In the ever-evolving world of Java development, performance remains a paramount concern. From high-throughput microservices built with Spring Boot to data-intensive applications leveraging Hibernate, the efficiency of the Java Virtual Machine (JVM) is critical. A significant factor in this performance equation is Garbage Collection (GC) latency—the infamous “stop-the-world” pauses that can grind an application to a halt. The latest Java ecosystem news is filled with advancements in this area, particularly with the evolution of low-pause collectors in recent releases like Java 17 and Java 21. However, accurately measuring and comparing the performance of these collectors under realistic workloads has always been a challenge.
This is where the latest Amazon Corretto news brings a powerful new tool to the forefront for developers. The Amazon Corretto team, known for its production-ready distribution of OpenJDK, has open-sourced Heapothesys, a specialized GC latency benchmark. Unlike general-purpose benchmarks, Heapothesys is meticulously designed to isolate and stress the JVM’s memory management subsystem. This article provides a comprehensive technical guide to understanding, using, and mastering Heapothesys to make data-driven decisions about GC tuning, helping you unlock the full potential of your Java applications.
Understanding Garbage Collection and the Benchmarking Challenge
Before diving into Heapothesys, it’s essential to grasp why dedicated GC benchmarking is so crucial. Every Java developer has, at some point, faced performance issues that were eventually traced back to long or frequent GC pauses. These pauses directly impact user experience and system reliability.
The Impact of GC Latency
Garbage Collection is the JVM’s automatic memory management process. While it frees developers from manual memory deallocation, it’s not without cost. When a “stop-the-world” (STW) pause occurs, all application threads are halted while the collector does its work. For a web service built with Spring Boot, a 200ms pause could mean a failed health check or a breached Service Level Agreement (SLA). For a desktop application using JavaFX, it could mean a frozen UI. The goal of modern garbage collectors like G1GC, ZGC, and Shenandoah—major highlights in recent Java 17 news and Java 21 news—is to minimize the length and frequency of these pauses. However, their effectiveness varies dramatically based on the application’s specific memory allocation patterns.
Why Traditional Benchmarks Fall Short
Trying to measure GC performance using a standard application load test can be misleading. The results are often noisy, mixing the performance of application code, database access, network latency, and GC activity. It’s difficult to isolate the impact of a specific JVM flag or a different GC algorithm. Microbenchmarks, while useful for small code paths, fail to simulate the complex object allocation and survival rates of a real-world application. This is the problem Heapothesys was created to solve. It generates a synthetic, yet highly configurable, workload that directly targets the heap and the garbage collector, providing a clean signal for analysis.
A Practical Guide to Using Heapothesys
Heapothesys is a command-line tool that simulates an application’s memory allocation behavior. By controlling parameters like allocation rate, object size, and object lifetime, you can model various scenarios and measure how the GC responds. Let’s walk through how to get started.
Setup and Initial Run
First, you need to obtain and build Heapothesys. It’s a standard Java project, easily built with Maven or Gradle. You can clone the repository directly from the Corretto GitHub page.
# Clone the repository
git clone https://github.com/corretto/heapothesys.git
cd heapothesys
# Build the project using Maven
mvn clean packageOnce built, you’ll find the executable JAR in the `target/` directory. You can now run your first basic benchmark. The following command runs the benchmark for 60 seconds with an allocation rate of 100 MB/s, using 4 threads.
java -jar target/heapothesys-1.0.jar -d 60 -a 100 -t 4The output will provide detailed statistics, including pause time percentiles (p50, p90, p99, max), which are crucial for understanding the user-perceived latency of your application.
Simulating Different Application Workloads
The true power of Heapothesys lies in its configurability. You can simulate various application profiles. For instance, a typical web application handling REST API requests generates a vast number of short-lived objects (request/response objects, DTOs, etc.). We can model this by specifying a short object lifetime.
The `-l` parameter controls the “mid-life” crisis age, essentially the number of GC cycles an object is expected to survive. A low value simulates short-lived objects. The `-s` parameter controls the size of allocated objects.
# Simulate a web server: 200 MB/s allocation rate, 16 threads,
# small objects (128 bytes), and very short-lived objects (survive ~1 GC cycle)
java -Xmx4g -jar target/heapothesys-1.0.jar \
-d 120 \
-a 200 \
-t 16 \
-s 128 \
-l 1This kind of simulation is invaluable for tuning the young generation size or deciding if a particular GC is suitable for your high-traffic Spring Boot service. It provides insights that are directly applicable to real-world scenarios, reflecting the ongoing trends in Java performance news.
Advanced Techniques for In-Depth GC Analysis
Once you’re comfortable with basic runs, you can leverage Heapothesys for more advanced analysis, such as comparing garbage collectors or integrating with professional profiling tools. This is where you can truly see the impact of the latest JVM news and OpenJDK advancements.
Comparing G1GC, ZGC, and Shenandoah
One of the most common use cases for Heapothesys is to make an evidence-based decision on which garbage collector to use. Let’s compare the default G1GC with the low-latency ZGC, a highlight of recent Java 11 news and its subsequent enhancements in Java 21. We will run the same workload twice, once for each collector, and enable GC logging to capture detailed information.
# WORKLOAD: 500 MB/s allocation rate, 8GB heap, run for 3 minutes
# Test 1: Using G1GC (default on modern JVMs)
java -Xmx8g -XX:+UseG1GC -Xlog:gc*:file=g1gc.log \
-jar target/heapothesys-1.0.jar -d 180 -a 500
# Test 2: Using ZGC (low-latency collector)
java -Xmx8g -XX:+UseZGC -Xlog:gc*:file=zgc.log \
-jar target/heapothesys-1.0.jar -d 180 -a 500After these runs, you can compare the percentile latency figures from the Heapothesys output and analyze the `g1gc.log` and `zgc.log` files. You will likely observe that ZGC provides significantly lower maximum pause times, albeit sometimes at the cost of slightly lower throughput. This empirical data is crucial for services with strict latency requirements. This approach is also relevant for developers exploring modern frameworks and paradigms like Reactive Java news, where non-blocking execution can be severely hampered by STW pauses.
Automation with Shell Scripting
To ensure consistency and enable regression testing, it’s a best practice to automate your benchmark runs. This is especially useful when testing the impact of a new JVM version or a change in application dependencies. This concept, borrowed from unit testing with tools like JUnit, can be applied to performance.
Here is a simple shell script to automate the comparison between G1GC and ZGC:
#!/bin/bash
HEAP_SIZE="-Xmx4g"
HEAPOTHESYS_JAR="target/heapothesys-1.0.jar"
WORKLOAD_ARGS="-d 120 -a 250 -t 8"
TIMESTAMP=$(date +%F_%T)
echo "--- Running Benchmark with G1GC ---"
java $HEAP_SIZE -XX:+UseG1GC -Xlog:gc*:file=g1gc-$TIMESTAMP.log \
-jar $HEAPOTHESYS_JAR $WORKLOAD_ARGS > results_g1gc_$TIMESTAMP.txt
echo "--- Running Benchmark with ZGC ---"
java $HEAP_SIZE -XX:+UseZGC -Xlog:gc*:file=zgc-$TIMESTAMP.log \
-jar $HEAPOTHESYS_JAR $WORKLOAD_ARGS > results_zgc_$TIMESTAMP.txt
echo "--- Benchmark Complete. Results saved to files with timestamp $TIMESTAMP ---"This script runs both tests and saves the console output and GC logs to timestamped files, creating a repeatable experiment that can be integrated into a CI/CD pipeline to catch performance regressions early.
Best Practices, Pitfalls, and the Broader Ecosystem
While Heapothesys is an incredibly powerful tool, using it effectively requires adherence to benchmarking best practices. Misinterpreting results can lead to poor tuning decisions.
Key Considerations for Accurate Benchmarking
- Isolate Your Environment: Always run benchmarks on a machine with minimal other processes running. Cloud environments can be “noisy,” so be aware of potential interference from other tenants.Warm-Up the JVM: The JVM’s performance changes over time due to Just-In-Time (JIT) compilation. Initial results are not representative. Heapothesys has a built-in warm-up period, but for long-running tests, it’s important to discard the initial phase of data.Don’t Over-Generalize: Heapothesys provides data about GC performance under a *synthetic* load. While you should model your application’s behavior as closely as possible, always validate your findings with profiling on your actual application using tools like Java Flight Recorder (JFR).
Connecting to Modern Java Trends
The insights gained from Heapothesys are more relevant than ever. With the rise of Project Loom and virtual threads, as discussed in the latest Java virtual threads news, applications can now handle millions of concurrent tasks. While virtual threads are lightweight, the objects they create are not, putting new and unique pressures on the GC. Using Heapothesys to simulate a workload with a massive number of threads (`-t 1000000`) can help you prepare your services for this new concurrency model. Similarly, as AI workloads in Java grow with libraries like Spring AI and LangChain4j, understanding the complex object graphs and allocation patterns they create is key to maintaining performance, making GC tuning a critical skill.
Conclusion and Next Steps
The Java landscape is one of constant innovation, from the core platform enhancements discussed in OpenJDK news to the rich ecosystem of frameworks and tools. In this environment, performance tuning remains a non-trivial but essential discipline. Garbage Collection latency is a fundamental challenge, and tools that provide clear, actionable data are invaluable.
Amazon Corretto’s Heapothesys stands out as a significant contribution to the community. It empowers developers, SREs, and performance engineers to move beyond guesswork and make informed, data-driven decisions about their JVM and GC configurations. By simulating targeted workloads, comparing collectors, and integrating with standard profiling tools, you can demystify GC behavior and systematically improve the latency and reliability of your applications. We encourage you to clone the Heapothesys repository, run it against your target JVM distribution—whether it’s Corretto, Adoptium, or another—and start your journey toward mastering JVM performance.