TL;DR: Use auto-instrumentation from OpenTelemetry. Traces will happen. Then your code can use global library functions to customize those traces with your specific important data.

Anders Østhus is a DevOps Engineer on the Digital Tools team at Proactima AS, a consulting firm based in Norway that offers services and expertise in risk management, cybersecurity, healthcare, environmental solutions, and more. It can be difficult to orient yourself in the distributed tracing space, and getting all the parts of a tracing setup to play well with each other can be a bit tricky. But the benefits of tracing are undeniable.

In the past, we’ve written about what instrumentation is and the insights it provides. Instrumenting your code generates telemetry that shows you how your system is performing, and whether your system is healthy. Like with most other companies, at Honeycomb we don’t write all of the code that runs in our systems.

With the Elastic 7.16 release, Osquery Manager is now generally available for Elastic Agent, making it easier than ever to deploy and run Osquery across your environments. By collecting Osquery data and combining it with the power of the Elastic Stack, you can greatly expand your endpoint telemetry, enabling enhanced detection and investigation, and improved hunting for vulnerabilities and anomalous activities.

Grafana Tempo has had quite a year. Just eight months after it was announced at ObservabilityCON 2020, the open source tracing solution went GA. Since the Tempo team released v1.0 in June, we have ingested more than 39 trillion spans, a 26x increase from last year. We also introduced Grafana Enterprise Traces, which is powered by Tempo, to the Grafana Enterprise Stack.

OpenTelemetry is a project by the Cloud Native Computing Foundation aimed to standardize the way that application telemetry data is recorded and utilized by platforms downstream. This application trace data can be valuable for application owners to understand the relationship between the components and services in their code, the request volume and latency introduced in each step, and ultimately where the bottlenecks are that are resulting in poor user experience.

At Honeycomb Developer Week, I got an opportunity to walk through a couple of fun new features we’ve shipped since August and ways that we’ve been able to improve Honeycomb for you. Hearing feedback from our users and customers— through support requests, in the Pollinators community, from Twitter, etc.—helps us make Honeycomb better for you.

In this tutorial, we will go through a working example of a Node.js application auto-instrumented with OpenTelemetry. In our example we’ll use Express, the popular Node.js web application framework. Our example application is based on two locally hosted services sending data to each other. We will instrument this application with OpenTelemetry’s Node.js client library to generate trace data and send it to an OpenTelemetry Collector.

In this blog series, we share the application instrumentation steps for distributed tracing with OpenTelemetry standards across multiple languages. Earlier, we covered Java Application Manual Instrumentation for Distributed Traces, Golang Application Instrumentation for Distributed Traces, Node JS Application for Distributed Traces, and DotNet Application Instrumentation for Distributed Traces. In this blog post, we are going to cover.

We are living in a data world. Data describes and controls almost every aspect of our life, from the president's elections to everyday grocery shopping. Data grows exponentially and so does the complexity of applications that manage that data. We all know the recent shift to microservices and other revolutionary changes that happened in the way we design, develop, deploy and operate modern applications.