Incident Library
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Cases2 cases

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  1. Symptoms

    From April 11, 2022 at 13:17 UTC customers and Gitpod staff reported that Gitpod workspaces were intermittently becoming unresponsive. CPU usage of nodes and pods looked normal, with nothing maxed out, and at 13:58 UTC the team found that the workspaces were IO bound rather than CPU bound.

    Trigger

    The ws39 generation of workspace clusters, released to all regional clusters on April 8th at 20:08 UTC and to the XL clusters on April 9th at 17:45 UTC, split the 1TB disk into smaller RAID0 disks, changed CPU limiting and workspace networking, and moved to faster machine types.

    Root cause
    Component
    Contributing factor
    Detection

    Customers and Gitpod staff reported on April 11th at 13:17 UTC that workspaces were intermittently unresponsive, and the team opened an incident in Slack through its incident.io integration. On April 17th the team noticed increased CPU load on the workspace nodes and opened an internal incident.

    Remediation
    • Shifted workspace starts back to the ws38 clusters on April 11th
    • Built ws39a clusters with c2d machines and local SSDs and moved traffic to them
    • Requested a limit increase from Let's Encrypt
    • Routed EU XL traffic to the regional EU cluster and built ws-eu39bxl to replace the crashed ws-eu39xl
    • Cleaned up workspaces stuck in a stopping state so users could start them again
    • Released ws40 with 1TB boot disks to get more IO bandwidth
    • Removed the workspaces that consumed excessive CPU time for crypto-mining
    • Blocked the fraudulent users behind the crypto-mining from using Gitpod
    • Rolled back the ws-daemon change that broke CPU limiting and shipped the fix in the ws34 clusters
    • Blocked ACME HTTP requests to workspace ports so Let's Encrypt rate limits would not be hit again
    • Rolled out IO limiting with the ws41xl and ws41 clusters and moved traffic to them step by step
    Action items
    • Invest in test automation so that test results stop releases like this one
    • Vary the interval between traffic shifts to new clusters, with more time for larger releases
    • Add a risk assessment stage, to be automated, that governs changeset size and rollout speed of workspace cluster rollouts
    • Reduce the impact teams have on each other
    • Add review criteria that find backwards incompatible changes before pull requests are merged, and split such pull requests
    • Recommend rollback as the primary means of mitigation in the incident procedures, and look at automating it
    • Make the status page show the workspace service as down when it is, and update the playbooks
    • Keep incident communication in a single Slack channel and Google Meet
    • Put 10% of the engineering staff on the on-call rotation from May 9th and use that time on observability, alerting and stability
    Lessons

    Gitpod concluded that it should have committed to rolling back on April 11th instead of building and shipping fixes, which kept customers on degraded workspaces longer than necessary. A large release so soon after the previous one needed a risk assessment and slower traffic shifts, and backwards incompatible changes that block a rollback need to be found before pull requests are merged. Failure-free operations require experience with failure.

  2. Symptoms

    Services on Buffer's kops-managed cluster on AWS (about 60 nodes and 1500 containers) answered more slowly than they should have. The container throttling metric showed that many containers were being throttled whatever their CPU usage was, including containers that never came close to their CPU limit.

    Trigger

    CPU limits on user-facing services, enforced through CFS quota by a Linux kernel with a bug that throttled containers even when they were well under their limit.

    Root cause
    Component
    Contributing factor
    Detection

    While reviewing the container throttling metric, the team noticed many containers being throttled even though their CPU usage stayed far below their limits.

    Remediation
    • removed CPU limits from all services directly or indirectly on the users' critical path
    • moved those services to dedicated tainted nodes and gave them matching tolerations
    • set each service's CPU request to its peak usage over a few months plus a margin of more than 20%, and set memory requests the same way
    Action items
    • Move nodes to a kernel that contains the CFS throttling fix (4.19 or later), which for Buffer means a kops version whose host image is Ubuntu 20.04
    • After the kernel upgrade, check whether throttling is fully gone and update the post
    Lessons

    A CPU limit can slow a service down even when the service uses a small part of it, because of how the kernel enforces CFS quota on older kernels; the throttling metric shows it, the CPU usage graph does not. Upgrading the kernel is the safer fix. Removing limits works for latency, but only together with honest CPU and memory requests, isolation of the unbounded services, and monitoring of node usage, since unbounded containers can starve the kubelet.

Check your cluster for this

These 2 cases involve Container CPU limit and Linux kernel. 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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