Cases14 cases

6 to 10 of 14

1. IL-0174 After an upgrade to Fluent Bit 2.0, logging sidecars that used the Kafka output plugin kept restarting with SIGSEGV because the plugin read a corrupted record as a map without checking its type

   Hyperconnect2023

   Symptoms

   Hyperconnect upgraded the Fluent Bit sidecar of one service with little user impact to Fluent Bit 2.0 and watched it for several days. Only the containers that used the Kafka output plugin were being restarted, each time on SIGSEGV. The stack trace printed at the crash pointed into the Kafka output plugin, but no core file was left behind, because Fluent Bit ran inside Kubernetes.

   Container crash loop

   Trigger

   Upgrading the Fluent Bit logging sidecar to version 2.0 (the 2.0.3 image) on a service with low user impact.

   Root cause

   Software defect

   Component

   Telemetry agent

   Contributing factor

   Limited debug access

   Detection

   The team monitored the upgraded sidecars for several days after the upgrade and saw that the containers using the Kafka output plugin were restarting with SIGSEGV.

   Remediation

   • patched the Fluent Bit Kafka output plugin to skip data that is not a map before calling produce_message()
   • ran a custom build with the patch as the sidecar, which stopped the segmentation faults
   • submitted the patch upstream as fluent-bit pull request 6452, which was merged eight days later

   Lessons

   A segmentation fault in a container can be debugged from its core file, but Kubernetes does not keep one by default: a volume mounted into the pod and a core_pattern pointing into it on the node are needed first. The debug image of the same version, run locally on the same CPU architecture with the matching source checkout, gives a gdb environment that matches production. Core files contain part of the process memory and may hold sensitive data, so they should be shared with care.

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2. IL-0501 Every node of the UToronto Kubernetes cluster on Azure went NotReady and the JupyterHub became unreachable

   2i2c2022

   Symptoms

   On 30 August 2022 at 11:15 AM Pacific time, the UToronto JupyterHub at jupyter.utoronto.ca stopped responding and users could not reach it at all. The outage was reported through 2i2c's support desk.

   Service outageNode NotReady

   Trigger

   Microsoft Azure rolled out an Ubuntu update to all Ubuntu 18.04 nodes, which triggered a systemd bug that broke networking.

   Root cause

   Breaking change in an upgrade

   Component

   Node operating system image

   Contributing factor

   Limited debug access

   Detection

   The outage was reported at 11:15 AM through 2i2c's Freshdesk support desk because jupyter.utoronto.ca was not responding. At 11:25 AM the customer's representative forwarded the Microsoft advisory on the same ticket, which pointed the team to the cause.

   Remediation

   • Started an upgrade of the cluster's Kubernetes and node versions, which replaced all old nodes with new working ones

   Action items

   • Document how to SSH into the Kubernetes nodes
   • Find a way to be notified of Azure advisories

   Lessons

   Once the cause was known, a single action brought the hub back quickly. The team had no documented way to SSH into its Kubernetes nodes, and the customer's representative saw the Azure advisory and pointed it out on the support ticket, which saved a lot of time.

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3. IL-0113 The managed GKE control plane of Webhook Relay's main cluster failed because its API server certificates had never been rotated, and a manual credential rotation reported success without taking effect

   Webhook Relay2022

   Symptoms

   At 12:00 EST the health check system reported that the backend APIs, the frontend and the tunnels in the EU region were down. The team first took it for a transient error, such as an interrupted client request or a server evicted for memory or CPU pressure, but the main dashboard was offline as well.

   Service outageControl plane lagControl plane unresponsiveStale or inconsistent API readsCredentials unavailable

   Root cause

   Certificate or credential expiry

   Component

   GKE control planeKubernetes API serverNode poolRBAC and service accounts

   Contributing factor

   Limited debug accessSingle point of failure

   Detection

   The health check system sent a notification that the service was down; the team then found the main dashboard offline.

   Remediation

   • recreated the node pools by hand after the credential rotation failed to recreate them
   • duplicated the main cluster's services into a newly created GKE cluster
   • deleted the Kubernetes Service records in the old cluster so its load balancers could be attached to the new cluster
   • detached the StatefulSet from the old cluster and switched all writes to the main disk
   • exported the data from the temporary ClickHouse instance and inserted it into the main instance

   Action items

   • Change the deployment manifests so services can be switched between clusters quickly without a complicated persistent disk migration

   Lessons

   Recovery of existing infrastructure needs a time limit. Throughout the outage the old cluster looked about to recover, and the reluctance to give up on the salvage effort kept the team from starting over with a new cluster much sooner, which would have been the right call. On older GKE clusters, check the validity of the cluster CA certificate, for example by decoding masterAuth.clusterCaCertificate from gcloud container clusters describe and reading it with openssl x509. GKE's multiple availability zones made no difference in this failure.

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4. IL-0591 Gitpod workspaces in all regions became IO bound and unresponsive for days after the ws39 workspace cluster release

   Gitpod2022

   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.

   Throughput degradationSustained high node CPUNode NotReadyData loss

   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

   Faulty deployment

   Component

   Node root diskContainer CPU limit

   Contributing factor

   No staged rolloutGap in automated testsUnrepresentative stagingUnknown limitOverlapping failuresIneffective rollbackHarmful manual actionLimited debug access

   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.

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5. IL-0205 The GKE cluster autoscaler could not add nodes to GitLab.com's production cluster because Google Cloud ran out of SSD persistent disks in us-east1

   GitLab2021

   Symptoms

   At 08:05 UTC on 2021-06-21 the API service's Apdex (the share of requests that finish within a target time) and Rails request queuing began trending the wrong way. At 09:01 the team found the API deployment unhealthy: it needed more pods for the day's growing traffic, but no new nodes could be added to give it pod capacity. Users saw slow responses; the review found no sign that error rates rose.

   Scale-up failureHigh latencyPods PendingDeploys failing

   Trigger

   Google Cloud ran out of SSD persistent disk stock in us-east1, the region GitLab.com runs in, while the API service needed more nodes for rising user traffic.

   Root cause

   Cloud provider outage

   Component

   Cluster autoscalerNode pool

   Contributing factor

   Monitoring blind spotLimited debug accessOverlapping failures

   Detection

   There was no alert on resource availability. Apdex and Rails queuing began trending the wrong way at 08:05 UTC and an engineer declared the incident in Slack at 08:43; finding the cause took a lot of log digging: cluster autoscaler errors about node pools in backoff, then ZONE_RESOURCE_POOL_EXHAUSTED at the instance-group level, and finally Google Cloud support naming SSD as the exhausted resource.

   Remediation

   • added a node pool that uses pd-standard disks
   • scaled the new node pool by hand across all three zones to relieve the API
   • brought more pd-standard node pools online for the other blocked workloads
   • stopped production deployments and opened a Google Cloud support case about the cluster autoscaler not working with the new node pools
   • changed node pools and nodeSelectors so the remaining workloads ran on pd-standard nodes
   • removed the SSD-backed node pools after a day without problems

   Action items

   • Move Kubernetes services to pd-standard disks (delivery#1837)
   • Capture resource exhaustion errors as a metric

   Lessons

   A cloud region can run out of one specific resource, here SSD persistent disks, and the only error may be a generic ZONE_RESOURCE_POOL_EXHAUSTED that does not name it. GitLab could neither monitor nor reserve resource availability, so detection took 22 hours and the cause came from Google Cloud support. The workloads ran fine on pd-standard disks. A second problem found on the way, a cluster autoscaler that does not fall back to another matching node pool when one cannot scale, slowed the move to the new node pools.

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