CloudBees, the leader in Jenkins CI/CD pipeline, has enhanced garbage collection performance utilizing GCeasy. Ryan Smith, a Sr. DevOps Engineer at CloudBees, shares insights from his experience optimizing garbage collection performance, highlighting the significance of these improvements for the industry.
Uber is facing significant performance issues due to increased traffic, including memory bottlenecks like long garbage collection pauses and memory leaks. Their engineering team shared insights on optimization strategies, employing tools like GCeasy to address problems. Notably, they identified a misconfigured thread causing excessive object creation through extensive manual analysis, highlighting the complexity of managing numerous threads.
Dsquare is a specialized FX trading boutique excelling in short-term trading, leveraging algorithmic models in the foreign exchange market with daily volumes over $3 trillion. Jad Sarmo discusses the development of their high-performance, low-latency Java trading application, addressing challenges and optimizations, including the use of GCeasy for garbage collection performance.
A major automobile manufacturer significantly enhanced its middleware platform by optimizing garbage collection performance, achieving a 49.46% reduction in overall response time from 1.88 seconds to 0.95 seconds. This improvement was attained solely through adjustments to garbage collection settings, eliminating the need for code refactoring or costly changes.
Uber successfully saved 70,000 cores across 30 critical services by optimizing Go's Garbage Collection (GC) through a semi-automated tuning mechanism. This effort resulted in significant CPU utilization improvements, notably 65% in observability services and 30% in Uber Eats. GC tuning is essential for efficient resource management and application performance.
The article discusses the impact of network delays on application performance and presents a method to simulate network lag using BuggyApp as a proxy server. It provides a sample program to introduce delays in server responses and explains manual and automated approaches to diagnose network issues. The article highlights the importance of addressing network performance problems in chaos engineering.
Discover how object pointers can enhance performance in Java programs. Learn about the JVM's object representation in memory and its impact on application performance.
This post helps you troubleshoot JVM deadlock issues in ServiceNow MID server environments. It also explains the causes of deadlocks and demonstrates how to simulate them using a sample Java program. It concludes by stressing the need to monitor and solve deadlocks for better performance.
This article discusses intentionally filling up disk space to test system stability. A script fills disk space by writing data to a file. The Boomi process runs successfully, but the operating system reports a critical disk space shortage, prompting system warnings. It also highlights how the issue was identified and emphasizes the importance of monitoring the system.
This article talks about how the JVM stores objects in memory. It explains how alignment, padding, and memory usage are related. It also talks about how the alignment can be adjusted and those changes affect the performance, highlighting how managing memory and object sizes can be tricky.