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.

Scaling the deployment, in order to meet demand or extend capabilities, is a known challenge in many fields, but it’s particularly pertinent when scaling microservices. This article looks at the challenges of scaling microservices and examines best practices to overcome them while maintaining app quality, dev efficiency, and a good developer experience.

The needs of observability workloads can sometimes be orthogonal to the needs of compliance workloads. Honeycomb is designed for software developers to quickly fix problems in production, where reducing 100% data completeness to 99.99% is acceptable to receive immediate answers. Compliance and audit workloads require 100% data completeness over much longer (or "infinite") time spans, and are content to give up query performance in return.

eBPF, or Extended Berkeley Packet Filter, is a kernel technology available since Linux 4.4. It lets developers run programs without adding additional modules or modifying the kernel source code. Think of it as a lightweight, sandboxed virtual machine (VM) within the Linux kernel that lets you run Berkeley Packet Filter (BPF) bytecode that uses certain kernel resources. Utilizing eBPF removes the need to modify the kernel source code and improves the software’s capacity to use existing layers.

At Grafana Labs, we are constantly improving our feature set, and tracing is no different. Traces are often overshadowed by logs and metrics, but they’re a pillar of observability for a reason. Used correctly, organizations that can quickly and successfully follow a chain of events through a system gain a more holistic view of their systems and are better equipped to find and fix issues faster.

In the context of application performance monitoring (APM) and observability, traces and spans are fundamental concepts that help users to track and understand the flow of requests and operations within a system. They are essential in assisting users to identify bottlenecks, troubleshoot issues, and optimize application performance.

In today’s rapidly evolving software landscape, ensuring observability is crucial for building robust and reliable applications. One of the critical components of observability is metrics, which provide valuable insights into the performance and behavior of our systems. OpenTelemetry, an open-source observability framework, offers a standardized approach to capturing, exporting, and analyzing metrics. This blog post explores seven OpenTelemetry metrics for tracking better visibility.

Get excited about Grafana Tempo 2.2! Not only is this release on time, but it is also chock full of TraceQL features and performance improvements. I was honestly a little shocked by how much we have accomplished in the last three months when summarizing the changelog.

On-call shifts are part of every developer’s job – we’ve all been there. It’s 3am, suddenly you get an alert for an issue occurring in production. The microservices landscape is complicated and finding the root cause of an issue is like looking for a needle in a haystack. How can you get to the root of what’s happening in the system so you can analyze and resolve the issue quickly and effectively?

The OpenTelemetry Go project now supports automatic instrumentation via eBPF! This is a big milestone for the project and makes it significantly easier to generate data from your Go apps: The automatic instrumentation agent is still in s/alpha/beta today, but it’s ready for you to try on your applications!

When I first met Uria Franko, the CTO of Novacy, I immediately knew we’d hit it off. He was looking for an observability solution for his team with a specific need around Celery, after they had been using logs but found they lacked the depth and granularity they needed. Luckily, our mission at Helios is to help organizations gain visibility and drill down into services through traces. So this was a perfect match.