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Jawad Qureshi

Gen3 now offers the option to use OpenShift instead of plain Kubernetes

We are excited to share that Gen3 now supports running Gen3 Helm charts on Red Hat's OpenShift distribution of Kubernetes! Although the default for Gen3 Helm is to run on plain Kubernetes, Gen3 users who prefer the user-friendliness and robust built-in security tools offered through Openshift can now implement Gen3 with OpenShift.

If you are interested in deploying Gen3 with OpenShift, read on for important information about what changed and what defaults to manage when using OpenShift.

If you prefer to continue to use plain Kubernetes, all the Gen3 Helm defaults will continue to support that; you will not need to make any changes.

Making the Gen3 Helm chart run on OpenShift

Gen3's Helm chart was written for plain Kubernetes with an Ingress in front of it. OpenShift is Kubernetes, but its default security posture rejects a lot of what a normal Helm chart assumes:

  • Containers can't bind to ports below 1024
  • Containers can't run as a fixed UID unless you're specifically granted that
  • The root filesystem is expected to be read-only unless you explicitly declared otherwise

Gen3 Helm PR #552, merged June 2026, is the actual body of work that permits the Gen3 Helm chart to run under those constraints. We touched nearly every subchart in this effort, but mostly to just apply the same handful of patterns everywhere.

This post walks through what that PR actually changed and why, and then reports what we observed when testing a fresh deployment end-to-end (i.e., from login to a real data submission via gen3-sdk).

The core problem: In OpenShift, the containers can't do what they used to in plain Kubernetes

Two OpenShift defaults matter here, and almost everything in the PR is a consequence of one or the other:

  • Restricted Security Context Constraint (SCC) assigns an arbitrary non-root UID per namespace and won't let a container bind privileged ports (<1024) unless it is root. Every Gen3 service was written assuming it could listen on port 80, so that needed to change for OpenShift compatibility.
  • readOnlyRootFilesystem is often enforced, which breaks anything that writes to paths baked into the image — for example, nginx's pid file, its temp/ cache dirs, its logs.

Fix both, and most of the rest is bookkeeping.

Fix #1: stop hardcoding port 80

Every subchart's deployment.yaml had containerPort: 80 baked into the template, and the app itself was told (via CLI flag or env var, depending on the service) to listen on 80. The PR made the port a value instead, and added an explicit non-privileged default:

Diff
 # helm/arborist/templates/deployment.yaml
           ports:
             - name: http
-              containerPort: 80
+              containerPort: {{ .Values.service.targetPort }}
               protocol: TCP
   ...
-              /go/src/github.com/uc-cdis/arborist/bin/arborist
+              /go/src/github.com/uc-cdis/arborist/bin/arborist --port {{ .Values.service.targetPort }}
Diff
 # helm/arborist/values.yaml
 service:
   type: ClusterIP
   port: 80
+  targetPort: 8080

The same shape landed in fence, sheepdog, indexd, peregrine, portal, revproxy, guppy, hatchery, metadata, manifestservice, requestor, sower, ssjdispatcher, wts, cedar, audit, argo-wrapper, gen3-workflow, gen3-analysis, gen3-user-data-library, cohort-middleware, access-backend, dicom-server, ohif-viewer, ohdsi-atlas, ohdsi-webapi, orthanc — over 25 subcharts got a service.targetPort value and the corresponding template change.

service.port (what other pods see when they talk to the Service) stays at the conventional 80; targetPort (what the container itself actually binds) moves to something in the 8000s that any UID can bind.

The probes had to move with it — httpGet.port: 80 became httpGet.port: http, referencing the named port instead of a number, so a probe doesn't silently point at the wrong port if targetPort changes:

Diff
           livenessProbe:
             httpGet:
               path: /_status?timeout=20
-              port: 80
+              port: http

Fix #2: give nginx somewhere to write

revproxy and portal both run nginx, and nginx by default wants to:

  • write a pid file to /var/run, bind port 80,
  • resolve upstream service names via kube-dns.kube-system.svc.cluster.local (an in-cluster DNS name that doesn't exist the same way on every OpenShift cluster), and
  • write its temp/cache/log directories into paths baked into the image.

Every one of those breaks under a restricted, read-only-root SCC. The fix templates all of it:

Diff
 # helm/revproxy/nginx/nginx.conf
-user nginx;
+user {{ .Values.nginx.user }};
 worker_processes 4;
-pid /var/run/nginx.pid;
+pid {{ .Values.nginx.pidFile }};
 ...
+  client_body_temp_path /tmp/client_temp;
+  proxy_temp_path       /tmp/proxy_temp_path;
+  fastcgi_temp_path     /tmp/fastcgi_temp;
+  uwsgi_temp_path       /tmp/uwsgi_temp;
+  scgi_temp_path        /tmp/scgi_temp;
   ...
   server {
-    listen 80;
+    listen {{ .Values.service.targetPort }};
   ...
-    resolver kube-dns.kube-system.svc.cluster.local ipv6=off;
+    resolver {{ .Values.nginx.resolver }} ipv6=off;

and the deployment gets emptyDir volumes mounted over every path nginx needs to write to, since the rest of the image filesystem is read-only:

Diff
 # helm/revproxy/templates/deployment.yaml
       volumes:
+        - name: nginx-tmp
+          emptyDir: {}
+        - name: nginx-cache
+          emptyDir: {}
+        - emptyDir: {}
+          name: nginx-logs
   ...
           volumeMounts:
+          - mountPath: /var/log/nginx
+            name: nginx-logs
   ...
+          - name: nginx-tmp
+            mountPath: /tmp
+          - name: nginx-cache
+            mountPath: /var/cache/nginx

portal got equivalent treatment — its own nginx-tmp emptyDir, a portal-nginx ConfigMap mounted over /etc/nginx/nginx.conf and /etc/nginx/conf.d/nginx.conf, and explicit pod/container securityContext blocks wired into the template rather than left to inherit whatever the cluster defaulted to:

Diff
 # helm/portal/templates/deployment.yaml
       serviceAccountName: {{ include "portal.serviceAccountName" . }}
+      securityContext:
+        {{- toYaml .Values.podSecurityContext | nindent 8 }}
   ...
         - name: portal
           image: "{{ .Values.image.repository }}:{{ .Values.image.tag | default .Chart.AppVersion }}"
+          securityContext:
+            {{- toYaml .Values.securityContext | nindent 12 }}

The chart ships two variants of the portal/frontend-framework nginx config — gen3.nginx.conf/portal-as-root/ and .../gen3ff-as-root/ — so the right one gets mounted depending on which frontend (global.frontendRoot: portal or gen3ff) is actually enabled.

Fix #3: stop assuming a specific UID/GID

fence had podSecurityContext.fsGroup: 101 hardcoded — which was fine on whatever cluster that number meant something on, but meaningless (or actively wrong) on an OpenShift namespace with its own assigned UID/GID range. The PR just removes the assumption:

Diff
 # helm/fence/values.yaml
 podSecurityContext:
-  fsGroup: 101
+podSecurityContext: {}

and sheepdog (which previously had no securityContext/ podSecurityContext knobs at all) got them added, template and values both. However, these are commented-out by default so an operator explicitly opts-in rather than the chart making a decision for them:

YAML
# helm/sheepdog/values.yaml
podSecurityContext:
  {}
  # fsGroup: 2000

securityContext:
  {}
  # capabilities:
  #   drop:
  #   - ALL
  # readOnlyRootFilesystem: true
  # runAsNonRoot: true
  # runAsUser: 1000

This is the pattern examples/openshift_values.yaml leans on: pick a fixed runAsUser/fsGroup per service and set it explicitly, once you know what your namespace's SCC actually allows. (See the SCC section below — it's not always the plain-vanilla restricted-v2 you'd expect).

Fix #4: add an actual OpenShift Route

None of the above matters if there's no way to get external traffic in without an Ingress controller. revproxy gained a real Route template:

YAML
# helm/revproxy/values.yaml
openshiftRoute:
  enabled: false
  annotations: {}
  host: ""
  path: "/"
  targetPort: "http"
  tls:
    termination: "edge"
    insecureEdgeTerminationPolicy: "Redirect"
  wildcardPolicy: "None"

rendering a plain route.openshift.io/v1 object gated behind openshiftRoute.enabled, so it's opt-in and coexists with the chart's existing Ingress templates rather than replacing them.

Fix #5: kubectl isn't always the right tool

wts's OIDC-client-registration job uses kubectl patch to patch a Kubernetes Secret with the client ID/secret it gets back from fence. That's fine on plain Kubernetes; however, on some OpenShift setups, the permissions model around who can patch what differs enough that it's simpler to swap in the OpenShift CLI image and use oc instead — gated behind a flag so it's opt-in per deployment:

YAML
# helm/wts/values.yaml
oidc_job_openshift: true
YAML
# helm/wts/templates/wts-oidc.yaml
{{- if .Values.oidc_job_openshift }}
- name: oc
  image: image-registry.openshift-image-registry.svc:5000/openshift/cli:latest
  ...
  oc patch secret wts-oidc-client --type=merge -p "..."
{{- else }}
- name: kubectl
  image: {{ .Values.image.utilImage }}
  ...
{{- end }}

Fix #6: a gen3_load dependency that didn't need to be there

The shared _db_setup_job.tpl that is used by every service's dbcreate init job sourced a gen3/gen3setup helper via gen3_load before running its Postgres-readiness loop. The PR comments that out in favor of plain echo/shell — one less external dependency for a job that's really just "wait for Postgres, create the DB if it doesn't exist, patch a Secret so downstream pods know it's done." (Commit messages in the PR describe this alongside "postgresql 15+ support" and cronjob fixes for metadata and fence — the common thread across all of them is removing assumptions that didn't hold up outside the original target environment.)

What we validated by actually deploying it

Reading a diff tells you what changed; it doesn't tell you whether the result actually works end-to-end. We took a Gen3 values file built on top of this PR's changes (examples/openshift_values.yaml), did a clean helm uninstall / helm install cycle against a real OpenShift namespace, and tested Gen3 behavior through login and data submission. Four things surfaced that the PR's diff alone wouldn't show you:

global.hostname must match your Route host, exactly. The PR gives you openshiftRoute.host to set the Route's hostname, but fence's BASE_URL (which drives every OAuth/OIDC redirect, OAUTH2_JWT_ISS, and the CSP FRAME_ANCESTORS header) is built from a separate value, global.hostname. If you only set openshiftRoute.host and leave global.hostname as the chart default (localhost), the portal loads fine, but clicking "Login" will redirect to https://localhost/.... Not a bug in the PR — just a second value that needs to agree with the first one (easy to miss).

Namespace LimitRanges can undo the port/UID work in a different way. Plenty of OpenShift projects cap containers at a default CPU limit (commonly 200m) if no limit is set explicitly. We hit this twice:

  • postgresql's upstream chart requests 250m CPU with no explicit limit, which OpenShift outright rejects (FailedCreate, pod never even schedules) once the LimitRange injects its 200m default.
  • fence had no resources block in our values file at all, silently inherited the same 200m limit from OpenShift LimitRange, and got CPU-throttled under real login traffic — resulting in 8-24 second responses on /user/user. No errors anywhere -- just slow.

The solution is to use kubectl get limitrange -o yaml before you deploy, and give explicit resources to whatever is on your request hot path.

Check which SCC policies you actually have before assuming you need more. The namespace we deployed into surprisingly carried restricted-v2-anyuid, not plain restricted-v2 — an anyuid-flavored grant that permits fixed runAsUser values outside the namespace's assigned UID range. That's why the fixed UIDs in examples/openshift_values.yaml (1000, 1000660001, 1000950000) work at all. (If our namespace only had the restricted-v2 SCC, we would only have been able to use values in the assigned UID range.) To check what SCC policies are already granted, you can use kubectl get pod <pod> -o jsonpath='{.metadata.annotations.openshift\.io/scc}'. This can help you avoid an assumption that your cluster needs a specific SCC grant it might already have.

gen3-sdk requires validation of the server's certificate to connect to the server. If your Route host isn't under the cluster's actual router wildcard domain (our example was chosen to match a local /etc/hosts entry, not the cluster's real domain), the router's TLS cert won't validate. If using cURL to test connection, curl -k will permit connecting while skipping the security check. However, the Gen3 SDK classes Gen3Auth/Gen3Submission use plain requests with no verification override exposed. We could not use the Gen3 SDK to submit data in our disposable insecure dev cluster unless we globally disabled requests verification for the session. Of course, for anything closer to production, put the Route under the real router domain instead.

With those four addressed, the full path worked: Route → mock Google login → fence-create token-create for a scripted API key → gen3-sdk creating a Program, a Project, and an Experiment node under it, verified by reading the record back through peregrine's GraphQL endpoint. The openshift.md file in the Gen3 Helm repo has the exact commands for reproducing all of this, including the mock-auth/API-key/submission flow in full.

Where things stand

The chart-level work (PR #552) is the real substance here: over two dozen subcharts updated with a consistent, minimal pattern — parameterize the port, give nginx somewhere to write, stop hardcoding UIDs, add a Route. None of it is exotic; all of it is the specific set of assumptions that plain-Kubernetes Helm charts tend to make without realizing they're assumptions until OpenShift's defaults refuse to go along with them. What we added on top is smaller: a working reference values file, and the handful of environment-specific "gotchas" (hostname/BASE_URL agreement, LimitRange interactions, SCC variants, TLS verification) that only show up once you actually deploy and click through the thing.

Boost Your K8s Productivity with These Handy Tools

Managing Kubernetes clusters and resources can get complicated quickly. Thankfully, there are some great open source tools that make working with k8s much easier. In this post, I'll highlight some of my favorite k8s productivity boosters.

kubectl Aliases

One of the first things I do when setting up my workstation to work with Kubernetes environments is create a set of aliases for common kubectl commands. This saves a ton of typing! Some useful aliases include:

Text Only
alias k=kubectl
alias kg=kubectl get
alias kgp=kubectl get pod
alias kd=kubectl describe
alias ke=kubectl edit
Full list of aliases!
Text Only
if (( $+commands[kubectl] )); then
    __KUBECTL_COMPLETION_FILE="${ZSH_CACHE_DIR}/kubectl_completion"

    if [[ ! -f $__KUBECTL_COMPLETION_FILE ]]; then
        kubectl completion zsh >! $__KUBECTL_COMPLETION_FILE
    fi

    [[ -f $__KUBECTL_COMPLETION_FILE ]] && source $__KUBECTL_COMPLETION_FILE

    unset __KUBECTL_COMPLETION_FILE
fi

# This command is used a LOT both below and in daily life
alias k=kubectl

# Execute a kubectl command against all namespaces
alias kca='f(){ kubectl "$@" --all-namespaces;  unset -f f; }; f'

# Apply a YML file
alias kaf='kubectl apply -f'

# Drop into an interactive terminal on a container
alias keti='kubectl exec -ti'

# Manage configuration quickly to switch contexts between local, dev ad staging.
alias kcuc='kubectl config use-context'
alias kcsc='kubectl config set-context'
alias kcdc='kubectl config delete-context'
alias kccc='kubectl config current-context'

# List all contexts
alias kcgc='kubectl config get-contexts'

# General aliases
alias kdel='kubectl delete'
alias kdelf='kubectl delete -f'

# Pod management.
alias kgp='kubectl get pods'
alias kgpw='kgp --watch'
alias kgpwide='kgp -o wide'
alias kep='kubectl edit pods'
alias kdp='kubectl describe pods'
alias kdelp='kubectl delete pods'

# get pod by label: kgpl "app=myapp" -n myns
alias kgpl='kgp -l'

# Service management.
alias kgs='kubectl get svc'
alias kgsw='kgs --watch'
alias kgswide='kgs -o wide'
alias kes='kubectl edit svc'
alias kds='kubectl describe svc'
alias kdels='kubectl delete svc'

# Ingress management
alias kgi='kubectl get ingress'
alias kei='kubectl edit ingress'
alias kdi='kubectl describe ingress'
alias kdeli='kubectl delete ingress'

# Namespace management
alias kgns='kubectl get namespaces'
alias kens='kubectl edit namespace'
alias kdns='kubectl describe namespace'
alias kdelns='kubectl delete namespace'
alias kcn='kubectl config set-context $(kubectl config current-context) --namespace'

# ConfigMap management
alias kgcm='kubectl get configmaps'
alias kecm='kubectl edit configmap'
alias kdcm='kubectl describe configmap'
alias kdelcm='kubectl delete configmap'

# Secret management
alias kgsec='kubectl get secret'
alias kdsec='kubectl describe secret'
alias kdelsec='kubectl delete secret'

# Deployment management.
alias kgd='kubectl get deployment'
alias kgdw='kgd --watch'
alias kgdwide='kgd -o wide'
alias ked='kubectl edit deployment'
alias kdd='kubectl describe deployment'
alias kdeld='kubectl delete deployment'
alias ksd='kubectl scale deployment'
alias krsd='kubectl rollout status deployment'
kres(){
    kubectl set env $@ REFRESHED_AT=$(date +%Y%m%d%H%M%S)
}

# Rollout management.
alias kgrs='kubectl get rs'
alias krh='kubectl rollout history'
alias kru='kubectl rollout undo'

# Port forwarding
alias kpf="kubectl port-forward"

# Tools for accessing all information
alias kga='kubectl get all'
alias kgaa='kubectl get all --all-namespaces'

# Logs
alias kl='kubectl logs'
alias klf='kubectl logs -f'

# File copy
alias kcp='kubectl cp'

# Node Management
alias kgno='kubectl get nodes'
alias keno='kubectl edit node'
alias kdno='kubectl describe node'
alias kdelno='kubectl delete node'

I stole my k8s aliases from a Github Gist. Huge shoutout to Github User doevelopper

k9s

https://k9scli.io

k9s provides a terminal UI for interacting with your Kubernetes clusters. It's great for get a quick overview of pods, nodes, services etc. Some of the handy features include:

  • Live filtering of resources
  • Easy log viewing
  • Executing containers
  • Resource editing

k9s makes it super easy to manage Kubernetes in a terminal-centric workflow.

asciicast

kubectx and kubens

kubectx and kubens allow you to quickly switch between Kubernetes contexts and namespaces. This comes in handy when you're working with multiple clusters or namespaces.

Some examples:

kubens staging - switch to staging namespace
kubectx minikube - change context to minikube cluster

No more typing out full context and namespace names!

Here's a kubectx demo: kubectx demo

...and here's a kubens demo: kubens demo

Credit: Created and released by Ahmet Alp Balkan

Running K8s on Your Laptop: Exploring the Options

Kubernetes (often abbreviated as "K8s") is an open-source platform designed to automate deploying, scaling, and managing containerized applications. Initially, Kubernetes might seem more fitting for large scale, cloud environments. However, for learning, development, and testing purposes, running Kubernetes locally on your laptop is incredibly beneficial. Let's dive into the various ways you can achieve this.

1. Minikube

Pros:

  • Officially supported by Kubernetes.
  • Provides a full-fledged K8s cluster with just one node.
  • Supports many Kubernetes features out-of-the-box.
  • Easy to install and use.

Cons:

  • Can be resource-intensive.
  • Requires a virtual machine (VM) or a local container runtime.

Overview:

Minikube is essentially a tool that runs a single-node Kubernetes cluster locally inside a VM (by default). This makes it perfect for users looking to get a taste of Kubernetes without the complications of setting up a multi-node cluster.

2. Docker Desktop

Pros:

  • Comes integrated with Docker, a popular containerization tool.
  • Provides Kubernetes out-of-the-box, no additional installation required.
  • Does not require a VM for macOS and Windows.

Cons:

  • Limited to a single node.
  • Might not support all K8s features.

Overview:

Docker Desktop, available for both Windows and macOS, offers a simple way to start a Kubernetes cluster. By simply checking a box in the settings, you get a single-node K8s cluster running alongside your Docker containers.

3. Kind (Kubernetes IN Docker)

Pros:

  • Runs K8s clusters using Docker containers as nodes.
  • Lightweight and fast.
  • Can simulate multi-node clusters.

Cons:

  • Might be slightly more complex for beginners.
  • Intended primarily for testing Kubernetes itself.

Overview:

Kind is an innovative solution that allows you to run Kubernetes clusters where each node is a Docker container. It’s especially useful for CI/CD pipelines and testing Kubernetes itself.

4. MicroK8s

Pros:

  • Lightweight and fast.
  • Single command installation.
  • Offers various add-ons for enhanced functionality.

Cons:

  • Limited to Linux.
  • Not as widely adopted as other solutions.

Overview:

MicroK8s is a minimal Kubernetes distribution aimed at developers and edge computing. It's a snap package, which makes it extremely simple to install on any Linux distribution.

5. K3s

Pros:

  • Extremely lightweight.
  • Simple to install and run.
  • Suitable for edge, IoT, and CI.

Cons:

  • Strips out certain default K8s functionalities to remain light.

Overview:

K3s is a lightweight version of Kubernetes. It's designed for use cases where resources are a constraint or where you don't need the full feature set of standard Kubernetes.

6. Rancher Desktop

Pros:

  • Provides a user-friendly GUI for managing Kubernetes clusters.
  • Supports multi-node clusters.
  • Offers integration with Rancher for enhanced Kubernetes management.
  • Works on Windows, macOS, and Linux.

Cons:

  • Requires additional setup compared to some other options.
  • May consume more resources for multi-node clusters.

Overview:

Rancher Desktop is a versatile tool that simplifies the management of Kubernetes clusters on your local machine. It offers a user-friendly graphical interface, making it an excellent choice for users who prefer a visual approach to Kubernetes cluster management. Rancher Desktop can set up and manage multi-node clusters, which can be valuable for testing and development scenarios. Additionally, it integrates seamlessly with Rancher, providing even more advanced Kubernetes management capabilities.

Conclusion

Running Kubernetes on your laptop is feasible and offers a variety of methods, each catering to different use cases. Whether you’re a developer wanting to test out your applications, an enthusiast keen on learning Kubernetes, or even someone looking to set up CI/CD pipelines, there's an option for you.

It's essential to weigh the pros and cons of each method, consider your resource limitations, and the scope of your projects. Regardless of the option you choose, diving into the world of Kubernetes is an enriching experience, offering a deep dive into modern cloud-native development and operations.

Choosing the Right Lightweight Kubernetes Tool for Local Development

Kubernetes, the popular container orchestration platform, is a cornerstone of modern development and deployment. However, running Kubernetes locally for development and testing purposes requires efficient tools that don't consume excessive resources. In this article, we'll explore several lightweight Kubernetes tools for local development and discuss their pros and cons.

Of course, getting every bell and whistle working (like that handy ingress feature that routes external traffic around the cluster) might need some extra tweaking on a basic laptop setup. But hey, half the fun is figuring out how to configure your local environment to really sing, right? As we look at tools for local dev, we'll hit on ways to tune things up for peak Gen3 performance.

When it comes to local Kubernetes development, several solid options exist for standing up a dev cluster directly on your laptop. In this blog post, we will explore popular choices!

Now, here's the cool part - Gen3 works on any Kubernetes cluster, whether you've just spun one up on your laptop or have a full-blown production cluster. That means you can kick the tires locally before taking it out for a spin in the real world.

Kind (Kubernetes IN Docker)

Overview: Kind runs Kubernetes inside a Docker container, making it an excellent choice for local development and testing. It is also used by the Kubernetes team to test Kubernetes itself.

Pros: - Fast cluster creation (around 20 seconds). - Robust and reliable, thanks to containerd usage. - Suitable for CI environments (e.g., TravisCI, CircleCI).

Cons: - Ingress controllers needs to be deployed manually

Preferred for gen3

In my experience, this is the most preferred method for running Gen3 on a laptop especially when paired up with OrbStack instead of Docker/Rancher desktop. I use this as my preffered K8s on my M1 Macbook.

Docker for Desktop

Overview: Docker for Desktop is an accessible option for MacOS users. Enabling Kubernetes in the Docker For Mac preferences allows you to run Kubernetes locally.

Pros: - Widely used and well-supported. - No additional installations required. - Built images are immediately available in-cluster.

Cons: - Resource-intensive due to docker-shim usage. - Difficult to customize and troubleshoot.

MicroK8s

Overview: MicroK8s is recommended for Ubuntu users. It is installed using Snap and includes useful plugins for easy configuration.

Pros: - Minimal overhead on Linux (no VM). - Simplified configuration with plugins. - Supports a local image registry for fast image management.

Cons: - Resetting the cluster is slow and can be error-prone. - Best optimized for Ubuntu, may be less stable on other platforms.

Rancher Desktop

Overview: Rancher Desktop is an open-source alternative to Docker Desktop. It uses containerd by default and offers flexibility in choosing a container runtime.

Pros: - Cross-platform (MacOS/Linux/Windows). - Utilizes k3s, known for its speed and resource efficiency. - Ingress with Traefik works out of the box

Cons: - Rapidly evolving, not fully supported by all tools.

Minikube

Overview: Minikube is a versatile option offering high fidelity and customization. It supports various Kubernetes versions, container runtimes, and more.

Pros: - Feature-rich local Kubernetes solution. - Customizable with multiple options. - Supports a local image registry for efficient image handling.

Cons: - Initial setup complexity, especially with VM drivers. - Some advanced options may require manual configuration. - Resource-intensive if using a VM.

k3d

Overview: k3d runs k3s, a lightweight Kubernetes distribution, inside a Docker container. k3s removes optional and legacy features while maintaining compatibility with full Kubernetes.

Pros: - Extremely fast startup (less than 5 seconds on most machines). - Built-in local registry optimized for Tilt.

Cons: - Less widely used, leading to limited documentation. - Some tools may have slower adoption.

In conclusion, choosing the right lightweight Kubernetes tool for your local development depends on your specific needs and preferences. Each tool offers a unique set of advantages and drawbacks, so consider your project requirements and platform compatibility when making your decision.

Feel free to experiment with these tools and share your experiences in the Kubernetes development journey!