for an archive of these links, so every couple weeks I’m going to round them up here. I’ll also be adding a bit of color commentary, but still the goal is for this not to be a ton of work for me. :)

Log2File is a utility class for Android applications to record logs to a file (such as the SD card).

Usage Scenarios:

1. Unable to connect to a computer for debugging (e.g., USB port is occupied by USB OTG).
2. Logs are difficult to capture in real-time.
3. Bugs appear randomly and are not easily reproducible.
4. Other scenarios where persistent logging is needed.

0. Introduction

This article was originally published on my CSDN blog: http://blog.csdn.net/grackergao/article/details/18322749. I have now migrated it here. The source code is available on Github: https://github.com/Gracker/Android-Utils/blob/master/Log2File.java.

1. Problem Overview

Recently, while developing for a Nokia project, I encountered the following issue:

When I plugged in a Nokia X, the computer did not respond at all—it wasn’t recognized. My colleague’s Windows machine also failed to detect it. After searching Google for a long time, I finally found a solution. Since I didn’t record it originally and later forgot how to configure it when helping someone else, I decided to document it here for everyone.

2. Solution

If the adb command indicates that no devices are found, please ensure you have already completed these basic steps:

1. Enable USB Debugging (Settings - Developer Options - USB Debugging). If you don’t see Developer Options, go to “About” and tap the Build Number several times.
2. Restart Adb with Sudo: Try sudo adb kill-server and sudo adb start-server.

If it still doesn’t work, follow the steps below: