Because Android is designed for mobile devices, you should always be careful about how much random-access memory (RAM) your app uses. Although Android’s Dalvik virtual machine performs routine garbage collection, this doesn’t mean you can ignore when and where your app allocates and releases memory. In order to provide a stable user experience that allows the system to quickly switch between apps, it’s important that your app does not needlessly consume memory when the user is not interacting with it.
Even if you follow all the best practices for Managing Your App Memory during development (which you should), you still might leak objects or introduce other memory bugs. The only way to be certain your app is using as little memory as possible is to analyze your app’s memory usage with tools. This guide shows you how to do that.
The simplest place to begin investigating your apps memory usage is the Dalvik log messages. You'll find these log messages in logcat (the output is available in the Device Monitor or directly in IDEs such as Eclipse and Android Studio).
Every time a garbage collection occurs, logcat prints a message with the following information:
D/dalvikvm: <GC_Reason> <Amount_freed>, <Heap_stats>, <External_memory_stats>, <Pause_time>
GC_CONCURRENTGC_FOR_MALLOCGC_HPROF_DUMP_HEAPGC_EXPLICIT
GC_EXTERNAL_ALLOCFor example:
D/dalvikvm( 9050): GC_CONCURRENT freed 2049K, 65% free 3571K/9991K, external 4703K/5261K, paused 2ms+2ms
As these log messages stack up, look out for increases in the heap stats (the {@code 3571K/9991K} value in the above example). If this value continues to increase and doesn't ever seem to get smaller, you could have a memory leak.
To get a little information about what kind of memory your app is using and when, you can view real-time updates to your app's heap in the Device Monitor:
From your <sdk>/tools/ directory, launch the monitor tool.
The Heap view shows some basic stats about your heap memory usage, updated after every garbage collection. To see the first update, click the Cause GC button.
Figure 1. The Device Monitor tool, showing the [1] Update Heap and [2] Cause GC buttons. The Heap tab on the right shows the heap results.
Continue interacting with your app to watch your heap allocation update with each garbage collection. This can help you identify which actions in your app are likely causing too much allocation and where you should try to reduce allocations and release resources.
As you start narrowing down memory issues, you should also use the Allocation Tracker to get a better understanding of where your memory-hogging objects are allocated. The Allocation Tracker can be useful not only for looking at specific uses of memory, but also to analyze critical code paths in an app such as scrolling.
For example, tracking allocations when flinging a list in your app allows you to see all the allocations that need to be done for that behavior, what thread they are on, and where they came from. This is extremely valuable for tightening up these paths to reduce the work they need and improve the overall smoothness of the UI.
To use Allocation Tracker:
From your <sdk>/tools/ directory, launch the monitor tool.
The list shows all recent allocations, currently limited by a 512-entry ring buffer. Click on a line to see the stack trace that led to the allocation. The trace shows you not only what type of object was allocated, but also in which thread, in which class, in which file and at which line.
Figure 2. The Device Monitor tool, showing recent app allocations and stack traces in the Allocation Tracker.
Note: You will always see some allocations from {@code DdmVmInternal} and else where that come from the allocation tracker itself.
Although it's not necessary (nor possible) to remove all allocations for your performance critical code paths, the allocation tracker can help you identify important issues in your code. For instance, some apps might create a new {@link android.graphics.Paint} object on every draw. Moving that object into a global member is a simple fix that helps improve performance.
For further analysis, you may want to observe how your app's memory is divided between different types of RAM allocation with the following adb command:
adb shell dumpsys meminfo <package_name>
The output lists all of your app's current allocations, measured in kilobytes.
When inspecting this information, you should be familiar with the following types of allocation:
A nice characteristic of the PSS measurement is that you can add up the PSS across all processes to determine the actual memory being used by all processes. This means PSS is a good measure for the actual RAM weight of a process and for comparison against the RAM use of other processes and the total available RAM.
For example, below is the the output for Gmail’s process on a tablet device. There is a lot of information here, but key points for discussion are listed below.
Note: The information you see may vary slightly from what is shown here, as some details of the output differ across platform versions.
** MEMINFO in pid 9953 [com.google.android.gm] **
Pss Pss Shared Private Shared Private Heap Heap Heap
Total Clean Dirty Dirty Clean Clean Size Alloc Free
------ ------ ------ ------ ------ ------ ------ ------ ------
Native Heap 0 0 0 0 0 0 7800 7637(6) 126
Dalvik Heap 5110(3) 0 4136 4988(3) 0 0 9168 8958(6) 210
Dalvik Other 2850 0 2684 2772 0 0
Stack 36 0 8 36 0 0
Cursor 136 0 0 136 0 0
Ashmem 12 0 28 0 0 0
Other dev 380 0 24 376 0 4
.so mmap 5443(5) 1996 2584 2664(5) 5788 1996(5)
.apk mmap 235 32 0 0 1252 32
.ttf mmap 36 12 0 0 88 12
.dex mmap 3019(5) 2148 0 0 8936 2148(5)
Other mmap 107 0 8 8 324 68
Unknown 6994(4) 0 252 6992(4) 0 0
TOTAL 24358(1) 4188 9724 17972(2)16388 4260(2)16968 16595 336
Objects
Views: 426 ViewRootImpl: 3(8)
AppContexts: 6(7) Activities: 2(7)
Assets: 2 AssetManagers: 2
Local Binders: 64 Proxy Binders: 34
Death Recipients: 0
OpenSSL Sockets: 1
SQL
MEMORY_USED: 1739
PAGECACHE_OVERFLOW: 1164 MALLOC_SIZE: 62
Generally, you should be concerned with only the Pss Total and Private Dirty
columns. In some cases, the Private Clean and Heap Alloc columns also offer
interesting data. Here is some more information about the different memory allocations (the rows)
you should observe:
Dalvik HeapPss Total includes all Zygote
allocations (weighted by their sharing across processes, as described in the PSS definition above).
The Private Dirty number is the actual RAM committed to only your app’s heap, composed of
your own allocations and any Zygote allocation pages that have been modified since forking your
app’s process from Zygote.
Note: On newer platform versions that have the Dalvik
Other section, the Pss Total and Private Dirty numbers for Dalvik Heap do
not include Dalvik overhead such as the just-in-time compilation (JIT) and garbage collection (GC)
bookkeeping, whereas older versions list it all combined under Dalvik.
The Heap Alloc is the amount of memory that the Dalvik and native heap allocators keep
track of for your app. This value is larger than Pss Total and Private Dirty
because your process was forked from Zygote and it includes allocations that your process shares
with all the others.
.so mmap and .dex mmap.so (native) and .dex (Dalvik) code. The
Pss Total number includes platform code shared across apps; the Private Clean is
your app’s own code. Generally, the actual mapped size will be much larger—the RAM here is only
what currently needs to be in RAM for code that has been executed by the app. However, the .so mmap
has a large private dirty, which is due to fix-ups to the native code when it was loaded into its
final address.
UnknownPss Total for Unknown takes into account sharing with Zygote, and Private Dirty
is unknown RAM dedicated to only your app.
TOTALThe Private Dirty and Private Clean are the total allocations within your
process, which are not shared with other processes. Together (especially Private Dirty),
this is the amount of RAM that will be released back to the system when your process is destroyed.
Dirty RAM is pages that have been modified and so must stay committed to RAM (because there is no
swap); clean RAM is pages that have been mapped from a persistent file (such as code being
executed) and so can be paged out if not used for a while.
ViewRootImplAppContexts and ActivitiesNote: A {@link android.view.View} or {@link android.graphics.drawable.Drawable} object also holds a reference to the {@link android.app.Activity} that it's from, so holding a {@link android.view.View} or {@link android.graphics.drawable.Drawable} object can also lead to your app leaking an {@link android.app.Activity}.
A heap dump is a snapshot of all the objects in your app's heap, stored in a binary format called HPROF. Your app's heap dump provides information about the overall state of your app's heap so you can track down problems you might have identified while viewing heap updates.
To retrieve your heap dump:
From your <sdk>/tools/ directory, launch the monitor tool.
Figure 3. The Device Monitor tool, showing the [1] Dump HPROF file button.
If you need to be more precise about when the dump is created, you can also create a heap dump at the critical point in your app code by calling {@link android.os.Debug#dumpHprofData dumpHprofData()}.
The heap dump is provided in a format that's similar to, but not identical to one from the Java HPROF tool. The major difference in an Android heap dump is due to the fact that there are a large number of allocations in the Zygote process. But because the Zygote allocations are shared across all app processes, they don’t matter very much to your own heap analysis.
To analyze your heap dump, you can use a standard tool like jhat or the Eclipse Memory Analyzer Tool (MAT). However, first
you'll need to convert the HPROF file from Android's format to the J2SE HPROF format. You can do
this using the hprof-conv tool provided in the <sdk>/tools/
directory. Simply run the hprof-conv command with two arguments: the original HPROF
file and the location to write the converted HPROF file. For example:
hprof-conv heap-original.hprof heap-converted.hprof
Note: If you're using the version of DDMS that's integrated into Eclipse, you do not need to perform the HPROF converstion—it performs the conversion by default.
You can now load the converted file in MAT or another heap analysis tool that understands the J2SE HPROF format.
When analyzing your heap, you should look for memory leaks caused by:
The Eclipse Memory Analyzer Tool (MAT) is just one tool that you can use to analyze your heap dump. It's also quite powerful so most of its capabilities are beyond the scope of this document, but here are a few tips to get you started.
Once you open your converted HPROF file in MAT, you'll see a pie chart in the Overview, showing what your largest objects are. Below this chart, are links to couple of useful features:
You might want to use this view to find extra instances of classes for which you know there should be only a certain number. For example, a common source of leaks is additional instance of your {@link android.app.Activity} class, for which you should usually have only one instance at a time. To find a specific class instance, type the class name into the <Regex> field at the top of the list.
When you find a class with too many instances, right-click it and select List objects > with incoming references. In the list that appears, you can determine where an instance is retained by right-clicking it and selecting Path To GC Roots > exclude weak references.
What you should look for is anything that's retaining a portion of heap that's roughly equivalent to the memory size you observed leaking from the GC logs, heap updates, or allocation tracker.
When you see something suspicious, right-click on the item and select Path To GC Roots > exclude weak references. This opens a new tab that traces the references to that object which is causing the alleged leak.
Note: Most apps will show an instance of {@link android.content.res.Resources} near the top with a good chunk of heap, but this is usually expected when your app uses lots of resources from your {@code res/} directory.
Figure 4. The Eclipse Memory Analyzer Tool (MAT), showing the Histogram view and a search for "MainActivity".
For more information about MAT, watch the Google I/O 2011 presentation, Memory management for Android apps, which includes a walkthrough using MAT beginning at about 21:10. Also refer to the Eclipse Memory Analyzer documentation.
You may find it useful to compare your app's heap state at two different points in time in order to inspect the changes in memory allocation. To compare two heap dumps using MAT:
While using the tools described above, you should aggressively stress your app code and try forcing memory leaks. One way to provoke memory leaks in your app is to let it run for a while before inspecting the heap. Leaks will trickle up to the top of the allocations in the heap. However, the smaller the leak, the longer you need to run the app in order to see it.
You can also trigger a memory leak in one of the following ways:
Tip: You can also perform the above steps by using the "monkey" test framework. For more information on running the monkey test framework, read the monkeyrunner documentation.