Cases2 cases

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1. IL-0282 DynamoDB in US-East reached about 55% request errors as storage servers overloaded its metadata service

   Amazon Web Services2015

   Symptoms

   Early on Sunday 20 September 2015, the error rate of customer requests to Amazon DynamoDB in the US-East Region rose far beyond any level of the previous 3 years by 2:37am PDT and settled at about 55%. Some other AWS services in the region that depend on DynamoDB were affected as well.

   High error rateDatabase overload

   Trigger

   At 2:19am PDT a brief network disruption affected a portion of DynamoDB's storage servers, and they all requested their membership data from the metadata service at the same time.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No capacity planningMonitoring blind spotRetry stormCircular dependencyExcessive blast radius

   Detection

   For the errors that remained after the main recovery, a growing number of customer support cases on Monday about tables stuck in the updating or deleting stage and higher than normal error rates showed that the low overall error rate was hitting some customers hard.

   Remediation

   • Paused requests to the metadata service, which cut the retries and relieved its load
   • Added significant capacity to the metadata service once it could answer administrative requests
   • Turned requests to the metadata service back on and returned storage servers to the customer request path
   • Restored a metadata partition that was still not taking its full traffic, ending the remaining errors on Monday

   Action items

   • Significantly increased the capacity of the metadata service, which AWS had already done by the time of the summary
   • Add stricter monitoring on performance dimensions such as membership size, to plan capacity ahead
   • Reduce the rate at which storage nodes request membership data
   • Lengthen the time allowed to process membership queries
   • Segment DynamoDB into many metadata service instances, each serving only part of the storage server fleet
   • Change SQS metadata caching so send and receive keep working without long access to the metadata table
   • Partition work across the Auto Scaling servers to process a backlog of scaling jobs in parallel
   • Prune older scaling activities that have been superseded
   • Increase the capacity available to process scaling activities
   • Change CloudWatch caching of DynamoDB group membership data to refresh only the smallest set of metrics
   • Deliver CloudWatch metrics faster through write-through caching
   • Roll out the shorter timeout on the DynamoDB-dependent API call in Console login

   Lessons

   A low overall error rate can hide disproportionately high error rates for a few customers, and AWS should have posted to the dashboard sooner. A call that does not need to succeed, like the DynamoDB-dependent API call in Console login, should fail quickly instead of blocking on a very long timeout. Splitting the metadata service into many instances, each serving part of the fleet, is meant to contain the impact of software, capacity or infrastructure failures.

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2. IL-0337 EBS volumes in one US East Availability Zone got stuck after a network change cut EBS nodes off from their replicas

   Amazon Web Services2011

   Symptoms

   From 12:47 AM PDT on 21 April 2011, a subset of Amazon EBS volumes in one Availability Zone of the US East Region could no longer serve reads or writes, and EC2 instances that tried to use them got stuck as well. AWS called these "stuck" volumes. EBS API calls across the whole US East Region also returned high error rates and latencies during two periods on the first day.

   Storage disconnected or stale mountsNetwork unreachableControl plane lagHigh error rateFailover failureData loss

   Trigger

   During a routine network change to upgrade the capacity of the primary EBS network in one Availability Zone, traffic was to be shifted off one of the redundant routers. The shift was executed incorrectly and sent the traffic onto the lower-capacity replication network instead of the other primary router, which could not carry it.

   Root cause

   Operator error

   Component

   Cloud block storageDatabase

   Contributing factor

   Automation amplificationRetry stormNo headroomExcessive blast radiusSignal buried in noise

   Remediation

   • Rolled back the incorrect traffic shift, which restored network connectivity
   • Disabled all new Create Volume requests in the affected Availability Zone at 2:40 AM PDT
   • Disabled all communication between the degraded EBS cluster and the EBS control plane from 8:20 AM PDT
   • Stopped EBS servers in the degraded cluster from futilely contacting other servers for free space
   • Changed the EBS control plane so new EBS-backed instance launches stopped failing
   • Moved spare server capacity physically from across the US East Region into the degraded cluster
   • Adjusted the negotiation throttles so the new servers could join without flooding the old ones
   • Built a separate EBS control plane partitioned to the affected zone and finer-grained throttles, then processed the backlog
   • Restored the remaining volumes from S3 snapshots taken early in the event
   • Restored volumes on failed machines that had no snapshot after forensic work
   • Intervened by hand on the RDS multi-AZ instances that did not fail over automatically

   Action items

   • Audit the change process and add automation to prevent the traffic shift mistake
   • Change capacity planning and alarming to keep the extra safety capacity needed for large recovery events
   • Change the retry logic in EBS nodes to back off harder and reconnect to previous replicas instead of searching for new nodes
   • Test and deploy the fix for the race condition that crashed EBS nodes
   • Fix the RDS bug that kept multi-AZ instances from failing over automatically
   • Improve the EBS control plane timeout logic to prevent thread exhaustion when one zone's cluster is slow
   • Make the EBS control plane aware of Availability Zones so it sheds load when over capacity
   • Move more EBS control plane functions into per-cluster services that run in the same zone as the cluster
   • Offer multiple Availability Zones to Amazon VPC customers
   • Provide better tools for building multi-AZ applications that survive the loss of a zone
   • Host free webinars and publish whitepapers on fault-tolerant architecture
   • Build the fine-grained control and throttling used in recovery into the EBS nodes and automate the recovery models
   • Look at changes that keep volumes working while a cluster is degraded, including the ability to take a snapshot of a stuck volume
   • Send more frequent and detailed customer updates and staff developer support more widely during events
   • Develop tools that show through the APIs whether instances are impaired

   Lessons

   AWS writes that with more spare capacity the cluster would have absorbed the re-mirroring requests without a storm, and that it now understands how much capacity large recovery events need. The event also showed that the EBS control plane let a failure in one Availability Zone affect the others, so more work is needed to make zones behave as if completely independent. AWS adds that some customers had critical components in a single zone, and that multi-AZ deployments need to become easier to design and run.

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