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  • Root cause
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Cases2 cases

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  1. Symptoms

    Just after 6 a.m. Pacific Time on 22 February 2022, user tickets about problems connecting to Slack, problems reported by internal users, and pages and alerts to several engineering teams all arrived at almost the same moment. Many users could not connect: the Slack client could not finish booting, the step that fetches channel lists, preferences and recent conversations, and showed an error page.

    Trigger

    A percentage-based rollout step upgraded the Consul agent on 25% of the fleet, followed by a slow sequential restart of the agents. Each restart on a Memcached node removed it from the Consul service catalog, so Mcrib, the cache-tier control plane, replaced it with an empty spare or flushed it when it rejoined, and the cache hit rate dropped as Slack reached its daily peak traffic.

    Root cause
    Component
    Contributing factor
    Detection

    User tickets about connection problems, reports from internal users and pages and alerts to several engineering teams arrived almost at the same time, just after 6 a.m. Pacific Time.

    Remediation
    • Paused the Consul agent restart operation
    • Throttled client boot requests to reduce load on the database tier
    • Cut the client boot rate limit again after a too-large increase and then raised it in smaller steps
    • Changed the GDM membership query to read from Vitess only the data missing from Memcached
    • Let the query read from replicas as well as Vitess primaries
    • Raised the client boot rate limit slowly back to normal traffic levels as the caches refilled
    Action items
    • Change the process for Consul rollouts so upgrades do not trigger this failure mode again
    • Change the Mcrib control loop so a brief network outage or partition of cache nodes does not set off the same cascading failure
    • Make the GDM membership query read from a table sharded by channel
    • Check the other cache-fronted database queries for high-volume scatter queries
    • Explore longer-term ways to make the caching tier more resilient
    Lessons

    Slack describes performance bottlenecks, a cascading failure, dependence on a warm cache and a change as the trigger. Once the system had passed the tipping point, lower cache hit rates caused high database load that in turn kept the cache from filling, and a system in that state stays there until an outside intervention, a large cut in load or an increase in capacity, changes it. Mcrib contributed not by being wrong but by being faster than the old Consul lock scheme at replacing Memcached nodes, which increased churn in the cache tier. Client retries still added load despite backoff and jitter. A brief network partition of the cache nodes could set off the same failure, and the interaction between Consul and the caching architecture is now more widely understood.

  2. Symptoms

    At 4:45pm Pacific on 12 May 2020, Slack was alerted that it was serving more HTTP 503 errors than normal. A subset of the webapp fleet was under heavy load while the rest of the webapp instances were not.

    Trigger

    At the end of the US business day the webapp tier began its usual scale-down, and autoscaling terminated the oldest instances first, which were the only backends left in most HAProxy instances' state.

    Root cause
    Component
    Contributing factor
    Detection

    An alert for more HTTP 503 errors than normal. The alerting meant to catch stale HAProxy state was not working.

    Remediation
    • Did a rolling restart of the HAProxy fleet
    Action items
    • Move all ingress load balancing from HAProxy to Envoy Proxy, with endpoint discovery through an xDS control plane
    Lessons

    The alerting for this exact failure had stopped working, and nobody noticed because the HAProxy stack had run for a long time without needing changes, so few engineers worked with its monitoring and alerting. A slot limit that had been ample a few months earlier was reached after pandemic-driven growth and one large autoscaling event.

Check your cluster for this

These 2 cases involve Service discovery and In-memory cache. 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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