Cloud-Native on Brave New Geek

Using Google-Managed Certificates and Identity-Aware Proxy With GKE

Wed, 24 Jun 2020 11:31:44 -0500

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.

Zero-Trust Security on GCP With Context-Aware Access

Mon, 22 Jun 2020 14:54:15 -0500

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.

Liftbridge 1.0

Tue, 28 Apr 2020 13:12:21 -0500

Liftbridge has evolved a lot since making the first commit in October 2017, but the vision has remained the same: provide a message-streaming solution with a focus on simplicity and usability. This is demonstrated through many of the design and implementation decisions. A few examples include the use of NATS as the messaging backbone, avoiding heavy dependencies on runtimes like the JVM and external coordination systems like ZooKeeper, compiling down to a small, single static binary, opting for a gRPC-based API, and relying on plain YAML configuration. Liftbridge is written in Go, and the code is structured with the hopes that it’s relatively easy for someone to hop in and contribute to the project.

Microservice Observability, Part 2: Evolutionary Patterns for Solving Observability Problems

Fri, 03 Jan 2020 14:18:10 -0600

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.

Microservice Observability, Part 1: Disambiguating Observability and Monitoring

Thu, 03 Oct 2019 10:55:23 -0500

“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.

The Observability Pipeline

Wed, 12 Sep 2018 11:37:24 -0500

The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. In this world, servers are very much cattle, not pets. This shift has exposed deficiencies in some of the tools and practices we used in the world of servers-as-pets. It has also led to new tools and services created to help us support our systems.

Introducing Liftbridge: Lightweight, Fault-Tolerant Message Streams

Fri, 27 Jul 2018 17:42:49 -0500

Last week I open sourced Liftbridge, my latest project and contribution to the Cloud Native Computing Foundation ecosystem. Liftbridge is a system for lightweight, fault-tolerant (LIFT) message streams built on NATS and gRPC. Fundamentally, it extends NATS with a Kafka-like publish-subscribe log API that is highly available and horizontally scalable.

I’ve been working on Liftbridge for the past couple of months, but it’s something I’ve been thinking about for over a year. I sketched out the design for it last year and wrote about it in January. It was largely inspired while I was working on NATS Streaming, which I’m currently still the second top contributor to. My primary involvement with NATS Streaming was building out the early data replication and clustering solution for high availability, which has continued to evolve since I left the project. In many ways, Liftbridge is about applying a lot of the things I learned while working on NATS Streaming as well as my observations from being closely involved with the NATS community for some time. It’s also the product of scratching an itch I’ve had since these are the kinds of problems I enjoy working on, and I needed something to code.