Operations | Monitoring | ITSM | DevOps | Cloud

Apache Flink monitoring support is now available in the open source OpenTelemetry collector. You can check out the OpenTelemetry repo here! You can utilize this receiver in conjunction with any OTel collector: including the OpenTelemetry Collector and observIQ’s distribution of the collector. Below are quick instructions for setting up observIQ’s OpenTelemetry distribution, and shipping Apache Flink telemetry to a popular backend: Google Cloud Ops.

An app that works as expected is great, but if expected means a beachball for 10 seconds before the page loads, that’s… not so great. Customers want it all; an application that is stable and fast… Luckily, Sentry does more than tell you when something is broken in your code, it also tells you what’s slow and how to fix it.

Tracing is often the last thought in any observability strategy. While engineers prioritize logs and metrics, tracing is truly the hallmark of a mature observability platform, but it is also the most difficult to implement. Once tracing is in place, engineers typically discover something else – many tracing solutions aren’t particularly feature-rich.

You can now monitor Jetty for free using top of the line open source monitoring tools in OpenTelemetry. If you are as excited as we are, take a look at the details of this support in OpenTelemetry’s repo. The best part is that this receiver works with any OpenTelemetry collector: including the OpenTelemetry Collector and observIQ’s distribution of the collector. Jetty uses the JMX receiver.

In this part 1 of a blog series on serverless monitoring, we will learn how to run the observIQ Distro For OpenTelemetry Collector, referred to as “oiq-otel-collector”, in Google Cloud Run. There are many reasons that someone may want to run monitoring in a serverless state. In our example, we will be monitoring MongoDB Atlas, a cloud hosted version of MongoDB.

Observability is a mindset that lets you use data to answer questions about business processes. In short, collecting as much data as possible from the components of your business — including applications and key business metrics — then using an AI-powered tool to help consolidate and make sense of this huge volume of data gives you observability into your business. Having observability for your business and applications lets you make smarter decisions, faster.

Log tracking, trace log, or logging traces… Although these three terms are easy to interchange (the wordplay certainly doesn’t help!), compare tracing vs. logging, and you’ll find they are quite distinct. Logs, traces, and metrics are the three pillars of observability, and they all work together to measure application performance effectively. Let’s first understand what logging is.

If you've stumbled (or purposefully landed) on this blog post, chances are you are new to—or diving deeper—into the observability space, o11y for short. Suffice it to say, you’re not in Kansas anymore. Honeycomb in a lot of ways can serve as a yellow brick road into o11y, and this article should serve as an introduction into how Honeycomb facilitates implementing o11y into applications and distributed services.

Tracing, or more specifically distributed tracing or distributed request tracing, is the ability to follow a request through a system, joining the dots between all the individual system calls required to service a particular request. Although tracing logs have been around for some time, the trend toward distributed architectures, microservices, and containerization has elevated it from nice-to-have status to an essential piece of the observability puzzle.