Resolving Java Heap Space OutOfMemoryError

Java is one of the most popular programming languages, known for its portability, scalability, and rich ecosystem. One of the critical components of the Java Runtime Environment (JRE) is the heap space, which plays a crucial role in memory management. While Java’s automatic memory management via garbage collection is powerful, it is not immune to problems. One such problem that developers frequently encounter is the dreaded OutOfMemoryError: Java heap space. This error can bring your application to a halt, leading to downtime, user frustration, and even data loss if not properly managed.

In this blog post, we’ll dive into the concept of Java heap space, explore the common causes of OutOfMemoryError, and provide you with a step-by-step guide to resolving this issue. We will also discuss best practices for preventing this error in the future and how to monitor your application’s memory usage effectively.

Introduction to Java Heap Space

Java heap space is a portion of memory allocated to a Java application by the JVM (Java virtual machine) at runtime. This memory is used to store objects and classes. When you create an object using the new keyword in Java, the object is allocated in the heap space. The JVM divides the heap into two main regions: the young generation and the old generation (also known as the tenured generation).

• Young generation: This is where all new objects are initially allocated. The young generation is further divided into eden space and survivor spaces (S0 and S1). The garbage collector frequently cleans up this area, moving objects still in use to the old generation.
• Old generation: This is where long-lived objects reside. The garbage collector cleans up this area less frequently than the young generation. However, when it does, the process is more intensive and can lead to significant pauses in your application if not managed correctly.

When the JVM runs out of heap space and cannot allocate more memory for new objects, your application throws an OutOfMemoryError, signaling that your memory resources are exhausted.

What Causes OutOfMemoryError?

The OutOfMemoryError typically occurs when your Java application attempts to allocate more memory in the heap space than is available. Several factors can lead to this situation:

1. Memory Leaks

A memory leak happens when objects no longer needed are not released from memory. In Java, this can occur if references to objects are maintained unintentionally, preventing the garbage collector from reclaiming the memory. Over time, these unused objects accumulate, consuming more and more heap space until the JVM runs out of memory.

2. Large Data Structures

Applications that handle large datasets or require large in-memory caches can easily consume substantial amounts of heap space. For example, if you load an entire database table into memory or use large collections without proper management, you may quickly exhaust your heap space.

3. Improper JVM Configuration

The JVM provides several options to configure the heap space, such as -Xms for initial heap size and -Xmx for maximum heap size. If these settings are too low for your application’s needs, you may encounter an OutOfMemoryError even if your application is functioning correctly otherwise.

4. Inefficient Code

Poorly optimized code can lead to excessive memory usage. For example, creating unnecessary objects, using suboptimal algorithms, or failing to reuse objects can lead to higher memory consumption.

How to Identify an OutOfMemoryError

Before resolving an OutOfMemoryError, you need to identify the root cause. Here are some methods to diagnose the issue:

1. Analyze the Error Message

The JVM provides specific details when an OutOfMemoryError occurs. The error message often contains information about the type of memory that ran out, such as “Java heap space” or “GC overhead limit exceeded.” This message gives you an initial clue about the source of the problem.

2. Examine Heap Dumps

A heap dump is a snapshot of your application’s memory at a specific point in time. You can generate a heap dump automatically when an OutOfMemoryError occurs by adding the -XX:+HeapDumpOnOutOfMemoryError flag to your JVM options. Heap dumps can be analyzed using tools like Eclipse MAT (Memory Analyzer Tool) or VisualVM to identify memory leaks or large objects consuming excessive memory. However, use caution with heap dumps, as heap dump files can be as large as your JVM memory allocation. If you create the file without enough disk space (equivalent to the size of JVM memory allocation), you’ll crash your application.

3. Use Profiling Tools

Profiling tools like JProfiler or YourKit provide insights into memory usage, including which objects are consuming the most memory and how often garbage collection occurs. These tools help you pinpoint inefficient code or identify parts of your application that may be leaking memory.

4. Monitor Garbage Collection Logs

Garbage collection (GC) logs can reveal how frequently GC occurs and how much memory is being reclaimed during each GC cycle. If GC runs too often and reclaims little memory, it might indicate a memory leak or inadequate heap size.

Tutorial: Steps to Resolve Java Heap Space OutOfMemoryError

Once you’ve identified the cause of the OutOfMemoryError, you can take steps to resolve it. Below are several approaches you can use to fix the issue:

1. Increase Heap Size

The simplest solution is to increase the heap size allocated to your JVM. You can make this increase by adjusting the -Xms (initial heap size) and -Xmx (maximum heap size) parameters.

For example:

java -Xms512m -Xmx2048m -jar your-application.jar

This command sets the initial heap size to 512 MB and the maximum heap size to 2048 MB. However, be cautious when increasing the heap size, as it might only delay the problem if there is an underlying issue like a memory leak.

2. Identify and Fix Memory Leaks

Increasing the heap size will not solve the problem if your application has a memory leak. You must identify the leak using heap dumps or profiling tools and fix the underlying code. For example, remove unnecessary references to objects and use weak references where appropriate.

3. Optimize Code and Data Structures

Always review your code to ensure efficient memory usage. Stackify Prefix is a real-time code profiler that identifies why requests are slow within an application-and that can lead to pinpointing where in the code an OutOfMemoryError occurs. Traces displayed in Prefix include all the events within your code leading up to an exception, which is essential in determining the root cause. You should also consider the following optimizations:

• Use primitive types instead of objects where possible to reduce memory overhead.
• Avoid unnecessary object creation by reusing objects, especially in loops or frequently called methods.
• Optimize data structures like ArrayList and HashMap by setting initial capacities to avoid excessive resizing and memory usage.
• Use efficient algorithms to process large data sets, reducing the need for large in-memory data structures.

4. Adjust GC Settings

If your application is experiencing frequent garbage collection pauses, you might need to tune the GC settings. For instance, you can experiment with different garbage collectors like G1GC or CMS (Concurrent Mark Sweep) to find the one that best suits your application’s needs.

5. Reduce Memory Usage

If your application deals with large datasets, consider whether you can reduce the memory footprint in one of these ways:

• Streaming data instead of loading everything into memory at once.
• Using external storage for large datasets rather than keeping them in memory.
• Compressing data before storing it in memory to reduce its size.

6. Consider Out-of-Process Caching

If your application relies heavily on in-memory caches, consider using an out-of-process caching solution like Redis or Memcached. These tools allow you to offload caching to a separate process, freeing up heap space in your JVM.

Best Practices for Preventing OutOfMemoryError

Preventing OutOfMemoryError is better than having to resolve it. Here are some best practices to keep your application running smoothly:

1. Regularly Monitor Memory Usage

Monitor your application’s memory usage through monitoring tools like Stackify APM. Providing detailed insights into memory usage, Stackify APM helps you detect issues before they lead to OutOfMemoryError.

2. Implement Efficient Memory Management

Design your application with memory efficiency in mind. Use appropriate data structures, avoid holding onto unnecessary object references, and prefer streaming over loading extensive datasets into memory.

3. Set Up Proper GC Tuning

Properly tuning the garbage collector based on your application’s behavior can significantly reduce the risk of OutOfMemoryError. Review and adjust GC settings regularly as needed.

4. Perform Load Testing

Before deploying your application, perform load testing to simulate real-world usage. This helps you identify potential memory issues and optimize your application’s memory usage under load.

5. Use Memory Profiling During Development

During development, use profiling tools to analyze memory usage and optimize your code. This proactive approach helps catch memory inefficiencies early in the development cycle.

Monitoring with Stackify

Monitoring your Java application’s memory usage is critical for preventing OutOfMemoryError. Tools like Stackify APM offer comprehensive monitoring and diagnostics for Java applications, including memory usage, GC activity, and more. Stackify APM capabilities help you detect and resolve memory issues before they impact your application.

If you’re not already using a monitoring solution, consider starting with a free trial of Stackify APM. This can help you ensure your application remains stable, efficient, and free from memory-related issues.

Improve Your Code with Retrace APM

Stackify's APM tools are used by thousands of .NET, Java, PHP, Node.js, Python, & Ruby developers all over the world.
Explore Retrace's product features to learn more.

• App Performance Management
• Code Profiling
• Error Tracking
• Centralized Logging
• App & Server Metrics

Learn More

Author

Stackify Team

More articles by Stackify Team