Optimizing Java Code(Pro Android Apps Performance Optimization)
About the Author
Hervé Guihot started learning about computers more than 20 years ago with
an Amstrad CPC464. Although the CPC464 is most likely the reason why he still
appreciates green-screened devices (ask him about his phone), Hervé started
working with Android as it became a popular platform for application
development. It was also was the only platform that combined two of his main
passions: software and pastriesAndroid是唯一把Herve两个最爱甜点和软件搭配在一起的平台. After many years working in the world of interactive and digital television, he is focused on bringing Android to more devices to encourage more developers to leverage the power of Android and more people to have access to the technology(Herve现在专注于将Android应用在更多设备并且鼓励更多开发者利用Android的强大功能). Hervé is currently a software engineering manager in MediaTek (www.mediatek.com), a leading fabless semiconductor(芯片设计) company for wireless communications(无线通信) and digital multimedia solutions(数字多媒体解决方案). He holds an engineering degree(电信工程学士) from the Institut de Formation Supérieure en Informatique et Télécommunication in Rennes, Brittany, and you can sometimes find him waiting in line for anéclair on 18th and Guerrero(发现他在18号大街和格雷罗大街的交汇处买泡芙).
Introduction
Android quickly became almost ubiquitous(普遍存在的). With the world transitioning from feature phones to smartphones, and then discovering that tablets(平板) are, after all, devices we can hardly live without, application developers today have a choice between mostly two platforms: Android and iOS. Android lowered, some may even say broke, the barrier of entry for application developers, because all you need to write Android applications is a computer (and of course some programming knowledge). Tools are free, and almost anyone can now write applications reaching millions of customers. With Android now spreading to a variety of devices, from tablets to televisions, it is important to make sure your applications can not only run well on all these devices but also run better than competing applications. After all, the barrier of entry was lowered for all application developers and you will in many cases find yourself competing for a slice of the ever-growing Android applications market. Whether you write applications to make a living, achieve stardom, or simply make the world a better place, performance will be one of the their key elements.
This book assumes you already have some familiarity with Android application developmentbut want to go one step further and explore what can make your applications run faster. Although the Android tools and online documentation make it easy to create applications, performance optimization is sometimes more of an art than a science and is not documented as thoroughly(性能优化与其说是科学不如说是一门艺术无定法可循). I wrote Pro Android Apps Performance Optimization to help you find easy ways to achieve good performance on virtually all Android devices(我写这本书就是要找到简便的优化方法以便在所有Android设备上取得不错的性能), whether you are trying to optimize an existing application or are writing an application from scratch. Android allows developers to use Java, C/C++, and even assembly languages(汇编语言), and you can implement performance optimizations in many different ways, from taking advantage of the CPU features (利用CPU特性)to simply using a different language more tailored to a specific problem(针对特定问题使用合适的汇编语言).
Chapter 1 focuses on optimizing your Java code. Your first applications will most likely exclusively use the Java language(基本是用Java开发的), and we will see that algorithms themselves are more important than their implementation(算法选择比算法实现重要). You will also learn how to take advantage of simple techniques such as caching and minimizing memory allocations to greatly optimize your applications(比如缓存及减少内存分配来极大程度优化你的应用). In addition, you will learn how to keep your applications responsive, a very important performance indicator(如何使你的应用保持相应的方法这是一个很重要的性能指标), and how to use databases efficiently.
Chapter 2 takes you one step further (or lower(或者更底层), depending on who you talk to) and introduces the Android NDK. Even though the Java code can be compiled to native code since Android 2.2(从android 2.2 java代码可以即时编译为机器码),using C code to implement certain routines can yield better results(某些方法使用C代码可以有更好的效果). The NDK can also allow you to easily port existing code to Android without having to rewrite everything in Java(NDK还可以让你轻松将现有代码移植到Android而无需java里面重写一遍).
Chapter 3 takes you to the abyss of assembly language(带你走进汇编语言的底层). Albeit rarely used by most application developers(底层汇编语言很少被大多数应用的开发者用到), assembly language allows you to take advantage of every platform's specific instruction set and can be a great way to optimize your applications, though at the cost of increased complexity and maintenance(复杂度和维护成本). Though assembly code is typically limited to certain parts of an application(汇编语言代码通常限定于应用程序的某些特定部分), its benefits should not be ignored as tremendous results can be achieved thanks to carefully targeted optimizations(仔细而有针对性的优化会取得巨大性的效果).
Chapter 4 shows you how using less memory can improve performance. In addition tolearning simple ways to use less memory in your code(在内存中使用较少内存的简便方法), you will learn how memory allocationsand memory accesses have a direct impact on performance because of how CPUs are designed(由于CPU的设计方法内存分配和内存访问也会对性能有直接影响).
■ INTRODUCTION
xii
Chapter 5 teaches you how to use multi-threading in your Android applications in order tokeep applications responsive and improve performance as more and more Android devices canrun multiple threads simultaneously(为了越来越多可以同时运行多线程的Android设备提升性能).
Chapter 6 shows you the basics of measuring your applications' performance. In addition to learning how to use the APIs to measure time, you will also learn how to use some of the Android tools to have a better view of where time is spent in your applications(一些Android工具还可以方便查看应用程序执行时间耗费的具体情况).
Chapter 7 teaches you how to make sure your applications use power rationally(合理使用电量). As manyAndroid devices are battery-powered, conserving energy is extremely important(节点非常重要) because anapplication that empties the battery quickly will be uninstalled quickly. This chapter shows you how to minimize power consumption without sacrificing the very things that make Android
applications special(不必牺牲Android程序的特性最大限度减少功耗).
Chapter 8 introduces some basic techniques to optimize your applications' layouts andoptimize OpenGL rendering(优化OpenGL渲染).
Chapter 9 is about RenderScript, a relatively new Android component introduced in Honeycomb(蜂巢). RenderScript is all about performance为性能而生 and has already evolved quite a bit since its first release(从它首次发布以来已经有了很大改进). In this chapter you learn how to use RenderScript in your applications and also learn about the many APIs RenderScript defines.
I hope you enjoy this book and find many helpful tips in it. As you will find out, many techniques are not Android specific, and you will be able to re-use a lot of them on other platforms, for example iOS. Personally, I have a sweet tooth for assembly language(偏好汇编语言) and I hope the proliferation of the Android platform and support for assembly language in the Android NDK will entice(吸引) many developers, if only to learn a new skill. However, I do want to emphasize(强调、着重) that good design and good algorithms will often already take care of all performance optimizations you need. Good luck, and I am looking forward to your Android applications!
Chapter 1: Optimizing Java Code
1.1 How Android Executes Your Code
1.2 Optimizing Fibonacci(优化斐波那契数列)
1.2.1From Recursive To Iterative (从递归到迭代)
1.2.2BigInteger
1.3 Caching Results(缓存结果)
android.util.LruCache<K, V>
1.4 API Levels(API 等级)
Fragmentation
1.5 Data Structures
1.6 Responsiveness响应能力
1.6.1 Lazy initializations (推迟初始化)
1.6.2 StrictMode(严格模式)
1.7 SQLite
1.7.1 SQLite Statements
1.7.2 Transactions(事务)
1.7.3 Queries查询
1.8 Summary总结
Optimizing Java Code
Many Android application developers have a good practical knowledge of the Java
language from previous experience. Since its debut in 1995自从Java1995年问世以来, Java has become a very popular programming language. While some surveys(调查) show that Java lost its luster光芒 trying to compete with other languages like Objective-C or C#, some of these same surveys rank Java as the number 1 language popularity-wise. Naturally, with mobile devices outselling销量超过 personal computers and the success of the Android platform (700,000 activations per day in December 2011) Java is becoming more relevant(更为重要) in today’s market than ever before.
Developing applications for mobile devices can be quite different from developing applications for personal computers. Today’s portable便携式 devices can be quite powerful, but in terms of performance(性能方面), they lag落后 behind personal computers. For example, some benchmarks基准测试 show a quad-core四核 Intel Core i7 processor running about 20 times faster than the dual-core双核 Nvidia Tegra 2 that is found in the Samsung Galaxy Tab 10.1.
NOTE: Benchmark results are to be taken with a grain of salt(盐堆中的一粒) since they often measure only part of a system and do not necessarily represent a typical use-case(不代表典型的应用场景).
This chapter shows you how to make sure your Java applications perform well on
Android devices, whether they run the latest Android release(最新的android版本) or not. First, we take a look at how Android executes your code. Then, we review品评 several techniques to optimize the implementation of a famous mathematical series数列代码, including how to take advantage of the latest APIs Android offers. Finally, we review a few techniques to improve your application’s responsiveness and to use databases more efficiently(高效使用数据库的技巧).
Before you jump in, you should realize code optimization is not the first priority in your application development. Delivering(提供) a good user experience and focusing on code maintainability(提高代码的可维护性) should be among your top priorities. In fact, code optimization should be one of your last priorities, and may not even be part of the process altogether(甚至可能完全不去做). However, good practices can help you reach an acceptable level of performance without having
you go back to your code, asking yourself “what did I do wrong?” and having to spend additional resources to fix it.
How Android Executes Your Code
While Android developers use Java, the Android platform does not include a Java Virtual Machine (VM) for executing code. Instead, applications are compiled(编译成) into Dalvik bytecode(字节码), and Android uses its Dalvik VM to execute it. The Java code is still compiled into Java bytecode, but this Java bytecode is then compiled into Dalvik bytecode by the dex compiler(编译器), dx (an SDK tool). Ultimately, your application will contain only the Dalvik bytecode, not the Java bytecode. For example, an implementation of a method that computes the nth term of the Fibonacci series is shown in Listing 1�C1 together with the class definition. The Fibonacci
series is defined as follows:
F0 = 0
F1 = 1
Fn = Fn-2 + Fn-1 for n greater than 1
Listing 1�C1. Nave Recursive Implementation(简单递归实现) of Fibonacci Series
public class Fibonacci {
public static long computeRecursively (int n)
{
if (n > 1) return computeRecursively(n-2) + computeRecursively(n-1);
return n;
}
}
NOTE(注意): A trivial optimization(微小优化) was done by returning n when n equals 0 or 1 instead of adding another “if” statement to check whether n equals 0 or 1.
An Android application is referred to as an APK(称为APK) since applications are compiled into a file with the apk extension(打包成带有APK的扩展名) (for example, APress.apk), which is simply an archive file(这是一个简单的压缩文件). One of the files in the archive(压缩文件) is classes.dex, which contains the application’s bytecode(字节码). The Android toolchain(工具包) provides a tool, dexdump, which can convert the binary form(二进制码) of the code (contained in the APK’s classes.dex file) into human-readable(更易懂) format.
TIP: Because an apk file is simply a ZIP archive, you can use common archive tools such as
WinZip or 7-Zip to inspect the content of an apk file..
Listing 1�C2 shows the matching Dalvik bytecode.
Listing 1�C2. Human-Readable Dalvik Bytecode of Fibonacci.computeRecursively
002548: |[002548] com.apress.proandroid.Fibonacci.computeRecursively:(I)J
002558: 1212 |0000: const/4 v2, #int 1 // #1
00255a: 3724 1100 |0001: if-le v4, v2, 0012 // +0011
00255e: 1220 |0003: const/4 v0, #int 2 // #2
002560: 9100 0400 |0004: sub-int v0, v4, v0
002564: 7110 3d00 0000 |0006: invoke-static {v0},
Lcom/apress/proandroid/Fibonacci;.computeRecursively:(I)J
00256a: 0b00 |0009: move-result-wide v0
00256c: 9102 0402 |000a: sub-int v2, v4, v2
002570: 7110 3d00 0200 |000c: invoke-static {v2},
Lcom/apress/proandroid/Fibonacci;.computeRecursively:(I)J
002576: 0b02 |000f: move-result-wide v2
002578: bb20 |0010: add-long/2addr v0, v2
00257a: 1000 |0011: return-wide v0
00257c: 8140 |0012: int-to-long v0, v4
00257e: 28fe |0013: goto 0011 // -0002
The first number on each line specifies the absolute position of the code within the file. Except on the very first line (which shows the method name), it is then followed by one or more 16-bit bytecode units, followed by the position of the code within the method itself (relative position, or label), the opcode(操作码) mnemonic(助记) and finally the opcode’s parameter(s)冒号右边是操作码助记符及后面的不定个数参数. For example, the two bytecode units 3724 1100 at address 0x00255a translate to “if-le v4, v2, 0012 // +0011”, which basically means “if content of virtual(虚拟寄存器) register v4 is less than or equal to content of virtual register v2 then go to label 0x0012 by skipping 17 bytecode units” (1710 equals 1116). The term “virtual register”(术语虚拟寄存器) refers to the fact that these are not actual hardware registers(非真实的虚拟寄存器) but instead the registers used by the Dalvik virtual machine(也就是Delvik虚拟机使用的寄存器). Typically, you would not need to look at your application’s bytecode.
This is especially true(通常情况下) with Android 2.2 (codename Froyo) and later versions since a Just-In-Time (JIT) compiler(实时编译器) was introduced in Android 2.2. The Dalvik JIT compiler compiles the Dalvik bytecode into native code, which can execute significantly faster. A JIT compiler (sometimes referred to simply as a JIT) improves performance dramatically(显著提升性能) because:
Native code is directly executed by the CPU without having to be interpreted by a virtual machine.
Native code can be optimized for a specific architecture.
Benchmarks(基准测试) done by Google showed code executes 2 to 5 times faster with Android 2.2 than Android 2.1. While the results may vary depending on what your code does代码的具体功能, you can expect a significant increase in speed when using Android 2.2 and later versions. The absence of a JIT compiler in Android 2.1 and earlier versions may affect your optimization strategy significantly. If you intend to target devices running Android 1.5 (codename(代号) Cupcake), 1.6 (codename Donut), or 2.1 (codename éclair), most likely you will need to review more carefully what you want or need to provide in your application. Moreover, devices running these earlier Android versions are older devices, which are less powerful than newer ones. While the market share of Android 2.1 and earlier devices is shrinking, they still represent about 12% as of December 2011). Possible strategies are:
Don’t optimize at all. Your application could be quite slow on these older devices.
Require minimum API level 8 in your application, which can then be
installed only on Android 2.2 or later versions.
Optimize for older devices to offer a good user experience even when no JIT compiler is present. This could mean disabling features that are too CPU-heavy.
TIP(提示): Use android:vmSafeMode in your application’s manifest to enable or disable the JIT compiler. It is enabled by default (if it is available on the platform). This attribute was introduced in Android 2.2(这个属性是Android2.2引入的).
Now it is time to run the code on an actual platform and see how it performs. If you are familiar with recursion(递归) and the Fibonacci series, you might guess that it is going to be slow. And you would be right. On a Samsung Galaxy Tab 10.1, computing the thirtieth Fibonacci number takes about 370 milliseconds. With the JIT compiler disabled, it takes about 440 milliseconds. If you decide to include that function in a Calculator application, users will become frustrated because the results cannot be computed “immediately.”
From a user’s point of view, results appear instantaneous(瞬时计算) if they can be computed in 100 milliseconds or less. Such a response time guarantees a very good user experience, so this is what we are going to target. Optimizing Fibonacci
Listing 1�C3. Optimized Recursive Implementation of Fibonacci Series
The first optimization we are going to perform eliminates a method call消除一个方法的调用, as shown in Listing(代码清单) 1�C3. As this implementation is recursive, removing a single call in the method dramatically reduces the total number of calls. For example, computeRecursively(30) generated 2,692,537 calls while computeRecursivelyWithLoop(30) generated “only” 1,346,269. However, the
performance of this method is still not acceptable considering the responsetime
criteria defined above, 100 millisecondsorless, as
computeRecursivelyWithLoop(30) takes about 270 milliseconds to complete.
public class Fibonacci {
public static long computeRecursivelyWithLoop (int n)
{
if (n > 1) {
long result = 1;
do {
result += computeRecursivelyWithLoop(n-2);
n--;
} while (n > 1);
return result;
}
return n;
}
}
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NOTE: This is not a true tail-recursion（尾递归） optimization.
1.2.1 From Recursive To Iterative
For the second optimization, we switch from a recursive implementation to an iterative one. Recursive algorithms often have a bad reputation(坏名声) with developers, especially on embedded systems(嵌入式开发) without much memory, because they tend to consume a lot of stack space and, as we just saw, can generate too many method calls. Even when performance is acceptable, a recursive algorithm can cause a stack overflow and crash an application. An iterative implementation is therefore often preferred whenever
possible(尽量用迭代实现). Listing 1�C4 shows what is considered a textbook iterative implementation of the Fibonacci series.
Listing 1�C4. Iterative Implementation of Fibonacci Series
public class Fibonacci {
public static long computeIteratively (int n)
{
if (n > 1) {
long a = 0, b = 1;
do {
long tmp = b;
b += a;
a = tmp;
} while (--n > 1);
return b;
}
return n;
}
}
Because the nth term of the Fibonacci series is simply the sum of the two previous terms, a simple loop can do the job. Compared to the recursive algorithms, the complexity of this iterative algorithm is also greatly reduced because it is linear(线性的).
Consequently, its performance is also much better, and computeIteratively(30) takes less than 1 millisecond to complete. Because of its linear nature, you can use such an algorithm to compute terms beyond the 30th. For example, computeIteratively(50000) takes only 2 milliseconds to return a result and, by extrapolation(外推算法), you could guess computeIteratively(500000) would take between 20 and 30 milliseconds to complete. While such performance is more than acceptable, it is possible to to achieve even faster results with a slightly modified version of the same algorithm, as showed in Listing 1�C5.
This new version computes two terms per iteration(这个新版本每次迭代计算两项), and the total number of iterations is halved(减少一半). Because the number of iterations in the original iterative algorithm could be odd(奇数),
the initial values for a and b are modified accordingly: the series starts with a=0 and b=1，when n is odd, and it starts with a=1 and b=1 (Fib(2)=1) when n is even(偶数).
6 CHAPTER 1: Optimizing Java Code
Listing 1�C5. Modified Iterative Implementation of Fibonacci Series
public class Fibonacci {
public static long computeIterativelyFaster (int n)
{
if (n > 1) {
long a, b = 1;
n--;
a = n & 1;
n /= 2;
while (n-- > 0) {
a += b;
b += a;
}
return b;
}
return n;
}
}
Results show this modified iterative version is about twice as fast as the original one. While these iterative implementations are fast, they do have one major problem: they don’t return correct results. The issue lies with(在于) the return value being stored in a long value, which is 64-bit(64位). The largest Fibonacci number that can fit in a signed 64-bit value is 7,540,113,804,746,346,429 or, in other words, the 92nd Fibonacci number. While the methods will still return without crashing the application for values of n greater than 92, the results will be incorrect because of an overflow: the 93rd Fibonacci number would be negative! The recursive implementations actually have the same limitation, but one would have to be quite patient to eventually find out(耐心等待终究会得到结论).
NOTE(提示): Java specifies the size of all primitive types (except boolean): long is 64-bit, int is 32-bit, and short is 16-bit. All integer types are signed(有符号的).
BigInteger
Java offers just the right class(恰当的类) to fix this overflow problem: java.math.BigInteger. A BigInteger object can hold a signed integer of arbitrary （任意）size and the class defines all the basic math operations(数学运算) (in addition to some not-so-basic ones). Listing 1�C6 shows the
BigInteger version of computeIterativelyFaster.
TIP: The java.math package also defines BigDecimal in addition to BigInteger, while java.lang.Math provides math constant and operations(数学常数和算术运算). If your application does not need double precision, use Android’s FloatMath instead of Math for performance (although gains may vary depending on platform).
Listing 1�C6. BigInteger Version of Fibonacci.computeIterativelyFaster
public class Fibonacci {
public static BigInteger computeIterativelyFasterUsingBigInteger (int n)
{
if (n > 1) {
BigInteger a, b = BigInteger.ONE;
n--;
a = BigInteger.valueOf(n & 1);
n /= 2;
while (n-- > 0) {
a = a.add(b);
b = b.add(a);
}
return b;
}
return (n == 0) ? BigInteger.ZERO : BigInteger.ONE;
}
}
That implementation guarantees correctness as overflows can no longer occur.
However, it is not without problems because, again, it is quite slow: a call to
computeIterativelyFasterUsingBigInteger(50000) takes about 1.3 seconds to
complete. The lackluster(黯淡无光) performance(平平的表现) can be explained by three things:
BigInteger is immutable（不可变的）.
BigInteger is implemented using BigInt and native code.
The larger the numbers, the longer it takes to add them together.
Since BigInteger is immutable, we have to write “a = a.add(b)” instead of simply “a.add(b)”. Many would assume “a.add(b)” is the equivalent(等价成) of “a += b” and many would be wrong: it is actually the equivalent of “a + b”. Therefore, we have to write “a = a.add(b)” to assign(分配) the result. That small detail is extremely significant as “a.add(b)” creates a new BigInteger object that holds the result of the addition(来持有额外的值).
Because of BigInteger’s current internal implementation, an additional BigInt object is created for every BigInteger object that is allocated(分配). This results in twice as many objects being allocated during the execution of
computeIterativelyFasterUsingBigInteger: about 100,000 objects are created when
calling computeIterativelyFasterUsingBigInteger (50000) (and all of them but one will become available for garbage collection almost immediately(所有其他对象立刻变成被等待回收的垃圾)). Also, BigInt is implemented using native code and calling native code from Java (using JNI) has a certain overhead（一定开销）.
The third reason is that very large numbers do not fit in a single, long 64-bit value. For example, the 50,000th Fibonacci number is 34,7111�Cbit long.
NOTE: BigInteger’s internal implementation (BigInteger.java) may change in future Android releases版本. In fact, internal implementation of any class can change.
For performance reasons, memory allocations分配 should be avoided whenever possible in critical paths of the code. Unfortunately, there are some cases where allocations are needed, for example when working with immutable不可变 objects like BigInteger. The next optimization focuses on(侧重于) reducing the number of allocations by switching to a different algorithm. Based on the Fibonacci Q-matrix(Q矩阵), we have the following:
F2n-1 = Fn
2 + Fn-1
2
F2n = (2Fn-1 + Fn) * Fn
This can be implemented using BigInteger again (to guarantee correct results), as
shown in Listing 1�C7.
Listing 1�C7. Faster Recursive Implementation of Fibonacci Series Using BigInteger
public class Fibonacci {
public static BigInteger computeRecursivelyFasterUsingBigInteger (int n)
{
if (n > 1) {
int m = (n / 2) + (n & 1); // not obvious at first �C wouldn’t it be great to
have a better comment here?
BigInteger fM = computeRecursivelyFasterUsingBigInteger(m);
BigInteger fM_1 = computeRecursivelyFasterUsingBigInteger(m - 1);
if ((n & 1) == 1) {
// F(m)^2 + F(m-1)^2
return fM.pow(2).add(fM_1.pow(2)); // three BigInteger objects created
} else {
// (2*F(m-1) + F(m)) * F(m)
return fM_1.shiftLeft(1).add(fM).multiply(fM); // three BigInteger
objects created
}
}
return (n == 0) ? BigInteger.ZERO : BigInteger.ONE; // no BigInteger object
created
}
public static long computeRecursivelyFasterUsingBigIntegerAllocations(int n)
{
long allocations = 0;
if (n > 1) {
int m = (n / 2) + (n & 1);
allocations += computeRecursivelyFasterUsingBigIntegerAllocations(m);
allocations += computeRecursivelyFasterUsingBigIntegerAllocations(m - 1);
// 3 more BigInteger objects allocated
allocations += 3;
}
return allocations; // approximate number of BigInteger objects allocated when
computeRecursivelyFasterUsingBigInteger(n) is called
}
}
A call to computeRecursivelyFasterUsingBigInteger(50000) returns in about 1.6
seconds. This shows this latest implementation is actually slower than the fastest iterative implementation we have so far. Again, the number of allocations is the culprit(罪魁祸首) as around 200,000 objects were allocated (and almost immediately marked as eligible(合格) for garbage collection).
NOTE: The actual number of allocations is less than what
computeRecursivelyFasterUsingBigIntegerAllocations would return. Because
BigInteger’s implementation uses preallocated（预分配对象） objects such as BigInteger.ZERO, BigInteger.ONE, or BigInteger.TEN, there may be no need to allocate a new object for some operations. You would have to look at Android’s BigInteger implementation to know exactly how many objects are allocated.
This implementation is slower, but it is a step in the right direction nonetheless(毕竟). The main thing to notice is that even though we need to use BigInteger to guarantee correctness, we don’t have to use BigInteger for every value of n. Since we know the primitive type long can hold results for n less than or equal to 92, we can slightly modify the recursive implementation to mix BigInteger and primitive type, as shown in Listing 1�C8.
Listing 1�C8. Faster Recursive Implementation of Fibonacci Series Using BigInteger and long Primitive Type
public class Fibonacci {
public static BigInteger computeRecursivelyFasterUsingBigIntegerAndPrimitive(int n)
{
if (n > 92) {
int m = (n / 2) + (n & 1);
BigInteger fM = computeRecursivelyFasterUsingBigIntegerAndPrimitive(m);
BigInteger fM_1 = computeRecursivelyFasterUsingBigIntegerAndPrimitive(m -
1);
if ((n & 1) == 1) {
return fM.pow(2).add(fM_1.pow(2));
} else {
return fM_1.shiftLeft(1).add(fM).multiply(fM); // shiftLeft(1) to
multiply by 2
}
}
return BigInteger.valueOf(computeIterativelyFaster(n));
}
private static long computeIterativelyFaster(int n)
{
// see Listing 1�C5 for implementation
}
}
A call to computeRecursivelyFasterUsingBigIntegerAndPrimitive(50000) returns in
about 73 milliseconds and results in about 11,000 objects being allocated: a small modification in the algorithm yields results about 20 times faster and about 20 times fewer objects being allocated. Quite impressive! It is possible to improve the performance even further by reducing the number of allocations, as shown in Listing 1�C9. Precomputed results can be quickly generated when the Fibonacci class is first loaded, and these
results can later be used directly.
Listing 1�C9. Faster Recursive Implementation of Fibonacci Series Using BigInteger and Precomputed Results
public class Fibonacci {
static final int PRECOMPUTED_SIZE= 512;
static BigInteger PRECOMPUTED[] = new BigInteger[PRECOMPUTED_SIZE];
static {
PRECOMPUTED[0] = BigInteger.ZERO;
PRECOMPUTED[1] = BigInteger.ONE;
for (int i = 2; i < PRECOMPUTED_SIZE; i++) {
PRECOMPUTED[i] = PRECOMPUTED[i-1].add(PRECOMPUTED[i-2]);
}
}
public static BigInteger computeRecursivelyFasterUsingBigIntegerAndTable(int n)
{
if (n > PRECOMPUTED_SIZE - 1) {
int m = (n / 2) + (n & 1);
BigInteger fM = computeRecursivelyFasterUsingBigIntegerAndTable (m);
BigInteger fM_1 = computeRecursivelyFasterUsingBigIntegerAndTable (m - 1);
if ((n & 1) == 1) {
return fM.pow(2).add(fM_1.pow(2));
} else {
return fM_1.shiftLeft(1).add(fM).multiply(fM);
}
}
return PRECOMPUTED[n];
}
}
The performance of this implementation depends on PRECOMPUTED_SIZE: the bigger, the faster（更大就更快）. However, memory usage may become an issue since many BigInteger objects will be created and remain in memory for as long as the Fibonacci class is loaded(只要加载斐波那契). It is possible to merge合并 the implementations shown in Listing 1�C8 and Listing 1�C9, and use a combination of precomputed results and computations with primitive types(基本类型). For example, terms 0 to 92 could be computed using computeIterativelyFaster, terms 93
to 127 using precomputed results and any other term using recursion递归. As a developer, you are responsible for choosing the best implementation, which may not always be the fastest. Your choice will be based on various factors, including:
What devices and Android versions your application target
Your resources (people and time)
As you may have already noticed, optimizations tend to make the source code harder to read, understand, and maintain, sometimes to such an extent(范围) that you would not recognize your own code weeks or months later. For this reason, it is important to carefully think about what optimizations you really need and how they will affect your application development, both in the short term and in the long term. It is always recommended you first implement a working solution(能实现的解决方案) before you think of optimizing it (and make sure you save a copy of the working solution). After all, you may realize optimizations are not needed at all, which could save you a lot of time. Also, make sure you include comments in your code for everything that is not obvious to a person with ordinary skill in the art(被旧代码搞晕时). Your coworkers will thank you, and you may give yourself a pat(回忆) on the back as well when you stumble on some of your old code. My poor comment in Listing 1�C7 is proof.
NOTE: All implementations实现 disregard忽略 the fact that n could be negative. This was done intentionally to make a point, but your code, at least in all public APIs, should throw an IllegalArgumentException whenever appropriate(在适当时).
1.3 Caching Results 缓存结果
When computations are expensive, it may be a good idea to remember past results(把上次结果缓存起来) to make future requests faster. Using a cache is quite simple as it typically通常 translates to the pseudo-code伪代码 shown in Listing 1�C10. Listing 1�C10. Using a Cache
result = cache.get(n); // input parameter参数 n used as key
if (result == null) {
// result was not in the cache so we compute it and add it
result = computeResult(n);
cache.put(n, result); // n is the key, result is the value
}
return result;
The faster recursive algorithm to compute Fibonacci terms yields many duplicate
calculations and could greatly benefit from memoization. For example, computing the 50,000th term requires computing the 25,000th and 24,999th terms. Computing the 25,000th term requires computing the 12,500th and 12,499th terms, while computing the 24,999th term requires computing… the same 12,500th and 12,499th terms again! Listing 1�C11 shows a better implementation using a cache.
If you are familiar with Java, you may be tempted to use a HashMap as your cache, and it would work just fine. However, Android defines SparseArray, a class that is intended to be more efficient than HashMap when the key is an integer value: HashMap would require the key to be of type java.lang.Integer, while SparseArray uses the primitive type int for keys. Using HashMap would therefore trigger the creation of many Integer objects for the keys, which SparseArray simply avoids.
Listing 1�C11. Faster Recursive Implementation Using BigInteger, long Primitive TypeAnd Cache
public class Fibonacci {
public static BigInteger computeRecursivelyWithCache (int n)
{
SparseArray<BigInteger> cache = new SparseArray<BigInteger>();
return computeRecursivelyWithCache(n, cache);
}
private static BigInteger computeRecursivelyWithCache (int n,
SparseArray<BigInteger> cache)
{
if (n > 92) {
BigInteger fN = cache.get(n);
if (fN == null) {
int m = (n / 2) + (n & 1);
BigInteger fM = computeRecursivelyWithCache(m, cache);
BigInteger fM_1 = computeRecursivelyWithCache(m �C 1, cache);
if ((n & 1) == 1) {
fN = fM.pow(2).add(fM_1.pow(2));
} else {
fN = fM_1.shiftLeft(1).add(fM).multiply(fM);
}
cache.put(n, fN);
}
return fN;
}
return BigInteger.valueOf(iterativeFaster(n));
}
private static long iterativeFaster (int n) { /* see Listing 1�C5 for implementation
*/ }
}
Measurements showed computeRecursivelyWithCache(50000) takes about 20
milliseconds to complete, or about 50 fewer milliseconds than a call to
computeRecursivelyFasterUsingBigIntegerAndPrimitive(50000). Obviously, the
difference is exacerbated(加剧) as n grows: when n equals 200,000 the two methods
complete in 50 and 330 milliseconds respectively.
Because many fewer BigInteger objects are allocated创建, the fact that BigInteger is immutable不可变 is not as big of a problem when using the cache. However, remember that three BigInteger objects are still created (two of them being very short-lived) when fN is computed, so using mutable可变 big integers would still improve performance.
Even though using HashMap instead of SparseArray may be a little slower, it would have the benefit of making the code Android-independent(让代码不依赖Android), that is, you could use the exact same code in a non-Android environment (without SparseArray).
NOTE: Android defines multiple types of sparse arrays: SparseArray (to map integers to objects), SparseBooleanArray (to map integers to booleans), and SparseIntArray (to map integers to integers(键为整数、值为整数)).
1.3.1 android.util.LruCache<K, V>
Another class worth mentioning is android.util.LruCache<K, V>, introduced in Android 3.1 (codename Honeycomb MR1), which makes it easy to define the maximum size of the cache when it is allocated创建. Optionally, you can also override the sizeOf() method to change how the size of each cache entry is computed（每个缓存条目计算大小的方式）. Because it is only available in Android 3.1 and later, you may still end up having to use a different class to implement a cache in your own application if you target Android revisions older than 3.1. This is a
very likely scenario(方案，情节) considering Android 3.1 as of today represents only a very small portion of the Android devices in use. An alternative solution is to extend java.util.LinkedHashMap and override removeEldestEntry. An LRU cache (for Least Recently Used) discards the least recently used items first. In some applications, you may need exactly the opposite, that is, a cache that discards丢弃 the most recently used items first. Android does not define such an MruCache class for now, which is not surprising considering MRU caches are not as commonly used.
Of course, a cache can be used to store information other than computations（是用来存储而不是计算的）. A common use of a cache is to store downloaded data such as pictures and still maintain tight control over how much memory is consumed(但仍需严格控制使用量). For example, override LruCache’s
sizeOf method to limit the size of the cache based on a criterion other than simply the number of entries in the cache（不能以简单限制缓存中条目数为准则）. While尽管 we briefly discussed the LRU and MRU strategies, you may want to use different replacement strategies for your own cache to maximize cache hits缓存命中率. For example, your cache could first discard the items that are not costly to recreate, or simply randomly discard items. Follow a pragmatic务实 approach and design your cache accordingly. A simple replacement strategy such as LRU can yield great results and allow you to focus your resources on other, more important problems（把手上的资源留给更重要的项目）.
We’ve looked at several different techniques to optimize the computation of Fibonacci numbers. While each technique has its merits优点, no one implementation is optimal. Often the best results are achieved by combining multiple various techniques instead of relying on only one of them. For example, an even faster implementation would use precomputations, a cache mechanism, and maybe even slightly different formulas公式. (
Hint:what happens when n is a multiple of 4?) What would it take to compute FInteger.MAX_VALUE
in less than 100 milliseconds?
1.4 API Levels
The LruCache class mentioned above is a good example of why you need to know what
API level you are going to target. A new version of Android is released approximately every six months, with new APIs only available from that release. Any attempt to call an API that does not exist results in a crash(奔溃), bringing not only frustration for the user but also shame to the developer. For example, calling Log.wtf(TAG, “Really?”) on an Android 1.5 device crashes the application, as Log.wtf was introduced in Android 2.2 (API level 8). What a terrible failure indeed that would be. Table 1�C1 shows the performance improvements made in the various Android versions.
Table 1�C1. Android Versions
API level Version Name Significant performance improvements
1 1.0 Base
2 1.1 Base 1.1
3 1.5 Cupcake Camera start-up time, image capture time, faster
acquisition of GPS location, NDK support
4 1.6 Donut
5 2.0 éclair Graphics
6 2.0.1 éclair 0.1
7 2.1 éclair MR1
8 2.2 Froyo V8 Javascript engine (browser), JIT compiler, memory
management
9 2.3.0
2.3.1
2.3.2
Gingerbread Concurrent garbage collector, event distribution,
better OpenGL drivers
10 2.3.3
2.3.4
Gingerbread MR1
11 3.0 Honeycomb Renderscript, animations, hardware-accelerated 2D
graphics, multicore support
12 3.1 Honeycomb MR1 LruCache, partial invalidates in hardware-accelerated
views, new Bitmap.setHasAlpha() API
13 3.2 Honeycomb MR2
14 4.0 Ice Cream Sandwich Media effects (transformation filters), hardwareaccelerated
2D graphics (required)
However, your decision to support a certain target should normally not be based on which API you want to use, but instead on what market you are trying to reach. For example, if your target is primarily tablets and not cell phones, then you could target Honeycomb. By doing so, you would limit your application’s audience to a small subset of Android devices, because Honeycomb represents only about 2.4% as of December 2011, and not all tablets support Honeycomb. (For example, Barnes & Noble’s Nook uses Android 2.2 while Amazon’s Kindle Fire uses Android 2.3.) Therefore, supporting older Android versions could still make sense.
The Android team understood that problem when they released the Android
Compatibility兼容 package, which is available through the SDK Updater. This package contains a static library with some of the new APIs introduced in Android 3.0, namely the fragment APIs. Unfortunately, this compatibility package contains only the fragment APIs and does not address the other APIs that were added in Honeycomb. Such a compatibility package is the exception, not the rule. Normally, an API introduced at a specific API level is not available at lower levels, and it is the developer’s responsibility to choose APIs carefully.
To get the API level of the Android platform, you can use Build.VERSION.SDK_INT. Ironically, this field was introduced in Android 1.6 (API level 4), so trying to retrieve the version this way would also result in a crash on Android 1.5 or earlier. Another option is to use Build.VERSION.SDK, which has been present since API level 1. However, this field is now deprecated, and the version strings are not documented归档 (although it would be pretty easy to understand how they have been created).
TIP: Use reflection to check whether the SDK_INT field exists (that is, if the platform is
Android 1.6 or later). See Class.forName(“android.os.Build$VERSION”).getField(“SDK”). Your application’s manifest file should use the <uses-sdk> element to specify two important things:
The minimum API level required for the application to run
(android:minSdkVersion)
The API level the application targets (android:targetSdkVersion)
It is also possible to specify the maximum API level (android:maxSdkVersion), but using this attribute is not recommended. Specifying maxSdkVersion could even lead to applications being uninstalled automatically after Android updates. The target API level is the level at which your application has been explicitly tested.By default, the minimum API level is set to 1 (meaning the application is compatible with all
Android versions). Specifying an API level greater than 1 prevents the application from being installed on older devices. For example, android:minSdkVersion=”4” guarantees Build.VERSION.SDK_INT can be used without risking any crash. The minimum API level does not have to be the highest API level you are using in your application as long as you make sure you call only a certain API when the API actually exists, as shown in Listing 1�C12.
Listing 1�C12. Calling a SparseArray Method Introduced in Honeycomb (API Level 11)
if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.HONEYCOMB) {
sparseArray.removeAt(1); // API level 11 and above
} else {
int key = sparseArray.keyAt(1); // default implementation is slower
sparseArray.remove(key);
}
This kind of code is more frequent when trying to get the best performance out of your Android platform since you want to use the best API for the job while you still want your application to be able to run on an older device (possibly using slower APIs).
Android also uses these attributes for other things, including determining whether the application should run in screen compatibility mode. Your application runs in screen compatibility mode if minSdkVersion is set to 3 or lower, and targetSdkVersion is not set to 4 or higher. This would prevent your application from displaying in full screen on a tablet, for example, making it much harder to use. Tablets have become very popular only recently, and many applications have not been updated yet, so it is not uncommon to find applications that do not display properly on a big screen(在大屏幕上显示不正确还比较少见).
NOTE: Android Market uses the minSdkVersion and maxSdkVersion attributes to filter applications available for download on a particular device. Other attributes are used for filtering as well. Also, Android defines two versions of screen compatibility mode, and their behaviors differ. Refer to “Supporting Multiple Screens” on http://d.android.com/guide for a complete description.
Instead of checking the version number, as shown in Listing 1�C12, you can use reflection to find out whether a particular method exists on the platform. While this is a cleaner and safer implementation, reflection(反射) can make your code slower; therefore you should try to avoid using reflection where performance is critical. One possible approach is to call Class.forName() and Class.getMethod()to find out if a certain method exists in the static initialization block, and then only call Method.invoke() where performance is important.
1.4.1 Fragmentation版本分化
The high number of Android versions, 14 API levels so far, makes your target market
quite fragmented, possibly leading to more and more code like the one in Listing 1�C12. However, in practice, a few Android versions represent the majority of all the devices. As of December 2011, Android 2.x versions represent more than 95% of the devices onnecting to Android Market. Even though Android 1.6 and earlier devices are still in operation, today it is quite reasonable not to spend additional resources to optimize for these platforms.
The number of available devices running Android is even greater, with currently close to 200 phones(200个电话型号) listed on www.google.com/phone, including 80 in the United States alone.While the listed devices are all phones or tablets, they still differ in many ways: screen resolutions, presence of physical keyboard, hardware-accelerated graphics, processors处理器.Supporting the various configurations配置, or even only a subset子集, makes application development more challenging. Therefore it is important to have a good knowledge of the market you are targeting in order to focus your efforts on important features and optimizations.
NOTE: Not all existing Android devices are listed on www.google.com/phone as some countries are not listed yet, for example India and its dual-SIM Spice MI270 running Android 2.2.
Google TV devices (first released in 2010 by Logitech and Sony in the United States) are technically not so different from phones or tablets. However, the way people interact with these devices differs. When supporting these TV devices, one of your main challenges will be to understand how your application could be used on a TV. For example, applications can provide a more social experience on a TV: a game could offer a simultaneous multiplayer mode, a feature that would not make much sense on a phone.
1.5 Data Structures 数据结构
As the various Fibonacci implementations demonstrated, good algorithms and good
data structures are keys to a fast application. Android and Java define many data
structures you should have good knowledge of to be able to quickly select the right
ones for the right job. Consider choosing the appropriate data structures one of your
highest priorities.
The most common data structures from the java.util package are shown in Figure 1.1. To those data structures Android adds a few of its own, usually to solve or improve performance of common problems.
LruCache
SparseArray
SparseBooleanArray
SparseIntArray
Pair
NOTE: Java also defines the Arrays and Collections classes. These two classes contain only static methods, which operate on arrays and collections respectively. For example, use Arrays.sort to sort an array and Arrays.binarySearch to search for a value in a sorted array.
Figure 1�C1. Data structures in the java.util package
While one of the Fibonacci implementations used a cache internally (based on a sparse
array), that cache was only temporary and was becoming eligible for garbage collection
immediately after the end result was computed. It is possible to also use an LruCache to
save end results, as shown in Listing 1�C13.
Listing 1�C13. Using an LruCache to Remember Fibonacci Terms
int maxSize = 4 * 8 * 1024 * 1024; // 32 megabits
LruCache<Integer, BigInteger> cache = new LruCache<Integer, BigInteger> (maxSize) {
protected int sizeOf (Integer key, BigInteger value) {
return value.bitLength(); // close approximation of object’s size, in bits
}
};
…
int n = 100;
BigInteger fN = cache.get(n);
if (fN == null) {
fN = Fibonacci. computeRecursivelyWithCache(n);
cache.put(n, fN);
}
Whenever you need to select a data structure to solve a problem, you should be able to
narrow down your choice to only a few classes since each class is usually optimized for
AbstractCollection
AbstractList
AbstractSequentialList
LinkedList
ArrayList
Vector
Stack
AbstractQueue
PriorityQueue
AbstractSet
EnumSet
HashSet
LinkedHashSet
TreeSet
ArrayDeque
AbstractMap
EnumMap
HashMap
LinkedHashMap
IdentityHashMap
TreeMap
WeakHashMap
BitSet
Dictionary
Hashtable
Properties
Collection
Deque
List
Map
NavigableMap
NavigableSet
Queue
RandomAccess
Set
SortedMap
SortedSet
a specific purpose or provides a specific service. For example, choose ArrayList over
Vector if you don’t need the operations to be synchronized. Of course, you may alwayscreate your own data structure class, either from scratch (extending Object) or
extending an existing one.
NOTE: Can you explain why LruCache is not a good choice for computeRecursivelyWithCache’s
internal cache as seen in Listing 1�C11?
If you use one of the data structures that rely on hashing (e.g. HashMap) and the keys are of a type you created, make sure you override the equal and hashCode methods. A poor implementation of hashCode can easily nullify the benefits of using hashing.
TIP: Refer to http://d.android.com/reference/java/lang/Object.html for a good
example of an implementation of hashCode().
Even though it is often not natural for many embedded application developers, don’t hesitate to consider converting one data structure into another in various parts of your application: in some cases, the performance increase can easily outweigh远大于 the conversion overhead as better algorithms can be applied. A common example is the conversion of a collection to an array(集合转换成数组), possibly sorted. Such a conversion would obviously require memory as a new object needs to be created. On memory-constrained内存受限 devices, such allocation分配 may not always be possible, resulting in an OutOfMemoryError exception. The Java Language Specification says two things: The class Error and its subclasses are exceptions from which ordinary programs are not ordinarily expected to recover.
Sophisticated精密 programs may wish to catch and attempt to recover
from Error exceptions.
If your memory allocation is only part of an optimization and you, as a sophisticated
application developer, can provide a fallback mechanism (for example, an algorithm,
albeit slower, using the original data structure) then catching the OutOfMemoryError
exception can make sense as it allows you to target more devices. Such optional
optimizations make your code harder to maintain but give you a greater reach.
NOTE: Counterintuitively反直觉的, not all exceptions are subclasses of Exception. All exceptions are subclasses of Throwable (from which Exception and Error are the only direct subclasses).
In general, you should have very good knowledge of the java.util and android.util
packages since they are the toolbox virtually all components rely on. Whenever a new Android version is released, you should pay special attention to the modifications in these packages (added classes, changed classes) and refer to the API Differences 。Report on http://d.android.com/sdk. More data structures are discussed in java.util.concurrent, and they will be covered in Chapter 5.
1.6 Responsiveness响应速度
Performance is not only about raw（不成熟/生的） speed. Your application will be perceived(感知) as being fast as long as it appears fast to the user, and to appear fast your application must be responsive. As an example, to appear faster, your application can defer allocations(分配，配置) until objects are needed, a technique known as lazy initialization,（缓慢执行） and during the development process you most likely want to detect when slow code is executed in performancesensitive calls.
The following classes are the cornerstones of most Android Java applications:
Application
Activity
Service
ContentProvider
BroadcastReceiver
Fragment (Android 3.0 and above)
View
Of particular interest(特别注意) in these classes are all the onSomething() methods that are called from the main thread, such as onStart() and onFocusChanged(). The main thread, also referred to（也称作) as the UI thread, is basically the thread your application runs in. It is possible, though not recommended, to run all your code in the main thread. The main thread is where, among other things:
Key events are received (for example, View.onKeyDown() and Activity.onKeyLongPress()). Views are drawn (View.onDraw()). Lifecycle events(生命周期的产生) occur (for example, Activity.onCreate()).
NOTE: Many methods are called from the main thread by design被设计为. When you override a method, verify how it will be called. The Android documentation does not always specify whether a method is called from the main thread.
In general, the main thread keeps receiving notifications of what is happening, whether the events are generated from the system itself or from the user. Your application has only one main thread, and all events are therefore processed sequentially按顺序处理的. That being said, it becomes easy now to see why responsiveness could be negatively affected: the first event in the queue has to be processed before the subsequent events can be handled(在队列第一个被处理前，其他都是被挂起的), one at a time. If the processing of an event takes too long to complete, then other events have to wait longer for their turn.
An easy example would be to call computeRecursivelyWithCache from the main thread. While it is reasonably fast for low values of n(当n还很小时), it is becoming increasingly slower as n grows. For very large values of n you would most certainly be confronted with Android’s infamous Application Not Responding (ANR) dialog. This dialog appears when Android detects your application is unresponsive, that is when Android detects an input event has not been processed within 5 seconds or a BroadcastReceiver hasn’t finished executing within 10 seconds. When this happens, the user is given the option to simply wait or to “force close” the application (which could be the first step leading to your application being uninstalled（导致你的应用迈向被卸载的结局）).
It is important for you to optimize the startup sequence of all the activities, which
consists of the following calls:
onCreate
onStart
onResume
Of course, this sequence occurs when an activity is created, which may
actually be more often than you think. When a configuration change occurs,
your current activity is destroyed and a new instance is created, resulting in
the following sequence of calls:
onPause
onStop
onDestroy
onCreate
onStart
onResume
The faster this sequence completes, the faster the user will be able to use your
application again. One of the most common configuration changes is the
orientation change, which signifies the device has been rotated旋转.
NOTE: Your application can specify which configuration changes each of its activities wants to handle itself with the activity element’s android:configChanges attribute in its manifest. This would result in onConfigurationChanged() being called instead of having the activity destroyed.
Your activities’ onCreate() methods will most likely contain a call to setContentView or any other method responsible for inflating resources. Because inflating resources is a relatively expensive operation(开销较大的操作), you can make the inflation faster by reducing the complexity of your layouts (the XML files that define what your application looks like).
Steps to simplify the complexity of your layouts include:
Use RelativeLayout instead of nested扁平化 LinearLayouts to keep layouts
as “flat” as possible. In addition to reducing the number of objects allocated减少对象创建, it will also make processing of events faster.
Use ViewStub to defer creation of objects (see the section on lazy initialization).
NOTE: Pay special attention to your layouts in ListView as there could be many items in the list.Use the SDK’s layoutopt tool to analyze your layouts.
The basic rule is to keep anything that is done in the main thread as fast as possible in order to keep the application responsive. However, this often translates换一种说法 to doing as little as possible in the main thread. In most cases, you can achieve responsiveness simply by moving operations to another thread or deferring operations推迟操作, two techniques that typically do not result in code that is much harder to maintain. Before moving a task to another thread, make sure you understand why the task is too slow. If this is due to a bad algorithm or bad implementation, you should fix it since moving the task to another thread would merely be like sweeping dust under a carpet(地毯)欲盖弥彰.
1.6.1Lazy initializations 推迟初始化
Procrastination拖延 does have its merits after all. A common practice is to perform all
initializations in a component’s onCreate() method. While this would work, it means
onCreate() takes longer to return. This is particularly important in your application’s
activities: since onStart() won’t be called until after onCreate() returns (and similarly
onResume() won’t be called until after onStart() returns), any delay will cause the
application to take longer to start, and the user may end up frustrated.
For example, Android uses the lazy initialization concept with android.view.ViewStub,
which is used to lazily inflate resources at runtime. When the view stub is made visible, it is replaced by the matching inflated resources（相应） and becomes eligible for garbage collection(等待垃圾回收的对象).
Since memory allocations 分配take time, waiting until an object is really needed to allocate it can be a good option. The benefits of lazily allocating an object are clear when an object is unlikely to be needed at all. An example of lazy initialization is shown in Listing 1�C14,
which is based on Listing 1�C13. To avoid always having to check whether the object is null, consider the factory method pattern 考虑工厂方法模式.
Listing 1�C14. Lazily Allocating the Cache
int n = 100;
if (cache == null) {
// createCache allocates the cache object, and may be called from many places
cache = createCache();
}
BigInteger fN = cache.get(n);
if (fN == null) {
fN = Fibonacci. computeRecursivelyWithCache(n);
cache.put(n, fN);
}
Refer to Chapter 8 to learn how to use and android.view.ViewStub in an XML layout and how to lazily inflate展开 a resource.
1.6.2 StrictMode
You should always assume假定 the following two things when writing your application: The network is slow (and the server you are trying to connect to may
not even be responding).
File system access is slow.
As a consequence结论是, you should always try not to perform any network or file system access in your application’s main thread as slow operations may affect responsiveness. While your development environment may never experience any network issue or any file system performance problem, your users may not be as lucky as you are.
NOTE: SD cards do not all have the same “speed”. If your application depends heavily on the performance of external storage then you should make sure you test your application with various SD cards from different manufacturers.
Android provides a utility to help you detect such defects in your application. StrictMode is a tool that does its best to detect bad behavior. Typically, you would enable StrictMode when your application is starting, i.e when its onCreate() method is called, as shown in Listing 1�C15. Listing 1�C15. Enabling StrictMode in Your Application
public class MyApplication extends Application {
@Override
public void onCreate ()
{
super.onCreate();
StrictMode.setThreadPolicy(new StrictMode.ThreadPolicy.Builder()
.detectCustomSlowCalls() // API level 11, to use with StrictMode.noteSlowCode
.detectDiskReads()
.detectDiskWrites()
.detectNetwork()
.penaltyLog()
.penaltyFlashScreen() // API level 11
.build());
// not really performance-related, but if you use StrictMode you might as well
define a VM policy too
StrictMode.setVmPolicy(new StrictMode.VmPolicy.Builder()
.detectLeakedSqlLiteObjects()
.detectLeakedClosableObjects() // API level 11
.setClassInstanceLimit(Class.forName(“com.apress.proandroid.SomeClass”), 100) //
API level 11
.penaltyLog()
.build());
}
}
StrictMode was introduced in Android 2.3(2.3版本被引进), with more features added in Android 3.0, so you should make sure you target the correct Android version and make sure your code is executed only on the appropriate platforms, as shown in Listing 1�C12. Noteworthy methods introduced in Android 3.0 include detectCustomSlowCall() and noteSlowCall(), both being used to detect slow, or potentially slow, code in your application. Listing 1�C16 shows how to mark your code as potentially slow code.
Listing 1�C16. Marking Your Own Code as Potentially Slow
public class Fibonacci {
public static BigInteger computeRecursivelyWithCache(int n)
{
StrictMode.noteSlowCall(“computeRecursivelyWithCache”); // message can be
anything
SparseArray<BigInteger> cache = new SparseArray<BigInteger>();
return computeRecursivelyWithCache(n, cache);
}
…
}
A call to computeRecursivelyWithCache from the main thread that takes too long to
execute would result in the following log if the StrictMode Thread policy is configured to detect slow calls:
StrictMode policy violation; ~duration=21121 ms:
android.os.StrictMode$StrictModeCustomViolation: policy=31 violation=8 msg=
computeRecursivelyWithCache
Android provides some helper methods to make it easier to allow disk reads and writes from the main thread temporarily, as shown in Listing 1�C17.
Listing 1�C17. Modifying the Thread Policy to Temporarily Allow Disk Reads
StrictMode临时读写.ThreadPolicy oldPolicy = StrictMode.allowThreadDiskReads();
// read something from disk
StrictMode.setThreadPolicy(oldPolicy);
There is no method for temporarily allowing network access, but there is really no reason to allow such access even temporarily in the main thread as there is no reasonable way to know whether the access will be fast. One could argue there is also no reasonable way to know the disk access will be fast, but that’s another debate争论.
NOTE: Enable StrictMode only during development, and remember to disable it when you deploy your application发布时要禁用他. This is always true, but even more true if you build the policies using the detectAll() methods as future versions of Android may detect more bad behaviors.
1.7 SQLite
Most applications won’t be heavy users of SQLite, and therefore you very likely won’t
have to worry too much about performance when dealing with databases. However, you need to know about a few concepts in case you ever need to optimize your SQLiterelated
code in your Android application:
SQLite statements
Transactions
Queries
NOTE: This section is not intended to be a complete guide to SQLite but instead provides you
with a few pointers to make sure you use databases efficiently. For a complete guide, refer to www.sqlite.org and the Android online documentation.
The optimizations covered in this section do not make the code harder to read and
maintain, so you should make a habit of applying them.
17.1 SQLite Statements
At the origin, SQL statements are simple strings, for example:
CREATE TABLE cheese (name TEXT, origin TEXT)
INSERT INTO cheese VALUES (‘Roquefort’, ‘Roquefort-sur-
Soulzon’)
The first statement would create a table named “cheese” with two columns named
“name” and “origin”. The second statement would insert a new row in the table.
Because they are simply strings, the statements have to be interpreted解释, or compiled编译, before they can be executed. The compilation of the statements is performed internally内部编译执行 when you call for example SQLiteDatabase.execSQL, as shown in Listing 1�C18.
Listing 1�C18. Executing Simple SQLite Statements
SQLiteDatabase db = SQLiteDatabase.create(null); // memory-backed database
db.execSQL(“CREATE TABLE cheese (name TEXT, origin TEXT)”);
db.execSQL(“INSERT INTO cheese VALUES (‘Roquefort’, ‘Roquefort-sur-Soulzon’)”);
db.close(); // remember to close database when you’re done with it
NOTE: Many SQLite-related methods can throw exceptions.
As it turns out, executing SQLite statements can take quite some time. In addition to the compilation编辑, the statements themselves may need to be created. Because String is also immutable, this could lead to the same performance issue we had with the high number of BigInteger objects being allocated分配 in computeRecursivelyFasterUsingBigInteger. We are now going to focus on the performance of the insert statement. After all, a table should be created only once, but many rows could be added, modified, or deleted.
If we want to build a comprehensive全面 database of cheeses (who wouldn’t?), we would end up with many insert statements, as shown in Listing 1�C19. For every insert
statement, a String would be created and execSQL would be called, the parsing of the SQL statement being done internally内部解析 for every cheese added to the database.
Listing 1�C19. Building a Comprehensive Cheeses Database
public class Cheeses {
private static final String[] sCheeseNames = {
“Abbaye de Belloc”,
“Abbaye du Mont des Cats”,
…
“Vieux Boulogne”
};
private static final String[] sCheeseOrigins = {
“Notre-Dame de Belloc”,
“Mont des Cats”,
…
“Boulogne-sur-Mer”
};
private final SQLiteDatabase db;
public Cheeses () {
db = SQLiteDatabase.create(null); // memory-backed database
db.execSQL(“CREATE TABLE cheese (name TEXT, origin TEXT)”);
}
public void populateWithStringPlus () {
int i = 0;
for (String name : sCheeseNames) {
String origin = sCheeseOrigins[i++];
String sql = “INSERT INTO cheese VALUES(\”” + name + ”\”,\”” + origin +
”\”)”;
db.execSQL(sql);
}
}
}
Adding 650 cheeses to the memory-backed database内存数据库 took 393 milliseconds on a Galaxy Tab 10.1, or 0.6 microsecond per row.
An obvious improvement is to make the creation of the sql string, the statement to
execute, faster. Using the + operator to concatenate连接 strings is not the most efficient method in this case, and it is possible to improve performance by either using a StringBuilder object or calling String.format. The two new methods are shown in Listing 1�C20. As they simply optimize the building of the string to pass to execSQL,
these two optimizations are not SQL-related per se.
Listing 1�C20. Faster Ways to Create the SQL Statement Strings
public void populateWithStringFormat () {
int i = 0;
for (String name : sCheeseNames) {
String origin = sCheeseOrigins[i++];
String sql = String.format(“INSERT INTO cheese VALUES(\”%s\”,\”%s\”)”, name,
origin);
db.execSQL(sql);
}
}
public void populateWithStringBuilder () {
StringBuilder builder = new StringBuilder();
builder.append(“INSERT INTO cheese VALUES(\””);
int resetLength = builder.length();
int i = 0;
for (String name : sCheeseNames) {
String origin = sCheeseOrigins[i++];
builder.setLength(resetLength); // reset position
builder.append(name).append(“\”,\””).append(origin).append(“\”)”); // chain
calls
db.execSQL(builder.toString());
}
}
The String.format version took 436 milliseconds to add the same number of cheeses,
while the StringBuilder version returned in only 371 milliseconds. The String.format
version is therefore slower than the original one, while the StringBuilder version is only marginally faster快了一点点. Even though these three methods differ in the way they create Strings, they all have in common the fact that they call execSQL, which still has to do the actual compilation编译 (parsing) of the statement. Because all the statements are very similar (they only differ by the name and origin of the cheese), we can use compileStatement to compile the statement only once, outside the loop循环外. This implementation is shown in Listing 1�C21.
Listing 1�C21. Compilation of SQLite Statement public void populateWithCompileStatement () {
SQLiteStatement stmt = db.compileStatement(“INSERT INTO cheese VALUES(?,?)”);
int i = 0;
for (String name : sCheeseNames) {
String origin = sCheeseOrigins[i++];
stmt.clearBindings();
stmt.bindString(1, name); // replace first question mark with name
stmt. bindString(2, origin); // replace second question mark with origin
stmt.executeInsert();
}
}
Because the compilation of the statement is done only once instead of 650 times and because the binding of the values is a more lightweight operation than the compilation编译, the performance of this method is significantly faster as it builds the database in only 268 milliseconds. It also has the advantage of making the code easier to read. Android also provides additional APIs to insert values in a database using a ContentValues object, which basically contains the binding information between column names and values. The implementation, shown in Listing 1�C22, is actually very close to populateWithCompileStatement, and the “INSERT INTO cheese VALUES” string does not even appear as this part of the insert statement is implied by the call to db.insert().
However, the performance of this implementation is below what we achieved with
populateWithCompileStatement since it takes 352 milliseconds to complete.
Listing 1�C22. Populating the Database Using ContentValues
public void populateWithContentValues () {
ContentValues values = new ContentValues();
int i = 0;
for (String name : sCheeseNames) {
String origin = sCheeseOrigins[i++];
values.clear();
values.put(“name”, name);
values.put(“origin”, origin);
db.insert(“cheese”, null, values);
}
}
The fastest implementation is also the most flexible one as it allows more options in the statement. For example, you could use “INSERT OR FAIL” or “INSERT OR IGNORE” instead of simply “INSERT”.
NOTE: Many changes were made in Android 3.0’s android.database and android.database.sqlite packages. For instance, the managedQuery, startManagingCursor, and stopManagingCursor methods in the Activity class are all deprecated in favor of CursorLoader.
Android also defines a few classes that can improve performance. For example, you can use DatabaseUtils.InsertHelper to insert multiple rows in a database while compiling the SQL insert statement only once. It is currently implemented the same way we implemented populateWithCompileStatement although it does not offer the same flexibility as far as options are concerned (for example, FAIL or ROLLBACK).
Not necessarily related to performance, you may also use the static methods in the
DatabaseUtils class to simplify your implementation.
1.7.2 Transactions
The examples above did not explicitly显示 create any transaction, however one was
automatically created for every insertion and committed immediately after each
insertion. Creating a transaction explicitly allows for two basic things:
Atomic commit原子提交
Better performance
The first feature is important but not from a performance point of view. Atomic commit means either all or none of the modifications to the database occur. A transaction cannot be only partially committed. In our example, we can consider the insertion of all 650 cheeses as one transaction. Either we succeed at building the complete list of cheeses or we don’t, but we are not interested in a partial list. The implementation is shown in Listing 1�C23.
Listing 1�C23. Insertion of All Cheeses in a Single Transaction
public void populateWithCompileStatementOneTransaction () {
try {
db.beginTransaction();
SQLiteStatement stmt = db.compileStatement(“INSERT INTO cheese
VALUES(?,?)”);
int i = 0;
for (String name : sCheeseNames) {
String origin = sCheeseOrigins[i++];
stmt.clearBindings();
stmt.bindString(1, name); // replace first question mark with name
stmt. bindString(2, origin); // replace second question mark with origin
stmt.executeInsert();
}
db.setTransactionSuccessful(); // remove that call and none of the changes
will be committed!
} catch (Exception e) {
// handle exception here
} finally {
db.endTransaction(); // this must be in the finally block
}
}
This new implementation took 166 milliseconds to complete. While this is quite an
improvement (about 100 milliseconds faster), one could argue both implementations
were probably acceptable for most applications as it is quite unusual to insert so many rows so quickly. Indeed, most applications would typically access rows only once in a while, possibly as a response to some user action. The most important point is that the database was memory-backed and not saved to persistent storage (SD card or internal Flash memory). When working with databases, a lot of time is spent on accessing persistent storage (read/write), which is much slower than accessing volatile memory.
By creating the database in internal persistent storage, we can verify the effect of having a single transaction. The creation of the database in persistent storage is shown in Listing 1�C24.
Listing 1�C24. Creation of Database On Storage
public Cheeses (String path) {
// path could have been created with getDatabasePath(“fromage.db”)
// you could also make sure the path exists with a call to mkdirs
// File file = new File(path);
// File parent = new File(file.getParent());
// parent.mkdirs();
db = SQLiteDatabase.openOrCreateDatabase(path, null);
db.execSQL(“CREATE TABLE cheese (name TEXT, origin TEXT)”);
}
When the database is on storage and not in memory, the call to
populateWithCompileStatement takes almost 34 seconds to complete (52 milliseconds
per row), while the call to populateWithCompileStatementOneTransaction takes less than
200 milliseconds. Needless to say, the one-transaction approach is a much better
solution to our problem. These figures obviously depend on the type of storage being used. Storing the database on an external SD card would make it even slower and therefore would make the one-transaction approach even more appealing.
NOTE: Make sure the parent directory exists when you create a database on storage. See
Context.getDatabasePath and File.mkdirs for more information. For convenience, use
SQLiteOpenHelper instead of creating databases manually.
Queries The way to make queries faster is to also limit the access to the database, especially on storage. A database query simply returns a Cursor object, which can then be used to iterate through the results. Listing 1�C25 shows two methods to iterate through all the rows. The first method creates a cursor that gets both columns in the database whereas the second method’s cursor retrieves only the first column.
Listing 1�C25. Iterating Through All the Rows
public void iterateBothColumns () {
Cursor c = db.query(“cheese”, null, null, null, null, null, null);
if (c.moveToFirst()) {
do {
} while (c.moveToNext());
}
c.close(); // remember to close cursor when you are done (or else expect an
exception at some point)
}
public void iterateFirstColumn () {
Cursor c = db.query(“cheese”, new String[]{“name”}, null, null, null, null,
null); // only difference
if (c.moveToFirst()) {
do {
} while (c.moveToNext());
}
c.close();
}
As expected跟设想一样, because it does not have to read data from the second column at all, the second method is faster: 23 milliseconds vs. 61 milliseconds (when using multiple transactions). Iterating through all the rows is even faster when all the rows are added as one transaction: 11 milliseconds for iterateBothColumns vs. 7 milliseconds for iterateFirstColumn. As you can see, you should only read the data you care about. Choosing the right parameters in calls to query can make a substantial difference in performance. You can reduce the database access even more if you only need up to a certain number of rows, and specify the limit parameter in the call to query.
TIP: Consider using the FTS (full-text search) extension to SQLite for more advanced search features (using indexing). Refer to www.sqlite.org/fts3.html for more information.
Summary
Years ago, Java had a bad reputation for performance因为性能问题广受诟病, but today this is no longer true.The Dalvik virtual machine虚拟机, including its Just-In-Time compiler, improves with every new release of Android. Your code can be compiled into native code that takes advantage of the latest CPU architectures 架构without you having to recompile anything. While implementation is important, your highest priority should be to carefully select data structures and algorithms. Good algorithms can be pretty forgiving and perform quite well even without you optimizing anything. On the other hand, a bad algorithm almost always gives poor results, no matter how hard you work on its implementation. Finally, never sacrifice responsiveness. It may make your application development a little
bit more difficult, but the success of your application depends on it.