Like city kids finding an open hydrant on a sweltering summer day, software engineers love Wavefront. The ultra-responsiveness of the charts in-browser, the stability of metric ingestion platform at scale, the power of the analytics query language – there is much to gush about. So it comes as little surprise to us, that DevOps and development engineers are blogging and sharing all their neat ways for using Wavefront to improve quality of their revenue-generating cloud applications.
No need to drink from this gushing hose of content. Here’s a curated list of recent blog posts authored by engineers at leading organizations using Wavefront.
Space Ape Games
Lead DevOps Engineer, Louis McCormack, writes about his contribution to a ruby gem (client) for speaking to Wavefront, as well as another open source project he pioneered, go-wavefront, which is a set of Golang libraries and a bundled CLI for interacting with the Wavefront API.
DevOps Engineer, Nathan Mclean, details his creation of a custom Terraform provider to automate management of Wavefront resources like Alerts, Alert Targets, and Dashboards, as they interact with the Wavefront API and want to move away from configuring these components by hand.
Senior Software Engineer, Hakan Baba, and Cofounder/VP Technology, Sam Ghods, outline how they rely on real-time metrics analytics from Wavefront to manage several large-scale Kubernetes clusters. They delineate expected vs. unexpected behavior using alerts, and this includes when a metric stops generating new data. They then describe their experiences and lessons for how to handle delayed or missing metrics in alerts.
Senior Systems Engineer, Matt Klein, has been regularly publishing on the progress at Lyft with Envoy, a service mesh for making microservices safe, fast, reliable, and observable. In this blog, Matt muses further about observability in the age of the service mesh and releases a snapshot of some of Lyft’s internal Envoy dashboards and some of the stats they look at. The pre-aggregation pipeline ultimately writes stats out to Wavefront, and the dashboards are driven by Wavefront’s query analytics engine.
British Gas Connected Homes
SRE, Robert Fisher, details how visibility into the status and performance of all aspects of a complex Puppet configuration is a big problem for enterprises. He then reviews the recent journey of his company and how they systematically tamed their “wild” puppet implementation using detailed visibility provided by the Wavefront. Code samples are included.
In this blog post, Robert reviews the new Wavefront CLI, major enhancements released this past summer to make Wavefront’s API coverage the most complete and simple to use for automation in the cloud monitoring industry. The blog is a comprehensive review of the installation and basics of our CLI, covering alerts, dashboards, proxies, data sources, events, maintenance windows, cloud integrations, queries, and more.
Principal System Engineer, Pontus Rydin, details a good use case to highlight the power of Wavefront’s metrics analytics – to identify which VMs have the strongest periodic variation (seasonality) on some metrics (in this case, CPU utilization). The use case for this is around capacity planning; with a better understanding of virtualized workload behavior, one can place workloads in such a way that their peak demands don’t coincide. The blog neatly steps through doing this with sample Python code, SciPy, and the Wavefront API. There’s some extra credit for super geeks at the end too!
In this blog post, Pontus guides you through the steps to export metrics from vRealize Operations to Wavefront. The author had recently released a Fling for bulk exporting vRealize Operations data and he them modifies that tool a bit to make it export data to Wavefront.
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