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In this article by Enrique López Mañas and Diego Grancini, authors of the book Android High Performance Programming explains how memory is the matter to focus on. A bad memory managed application can affect the behavior of the whole system or it can affect the other applications installed on our device in the same way as other applications could affect ours. As we all know, Android has a wide range of devices in the market with a lot of different configurations and memory amounts. It’s up to the developers to understand the strategy to take while dealing with this big amount of fragmentation, the pattern to follow while developing, and the tools to use to profile the code. This is the aim of this article. In the following sections, we will focus on heap memory.

We will take a look at how our device handles memory deepening, what the garbage collection is, and how it works in order to understand how to avoid common developing mistakes and clarify what we will discuss to define best practices. We will also go through patterns definition in order to reduce drastically the risk of what we will identify as a memory leak and memory churn. This article will end with an overview of official tools and APIs that Android provides to profile our code and to find possible causes of memory leaks and that aren’t deepened.

(For more resources related to this topic, see here.)


Before starting the discussion about how to improve and profile our code, it’s really important to understand how Android devices handle memory. Then, in the following pages, we will analyze differences between the runtimes that Android uses, know more about the garbage collection, understand what a memory leak and memory churn are, and how Java handles object references.

How memory works

Have you ever thought about how a restaurant works during its service? Let’s think about it for a while. When new groups of customers get into the restaurant, there’s a waiter ready to search for a place to allocate them. But, the restaurant is a limited space. So, there is the need to free tables when possible. That’s why, when a group has finished to eat, another waiter cleans and prepares the just freed table for other groups to come. The first waiter has to find the table with the right number of seats for every new group. Then, the second waiter’s task should be fast and shouldn’t hinder or block others’ tasks. Another important aspect of this is how many seats are occupied by the group; the restaurant owner wants to have as much free seats as possible to place new clients. So, it’s important to control that every group fills the right number of seats without occupying tables that could be freed and used in order to have more tables for other new groups.

This is absolutely similar to what happens in an Android system. Every time we create a new object in our code, it needs to be saved in memory. So, it’s allocated as part of our application private memory to be accessed whenever needed and the system keeps allocating memory for us during the whole application lifetime. Nevertheless, the system has a limited memory to use and it cannot allocate memory indefinitely. So, how is it possible for the system to have enough memory for our application all the time? And, why is there no need for an Android developer to free up memory? Let’s find it out.

Garbage collection

The Garbage collection is an old concept that is based on two main aspects:

  • Find no more referenced objects
  • Free the memory referenced by those objects

When that object is no more referenced, its “table” can be cleaned and freed up. This is, what it’s done to provide memory for future new objects allocations. These operations of allocation of new objects and deallocation of no more referenced objects are executed by the particular runtime in use in the device, and there is no need for the developer to do anything just because they are all managed automatically. In spite of what happens in other languages, such as C or C++, there is no need for the developer to allocate and deallocate memory. In particular, while the allocation is made when needed, the garbage collection task is executed when a memory upper limit is reached. Those automatic operations in the background don’t exempt developers from being aware of their app’s memory management; if the memory management is not well done, the application can be lead to lags, malfunctions and, even, crashes when an OutOfMemoryError exception is thrown.

Shared memory

In Android, every app has its own process that is completely managed by the runtime with the aim to reclaim memory in order to free resources for other foreground processes, if needed. The available amount of memory for our application lies completely in RAM as Android doesn’t use swap memory. The main consequence to this is that there is no other way for our app to have more memory than to unreferenced no longer used objects. But Android uses paging and memory mapping; the first technique defines blocks of memory of the same size called pages in a secondary storage, while the second one uses a mapping in memory with correlated files in secondary storage to be used as primary.

They are used when the system needs to allocate memory for other processes, so the system creates paged memory-mapped files to save Dalvik code files, app resources, or native code files. In this way, those files can be shared between multiple processes. As a matter of fact, Android system uses a shared memory in order to better handle resources from a lot of different processes. Furthermore, every new process to be created is forked by an already existing one that is called Zygote. This particular process contains common framework classes and resources to speed up the first boot of the application. This means that the Zygote process is shared between processes and applications. This large use of shared memory makes it difficult to profile the use of memory of our application because there are many facets to be consider before reaching a correct analysis of memory usage.


Some functions and operations of memory management depend on the runtime used. That’s why we are going through some specific features of the two main runtime used by Android devices. They are as follows:

  • Dalvik
  • Android runtime (ART)

ART has been added later to replace Dalvik to improve performance from different point of view. It was introduced in Android KitKat (API Level 19) as an option for developer to be enabled, and it has become the main and only runtime from Android Lollipop (API Level 21) on. Besides the difference between Dalvik and ART in compiling code, file formats, and internal instructions, what we are focusing on at the moment is memory management and garbage collection. So, let’s understand how the Google team improved performance in runtimes garbage collection over time and what to pay attention at while developing our application.

Let’s step back and return to the restaurant for a bit more. What would happen if everything, all employees, such as other waiters and cooks, and all of the services, such as dishwashers, and so on, stop their tasks waiting for just a waiter to free a table? That single employee performance would make success or fail of all. So, it’s really important to have a very fast waiter in this case. But, what to do if you cannot afford him? The owner wants him to do what he has to as fast as possible, by maximizing his productivity and, then, allocating all the customers in the best way and this is exactly what we have to do as developers. We have to optimize memory allocations in order to have a fast garbage collection even if it stops all the other operations.

What is described here is just like the runtime garbage collection works. When the upper limit of memory is reached, the garbage collection starts its task pausing any other method, task, thread, or process execution, and those objects won’t resume until the garbage collection task is completed. So, it’s really important that the collection is fast enough not to impede the reaching of the 16 ms per frame rule, resulting in lags, and jank in the UI. The more time the garbage collection works, the less time the system has to prepare frames to be rendered on the screen.

Keep in mind that automatic garbage collection is not free; bad memory management can lead to bad UI performance and, thus, bad UX. No runtime feature can replace good memory management. That’s why we need to be careful about new allocations of objects and, above all, references.

Obviously, ART introduced a lot of improvement in this process after the Dalvik era, but the background concept is the same; it reduces the collection steps, it adds a particular memory for Bitmap objects, it uses new fast algorithms, and it does other cool stuff getting better in the future, but there is no way to escape that we need to profile our code and memory usage if we want our application to have the best performance.

Android N JIT compiler

The ART runtime uses an ahead-of-time compilation that, as the name suggests, performs compilation when the applications are first installed. This approach brought in advantages to the overall system in different ways because, the system can:

  • Reduce battery consumption due to pre-compilation and, then, improve autonomy
  • Execute application faster than Dalvik
  • Improve memory management and garbage collection

However, those advantages have a cost related to installation timings; the system needs to compile the application at that time, and then, it’s slower than a different type of compiler.

For this reason, Google added a just-in-time (JIT) compiler to the ahead-of-time compiler of ART into the new Android N. This one acts when needed, so during the execution of the application and, then, it uses a different approach compared to the ahead-of-time one. This compiler uses code profiling techniques and it’s not a replacement for the ahead-of-time, but it’s in addition to it. It’s a good enhancement to the system for the advantages in terms of performance it introduces.

The profile-guided compilation adds the possibility to precompile and, then, to cache and to reuse methods of the application, depending on usage and/or device conditions. This feature can save time to the compilation and improve performance in every kind of system. Then, all of the devices benefit of this new memory management. The key advantages are:

  • Less used memory
  • Less RAM accesses
  • Lower impact on battery

All of these advantages introduced in Android N, however, shouldn’t be a way to avoid a good memory management in our applications. For this, we need to know what pitfalls are lurking behind our code and, more than this, how to behave in particular situations to improve the memory management of the system while our application is active.

Memory leak

The main mistake from the memory performance perspective a developer can do while developing an Android application is called memory leak, and it refers to an object that is no more used but it’s referenced by another object that is, instead, still active. In this situation, the garbage collector skips it because the reference is enough to leave that object in memory. Actually, we are avoiding that the garbage collector frees memory for other future allocations. So, our heap memory gets smaller because of this, and this leads to the garbage collection to be invoked more often, blocking the rest of executions of the application. This could lead to a situation where there is no more memory to allocate a new object and, then, an OutOfMemoryError exception is thrown by the system. Consider the case where a used object references no more used objects, that reference no more used objects, and so on; none of them can be collected, just because the root object is still in use.

Memory churn

Another anomaly in memory management is called memory churn, and it refers to the amount of allocations that is not sustainable by the runtime for the too many new instantiated objects in a small period of time. In this case, a lot of garbage collection events are called many times affecting the overall memory and UI performance of the application. The need to avoid allocations in the View.onDraw() method, is closely related to memory churn; we know that this method is called every time the view needs to be drawn again and the screen needs to be refreshed every 16.6667 ms. If we instantiate objects inside that method, we could cause a memory churn because those objects are instantiated in the View.onDraw() method and no longer used, so they are collected very soon. In some cases, this leads to one or more garbage collection events to be executed every time the frame is drawn on the screen, reducing the available time to draw it below the 16.6667 ms, depending on collection event duration.


Let’s have a quick overview of different objects that Java provides us to reference objects. This way, we will have an idea of when we can use them and how. Java defines four levels of strength:

  • Normal: It’s the main type of reference. It corresponds to the simple creation of an object and this object will be collected when it will be no more used and referenced, and it’s just the classical object instantiation:
    SampleObject sampleObject = new SampleObject();
  • Soft: It’s a reference not enough strong to keep an object in memory when a garbage collection event is triggered. So, it can be null anytime during the execution. Using this reference, the garbage collector decides when to free the object memory based on memory demand of the system. To use it, just create a SoftReference object passing the real object as parameter in the constructor and call the SoftReference.get() method to get the object:
    SoftReference<SampleObject> sampleObjectSoftRef = new 
    SoftReference<SampleObject>(new SampleObject());
    SampleObject sampleObject = sampleObjectSoftRef.get();
  • Weak: It’s exactly as SoftReferences, but this is weaker than the soft one:
    WeakReference<SampleObject> sampleObjectWeakRef = new 
    WeakReference<SampleObject>(new SampleObject());
  • Phantom: This is the weakest reference; the object is eligible for finalization. This kind of references is rarely used and the PhantomReference.get() method returns always null. This is for reference queues that don’t interest us at the moment, but it’s just to know that this kind of reference is also provided.

These classes may be useful while developing if we know which objects have a lower level of priority and can be collected without causing problems to the normal execution of our application. We will see how can help us manage memory in the following pages.

Memory-side projects

During the development of the Android platform, Google has always tried to improve the memory management system of the platform to maintain a wide compatibility with increasing performance devices and low resources ones. This is the main purpose of two project Google develops in parallel with the platform, and, then, every new Android version released means new improvements and changes to those projects and their impacts on the system performance. Every one of those side projects is focusing on a different matter:

  • Project Butter: This is introduced in Android Jelly Bean 4.1 (API Level 16) and then improved in Android Jelly Bean 4.2 (API Level 17), added features related to the graphical aspect of the platform (VSync and buffering are the main addition) in order to improve responsiveness of the device while used.
  • Project Svelte: This is introduced inside Android KitKat 4.4 (API Level 19), it deals with memory management improvements in order to support low RAM devices.
  • Project Volta: This is introduced in Android Lollipop (API Level 21), it focuses on battery life of the device. Then, it adds important APIs to deal with batching expensive battery draining operations, such as the JobSheduler or new tools such as the Battery Historian.

Project Svelte and Android N

When it was first introduced, Project Svelte reduced the memory footprint and improved the memory management in order to support entry-level devices with low memory availability and then broaden the supported range of devices with clear advantage for the platform.

With the new release of Android N, Google wants to provide an optimized way to run applications in background. We know that the process of our application last in background even if it is not visible on the screen, or even if there are no started activities, because a service could be executing some operations. This is a key feature for memory management; the overall system performance could be affected by a bad memory management of the background processes.

But what’s changed in the application behavior and the APIs with the new Android N? The chosen strategy to improve memory management reducing the impact of background processes is to avoid to send the application the broadcasts for the following actions:

  • ConnectivityManager.CONNECTIVITY_ACTION: Starting from Android N, a new connectivity action will be received just from those applications that are in foreground and, then, that have registered BroadcastReceiver for this action. No application with implicit intent declared inside the manifest file will receive it any longer. Hence, the application needs to change its logics to do the same as before.
  • Camera.ACTION_NEW_PICTURE: This one is used to notify that a picture has just been taken and added to the media store. This action won’t be available anymore neither for receiving nor for sending and it will be for any application, not just for the ones that are targeting the new Android N.
  • Camera.ACTION_NEW_VIDEO: This is used to notify a video has just been taken and added to the media store. As the previous one, this action cannot be used anymore, and it will be for any application too.

Keep in mind these changes when targeting the application with the new Android N to avoid unwanted or unexpected behaviors.

All of the preceding actions listed have been changed by Google to force developers not to use them in applications. As a more general rule, we should not use implicit receivers for the same reason. Hence, we should always check the behavior of our application while it’s in the background because this could lead to an unexpected usage of memory and battery drain. Implicit receivers can start our application components, while the explicit ones are set up for a limited time while the activity is in foreground and then they cannot affect the background processes.

It’s a good practice to avoid the use of implicit broadcast while developing applications to reduce the impact of it on background operations that could lead to unwanted waste of memory and, then, a battery drain.

Furthermore, Android N introduces a new command in ADB to test the application behavior ignoring the background processes. Use the following command to ignore background services and processes:

adb shell cmd appops set RUN_IN_BACKGROUND ignore

Use the following one to restore the initial state:

adb shell cmd appops set RUN_IN_BACKGROUND allow

Best practices

Now that we know what can happen in memory while our application is active, let’s have a deep examination of what we can do to avoid memory leaks, memory churns, and optimize our memory management in order to reach our performance target, not just in memory usage, but in garbage collection attendance, because, as we know, it stops any other working operation.

In the following pages, we will go through a lot of hints and tips using a bottom-up strategy, starting from low-level shrewdness in Java code to highest level Android practices.

Data types

We weren’t joking; we are really talking about Java primitive types as they are the foundation of all the applications, and it’s really important to know how to deal with them even though it may be obvious. It’s not, and we will understand why.

Java provides primitive types that need to be saved in memory when used: the system allocate an amount of memory related to the needed one requested for that particular type. The followings are Java primitive types with related amount of bits needed to allocate the type:

  • byte: 8 bit
  • short: 16 bit
  • int: 32 bit
  • long: 64 bit
  • float: 32 bit
  • double: 64 bit
  • boolean: 8 bit, but it depends on virtual machine
  • char: 16 bit

At first glance, what is clear is that you should be careful in choosing the right primitive type every time you are going to use them.

Don’t use a bigger primitive type if you don’t really need it; never use long, float, or double, if you can represent the number with an integer data type. Otherwise, it would be a useless waste of memory and calculations every time the CPU need to deal with it and remember that to calculate an expression, the system needs to do a widening primitive implicit conversion to the largest primitive type involved in the calculation.


Autoboxing is the term used to indicate an automatic conversion between a primitive type and its corresponding wrapper class object. Primitive type wrapper classes are the followings:

  • java.lang.Byte
  • java.lang.Short
  • java.lang.Integer
  • java.lang.Long
  • java.lang.Float
  • java.lang.Double
  • java.lang.Boolean
  • java.lang.Character

They can be instantiated using the assignation operator as for the primitive types, and they can be used as their primitive types:

Integer i = 0;

This is exactly as the following:

Integer i = new Integer(0);

But the use of autoboxing is not the right way to improve the performance of our applications; there are many costs for that: first of all, the wrapper object is much bigger than the corresponding primitive type. For instance, an Integer object needs 16 bytes in memory instead of 16 bits of the primitive one. Hence, the bigger amount of memory used to handle that. Then, when we declare a variable using the primitive wrapper object, any operation on that implies at least another object allocation. Take a look at the following snippet:

Integer integer = 0;

Every Java developer knows what it is, but this simple code needs an explanation about what happened step by step:

  • First of all, the integer value is taken from the Integer value integer and it’s added 1:
    int temp = integer.intValue() + 1;
  • Then the result is assigned to integer, but this means that a new autoboxing operation needs to be executed:
    i = temp;

Undoubtedly, those operations are slower than if we used the primitive type instead of the wrapper class; no needs to autoboxing, hence, no more bad allocations. Things can get worse in loops, where the mentioned operations are repeated every cycle; take, for example the following code:

Integer sum = 0;
for (int i = 0; i < 500; i++) {
    sum += i;

In this case, there are a lot of inappropriate allocations caused by autoboxing, and if we compare this with the primitive type for loop, we notice that there are no allocations:

int sum = 0;
for (int i = 0; i < 500; i++) {
    sum += i;

Autoboxing should be avoided as much as possible. The more we use primitive wrapper classes instead of primitive types themselves, the more waste of memory there will be while executing our application and this waste could be propagated when using autoboxing in loop cycles, affecting not just memory, but CPU timings too.

Sparse array family

So, in all of the cases described in the previous paragraph, we can just use the primitive type instead of the object counterpart. Nevertheless, it’s not always so simple. What happens if we are dealing with generics? For example, let’s think about collections; we cannot use a primitive type as generics for objects that implements one of the following interfaces. We have to use the wrapper class this way:

List<Integer> list;
Map<Integer, Object> map;
Set<Integer> set;

Every time we use one of the Integer objects of a collection, autoboxing occurs at least once, producing the waste outlined above, and we know well how many times we deal with this kind of objects in every day developing time, but isn’t there a solution to avoid autoboxing in these situations? Android provides a useful family of objects created on purpose to replace Maps objects and avoid autoboxing protecting memory from pointless bigger allocations; they are the Sparse arrays.

The list of Sparse arrays, with related type of Maps they can replace, is the following:

  • SparseBooleanArray: HashMap<Integer, Boolean>
  • SparseLongArray: HashMap<Integer, Long>
  • SparseIntArray: HashMap<Integer, Integer>
  • SparseArray<E>: HashMap<Integer, E>
  • LongSparseArray<E>: HashMap<Long, E>

In the following, we will talk about SparseArray object specifically, but everything we say is true for all other object above as well.

The SparseArray uses two different arrays to store hashes and objects. The first one collects the sorted hashes, while the second one stores the key/value pairs ordered conforming to the key hashes array sorting as in Figure 1:

Android High Performance Programming

Figure 1: SparseArray’s hashes structure

When you need to add a value, you have to specify the integer key and the value to be added in SparseArray.put() method, just like in the HashMap case. This could create collisions if multiple key hashes are added in the same position.

When a value is needed, simply call SparseArray.get(), specifying the related key; internally, the key object is used to binary search the index of the hash, and then the value of the related key, as in Figure 2:

Android High Performance Programming

Figure 2: SparseArray’s workflow

When the key found at the index resulting from binary search does not match with the original one, a collision happened, so the search keeps on in both directions to find the same key and to provide the value if it’s still inside the array. Thus, the time needed to find the value increases significantly with a large number of object contained by the array.

By contrast, a HashMap contains just a single array to store hashes, keys, and values, and it uses largest arrays as a technique to avoid collisions. This is not good for memory, because it’s allocating more memory than what it’s really needed. So HashMap is fast, because it implements a better way to avoid collisions, but it’s not memory efficient. Conversely, SparseArray is memory efficient because it uses the right number of object allocations, with an acceptable increase of execution timings.

The memory used for these arrays is contiguous, so every time you remove a key/value pair from SparseArray, they can be compacted or resized:

  • Compaction: The object to remove is shifted at the end and all the other objects are shifted left. The last block containing the item to be removed can be reused for future additions to save allocations.
  • Resize: All the elements of the arrays are copied to other arrays and the old ones are deleted. On the other hand, the addition of new elements produces the same effect of copying all elements into new arrays. This is the slowest method, but it’s completely memory safe because there are no useless memory allocations.

In general, HashMap is faster while doing these operations because it contains more blocks than what it’s really needed. Hence, the memory waste.

The use of SparseArray family objects depends of the strategy applied for memory management and CPU performance patterns because of calculations performance cost compared to the memory saving. So, the use is right in some situations. Consider the use of it when:

The number of object you are dealing with is below a thousand, and you are not going to do a lot of additions and deletions.

You are using collections of Maps with a few items, but lots of iterations.

Another useful feature of those objects is that they let you iterate over indexing, instead of using the iterator pattern that is slower and memory inefficient. The following snippet shows how the iteration doesn’t involve objects:

// SparseArray
for (int i = 0; i < map.size(); i++) {
    Object value = map.get(map.keyAt(i));

Contrariwise, the Iterator object is needed to iterate through HashMaps:

// HashMap
for (Iterator iter = map.keySet().iterator(); 
    iter.hasNext(); ) {
    Object value =;

Some developers think the HashMap object is the better choice because it can be exported from an Android application to other Java ones, while the SparseArray family’s object don’t. But what we analyzed here as memory management gain is applicable to any other cases. And, as developers, we should strive to reach performance goals in every platform, instead of reusing the same code in different platform, because different platform could have been affected differently from a memory perspective. That’s why, our main suggestion is to always profile the code in every platform we are working on, and then make our personal considerations on better or worse approaches depending on results.


An ArrayMap object is an Android implementation of the Map interface that is more memory efficient than the HashMap one. This class is provided by the Android platform starting from Android KitKat (API Level 19), but there is another implementation of this inside the Support package v4 because of its main usage on older and lower-end devices.

Its implementation and usage is totally similar to the SparseArray objects with all the implications about memory usage and computational costs, but its main purpose is to let you use objects as keys of the map, just like the HashMap does. Hence, it provides the best of both worlds.


We defined a lot of best practices to help keep a good memory management, introducing helpful design patterns and analyzing which are the best choices while developing things taken for granted that can actually affect memory and performance. Then, we faced the main causes for the worst leaks in Android platform, those related to main components such as Activities and Services. As a conclusion for the practices, we introduced APIs both to use and not to use. Then, other ones able to define a strategy for events related to the system and, then, external to the application.

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