Systems | Development | Analytics | API | Testing

Building Smart O11y for Kuma With Elastic Observability

This blog was co-created by Ricardo Ferreira (Elastic) and Viktor Gamov (Kong). We love our microservices, but without a proper observability (O11y) strategy, they can quickly become cold, dark places cluttered with broken or unknown features. O11y is one of those technologies deemed created by causation: the only reason it exists is that other technologies pushed for it. There wouldn’t be need for O11y if, for example, our technologies haven’t gotten so complex across the years.

Using Elastic ML to Observe Your Kuma API Observability Metrics

Observability is catching on these days as the de-facto way to provide visibility into essential aspects of systems. It would be unwise for you not to leverage it with Kuma service mesh — the place that allows your services to communicate with the rest of the world. However, many observability solutions restrict themselves to the works: simple metric collection that provides them with dashboards. Expecting users to simply sit on their chairs and look at those metrics all day long is an invitation to failure, as we know that one can only do so much when they get tired and bored.

Building With Insomnia as a REST API Client

As more companies invest in a cloud native infrastructure, they’re choosing to prioritize their applications as microservices—architecting them into distinct servers. Each component is responsible for one (and only one) feature. For example, you might have Server A responsible for handling billing logic, Server B for handling user interaction and Server C for handling third-party user interactions.

How We Got a 12% Increase in RPS and a 37% Drop in Latency

At Kong, we run performance testing in CI in every commit or pull request that has a potential performance impact, as well as on each release. Thanks to the performance testing framework and its integration with Github Actions, we can easily get basic metrics like RPS and latency. Also, flame graphs to pinpoint the significant part that draws down performance. With that workflow in place, we figured one of the most significant parts of Kong’s hotpath is Nginx variable accesses.

4 Ways to Leverage Kong's jq Plugin

As part of the Kong Gateway 2.6 release, we shipped a brand new jq plugin for anyone with an enterprise license to use. It’s like we combined the request and response transformer plugins to form a single, more powerful plugin—supercharging the way we work with request and response bodies. If you’re not familiar with jq, it’s a JSON processing language that allows you to manipulate any JSON document and transform it however you need.

Kong and Neosec: Behavioral Analytics With Response Automation

The Neosec platform integrates with Kong Gateway Enterprise to provide automated and continuous API discovery, API risk posture alerting and API protection through behavioral analytics and response automation. And it does all that while being out of band, using the logs shipped from Kong to Neosec.