DevOps is an essential part of software development in the modern IT world. However, as the application becomes more and more complex, managing infrastructure can become challenging. One of the essential aspects of driving such infrastructure is metrics collection and performance monitoring. There are plenty of tools available for monitoring a system or an infrastructure, but we are going to focus on, analyze, and look at the architecture of Prometheus.
One of the top Elite partners I enjoy working with is an MSP based out of White Salmon, Washington called Radcomp. Over the years, I’ve worked closely with their manager of operations, Tyrell Midland. We’ve had all sorts of candid conversations in that time, but one we had last week I thought would make a great blog post.
Adoption of service meshes like Istio is increasing. As a result, Speedscale has developed a webassembly plugin. We extended Envoy using Rust, and no changes are required to your Istio configuration. This allows us to leverage the same sidecars that you have deployed throughout your environment to inspect API traffic. Once we are listening through Istio, the typical Speedscale magic can take place. We can use the data to build integration/performance test suites and autogenerate service mocks.
A common pain point we repeatedly hear from our customers that use Desktop as a Service (DaaS)/Virtual Desktop Infrastructure (VDI) environments is, “We have monitoring in place for physical hosts and infrastructure, but our employees still complain a lot.” If DaaS or VDI is part of your IT environment and you lack visibility into such environments to ensure effective employee experience, read on.
Slack experienced meteoric growth between 2017 and 2020—but that level of growth came with growing pains. In his talk at the 2021 o11ycon+hnycon, Frank Chen (LinkedIn), a Slack Senior Staff Engineer, detailed one of Slack’s biggest pain points in that period: flaky tests. A flaky test returns both a passing and failing result despite no changes in the code. At one point, between 2017 and 2020, Slack’s flaky test rate reached as high as 50%.
In a previous blog, we discussed how to monitor, troubleshoot, and fix high %CPU issues. We also revealed a system API that could have an unexpected impact on CPU consumption. In this episode, we’ll discuss another time-related performance aspect that is unique to security software: application startup time. You don’t need to be a developer to benefit from this article.
Another generic error message from our favorite FaaS provider AWS Lambda. And again, there are multiple reasons why this issue could arise. Let’s first look at the basics of AWS Lambda to get a better intuition for when things go wrong later. Lambda is an asynchronous event-based service at heart.
We’ve been monitoring 100,000’s of serverless backend components for 3+ years at Dashbird. In our experience, Serverless infrastructure failures boil down to: These isolated faults become causes of failure due to dependencies in our cloud architectures (ref. Difference of Fault vs. Failure). If a serverless Lambda function relies on a database that is under stress, the entire API may start returning 5XX errors.
