[Oracle Dev Days 2025] Optimizing Java Performance: Choosing the Right Garbage Collector
Jean-Philippe BEMPEL , a seasoned developer at Datadog and a Java Champion, delivered an insightful presentation on selecting and tuning Garbage Collectors (GCs) in OpenJDK to enhance Java application performance. His talk, rooted in practical expertise, unraveled the complexities of GCs, offering a roadmap for developers to align their choices with specific application needs. By dissecting the characteristics of various GCs and their suitability for different workloads, Jean-Philippe provided actionable strategies to optimize memory management, reduce production issues, and boost efficiency.
Understanding Garbage Collectors in OpenJDK
Garbage Collectors are pivotal in Java’s memory management, silently handling memory allocation and reclamation. However, as Jean-Philippe emphasized, a misconfigured GC can lead to significant performance bottlenecks in production environments. OpenJDK offers a suite of GCs—Serial GC, Parallel GC, G1, Shenandoah, and ZGC—each designed with distinct characteristics to cater to diverse application requirements. The challenge lies in selecting the one that best matches the workload, whether it prioritizes throughput or low latency.
Jean-Philippe began by outlining the foundational concepts of GCs, particularly the generational model. Most GCs in OpenJDK are generational, dividing memory into the Young Generation (for short-lived objects) and the Old Generation (for longer-lived objects). The Young Generation is further segmented into the Eden space, where new objects are allocated, and Survivor spaces, which hold objects that survive initial collections before promotion to the Old Generation. Additionally, the Metaspace stores class metadata, a critical but often overlooked component of memory management.
Serial GC: Simplicity for Constrained Environments
The Serial GC, one of the oldest collectors, operates with a single thread and employs a stop-the-world approach, pausing all application threads during collection. Jean-Philippe highlighted its suitability for small-scale applications, particularly those running in containers with less than 2 GB of RAM, where it serves as the default GC. Its simplicity makes it ideal for environments with limited resources, but its stop-the-world nature can introduce noticeable pauses, making it less suitable for latency-sensitive applications.
To illustrate, Jean-Philippe explained the mechanics of the Young Generation’s Survivor spaces. These spaces, S0 and S1, alternate roles as source and destination during minor GC cycles, copying live objects to manage memory efficiently. Objects surviving multiple cycles are promoted to the Old Generation, reducing the overhead of frequent collections. This generational approach leverages the hypothesis that most objects die young, minimizing the cost of memory reclamation.
Parallel GC: Maximizing Throughput
For applications prioritizing throughput, such as batch processing jobs, the Parallel GC offers significant advantages. Unlike the Serial GC, it leverages multiple threads to reclaim memory, making it efficient for systems with ample CPU cores. Jean-Philippe noted that it was the default GC until JDK 8 and remains a strong choice for throughput-oriented workloads like Spark jobs, Kafka consumers, or ETL processes.
The Parallel GC, also stop-the-world, excels in scenarios where total execution time matters more than individual pause durations. Jean-Philippe shared a benchmark using a JFR (Java Flight Recorder) file parsing application, where Parallel GC outperformed others, achieving a throughput of 97% (time spent in application versus GC). By tuning the Young Generation size to reduce frequent minor GCs, developers can further minimize object copying, enhancing overall performance.
G1 GC: Balancing Throughput and Latency
The G1 (Garbage-First) GC, default since JDK 9 for heaps larger than 2 GB, strikes a balance between throughput and latency. Jean-Philippe described its region-based memory management, dividing the heap into smaller regions (Eden, Survivor, Old, and Humongous for large objects). This structure allows G1 to focus on regions with the most garbage, optimizing memory reclamation with minimal copying.
In his benchmark, G1 showed a throughput of 85%, with average pause times of 76 milliseconds, aligning with its target of 200 milliseconds. However, Jean-Philippe pointed out challenges with Humongous objects, which can increase GC frequency if not managed properly. By adjusting region sizes (up to 32 MB), developers can mitigate these issues, improving throughput for applications like batch jobs while maintaining reasonable pause times.
Shenandoah and ZGC: Prioritizing Low Latency
For latency-sensitive applications, such as HTTP servers or microservices, Shenandoah and ZGC are the go-to choices. These concurrent GCs minimize pause times, often below a millisecond, by performing most operations alongside the running application. Jean-Philippe highlighted Shenandoah’s non-generational approach (though a generational version is in development) and ZGC’s generational support since JDK 21, making the latter particularly efficient for large heaps.
In a latency-focused benchmark using a Spring PetClinic application, Jean-Philippe demonstrated that Shenandoah and ZGC maintained request latencies below 200 milliseconds, significantly outperforming Parallel GC’s 450 milliseconds at the 99th percentile. ZGC’s use of colored pointers and load/store barriers ensures rapid memory reclamation, allowing regions to be freed early in the GC cycle, a key advantage over Shenandoah.
Tuning Strategies for Optimal Performance
Tuning GCs is as critical as selecting the right one. For Parallel GC, Jean-Philippe recommended sizing the Young Generation to reduce the frequency of minor GCs, ideally exceeding 50% of the heap to minimize object copying. For G1, adjusting region sizes can address Humongous object issues, while setting a maximum pause time target (e.g., 50 milliseconds) can shift its behavior toward latency sensitivity, though it may not compete with Shenandoah or ZGC in extreme cases.
For concurrent GCs like Shenandoah and ZGC, ensuring sufficient heap size and CPU cores prevents allocation stalls, where threads wait for memory to be freed. Jean-Philippe emphasized that Shenandoah requires careful heap sizing to avoid full GCs, while ZGC’s rapid region reclamation reduces such risks, making it more forgiving for high-allocation-rate applications.
Selecting the Right GC for Your Workload
Jean-Philippe concluded by categorizing workloads into two types: throughput-oriented (SPOT) and latency-sensitive. For SPOT workloads, such as batch jobs or ETL processes, Parallel GC or G1 are optimal, with Parallel GC offering easier tuning for predictable performance. For latency-sensitive applications, like microservices or databases (e.g., Cassandra), ZGC’s generational efficiency and Shenandoah’s low-pause capabilities shine, with ZGC being particularly effective for large heaps.
By analyzing workload characteristics and leveraging tools like GC Easy for log analysis, developers can make informed GC choices. Jean-Philippe’s benchmarks underscored the importance of tailoring GC configurations to specific use cases, ensuring both performance and stability in production environments.
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