Cases36 cases

31 to 35 of 36

1. IL-0057 Higher p99 latency after moving a reverse proxy to EKS, from outgoing connections delayed by a SNAT conntrack race

   Prezi2019

   Symptoms

   Prometheus alerts fired on tail latency: from 7/24, the p99 response time of a few services went up after Prezi moved its Nginx-based reverse proxy, smartgateway, from dedicated Elastic Beanstalk machines onto shared Kubernetes worker nodes. The bump affected only the tail, so the earlier monitoring of average response times would most certainly have missed it.

   High latencyIntermittent timeouts

   Trigger

   Moving the reverse proxy onto shared EKS worker nodes, where its outgoing connections went through source NAT on the node and some SYN packets were dropped by a race in Netfilter's conntrack.

   Root cause

   DNS or network fault

   Component

   conntrack tableAWS VPC CNILinux kernelNode operating system image

   Contributing factor

   Unreviewed defaultsUnpatched known bug

   Detection

   Prometheus alerts on p99 latency fired after the reverse proxy migration.

   Remediation

   • added AWS X-Ray tracing and an $upstream_connect_time trace attribute to locate the delay
   • ran more pods of the service on more nodes, which reduced but did not end the delays
   • set AWS_VPC_K8S_CNI_EXTERNALSNAT=true on the AWS VPC CNI to turn off SNAT on the nodes

   Lessons

   Alerting on tail latency instead of averages caught a problem that would otherwise have gone unnoticed, and tracing turned a vague slowdown into a measured connect delay. Delays of exactly 1 or 3 seconds on new outgoing connections point to dropped SYN packets, and on EKS the SNAT done by the VPC CNI is where to look.

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2. 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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3. IL-0011 HAProxy ingress piled requests onto the same pods because a Go template visited the backend map in sorted pod IP order

   Skyscanner2019

   Symptoms

   On a Sunday, a team using one of Skyscanner's Kubernetes clusters manually paged the out-of-hours Kubernetes support because their service showed elevated latency, worst at the p90 and maximum levels.

   High latencyLoad imbalanceOOMKilledIntermittent connection failures

   Trigger

   Upgrading the HAProxy ingress controller to v0.6 and going back from Service virtual IPs to individual pod IPs as backends, on the understanding that the sort-backends flag introduced in v0.5, false by default, meant backends were randomly shuffled.

   Root cause

   Software defect

   Component

   Ingress controllerHAProxyConfiguration templating

   Contributing factor

   Unrepresentative stagingUnverified assumptionIgnored early signal

   Detection

   A cluster user manually paged the out-of-hours Kubernetes support about elevated latency; Prometheus queries then showed the uneven request distribution across pods.

   Remediation

   • switched the affected service from pod IPs back to the Service virtual IP through a config change in the internal deployment tool Slingshot
   • patched the controller to keep each backend's servers in an insertion-ordered slice and changed the template to iterate over that slice
   • tested the patch in sandbox clusters and rolled it out to the production clusters

   Action items

   • Run the internal controller patch in production and return to pod IPs as the default backends
   • Raise the problem upstream with a GitHub issue and pull request
   • Test changes to cluster components against the specific behaviour and conditions that matter

   Lessons

   The team already knew that sorted backends cause hot-podding and still did not test the new flag well enough to see that it did not work; changes to cluster components need tests aimed at the exact behaviour that matters, under conditions like production. A keyword can behave differently in different parts of a language: range over a map is unordered in Go code but sorted in text/template. A small cluster component can do outsized harm to every user of the cluster and, in the end, to customers.

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4. 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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5. IL-0075 Kubernetes API servers pegged on CPU when the cluster grew to about 290 nodes, traced to Datadog agents polling the API

   Civis Analytics2019

   Symptoms

   After Civis released its new script infrastructure on Kubernetes internally in September 2018, developers reported that kubectl commands were slow, and users intermittently could not start notebooks and services.

   Control plane lagControl plane unresponsiveHigh latencyHigh error rateDNS resolution failures

   Trigger

   Migrating clients' Python, R and container scripts from the in-house Bocce system to Kubernetes (the project called Croquet) pushed the cluster to about 290 EC2 instances under a large script load.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverTelemetry agentetcd

   Contributing factor

   Unknown limitUnverified assumptionMonitoring blind spotNo capacity planning

   Detection

   Developer reports of slow kubectl commands and user reports of notebooks and services failing to start, then high CPU on the master nodes.

   Remediation

   • moved internal users back to the old Bocce infrastructure
   • resized the three master nodes from m4.large to m4.4xlarge
   • changed the Datadog setup so per-node agents no longer polled the API server for service metadata
   • raised max-requests-inflight for non-mutating requests from 400 to 1200
   • grew the etcd EBS volumes from 20 to 100 GB (100 to 300 IOPS)
   • switched client scripts from the ClusterFirst DNS policy to Default

   Action items

   • Upgrade etcd from 2.2.1 to 3.x once Kops supports a safe migration
   • Solve the in-cluster DNS timeouts so client scripts can talk to other workloads in the cluster

   Lessons

   Kubernetes can reach thousands of nodes only after tuning, and third-party DaemonSets are a common cause of control plane load: an agent on every node that polls or watches the API server multiplies with the cluster size. Datadog, kube2iam and the fluentd metadata filter all showed this. A small configuration change often fixes it, but finding it took load testing and the API server access logs.

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