Cases3 cases

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1. IL-0281 PostHog's Logs ClickHouse cluster silently deleted almost all its S3 data, losing US logs older than 3 days

   PostHog2026

   Symptoms

   On 19 February 2026, a node of the ClickHouse cluster behind PostHog's Logs product crashed, restarted and could not start again because it could not find a large amount of its data. Engineers then found that almost all of the Logs data stored on S3 was gone, so logs collected in the US region more than 3 days earlier were lost. Only the Logs product was affected.

   Data loss

   Trigger

   At 10:54 UTC a routine mutation was run to materialise an index. When it finished at 11:02, it set off a bug in ClickHouse's zero-copy replication.

   Root cause

   Software defect

   Component

   DatabaseDatabase replicationObject storageDatabase backups and snapshots

   Contributing factor

   Monitoring blind spotSingle point of failureUntested backup

   Detection

   No automated alarm fired. At 19:40 UTC a node in the cluster crashed, restarted and failed to start because of the missing data, and the engineers who investigated found that most of the S3-backed data was gone.

   Remediation

   • Switched Logs to a new table without zero-copy replication and caught up on recent messages
   • Backfilled the Logs data from the 3 days of Kafka history

   Action items

   • Disable zero-copy replication on all clusters
   • Audit the whole Logs ClickHouse configuration and keep experimental features out of production
   • Enable S3 versioning on the storage buckets so objects deleted by a database bug can be recovered
   • Spot-check configurations and data integrity strategies for single points of failure before a product becomes generally available

   Lessons

   Running Logs in its own cluster kept every other product unaffected, and 3 days of Kafka retention saved the recent data. An experimental setting chosen for an early internal stage was allowed to stay in production. Monitoring covered only incoming and recent data, so petabytes were deleted over 8 hours without an alarm. Relying on database replication alone over shared storage was a single point of failure, and S3 versioning would have let the deleted files be recovered.

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2. IL-0517 Gitpod workspaces failed to start worldwide and lost data when CoreDNS lookups to 8.8.8.8 timed out in its clusters

   Gitpod2022

   Symptoms

   At around 5.44pm UTC on 25 January 2022, Gitpod saw a high error rate when pulling images from Google Container Registry (gcr.io), with the error "cannot get manifest". About 10 minutes later users reported that they could not start workspaces.

   Image pull failuresDNS resolution failuresService outageData loss

   Root cause

   DNS or network fault

   Component

   CoreDNSContainer registry and its credentialsObject storage

   Contributing factor

   Single point of failure

   Detection

   The team noticed a high error rate on image pulls from GCR at around 5.44pm UTC; about 10 minutes later users reported that workspaces would not start, and the on-call team started incident response.

   Remediation

   • Created new workspace clusters in the EU and the US with a werft job
   • Redirected traffic to the new clusters

   Action items

   • Keep workspace data on cloud provider persistent volumes that survive node and cluster failures
   • Let users download their files from a workspace that cannot be started
   • Improve the workspace backup process
   • Use three DNS servers instead of one: a GCP-internal one, 8.8.8.8 and Cloudflare's 1.1.1.1
   • Do more DNS resiliency work, tracked in an epic
   • Improve support for multi-region clusters and later go multi-cloud

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3. IL-0229 bunny.net's DNS servers crashed in a loop on a corrupted routing database file and could not fetch a fix from its CDN

   bunny.net2021

   Symptoms

   On 22 June 2021, within minutes of an update released at 8:25 AM UTC, bunny.net's global fleet of close to 100 DNS servers kept crashing and restarting, more than 60% of traffic was lost, hundreds of Gbits of throughput disappeared, and over 750,000 websites were affected. After 10 minutes the team realised that the DNS servers were restarting and dying and that nothing could bring them back up in that state.

   Container crash loopDNS resolution failuresService outageMisrouted requestsLoad imbalance

   Trigger

   At 8:25 AM UTC bunny.net released an update meant to reduce the download size of the SmartEdge optimization database, and it uploaded a corrupted file to Edge Storage.

   Root cause

   Faulty deployment

   Component

   Authoritative DNS servers and recordsCDN and edge networkObject storage

   Contributing factor

   Circular dependencySingle point of failureAutomation amplificationIneffective rollbackHarmful manual action

   Detection

   About 10 minutes after the release, the team realised that the DNS servers were restarting and dying.

   Remediation

   • Deployed by hand an update that disabled the SmartEdge system on the DNS nodes
   • Routed all requests through Frankfurt instead of Madrid on a couple of DNS servers
   • Migrated the deployment systems and files to a third-party cloud storage service
   • Rewired the deployment system and DNS software to the new storage and deployed
   • Synced the databases and deployed the newest file sets through the third-party service

   Action items

   • Phase out the BinaryPack library as a hot-fix
   • Test third-party libraries more extensively
   • Migrate the internal APIs to an independent third-party service
   • Find how to stop non-critical software from becoming a single point of failure across clusters
   • Make the DNS system keep a local copy of all backup data with automatic failure detection

   Lessons

   bunny.net had built its systems to rely on each other, including the critical parts of its internal infrastructure, so one corrupted file brought down several layers of redundancy with no way back. A part of the infrastructure treated as non-critical turned out to be a single point of failure for several clusters at once, which canary deployments did not catch. Building infrastructure inside its own ecosystem lets one failure cascade through all of it. In the rush to fix the outage the team made typos and mistakes, and the final deployment came after it calmed down and double-checked everything.

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