Cases19 cases

11 to 15 of 19

1. IL-0220 An overloaded AWS Transit Gateway dropped packets on 4 January 2021, and Slack's web tier saturated and went down

   Slack2021

   Symptoms

   During the Americas' morning of 4 January 2021, an external monitoring service paged Slack because error rates were creeping up. While the investigation was starting, Slack's own dashboarding and alerting service became unavailable, so responders had to work without their usual dashboards and alerts.

   High error rateMonitoring outagePacket lossService outageScale-up failureFile descriptor exhaustionCloud API rate limit exhausted

   Trigger

   On the first working day after the holidays, Slack's traffic went from the quietest time of the year to one of its biggest days overnight, with cold client caches pulling more data than usual, and one of Slack's AWS Transit Gateways did not scale fast enough for it.

   Root cause

   Cloud provider outage

   Component

   Transit gateway or network interconnectAutomated capacity manager

   Contributing factor

   Circular dependencyAutomation amplification

   Detection

   An external monitoring service paged Slack when error rates started creeping up during the Americas' morning. AWS engineers were separately alerted to the packet drops by AWS's internal monitoring.

   Remediation

   • Disabled automated downscaling of the web tier to keep serving capacity and allow investigation
   • Cleared up the broken, partly provisioned instances that filled the autoscaling groups
   • Repaired provision-service so new web tier instances could be provisioned and enter service
   • AWS engineers increased the Transit Gateway capacity manually across all Availability Zones

   Action items

   • AWS reviews its Transit Gateway scaling algorithms for large packet-per-second increases
   • Request a preemptive upscaling of the Transit Gateways at the end of the next holiday season
   • Run the dashboard service instances in the same VPC as their database to remove the Transit Gateway dependency
   • Load test provision-service regularly
   • Reevaluate health-checking and autoscaling configurations so they cannot overload provision-service again

   Lessons

   Monitoring has to stay as independent as possible of the infrastructure it watches, or it fails when it is needed most. Here the dashboard and alerting services failed because they ran in a different VPC from their databases and so depended on the Transit Gateways. provision-service had been load tested before, but this event exceeded the earlier scale-ups.

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2. IL-0384 Amazon Kinesis front-end servers in US-EAST-1 could not route requests, so Kinesis and services built on it failed

   Amazon Web Services2020

   Symptoms

   At 5:15 AM PST on 25 November 2020, alarms fired for errors on putting and getting Amazon Kinesis records in the Northern Virginia (US-EAST-1) Region. From the same time CloudWatch, which processes metric and log data through Kinesis Data Streams, and CloudWatch Events and EventBridge returned more errors.

   High error rateHigh latencyMonitoring outage

   Trigger

   A relatively small addition of capacity to the Kinesis front-end fleet, made between 2:44 AM and 3:47 AM PST.

   Root cause

   Capacity exhaustion

   Component

   Message brokerServerless function platformTelemetry agent

   Contributing factor

   Unknown limitExcessive blast radiusCircular dependency

   Detection

   Alarms for errors on putting and getting Kinesis records fired at 5:15 AM PST, and the teams began reviewing logs.

   Remediation

   • Removed the newly added front-end capacity
   • Added a configuration so front-end servers load bootstrap data from the authoritative metadata store instead of neighbouring servers
   • Restarted the front-end fleet gradually, a few hundred servers per hour from 10:07 AM PST
   • Added Cognito capacity to buffer more calls to Kinesis
   • Deployed a change that reduced Cognito's dependency on Kinesis
   • Mitigated the memory contention on the Lambda invocation hosts
   • Informed customers through the backup Service Health Dashboard tool, the Personal Health Dashboard and a global banner

   Action items

   • Move the Kinesis front-end to larger CPU and memory servers so fewer servers, and fewer threads per server, are needed
   • Add fine-grained alarming for thread consumption
   • Finish testing a higher operating system thread count limit
   • Greatly improve the cold-start time of the front-end fleet
   • Move the front-end server cache to a dedicated fleet
   • Move a few large AWS services such as CloudWatch to a separate, partitioned front-end fleet
   • Accelerate the cellularization of the front-end fleet to match the back-end
   • Change the Cognito webservers so they sustain Kinesis API errors without exhausting their buffers
   • Keep 3 hours of metric data in the CloudWatch local metrics data store, first in US-EAST-1 and then globally
   • Train support engineers regularly on the backup tool for posting to the Service Health Dashboard

   Lessons

   In the Kinesis front-end, the number of threads each server keeps grows directly with the number of servers in the fleet, so adding servers can push every server past an operating system limit at the same time. AWS sees cellularization of the front-end, already done for the back-end, as the way to keep the shard-map cache within a tested and operated range and to guard against unknown scaling limits.

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3. IL-0272 New Cassandra nodes served reads for data they did not hold, so Monzo records looked missing and payments failed

   Monzo2019

   Symptoms

   On 29 July 2019 at 13:14 BST, four minutes after six new servers were added to Monzo's Cassandra cluster at 13:10, automated alerts showed a small percentage of Mastercard card transactions failing, and the tool Customer Operations uses to talk to customers through in-app chat stopped working. Customers could not log in, send or receive payments or withdraw cash, and saw inaccurate balances and transactions.

   Inconsistent reads from a data storeHigh error rate

   Trigger

   At 13:10 BST Monzo added six new servers to its 21-server production Cassandra cluster to add capacity, with auto_bootstrap set to false.

   Root cause

   Configuration change

   Component

   Database

   Contributing factor

   Unverified assumptionUnrepresentative stagingMonitoring blind spotExcessive blast radiusCircular dependency

   Detection

   At 13:14 automated alerts reported a small percentage of Mastercard card transactions failing, and Customer Operations reported that its customer communication tool was not working; an incident was declared at 13:15.

   Remediation

   • Deployed a fix for an error-handling bug in a Mastercard service through a planned fallback mechanism, because the deployment tooling was failing
   • Decommissioned the six new Cassandra servers one by one so data ownership returned to the original servers
   • Reconciled the data written while the new servers were serving reads and writes, by replaying externally stored events and running consistency checks

   Action items

   • Fix the use of auto_bootstrap
   • Review and document the decisions behind the other Cassandra settings and write more extensive runbooks for operations such as scale-ups and restarts
   • Expose more Cassandra metrics and consider alerting on strong shifts in metrics such as 'row not found'
   • Split the single Cassandra cluster into several smaller clusters

   Lessons

   Monzo had gaps in its knowledge of rarer Cassandra operations such as adding servers. The scale-up test with one server could not show the problem, because quorum still came from the original replicas, so it did not match adding six servers in production. The lack of metrics pointing at Cassandra delayed the response, and a single shared cluster both let one change have a far-reaching impact and made the failing part harder to find.

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4. IL-0222 Google Cloud US regions had heavy packet loss and some went unreachable after the network control plane was descheduled

   Google Cloud2019

   Symptoms

   On Sunday 2 June 2019, with user impact beginning between 11:47 and 11:49 US/Pacific, Google Cloud projects in several US regions saw elevated packet loss from network congestion, and some regions became unreachable. Customers saw higher latency, intermittent errors and lost connectivity to instances in us-central1, us-east1, us-east4, us-west2, northamerica-northeast1 and southamerica-east1, while us-west1, Europe and Asia had no regional congestion.

   Packet lossNetwork unreachableHigh latencyHigh error rateMonitoring outage

   Trigger

   At 11:45 US/Pacific a relatively rare type of datacenter maintenance event started in one physical location, and the automation that runs it descheduled logical clusters that also held network control plane jobs in other physical locations.

   Root cause

   Software defect

   Component

   Network control plane (SDN)BGP routingBackbone network (WAN)

   Contributing factor

   Excessive blast radiusCircular dependencyLimited debug accessIneffective rollback

   Detection

   Google engineers were alerted to the failure two minutes after it began and started the incident process used for the most serious production incidents. The first customer notice appeared on the status page at 12:53 US/Pacific.

   Remediation

   • Lowered the priority of less critical network traffic classes to reduce congestion
   • Halted the datacenter automation software responsible for the maintenance event
   • Rescheduled the network control plane and its supporting infrastructure in each location
   • Rebuilt and redistributed the lost network control plane configuration, rolling it out from 14:03
   • Directed service traffic away from the affected regions
   • Moved the Cloud VPN control plane in three regions out of its fail-safe state

   Action items

   • Keep the maintenance automation off until safeguards stop it descheduling jobs in several physical locations at once
   • Harden the cluster management software to reject such requests whatever their origin
   • Reconfigure the network control plane to reject maintenance requests of the type involved
   • Make the network control plane in each location persist its configuration
   • Let the network stay in fail static mode longer when the control plane is lost
   • Review, update and test emergency response tooling and procedures, including customer communication tooling, for such network failures
   • Extend continuous disaster recovery testing to this and similarly catastrophic failures

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5. IL-0492 Hosted Graphite's self-hosted load balancers hit their connection limit in an AWS outage, timing out its site and APIs

   Hosted Graphite2018

   Symptoms

   On a Friday night at 23:28 UTC, Hosted Graphite's monitoring showed its website intermittently becoming unavailable: periodic checks of the website and of the graph rendering API were timing out. Traffic dropped across all ingestion protocols at the same time.

   Intermittent timeoutsIntermittent connection failures

   Trigger

   An AWS connectivity problem in us-east1 and us-west2 slowed down connections from AWS hosts in those regions to Hosted Graphite's load balancers without breaking them.

   Root cause

   Capacity exhaustion

   Component

   On-premises load balancer

   Contributing factor

   Circular dependency

   Detection

   Monitoring reported the website intermittently unavailable at 23:28 UTC, when periodic checks of the website and the graph rendering API started timing out.

   Action items

   • Enforce stricter client timeouts
   • Raise the load balancers' connection limits
   • Use the new visualizations built from load balancer logs when troubleshooting the load balancing layer

   Lessons

   An outage at a provider that did not host the service's website still made the service unavailable, here because slow clients in the affected regions used up the connection limit of the load balancers that served everyone.

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