Cases2 cases

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1. IL-0103 During an EKS node AMI update, Istio sidecars crash-looped because hundreds of pods restarting at once overloaded the external certificate authority behind istiod

   Tetrate (unnamed U.S. federal customer)2025sev1

   Symptoms

   While the customer was updating the AMIs of its Amazon EKS nodes across more than 30 clusters running Istio, sidecar-injected workloads in one cluster failed to start and showed CrashLoopBackOff.

   Container crash loopProbe failures

   Trigger

   Updating the EKS node AMIs, which started hundreds of sidecar-injected pods at the same time.

   Root cause

   Dependency failure

   Component

   IstioAdmission webhookNode operating system image

   Contributing factor

   Unreviewed defaultsUnknown limit

   Detection

   Sidecar-injected workloads in the updated cluster failed to start and showed CrashLoopBackOff; the customer raised a Severity 1 ticket and a Tetrate engineer was reviewing logs within six minutes.

   Remediation

   • scaled istiod down temporarily from 40 replicas to 1 to slow Istio's internal processes and ease the load on the CA
   • deleted the CertificateSigningRequests stuck in the Approved, Failed state
   • restarted the pods gradually in a controlled order instead of all at once

   Lessons

   When a mesh hands certificate signing to an external CA, every pod start becomes a signing request to that CA, so a mass restart such as a node image rollout can overload it. Sidecars then abort under istiod's 10-second default CA timeout even though the CSRs are eventually signed. Restarting pods in controlled batches keeps the request rate within what the CA can handle. The author's point is that diagnosing such a failure means looking at the whole service mesh ecosystem, including its external dependencies, not only Istio.

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2. IL-0037 Image registry rate-limited a whole region after a crash-looping DaemonSet with imagePullPolicy Always pulled through three NAT instances

   Datadog2019

   Symptoms

   Users reported that their jobs were not starting, with some sign that image pulls were involved. Many pods across the cluster were in error states related to image pulls, while applications that were already running were not affected.

   Image pull failuresService outage

   Trigger

   A change to the permissions on a storage bucket that one application needed to read. The application could no longer start, and its DaemonSet, running on roughly a thousand nodes of the cluster, went into a crash loop with imagePullPolicy: Always, so every restart pulled the image again although it was already on the node.

   Root cause

   Capacity exhaustion

   Component

   Container registry and its credentialsNAT gatewayApplication databaseAdmission webhook

   Contributing factor

   Automation amplificationSingle point of failureUnknown limitHarmful manual actionExcessive blast radius

   Detection

   Users reported that their jobs were not starting; monitoring had not caught it first.

   Remediation

   • replaced the affected NAT instances so registry traffic came from new source IPs
   • reconfigured the static IPs and firewall rules so applications could reach the Cloud SQL database again

   Action items

   • Add an admission webhook that rejects floating image tags, starting with latest
   • Apply the image tag webhook to workload objects only, not to pods, after it blocked pods created by in-cluster controllers

   Lessons

   The speakers' takeaway is to be very careful with DaemonSets in large clusters: they say they had broken everything DaemonSets depend on, including DNS, Vault and the image repository, and that it is very easy to break things when managing them by the thousand. imagePullPolicy: Always made every crash-loop restart pull the image again. Sending all registry traffic through three NAT instances put the whole region behind three source IPs, which the registry rate-limited, and changing those IPs cut the connections to a critical database reached over the internet through them. An admission webhook on pods rejects the pods that controllers in the cluster create, where no user sees the error.

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