Your liveness and readiness HTTP endpoints can generate a bunch of useless Spans and can mess with your latency metrics. On this post we’ll learn how to configure Kamon Telemetry to completely ignore these enpoints for good.
As distributed environments become more complex, users often use distributed tracing tools to improve the visibility of issues evident within their traces. Throughout this post, we will examine some of the best open-source and other generally popular distributed tracing tools available today.
Distributed tracing is a method of tracking requests as they propagate through a distributed system. A trace is built from spans. Each span represents an interaction, like an HTTP request, a DB query, a serverless function invocation, etc. A trace is essentially a tree of spans. Based on the collected span data, a distributed tracing platform can capture all the interactions between the different architectural components and tie them together with a trace ID.
In this post, we’ll look at how you can use OpenTelemetry to monitor your unit tests and send that data to Honeycomb to visualize. It’s important to note that you don’t need to adopt Honeycomb, or even OpenTelemetry, in your production application to get the benefit of tracing. This example uses OpenTelemetry purely in the test project and provides great insights into our customer’s code. We’re going to use xUnit as the runner and framework for our tests.
OpenTracing was an open-source project developed to provide vendor-neutral APIs and instrumentation for distributed tracing across a variety of environments. As it is often extremely difficult for engineers to see the behaviour of requests when they are working across services in a distributed environment, OpenTracing aimed to provide a solution to heighten observability.
OpenTelemetry is a free and open-source software initiative with the objective of supplying software developers with the means to create distributed systems. OpenTelemetry was developed by engineers at Google, and developers have the ability to utilize it to create a standard foundation for the construction of distributed systems. The goal is to enable developers to write code once and then deploy it in any location of their choosing.
OpenTelemetry is a collection of tools and APIs for collecting, processing, and exporting telemetry data from software. It is used to instrument applications for performance monitoring, logging, tracking, tracing, and other observability purposes. What is Telemetry? The word is derived from the Greek “tele” meaning “remote,” and metron meaning “measure.” So, it’s the collection of metrics and their automatic to a receiver for monitoring.
The scenario: you want to see distributed traces, maybe for your web app. You’ve set up an OpenTelemetry collector to receive OTLP traces in JSON over HTTP, and send those to Honeycomb (how to do that is another post, and we’ll link it here when it’s up).
Testing web applications from the user interface (UI) is a must for every customer-facing product, from e-commerce portals to cyber security dashboards. Often, a broken or inefficient UI experience can make or break whether end users adopt a product quickly and trouble-free. This is why developers have embraced UI testing as a critical part of their development process.
Systems and applications alike have become progressively distributed as microservices, open-source tools, and containerisation have gained traction. In order to actively monitor and respond quickly to issues that arise in our environment, distributed tracing has proven to be vital for businesses such as Uber, Postmates, Hello Fresh and TransferWise. It is, however, important to clarify what distributed tracing actually means.
Cloud native and microservice architectures bring many advantages in terms of performance, scalability, and reliability, but one thing they can also bring is complexity. Having requests move between services can make debugging much more challenging and many of the past rules for monitoring applications don’t work well. This is made even more difficult by the fact that cloud services are inherently ephemeral, with containers constantly being spun up and spun down.
Telemetry data is a powerful tool for understanding the behavior of complex systems. OpenTelemetry provides a platform-agnostic, open-source way to collect, process, and store telemetry data. This post explores the OpenTelemetry collector architecture, specifically focusing on the Collectors component. We'll look at how collectors work and how they can be used to process telemetry data from any system or application. We'll also discuss some benefits of using OpenTelemetry for your telemetry needs.
The OpenTelemetry Collector is an application written in Go. The GitHub readme does a great job of describing it: So the OpenTelemetry collector is a Go binary that does exactly what its name implies: it collects data and sends it to a back-end. But there’s a lot of functionality that lies in between. What a neat service! A local destination for data that handles the final sending of Open Telemetry information to your back end.
