Modern-day software systems emit millions of log lines per minute. Cloud computing and containerization have made it easy to have distributed systems. Distributed systems emit logs from multiple sources. While developers have always used logs to debug stand-alone applications, centralized logging solves the challenges of modern-day distributed software systems.

We are delighted to announce the alpha release of the Elastic OpenTelemetry Distribution for Node.js. This distribution is a light wrapper around the OpenTelemetry Node.js SDK that makes it easier to get started using OpenTelemetry to observe your Node.js applications.

Welcome to SigNal 36, the 36th edition of our monthly product newsletter! We crossed 10 Million Docker downloads for our open source project. We’ve enhanced our Dashboards UX and incorporated feedback from users in different areas of our product. Let’s see what humans of SigNoz were up to in the month of April 2024.

When OpenTelemetry first came into the picture with the merger of OpenCensus and OpenTracing in 2019, it was pretty much all about classic telemetry data, namely- logs, metrics, and traces. Since then, OpenTelemetry has become an indispensable tool in the modern observability landscape. With frequent integrations and introduction to new capabilities every year or so, it has poised itself as an invaluable tool for cloud enterprises.

Both Grafana Tempo and Jaeger are tools aimed at distributed tracing for microservice architecture. Jaeger was released as an open-source project by Uber in 2015, while Tempo is a newer product announced in October 2020. Jaeger is a popular open-source tool that graduated as a project from Cloud Native Computing Foundation. Grafana Tempo is a high-volume distributed tracing tool deeply integrated with other open-source tools like Prometheus and Loki.

We’re excited to bring you relational fields, a new feature that allows you to query spans based on their relationship to each other within a trace. Previously, queries considered spans in isolation: You could ask about field values on spans and aggregate them based on matching criteria, but you couldn’t use any qualifying relationships about where and how the spans appear in a trace.

In the complex world of microservices and distributed systems, achieving transparency and understanding the intricacies and inefficiencies of service interactions and request flows has become a paramount challenge. Distributed tracing is essential in understanding distributed systems. But distributed tracing, whether manually applied or auto-instrumented, is usually rather coarse-grained.

OpenTelemetry has emerged as a key open source tool in the observability space. And as organizations use it to manage more of their telemetry data, they also need to understand how to make it work across their various environments. This guide is focused on scaling the OpenTelemetry Collector deployment across various Linux hosts to function as both gateways and agents within your observability architecture.

As we’ve already shared, Elastic is committed to helping OpenTelemetry (OTel) succeed, which means, in some cases, building distributions of language SDKs. Elastic is strategically standardizing on OTel for observability and security data collection. Additionally, Elastic is committed to working with the OTel community to become the best data collection infrastructure for the observability ecosystem.

You're probably familiar with the concept of real user monitoring (RUM) and how it's used to monitor websites or mobile applications. If not, here's the short version: RUM requires telemetry data, which is generated by an SDK that you import into your web or mobile application. These SDKs then hook into the JS runtime, the browser itself, or various system APIs in order to measure performance.