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Memory leaks in Java


In the Garbage collection example, we implied that Java solves the problem of memory leaks. This is not actually true. A Java program can leak memory, though the causes of the leaks are rather different.

Reachable objects can leak

Consider the following naive stack implementation.

public class NaiveStack {
    private Object[] stack = new Object[100];
    private int top = 0;

    public void push(Object obj) {
        if (top >= stack.length) {
            throw new StackException("stack overflow");
        stack[top++] = obj;

    public Object pop() {
        if (top <= 0) {
            throw new StackException("stack underflow");
        return stack[--top];

    public boolean isEmpty() {
        return top == 0;

When you push an object and then immediately pop it, there will still be a reference to the object in the stack array.

The logic of the stack implementation means that that reference cannot be returned to a client of the API. If an object has been popped then we can prove that it cannot "be accessed in any potential continuing computation from any live thread". The problem is that a current generation JVM cannot prove this. Current generation JVMs do not consider the logic of the program in determining whether references are reachable. (For a start, it is not practical.)

But setting aside the issue of what reachability really means, we clearly have a situation here where the NaiveStack implementation is "hanging onto" objects that ought to be reclaimed. That is a memory leak.

In this case, the solution is straightforward:

    public Object pop() {
        if (top <= 0) {
            throw new StackException("stack underflow");
        Object popped = stack[--top];
        stack[top] = null;              // Overwrite popped reference with null.
        return popped;

Caches can be memory leaks

A common strategy for improving service performance is to cache results. The idea is that you keep a record of common requests and their results in an in-memory data structure known as a cache. Then, each time a request is made, you lookup the request in the cache. If the lookup succeeds, you return the corresponding saved results.

This strategy can be very effective if implemented properly. However, if implemented incorrectly, a cache can be a memory leak. Consider the following example:

public class RequestHandler {
    private Map<Task, Result> cache = new HashMap<>();

    public Result doRequest(Task task) {
        Result result = cache.get(task);
        if (result == null) {
            result == doRequestProcessing(task);
            cache.put(task, result);
        return result;

The problem with this code is that while any call to doRequest could add a new entry to the cache, there is nothing to remove them. If the service is continually getting different tasks, then the cache will eventually consume all available memory. This is a form of memory leak.

One approach to solving this is to use a cache with a maximum size, and throw out old entries when the cache exceeds the maximum. (Throwing out the least recently used entry is a good strategy.) Another approach is to build the cache using WeakHashMap so that the JVM can evict cache entries if the heap starts getting too full.