For the third consecutive year, we’re happy to announce that Kong has been recognized as a Leader in the Gartner Magic Quadrant for Full Life Cycle API Management and is positioned furthest to the right for Completeness of Vision. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document.
Observability is a critical part of Kong’s API Gateway. In this post, we’ll describe two options to monitor Kong Gateway using Prometheus. Prometheus is an open source system monitor toolkit built at SoundCloud that is now widely adopted. StatsD was originally a simple daemon developed by Etsy to aggregate and summarize application metrics. Prometheus provides a StatsD exporter to collect metrics that are sent in StatsD format.
Today we’re excited to announce the release of Kong Mesh and Kuma 2.0. With this new major release, we’re announcing the first availability of our next-generation policies, in addition to new eBPF capabilities. 2.0 is also significant as we have unified the version scheme between Kong Mesh and Kuma. Previously, Kuma versions had an n-1 version naming convention when compared with Kong Mesh.
As a Solutions Engineer here at Kong, one question that frequently comes across my desk is “how can I transform a Kong logging plugin message into a format that my insert-observability-stack-here understands, i.e. ELK, Loki, Splunk, etc.?” In this blog, I’m going to show you how to easily accomplish converting a Kong logging payload to the Elastic Common Schema. In order to accomplish this task, we’re going to be running Kong Gateway in Kubernetes and using two Kong plugins.