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The latest News and Information on Distributed Tracing and related technologies.
Shedding Light on Kafka's Black Box Problem (with OpenTelemetry)
"All language is but a poor translation." — Franz Kafka This quote by Franz Kafka reminds me of the time when I used to look at metrics from “Apache Kafka” topics trying to figure out what was causing the huge lags and manually deleting the messages in certain partitions to get rid of polluted messages. Yep, pretty lost in translation. I wasn’t aware of the power of observability for a Kafka producer-topic-consumer system.
Easy Way to Convert Wavefront Metrics Using OpenTelemetry
Once upon a time in the world of metrics, Wavefront was a pioneer. Before Prometheus took over and tools like OpenTelemetry unified tracing and metrics, Wavefront brought something novel to the table: human-readable metrics with real-time querying and tag-based dimensionality. In enterprise environments running VMware or early microservices, it offered a scalable way to understand a system's behavior. But as the telemetry landscape evolved, many systems that spoke Wavefront were left behind.
Using the OpenTelemetry Operator to boost your observability
If you’ve ever wrangled sidecars or sprinkled instrumentation code just to get basic trace data, you know the setup overhead isn’t always worth the payoff. But what if it was… just easier? That’s where the OpenTelemetry Operator for Kubernetes steps in… and it plays great with Coralogix out of the box!
OpenTelemetry vs Micrometer: Here's How to Decide
In a distributed system, things break in unexpected ways. That’s why observability isn’t optional—it’s how you understand what’s going on under the hood. If you’re comparing tools to instrument your services, OpenTelemetry and Micrometer are two names you’ll run into. Both are used to collect metrics, but they take very different approaches—especially when it comes to flexibility, vendor support, and what you can do with the data.
Set Up Tracing for a Ruby on Rails Application in AppSignal
In this guide, we'll harness AppSignal to detect, diagnose, and remove performance bottlenecks and employ proper tracing in a Ruby on Rails application. From setting up tracing to capturing errors and logging, we’ve got you covered. We'll ensure our application runs smoother than ever, even under the heaviest loads! But first, let's quickly touch on how to define tracing and its benefits.
OpenTelemetry with Prometheus: better integration through resource attribute promotion
With the 3.0 release, Prometheus firmly established itself as the leading metrics database for OpenTelemetry. A lot of work has gone into integrating the two open source projects, including a major Prometheus enhancement we’re really excited about: resource attribute promotion.
Grafana Tempo vs Jaeger: Key Features, Differences, and When to Use Each
Both Grafana Tempo and Jaeger are distributed tracing tools designed for modern microservice architectures. Jaeger, released as an open-source project by Uber in 2015, has matured into a graduated CNCF project. Tempo, announced by Grafana Labs in October 2020, is a newer entrant focused on high-volume tracing with a unique storage architecture. Before comparing these tools in detail, let's quickly review what distributed tracing is and why it matters.
Learning from LFX Mentorship @ CNCF - Jaeger
Hariom Gupta Follow 4 min read· 1 hour ago -- Listen Share Starting this journey was both exciting and fulfilling — and now, here I am at the finish line, having successfully completed the LFX Mentorship Program and reflecting on the experience through this blog. The past three months have been incredible — surpassing my expectations in so many ways.
Tracing Funnels - Define funnels between spans | SigNoz Launch Week 4.0 Day 5
Build funnels directly on your traces and get instant answers to questions like: What fraction of spans made it from event A to event B? Between which spans are most requests failing? What is the latency between key spans? Traditional observability tools let you inspect traces and spans, but they can’t aggregate or analyze how requests flow across multiple services or stages in your system. In asynchronous, distributed architectures, the root span rarely tells the full story-and there’s no way to measure conversion, drop-off, or latency between arbitrary steps across all traces.