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Managing JVM Applications in Production

Defensive Driving

Article from ADMIN 92/2026

By Henner Schmidt , By Max Jonas Werner

Java's memory management has quite a steep learning curve when you are tasked with operating a Java Virtual Machine efficiently in production. We guide you through the waters of keeping applications up and running and what signals to look for to prevent crashes.

Thirty years of unbroken compatibility promises accumulated to quite a number of choices between different concepts in the Java Virtual Machine (JVM) runtime environment (Figure 1). Looking at some of those ideas today makes them appear to be a statement of Zeitgeist more than anything else, but you still have to choose the parameters for running your applications. Understanding how the JVM manages memory and how to observe the many metrics it exposes is essential to operating JVM applications in production.

Figure 1: Java Virtual Machine tuning is an art in and of itself. Over the last 30 years, many knobs have been added, but only a subset is crucial for operators to know.

To observe a JVM application's behavior and memory usage, operators need more than a few heap graphs: They need a reliable way to observe JVM memory usage, interpret it correctly, and spot failure patterns early enough to intervene. In the following sefctions, we dive into the details of JVM memory observation: which metrics exist, how to retrieve them, how to visualize them in Grafana, and what memory trends tend to precede JVM memory failures.