How does it handle memory leak investigations?

It provides specific commands for generating heap dumps (jmap) and analyzing object retention patterns using industry-standard tools and methodologies.

Does this skill require specific Java versions?

While many commands work with older versions, it is optimized for modern JDKs (Java 11, 17, and 21+) to take advantage of features like ZGC and JFR.

Which Garbage Collectors does this skill support?

The skill supports a wide range of GCs including G1, ZGC (including Generational ZGC), Shenandoah, Parallel, and Serial GC, providing specific presets for each.

Can I use this skill to optimize Java applications in Docker?

Yes, it includes container-optimized presets such as MaxRAMPercentage and UseContainerSupport to ensure the JVM operates efficiently within cgroup limits.

Does it help with code-level performance testing?

Absolutely. It provides standardized templates for JMH (Java Microbenchmark Harness) to help you write scientifically accurate performance tests.

Java Performance Tuning

Name: Java Performance Tuning
Author: pluginagentmarketplace

bypluginagentmarketplace

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Analytics & Monitoring

Optimizes JVM performance through advanced garbage collection tuning, memory analysis, and application profiling.

About

The Java Performance Tuning skill provides a comprehensive toolkit for developers to diagnose and resolve bottlenecks within the Java Virtual Machine. It offers expert guidance on optimizing Garbage Collection (GC) for specific workloads—ranging from low-latency ZGC configurations to high-throughput G1 settings—and facilitates deep technical analysis through CPU profiling, heap dump investigations, and JMH benchmarking. Whether you are troubleshooting memory leaks in production or fine-tuning containerized microservices for resource efficiency, this skill automates the generation of precise diagnostic commands and configuration presets.

Key Features

Comprehensive profiling workflows using JFR, jstack, and Async Profiler
Expert GC tuning presets for low-latency, high-throughput, and memory-constrained environments
Cloud-native optimization for JVMs running in Docker and Kubernetes
1 GitHub stars
Automated memory leak detection and heap analysis strategies
JMH (Java Microbenchmark Harness) template generation for accurate code benchmarking

Use Cases

Identifying and fixing memory leaks in long-running server processes
Optimizing JVM resource utilization for containerized microservices to reduce infrastructure costs
Reducing tail latency and GC pause times in high-frequency enterprise applications

About

Key Features

Comprehensive profiling workflows using JFR, jstack, and Async Profiler
Expert GC tuning presets for low-latency, high-throughput, and memory-constrained environments
Cloud-native optimization for JVMs running in Docker and Kubernetes
1 GitHub stars
Automated memory leak detection and heap analysis strategies
JMH (Java Microbenchmark Harness) template generation for accurate code benchmarking

Use Cases

Identifying and fixing memory leaks in long-running server processes
Optimizing JVM resource utilization for containerized microservices to reduce infrastructure costs
Reducing tail latency and GC pause times in high-frequency enterprise applications