This is public postmortem for an a complete shutdown of our internal Kubernetes cluster. It’s shared with you all so everyone may learn.

Background

Saltside has multiple Kubernetes clusters for different use cases. This postmortem covers an outage on np.k8s.saltside.io . This is our nonproduction cluster used for general developer experiments and as a target for CI builds. I lead the SRE team at Saltside. We’re responsible for provisioning and maintaining these clusters.

Impact

Internal Kubernetes services unavailable inside np.k8s.saltside.io

Tiller unavailable ( helm commands fail, no helm installs)

commands fail, no Unable to create new pods

Existing pods in Pending phase become Unknown

phase become Flaky delete behavior

Timeline

I had completed a round of work on our application’s Helm chart and needed to test the CI process on my machine before making some commits. The CI process builds charts for all our markets (Efritin.com, Tonaton.com, Ikman.lk, and Bikroy.com), installs them on a cluster ( np.k8s.saltside.io ), and runs helm test against each release. I fired off the command and monitored pods with watch kubectl get pods . All pods came to the Running phase after sometime. I continued watching the pods incase of failed probes triggering extra restarts. I noticed some pods entered the Pending phase after being Running . I shrugged this off as a transient issue that would resolve itself. Nothing was broken or critical at this point, plus I had a meeting to attend. I expected things to resolve during the meeting.

I checked on things two hours later. The issue had not resolved itself. It became larger. I noticed pods in an Unknown state. This was new to me and really piqued my interest and made me thing something had definitely gone wrong internally.

I used kubectl describe pod to find more information. The pod event logs were full of image pulling errors from the container runtime (Docker in our case). Again, I largely wrote this problem off because:

This is the nonproduction cluster

Node use t2.medium

Historically we’ve had a lot of instability from the official Docker registry, so failed pulled are nothing new

My estimation at his point was things has gotten overwhelmed pulling all the images. I decided it would be easier to start deleting things given the uncertainty around the root cause and low business impact of the current conditions.

I attempted to start with deleting Helm releases. This would delete everything ( Deployment , Service , Pod , ConfigMap , Secret ) without needing to delete individual resources. Unfortunately the tiller pods in kube-system had started trashing. They were in a crash loop for an unknown reason. Now the only option was to delete things outside of helm and worry about helm afterwards.

Initial kubectl delete commands seemed to work successfully but nothing actually was removed. I started to assume there was some strange internal inconsistency that was being exacerbated by CPU/Memory/Disk consumption. A quick google search (naturally leading to Stack overflow) revealed a way to forcibly delete resources. I added --force --grace-period=0 and attempted to delete pods. The pods where removed from Kubernetes (not known in kubectl get ). However I did not verify at Docker level (which should have been done). Now I needed turned my attention to restoring the tiller service.

I ran kubectl get pods -n kube-system to see the internal state there. Pods were in a variety of undesired states. Notably some where in DeadNode . This was another I had not seen before — another indicator of something going seriously wrong. This prompted me to inspect the nodes with kubectl describe nodes . Some nodes reported the container runtime was done and others reported the kubelet was done for various failed checks. I attempted to SSH into the instances but my connections repeatedly timeout. I decided it would be easier to simply terminate these instances and let the ASG sort it out. I terminated the three worker nodes and waited for new instances to join the cluster. Sure enough everything was back to normal after about 10 minutes.

Observations

This sequence of events was quite concerning. The normal activity of deploying our application could collapse the cluster and leave things in disarray. Terminating worker nodes is undesirable solution because pods are not drained to other nodes which would like create unavailability for our customers. It works, but it cannot be standard procedure for these kind of outages. Beyond that there were other important observations about out current setup:

Node unavailability was not picked up any monitoring. kubectl get nodes reported that all three worker nodes were not ready, but nothing was reported.

reported that all three worker nodes were not ready, but nothing was reported. We had no pods/phase metrics (i.e. Number of pods in Unknown or Pending ).

or ). Deploying a small number of a containers in parallel completely overloaded the cluster

Tiller pods failed to reschedule because of CPU limits. This is curious because there were no CPU request issues at the node level. This warrant future investigation.

helm init runs tiller with a single replica. This is not HA. It’s also uncertain what the HA story is with tiller.

runs tiller with a single replica. This is not HA. It’s also uncertain what the HA story is with tiller. We had no node (CPU/Memory/Disk) metrics

We did not understand what the Kubelet checks test under the covers (e.g. What is “Disk Pressure?”)

We had no way to throttle the number of newly created pods. This would not solve the problem, but it would mitigate risk in future scenarios. There’s no need to DoS our own cluster in case something goes wrong.

We had no CloudWatch metrics in DataDog for this AWS account.

Outcomes

There were some short term actions to take based on the observations. First, I wanted to repeat the process with more telemetry to better diagnose the system bottleneck. I fixed the issue with the DataDog integration and our nonproduction AWS account so there was some data. I suspected bottleneck was either the disk or the Docker daemon itself. I repeated the process and watched the EBS metrics. The results were conclusive:

Worker node EBS metrics during chart installs

The graphs show the EBS queue length hockey sicking. It turns out that Kops used 20GB gp2 EBS volumes by default. gp2 IOPs are tied to disk size. We needed more IOPs and more disk space ( df reported 90% usage). This also revealed kops does not support EBS optimization. Hopefully this if fixed (GitHub issue) in future release. This was may not solve the problem, but it will increase overall performance. This is also a problem for large instance types that require EBS optimization. I scaled up the root EBS volume to 120GB and repeated the exercise. I also deployed DaemonSet that ran a sleep loop inside common base images (e.g. ruby:2.4 or alpine:3.2 ). This hack work around “pre-pulled” images on nodes until Kops supports user supported hooks. This time charts installed successfully without killing the Kubelet. However other things broke so the cluster still cannot support this amount of parallel container operations.

I also spend time investigating the other observations.

The DataDog agent does not yet support the full suite of kube-state-metrics (notably the pod/phase metrics). This planned for a future release.

(notably the pod/phase metrics). This planned for a future release. We need to configure different allocation limits for different layers in Kubernetes.

There are two things that keep me up at night right now.