Cases7 cases

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1. IL-0047 Pod-to-pod requests delayed by 5, 10 or 15 seconds because of DNS lookups lost to a conntrack race

   MindTickle2019

   Symptoms

   Pod-to-pod calls were intermittently slow and the web application felt slow overall. A request that pod A sent at time x reached pod B at x + 5s, x + 10s or x + 15s, always a multiple of five seconds.

   Intermittent timeoutsHigh latencyDNS resolution failures

   Trigger

   The C library resolver sent the A and AAAA queries for each lookup on the same port; where DNAT or SNAT translation takes place, a race condition in conntrack sometimes left one of the queries unanswered, and the resolver waited 5 seconds before retrying.

   Root cause

   DNS or network fault

   Component

   conntrack tableClient-side DNS resolver and cacheLinux kernel

   Contributing factor

   Unreviewed defaults

   Detection

   The team noticed intermittent delays in pod-to-pod calls and slowness in the web application, and that the delays were always multiples of five seconds.

   Remediation

   • added the single-request-reopen option to dnsConfig in the deployment specs
   • set GRPC_DNS_RESOLVER=native in the Dockerfiles of gRPC applications so they used the libc resolver
   • checked with tcpdump port 53 that A and AAAA queries left from different ports

   Lessons

   Delays that are exact multiples of five seconds point to DNS lookups being retried after the resolver's 5-second timeout. A resolver option set in the pod spec only helps processes that use the libc resolver; runtimes with their own resolver, such as gRPC with c-ares, ignore it.

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2. IL-0035 Worker nodes went NotReady after a new ElastAlert rule without memory limits used up node memory

   NU.nl2019

   Symptoms

   The NU.nl development team got reports that several services on one of its Kubernetes clusters behaved erratically: occasional error pages, slow responses and timeouts. The affected services were mostly internal tools and dashboards.

   Node NotReadyHigh error rateHigh latencyIntermittent timeoutsMonitoring outageOOMKilled

   Trigger

   A new ElastAlert rule, deployed shortly before from the command line, that fired if the Cloudfront-* Elasticsearch indexes stopped receiving documents; those indexes take a few million requests per hour, and the rule made ElastAlert's memory grow without bound.

   Root cause

   Configuration change

   Component

   Container memory limitkubeletPrometheus

   Contributing factor

   No resource requests or limitsUnreviewed defaultsNo alert on limitMonitoring blind spotExcessive blast radius

   Detection

   Users reported error pages, slow responses and timeouts from several services; no alert fired.

   Remediation

   • terminated the NotReady nodes one by one so the auto-scaling group replaced them (the new nodes also went NotReady, but Prometheus and Grafana landed on a stable node)
   • found the ElastAlert pods' memory growth on the Grafana pod memory dashboard
   • fixed the ElastAlert problem that caused the memory growth

   Action items

   • Define alerts on resource usage such as CPU, memory and disk space
   • Keep researching an alerting strategy that combines Elasticsearch, Prometheus Alertmanager and New Relic
   • Consider exporting metrics out of the cluster and running Grafana outside it, on EC2 or a separate Kubernetes cluster
   • Either visualise the important metrics in the ELK stack or make Prometheus and Grafana more available, and improve the metrics strategy
   • Set resource limits for ElastAlert through the Helm values
   • Configure namespaces with default memory requests and limits
   • Isolate customer-facing workloads from build, logging and monitoring workloads (a dedicated node group, or later a separate tools cluster)
   • Deploy everything through automation such as Jenkins pipelines, and consider a GitOps approach
   • Deploy through a pipeline that includes a smoke test step
   • Make Kubernetes and cloud infrastructure work part of the team's sprints, picked up by all team members while pairing with more experienced members, and hold workshops that go deep into specific topics

   Lessons

   Fixing the root cause covers only a small part of what an incident can teach; the chain of smaller failures around it is where most of the learning is. A pod without a memory limit can take whole nodes down, so limits need defaults at the namespace level. Monitoring that runs inside the cluster it watches goes blind with it. Changes installed by hand from the command line leave the rest of the team without a record of what changed.

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