Cases4 cases

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1. IL-0145 A power sag and chiller failure in one Australia East availability zone took down the AKS control plane's compute and its non-zone-redundant SQL Database operation queue, blocking AKS create, update and delete operations in Australia East and Australia Southeast

   Microsoft Azure2023

   Symptoms

   From about 10:30 UTC on 30 August 2023, customers had trouble accessing or using Azure, Microsoft 365 and Power Platform services in Australia East. The affected Azure services included Azure Kubernetes Service (AKS) along with storage, SQL Database, Cosmos DB, virtual machines and Azure Resource Manager.

   Control plane unresponsiveHigh error rateService outage

   Trigger

   At 08:41 UTC a lightning strike on electrical infrastructure about 18 miles away caused a utility voltage sag that shut down the cooling chillers of datacenters in one Australia East availability zone; 13 chillers failed to restart automatically and five could not be restarted, so two data halls overheated and their infrastructure was shut down.

   Root cause

   Cloud provider outage

   Component

   AKS control planeApplication database

   Contributing factor

   Single point of failureExcessive blast radius

   Detection

   Cosmos DB impact was detected by monitoring at 10:47 UTC, first automated communications went to Azure Service Health at 10:48 UTC, the public Azure Status page was updated at 11:30 UTC, and all subscriptions in Australia East were sent portal communications at 11:36 UTC.

   Remediation

   • shut down the remaining networking, compute and storage in the two affected data halls so the chilled water loop could cool down
   • restarted the five chillers manually at 12:12 UTC
   • restored power to all hardware by 15:10 UTC and brought infrastructure back in phases
   • restored AKS operations through the recovery of the SQL Database operation queue, with no other mitigation

   Action items

   • AKS: convert all operation queue SQL Database databases to be zone redundant (estimated September 2023)
   • AKS: replace all cross-region SQL Database queue usage with zone-redundant Service Bus queues (estimated September 2023)
   • Change the control timing logic of the Chiller Management System so chillers do not fail to restart after voltage fluctuations
   • Change the emergency procedure for manual restarts of simultaneous chiller failures from an adjacent sequence to a load-based sequence per data hall
   • Increase technician staffing at the datacenter as a temporary measure for manual chiller restarts
   • Storage: optimise the large-scale recovery process and update troubleshooting guides (estimated December 2023)
   • Azure Service Fabric: improve reliability of SQL Database tenant ring recovery (estimated December 2023)
   • SQL Database: review the trigger criteria of auto-failover groups (estimated October 2023)
   • SQL Database: upgrade internal tooling to enable mass migration of databases (estimated December 2023)
   • Cosmos DB: optimise Service Managed Failover for single-region-write accounts (estimated November 2023)
   • ARM: complete the migration of its storage layer to the zonally redundant architecture (estimated December 2023)
   • Incident management: harden readiness, process and playbook for power-down scenarios (estimated October 2023)

   Lessons

   A control plane spread across availability zones still fails with a zone when one of its dependencies, here the SQL Database operation queue, is not zone redundant. When a second region uses the same database, the failure crosses into that region as well. Workloads already running were not expected to be affected; what stopped was everything that needs the managed service's control plane, such as scaling and cluster changes.

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2. IL-0196 Adding a second Redis replica and lowering memory limits in one RedisFailover change restarted the only Redis pod that held data, and Sentinel promoted the empty new replica, erasing the data

   Palark2020

   Symptoms

   Right after an updated RedisFailover resource was rolled out, the large Redis database lost all its data and the service went down; the data had to be restored from a backup taken just before the change.

   Data lossService outage

   Trigger

   A RedisFailover change that raised the number of Redis replicas from one to two and, in the same rollout, lowered the containers' memory requests and limits from 22 GB to 19 GB.

   Root cause

   Software defect

   Component

   Application databasePersistent volumes and stateful setsContainer memory limit

   Contributing factor

   No staged rolloutSingle point of failureUnverified assumption

   Remediation

   • restored the Redis data from the backup prepared before the rollout

   Action items

   • Report the readiness probe flaw to the Redis Operator project and test the developers' fix
   • Reconsider the use of the Spotahome Redis Operator
   • Clean up the Redis databases and fix the application code that created the excess keys
   • Consider Redis built-in clustering and sharding

   Lessons

   Changing the replica count and the pod resources in one rollout turned a seemingly harmless change into a rolling restart of the only pod with data while the new replica was still empty. A readiness probe that only checks that Redis answers a ping does not tell whether a replica has finished syncing. Operators for critical stateful components need to be evaluated for maturity before production use, and the dump.rdb file should be backed up before any change to the Redis cluster.

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3. 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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4. IL-0064 CPU-heavy pods packed onto one Google Cloud node drove it to kernel panics, and the crash moved from node to node until the site was down

   Moonlight2019

   Symptoms

   On Friday, January 18, Moonlight's API began returning occasional errors that looked like connection problems with its Redis database. The API checks Redis on every authenticated request to validate the session. At the same time the Kubernetes monitoring reported some nodes and pods as unresponsive.

   High error rateIntermittent connection failuresNode NotReadyService outageSustained high node CPU

   Trigger

   The Kubernetes scheduler put several CPU-heavy pods on the same node, including all three replicas of the website and the server-side rendering pods.

   Root cause

   Capacity exhaustion

   Component

   Pod scheduling and placementLinux kernelApplication database

   Contributing factor

   Single point of failureUnverified assumptionAutomation amplification

   Detection

   API errors that looked like Redis connectivity problems, and Kubernetes monitoring that showed nodes and pods as unresponsive. Google Cloud Support later found the link between sustained 100% CPU and kernel panics.

   Remediation

   • added anti-affinity rules to all major deployments so their pods spread across nodes

   Action items

   • Keep working with Google Cloud support to find and fix the cause of the node kernel panics

   Lessons

   Three replicas give no fault tolerance if the scheduler places them all on one node; anti-affinity rules make the spread explicit. A node failure that comes from the workload itself moves with the workload when the scheduler reschedules it.

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