Operations | Monitoring | ITSM | DevOps | Cloud

Curious how to get data flowing from your Go application into Honeycomb using OpenTelemetry? This guide will help you step by step through adding OpenTelemetry into your web service and sending the data to Honeycomb.

Last week Sumo Logic announced our new Observability Suite, which included the public introduction of the closed beta for our distributed tracing capabilities as part of our Microservices Observability solution. This new solution will provide end-to-end visibility into user transactions across services, as well as seamless integration into performance metrics and logs to accelerate issue resolution and root-cause analysis. In this blog, we’ll explore the new solution in detail.

Logs, metrics and traces are the three pillars of the Observability world. The distributed tracing world, in particular, has seen a lot of innovation in recent months, with OpenTelemetry standardization and with Jaeger open source project graduating from the CNCF incubation. According to the recent DevOps Pulse report, Jaeger is used by over 30% of those practicing distributed tracing.

Nothing is more frustrating than feeling like you’ve finally found the perfect trace only to see that you’re missing critical spans. In fact, a common question for new users and operators of Jaeger, the popular distributed tracing system, is: “Where did all my spans go?” In this post we’ll discuss how to diagnose and correct lost spans in each element of the Jaeger ingestion pipeline.

Jaeger primarily supports two backends: Cassandra and Elasticsearch. Here at Grafana Labs we use Scylla, an open source Cassandra-compatible backend. In this post we’ll look at how we run Scylla at scale and share some techniques to reduce load while ingesting even more spans. We’ll also share some internal metrics about Jaeger load and Scylla backend performance. Special thanks to the Scylla team for spending some time with us to talk about performance and configuration!

If you are familiar with OpenTracing and OpenCensus, then you have probably already heard of the OpenTelemetry project. OpenTelemetry merges the OpenTracing and OpenCensus projects to provide a standard collection of APIs, libraries, and other tools to capture distributed request traces and metrics from applications and easily export them to third-party monitoring platforms.

Jaeger primarily supports two backends: Cassandra and Elasticsearch. Here at Grafana Labs we use Scylla, an open source Cassandra-compatible backend. In this post we’ll look at how we run Scylla at scale and share some techniques to reduce load while ingesting even more spans. We’ll also share some internal metrics about Jaeger load and Scylla backend performance. Special thanks to the Scylla team for spending some time with us to talk about performance and configuration!

I am spending a considerable amount of time recently on distributed tracing topics. In my previous blog, I discussed different pros and cons of various approaches to collecting distributed tracing data. Right now I would like to draw your attention to the analysis back-end: what does it take to be good at analyzing transaction traces?

OpenTelemetry is an instrumentation standard for application monitoring - both for monitoring metrics & distributed tracing. In this blog, we take you through a hands on guide on how to run this on Kubernetes.

Today, users and customers are driven by response rates to their online requests. It’s no longer good enough to just have a request run to completion, it also has to fit within the perceived limits of “fast enough”. Yet, as we continue to build cloud-native applications with microservice architectures, driven by container orchestration like Kubernetes in public clouds, we need to understand the behavior of our system across all aspects, not just one.