Incident Library
Applied filters ComponentApplication databaseSymptomHigh error rateOrganisationOpenAIClear allEvery case under the filters, with no sentence; newest first.
Suggested filters Search, and the values found in your words appear here.

Graph

  • Root cause
  • Component
  • Symptom
  • Contributing factor
  • Case

Cases2 cases

1 to 2 of 2

  1. Symptoms

    From 3:30am to 7:08am PT, chatgpt.com and a small number of OpenAI APIs returned elevated error rates. The status page classed the incident as a full outage.

    Trigger

    A failure of Cosmos DB in ChatGPT, at OpenAI's service provider.

    Root cause
    Component
    Contributing factor
    Remediation
    • the service provider recovered the affected Cosmos DB databases at 4:25am PT
    • fixed the web server's health checking logic, which ended the pod crash loop
    Action items
    • Change the web systems to degrade more gracefully when they fail, including concurrency control and better load shedding
    • Make the database layer more resilient to service provider outages (described as already underway)
    Lessons

    The database came back at 4:25am PT, yet users saw errors until 7:08am PT: the web tier's own health checks kept removing pods after the dependency had recovered. The planned changes target the web tier's behaviour under failure, with concurrency control and load shedding, rather than the database alone.

  2. 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.

    Trigger

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

    Root cause
    Component
    Contributing factor
    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.

Check your cluster for this

These 2 cases involve Application database and Ingress controller. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

By root cause2