Cases33 cases

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1. IL-0490 Records were missing from LootLocker's new MySQL database after a migration cutover and the service degraded for 3 hours

   LootLocker2026sev2

   Symptoms

   About an hour after LootLocker cut over from a managed database provider to its own MySQL deployment on 2026-04-28, data anomalies showed up in production. A small window of writes made shortly before the migration, spanning 2026-04-19 to 2026-04-20, had never reached the new database, and the service stayed degraded for about three hours after the cutover.

   Data loss

   Trigger

   On 2026-04-20 at about 16:20 BST, a routine credential rotation, made as part of an unrelated configuration fix, changed the credentials that the temporary replication process used for its database connection, and the process crashed.

   Root cause

   Certificate or credential expiry

   Component

   DatabaseDatabase replication

   Contributing factor

   Change with a delayed effectMonitoring blind spotUnverified assumptionNo safeguard on destructive change

   Detection

   Data anomalies were detected in production at about 11:30 BST, about an hour after the cutover.

   Remediation

   • Restored normal service behaviour first and deferred the root cause analysis
   • Requested a restore of the deleted source database from the previous provider
   • Replayed the affected records for impacted users from the restored source database

   Action items

   • Perform technical changes of comparable risk in a scheduled maintenance window instead of as live cutovers
   • Require a pre-cutover checklist that verifies replication or sync processes are live and caught up just before cutover
   • Require an explicit downstream impact review for credential rotations of service accounts used by long-running processes
   • Keep source systems until integrity in the target has been independently verified after cutover
   • Publish planned maintenance and migration notices in the in-product News feed as well as Discord, with clear impact statements and more advance notice

   Lessons

   The failure was a silent side effect of an unrelated change made eight days earlier: the replication process recovered and kept working, so there was no signal that data had been dropped, and a pre-cutover integrity check would have caught it. Deleting the source database before validating the new one made recovery much harder. Choosing a live migration to avoid downtime left no clean point to verify integrity, and the resulting degradation and two days of piecemeal recovery far exceeded what a short planned maintenance window would have cost. The announcement on Discord was not clear enough about the impact and came with too little notice.

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2. IL-0288 ZooKeeperWait rose to thousands of seconds on three redundant ClickHouse instances, degrading a CDN77 data pipeline

   CDN772026

   Symptoms

   At 13:30 UTC on 9 March 2026, CDN77's data team was alerted to a sudden rise in the ZooKeeperWait metric on the ClickHouse cluster behind one of its large data pipelines. Alerting for this cluster size is set at 10 sec for the low watermark and 20 sec for the high one, and the metric reached thousands of seconds. The same rise appeared on three symmetrically redundant ClickHouse instances in different datacenters, each with its own ZooKeeper instance, and the pipeline stayed degraded for over an hour, with no observable impact on internal users, customers or partners.

   Replication lagThroughput degradation

   Trigger

   From 13:15 UTC two client machines that had been cut off from the network for several months came back and their log shippers sent months of queued, very small batches of health check data with old timestamps, spanning a very large time range.

   Root cause

   Capacity exhaustion

   Component

   DatabaseCoordination and key-value storeDatabase replicationTelemetry agent

   Contributing factor

   Monitoring blind spotUnreviewed defaultsIgnored early signal

   Detection

   An alert on the ZooKeeperWait metric fired at 13:30 UTC, fifteen minutes after the problematic inserts began at 13:15. The cluster's alerting uses a low watermark of 10 sec and a high watermark of 20 sec.

   Remediation

   • Dropped and recreated the one table partitioned by 5 minutes that had not cleared its backlog of parts
   • Broadened that table's partitioning from minutes to hours

   Action items

   • Reject out-of-range timestamps in the root MaterializedView tables
   • Record statistics on rejected rows in a second materialized view
   • Refine the deliberately wide rejection boundaries as data on rejections comes in
   • Keep a minimal test scenario with an insert query whose timestamps span days or months
   • Make a range of observability improvements

   Lessons

   Data structure matters even in a pipeline built around volume: incoherent timestamps can destabilise it as effectively as changes in cardinality. A metric that looks odd and cannot be explained at once should not be set aside under incident pressure; the falling mean insert size was what led to the cause. Means and medians each have their place, and here the mean, not the median, exposed the problem. Partitioning has to be chosen carefully: too few partitions give oversized parts, slow mutations and costly or ineffective TTL, while too many slow column scans, add final merging to aggregating queries and overload the Keeper.

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3. 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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4. IL-0305 Replicas of one Intercom Vitess shard hit 100% CPU and fell up to 2.2 hours behind, serving stale inbox data

   Intercom2026

   Symptoms

   From 17:03 UTC on January 27, 2026, a small subset of Intercom customers, fewer than 1%, saw inconsistent latency and stale data when reading from the platform. At 17:25 UTC an alarm on an excessive message backlog in a worker declared an incident automatically.

   Replication lagDatabase overloadHigh latency

   Trigger

   An internal app deletion process, running more concurrent deletion steps per app than usual with no rate limiting, and the ETL data pipeline both put heavy load on the same database shard at the same time.

   Root cause

   Capacity exhaustion

   Component

   DatabaseDatabase replication

   Contributing factor

   No headroom

   Detection

   An alarm on an excessive message backlog in a worker fired at 17:25 UTC and declared an incident automatically.

   Remediation

   • Disabled ETL processing to reduce load on the affected shard
   • Scaled the app deletion workers to zero
   • Re-enabled ETL carefully once monitoring showed stable metrics

   Action items

   • Increase the capacity of shards to leave headroom for unexpected load spikes
   • Add rate limiting and resource controls to the app deletion process so it backs off when database load is high
   • Review how background work such as ETL and maintenance tasks competes with customer-facing operations for database capacity
   • Alert on replication lag earlier and link it to specific workloads
   • Communicate proactively with affected customers in incidents with a limited blast radius

   Lessons

   Because data is spread across many independent shards, the overload stayed on one shard and reached fewer than 1% of customers instead of the whole platform.

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5. IL-0368 PlanetScale's control plane went down and some database branches failed queries during the AWS us-east-1 outage

   PlanetScale2025

   Symptoms

   At 7:13 UTC on 20 October 2025, PlanetScale's continuous tests in production started failing because they could not create new database branches. The service that creates, resizes and configures database branches was unavailable, a near-total control plane outage, while existing database branches kept their capacity and connectivity.

   Service outageScale-up failureNetwork unreachableHigh error rateReplication lagDNS resolution failures

   Trigger

   The incident started with a DNS misconfiguration at one of PlanetScale's service providers. An Amazon DynamoDB outage impacted AWS STS, and several hours of capacity constraints and network instability in AWS us-east-1 followed.

   Root cause

   Cloud provider outage

   Component

   DatabaseDatabase replicationDatabase proxy and connection poolerObject storage

   Contributing factor

   –

   Detection

   PlanetScale's continuous testing in production started failing and alerted engineers at 7:13 UTC. At 10:05 UTC an alert fired for a Kubernetes operator in a large single-tenant customer installation exhausting its resources.

   Remediation

   • Disallowed new databases in us-east-1 and made us-east-2 the default region for new databases
   • Delayed new backups and cancelled pending backups that were waiting for an EC2 instance
   • Advised PlanetScale Managed customers with vtgate autoscaling to shed load
   • Paused draining and terminating EC2 instances older than 30 days
   • Kept vacant EC2 instances for reuse instead of terminating them
   • Bin-packed vtgate processes more tightly for customers with autoscaling
   • Sent manual reparent requests to move primaries to healthier availability zones
   • Restarted the edge load balancer and vtgate processes that did not recover after the partitions healed

   Action items

   • Understand and become resilient to the failure modes of SaaS dependencies for CI/CD, SSO, web application hosting and incident communication
   • Investigate more ambitious ways to reduce runtime dependence on internal and AWS services
   • Look into using all six us-east-1 availability zones to tolerate zonal outages and partitions

   Lessons

   Keeping the control plane separate from the data plane meant the control plane outage did not affect customer databases, and redundancy, automated failover and zonal traffic routing limited the effect of the network partitions. Network partitions are hard to reason about, test and tolerate: with three availability zones a system survives losing one zone only if the other two can still reach each other reliably.

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