Cases3 cases

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1. IL-0284 Workers KV failed 90.22% of requests in a third-party storage outage, breaking Access, WARP and many Cloudflare products

   Cloudflare2025

   Symptoms

   At 17:52 UTC on 12 June 2025, Cloudflare's WARP team saw registrations of new devices failing and declared an incident. Minutes later the Cloudflare Access team got an alert on a rapid rise in error rates, and Service Level Objectives for several other services fell below target. All of them shared one cause: Workers KV was unavailable.

   High error rateService outageCredentials unavailable

   Trigger

   Part of the storage infrastructure behind Workers KV is backed by a third-party cloud provider, and that provider had an outage. Workers KV could then no longer serve cold reads or writes from its central data store.

   Root cause

   Cloud provider outage

   Component

   Database

   Contributing factor

   Single point of failureExcessive blast radiusCircular dependencyRetry storm

   Detection

   The WARP team saw registrations of new devices failing at 17:52 UTC and declared an incident. At 18:05 the Access team received an alert on a rapid increase in error rates and Service Level Objective alerts fired across several teams; at 18:06 the incidents were combined once Workers KV unavailability was identified as the shared cause.

   Remediation

   • Activated the Challenges and Turnstile kill switches so visitors were not blocked
   • Degraded Gateway rules that referenced identity or device posture state to remove the Workers KV dependency
   • Force-dropped Access identity and device posture requests to shed load on Workers KV
   • Worked on a Workers KV release against an alternative backing datastore in case the storage stayed down
   • Restored Access and Device Posture calls to Workers KV once the third-party service was back

   Action items

   • Avoid singular dependencies on storage infrastructure that Cloudflare does not own
   • Improve the ability to recover critical services, including Access, Gateway and WARP
   • Add redundancy to Workers KV's storage infrastructure and remove the dependency on any single provider
   • Make each affected product resilient to the loss of any single point of failure, third-party dependencies included
   • Build tooling to re-enable namespaces progressively during storage incidents, so services come back without overloading Cloudflare's own infrastructure

   Lessons

   Cloudflare holds itself responsible for its choice of dependencies and how it architects around them, even when a vendor failure was the trigger. Building many products on Workers KV normally makes them more robust than separate storage services would, but here the cascading failure of Workers KV made the outage worse and widened its blast radius.

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2. 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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3. 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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