Are you looking for Kubernetes monitoring tools? Then you have come to the right place. Kubernetes has grown to become the container orchestration platform of choice. It simplifies managing your containerized workloads. You get the power of automating deployments, scaling resources, and keeping your applications running smoothly. But with great power comes added responsibility. And like any complex system, Kubernetes needs monitoring.

This is the second post in a 3-part series about shifting Observability left. If you have not had a chance to read the first, you can find it here. In today’s complex microservices deployments, gaining visibility into deployments is vital for optimal system performance and scalability. This has become even more important as the tech industry has moved toward microservice architecture reliance. Navigating through logs has become increasingly complex as requirements have grown.

Observability is often defined in the context of three pillars: logs, metrics, and traces. Modern-day cloud-native applications are complex and dynamic. To avoid surprises and performance issues, you need a robust observability stack. But is observability limited to collecting logs, metrics, and traces? How is observability evolving to make our systems more observable? In this tutorial, we cover.

If you’ve landed on this blog, you’re likely either considering starting your OpenTelemetry journey or you are well on your way. As OpenTelemetry adoption has grown, not only within the observability community but also internally at Grafana Labs and among our users, we frequently get requests around how to best implement an OpenTelemetry strategy.

At Lumigo, building developer-first tools has always been at the forefront of our approach to troubleshooting and debugging. As developers ourselves, we have experienced firsthand the frustration and intricacies of sifting through logs looking for answers. We’ve also felt the pressure of the clock ticking, with production issues waiting to be resolved and the need for timely answers to surfaced application issues.

Monitoring distributed systems means collecting data from various sources, including servers, containers, and applications. In large organizations, this data distribution makes it harder to get a single view of the performance of their entire system. OpenTelemetry helps you streamline your full-stack observability efforts by giving you a single, universal format for collecting and sending telemetry data. Thus, OpenTelemetry makes improving performance and troubleshooting issues easier for teams.

Lumigo is excited to announce its microservice troubleshooting platform now provides developers and DevOps with the power of OpenTelemetry (OTel) with a single click. Lumigo has long been the leading troubleshooting platform for serverless, but now, users can harness its best-in-class debugging and observability platform for all microservices-based environments.

In this tutorial, you will learn how to collect logs using the Loki receiver in OpenTelemetry Collector to send logs to SigNoz. If you’re using Promtail to collect logs, you can send them to SigNoz instead of Loki via the OpenTelemetry Collector. In this tutorial, we cover: If you want to jump straight into implementation, start with this prerequisites section.

For extremely high throughput web applications, it is important to load balance the traffic across multiple servers. However, load balancing the traffic alone is not enough at times. The reverse proxy server that handles the workload needs to be performant, too. In our previous article, we discussed the NGINX reverse proxy server and understood how to monitor it. In this article, we set up monitoring for an even more performant reverse proxy server - HAProxy.