This post examines the performance characteristics of HashMap, Hashtable, and ConcurrentHashMap through practical examples. It finds that while HashMap is fastest, it is not thread-safe, risking infinite loops. ConcurrentHashMap offers comparable speed with thread safety. Hashtable, however, is significantly slower due to synchronization limiting thread access.
The java.lang.ClassLoader#loadClass() API is essential for loading Java classes but can degrade application performance due to thread contention. Frequent calls can cause threads to block, impacting responsiveness. To mitigate these issues, it's advised to load classes once at startup and reuse them, rather than repeatedly during execution.
yCrash addresses the limitations of traditional Application Performance Monitoring tools by providing insights into the root causes of problems. It captures and analyzes various technology stack artifacts like logs and dumps. NaveenKumar Namachivayam offers a video series to guide users in learning to utilize yCrash effectively.
This post discusses the advantages of setting the initial heap size equal to the maximum heap size for Java applications. Benefits include improved availability, performance, and application startup time while maintaining cost-effectiveness in cloud environments. It highlights real-world tests showing better response times and reduced garbage collection pause times with this configuration.
The post discusses a memory leak problem encountered in a Java application running on AWS. After initial unresponsiveness, the yCrash tool identified the leak stemmed from an anonymous inner class. The issue was resolved by moving the 'pm.stopReporting()' method into a 'finally' clause, ensuring proper cleanup and garbage collection.
The 'java.util.UUID#randomUUID()' API in Java uses operating system entropy to generate unique identifiers. A lack of entropy can lead to blocked threads, causing application unresponsiveness. To mitigate this, one can upgrade to RHEL 7, install Haveged for better randomness, or configure Java to use '/dev/urandom' for number generation.
This article discusses optimizing garbage collection (GC) performance in applications, highlighting its importance for reducing pause times, improving response times, and lowering cloud costs. Key performance indicators like GC pause time, throughput, and CPU usage are analyzed. A case study illustrates how reducing the young generation size significantly improved GC metrics and overall performance.
ConFoo Montreal is a multi-technology conference where global speakers share knowledge. Architect Ram Lakshmanan presented on "LET'S CRASH OUR APPLICATIONS!" covering issues like memory leaks, CPU spikes, and deadlocks. Understanding these triggers aims to simplify diagnosis and resolution of application performance problems.
ConFoo Montreal is a conference for developers featuring Architect Ram Lakshmanan, who will present on "16 artifacts to capture when there is a production problem." His session ranks 5th among 155. The presentation highlights essential artifacts and tools for effectively troubleshooting production issues.
GC log analysis offers benefits such as reduced GC pause time, cost savings in cloud computing, outage prediction, and valuable metrics for capacity planning. Key steps include enabling GC logs, measuring during peak traffic periods, and utilizing tools like GCeasy and IBM GC visualizer for effective analysis.