Cases4 cases

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1. IL-0178 On EKS, HPA scale-in returned 502 errors because containers stopped before their pod IPs left the Service endpoints, and after blue/green switches conntrack kept sending established connections to deleted pods behind an instance-mode ALB

   Lotte E-commerce2022

   Symptoms

   LOTTE ON, Lotte E-commerce's online shopping mall of about 40 microservices on Amazon EKS, returned errors for a period of time whenever the Horizontal Pod Autoscaler (HPA) scaled pods in after load dropped. Requests reached containers that had already been deleted and failed with 502 errors.

   High error rateIntermittent timeoutsStale endpoint routing

   Trigger

   HPA scale-in deleting pods, and the deletion of Blue pods after a blue/green switch of the Service label selector.

   Root cause

   DNS or network fault

   Component

   Horizontal pod autoscalerPod terminationkube-proxyconntrack tableCloud load balancerIngress controller

   Contributing factor

   Unreviewed defaults

   Remediation

   • delayed container shutdown on pod deletion (a wait in the SIGTERM handler or a preStop sleep, capped by terminationGracePeriodSeconds) so containers stop only after the pod has left the Service endpoints
   • switched some workloads' Ingress-created ALBs from instance mode to IP mode so traffic goes straight to pod IPs without iptables and conntrack

   Action items

   • Move all workloads to ALB IP mode gradually

   Lessons

   Removing a pod from Service endpoints and stopping its containers happen at the same time in Kubernetes, so a pod can stop while traffic is still routed to it unless shutdown is delayed with a preStop hook or in the application. With an instance-mode ALB, traffic passes through node iptables and conntrack, and established connections can keep going to a pod IP after its iptables rules are gone. IP mode avoids that path, within the limit of 1000 targets per target group.

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2. IL-0461 Every rollout of Datadog's metrics query service on Kubernetes raised errors that the logs blamed on DNS

   Datadog2022

   Symptoms

   From September 2021, each rollout of an update to Datadog's metrics query service, which serves real-time and historical metric data to the web frontend and to monitor evaluation, came with a rise in errors. Clients retried failed queries, so few errors reached users, but the retries slowed dashboards and monitors. The service's logs showed DNS errors stopping it from connecting to the data stores it depends on.

   High error rateDNS resolution failuresHigh latencyOOMKilledPacket lossStale endpoint routing

   Trigger

   Rollouts of the metrics query service, which delete the old pods. The client behaviour behind the failures came from an August 2021 rollout of the metrics query clients that applied round_robin as the new default gRPC load balancing policy.

   Root cause

   Configuration change

   Component

   conntrack tableCiliumLinux kernelCoreDNSClient-side DNS resolver and cacheexternal-dnsHTTP and RPC client library

   Contributing factor

   Change with a delayed effectRetry stormUnknown limitMonitoring blind spot

   Detection

   Errors rose during rollouts of the metrics query service, and the service's logs showed DNS errors when connecting to its data stores.

   Remediation

   • Raised the memory of the node-local-dns pods from 64 MB to 256 MB, which stopped the OOM kills but not the DNS errors
   • Tested larger instance types and confirmed that scaling up the service's infrastructure resolved the errors
   • Removed the custom gRPC reconnect parameters from the clients to restore the defaults

   Action items

   • Contribute a change to Cilium that adds an unreachable route for a deleted pod's IP so clients get an ICMP error

   Lessons

   The errors looked like DNS but were not. ENA metrics and VPC Flow Logs gave the visibility into VPC connection tracking that host metrics did not, and Cilium, Kubernetes and AWS networking interact in ways that can make reverse path filtering drop packets it should allow. DNS propagation time depends on many settings. The causes were changes Datadog had made itself, to work around a dependency bug, to resolve earlier incidents and to override default reconnect parameters, which looked safe at the time and showed their effects much later, when the link was hard to see.

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3. IL-0049 Partial DNS outage after CoreDNS scaled down and kube-proxy failed to delete stale conntrack entries

   Preply2020

   Symptoms

   Prometheus detected a high rate of 5xx responses on three services, called A, B and C in the write-up, and paged the on-call engineers through VictorOps at 22:18 EET. The 5xx rate was also visible in Kibana.

   DNS resolution failuresHigh error rateStale endpoint routing

   Trigger

   Because load in the cluster was low, the CoreDNS autoscaler reduced the number of CoreDNS pods from 3 to 2.

   Root cause

   DNS or network fault

   Component

   CoreDNSkube-proxyconntrack table

   Contributing factor

   Automation amplificationIgnored early signalNo caching

   Detection

   Prometheus alerted on a high 5xx rate on services A, B and C and paged on-call through VictorOps.

   Remediation

   • reverted the latest release of the affected service
   • a regular deploy created new nodes, the CoreDNS autoscaler added CoreDNS pods, and the conntrack table was rewritten

   Action items

   • Disable the autoscaler for CoreDNS
   • Run a DNS cache server in production
   • Monitor conntrack usage

   Lessons

   Scaling CoreDNS down is not free: if kube-proxy fails to clear conntrack entries for the removed pod, clients keep sending DNS queries to it. Known DNS weaknesses in a cluster should be prioritised before they cause an outage, and fixes that only treat consequences leave the underlying conntrack problem in place.

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4. IL-0013 The conntrack table filled up on downsized GKE nodes and a high-traffic aggregation proxy saw climbing connection resets

   loveholidays2020

   Symptoms

   Shortly after a round of changes, loveholidays saw connectivity problems and raised error rates on its aggregation service. The service is a proxy between two other services of more than 100 instances each, handles over 10,000 requests per second continuously and can spike to four times that.

   Connection resetsHigh error rateIntermittent connection failures

   Trigger

   Three changes together: a change in service architecture, halving the RAM of the nodes that run the aggregation service, and sharding a downstream service, which doubled the number of connections to track.

   Root cause

   Capacity exhaustion

   Component

   conntrack tablekube-proxyNode poolHAProxy

   Contributing factor

   Unknown limitNo capacity planningNo alert on limit

   Detection

   Rising error rates and climbing connection reset errors in HAProxy, then node_exporter conntrack metrics compared with their limit.

   Remediation

   • moved the aggregation service to nodes with more RAM
   • limited the number of aggregation service pods per node

   Action items

   • Alert when conntrack table use goes above 75%, with (node_nf_conntrack_entries / on (pod) node_nf_conntrack_entries_limit / on (pod) group_right kube_pod_info) > 0.75

   Lessons

   The conntrack limit is tied to node memory, so resizing a node pool silently resizes the connection table, and on GKE the ratio did not match the commonly cited formula. A high-traffic proxy on small nodes can exhaust it, and the symptoms are connection resets and timeouts, so conntrack use needs its own metric and alert.

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