The advent of Machine Learning (ML) has unlocked new possibilities in various domains, including full lifecycle Application Performance Monitoring (APM). Maintaining peak performance and seamless user experiences poses significant challenges with the diversity of modern applications. So where and how does ML and APM fit together? Traditional monitoring methods are often reactive, resolving concerns after the process already affected the application’s performance.

AVD (Azure Virtual Desktop) has grown in popularity and for many reasons many organizations are choosing to consume AVD via a specialist Managed Service Provider (MSP). For the MSPs offering AVD, monitoring and troubleshooting AVD especially for multi-tenant environments is challenging and involves significant, costly and skilled effort using native tools such as Azure Insights or Azure Monitor.

For high throughput systems that focus on gathering continuous data or have a heavy read-only traffic, NoSQL databases came as a blessing. NoSQL databases, due to their unstructured nature of data, allow relatively faster inserts as well as reads compared to relational databases. One such database that’s quite popular today is MongoDB. In this article, our focus would be to understand how to extract metrics out of MongoDB and ship them to Signoz using the Open Telemetry collector.

Let's dive deep into the realm of Memcached, where we'll uncover the power of monitoring with OpenTelemetry and SigNoz. This isn't just about caching data; it's about watching over Memcached like a vigilant guardian, ensuring it performs at its best, and optimizing your application's speed. In this tutorial, you will install OpenTelemetry Collector to collect Memcached metrics that should be monitored for performance and then send the collected data to SigNoz for visualization and alerts.

At Raygun, we’re processing billions of events per month for our customers, so it’s well worthwhile looking for the most efficient data storage solutions. Way back when we started out, we chose lz4net to store data, which served our purposes well for many years. As we grew, though, we realized this was getting expensive, and was starting to undermine our business model. This post is focused on how we made the switch to the K4os.Compression.LZ4 rewrite, attaining significant performance gains.

Database monitoring is an important aspect to look at for a high-volume or high-traffic system. The database performance drastically impacts the response times for the application. In this tutorial, you will install OpenTelemetry Collector to collect MySQL metrics and then send the collected data to SigNoz for monitoring and visualization. In this tutorial, we cover: If you want to jump straight into implementation, start with this pre-requisites section.

In the realm of modern software development, achieving true observability is paramount for understanding application behavior and performance. This demonstration focuses on a.NET application that harnesses the capabilities of OpenTelemetry to seamlessly integrate logging and tracing functionalities. OpenTelemetry, a key player in the Cloud Native Computing Foundation, provides a unified framework for comprehensive observability.

OpenTelemetry provides a component called OpenTelemetry Collector, which can be used to collect data from multiple sources. Prometheus is a popular metrics monitoring tool that has a wide adoption. If you’re using Prometheus SDKs to generate metrics, you can collect them via OpenTelemetry collector and send them to a backend of your choice.

Monitoring Apache web server metrics ensures your web server performs efficiently, securely, and reliably. In this tutorial, you will configure OpenTelemetry Collector to collect Apache metrics and send them to SigNoz for monitoring and visualization. We cover: If you want to jump straight into implementation, start with this prerequisites section.