Cases2 cases

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1. IL-0117 A scheduled maintenance and an ingress upgrade on OpenAI's user-facing clusters caused a networking control plane regression that briefly broke the data plane, and the services that restarted then overwhelmed a backend persistence store with a heavy first-start query

   OpenAI2024

   Symptoms

   On August 16, 2024 from 11:38 AM to 1:16 PM PT, the OpenAI primary API was unreliable: ChatGPT conversations succeeded less often, and logging in and creating accounts were affected. The status page called it elevated error rates for ChatGPT and the Platform API. The incident came in two waves, of 44 and 15 minutes.

   High error rateDatabase overload

   Trigger

   A scheduled maintenance combined with an upgrade to the ingress of OpenAI's user-facing clusters.

   Root cause

   Breaking change in an upgrade

   Component

   Ingress controllerApplication database

   Contributing factor

   No staged rolloutAutomation amplification

   Action items

   • Mitigate the networking control plane regression and validate that control plane restarts do not interfere with the clusters' data plane
   • Change service software to start faster and remove the first-start query, relieving the persistence layer
   • Deploy configuration changes that optimise the networking control plane's effect on the clusters' ability to handle traffic
   • Remove the expensive database query from critical startup paths
   • Add monitoring and alerting for networking control plane issues
   • Introduce staged rollouts with longer soak time for infrastructure changes
   • Audit systems for other slow queries that may affect service start time

   Lessons

   A brief connectivity loss turned into a long outage because recovery itself was expensive: the services that restarted overwhelmed one backend persistence store with a heavy first-start query. Keeping costly queries off startup paths shortens recovery, and staged rollouts with longer soak time limit how many systems an infrastructure regression reaches before it is caught.

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2. IL-0115 One of OpenAI's three production AKS clusters went offline through a human error, and rebuilding it took 10½ hours because an AKS Kubernetes upgrade broke the ingress and AKS cannot be downgraded

   OpenAI2022

   Symptoms

   At 18:56 PDT on 23 June 2022 (01:56 UTC on 24 June) several models went offline at once: some fine-tuned curie and davinci models, the Codex beta models code-davinci-001 and code-cushman-001, the legacy GPT models curie, babbage and ada, and all Embeddings models.

   Service outageHigh error rateIntermittent timeouts

   Trigger

   A destructive operation on one of the three production clusters, caused by human error that the existing safeguards did not catch, took the whole cluster offline.

   Root cause

   Operator error

   Component

   Ingress controllerModel hub

   Contributing factor

   No safeguard on destructive changeNo headroomDeferred upgradesOverlapping failures

   Remediation

   • re-allocated the models from the lost cluster to the remaining two clusters
   • rebuilt the VPN link with Azure support after losing the connection to AKS during cluster setup
   • built workarounds with Azure support after losing the connection to Azure Private Link
   • tore down the cluster and rebuilt it from scratch after the upgraded Kubernetes version broke the ingress
   • moved traffic back into the rebuilt cluster between 05:30 and 06:30 PDT

   Action items

   • Add checks and locks so that this and similar production-critical infrastructure cannot be modified by accident
   • Add better visibility into the operations being performed and make parts of the system more lexicographically distinct
   • Keep working with Azure on the AKS network connectivity issues so they do not disrupt new cluster operations
   • Update playbooks with the new cluster failure modes and change the setup steps for new clusters and infrastructure
   • Upgrade Kubernetes systematically and set aside time so the clusters do not fall behind again
   • Shard fine-tuned models across multiple clusters and work towards more automatic cross-cluster replication
   • Distribute and replicate Azure Blob Storage so fine-tuned models load faster and more reliably
   • Finish rolling out node-to-node caching to relieve Azure Blob Storage bottlenecks
   • Change how model weights are stored so they are more efficient and faster to load

   Lessons

   Losing one of three clusters exhausted the GPU headroom, and GPUs could not be added on short notice, so full recovery depended on bringing the same cluster back. Rebuilding it exposed a deferred Kubernetes upgrade: the newer version broke the ingress, and because AKS cannot downgrade there was no way back except a full rebuild. Recovering many large models at once is limited by the bandwidth of the storage they load from, and the caches that normally hide this do not help when everything moves to a different cluster.

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