This article discusses how to optimize memory allocation in HashMaps by understanding the differences between allocation and mapping capacity. It emphasizes the importance of choosing the correct initial capacity, ideally a power of two, to mitigate space overhead and prevent performance degradation. Several formulas for calculating proper capacity are reviewed.
OutOfMemoryErrors in applications occur when memory allocation fails. This article discusses effective practices for analyzing such errors, including automatic heap dumps and using JVM parameters. It highlights script usage for automatic actions upon encountering errors and emphasizes the importance of proactive monitoring and automated incident reporting to reduce application downtime.
The annual retreat in Udaipur turned into a memorable surprise celebration for Mahesh, our Lead Engineer, who is getting married soon. Despite challenges in planning due to virtual traditions and scheduling mishaps, the team successfully organized a touching Haldi ceremony and video tribute, leaving Mahesh pleasantly astonished and grateful for the heartfelt gestures.
The Java Virtual Machine (JVM) performance training program offers engineers essential troubleshooting skills for production issues, significantly reducing resolution time. Participants learn to optimize CPU, memory, and response times through hands-on labs and expert instruction. This course has propelled many engineers into higher roles. Offered remotely, it includes bonus DevOps tools and valuable resources.
DevopsCon, held in Munich, featured a talk by our architect on “TOP 5 PRODUCTION PERFORMANCE PROBLEMS.” The session revealed common performance issues in production environments and offered insights on early detection and practical solutions to enhance operational performance and user experiences. Conference materials and photographs were shared.
DevopsCon, a global conference in Munich, featured a talk on "PREDICTING PRODUCTION OUTAGES: UNLEASHING THE POWER OF MICRO-METRICS." The session highlighted the limitations of macro-metrics in performance monitoring and introduced micro-metrics as effective lead indicators for predicting application performance, enabling early detection of potential outages.
Spring Boot is a popular framework for developing Java web applications that often encounter thread deadlocks, halting performance. This blog discusses deadlock causes, examples, and troubleshooting techniques using the yCrash tool. Solutions include timeout locks, utilizing concurrency utilities, and avoiding nested locks to mitigate deadlock issues effectively.
Micro-Metrics Monitoring (m3) by yCrash offers a non-intrusive approach to application performance monitoring, predicting performance issues before they emerge. By capturing detailed Micro-Metrics and utilizing Machine Learning for analysis, m3 enhances troubleshooting efficiency and root cause identification while integrating seamlessly with other tools, all at a cost-effective rate.
Shenandoah GC enhances Java applications by minimizing pause times through concurrent garbage collection and region-based memory management. It is ideal for low-latency and large heap applications, offering various tuning parameters and modes to optimize performance. Effective analysis of garbage collection logs allows developers to fine-tune Shenandoah for their specific needs.
Virtual Threads in Java, introduced in version 21, significantly enhance multi-threaded applications by reducing thread management overhead. This article explores their advantages over traditional platform threads through experimentation involving one million threads. Virtual Threads operate efficiently within heap memory, preventing OutOfMemoryError, and allow easier application scalability and performance improvements.