Operations | Monitoring | ITSM | DevOps | Cloud

We’ve released support for tracing header interoperability in all of our Beelines. This means you can now mix and match distributed services instrumented with Beelines or with OpenTelemetry, and your traces will be preserved in Honeycomb!

Back in March, we introduced the Grafana Cloud Agent, a subset of Prometheus built for hosted metrics. It uses a lot of the same battle-tested code as Prometheus and can save 40 percent on memory usage. Ever since the launch, we’ve been adding features to the Agent. Now, there’s a clustering mechanism, additional Prometheus exporters, and support for Loki. Our latest feature: Grafana Tempo! It’s an easy-to-operate, high-scale, and cost-effective distributed tracing system.

Implementing distributed tracing is fast becoming a fundamental expectation when building modern (distributed) systems. However, this is yet another thing for developers to learn, and configuring distributed tracing on Kubernetes is hard, right? Actually, no. Getting started with Jaeger on Kubernetes has never been easier.

Yesterday we were excited to announce Logz.io Distributed Tracing, the most recent addition to our Cloud-Native Observability Platform. This is such a special launch for us because it makes Logz.io the only place where engineers can use the best open source monitoring tools for logs, metrics, and traces – known as the ‘three pillars’ to observability – together in one place.

Today was a monumental day for Logz.io and our entire community. There is nothing more inspiring than seeing how people use the technology we’ve built to enhance their businesses. At ScaleUP 2020, our first ever global user conference, we hosted an exciting day of technical, customer-led sessions with our community. We also had the privilege of unveiling some ground-breaking new solutions and enhancements to our end-to-end cloud-native observability platform.

Grafana Tempo, the recently announced distributed tracing backend, relies on integrations with other data sources for trace discovery. Tempo’s job is to store massive numbers of traces, place them in object storage, and retrieve them by id. Logs and exemplars allow users to quickly and more powerfully jump directly to traces than ever before. Let’s dig into some examples with a live playground to try it out!

These days, the internal workings of Linux applications involve many different moving parts. Sometimes, it can be rather difficult to debug them when things go wrong or run slower than expected. Tracing an application’s execution is one way of understanding potential issues without diving into the source code. To this end, we wrote an app-tracing tool called etrace, designed to detect performance bottlenecks and runtime issues in snaps.