When our application confronts OutOfMemoryError, our typical response is to increase -Xmx. Increasing -Xmx might work in a few cases, but it will not work in several cases. In this post, let’s discuss what are the potential consequences of increasing -Xmx to our application & effective alternative solutions.
What happens when you set -Xmx?
Fig: JVM Memory Regions
There is a misunderstanding that when you set -Xmx, it sets the maximum memory size of the entire Java process. It’s not true. When you set -Xmx, you are only setting the size of one of the important memory regions of the Java process, which is ‘Java Heap’ region. Besides this region, there are several other regions in that Java Native Memory:
1. Metaspace
2. Threads
3. Code Cache
4. Direct Buffer
5. GC
6. JNI
7. Misc
Most of OutOfMemoryError will not be addressed
There are 9 types of OutOfMemoryError. Say suppose ‘Metaspace’ region saturates, you will get ‘java.lang.OutOfMemoryError: Metaspace‘, similarly when ‘Threads’ region saturates, you will get ‘java.lang.OutOfMemoryError: Unable to create new native threads‘. Similarly, when each native region saturates, you get different types of OutOfMemoryError.
Let’s say there is heavy memory consumption in the ‘Threads’ region your application experiences ‘OutOfMemoryError: Unable to create new native threads’. Our typical response is to increase the -Xmx. In this scenario increase increasing -Xmx will not address the OutOfMemoryError rather it will worsen the situation. Yes, think about it. When you increase -Xmx, you are increasing the size of the Heap memory region, which leaves less room other memory regions in the device. This will cause the threads memory region capacity to shrink further. Thus, you will get OutOfMemoryError thrown even more quickly.
Increasing -Xmx will have an effect, only if your application is suffering from ‘java.lang.OutOfMemoryError: Java heap space‘ or ‘java.lang.OutOfMemoryError: GC Overhead Limit Exceeded‘ and it will not any effect when other types of OutOfMemoryError is thrown.
Buying Extra Time, Not Solving Root Cause
JVM throws java.lang.OutOfMemoryError: Java heap space under two circumstances:
1. Increase In Traffic Volume: Your application traffic volume might increase during a particular time window (say if you’re a banking application, you might see a surge in the traffic volume from 8am to 10am) or maybe your business is doing well and organically traffic has been building up in the application. If there is a surge in the traffic volume & sufficient memory isn’t allocated you will get OutOfMemoryError
2. Buggy Code: Buggy code in the application can cause the unnecessary objects to be retained in heap memory. Building up Unnecessary objects in the heap memory over a period of time, will result in OutOfMemoryError.
If your application is experiencing OutOfMemoryError due to scenario #1 Increase in Traffic Volume, then increasing -Xmx will address the OutOfMemoryError. However, on the other hand if application is experiencing OutOfMemoryError due to scenario #2 Buggy Code, increasing the -Xmx, will not solve the problem. Your application might be able to run a little longer, but you will still experience OutOfMemoryError. Most of the time OutOfMemoryError happens because of scenario #2. They say, ‘Greatness can’t be bought, it can only be earned’. Throwing more memory at the problem is like trying to buy greatness, which will not work. You need to earn it, you need to do proper Heap Dump analysis and identify the root cause of the leak.
Longer GC Pauses
When you try to increase the -Xmx, you are inheriting the risk of longer GC pauses. Yes, because when you increase heap size, more objects can be retained in memory. It means Garbage Collector has to do more work to reclaim the retained objects in the memory. More Garbage Collector work translates to increased GC pause time. You will have to do additional GC tuning to reduce the GC Pause times.
If you are looking to tune your GC settings and reduce GC pause times, this post can help you: Reduce Long Garbage Collection Pauses
Increase in CPU & Memory Consumption
How many staff are required to manage a small soccer field? Similarly, think how many more staff will be required to manage a large soccer field. Definitely more resources will be needed. Similarly, when you increase the size of -Xmx, you are increasing the size of the JVM playing field, thus JVM & Garbage Collector will need more CPU cycles and Memory to manage it.
- More objects to track: Every allocation, promotion, and reclamation involves scanning and updating metadata. A larger heap means more references to walk through during garbage collection.
- CPU demand: The garbage collector threads need more CPU cycles to keep up with a bigger heap. If your application was already CPU-sensitive, this can cause noticeable contention between GC and your application threads.
- More GC memory overhead: Internal GC structures (remembered sets, card tables, region tracking) scale with the heap. That means memory is consumed not just by your objects, but also by the bookkeeping needed to manage them.
Increased Computing Cost
By increasing -Xmx you are actually doing a vertical scaling. Unlike horizontal scaling, vertical scaling is not only scalable beyond a point but also quite expensive. Cloud providers price memory-heavy instances at a premium. For example, on AWS EC2 pricing as of 2025 is:
- A m7g.large instance (2 vCPUs, 8 GB RAM) costs about $0.038 per hour.
- A m7g.2xlarge instance (8 vCPUs, 32 GB RAM) jumps to about $0.302 per hour.
That’s nearly 8× the cost for only 4× the memory. And if you move up to memory-optimized families (like r7g), the price jump is even steeper.
So each time you increase -Xmx and move to a bigger box, you are burning through your cloud budget. Multiply that across dozens or hundreds of JVMs, and the monthly bill may get jaw-dropping. If time permits consider reading this post: How GC Inefficiency Costs Enterprises Millions
Troubleshooting can become harder
Similarly, if you need to troubleshoot any memory problem, you will have to capture heap dumps from the application. A heap dump is basically a file which contains all information about your application’s memory like what objects were present, what are their references, how much memory each object occupies, …. Heap dump file size will be more or less equal to the size of your heap. So if you have large -Xmx (like 64GB, 128GB), then the heap dump file size will also be large. Analyzing large size heap dumps is difficult. Even world’s best heap dump tools like Eclipse MAT, HeapHero have challenges in parsing heap dumps that are more than 100gb. Reproducing the memory problems in the test lab, storing these heap dump files, and sharing these large heap dump files across your co-workers in your organization are all associated challenges.
What are the Alternate Solutions to Increasing -Xmx?
Below are the alternate solutions instead of increasing -Xmx:
1. Heap Dump Analysis: If your application experiences OutOfMemoryError, then instead of increasing -Xmx, first capture the heap dump from the application and analyze it to isolate the root cause of memory leak. Fix the buggy code that is causing the memory leak. Just increasing the heap size will only buy more time before the JVM crash.
2. Native Memory Tracking: If OutOfMemoryError is happening in the native memory region, just increasing the heap size will not have any impact, in fact it can only worsen the situation as pointed out earlier. To mitigate this problem, do native memory tracking and identify the region that is suffering from the memory problem and address it appropriately.
3. Study Garbage Collection Behavior: By studying the Garbage Collection log, you will be able to identify whether you have over-allocated or under-allocated your -Xmx. Overallocation can cause an increase in the computing cost, under allocation will degrade the application performance. Based on GC KPIs, tune to the appropriate heap size.
Conclusion
In this post we learnt the consequence of increasing -Xmx without proper due diligence includes not solving OutOfMemoryError, Longer GC Pauses, increased computing cost and harder troubleshooting. Right alternate solutions are doing heap dump analysis, native memory tracking & studying the garbage collection behavior.
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