(This is the first post by our new head of Customer Success, Irving.) Sampling is a must for applications at scale; it’s a technique for reducing the burden on your infrastructure and telemetry systems by only keeping data on a statistical sample of requests rather than 100% of requests. Large systems may produce large volumes of similar requests which can be de-duplicated.

We’re big fans of AWS Lambda at Honeycomb. As you may have read, we recently made some major improvements to our storage engine by leveraging Lambda to process more data in less time. Making a change to a complex system like our storage engine is daunting, but can be made less so with good instrumentation and tracing. For this project, that meant getting instrumentation out of Lambda and into Honeycomb.

We’ve been working on integrating call tracing in the server to provide exact measurements of all API and DB calls. We’ve picked OpenTracing—a lovely open source project that allows you to set up trace reporting and enables you to support Distributed tracing.

At the Grafana Labs booth at KubeCon + CloudNativeCon in San Diego this week, we showed a demo of a future feature for Grafana: distributed tracing datasources. Until now, Grafana has been bringing together metrics and logs, to be viewed side-by-side on one screen. Now we’re adding tracing, which has been a missing puzzle piece for even more observability in Grafana.

There’s been a fair bit of buzz lately about OpenTelemetry, which is the next major version of the OpenTracing and OpenCensus projects. The leadership of those two projects have come together to create OpenTelemetry, which combines the best parts of OpenTracing and OpenCensus to create one open source project to help with your instrumentation needs.

As explained on the Eclipse Che website, “Che brings your Kubernetes application into your development environment and provides an in-browser IDE, allowing you to code, build, test and run applications exactly as they run on production from any machine”. However when deployed in your production environment, those same applications can be monitored using observability tools to understand their performance to help inform future improvements.

Last week, we spoke with a lot of folks at PagerDuty Summit, where we explored the power of error monitoring in a world that’s always on (or on-call). What a neat tweet. Throughout our conversations, one thing became clear: developers are seeking out increased efficiency — they want to minimize time to error detection and resolution. They want to know about issues, find the root cause, and fix them quickly so that they can move on to other things — like writing more code.

Founded in 2014, Weaveworks Inc. makes software that helps developers and DevOps teams build, run and manage containerized applications on Kubernetes. Its products include GitOps-based cluster management, and application delivery, observability and monitoring solutions for services running on Kubernetes. It is a founding member of the Cloud Native Computing Foundation.

At Datadog, we’ve always been committed to ensuring that our libraries and software that run on your systems are open source. We believe that transparency into how we collect data and integrate with your applications is key for building trust.