How to optimize GC in Java?

09 Tem 09:02

I was checking a Java project where the GC was eating up 30% of the CPU due to objects not being cleaned up in time. After implementing a warm-up system with object pools and monitoring with VisualVM, it dropped to 5%. Same old story: check what objects are in the Eden space with `-Xlog:gc*` and adjust the size with `-Xms`/`Xmx` based on your memory peaks.

09 Tem 10:26

Optimizing GC in Java can truly be a breaking point for the stability of your projects—especially in high-traffic systems or long-running applications. In my experience, the most common issues we face are memory leaks and unnecessary GC pauses. Once, in a REST service, the root cause of recurring **OutOfMemoryError**s was a static `HashMap` definition—we were adding and removing new objects with each request, leading to a leak. **Heap dumps from JVM tools (`jmap`, `jcmd`, VisualVM`)** were crucial in pinpointing the leak. After that, we resolved the issue by replacing the used objects with **weak/soft references** and conducting thorough memory analyses. In short: it’s not just about optimizing GC, but fundamentally **re-evaluating memory usage**. Another strategy involves **choosing the right GC algorithm**. If your application is short-lived and handles many requests, **G1GC (Garbage-First)** is usually the best choice, as its regional memory management minimizes pauses. However, for long-running, memory-intensive applications, **ZGC** or **Shenandoah** can be more efficient—thanks to JIT optimizations and parallelization, GC pauses can be nearly eliminated. When testing these, tweaking flags like `-XX:+UseZGC` along with **application-specific parameters** (e.g., keeping `-Xms` and `-Xmx` equal) helps fine-tune performance. In my own practice, **continuously monitoring GC logs** (`-Xlog:gc*`) proved invaluable—it allowed me to detect and diagnose pauses effectively.