There are standalone distributed tracing tools like Jaeger, and there are APM tools that do not provide distributed tracing capabilities. In this article, we will see how distributed tracing complements an APM tool for a holistic performance monitoring experience. Both APM and distributed tracing are critical tools to understand the performance of your applications. And if your application is facing performance issues impacting customer experience, you need to understand what’s causing it fast.

This week we have turned on full trace retention search (beta) in Grafana Cloud Traces. This feature was also introduced in the recent release of Grafana Tempo v1.3. Previously, if you brought up your Grafana Cloud Traces data source, you were greeted with this message: This message simply warned the user that the Grafana Tempo search was calibrated for recent traces only, regardless of the selected time window.

The boom of digital commerce is making all businesses take a closer look at how they deliver great customer experiences. To stay competitive, businesses today are using cloud-native architectures, because the cloud-native applications they produce deploy quickly and better support the continuous improvement cycles of agile methodology. Behind the practicality of keeping online customers happy are distributed cloud environments that business applications use for each customer interaction.

GraphQL is a query language for APIs developed by Facebook in 2012. It was then open-sourced in 2015. GraphQL enables frontend developers or consumers of APIs to request the exact data that they need, with no over-fetching or under-fetching. In this article, we will learn how to monitor GraphQL APIs with the open-source APM tool, SigNoz. GraphQL has become a popular alternative to REST because of its ease of use. It enables developers to pull data from multiple data sources in a single API call.

Today, the Honeycomb OpenTelemetry Distribution for Java reaches a major milestone with a 1.0 release. This is the first Honeycomb OTel Distro to come out of beta and go into the general release category, and you can expect more to come!

StackState has always believed in the importance of open source and open standards, and we’ve demonstrated our commitment through ongoing support of open technologies. From the beginning, StackState supported StatsD and OpenMetrics. Even our agent is open source, designed to help organizations easily onboard our platform and to give them an extensible open way to observe their services. StackState is now proud to announce our next big open source step.

Microservices architecture allows technology companies to build application services around business capabilities. It enables rapid development and also boosts developer productivity. But it also introduces complexity. Troubleshooting and operating an internet-scale application based on microservices is hard. And that’s where distributed tracing comes into the picture. Traditional monolithic application architecture is easy to develop, deploy and monitor.

TL;DR: startSpan is easier and measures a duration. Use it if your work won’t create any subspans. startActiveSpan requires that you pass a callback for the work in the span, and then any spans created during that work will be children of this active span. I’m instrumenting a Node.js app with OpenTelemetry, and adding some custom instrumentation. For this important activity that I’m doing (let’s call it “retrieve number”), I’m creating a custom span.

In 2020 we released distributed tracing for AWS Lambda functions written in Python, Node.js, and Ruby, providing you with health and performance insights across your serverless applications. Since then, we’ve expanded our support to additional Lambda runtimes such as Java and Go, and are pleased to announce that real-time distributed tracing is now also available for.NET Lambda functions.