Incident Library
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Cases4 cases

1 to 4 of 4

  1. Symptoms

    From 06:54 US/Pacific on 8 January 2025, Google Cloud Pub/Sub customers in ten regions could not publish messages or subscribe to them. Internal telemetry alerted Google engineers at 07:03, nine minutes after the impact started.

    Trigger

    From 06:54 to 07:30 US/Pacific a bad service configuration change, which over-restricted the permission to access the regional metadata database of Cloud Pub/Sub, was rolled out to multiple regions within a short time.

    Root cause
    Component
    Contributing factor
    Detection

    Internal telemetry alerted Google engineers at 07:03 US/Pacific, nine minutes after the impact started. A second internal telemetry alert at 09:07 showed the ordered subscriptions that could not consume their backlog.

    Remediation
    • Rolled back the configuration change that over-restricted access to the metadata database
    • Identified and repaired every affected ordered subscription so the subscriptions received their backlog
    Action items
    • Enforce parity between pre-production and production environments so configuration changes are caught before production
    • Review change management so configuration changes roll out progressively according to their priority
    • Add monitoring that detects ordering metadata inconsistency
    • Fix the bug in Cloud Pub/Sub ordering metadata management that left ordered messages undelivered
  2. Symptoms

    From 00:17 UTC on 30 November 2024, client connections to Wasabi Cloud Storage and the Web Console failed in every storage region, and all API calls returned HTTP 5XX errors.

    Root cause
    Component
    Contributing factor
    –
    Remediation
    • Configured internal servers by hand to route requests across multiple database instances, which restored service
    • After service was restored, worked on the root cause by recovering the internal messaging queue and resuming its automated handling of client requests
  3. Symptoms

    From 10:32 UTC on 6 August 2024, Wasabi's S3 service and user services failed in every region: users had trouble logging into the Web Console and requests to Wasabi regions returned errors.

    Trigger

    At 10:32 UTC on 6 August, Wasabi's queueing service reached full capacity, and that made the database cache unresponsive.

    Root cause
    Component
    Contributing factor
    –
    Remediation
    • Restarted the primary database to clear stale connections and cleared the queueing service's queue at the same time, which did not fully restore the database
    • Promoted the secondary database instance to primary, after which S3 was fully operational at 11:20 UTC
    • Restarted the database again between 13:17 and 13:23 UTC to fully incorporate the queueing service library
    • Promoted the original primary database back to production on 7 August, which stopped the user service timeouts
    • Recreated and redeployed the automation cluster that the automation service could not see, which fully restored S3
  4. Symptoms

    On Thursday night, 26 March 2015, and again on Friday, 27 March, Parse.ly's data processing backend stopped. Monitoring tools such as Munin, Sentry and Pingdom reported failures widely, PagerDuty woke the on-call engineer, and no new analytics data appeared in the dashboards at dash.parsely.com and preview.parsely.com for several hours.

    Trigger

    Data volume reached a point where the Kafka brokers, running on modest Amazon EC2 nodes without high network capacity, hit operating system network limits under the outbound read traffic and became unavailable.

    Root cause
    Component
    Contributing factor
    Detection

    Munin, Sentry and Pingdom reported the failures widely, Graphite graphs showed them, and PagerDuty woke the on-call engineer when the first outages started in the night.

    Remediation
    • Added new brokers to expand the Kafka cluster
    • Corrected the configuration of the new brokers and deployed them to the cluster, after which network utilization per node dropped and processing resumed
    • Turned the downstream consumer systems back on slowly through Apache Storm and reprocessed the backlog
    • Began restoring the data to customer accounts from the raw data archived in Amazon S3
    Lessons

    CPU and disk I/O are good signs of machine health, but creeping network utilization is harder to monitor, because 100% of network capacity is not well defined on cloud VMs that share host machines. When every node of a cluster runs close to the same limit, losing one node can take down the whole cluster, so failure planning should cover an entire subsystem going offline, not just one or two machines. Keeping raw data archival in simple files and S3 uploads, separate from the Kafka stream, meant there was no risk of permanent data loss.

Check your cluster for this

These 4 cases involve Message broker and Database. 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.

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