The Evolution of Java Garbage Collection: A Comprehensive Guide to Modern GC Algorithms and…

This content originally appeared on Level Up Coding – Medium and was authored by Arpit Jindal

The Evolution of Java Garbage Collection: A Comprehensive Guide to Modern GC Algorithms and Optimization Strategies

Java’s automatic memory management has been one of its defining features since inception, freeing developers from the complexity of manual memory allocation and deallocation. However, as applications have grown in scale and performance requirements have become more demanding, the evolution of garbage collection algorithms has become critical to Java’s continued relevance in high-performance computing environments.

This comprehensive guide explores the journey of Java garbage collectors from the early days of Serial GC to today’s cutting-edge implementations like Generational ZGC, providing architects and senior developers with the insights needed to make informed decisions about GC selection and optimization for modern applications.

The Historical Evolution of Java Garbage Collectors

The story of Java garbage collection spans nearly three decades of continuous innovation and optimization. Understanding this evolution is crucial for appreciating why certain collectors exist and when they should be used.

The Foundation Era (Java 1.2 — Java 6)

• Serial Garbage Collector (1998) marked the beginning of Java’s GC journey. As a single-threaded, stop-the-world collector, Serial GC was designed for the computing constraints of the late 1990s. It uses a simple mark-and-sweep algorithm with compaction, making it predictable but unsuitable for multi-core systems.
• Concurrent Mark Sweep (CMS) Collector was introduced in Java 1.4 (2002) as the first attempt at reducing pause times. CMS performed most of its work concurrently with application threads, significantly reducing stop-the-world pauses. However, it suffered from fragmentation issues since it didn’t compact the heap during normal operations.
• Parallel Garbage Collector arrived with Java 5 (2004), leveraging multiple threads to speed up garbage collection on multi-core systems. It became the default collector for server-class machines starting from Java 5 through Java 8, focusing on maximizing throughput rather than minimizing pause times.

The Modern Era (Java 7 — Java 11)

• Garbage-First (G1) Collector was introduced experimentally in Java 6 and became fully supported in Java 7. G1 represented a paradigm shift with its region-based heap organization and ability to meet pause-time goals. It became the default garbage collector starting with Java 9, effectively replacing both Parallel GC and CMS for most use cases.
• Epsilon GC appeared in Java 11 as an experimental “no-op” garbage collector. While it doesn’t actually perform garbage collection, Epsilon serves important roles in performance testing and benchmarking.
• Z Garbage Collector (ZGC) made its debut as an experimental feature in Java 11. ZGC was designed from the ground up for ultra-low latency applications, promising pause times under 10 milliseconds regardless of heap size. It achieves this through the use of colored pointers and concurrent relocation techniques.

The Contemporary Era (Java 12 — Java 21)

• Shenandoah GC was introduced in OpenJDK 12, focusing on low pause times through concurrent compaction. Unlike G1, Shenandoah performs most of its collection work concurrently, including compaction, resulting in more consistent pause times.
• The deprecation and removal of CMS occurred in Java 9 and Java 14 respectively, as G1GC and newer collectors provided superior performance characteristics without CMS’s fragmentation problems.
• Generational ZGC was introduced in Java 21, combining ZGC’s ultra-low latency with generational collection efficiency. This represents the latest evolution in Java’s GC technology, offering both performance and efficiency improvements.

The Innovation Era (Java 22 — Java 24)

• Generational ZGC becomes production-ready in Java 22, moving beyond experimental status. This version refines the combination of ZGC’s ultra-low pause times with generational collection, offering even better throughput and memory efficiency, especially for applications with lots of short-lived objects.
• Shenandoah enhancements in Java 23 focus on improved scalability and reduced overhead when running on very large heaps. These improvements further solidify Shenandoah’s position as a strong choice for low-pause scenarios, especially in high-performance server environments.
• JEP 476: ZGC: Concurrent class unloading (Java 23) adds the ability for ZGC to unload unused classes concurrently, reducing stop-the-world phases even further and improving overall performance in applications with dynamic class loading.

Deep Dive into Garbage Collectors

Understanding the internal mechanisms of modern garbage collectors is essential for making informed architectural decisions and optimization strategies.

G1 Garbage Collector: The Balanced Approach

G1GC revolutionized Java garbage collection through its region-based heap organization. The heap is divided into equally-sized regions (typically 1MB to 32MB), with each region serving as either Eden, Survivor, or Old generation space.

• Key Internal Mechanisms:
  G1’s concurrent marking cycle operates in phases: Initial Mark, Concurrent Mark, Remark, and Cleanup. During the concurrent marking phase, G1 builds a snapshot of live objects while the application continues running. The evacuation process copies live objects from selected regions to new regions, achieving compaction without stopping the entire application.
• Performance Impact:
  G1’s predictable pause times come from its ability to incrementally collect regions based on garbage density. The collector maintains detailed statistics about each region’s occupancy and collection cost, allowing it to prioritize regions with the highest garbage-to-effort ratio.

ZGC: Ultra-Low Latency at Scale

ZGC’s architecture centers around colored pointers and load barriers. Unlike traditional collectors that use separate metadata structures, ZGC embeds object state information directly into 64-bit pointers using unused address bits.

• Key Internal Mechanisms:
  ZGC performs concurrent relocation through a sophisticated three-phase cycle: Mark, Relocation Set Selection, and Relocation. The load barrier mechanism ensures that whenever the application loads an object reference, it gets the most current forwarding information if the object has been relocated.
• Performance Impact:
  ZGC’s pause times are independent of heap size because most work occurs concurrently. The trade-off is higher CPU overhead due to load barriers and the need for more complex synchronization mechanisms. ZGC can handle heaps up to 16TB while maintaining sub-millisecond pause times.

Shenandoah: Concurrent Compaction Pioneer

Shenandoah’s innovation lies in concurrent compaction without relocation sets. It uses forwarding pointers and a technique called “Brooks forwarding” to enable live object movement while the application runs.

• Key Internal Mechanisms:
  Shenandoah operates in three concurrent phases: Mark, Concurrent Evacuation, and Concurrent Update References. The SATB (Snapshot-At-The-Beginning) marking ensures consistency during concurrent operations. Unlike ZGC, Shenandoah doesn’t use colored pointers but instead maintains forwarding information through object headers.
• Performance Impact:
  Shenandoah achieves consistent low pause times across different heap sizes, typically under 10 milliseconds. It generally offers better throughput than ZGC for CPU-bound workloads but may have slightly higher pause times than ZGC in extreme scenarios.

Performance Analysis and Comparison

Modern garbage collectors represent different trade-offs between throughput, latency, and resource utilization. Understanding these trade-offs is crucial for selecting the appropriate collector for specific use cases.

Throughput vs. Latency Trade-offs

• Parallel GC excels in throughput-oriented scenarios, achieving up to 98% application thread utilization. However, its stop-the-world approach can result in pause times exceeding 800 milliseconds for large heaps, making it unsuitable for latency-sensitive applications.
• G1GC strikes a balance between throughput and latency, typically achieving 96% throughput with pause times under 200 milliseconds. Its predictable pause time goals make it suitable for most enterprise applications with moderate latency requirements.
• ZGC and Shenandoah prioritize latency over throughput, with pause times consistently under 5 milliseconds. ZGC achieves sub-millisecond pauses but with slightly lower throughput (94%) and higher CPU usage (30%) compared to G1GC.

CPU and Memory Overhead Analysis

• Memory overhead varies significantly among collectors. G1GC typically requires 2–16% additional heap space for remembered sets and other metadata. ZGC’s colored pointers technique eliminates the need for separate metadata structures but requires 64-bit platforms and may use more native memory for internal structures.
• CPU overhead is highest in concurrent collectors. ZGC’s load barriers and Shenandoah’s concurrent compaction require approximately 5–10% additional CPU resources compared to stop-the-world collectors. However, this overhead often provides better overall system responsiveness.

Strategic GC Selection Guidelines

Choosing the right garbage collector requires careful consideration of application characteristics, performance requirements, and operational constraints.

Application Profile Analysis

• Memory allocation patterns significantly influence GC performance. Applications with high allocation rates and short-lived objects benefit from generational collectors like G1GC or Generational ZGC. Long-lived object applications may see better results with non-generational approaches.
• Heap size considerations are crucial for GC selection. For heaps under 4GB, G1GC typically provides optimal balance. Beyond 32GB, ZGC or Shenandoah become necessary to maintain acceptable pause times. Serial GC remains viable for small applications with heaps under 100MB.

Latency vs. Throughput Requirements

• Ultra-low latency applications such as high-frequency trading systems require ZGC’s sub-millisecond pause times. The additional CPU overhead is justified by the business value of consistent response times.
• Batch processing applications prioritize throughput over latency, making Parallel GC or G1GC optimal choices. The occasional long pause is acceptable in exchange for maximum processing efficiency.
• Interactive applications need balanced performance, where G1GC’s predictable pause times provide good user experience without excessive resource overhead.

Advanced Optimization Techniques and Examples

Effective garbage collection tuning requires systematic analysis and iterative refinement based on application-specific metrics and requirements.

G1GC Optimization Strategies

• Pause time tuning begins with setting appropriate goals using -XX:MaxGCPauseMillis. Starting with 200ms and adjusting based on actual performance is recommended. Monitor GC logs to ensure the target is consistently met without excessive throughput degradation.
• Region size optimization through -XX:G1HeapRegionSize can significantly impact performance. For applications with many large objects, increasing region size to 32MB may reduce GC overhead. Conversely, applications with predominantly small objects may benefit from smaller 1-8MB regions.

Example G1GC Configuration:

-XX:+UseG1GC
-XX:MaxGCPauseMillis=200
-XX:G1HeapRegionSize=16m
-XX:G1NewSizePercent=20
-XX:G1MaxNewSizePercent=40
-XX:InitiatingHeapOccupancyPercent=45

ZGC Optimization Approaches

• Generational ZGC configuration in Java 21 provides significant performance improvements. Enable both flags to use the latest implementation:

-XX:+UseZGC -XX:+ZGenerational

• Memory pre-touching with -XX:+AlwaysPreTouch reduces allocation latency by committing memory pages upfront. This is particularly beneficial for ZGC in containers where memory allocation delays can impact pause times.
• NUMA optimization using -XX:+UseNUMA improves performance on multi-socket systems by ensuring memory allocation locality.

Shenandoah Optimization Techniques

• Heuristics selection dramatically affects Shenandoah performance. The adaptive heuristics (-XX:ShenandoahGCHeuristics=adaptive) work well for most applications, while static heuristics may be better for predictable workloads.

Example Shenandoah Configuration:

-XX:+UseShenandoahGC
-XX:ShenandoahGCHeuristics=adaptive
-XX:+AlwaysPreTouch
-XX:+UseNUMA
-XX:-UseBiasedLocking

Future Outlook and Recommendations

The future of Java garbage collection continues to evolve with emerging technologies and changing application requirements.

Emerging Technologies

• Generational ZGC represents the current pinnacle of low-latency garbage collection. As it matures and becomes the default ZGC implementation, more applications will benefit from its combination of ultra-low latency and generational efficiency.
• Machine Learning Integration is beginning to influence GC optimization. Automated tuning systems that learn from application behavior patterns may reduce the manual effort required for optimal GC configuration.
• Project Loom Integration will require garbage collectors to handle the unique memory patterns of virtual threads efficiently. Future GC implementations will likely optimize for the high-volume, short-lived virtual thread allocation patterns.

Best Practices Summary

1. Start with G1GC for most new applications unless specific requirements dictate otherwise
2. Monitor application behavior before optimizing GC settings
3. Consider ZGC or Shenandoah for applications requiring consistent sub-10ms pause times
4. Test thoroughly when changing GC algorithms or tuning parameters
5. Measure actual impact rather than relying solely on benchmarks

The evolution of Java garbage collection reflects the platform’s commitment to performance and developer productivity. As applications continue to scale and latency requirements become more stringent, understanding these sophisticated memory management systems becomes increasingly critical for architects and senior developers building the next generation of Java applications.

Modern garbage collectors like G1GC, ZGC, and Shenandoah provide powerful tools for meeting diverse performance requirements. The key to success lies in understanding application characteristics, selecting appropriate collectors, and systematically optimizing based on measured performance data rather than theoretical assumptions.

References:

• https://dzone.com/articles/java-garbage-collectors-overview
• https://www.geeksforgeeks.org/java/garbage-collection-in-java/
• https://www.eginnovations.com/blog/what-is-garbage-collection-java/
• https://blogs.oracle.com/javamagazine/post/java-garbage-collectors-evolution

The Evolution of Java Garbage Collection: A Comprehensive Guide to Modern GC Algorithms and… was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.

This content originally appeared on Level Up Coding – Medium and was authored by Arpit Jindal