Introduction

Large-scale computing has evolved significantly over the past 20 years or so. For me, one of the most exciting areas is the convergence of modern software solutions, like big data and AI, with the dinosaur that won't die, the mainframe. Mainframes have always held a special place in my heart - my first university programs ran on an IBM 360 while I plugged away on a real IBM 3270 green screen terminal over a thousand miles away. On the other hand, I've also been a huge fan of Java since its introduction. Now, imagine the power of a Java application, like an Integration Server, running in a Linux partition on half a mainframe powered by 120 CPUs, 20TB of RAM, redundant flash secondary storage, and the rock-solid stability of Big Iron.

"Cache enormous data sets for immediate retrieval?" "No worries!"

"Process transaction volumes that would choke a normal server?" "Any time! Except now. Not now, I got to take out the garbage."

Large heaps have historically posed a challenge for Java GC. If only there were a garbage collector that could somehow manage multi-terabyte heaps and still maintain latency. Meet the Z Garbage Collector (ZGC), an ultra-low-latency garbage collector introduced in JDK 11 and significantly enhanced with the Generational ZGC in JDK 21. It is designed to handle extremely large heaps (up to 16TB) while maintaining pause times under 1 millisecond, regardless of heap size. Its advanced implementation combines concurrent algorithms, pointer coloring, and barrier mechanisms to achieve predictable performance in modern, latency-sensitive applications.

Let's dig into the technical implementation of ZGC, including its generational enhancements.

Key Goals and Characteristics

1. Ultra-Low Latency:
   • ZGC minimizes stop-the-world (STW) pauses to under 1 millisecond by performing most GC operations concurrently with application threads.
2. Scalability:
   • ZGC scales efficiently to terabyte-scale heaps, making it ideal for memory-intensive applications.
3. Concurrent Compaction:
   • ZGC performs concurrent heap compaction to eliminate memory fragmentation, ensuring efficient use of heap space.
4. Generational Design (JDK 21):
   • The Generational ZGC partitions the heap into young and old generations, optimizing memory reclamation for short-lived and long-lived objects, improving throughput and efficiency.

Core Concepts and Components

1. Pointer Coloring
   • ZGC uses 64-bit object pointers to encode metadata directly into the pointer itself. This is known as pointer coloring.
   • Metadata stored in the pointer includes:
     • Marking State: Tracks whether an object is live or garbage.
     • Relocation State: Tracks whether an object has been moved during compaction.
     • Generational State (JDK 21): Tracks whether an object belongs to the young or old generation.
   • Pointer coloring eliminates the need for separate data structures to track object states, reducing memory overhead and improving efficiency.
2. Load Barriers
   • ZGC uses load barriers to intercept all memory reads (object references) by the application.
   • The load barrier ensures the application only interacts with valid and up-to-date object references. If an object has been relocated (moved during compaction), the load barrier resolves the reference and updates it to the new location.
   • Load barriers are critical for enabling concurrent compaction and relocation.
3. Concurrent Phases
   • ZGC performs most of its GC operations concurrently with application threads, minimizing STW pauses. Its workflow consists of the following concurrent phases:
   • Concurrent Marking
     • Traverses the object graph starting from the root set (e.g., thread stacks, global references) to identify all live objects.
     • Uses pointer coloring to mark live objects without impacting application performance.
   • Concurrent Relocation (Compaction)
     • Moves live objects to new memory locations to compact the heap and reduce fragmentation.
     • Updates references to relocated objects using the load barrier.
   • Concurrent Reference Updates:
     • Updates all object references to point to their new locations after relocation.
     • This phase is aided by pointer coloring and the load barrier, ensuring correctness even as relocation occurs.
   • Concurrent Generational Handling (JDK 21):
     • The generational design introduces young and old generations:
       • Young Generation: Objects that are short-lived are collected more frequently and quickly.
       • Old Generation: Long-lived objects are collected less frequently.
     • This generational approach reduces the workload on the old generation, improving throughput efficiency.
4. Region-Based Heap Layout
   • The ZGC heap is divided into ZPages, which are fixed-size memory regions. ZPages come in three sizes:
     • Small (2MB): For regular objects.
     • Medium (32MB): For larger objects.
     • Large (1GB): For humongous objects.
   • Each ZPage is independently managed, allowing ZGC to focus on individual regions during collection, improving efficiency and scalability.
5. Thread-Local Allocation Buffers (TLABs)
   • ZGC uses Thread-Local Allocation Buffers (TLABs) to optimize object allocation.
   • Each thread allocates objects in its own buffer, reducing contention and improving performance.

Generational ZGC Enhancements (JDK 21)

The introduction of Generational ZGC in JDK 21 adds a generational model to the previously non-generational ZGC. This enhancement brings several benefits:

1. Young Generation:

   • Newly allocated objects are placed in the young generation, which is collected more frequently.
   • Since most objects are short-lived, this reduces the workload on the old generation and improves throughput.

2. Old Generation:

   • Long-lived objects are promoted to the old generation, which is collected less frequently.
   • This separation reduces the need for global marking cycles over the entire heap.

3. Generational Barriers:

   • ZGC uses write barriers to track references between generations, ensuring efficient collection and promotion of objects from the young to the old generation.

4. Improved Throughput:

   • By focusing on short-lived objects in the young generation, Generational ZGC reduces the frequency of full heap scans, improving CPU and memory efficiency.

Key Phases of ZGC (Generational)

1. Young Generation Collection:

   • Triggers when the young generation is full.
   • Collects short-lived objects quickly and efficiently.
   • Surviving objects are promoted to the old generation.

2. Old Generation Collection:

   • Triggers when the old generation reaches a certain threshold.
   • Uses concurrent marking and compaction to reclaim memory without impacting application performance.

3. Global Marking (Full Heap):

   • Occurs less frequently than in non-generational ZGC.
   • Traverses the entire heap to identify live objects, typically during old generation collection.

Performance Features

1. Ultra-Low Latency:

   • ZGC delivers sub-millisecond pauses, even for terabyte-scale heaps, by performing most operations concurrently.

2. Scalability:

   • ZGC scales efficiently to heaps ranging from a few gigabytes to 16TB, making it ideal for large-scale applications.

3. Fragmentation Handling:

   • ZGC's concurrent compaction eliminates fragmentation, ensuring efficient memory utilization.

4. Improved Throughput (Generational Model):

   • The generational design optimizes memory reclamation for short-lived objects, reducing the workload on the old generation and improving overall throughput.

Tuning Parameters for ZGC

1. -XX:+UseZGC: Enables ZGC.
2. -XX:SoftMaxHeapSize=<size>: Allows ZGC to dynamically adjust heap usage.
3. -XX:ZUncommitDelay=<time>: Configures the delay for releasing unused memory back to the operating system.
4. -XX:MaxHeapSize=<size>: Sets the maximum heap size.
5. -XX:ZProactive: Enables proactive GC cycles to avoid heap pressure.

Advantages of ZGC

1. Sub-Millisecond Pause Times:
   • Ideal for latency-critical applications, such as financial systems, gaming, and real-time analytics.
2. Scalability:
   • Handles heaps up to 16TB efficiently.
3. Generational Model:
   • Improves throughput while retaining ultra-low latency.
4. Concurrent Compaction:
   • Prevents fragmentation without STW pauses.

Limitations of ZGC

1. Memory Overhead:
   • Pointer coloring adds metadata, increasing memory requirements slightly.
2. Higher CPU Usage:
   • Concurrent operations consume additional CPU cycles, which may impact throughput in CPU-constrained environments.

Use Cases

• Real-Time Systems: Applications requiring ultra-low latency (<1ms), such as trading systems and gaming.
• Large Heaps: Memory-intensive workloads with terabyte-scale heaps, such as in-memory databases and big data applications.
• Mixed Latency and Throughput: Applications requiring a balance of low latency and improved throughput, such as microservices and web servers.

Conclusion

ZGC's technical implementation, bolstered by its generational model, makes it one of the most advanced garbage collectors available. It combines ultra-low latency, scalability, and improved throughput to support modern, large-scale, and latency-sensitive applications. With its ability to handle both young and old generations effectively, Generational ZGC is a state-of-the-art solution for diverse Java workloads. It's ability to manage massive heaps with sub-millisecond GC pauses is heretofore unheard of in the Java world. I can't wait to see what comes next.

Until next time, happy integrating!