If you happen to be running multiple clusters, each with a large number of services, you’ll find that it’s rather impractical to use static alerts, such as “number of pods < X” or “ingress requests > Y”, or to simply measure the number of HTTP errors. Values fluctuate for every region, data center, cluster, etc. It’s difficult to manually adjust alerts and, when not done properly, you either get way too many false-positives or you could miss a key event.

This is a companion post to a recent webinar with an OpsRamp partner, Flycast Partners. Check out the recorded webinar on YouTube and slides on SlideShare for the potential impacts of ITSM and ITOM silos and how a modern ITOM platform can help improve bi-directional collaboration and reduce mean-time-to-resolution.

When developing web services and tuning the infrastructure that runs them, you’ll want to make sure that they handle requests quickly enough, and at a high enough volume, to meet your requirements. ApacheBench (ab) is a benchmarking tool that measures the performance of a web server by inundating it with HTTP requests and recording metrics for latency and success.

HashiCorp Consul is agent-based cluster management software that addresses the challenge of sharing network and configuration details across a distributed system. Consul handles service discovery and configuration for potentially massive clusters of hosts, spread across multiple datacenters.

In Part 1, we looked at metrics and logs that can give you visibility into the health and performance of your Consul cluster. In this post, we’ll show you how to access this data—and other information that can help you troubleshoot your Consul cluster—in four ways: Consul provides a built-in CLI and API that you can use to query the most recent information about your cluster, giving you a high-level read into Consul’s health and performance.

You can use our API to trigger an on demand run of both the uptime check and the broken links checker. If you add this to, say, your deploy script, you can have near-instant validation that your deploy succeeded and didn't break any links & pages. Our API allows you to trigger an on demand run for every check we do. But, it's an API - so it requires a set of IDs. First, let's find the different checks your site has.

I am delighted to announce a big new initiative that we have been working on here at Squared Up. Our engineers have been working their socks off to build a new product, Squared Up for Azure, and it’s shaping up very nicely. It’s not ready for you to play with quite yet, but we plan to have an early release available for enthusiastic testers in late September (sign up below!).

The Grafana Labs community has more than 600 developers around the world who contribute to our open source projects. From time to time, they also ask really great questions about how to get started in Grafana, how to solve an issue, or how to implement best practices for various functions. Here are three of the most popular questions on the Grafana community board right now – and the answers from Grafana team members and fellow developers.

In our Sentry for Data series, we explain precisely why Sentry is the perfect tool for your data team. The present post focuses on how we optimized Airflow for deeper insights into what goes wrong when our data pipelines break. Data enables Sentry’s go-to-market teams by generating high-quality leads and tailored marketing campaigns. Of course, data is also used to steer the business by influencing how we think about Sentry pricing, future opportunities, and feature roadmap.

Quite often I run projects at existing VMware customers that are building a new Digital Workspace. And quite often, that Digital Workspace contains a VDI. Especially when a customer is already using VMware technology in their data center, vRealize Operations (vROps) is used to monitor the environment. Although vROps is a great solution, it’s not really sufficient to monitor the desktop side of things.