Cases47 cases

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1. IL-0056 Calls from EKS pods to services behind an NLB in another VPC failed intermittently because the AWS VPC CNI did source NAT on the node

   MindTickle2020

   Symptoms

   On the Saturday the production cluster was moved from kops to EKS, the platform became very slow and too unstable to keep running, and the team reverted.

   Intermittent connection failuresHigh latency

   Trigger

   Production was moved from a kops cluster using Weave networking to a new EKS cluster using the AWS VPC CNI, without external SNAT enabled.

   Root cause

   DNS or network fault

   Component

   AWS VPC CNI

   Contributing factor

   Unreviewed defaultsUnverified assumption

   Detection

   The team lead saw the platform behaving very slowly after the switch to EKS and phoned the author.

   Remediation

   • reverted production to the kops cluster
   • set AWS_VPC_K8S_CNI_EXTERNALSNAT=true in the AWS VPC CNI

   Action items

   • Monitor and alert on the AWS VPC CNI

   Lessons

   In a networking problem every component in the stack is a suspect; ruling out EKS early cost a lot of time. A DaemonSet that pinged every component from every other one exposed the anomaly that led to the fix. The team had only skimmed the AWS CNI documentation before the migration; knowing how the plugin works internally would have led to better judgements and hypotheses while debugging.

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2. IL-0190 Cortex distributors kept a stuck connection to a dead etcd leader after a Kubernetes upgrade terminated its node, failing writes to Hosted Prometheus for 12 minutes

   Grafana Labs2020

   Symptoms

   About a minute after the etcd leader changed, two replicas of the Cortex distributor started logging "context deadline exceeded" when they tried to read keys from etcd. Writes from some customers that landed on those two replicas failed, while the other distributor replicas kept working, so the service saw an error rate of about 20% on writes to the London region of Hosted Prometheus.

   High error rateIntermittent timeouts

   Trigger

   A scheduled Kubernetes version upgrade abruptly terminated the node that ran the etcd leader at 11:19:00 UTC.

   Root cause

   Software defect

   Component

   etcdNode pool

   Contributing factor

   Unreviewed defaultsNo safeguard on destructive change

   Detection

   An SLO-based alert paged the on-call engineer at 11:31, 11 minutes after the errors started. At a 20% error rate the monthly SLA would have taken about 18 hours to breach.

   Remediation

   • Restarted the two affected Cortex distributor replicas at 11:32, which stopped the errors

   Action items

   • Enable gRPC keepalive probes in the Cortex etcd client (DialKeepAliveTime 10s, DialKeepAliveTimeout, PermitWithoutStream)
   • Make the Kubernetes upgrade process terminate pods and machines gracefully, including the etcd pods managed by the etcd operator

   Lessons

   When a node dies abruptly, clients holding TCP connections to it are not told; without keepalive probes an etcd client can keep sending requests into a dead connection long after a new leader exists. A failover that works on the server side still needs clients that detect the dead peer. Node replacement during cluster upgrades should drain pods gracefully, including pods owned by operators that the upgrade scripts do not know about, and should give instances enough time to shut down.

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3. IL-0015 A GKE cluster's /16 pod address range ran out at 256 nodes, leaving new pods Pending and node scale-ups failing

   loveholidays2020

   Symptoms

   A colleague wrote in Slack that his application was taking a long time to deploy. A deployment that normally finishes in minutes had been running for about an hour: about half of the new pods were running and serving traffic, and the other half were Pending.

   Pods PendingIP address exhaustionScale-up failure

   Trigger

   The cluster reaching 256 nodes, the most its /16 pod secondary range could hold at 256 addresses per node; every further node creation failed.

   Root cause

   Capacity exhaustion

   Component

   VPC subnet or pod address rangeCluster autoscalerNode pool

   Contributing factor

   Unreviewed defaultsUnknown limit

   Detection

   A colleague reported in Slack that a deployment was slow; the pending pods' events and the cluster autoscaler status ConfigMap showed the exhausted IP space.

   Remediation

   • lowered the maximum pods per node on selected node pools, cutting IP consumption by about 30%
   • moved some node pools to larger instance types

   Action items

   • Lower the maximum pods per node on node pools that do not run many pods
   • Use larger instance types in some node pools

   Lessons

   In a VPC-native GKE cluster the number of nodes, not the number of pods, uses up the pod range: by default each node reserves 256 addresses, so even a /16 is full at 256 nodes. Maximum pods per node and node size are the practical levers; larger nodes pack better but take more pods down when they fail.

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4. IL-0033 Uneven load across kube-dns pods and a DNS packets-per-second limit pushed service latency to 20 seconds

   Airbnb2019

   Symptoms

   On May 10 the latency graphs of several services showed a few spikes of up to about one second, then latency climbed to five seconds and then to 20 seconds, and it stayed there for a long time. The same pattern appeared on many services, not just one.

   High latencyIntermittent timeoutsLoad imbalanceDNS resolution failures

   Trigger

   Several services sent a high volume of external DNS queries, all of which went through kube-dns.

   Root cause

   Capacity exhaustion

   Component

   kube-dnskube-proxyClient-side DNS resolver and cache

   Contributing factor

   Unreviewed defaults

   Detection

   Latency graphs of several services showed the rise; engineers then looked at load per kube-dns pod.

   Lessons

   By default a pod discovers DNS through the kube-dns cluster IP and uses the ClusterFirst policy, so even external lookups go through kube-dns. If a pod does not need cluster DNS, set its dnsPolicy to Default or None. Check the DNS policy early and often. iptables picks backends at random and is not a load balancer, so pods behind a cluster IP, kube-dns included, can carry very different loads.

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5. IL-0189 A JVM service moved from EC2 to Kubernetes answered up to ten times slower because KIAM's 15-minute credentials made the AWS Java SDK fetch new credentials on every request

   Adevinta2019

   Symptoms

   After a Spring-based JVM microservice was deployed to Kubernetes and part of production traffic was routed to it, request latency there was up to 10 times higher than on EC2. Behind the NGINX ingress controller the backend took 100 to 200 ms to answer, where the EC2 instances took about 20 ms.

   High latency

   Trigger

   Moving the service onto the Kubernetes platform, where KIAM served IAM credentials to pods with its default 15-minute lifetime.

   Root cause

   Configuration change

   Component

   Credential broker for pods

   Contributing factor

   Unreviewed defaults

   Detection

   After part of production traffic was routed to the Kubernetes deployment, its request latency was seen to be up to 10 times higher than on EC2.

   Remediation

   • reconfigured KIAM to request IAM credentials with a longer expiration period

   Lessons

   KIAM's short credential lifetime for pods and the AWS Java SDK's eager refresh of any credential within 15 minutes of expiry are each reasonable, but together they force a metadata round trip on every request. When latency rises after a move to Kubernetes, the cause is often the interaction of components that never ran together before rather than Kubernetes itself. Here a packet capture from inside the container found what latency metrics and DNS tests had not.

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