Cases2 cases

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1. IL-0535 An internal Kinesis Data Streams cell in US-EAST-1 degraded, raising errors in CloudWatch Logs, S3 events and Lambda

   Amazon Web Services2024

   Symptoms

   From 2:45 PM PDT on July 30th, 2024, several AWS services in the Northern Virginia (US-EAST-1) Region had higher latency and error rates: CloudWatch Logs, Amazon Data Firehose, the event framework that publishes Amazon S3 events, Amazon ECS, AWS Lambda, Amazon Redshift and AWS Glue. All of them used one internal cell of Amazon Kinesis Data Streams, whose requests had started to see elevated latency and errors.

   High latencyHigh error rateMonitoring outageProbe failures

   Trigger

   At 9:09 AM PDT on July 30th a routine deployment started in one Availability Zone of the Kinesis cell, and taking hosts in and out of service during the deployment set off the failure.

   Root cause

   Software defect

   Component

   Message brokerServerless function platformObject storage

   Contributing factor

   Unrepresentative stagingAutomation amplification

   Remediation

   • Deployed a change that shed load by reducing incoming stream volume from less time-sensitive internal workloads
   • Added capacity to the subsystem that provisions secure connections for Kinesis requests
   • Added more capacity at 6:32 PM PDT to further improve connection provisioning

   Action items

   • Keep the added connection provisioning capacity, load-shedding tooling and changed connection limits in place in US-EAST-1
   • Apply the same changes in every other region that has cells with the new Kinesis Data Streams architecture
   • Update the cell management system to better manage the workload that triggered the issue

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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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