category
Cloud
- #95 6 min
Using Google-Managed Certificates and Identity-Aware Proxy With GKE
Ingress on Google Kubernetes Engine (GKE) uses a Google Cloud Load Balancer (GCLB). GCLB provides a single anycast IP that fronts all of your backend compute instances along with a lot of other rich features. In order to create a GCLB that uses HTTPS, an SSL certificate needs to be associated with the ingress resource. This certificate can either be self-managed or Google-managed. The benefit of using a Google-managed certificate is that they are provisioned, renewed, and managed for your domain names by Google. These managed certificates can also be configured directly with GKE, meaning we can configure our certificates the same way we declaratively configure our other Kubernetes resources such as deployments, services, and ingresses.
- #94 6 min
Zero-Trust Security on GCP With Context-Aware Access
A lot of our clients at Real Kinetic leverage serverless on GCP to quickly build applications with minimal operations overhead. Serverless is one of the things that truly differentiates GCP from other cloud providers, and App Engine is a big component of this. Many of these companies come from an on-prem world and, as a result, tend to favor perimeter-based security models. They rely heavily on things like IP and network restrictions, VPNs, corporate intranets, and so forth. Unfortunately, this type of security model doesn’t always fit nicely with serverless due to the elastic and dynamic nature of serverless systems.
- #91 17 min
Microservice Observability, Part 2: Evolutionary Patterns for Solving Observability Problems
In part one of this series, I described the difference between monitoring and observability and why the latter starts to become more important when dealing with microservices. Next, we’ll discuss some strategies and patterns for implementing better observability. Specifically, we’ll look at the idea of an observability pipeline and how we can start to iteratively improve observability in our systems. To recap, observability can be described simply as the ability to ask questions of your systems without knowing those questions in advance. This requires capturing a variety of signals such as logs, metrics, and traces as well as tools for interpreting those signals like log analysis, SIEM, data warehouses, and time-series databases. A number of challenges surface as a result of this. Clint Sharp does a great job discussing the key problems, which I’ll summarize below along with some of my own observations.
- #90 9 min
Microservice Observability, Part 1: Disambiguating Observability and Monitoring
“Pets versus cattle” has become something of a standard vernacular for describing the shift in how we build systems. It alludes to the elastic and dynamic nature of these (typically, but not necessarily) container-based systems with on-demand scaling and more transparent fault-tolerance. I’ve talked before about this transition before and specifically how it relates to monitoring. In particular, with these more dynamic, microservice-based systems, the conversation starts to shift away from traditional monitoring toward observability. In this series, I’ll describe that distinction, explain why it matters, and share some concrete tactical items for implementing observability in a microservice environment.
- #89 6 min
What’s Going on with GKE and Anthos?
GCP’s Slippery Slide into Enterprise When former Oracle exec Thomas Kurian took over for Diane Greene as Google Cloud’s CEO, a lot of people expressed concern about what this meant for the future of GCP. Vendor lock-in is already at the forefront of the minds of many cloud adopters, and Oracle is notorious for locking customers into expensive and prolonged contracts. However, I thought the move was smart on Google’s part.