Amazon Corretto Unveils Heapothesys: A Deep Dive into JVM GC Latency Benchmarking for Modern Java Applications

Introduction

In the world of high-performance Java applications, garbage collection (GC) is often the silent performance killer. While the JVM’s automatic memory management is a cornerstone of Java’s productivity, the pauses it introduces can wreak havoc on latency-sensitive services, impacting user experience and threatening service level objectives (SLOs). As the Java ecosystem continues to evolve with new releases and performance enhancements, understanding and tuning GC behavior has become more critical than ever. This is a central theme in recent Amazon Corretto news and the broader JVM news landscape.

To address this challenge, the Amazon Corretto team, a major contributor to the OpenJDK project, has released Heapothesys—an open-source GC latency benchmark. Unlike synthetic microbenchmarks that often fail to represent real-world conditions, Heapothesys is a collection of workloads designed specifically for application developers. It provides a standardized and realistic way to measure, compare, and understand the latency characteristics of different GC algorithms under various memory allocation patterns. This article provides a comprehensive technical deep dive into Heapothesys, exploring how you can leverage it to demystify GC performance and make informed tuning decisions for your modern Java applications.

Section 1: The Core Challenge of GC Latency and the Role of Heapothesys

Before diving into the practical aspects of Heapothesys, it’s essential to understand the problem it aims to solve. Garbage collection latency, or “pause time,” refers to the moments when an application’s execution is halted so the JVM can reclaim memory. For many applications, these pauses are negligible, but for interactive services, financial trading platforms, or real-time data processing systems, even millisecond-level delays can be catastrophic.

Why GC Latency is a Critical Metric

GC pauses can be broadly categorized into “stop-the-world” (STW) events, where all application threads are frozen, and concurrent phases, which run alongside the application. The primary goal of modern garbage collectors is to minimize the duration and frequency of these STW pauses. High latency can lead to:

• Poor User Experience: Slow API responses and UI freezes.
• SLA Breaches: Failing to meet response time guarantees.
• Cascading Failures: A paused service can cause timeouts and back-pressure in downstream services in a microservices architecture.

The continuous evolution seen in Java 11 news, Java 17 news, and now Java 21 news has brought several advanced garbage collectors like G1, ZGC, and Shenandoah to the forefront, each offering different trade-offs between latency, throughput, and memory footprint. Choosing the right one is not a one-size-fits-all decision; it depends entirely on your application’s specific memory allocation behavior.

Introducing Heapothesys: A Developer-Centric Benchmark

Heapothesys enters the scene as a tool to bridge the gap between GC theory and application reality. It simulates various memory allocation and object survival patterns, known as “workloads,” that mimic different types of real-world applications. By running these workloads against different GC algorithms, developers can gather empirical data on how each collector will likely perform for their specific use case. This is a significant piece of Java performance news, as it empowers developers to move beyond guesswork and make data-driven tuning decisions.

For example, you can simulate an application with a large, stable set of long-lived objects (like a cache) or an application with a high rate of short-lived “churn” objects (like a stateless request-response service). This practical approach is what makes Heapothesys a valuable addition to the Java ecosystem news.

# A conceptual preview of running a workload
# We will cover this in detail in the next section
java -Xmx2g -XX:+UseG1GC -jar heapothesys.jar --workload churn --duration 60s

Section 2: Getting Hands-On with Heapothesys

Heapothesys is designed to be straightforward to set up and run. It is built with Maven, making it easy to integrate into any standard Java development workflow. This section will guide you through cloning, building, and running your first benchmark.

Setting Up Your Benchmarking Environment

First, you need to clone the official Heapothesys repository from GitHub and build the project. You’ll need a JDK (like Amazon Corretto 17) and Maven installed.

# 1. Clone the repository
git clone https://github.com/corretto/heapothesys.git

# 2. Navigate into the project directory
cd heapothesys

# 3. Build the project using the Maven wrapper
# This will download dependencies and compile the source code
./mvnw clean install

Once the build is successful, you will find the executable JAR file in the target/ directory. This JAR contains all the workloads and the benchmarking harness, ready to be executed.

Running a Basic Benchmark and Analyzing Results

Running a benchmark involves invoking the JAR file with standard Java command-line flags to configure the JVM (heap size, GC algorithm) and Heapothesys-specific arguments to configure the workload.

Let’s run the mid-live-set workload, which simulates an application with a moderately sized set of long-lived objects, using the G1 Garbage Collector. We’ll set a 2GB heap, run the test for 60 seconds, and log the GC activity to a file for later analysis.

# Run Heapothesys with G1GC on a 2GB heap
# --workload: Specifies the memory allocation pattern to simulate
# --duration: How long the benchmark should run
# --allocation-rate: The rate at which new objects are created
# -Xlog:gc*:...: Standard JVM flag to log GC events
java -Xms2g -Xmx2g \
     -XX:+UseG1GC \
     -Xlog:gc*:file=g1_mid-live-set.log \
     -jar target/heapothesys.jar \
     --workload mid-live-set \
     --duration 60s \
     --allocation-rate 256m

While the benchmark is running, Heapothesys will print summary statistics to the console, including key latency percentiles (p50, p90, p99, max). However, the most valuable data is in the GC log file (g1_mid-live-set.log). You can use tools like GCViewer or online analyzers to parse this log and visualize pause times, heap usage, and other critical metrics. This analysis allows you to answer questions like: “What was my application’s longest pause time?” and “How much time was spent in GC overall?” This kind of detailed insight is invaluable and a core theme in recent OpenJDK news.

Section 3: Advanced Workloads and Comparative Analysis

The true power of Heapothesys lies in its ability to compare different GC configurations under workloads that closely match your application’s profile. This allows you to test hypotheses and validate tuning choices before deploying them to production.

Comparing G1, ZGC, and Shenandoah

Modern Java versions offer powerful low-latency garbage collectors like ZGC and Shenandoah. Heapothesys is the perfect tool for comparing them against the default G1GC. Let’s design an experiment to test the churn workload, which simulates an application with a high rate of object allocation and death—a common pattern in web services and applications built with frameworks like Spring Boot.

We’ll run the same workload three times, each with a different GC, and direct the logs to separate files for comparison. This is highly relevant for anyone following Java 17 news or Java 21 news, as these collectors have reached full maturity in recent releases.

# Experiment Parameters
HEAP_SIZE="4g"
ALLOC_RATE="512m"
DURATION="120s"
WORKLOAD="churn"
JAR_PATH="target/heapothesys.jar"

# Test 1: G1 GC (The default)
echo "Running with G1 GC..."
java -Xmx$HEAP_SIZE -XX:+UseG1GC -Xlog:gc*:file=g1_$WORKLOAD.log \
     -jar $JAR_PATH --workload $WORKLOAD --duration $DURATION --allocation-rate $ALLOC_RATE

# Test 2: ZGC (Optimized for ultra-low latency)
echo "Running with ZGC..."
java -Xmx$HEAP_SIZE -XX:+UseZGC -Xlog:gc*:file=zgc_$WORKLOAD.log \
     -jar $JAR_PATH --workload $WORKLOAD --duration $DURATION --allocation-rate $ALLOC_RATE

# Test 3: Shenandoah GC (Another excellent low-latency option)
echo "Running with Shenandoah GC..."
java -Xmx$HEAP_SIZE -XX:+UseShenandoahGC -Xlog:gc*:file=shenandoah_$WORKLOAD.log \
     -jar $JAR_PATH --workload $WORKLOAD --duration $DURATION --allocation-rate $ALLOC_RATE

After running these commands, you can analyze the three log files. You will likely observe that for a high-churn workload, ZGC and Shenandoah deliver significantly lower maximum pause times and better p99 latencies compared to G1, albeit sometimes at the cost of slightly lower overall throughput. This is exactly the kind of trade-off Heapothesys helps you quantify.

Extending Heapothesys with Custom Workloads

While the built-in workloads cover many common scenarios, you can also implement your own to perfectly mirror your application’s unique memory behavior. This is done by implementing the Workload interface. This extensibility is a testament to the tool’s thoughtful design and is great news for the broader Java ecosystem.

Here is a simplified skeleton of what a custom workload implementation looks like in Java. You would compile this into a JAR and add it to the classpath when running Heapothesys.

package com.mycompany.workloads;

import com.amazon.corretto.heapothesys.workload.Workload;
import com.amazon.corretto.heapothesys.object_layout.ObjectLayout;

import java.util.LinkedList;
import java.util.List;

// A custom workload simulating a session cache
public class SessionCacheWorkload implements Workload {
    private List<byte[]> sessionData;
    private static final int SESSION_SIZE = 4 * 1024; // 4 KB sessions
    private static final int CACHE_CAPACITY = 10000;

    @Override
    public void init(long heapSize, ObjectLayout layout) {
        this.sessionData = new LinkedList<>();
        System.out.println("Initializing SessionCacheWorkload...");
    }

    @Override
    public void run() {
        // Simulate adding a new session
        sessionData.add(new byte[SESSION_SIZE]);

        // Simulate session expiry by removing the oldest if cache is full
        if (sessionData.size() > CACHE_CAPACITY) {
            sessionData.remove(0);
        }
    }

    @Override
    public String getName() {
        return "SessionCacheWorkload";
    }
}

This level of customization allows you to create highly accurate performance tests, a topic of great interest in discussions around Spring Boot news and high-performance microservices.

Section 4: Best Practices and Optimization Strategies

To get meaningful results from Heapothesys or any benchmark, it’s crucial to follow established best practices. Garbage collection performance is a complex domain, and small mistakes in methodology can lead to misleading conclusions.

Benchmarking Best Practices

• Use a Dedicated Environment: Never benchmark on your development machine, which is subject to unpredictable background processes. Use a server with a hardware and OS configuration as close to production as possible.
• Warm Up the JVM: The JVM performs numerous optimizations during the initial phase of execution (like JIT compilation). Your measurements should only begin after a sufficient warm-up period to ensure you’re testing steady-state performance. Heapothesys runs have a built-in warm-up phase.
• Run for Sufficient Duration: Short runs may not trigger enough GC cycles to be representative. A run of at least 2-5 minutes is recommended, and longer for complex workloads.
• Control Variables: When comparing GCs, ensure all other variables—heap size, allocation rate, JDK version, and hardware—remain identical.
• Repeat and Aggregate: Run each benchmark multiple times to account for performance variability and ensure your results are repeatable.

Translating Findings into Production Tuning

The insights gained from Heapothesys are not just academic. They directly inform the JVM flags you use in production. For instance, if your service is an API gateway where low latency is paramount, and your Heapothesys results show ZGC performs best for your churn-like workload, you can confidently choose it for production.

This is especially relevant as the community discusses Project Loom news. The introduction of millions of virtual threads will likely create novel memory allocation patterns. Tools like Heapothesys will be indispensable for understanding how GCs handle the massive number of small, short-lived stack chunks associated with Java virtual threads.

Your final JVM configuration might look something like this, tailored based on your benchmark findings:

# Production flags for a Latency-Sensitive Service (based on ZGC winning the benchmark)
-server -Xms8g -Xmx8g -XX:+UseZGC -XX:+ZGenerational -XX:SoftMaxHeapSize=6g -Dspring.profiles.active=production

# Production flags for a Throughput-Oriented Batch Job (based on G1 winning the benchmark)
-server -Xms8g -Xmx8g -XX:+UseG1GC -XX:MaxGCPauseMillis=250 -Dspring.profiles.active=production

Conclusion

The release of Heapothesys by the Amazon Corretto team is a significant event in the Java performance news cycle. It provides a powerful, accessible, and developer-focused tool for navigating the complex world of JVM garbage collection. By simulating realistic application workloads, it empowers developers to move beyond folklore and make data-driven decisions about GC selection and tuning. It allows teams to quantify the trade-offs between different collectors, validate the impact of migrating to new Java versions, and prepare for future JVM innovations like those from Project Loom and Project Valhalla.

As a next step, clone the Heapothesys repository, identify the built-in workload that most closely matches your application’s profile, and run a comparative benchmark of G1, ZGC, and Shenandoah. The insights you gain will be invaluable for building more responsive, reliable, and performant Java applications. This tool is a must-have for any serious Java developer concerned with application performance.