A major insurance company improved application performance by tuning Garbage Collection (GC). They identified consecutive Full GC events as the bottleneck and explored three solutions. By increasing the heap size and adjusting GC settings, they significantly enhanced GC efficiency, resulting in a 23% increase in application throughput, a 15% reduction in response time, and a 50% decrease in CPU usage.
GC logs are vital for reducing GC pause time, predicting memory issues, and improving application response time. Enable GC logs with specific settings, avoid log file rotation, and monitor logs regularly on all production JVMs to gain valuable insights for performance improvement.
Tuning Garbage Collection (GC) is essential for improving application performance. Important metrics include GC latency, throughput, memory size, and CPU usage. By analyzing these metrics, applications can become more efficient. Optimizing GC settings involves trade-offs, like balancing low latency with higher CPU usage. Understanding and adjusting these metrics in line with application performance results in better-performing systems.
Garbage Collection (GC) analysis is essential for application performance. Follow three steps: capture the GC log, use analysis tools, and study key metrics. Enable GC logging with specific settings. Use GC log analysis tools to examine key metrics and resolve memory and GC problems for better performance. Effective GC analysis can make you a hero in your organization.
As a Java engineer, picking the right GC algorithm is key for application performance. Options include Serial, Parallel, CMS (deprecated), G1, Shenandoah, ZGC, and Epsilon. Each one has unique features and suits different situations. Use a flowchart to help choose the best algorithm based on your performance goals and heap size. Always conduct thorough performance testing before making a switch.
This is an investigative piece on the performance of string deduplication in different versions of Java. The investigation compared Java versions 11, 17, and 21 and their ability to remove duplicate strings. It utilized a WebCrawler application and JMeter load testing to gather data. The findings revealed that Java 11 outperformed versions 17 and 21, eliminating 34.3% of duplicates in 1,264.442 milliseconds. However, newer versions showed a decline in performance, deduplicating fewer strings over longer periods of time.
Node.js applications may suffer from unresponsiveness due to long Garbage Collection pauses or memory leaks. Enabling GC traces with '--trace-gc' helps monitor memory usage and potential bottlenecks. This article details on how certain tools can provide graphs and metrics for easy interpretation, aiding in optimizing performance and memory management.
The post discusses optimizing a Java application used for controlling warehouse robots, which faced performance issues due to long Garbage Collection (GC) pauses. By analyzing the GC log, it identified a large heap size and the CMS GC algorithm as culprits. Switching to the G1 GC algorithm reduced GC pauses significantly, enhancing application performance without major structural changes.
Sridhar Vembu, CEO of Zoho, inspires many with his success story in building a major SaaS business. He emphasizes the financial benefits of optimizing automatic garbage collection, which can save companies billions annually by reducing application pause times that hinder performance and inflate cloud costs, as shown by successes at Uber and an automobile company.