Operations | Monitoring | ITSM | DevOps | Cloud

When it comes to Java applications, to make sure they run at peak performance, it’s critical to close the resource gap between the code and the virtual machine it’s running on – if there is one. The way to do that is by peaking into and fine-tuning the Java Virtual Machine (JVM). However, that’s easier said than done. Many factors can influence JVM performance, and during tuning, you must consider all of them. Though, there are ways around that to make it not be such a pain.

What is the bare minimum you need to build, test and run your Java application in Docker container? The recipe: Create a separate Docker image for each step and optimize the way you are running it. I started working with Java in 1998, and for a long time, it was my main programming language. It was a long love–hate relationship. During my work career, I wrote a lot of code in Java. Despite that fact, I don’t think Java is usually the right choice for microservices.

The Java Virtual Machine (JVM) dynamically manages memory for your applications, ensuring that you don’t need to manually allocate and release memory in your code. But anyone who’s ever encountered a java.lang.OutOfMemoryError exception knows that this process can be imperfect—your application could require more memory than the JVM is able to allocate.

Managing an application’s performance, and specifically keeping it in good condition, is one of the hardest challenges in software development. That’s true for virtually any programming language and platform. Java is no exception to this rule. But beyond that, Java also presents some unique challenges of its own.

Equality is an essential concept when programming, not only in Java but in pretty much all programming languages. After all, much of what we do when writing code has to do with comparing values and then making decisions based on the results of such comparisons.

Exception handling in Java isn’t an easy topic. Beginners find it hard to understand and even experienced developers can spend hours discussing how and which Java exceptions should be thrown or handled. That’s why most development teams have their own set of rules on how to use them.

It’s one thing to write code that works. But what about clean, readable, concise code? That’s another thing entirely. To create an app that solves one problem? Not that hard. What about one that not only solves the problem, but it’s also easy and pleasurable to use? Now we’re talking. You could apply the same reasoning for many software properties, which would make for a long article. Instead, let’s focus on only one of those properties: performance.

If you have deployed a Java application in production, you’ve probably encountered a situation where the application suddenly starts to take up a large amount of CPU. When this happens, application response becomes sluggish and users begin to complain about slow response. Often the solution to this problem is to restart the application and, lo and behold, the problem goes away – only to reappear a few days later.

Whether you’re investigating memory leaks or debugging errors, Java Virtual Machine (JVM) runtime metrics provide detailed context for troubleshooting application performance issues. For example, if you see a spike in application latency, correlating request traces with Java runtime metrics can help you determine if the bottleneck is the JVM (e.g., inefficient garbage collection) or a code-level issue.

Java applications deployed in production must be multi-threaded for scalability. Threads can be used for performing different tasks or they could be executing the same task to service different user requests. Often, it is necessary to synchronize between different threads.