Cases5 cases

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1. 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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2. IL-0287 One overloaded shard of MongoDB's internal monitoring database delayed Atlas metrics, alerts and cluster operations

   MongoDB2025

   Symptoms

   On 7 October 2025 MongoDB reported a delay in the metric ingestion pipeline of MongoDB Atlas and Cloud Manager. Metrics in Atlas dashboards arrived late or went missing, Host Down alerts did not fire, and Atlas cluster management operations such as provisioning and resizing were delayed, while customer clusters themselves stayed healthy and available.

   Monitoring outageDatabase overloadLoad imbalance

   Trigger

   The rollout of an updated internal software version caused a temporary spike in resource demand on the internal database cluster.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   Inefficient queryNo headroom

   Remediation

   • Took corrective actions to stabilise the system and monitored their effect
   • Implemented a fix and monitored the results until the incident was resolved

   Action items

   • Shard the collections with concentrated load to spread traffic over more nodes
   • Optimise inefficient queries
   • Provision additional infrastructure capacity for higher traffic
   • Refine capacity planning to anticipate future load spikes, using metrics from this event
   • Redesign software rollouts to account for the predictable rise in resource demand during a rollout
   • Keep monitoring how the sharding and query changes resolve the hotspots
   • Expand the detection of elevated load conditions for faster anomaly detection and response

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3. IL-0270 Discord's ScyllaDB authentication cluster overloaded during an OS upgrade, and logins failed for about 50 minutes

   Discord2023

   Symptoms

   On 6 November 2023 at 10:52 AM PST, when an upgrade script drained all connections from zone [c] of the scylla-authentication ScyllaDB cluster, Discord's availability, measured as the share of HTTP requests that return a 200-level response, fell at once to about 25%. Users could not connect, log in, read or send messages.

   Service outageDatabase overloadHigh latency

   Trigger

   During an OS upgrade that takes one zone of a ScyllaDB cluster offline at a time, the upgrade script drained all connections from zone [c] of scylla-authentication at 10:52 AM, while one node in the just-upgraded zone [b] had a degraded NVMe Local SSD.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No headroomOverlapping failuresDependence on a warm cacheUnverified assumptionIgnored early signalRetry storm

   Detection

   An incident response call had already started when the first alerts fired at 10:56 AM, four minutes after the start. The time spent by downstream service graph showed the authentication service spending far more time on user requests than normal, and the authentication alerts fired moments later.

   Remediation

   • Turned off sending typing events and marking messages as read to shed load from the authentication cluster
   • Waited for the zone [c] RAID rebuild instead of restarting nodes, and the two zone [c] nodes came back online at 11:41 AM
   • Restarted the two zone [b] nodes one at a time

   Action items

   • Double the size of the scylla-authentication cluster so each node takes half as many requests
   • Rebuild scylla-authentication-b-2 with new, non-degraded disks
   • Build a way to detect disk degradation that is too subtle to affect overall latency but still threatens reliability
   • Move upgrade operations into a more automatic control plane for the Scylla clusters that analyses cluster state, such as cache hit rate
   • Increase the redundancy of the scylla-authentication cluster; the options being explored, not yet decided, are a higher replication factor or a hot-standby cluster in another region

   Lessons

   Taking a whole zone out of a three-zone cluster leaves no margin, so one additional slow node in another zone was enough to overload it. For some Scylla clusters the row cache hit rate of restarted nodes must reach a safe level before the next zone goes down, and a short zonal test beforehand gave false confidence. The small but critical authentication cluster needs to be run differently from Discord's much larger Scylla clusters, with more redundancy.

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4. IL-0326 A retention clean-up job filled the disk of MailPace's managed Postgres, which stayed offline for just over two hours

   MailPace2023

   Symptoms

   At 13:36 BST on Friday August 11th 2023, minutes after MailPace reran its data clean-up job with more memory, notifications arrived that all database transactions were failing across multiple services. The managed Postgres database had gone offline, did not come back when restarted, and reported that it was out of space.

   Node disk fullService outageWrites rejected

   Trigger

   The daily retention clean-up job, which had been failing with killed, was rerun with its memory raised from 1gb to 8gb, and its large destroy_all deletions filled the database disk.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   Inefficient queryMonitoring blind spotNo headroom

   Detection

   Notifications that all database transactions were failing across multiple services arrived as soon as the outage began; the provider was automatically notified at 99% storage. MailPace's two status pages did not detect the outage.

   Remediation

   • Contacted the managed database provider's support
   • Notified users on Twitter and Mastodon
   • Prepared an offsite backup from six hours earlier on a new database instance as a fallback
   • The provider migrated the database to a larger instance
   • Removed the old data in production by hand, day by day, with an updated query

   Action items

   • Delete old data in batches of 50 records with in_batches in the clean-up job
   • Run the clean-up job four times a day instead of once
   • Stay on a much larger database instance with room to grow
   • Add cron monitoring to all cron jobs
   • Point the status pages at a /health endpoint that checks database access and returns a 500 error on failure
   • Investigate a hot-replica database for automated failover

   Lessons

   Several causes came together: a clean-up job that failed silently, a naive deletion that grew huge once data had piled up, a database plan without headroom for growth, and status pages that missed the outage, presumably because https://app.mailpace.com still answered 200 while the database was down. MailPace also advises that critical services such as email sending be set up with more than one provider.

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5. IL-0383 PagerDuty's shared Cassandra cluster degraded during a repair, delaying notifications and losing incoming events

   PagerDuty2014sev1

   Symptoms

   On 3 June 2014, PagerDuty's notification pipeline performed poorly and some notifications were delayed after the Cassandra cluster behind it became heavily degraded. About 3% of events sent to the integration API could not be received.

   Database overloadHigh error rateHigh latency

   Trigger

   On 3 June 2014 the Cassandra background repair process started its normal run on one node of the cluster while other high load was on the cluster; on 4 June the team restarted the postponed repair by hand.

   Root cause

   Capacity exhaustion

   Component

   Database

   Contributing factor

   No headroomExcessive blast radiusHarmful manual actionIgnored early signal

   Remediation

   • Reduced the load on the Cassandra cluster
   • Stopped the Cassandra repair process
   • Cut communication between some Cassandra nodes to stabilise the cluster
   • On 4 June, deleted all in-flight data in the notification pipeline in a "factory reset"
   • Brought downstream systems back into a consistent state with the emptied pipeline by hand

   Action items

   • Scale the existing Cassandra nodes vertically to bigger, faster servers
   • Set up multiple Cassandra clusters and spread the workloads across them
   • Set system load thresholds at which the Cassandra clusters are scaled out horizontally ahead of need
   • Upgrade the current and new clusters to a more recent Cassandra version
   • Add more load shedding techniques to control Cassandra under high load
   • Bring more Cassandra expertise in-house

   Lessons

   PagerDuty concluded that its Cassandra cluster was underscaled and shared by too many services with different load and usage patterns, and that the scaling work should have come before the planned Cassandra version upgrade.

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