Grafana Labs works everyday to break traditional data boundaries with metric-visualization tools accessible across entire organizations. It began as a pure open-source project and has since expanded into supported subscription services. The Grafana open-source project is a platform for monitoring and analyzing time series data. There are also subscription offerings such as the supported Grafana Enterprise version. Grafana Labs’ engineers service more than 150,000 active installations.

Over the past few years, and coupled with the growing adoption of microservices, distributed tracing has emerged as one of the most commonly used monitoring and troubleshooting methodologies. New tracing tools and frameworks are increasingly being introduced, driving adoption even further. One of these tools is Jaeger, a popular open source tracing tool. This article explores the integration of Jaeger with the ELK Stack for analysis and visualization of traces.

Tracing is one of the key tools that Honeycomb offers to make sense of data. Over the last few weeks, we’ve made a number of improvements to our tracing interface — and, put together, those changes let you think about traces in a whole new way! Tracing makes it easier to understand control flow within a distributed system. We render traces with waterfall diagrams, which capture the execution history of individual requests.

In the previous post, we took a look at the building blocks of Service Mesh Istio, got familiar with the system, and went through the questions that new Istio users often ask. In this post, we will look at how to organize the collection of tracing information over the network.

Distributed tracing has become a de-facto standard for monitoring distributed architectures, helping engineers to pinpoint errors and identify performance bottlenecks. Zipkin is one of the popular open source “tracers” available in the market, and I’m now happy to inform our users that we’ve recently introduced a new integration that allows users to easily ship trace data collected by Zipkin to Logz.io!

We’re very pleased to announce the official launch of our new Java tracer for AWS Lambda. This release extends Lumigo’s real-time monitoring and observability tools to all JVM-based AWS Lambda applications, with support for Apache Groovy, Scala and Kotlin among others.

Recently, OpenTelemetry has been announced as a new CNCF sandbox project resulting from a merger of OpenTracing and OpenCensus [1], [2], [3], [4]. Several people have already asked me what OpenTelemetry means for the Jaeger project (incubating at CNCF), and whether it is going to replace Jaeger. I will attempt to answer these questions in this post.

Microservices and containers multiplied the complexity of Ticketmaster’s software system. Its engineers solved their debugging problems with Jaeger, an open-source tracing tool from Uber incubating at CNCF.

This post illustrates some practical examples of distributed context propagation. More detailed examples are presented in Chapter 10 of my book, Mastering Distributed Tracing.

Recently, Toshok was telling a story about the kind of thing he talks about a lot—improving the performance of some endpoint or page or other. Obviously, we spend a lot of time thinking about how to improve the experience of our users, but what caught my attention this time was that what he was describing sounded like a new kind of testing in production—so I asked him to go into a bit more detail.