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Performance Tips

This document primarily covers micro-optimizations that can improve overall app performance when combined, but it's unlikely that these changes will result in dramatic performance effects. Choosing the right algorithms and data structures should always be your priority, but is outside the scope of this document. You should use the tips in this document as general coding practices that you can incorporate into your habits for general code efficiency.

There are two basic rules for writing efficient code:

One of the trickiest problems you'll face when micro-optimizing an Android app is that your app is certain to be running on multiple types of hardware. Different versions of the VM running on different processors running at different speeds. It's not even generally the case that you can simply say "device X is a factor F faster/slower than device Y", and scale your results from one device to others. In particular, measurement on the emulator tells you very little about performance on any device. There are also huge differences between devices with and without a JIT: the best code for a device with a JIT is not always the best code for a device without.

To ensure your app performs well across a wide variety of devices, ensure your code is efficient at all levels and aggressively optimize your performance.

Avoid Creating Unnecessary Objects

Object creation is never free. A generational garbage collector with per-thread allocation pools for temporary objects can make allocation cheaper, but allocating memory is always more expensive than not allocating memory.

As you allocate more objects in your app, you will force a periodic garbage collection, creating little "hiccups" in the user experience. The concurrent garbage collector introduced in Android 2.3 helps, but unnecessary work should always be avoided.

Thus, you should avoid creating object instances you don't need to. Some examples of things that can help:

A somewhat more radical idea is to slice up multidimensional arrays into parallel single one-dimension arrays:

Generally speaking, avoid creating short-term temporary objects if you can. Fewer objects created mean less-frequent garbage collection, which has a direct impact on user experience.

Prefer Static Over Virtual

If you don't need to access an object's fields, make your method static. Invocations will be about 15%-20% faster. It's also good practice, because you can tell from the method signature that calling the method can't alter the object's state.

Use Static Final For Constants

Consider the following declaration at the top of a class:

static int intVal = 42;
static String strVal = "Hello, world!";

The compiler generates a class initializer method, called <clinit>, that is executed when the class is first used. The method stores the value 42 into intVal, and extracts a reference from the classfile string constant table for strVal. When these values are referenced later on, they are accessed with field lookups.

We can improve matters with the "final" keyword:

static final int intVal = 42;
static final String strVal = "Hello, world!";

The class no longer requires a <clinit> method, because the constants go into static field initializers in the dex file. Code that refers to intVal will use the integer value 42 directly, and accesses to strVal will use a relatively inexpensive "string constant" instruction instead of a field lookup.

Note: This optimization applies only to primitive types and String constants, not arbitrary reference types. Still, it's good practice to declare constants static final whenever possible.

Use Enhanced For Loop Syntax

The enhanced for loop (also sometimes known as "for-each" loop) can be used for collections that implement the Iterable interface and for arrays. With collections, an iterator is allocated to make interface calls to hasNext() and next(). With an ArrayList, a hand-written counted loop is about 3x faster (with or without JIT), but for other collections the enhanced for loop syntax will be exactly equivalent to explicit iterator usage.

There are several alternatives for iterating through an array:

static class Foo {
    int mSplat;

Foo[] mArray = ...

public void zero() {
    int sum = 0;
    for (int i = 0; i < mArray.length; ++i) {
        sum += mArray[i].mSplat;

public void one() {
    int sum = 0;
    Foo[] localArray = mArray;
    int len = localArray.length;

    for (int i = 0; i < len; ++i) {
        sum += localArray[i].mSplat;

public void two() {
    int sum = 0;
    for (Foo a : mArray) {
        sum += a.mSplat;

zero() is slowest, because the JIT can't yet optimize away the cost of getting the array length once for every iteration through the loop.

one() is faster. It pulls everything out into local variables, avoiding the lookups. Only the array length offers a performance benefit.

two() is fastest for devices without a JIT, and indistinguishable from one() for devices with a JIT. It uses the enhanced for loop syntax introduced in version 1.5 of the Java programming language.

So, you should use the enhanced for loop by default, but consider a hand-written counted loop for performance-critical ArrayList iteration.

Tip: Also see Josh Bloch's Effective Java, item 46.

Consider Package Instead of Private Access with Private Inner Classes

Consider the following class definition:

public class Foo {
    private class Inner {
        void stuff() {

    private int mValue;

    public void run() {
        Inner in = new Inner();
        mValue = 27;

    private void doStuff(int value) {
        System.out.println("Value is " + value);

What's important here is that we define a private inner class (Foo$Inner) that directly accesses a private method and a private instance field in the outer class. This is legal, and the code prints "Value is 27" as expected.

The problem is that the VM considers direct access to Foo's private members from Foo$Inner to be illegal because Foo and Foo$Inner are different classes, even though the Java language allows an inner class to access an outer class' private members. To bridge the gap, the compiler generates a couple of synthetic methods:

/*package*/ static int Foo.access$100(Foo foo) {
    return foo.mValue;
/*package*/ static void Foo.access$200(Foo foo, int value) {

The inner class code calls these static methods whenever it needs to access the mValue field or invoke the doStuff() method in the outer class. What this means is that the code above really boils down to a case where you're accessing member fields through accessor methods. Earlier we talked about how accessors are slower than direct field accesses, so this is an example of a certain language idiom resulting in an "invisible" performance hit.

If you're using code like this in a performance hotspot, you can avoid the overhead by declaring fields and methods accessed by inner classes to have package access, rather than private access. Unfortunately this means the fields can be accessed directly by other classes in the same package, so you shouldn't use this in public API.

Avoid Using Floating-Point

As a rule of thumb, floating-point is about 2x slower than integer on Android-powered devices.

In speed terms, there's no difference between float and double on the more modern hardware. Space-wise, double is 2x larger. As with desktop machines, assuming space isn't an issue, you should prefer double to float.

Also, even for integers, some processors have hardware multiply but lack hardware divide. In such cases, integer division and modulus operations are performed in software—something to think about if you're designing a hash table or doing lots of math.

Know and Use the Libraries

In addition to all the usual reasons to prefer library code over rolling your own, bear in mind that the system is at liberty to replace calls to library methods with hand-coded assembler, which may be better than the best code the JIT can produce for the equivalent Java. The typical example here is String.indexOf() and related APIs, which Dalvik replaces with an inlined intrinsic. Similarly, the System.arraycopy() method is about 9x faster than a hand-coded loop on a Nexus One with the JIT.

Tip: Also see Josh Bloch's Effective Java, item 47.

Use Native Methods Carefully

Developing your app with native code using the Android NDK isn't necessarily more efficient than programming with the Java language. For one thing, there's a cost associated with the Java-native transition, and the JIT can't optimize across these boundaries. If you're allocating native resources (memory on the native heap, file descriptors, or whatever), it can be significantly more difficult to arrange timely collection of these resources. You also need to compile your code for each architecture you wish to run on (rather than rely on it having a JIT). You may even have to compile multiple versions for what you consider the same architecture: native code compiled for the ARM processor in the G1 can't take full advantage of the ARM in the Nexus One, and code compiled for the ARM in the Nexus One won't run on the ARM in the G1.

Native code is primarily useful when you have an existing native codebase that you want to port to Android, not for "speeding up" parts of your Android app written with the Java language.

If you do need to use native code, you should read our JNI Tips.

Tip: Also see Josh Bloch's Effective Java, item 54.

Performance Myths

On devices without a JIT, it is true that invoking methods via a variable with an exact type rather than an interface is slightly more efficient. (So, for example, it was cheaper to invoke methods on a HashMap map than a Map map, even though in both cases the map was a HashMap.) It was not the case that this was 2x slower; the actual difference was more like 6% slower. Furthermore, the JIT makes the two effectively indistinguishable.

On devices without a JIT, caching field accesses is about 20% faster than repeatedly accessing the field. With a JIT, field access costs about the same as local access, so this isn't a worthwhile optimization unless you feel it makes your code easier to read. (This is true of final, static, and static final fields too.)

Always Measure

Before you start optimizing, make sure you have a problem that you need to solve. Make sure you can accurately measure your existing performance, or you won't be able to measure the benefit of the alternatives you try.

You may also find Traceview useful for profiling, but it's important to realize that it currently disables the JIT, which may cause it to misattribute time to code that the JIT may be able to win back. It's especially important after making changes suggested by Traceview data to ensure that the resulting code actually runs faster when run without Traceview.

For more help profiling and debugging your apps, see the following documents:

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