HashiCorp Consul Service Mesh on Kubernetes Series - Part 1 - Introduction and Setup

Modern cloud-native architectures rely heavily on microservices, and Kubernetes has become the go-to platform for deploying, managing, and scaling these distributed applications. As the number of microservices grows, ensuring secure, reliable, and observable service-to-service communication becomes increasingly complex. This is where service mesh solutions, such as HashiCorp Consul, step in to provide a seamless approach to managing these challenges. In this blog post, we will delve into the integration of HashiCorp Consul Service Mesh with Kubernetes, exploring its architecture, features, and step-by-step deployment guide.

HashiCorp Consul Service Mesh on Kubernetes Series - Part 2 - Observability

Modern service meshes require robust observability to ensure seamless operations, proactive troubleshooting, and performance optimization. In this section, we explore the observability features of HashiCorp Consul Service Mesh, including visualizing the service mesh, querying metrics, distributed tracing, and logging and auditing.

Visualizing the Service Mesh

The Consul UI is used for visualizing the service mesh and its topology.

Use the watch command to send requests to the application continually. Make sure HTTP status code 200 is returned in the output.

HashiCorp Consul Service Mesh on Kubernetes Series - Part 4 - Security

Security is a fundamental aspect of any service mesh, ensuring that all service-to-service communication is secure, controlled, and auditable. HashiCorp Consul provides robust security features, including mutual TLS (mTLS), access control, and rate limiting.

mTLS

In this section, we will demonstrate mTLS with Consul. Consul enables and strictly enforces mTLS by default. All traffic sent through the Consul Connect Service Mesh is encrypted.

This section is slightly different from the Istio mTLS section because:

CAPV: Addressing Node Provisioning Issues Due to an Invalid State of ETCD

I recently ran into a strange scenario on a Kubernetes cluster after a sudden and unexpected crash it had experienced due to an issue in the underlying vSphere environment. In this case, the cluster was a TKG cluster (in fact, it happened to be the TKG management cluster), however, the same situation could have occurred on any cluster managed by Cluster API Provider vSphere (CAPV).

I have seen clusters unexpectedly crash many times before and most of the time, they successfully went back online when all nodes were up and running. In this case, however, some of the nodes could not boot properly, and Cluster API started attempting their reconciliation.

TKG 2.3: Fixing the Prometheus Data Source in the Grafana Package

With the release of TKG 2.3, the Grafana package was finally updated from version 7.5.x to 9.5.1. If you have deployed the new Grafana package (9.5.1+vmware.2-tkg.1) or upgraded your existing one to this version, you may have run into error messages in your Grafana dashboards.

For example, in the TKG Kubernetes cluster monitoring default dashboard, you may have run into the Failed to call resource error when opening the dashboard and noticed that a lot of the data is missing.

Tanzu Kubernetes Grid GPU Integration

I recently had to demonstrate Tanzu Kubernetes Grid and its GPU integration capabilities. Developing a good use case and assembling the demo required some preliminary research.

During my research, I reached out to Jay Vyas, staff engineer at VMware, SIG Windows lead for Kubernetes, a Kubernetes legend, and an awesome guy in general. :) For those who don’t know Jay, he is also one of the authors of the fantastic book Core Kubernetes (look it up!).