**This article is the second installment in our 3-part series on "Optimizing Java Performance: A Deep Dive into Memory Management" written by our Software Developer, Katsiaryna Auchynnikava.
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Java continues to be a cornerstone in enterprise-level and large-scale application development, and is one of the core technologies used within our application. Achieving optimal performance and efficient memory management remains crucial as users expect applications to be fast, responsive, and reliable. Performance optimization and memory management are not just about making applications run faster; they are about ensuring stability, reducing costs, and improving the overall user experience.
Introduction
In our previous blog, we explored the Java memory model and various garbage collection mechanisms. Today, we will focus on the key metrics for garbage collection: throughput, latency, and footprint. These metrics are crucial for understanding the performance of your Java applications and making informed decisions on JVM tuning.
Throughput
It is the ratio of the time spent by the JVM running application code to the total time spent by the JVM, including both application execution and garbage collection. Formula:
High throughput indicates that the application is running efficiently with minimal interruption from garbage collection. Optimizing for throughput ensures that the CPU and other resources are primarily focused on executing application logic rather than managing memory. While high throughput is desirable, it must be balanced with other metrics like latency and footprint. For instance, maximizing throughput may involve longer, less frequent garbage collection cycles, which could increase pause times (latency).
Different garbage collectors (e.g., Serial, Parallel, G1, ZGC) have varying impacts on throughput. The Parallel GC, for instance, is designed to maximize throughput. Properly tuning heap size and garbage collector settings can optimize throughput. For example:
- Parallel GC:
- G1 GC:
Throughput is a crucial metric in Java garbage collection that reflects the efficiency of the JVM in running application code versus performing garbage collection. By understanding and optimizing throughput, developers can ensure that their Java applications run smoothly and efficiently, balancing the needs of application responsiveness and resource utilization.
Latency
Latency is a vital performance metric for evaluating the performance of Java applications, particularly those that require high responsiveness and low delay. It measures the time taken for specific operations or events, such as garbage collection pauses, to complete. In the context of garbage collection (GC), latency specifically refers to the duration of stop-the-world (STW) pauses, where the application is temporarily halted to perform memory management tasks.
In latency-sensitive applications, such as real-time systems, financial trading platforms, gaming, and interactive user interfaces, even short delays can negatively impact user experience and system performance. Low latency is essential for maintaining smooth and responsive operations.
Low latency is essential for maintaining high responsiveness and a good user experience. Low latency is crucial for applications that require fast response times, such as real-time systems, interactive applications, and online transaction processing systems. For backend systems and services, high latency can impact the overall performance, leading to slower transaction processing and reduced throughput.
Different garbage collectors impact latency differently:
- Serial GC: May cause long pauses due to single-threaded collection
- Parallel GC: Uses multiple threads, reducing pause times compared to Serial GC but still can have significant pauses
- CMS (Concurrent Mark-Sweep): Minimizes pause times by performing most work concurrently but may suffer from fragmentation
- G1 GC: Aims for predictable, short pauses by focusing on specific regions
- ZGC and Shenandoah GC: Designed for ultra-low pause times, performing most tasks concurrently. ZGC and Shenandoah GC are optimized for minimal pause times and designed for low latency.
The size and configuration of the heap can significantly influence latency. Larger heaps may cause longer GC pauses if not managed efficiently. Proper tuning of initial and maximum heap sizes is crucial. Choose appropriate initial and maximum heap sizes to avoid frequent GCs and reduce pause times: -Xms<size> -Xmx<size>.
Latency is an important metric for assessing the performance of Java applications, particularly those that demand high responsiveness. By understanding and optimizing latency through careful selection of garbage collectors, tuning GC parameters, configuring heap sizes, and implementing application-level optimizations, developers can significantly improve the responsiveness and overall performance of their Java applications. Tools such as JVisualVM, YourKit, and JProfiler help visualize and analyze GC pauses, offering insights into latency and identifying performance bottlenecks.
Footprint
The footprint metric is a critical aspect of Java memory performance, referring to the amount of memory used by a Java application during its execution. It encompasses the memory occupied by the Java heap, native memory, and other data structures required by the Java Virtual Machine (JVM). The footprint metric measures the total memory usage of a Java application, including the Java heap, native memory for JVM internals, thread stacks, and other data structures. It indicates the application's memory consumption and is crucial for understanding its resource requirements. A smaller memory footprint is desirable, especially for applications running in environments with limited resources, such as embedded systems or cloud-based instances. Reducing the memory footprint can lead to more efficient resource utilization, lower costs, and improved performance.
Optimizing Footprint
- Heap Size Tuning: set appropriate initial and maximum heap sizes to balance memory usage and application performance:
- Choose the Right Garbage Collector: select a garbage collector that provides the best balance between footprint and performance for your application. For example, the G1 GC or Shenandoah GC may offer better footprint management for certain workloads.
- Class Metadata Management: Use the -XX:MetaspaceSize=<size> and -XX:MaxMetaspaceSize=<size> options to tune the metaspace, which stores class metadata. Properly configuring these options can help control the footprint related to class loading.
- Thread Stack Size: Optimize the thread stack size using the -Xss<size> option to reduce memory usage without causing stack overflow errors.
- Reducing Object Allocation: Optimize your application code to reduce unnecessary object allocation and improve memory reuse. This can help lower the overall memory footprint by decreasing the demand on the heap.
The footprint metric is essential for understanding and optimizing the memory performance of Java applications. By carefully tuning heap sizes, selecting the appropriate garbage collector, managing class metadata, and optimizing thread stack sizes, developers can reduce the memory footprint and improve the efficiency and performance of their applications. Tools like JVisualVM, YourKit, and JProfiler can help measure and analyze the memory footprint of a Java application. These tools provide insights into heap usage, thread memory, and native memory consumption.
In our last blog of the series, we will explore code-level optimization techniques, including efficient data structures, minimizing object creation, and optimizing loops and iterations. These practices will help you write more performant Java code and further improve your application's memory management.
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REFERENCES
1. Java Performance: The Definitive Guide by Scott Oaks
2. Performance Considerations - Oracle Documentation https://docs.oracle.com/en/graalvm/jdk/21/docs/reference-manual/native-image/optimizations-and-performance/MemoryManagement/#performance-considerations
3. Memory Management in the Java HotSpot Virtual Machine - Oracle Documentation - https://www.oracle.com/technetwork/java/javase/memorymanagement-whitepaper-150215.pdf
4. Memory-Related Metrics - Broadcom - https://techdocs.broadcom.com/us/en/ca-enterprise-software/it-operations-management/application-performance-management/21-3/using/apm-metrics/memory-related-metrics.html