CPU time and memory are the two primary resources every performance-sensitive application needs to use wisely. Java is now the language of choice for many such systems. While most developers have at least basic understanding of computational complexity, I believe that few, especially among those who primarily use Virtual-Machine based languages such as Java, understand well how their applications manage memory. This is not only a very interesting topic in itself, but can also help in improving performance of Java applications or in understanding better the behavior of common Java-based databases such as Apache Cassandra.

For many systems, memory usage is probably more likely to become an issue than CPU usage.

While rumors of Java being slow are greatly exaggerated, memory usage of applications written for the Java Platform does indeed tend to be higher than of similar applications written in lower-level languages like C++. Usually, this is not a problem: the gains in developer productivity outweigh the cost of a somewhat increased memory footprint. However, a developer should be aware of the costs and trade-offs, as well as of situations where memory usage can exceed expectations and cause undesired issues. This requires a higher level of understanding of how Java manages memory and internally represents data structures.

Much ado about nothing?

RAM is cheap these days and quite often optimizing memory usage makes little sense. A two- or ten-fold increase in memory usage over some theoretically possible minimum may seem wasteful, but the cost of developers’ time needed to optimize it is almost certain to be higher that the cost of a few additional gigabytes of RAM for the servers. But sometimes waste goes beyond that, especially when big data gets processed. With a farm containing tens or hundreds of machines, the possibility to run the same software with half as many or the need to upscale the park to twice the number do make a difference in cost even for a big company.

The increase in data size that needs to be held in memory at the same time may be the result of increased traffic to the site. Waste which is acceptable with a few requests per second may become deadly when traffic grows to tens or hundreds of requests per second. In the case of sudden spikes in traffic, providing and deploying additional machines may be technically not feasible or economically not sound even with cloud-based hosting.

Some environments have only limited resources available and are not easily upgraded, as is the case with mobile devices. With hardware upgrades impossible or very hard, limiting memory consumption may be the only option.

Last but not least, many pitfalls related to memory usage can easily be avoided, so that the code is better with no elegance lost. Due to interactions between the Garbage Collector (GC), the Just-In-Time Compiler (JIT), the Operating System (OS) and even hardware, using less memory may result in an overall faster application for free. This is something both developers and business like.

Specification and implementation

The environment in which Java applications run is rather complicated, and each layer influences how memory is used. Some behaviors are defined in the Java Language Specification (JLS) while others are consequences of choices made during the design of the Java Virtual Machine (JVM) and outlined in the [Java Virtual Machine Specification (JVMS)] (http://docs.oracle.com/javase/specs/jvms/se7/html/). The JVMS (§2.7) clearly states that The Java Virtual Machine does not mandate any particular internal structure for objects. Different implementations of the JVM may choose different solutions for implementing specific behaviors, so the Oracle JVM will likely have a different memory footprint than IBM’s J9 or Azul’s Zing. There will be differences between the same JVM running on different platforms (e.g. 64 vs 32-bit), and even between different versions of the same JVM since optimizations to the compiler and JRE are added with each upgrade.

Due to this volatility, some characteristics of JMV implementations stay constant over time but others change quite often. It is important to recognize this, since otherwise effort might be put into optimizations which could become no longer valid with the next release of the JDK. Any claims found on the web, including this article, should be taken with a grain of salt and checked for validity. Sometimes the source code of OpenJDK can be helpful. A tool such as Java Object Layout (JOL) can be used for analysis of specific classes’ layouts or for comparing different JDKs.

Unless noted otherwise, this article refers to Oracle JVM 8.

It’s not only Java any more

Apart from Java, many languages run on the JVM now. Scala, Groovy and Clojure are all affected by the JVM specification. They may also highlight specific aspects of the JVM. Languages with elements of functional programming, for example, need to create instances of classes which represent closures. This may lead to object creation taking place where developers don’t expect it.

These languages also tend to promote the use of immutable data structures. Immutability is good for concurrent programming and can help in ensuring algorithm correctness, but sometimes it comes at a cost. Where an imperative loop in Java might just modify a collection in-place, a nifty functional expression in Scala could lead to several copies of the collection with various modifications getting created. Mechanisms such as the use of persistent data structures for Scala’s default collections or the possibility of using withFilter() in place of filter() help deal with potential issues, but getting performance-critical code right may still require more insight than in Java.

Different kinds of memory

When we think of memory in Java, we most often think of heap memory and the well-known -Xmx command line argument. However, a running JVM actually uses several different kinds of memory. Apart from heap memory (where object instances are normally allocated), each thread has a separate stack memory area (for local variables and method call arguments), memory allocated by JNI code, native buffers, mapped files, and so on. The virtual machine itself also needs memory for internal housekeeping, JIT compilation and operating the Garbage Collector. Depending on the application, resident memory usage reported by system tools such as top may report 20% or 100% more usage than the heap size passed to -Xmx parameter.

In extreme cases, heap memory may be just a small fraction of all memory used. For example, our Cassandra cluster consists of machines with 192 GB RAM but the heap size is set to only 12 GB. We do, however, need all that RAM, and need it badly. Like many other applications that perform heavy I/O, Cassandra tries to optimize disk accesses by taking advantage of memory-mapped files mechanism which allows portions of data to be loaded from disk into RAM as needed. This process is managed by the Operating System without the application having to worry about caching or prioritizing different accesses. In top, using memory mapping on large files is usually manifested by having huge values in the VIRT and SHR columns (comparable to the size of data kept on disk). The size of data actually loaded into memory this way will increase the value in RES column together with memory allocated with more conventional approaches.

Since we mentioned resident and virtual memory, here’s one tip: when running JVM apps, make sure swap is turned off, period. Swapping data out from memory to disk is bad enough to bring any application to a crawl, but languages that use Garbage Collection are even more prone to grinding to a halt once they hit swap. This is because much of a JVM’s memory is actually garbage only waiting to be garbage-collected later on. Since there are few accesses, this data will be dumped to disk, resulting in a storm of I/O once the garbage collection step is invoked at some point. The complex object graph with many small objects referencing each other and scattered all over the allocated space makes for many random disk accesses and slows things down even more. If your machine has too little RAM for your app, upgrade it. Using a swap file or partition will only make matters worse.

How many bytes in a byte?

The answer to the question of how many bytes there are in a Java byte is far from obvious. Native memory aside, a byte could be a local variable of type byte, a Byte object or the element of an array. Actual memory usage will be different in each of these scenarios.

Primitive variables

A local variable or method call parameter of primitive type (such as byte) is allocated on the stack. When Java source code is compiled, it is normally not compiled directly to native code that could run on a particular CPU. Rather, it is compiled to bytecode (this has nothing to do with the fact that we used byte as our example type) which is an abstract language that the JVM can execute and possibly transform to native CPU code later on. However, in bytecode there are no instructions for handling variables of type byte directly. Instead, Java’s stack is divided into slots such that variables of type byte, char, short, int and float take up one slot while longs and doubles take two (see: JVMS §2.6.1). The same operation codes are used for manipulating bytes and ints on the stack (JVMS §2.11.1). This has far-reaching consequences. In theory one needs only a single byte of memory for storing the range of values of Java byte type (-128 to +127). However, the specification says the same slot is to be used for a byte and for an int which has a much larger range of values. In practice, most implementations simply reserve 4 bytes for each slot, meaning that a byte on the stack takes 4 bytes instead of just one.

Note that neither JLS nor JVMS mandate a specific layout of variables or objects in memory of the running application (but they do for class files). The JVMS only says that bytecode operations work on slots and that the same slot size is used for a byte and for an int, but not how these slots should be implemented. Most JVMs take the route described above which is a reasonable way of fulfilling the specification, but not the only one. It would be possible to create a JVM which allocated 17 bytes on the stack for each slot and it would still be standards-compliant, though rather wasteful. There are many other cases where different JVMs implement some mechanism in a similar way even though other solutions would quite well be possible. Such solutions are not part of any standard but due to their popularity one can (at least for now) rely on them for the purpose of tuning memory usage. Such implementations are usually chosen because of the way popular hardware (like Intel CPU chips) work, so they may change in the future but not quite as fast as minor quirks which may change with each JDK release.


When a variable of some object type is declared in a Java application, the declaration only creates an object reference. Only when an object instance is created using the new operator, does an actual object get created. This easily explains the behavior of object references which developers new to Java sometimes find confusing. The reference behaves like a primitive: if a value is assigned to the reference, the reference changes its value to point to another object but the objects themselves are not affected. Similarly, if a reference is passed to a method as a parameter, one cannot modify what the external reference points to, but by following the reference, one can modify the state of the pointed-to object.

private void someMethod() {
    Byte b; // creates a reference on stack, no object instance is created
    b = new Byte((byte) 123); // allocates a new Byte object on the heap and sets b to point to it

Note that due to autoboxing, primitive types may in some situations get boxed in wrapper objects without the programmer coding it explicitly. An example would be using Java collections which can only store objects and will autobox any primitive values added to them. This means that a developer may well be creating lots of Byte objects while thinking that only primitive bytes are used.

Thus, for any object there will be some reference or references pointing to it and the object instance itself. If there are no references to the object left, it can be removed by the Garbage Collector.

A reference is a unique handle by which an object can be accessed. Most Java applications create lots of object references since everything which is not a primitive value, including arrays, is represented as an object. Many references are also used behind the scenes. For example, this is passed implicitly for every method call in an object and references allow objects to know their types and corresponding Class objects. Implementations differ, with some JVMs encoding basic information about an object inside the reference in order to speed up some operations and others using a simple pointer to a data structure. However, at some point, the JVM needs to use pointers (like those known from C++) in order to access object instances. While 32-bit CPUs were in common use, pointers were 32 bits in size. Moving to 64-bit JVMs meant that the size of the pointer was increased twice which caused a huge increase in memory usage for most apps and decreased performance for many. Oracle JVM has implemented a special optimization called “compressed oops” which allows most pointers (and references) to stay at 32 bits even on a 64-bit JVM as long as the heap size doesn’t exceed 32 GB.

An object instance itself is a much more complicated beast than a reference. First off, objects are aligned in memory to 8-byte boundaries. This allows the “compressed oops” trick to work but at the same time it means that some space is necessarily wasted. The object instance also contains a header of 8-12 bytes which precedes any instance fields declared in the class. Together with a 4-byte reference, that adds up to 20 bytes even for an object which doesn’t store any useful information. There are additional rules related to ordering of fields and their alignment when inheritance comes into play.

As a result, a boxed Byte will take up at least 20 bytes (4 for the reference and 16 for the object instance). That’s 20 times more than one might expect at first. In the case of Double, we end up with 28 bytes instead of 8 because the 8-byte payload plus 12 byte header are rounded up to 24 bytes due to the 8-byte object alignment.

A more detailed discussion of compressed oops and object header layout can be found in Oracle’s HotSpot wiki.

Creating lots of small objects can not only consume lots of memory but also put the Garbage Collector under stress. When creating primitive object wrappers, it is likely that many of them will contain the same values, for example small numbers such as 0 or 1 in the case of Integer. Using Integer.valueOf(1) instead of new Integer(1) will return a cached instance of the Integer wrapper (for small numbers) and avoid creating unnecessary instances of this immutable type. Do use valueOf() if you need to box many primitives. In some cases it may make sense to cache other object instances in a similar manner. String has the String.intern() method which can be used for this purpose, though there are a number of pitfalls. Making your own classes immutable allows for instances to be cached and re-used without the risk of modifying their contents by unrelated pieces of code.


An array is an object in Java, so it needs a reference to point to it and it has a header which contains the array’s length. Within an array, consecutive values are usually packed more effectively than on the stack: bytes, for example, take only one byte of memory each. However, an array of booleans uses a byte for each entry while int-sized slots are used on the stack (JVMS §2.3.4). In the case of arrays of boxed Byte objects or of collections which only work with boxed types, each entry contains a reference to an object on the heap, meaning that an image manipulation program which used Byte[] instead of byte[] in order to load a 5 MB image file, would be using 100 MB of memory instead of 5 MB. The issue would be aggravated by memory fragmentation and GC overhead. If there were few unique values, using valueOf() might reduce this figure to 20 MB since there would be no need to allocate more than just a few unique instances of wrapper objects (but space would still have to be allocated for five million four-byte references instead of single-byte bytes).

Collecting the debt

Most of the time, complex data structures are built from Java collections such as Lists, Sets and Maps. These structures are themselves objects and they store object references inside which means every primitive object needs to be wrapped inside their boxed counterpart. The effects this brings about onto memory usage were outlined in the previous paragraph. But this is not all. Java’s collection classes were designed with code simplicity (never mind the awful API design) rather than memory effectiveness in mind. This has some peculiar consequences. A HashSet, for example, may take up more memory than a HashMap because it contains a HashMap internally (check the code yourself ). Maps are also quite ineffective memory-wise because the entrySet() operation was placed in the Map interface, so Map implementations that come with the JDK actually store materialized instances of Map.Entry inside. This means for each key-value pair, apart from space used in the hashtable, an additional object with 16 bytes of payload (that’s 32 bytes storage per object together with the header and an additional reference). Resolving hash conflicts is done by chaining, so the values stored in the hashtable are not direct values but rather collections (list or tree-based since JDK 8), each carrying its own overhead. Add to it that arrays allocated for hash tables or ArrayLists need some free space above the capacity actually in use so that new objects can be inserted and you will easily see that the overhead tends to get rather large. When objects are inserted into a collection and then removed, memory may not get freed immediately, sometimes not at all (e.g. the hash table inside a HashMap). A HashMap with a single entry takes about 164 bytes of memory in addition to the contents of key and value objects (4 B for the reference, 48 B for HashMap object, 16 B + 16*4 B for the internal array + 32 for the entry node).

There are a number of alternative collection libraries, some of which may reduce the memory footprint by using more memory-efficient data structures or by providing special implementations for primitive data types. This is in itself a topic for a separate blog post, so just to get you inspired, here are some of the alternative Java collection libraries:

Here are some general hints for working with collections:

  • Small collections carry big overheads.
  • If you know there will be lots of empty collections, and they are effectively immutable, use a single instance instead of allocating a new one each time. Collections.emptySet() and friends return singletons.
  • Primitive wrappers may greatly increase memory usage.
  • Operations such as Map.keySet() may cause significant amounts of memory to be allocated. When iterating, entrySet() is usually most effective for Java built-in collections, other implementations may differ. Trove comes with expressions for effective, pseudo-functional iteration over its collection classes.
  • Preallocate space if you know the size of a collection up-front (many classes come with methods such as ensureCapacity()). When an array-based collection such as ArrayList or HashMap grows, it needs to enlarge its internal array. Since there is no realloc() in Java, this means that at some point the old array is still in use while the new, larger one, needs to be allocated and data copied into it. If the array is increased by a factor of 2, as is common, this means that at one point adding an element to a collection of N elements needs to have memory for 3*N allocated for some time.
  • If collections are your bottleneck, consider alternatives to the collection library provided by the JDK.

Stringing it all together

Strings are a common element of most Java apps. They are immutable representations of character sequences. Strings contain arrays of Java chars which are two-byte values (Unicode UTF-16 representation, with char representing one unit). A String instance contains a reference to a char array that stores the characters as well as a cached value of the hash code. The latter is an interesting example of an immutable object (String) containing a field (hash) whose value is modified, without even any synchronization, and yet the object stays immutable and thread-safe. Watch but don’t do this at home.

So, the memory footprint of a String is roughly 4 + 24 + 16 + 2*length bytes (reference + string object + array). Two things to notice are:

  • An empty String uses about 44 bytes.
  • The size in memory will usually be larger than the size of your UTF-8 encoded input data such as JSON message since chars are two bytes each and UTF-8 with mostly ASCII will be close to one byte per character. However, there is an optimization in Oracle JVM which supposedly uses byte arrays instead of char arrays “when possible” for Strings containing only ASCII characters.

In JDK 6 and the earliest versions of JDK 7, String was implemented in a different way, which made the analysis of memory usage much more complicated. In addition to the char array, there were two int fields, offset and length, which allowed several Strings to share the same char array. This made it possible for String.substring() to allocate only the String object but to re-use the char array, using a different offset and length. At the same time, it led to 8 additional bytes of overhead and to some counterintuitive behaviors. The possibility of a big String never being removed from memory by GC because of a short substring sharing the same character array. That’s probably why the implementation was changed. Compare [String in JDK 6] (http://hg.openjdk.java.net/jdk6/jdk6/jdk/file/b2317f5542ce/src/share/classes/java/lang/String.java) and String in JDK 8 for an interesting read.


This text barely scratches the surface. A JVM performs very complex operations on a running app, many of which affect the memory footprint. New compiler versions add layer upon layer of optimizations to the already complicated runtime.

After reading this, you may be tempted to go ahead and start optimizing your code for memory usage. This is probably not a good idea, as in most cases code readability and maintainability are much more important than minor performance gains. As with any performance optimization, be very careful and always measure actual usage before and after any changes. Sometimes, very unintuitive things may happen, so always double check that purported optimizations actually do improve the memory footprint of your applications.

However, being aware of what goes on behind the scenes in the JVM will help you avoid writing code with excessive memory usage by default and to understand where gains are possible and where there is little to win. It is also plain interesting to know how complex the machinery that runs our apps needs to be in order to allow them to run smoothly.