Troubleshooting .NET Production problems using AI

.NET powers many critical applications, but troubleshooting its production issues presents challenges. While observability tools identify problems, they often fail to uncover root causes. yCrash enhances troubleshooting by capturing 16 artifacts, predicting outages with micro-metrics, and utilizing advanced analysis to provide detailed insights for effective issue resolution.

Visualising JVM Metrics Using Prometheus and Grafana

This article provides a comprehensive guide for visualizing Java Virtual Machine (JVM) metrics using Prometheus and Grafana. It explains how to set up the monitoring system, retrieve JVM metrics from applications, and create custom dashboards. The integration enhances problem detection and facilitates quicker resolution, boosting application performance and reliability.

How ‘yCrash Log’ uses AI & ML?

Application logs are crucial for engineers to troubleshoot production incidents, but manual inspection is often inefficient. The 'yCrash Log' tool utilizes AI and ML to analyze and structure unfiltered log data, identify errors, and provide solutions, improving incident response time and system reliability. It enhances traditional log management by automating root cause analysis.

Exposing JVM metrics over Actuator in Spring Boot 4 

In modern enterprise Java applications, standard logging falls short in diagnosing JVM issues such as memory leaks and CPU spikes. Spring Boot Actuator facilitates the exposure of vital JVM metrics and application health through HTTP endpoints, providing developers with essential insights for monitoring performance, triggering alerts, and diagnosing production problems efficiently.

Spring AI – Building intelligent apps in Java

The article provides a practical guide on utilizing Spring AI for automation in corporate environments, emphasizing its evolution from an early-stage tool to a robust framework by 2025. It outlines the significance of LLMs, their limitations, and how Spring AI enables Java developers to create intelligent applications, enhancing efficiency and decision-making through automation.

JVM Optimization in Real Systems

A Spring Boot application unexpectedly surged in JVM memory usage from 8GB to 61GB without any deployment or configuration changes. By diagnosing a ZipFile$Source memory leak with yCrash, the team identified excessive caching leading to the leak. By disabling caching and restarting the app, they reduced memory usage to 4GB effectively.

‘The Hidden Battle: Troubleshooting Issues in On-Prem Customer Deployments’ webinar

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.

Key Challenges in Troubleshooting Customer On-Premise Applications

Troubleshooting on-premise application issues is complicated due to limited access to customer systems and reliance on their support staff for vital diagnostic data. Challenges include incomplete information, security concerns, miscommunication, and environmental instabilities. Implementing the yc-360 Script can streamline artifact collection, improving diagnosis and reducing resolution times.

Top 5 Java Performance Problems

Java is a popular programming language that powers several mission critical applications all over the world. In this post let's discuss some of the commonly confronted performance problems by Java applications and potential solutions to solve them. Video In this video, we explored the most common issues that impact Java applications in production environments. Our... Continue Reading →

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