GC logs are essential for JVM performance analysis, but their complexity complicates interpretation. In a recent webinar, Ram Lakshmanan discussed how Deterministic AI can refine GC log analysis, enhancing accuracy and safety while addressing the limitations of traditional AI methods. This approach enables faster, reliable insights for troubleshooting performance issues.
The recent webinar focused on troubleshooting performance issues in customer-managed, on-premise environments. Experts discussed challenges like limited visibility and communication delays, emphasizing the need for essential artifacts for diagnosis. Participants learned practical strategies, including automation workflows and effective communication techniques, to enhance resolution accuracy and reduce turnaround times.
Automatic garbage collection in programming languages like Golang, Java, and Python simplifies memory management but may lead to high CPU usage. This article discusses methods to measure CPU consumption caused by garbage collection, including GC log analysis, monitoring tools, and examining CPU usage by GC threads, enabling performance optimizations for applications.
Modern programming languages offer automatic garbage collection, which simplifies memory management but can lead to increased CPU consumption and higher cloud hosting costs. This article outlines five strategies for developers to mitigate these issues: analyzing GC logs, switching GC algorithms, minimizing object creation, adjusting heap size, and scaling instances. Each approach aims to optimize application performance and reduce resource expenses.
The intern() function in Java's String class helps reduce memory consumption by eliminating duplicate string objects, storing intern strings in the JVM heap memory. Earlier, intern strings were in the Perm Generation, but since Java 7, they reside in the Heap region. A practical experiment confirmed this memory behavior.
