Cases24 cases

12 to 16 of 24

1. IL-0202 A burst of queries and database writes exhausted connection pools and memory on Honeycomb's ingest hosts, and Kubernetes crash-loop back-off kept failed hosts offline so ingest capacity stayed too low to recover

   Honeycomb2023sev1

   Symptoms

   Standard alerting paged Honeycomb's on-call engineers about the performance and stability of event ingestion, and the automated load-shedding mechanisms tripped and sent further notifications. The status page described a higher than normal error rate and response time on the ingestion service.

   High error rateHigh latencyContainer crash loopService outageDatabase overload

   Trigger

   An abnormally large number of queries arrived from many separate connections within a few minutes.

   Root cause

   Capacity exhaustion

   Component

   DatabaseIn-memory cache

   Contributing factor

   Retry stormAutomation amplification

   Detection

   Standard alerting notified the on-call engineers about ingestion performance and stability, and the automated load-shedding mechanisms added notifications when they tripped. The first status post, about alerts on a higher than normal error rate and response time, went up at 18:28 PST (02:28 UTC), 13 minutes after the 02:15 UTC start.

   Remediation

   • Scaled the ingest fleet up manually and aggressively
   • Cut off most ingest for a few minutes so the ingest hosts could come back with enough capacity
   • Temporarily lowered the rate limits for the most aggressive traffic sources until their traffic subsided

   Action items

   • Keep the ingest fleet scaled up to prevent a repeat while longer-term fixes are worked on
   • Decide on the projects that will address these failure paths

   Lessons

   Load shedding and auto-scaling were in place, yet a burst that arrived faster than the cache could spread its writes still took the ingest hosts down. During recovery, Kubernetes' crash loop back-off worked against the operators: the hosts that had failed stayed offline while the fleet needed every host. Clients that retry whole OTel batches after a partial drop can multiply traffic many times over, so per-source rate limits were what finally stabilised ingest.

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2. IL-0386 After a move to Google Cloud, Postgres connection timeouts and memcached i/o timeouts spiked on GKE nodes

   incident.io2023

   Symptoms

   Shortly after incident.io moved its infrastructure fully into Google Cloud, the application saw a larger-than-normal number of Postgres connection timeouts, and new database connections per second were in the hundreds, spiking up and down. At the same time, the memcached instance used for transient data and faster API responses had sporadic connection timeouts.

   Intermittent timeoutsPacket loss

   Trigger

   After the move to Google Cloud, the application opened hundreds of new Postgres connections per second, and an accidental database join made it send thousands of duplicate calls to an external third party in parallel.

   Root cause

   Capacity exhaustion

   Component

   CNI pluginDatabaseIn-memory cache

   Contributing factor

   Unreviewed defaultsOverlapping failures

   Detection

   An incident was declared when the number of Postgres connection timeouts rose above normal. The remaining cache timeouts were traced through the logs of the Kubernetes node that served an affected request, the request's trace and the node's metrics.

   Remediation

   • Doubled the maximum Postgres connection lifespan from 15m to 30m
   • Made the Postgres connection pools static so they always keep N connections open
   • Raised the memcache client's MaxIdleConns from 2 to 20
   • Moved temporarily from Google's hosted memcached to a memcached inside the Kubernetes cluster
   • Shipped the fix for the accidental database join that made thousands of duplicate external calls

   Action items

   • Set more sensible concurrency limits for outbound network calls

   Lessons

   Network timeouts are not always local to one request or pod: when they appear, the whole activity of the node, including other pods on it, needs to be checked. Staying within a provider's rate limits does not keep outbound calls from affecting a node, so outbound network calls need more sensible concurrency limits.

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3. IL-0145 A power sag and chiller failure in one Australia East availability zone took down the AKS control plane's compute and its non-zone-redundant SQL Database operation queue, blocking AKS create, update and delete operations in Australia East and Australia Southeast

   Microsoft Azure2023

   Symptoms

   From about 10:30 UTC on 30 August 2023, customers had trouble accessing or using Azure, Microsoft 365 and Power Platform services in Australia East. The affected Azure services included Azure Kubernetes Service (AKS) along with storage, SQL Database, Cosmos DB, virtual machines and Azure Resource Manager.

   Control plane unresponsiveHigh error rateService outage

   Trigger

   At 08:41 UTC a lightning strike on electrical infrastructure about 18 miles away caused a utility voltage sag that shut down the cooling chillers of datacenters in one Australia East availability zone; 13 chillers failed to restart automatically and five could not be restarted, so two data halls overheated and their infrastructure was shut down.

   Root cause

   Cloud provider outage

   Component

   AKS control planeDatabase

   Contributing factor

   Single point of failureExcessive blast radius

   Detection

   Cosmos DB impact was detected by monitoring at 10:47 UTC, first automated communications went to Azure Service Health at 10:48 UTC, the public Azure Status page was updated at 11:30 UTC, and all subscriptions in Australia East were sent portal communications at 11:36 UTC.

   Remediation

   • shut down the remaining networking, compute and storage in the two affected data halls so the chilled water loop could cool down
   • restarted the five chillers manually at 12:12 UTC
   • restored power to all hardware by 15:10 UTC and brought infrastructure back in phases
   • restored AKS operations through the recovery of the SQL Database operation queue, with no other mitigation

   Action items

   • AKS: convert all operation queue SQL Database databases to be zone redundant (estimated September 2023)
   • AKS: replace all cross-region SQL Database queue usage with zone-redundant Service Bus queues (estimated September 2023)
   • Change the control timing logic of the Chiller Management System so chillers do not fail to restart after voltage fluctuations
   • Change the emergency procedure for manual restarts of simultaneous chiller failures from an adjacent sequence to a load-based sequence per data hall
   • Increase technician staffing at the datacenter as a temporary measure for manual chiller restarts
   • Storage: optimise the large-scale recovery process and update troubleshooting guides (estimated December 2023)
   • Azure Service Fabric: improve reliability of SQL Database tenant ring recovery (estimated December 2023)
   • SQL Database: review the trigger criteria of auto-failover groups (estimated October 2023)
   • SQL Database: upgrade internal tooling to enable mass migration of databases (estimated December 2023)
   • Cosmos DB: optimise Service Managed Failover for single-region-write accounts (estimated November 2023)
   • ARM: complete the migration of its storage layer to the zonally redundant architecture (estimated December 2023)
   • Incident management: harden readiness, process and playbook for power-down scenarios (estimated October 2023)

   Lessons

   A control plane spread across availability zones still fails with a zone when one of its dependencies, here the SQL Database operation queue, is not zone redundant. When a second region uses the same database, the failure crosses into that region as well. Workloads already running were not expected to be affected; what stopped was everything that needs the managed service's control plane, such as scaling and cluster changes.

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4. IL-0207 A routine task run from an engineer's local environment switched to production and deleted GitLab.com's Consul release, so Patroni lost Consul and GitLab.com returned 5xx errors to all traffic

   GitLab2022

   Symptoms

   GitLab's monitoring and alerting fired at once. One of the alerts reported a Patroni failover, and users of GitLab.com received 5XX errors; the CDN counted about 13 million requests answered with 503 during the outage.

   Service outageHigh error rateResources deleted

   Trigger

   An engineer ran a routine task with GitLab's tools in a local development environment; during a local deploy or rollback operation the tool's context changed from local development to production, and the change was applied to production.

   Root cause

   Operator error

   Component

   Database

   Contributing factor

   No safeguard on destructive change

   Detection

   GitLab's monitoring and alerting fired immediately; one of the alerts reported a Patroni failover.

   Remediation

   • re-applied the Consul release to the production Kubernetes cluster, with manual steps because the tooling did not support the redeploy directly

   Action items

   • Improve the Kubernetes deployment process to reduce the risk of running harmful deployment operations against production from local environments (https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/16875)
   • Integrate Teleport with the Kubernetes clusters, which could give SREs a read-only role by default (https://gitlab.com/gitlab-com/gl-infra/reliability/-/issues/16221)

   Lessons

   A tool that can switch its target from a local environment to production during a deploy or rollback turns routine local work into a production change. GitLab plans to improve its Kubernetes deployment process so that harmful deployment operations are less likely to be run against production from local environments, and to integrate Teleport with its clusters, which could give SREs a read-only role by default. Detection and diagnosis were fast because the alerts led straight from Patroni to its dependency, Consul. Bringing the deleted release back needed manual work, because the tooling did not make the redeploy straightforward, but it was still done relatively quickly.

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5. IL-0500 UToronto JupyterHub logins and server starts returned 500 errors when the hub's sqlite database disk filled up

   2i2c2022

   Symptoms

   On 31 October 2022 at 6:27 PM Pacific time, users of the UToronto JupyterHub run by 2i2c could no longer log in or start their servers, and the hub answered with 500 errors. Users who were already logged in could keep using the hub.

   High error ratePersistent volume full

   Trigger

   The JupyterHub log file, written to the same 10G disk as the hub's sqlite database, grew until the disk was completely full.

   Root cause

   Capacity exhaustion

   Component

   DatabasePersistent volumes and stateful setsJupyterHub

   Contributing factor

   No alert on limit

   Detection

   A user emailed 2i2c support at 7:13 PM that users could not log in; the team acknowledged the issue at 8:33 PM. There was no alert for the problem.

   Remediation

   • Compressed the hub log file into /tmp from a shell in the hub pod, because it could not be compressed in place on the full disk
   • Copied the compressed log to a local machine so the hub pod could be restarted without losing it
   • Resized the hub database disk (the hub-db-dir PVC) from 10G to 60G
   • Restarted the hub pod, which came back with the larger disk and restored service

   Action items

   • Stop keeping hub logs on the same disk as the hub database
   • Add a Grafana graph of free space on the JupyterHub database disk
   • Alert on elevated error rates

   Lessons

   Kubernetes dynamic volume resizing let the hub database disk grow from 10G to 60G without much effort. The risk of keeping logs on the database disk had been identified during the earlier migration, but the team had no solid process for following it through, and because no alert existed the team had to wait for a person to report the outage.

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