Back in the day, monitoring applications from end to end was—for the most part—significantly easier than it is today. Though the basics of instrumentation and observability for metrics like CPU, memory, and I/O throughput haven’t changed, the way applications are built has changed significantly. There were, at most, a handful of application servers and likely a single database server.

Grafana Tempo 1.3 has been released! We are proud to add the capability to search the backend datastore. This feature will also appear soon in Grafana Cloud Traces. If you want to dig through the nitty-gritty details, you can always check out the v1.3 changelog. If that’s too much, this post will cover the big ticket items. You can also register for our upcoming webinar “Distributed tracing in Grafana: From Tempo OSS to Enterprise” on Jan.

In distributed tracing, sampling is frequently used to reduce the number of traces that are collected and stored in the backend. This is often desirable because it is easy to produce more data than can be efficiently stored and queried. Sampling allows us to store only a subset of the total traces produced.

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.

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.