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.

In the realm of data and complex scenarios, we humans naturally gravitate towards visualizing things as entities with attributes, rather than just raw data. Consider the phrase, “The response time on our Ad Generation service has increased.” It immediately resonates with the audience supporting the service.