Application performance management (APM) has moved beyond traditional monitoring to become an essential tool for developers, offering deep insights into applications at the code level. With APM, teams can not only detect issues but also understand their root causes, optimizing software performance and end-user experiences. The modern landscape presents a wide range of APM tools and companies offering different solutions. Additionally, OpenTelemetry is becoming the open ingestion standard for APM.

This guest post is written by Ian Duncan, Staff Engineer - Stability Team at Mercury. To view the original post, go to Ian's website. At work, we use OpenTelemetry extensively to trace execution of our Haskell codebase. We struggled for several months with a mysterious tracing issue in our production environment wherein unrelated web requests were being linked together in the same trace, but we could never see the root trace span.

While support for logging in the OpenTelemetry Python project is listed as 'experimental,' it's completely possible to send logs from your Python application. The Opentelemetry Collector has support for numerous existing logging systems, effectively exporting log data from wherever you were sending logs currently; you can also use the filelog receiver to tail and send logs from files. The only 'experimental' portion of the Python SDK is sending logs directly from code-level instrumentation.

In the fast-paced universe of software development, especially in the cloud-native realm, DevOps and SRE teams are increasingly emerging as essential partners in application stability and growth. DevOps engineers continuously optimize software delivery, while SRE teams act as the stewards of application reliability, scalability, and top-tier performance. The challenge?

The OpenTelemetry Collector is a useful application to have in your stack. However, deploying it has always felt a little time consuming: working out how to host the config, building the deployments, etc. The good news is the OpenTelemetry team also produces Helm charts for the Collector, and I’ve started leveraging them. There are a few things to think about when using them though, so I thought I’d go through them here.

It was a rainy day in Seattle at KubeCon + CloudNativeCon North America in December 2018 when I first encountered the term ‘OpenTelemetry.’ At that time, I was an active member of a working group focused on developing W3C Trace Context, a standard now extensively employed for context propagation in distributed systems.

In the fast-paced universe of software development, especially in the cloud-native realm, DevOps and SRE teams are increasingly emerging as essential partners in application stability and growth. DevOps engineers continuously optimize software delivery, while SRE teams act as the stewards of application reliability, scalability, and top-tier performance. The challenge?

Microservices architecture has become increasingly popular in modern software development due to its scalability, resilience, and flexibility. However, with the benefits of microservices come the challenges of debugging and monitoring these distributed systems. Using the Istio service mesh, OpenTelemetry distributed tracing, and Apica’s Kubernetes-native observability platform, developers can easily collect and visualize performance data in real-time to identify and fix issues quickly.

Our friends at Tracetest recently released an integration with Honeycomb that allows you to build end-to-end and integration tests, powered by your existing distributed traces. You only need to point Tracetest to your existing trace data source—in this case, Honeycomb. This guest post from Adnan Rahić walks you through how the integration works.

We often get asked, what's the best place to get started with OpenTelemetry - host metrics, traces, or even logs? Hosts Nočnica Mellifera and Pranay will talk about taking your first steps to gathering OpenTelemetry data Below is the recording and an edited transcript of the conversation. Find the conversation transcript below.