Cases5 cases

1 to 5 of 5

1. 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.

   View case
2. IL-0213 Roblox was down for 73 hours because key-value writes on its single Consul cluster slowed from under 300 ms to 2 seconds

   Roblox2021

   Symptoms

   On the afternoon of 28 October 2021 (13:37 PST), Vault performance at Roblox degraded and a single Consul server showed high CPU load. Consul cluster metrics showed that writes to its key-value (KV) store had a 50th percentile latency of 2 seconds instead of the usual under 300 ms. Players were not yet affected.

   High latencySustained high node CPUService outageMonitoring outage

   Trigger

   On 27 October at 14:00, one day before the outage, Roblox enabled the Consul streaming feature on the backend service responsible for traffic routing and, to prepare for end-of-year traffic, added 50% more nodes to traffic routing.

   Root cause

   Software defect

   Component

   Coordination and key-value storeService discoveryIn-memory cache

   Contributing factor

   Single point of failureOverlapping failuresMonitoring blind spotHarmful manual actionChange with a delayed effect

   Detection

   Roblox engineers began investigating when Vault performance degraded and a single Consul server had high CPU load, before players were affected. Consul cluster metrics then showed elevated KV write latency.

   Remediation

   • Replaced one Consul cluster node on the suspicion of degraded hardware
   • Replaced all Consul nodes with 128-core machines with faster NVMe SSDs
   • Shut down the Consul cluster and reset its state from a snapshot taken at the start of the outage, with access blocked by iptables
   • Blocked external traffic, disabled all non-essential Consul use, scaled services down and lowered health check frequency from 60 seconds to 10 minutes
   • Moved the Consul cluster back to 64-core servers
   • Disabled the streaming feature for all Consul systems, which brought KV write latency back to 300ms
   • Kept the slow Consul servers from staying elected as leader
   • Redeployed the caching system and fixed its wrong scheduling data and failed deployments
   • Restarted the stopped services at the right capacity levels
   • Let players back in with DNS steering in roughly 10% steps while checking database load, cache performance and stability

   Action items

   • HashiCorp builds laboratory benchmarks and scale tests that reproduce the streaming contention
   • HashiCorp improves the design of streaming so it does not contend under extreme load
   • Compact the BoltDB database with existing BoltDB tooling
   • Remove the circular dependency between the telemetry systems and Consul
   • Extend telemetry with targeted alerts on Consul and BoltDB performance and with visibility into traffic between services and Consul
   • Run the backend in an additional, geographically distinct data center and in multiple availability zones
   • Split critical services into their own dedicated Consul clusters
   • Move data that core services keep directly in the Consul KV store to a more suitable storage system
   • Delete obsolete KV data from Consul
   • Upgrade to a Consul version that replaces BoltDB with bbolt, in Q1
   • Redesign cache deployment so the cache system can be brought up quickly from a standing start
   • Deploy Nomad enhancements that make it easier to turn up large jobs after a long period of unavailability
   • Use mechanisms for faster machine configuration changes
   • Reintroduce streaming only after a new implementation has been tested at Roblox's scale and workload

   Lessons

   Two mostly unrelated problems deep inside Consul, contention in streaming under very high read and write load and BoltDB freelist growth, were hard to diagnose, and that difficulty was the main reason the outage lasted so long. A single Consul cluster serving every workload made the impact larger, and monitoring that depended on Consul hid the cause when it was needed most. Faster hardware did not help and may have hurt, because the contention was worse on NUMA machines. HashiCorp had benchmarked streaming at a similar scale, but the problem needed both a large number of streams and a high churn rate. Restarting a platform from a fully stopped state, including warming caches, took a long time and needed tools built for bootstrapping rather than incremental change.

   View case
3. IL-0277 Patching Elastic Cloud's ZooKeeper ensemble in us-east-1 lost quorum, and hosted clusters and Kibana went unreachable

   Elastic2019

   Symptoms

   On 4 February 2019 at about 02:50 UTC, Elastic Cloud customers with deployments in AWS us-east-1 lost stable access to their Elasticsearch clusters, and an increase in HTTP 5xx errors showed on the load balancer. Deployments were partially or completely unavailable until 09:28 UTC, Kibana access was disrupted for most customers in the region, and the Elastic Cloud User Console timed out when customers tried to view their deployments.

   High error rateService outageIntermittent timeoutsNode kernel panic

   Trigger

   A routine patching procedure in AWS us-east-1 replaced the first and second members of the three-node ZooKeeper ensemble with patched hosts between 02:25 and 02:45 UTC, following the same documented process used in the other 12 regions.

   Root cause

   Software defect

   Component

   Coordination and key-value storeContainer runtimeLinux kernelElasticsearch or OpenSearchconntrack table

   Contributing factor

   Monitoring blind spotRetry stormHealth check tied to a dependencyOverlapping failuresHarmful manual action

   Detection

   An increase in HTTP 5xx errors on the load balancer was identified at 02:50 UTC, the time the ZooKeeper ensemble lost its leader.

   Remediation

   • Restarted internal services in sweeping restarts at 03:22 and 06:00 to reduce client connection problems
   • Isolated the ZooKeeper ensemble by cutting off client connections
   • Rolled back the remaining ensemble member after its host became unstable
   • Rolled back the second ensemble member after its host became unstable
   • Rebooted the ZooKeeper hosts made unresponsive by the runc CPU softlocks
   • Re-established ZooKeeper quorum by removing client load and operator debugging, then reintroduced client load gradually
   • Increased the initLimit setting of the ZooKeeper observer layer used by the proxies
   • Scaled the proxy fleet up and out to handle the increased traffic
   • Applied a runbook that restarted the internal proxying containers and Kibana instances and set sysctl limits

   Action items

   • Reduce the dataset size carried by the ZooKeeper ensembles by over 60%
   • Reduce the number of health checks and intervals needed before a healthy proxy node joins the load balancing layer
   • Stop counting ZooKeeper connectivity in the proxy health checks once a proxy has its configuration and routes
   • Canary the rewritten proxy v2 architecture, which decouples the proxy service from ZooKeeper state, in production regions
   • Improve visibility into ZooKeeper with better logging and metrics for the ZooKeeper processes, the JVM and the container and host systems
   • Make Kibana route discovery more resilient
   • Fix the Kibana issues that amplify internal HTTP requests during availability events
   • Fix the Kibana issues that leak connections
   • Make internal monitoring agents use backoff with jitter
   • Formalize a maintenance matrix with set timeframes per region, so failures of critical services fall in non-critical times
   • Give runbooks for coordination layer maintenance a better decision criteria matrix
   • Set up a feedback loop with support leadership for incident communications through support and the status page

   Lessons

   Service metrics that reported the ZooKeeper hosts healthy were not enough to show the state of individual hosts or of the ensemble, so they gave a false signal that maintenance could continue. Because the platform depended on the coordination layer, losing its quorum caused a major service disruption in which every Elasticsearch cluster in the region saw some failures, and fixes that worked one by one fell short at the scale of the failure.

   View case
4. IL-0588 AppOptics metrics APIs returned 5xx as Kafka partitions stayed leaderless after an AWS power event hit their ZooKeeper

   AppOptics2018

   Symptoms

   On May 31, 2018 from 21:53 UTC the metrics APIs of AppOptics and Librato returned a 5xx for a majority of incoming metric data. APM metrics and traces sent from AppOptics APM were affected, and many alert notifications were delayed.

   High error rateFailover failureMonitoring outage

   Trigger

   A power event in one of AWS's availability zones in us-east-1 cut off nodes in several critical parts of the AppOptics and Librato infrastructure, including the ZooKeeper node that was then the leader of the ensemble coordinating the Kafka brokers.

   Root cause

   Cloud provider outage

   Component

   Message brokerCoordination and key-value store

   Contributing factor

   Single point of failureExcessive blast radiusMonitoring blind spot

   Remediation

   • Restarted one ZooKeeper node by hand to force a leader election, after which the ensemble regained quorum
   • Restarted every Kafka broker in a rolling restart until all partitions were back online
   • Worked through the backlog, including the backlog of alerts, and resumed normal operations

   Action items

   • Isolate the Kafka clusters better across availability zones and regions
   • Stop sharing one ZooKeeper ensemble across Kafka clusters
   • Run game days on this failure and check whether newer Kafka versions fix it
   • Put in place more checks that notify the team earlier about subsystems that need extra help to recover

   Lessons

   Running services across several availability zones and more than one Kafka cluster did not isolate the pipeline from the failure of one zone, because partitions of different clusters sat in the same zones and the clusters shared one ZooKeeper ensemble. More operational monitoring of the subsystems that needed help to recover would have led to a faster recovery.

   View case
5. IL-0362 PagerDuty could not dispatch notifications for 18 minutes when a peering point shared by two AWS regions degraded

   PagerDuty2013sev1

   Symptoms

   On Saturday 13 April 2013 at 8:11am Pacific Time, PagerDuty's on-call engineer was paged about one node of the notification dispatch system, and an attempt at 8:13 to bring the node back failed. Customers saw delayed notifications and 500 errors on PagerDuty's API endpoints.

   High latencyHigh error rateIntermittent connection failures

   Trigger

   At 7:57am Pacific Time, according to AWS, a peering point in Northern California began to degrade, causing connectivity problems for the two AWS regions that PagerDuty used as two of its three datacenters.

   Root cause

   Cloud provider outage

   Component

   Transit gateway or network interconnectCoordination and key-value store

   Contributing factor

   Single point of failureUnverified assumptionMonitoring blind spot

   Detection

   The PagerDuty on-call engineer was paged at 8:11am about an issue with one node of the notification dispatch system, and at 8:18 monitoring detected multiple-provider failures for notifications.

   Remediation

   • Restarted most of the failed coordinator processes by hand
   • The notification dispatch system reached quorum again and processed all queued notifications

   Action items

   • Add more logging and aggregate logs into a single searchable source
   • Add a process watcher that restarts failed coordinator processes automatically
   • Build a dashboard that shows inter-host connectivity
   • Train engineering staff on Cassandra and ZooKeeper
   • Investigate moving off one of the AWS regions to a new hosting provider and datacenter that avoids a single point of failure

   Lessons

   Datacenters chosen to be independent and physically separate can still share a network peering point, and a design built to survive the loss of one datacenter did not survive losing two at once.

   View case

By root cause3