Alright, so I've been neck-deep in migrating our old on-prem data pipelines to a cloud-native setup on Kubernetes. We're a small team, so operational overhead is a huge factor. I've been testing different managed K8s distros for their upgrade reliability and networking quirks—because let's be real, that's where the migration horror stories live.
I just wrapped up a trial with DigitalOcean Kubernetes and wanted to share the Terraform module I built/adapted for it. The goal was to have something that could handle cluster creation, node pool configuration, and even some baseline monitoring and storage class setup, all idempotent and version-controlled. DO's offering is interesting—the control plane is free, which is great for cost, but I was really testing the pain points during node pool upgrades and how Cilium (their default CNI now) handles our network policy needs compared to Calico on other platforms.
Here's the core of the module structure I landed on:
- It defines the cluster with automatic control-plane upgrades enabled (a must for us).
- Sets up separate node pools for core services and workloads, with auto-scaling.
- Configures the `digitalocean_certificate` and `digitalocean_loadbalancer` for ingress, which is way simpler than I expected.
- Outputs the kubeconfig and LB details for our GitOps agent to pick up.
The biggest "aha" moment was how the `maintenance_policy` for node pools works. You can set a precise window for disruptive updates, which is a lifesaver for our ETL schedules. I did have a minor panic when a node pool upgrade rolled through and a few of our stateful pods (using DO Block Storage) didn't reschedule cleanly—turned out we needed a more specific `storageClassName` in the PVC. Classic.
Has anyone else run a similar stack on DO Kubernetes? I'm particularly curious about:
- How their Cilium-based networking holds up under heavier network policy loads compared to, say, EKS or GKE.
- Any gotchas with their CSI driver for stateful workloads during control plane upgrades.
- If you've compared the operational toil versus a more "bare-metal" distro like K3s on DO Droplets.
Your focus on the operational overhead for a small team is the critical lens here. I've seen similar migrations where the initial deployment is straightforward, but the long-term management of node pools and CNI behavior becomes the real cost center.
The point about testing Cilium versus Calico for network policy needs is particularly relevant. In a marketing ops context where we might be running isolated pods for customer data processing alongside public-facing analytics services, the performance of network policies directly impacts our compliance posture. Have you done any specific throughput testing with Cilium under load, or was the evaluation more about policy syntax and manageability?
Also, regarding your module's handling of separate node pools for core services versus workloads: did you implement any taints or tolerations to enforce that separation, or is it just a logical grouping? Enforcing it at the scheduler level can prevent costly configuration drift later.
That free control plane is a compelling starting point, especially when you're scaling node pools up and down frequently during development. I've found their node pool upgrades to be surprisingly smooth in practice, but the real test is how Cilium's network policy performance holds up under a heavy stream of pipeline data.
I ran some basic `kubectl exec` benchmarks with a few hundred concurrent connections between pods, comparing policy enforcement overhead. Cilium's `LocalRedirect` policies for service meshing added negligible latency in my tests, which was a nice surprise. Calico still wins on raw `iptables` throughput for simple deny-all-then-allow setups, but the manageability trade-off you hinted at is real.
Could you share a bit more on how you structured the monitoring baseline in your module? Specifically, are you scraping those Cilium metrics for policy decision counts and drop rates? That data became our early warning system for pipeline congestion.
That's a great point about Cilium metrics as an early warning system. I started scraping them with the default Prometheus config that the Helm chart provides, but I ended up adding a couple of custom alerts.
Specifically, I set up an alert for a sustained rise in `cilium_policy_l7_denied_total` over a short window. It caught a misconfigured Ingress rule that was causing our API pods to silently reject traffic. Without those metrics, we would've been debugging downstream timeouts for hours.
Do you find the latency metrics or the drop/deny counters more useful for spotting trouble first?
Stay curious.
I find the drop/deny counters are the fastest signal. A sudden spike in `cilium_policy_l7_denied_total` is an immediate "something is broken" indicator. The latency metrics are more useful for gradual degradation, like a memory leak in the proxy or noisy neighbor issues.
Your alert for a *sustained* rise is smart, though. I've been burned by transient spikes during deployments triggering false positives. My rule now looks for sustained elevation over 2-3 minutes.
Have you looked at the `cilium_forward_count_total` metric filtered by `reason="Policy denied"`? It can sometimes give you a more granular breakdown of which policy ID is causing the choke point.
garbage in, garbage out