Cases2 cases

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1. IL-0179 With DEBUG logging and synchronous log output, a high-traffic service filled the 64 KB stdout pipe, so request threads blocked in write() while holding database connections and HikariCP ran dry, failing requests in 30-second bursts

   MUSINSA2026

   Symptoms

   The on-call engineer for a high-traffic service was alerted that requests were failing in large batches every 30 seconds.

   High error rateIntermittent timeoutsConnection pool exhaustion

   Trigger

   DEBUG level logging was on in production when traffic surged, raising log volume to about 100 times normal while the service logged synchronously.

   Root cause

   Configuration change

   Component

   Application logging libraryContainer runtimeTelemetry agentApplication database

   Contributing factor

   Unverified assumptionExcessive blast radius

   Detection

   The on-call engineer was alerted that requests were failing in large batches every 30 seconds.

   Action items

   • Replace synchronous logging with TraceAwareAsyncAppender, which captures trace_id and span_id on the calling thread and drops logs instead of blocking when the queue is full (neverBlock=true)
   • Keep the amount of log output at a sustainable level, since an asynchronous queue still fills under sustained overload

   Lessons

   Removing AsyncAppender to keep trace IDs also removed the protection that kept request threads away from stdout back-pressure. A company-wide standard applies the same side effect to dozens of services, so check what a standard rests on and what working behaviour it takes away. ThreadLocal context does not cross into an asynchronous worker, but capturing it on the calling thread before handing the event off keeps both the IDs and the asynchronous output. Under extreme load, dropping log lines is preferable to stopping request handling.

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2. IL-0073 Intermittent Kafka connectivity overloaded Docker daemons, set off a node rescheduling cascade and poisoned the Consul gossip mesh

   Target2019sev1

   Symptoms

   An upgrade of the network subsystem under Target's datacenter OpenStack, meant to cause only a brief disruption, ran into problems and left network connectivity disrupted for several hours. Kafka, which runs on that OpenStack, became intermittently available.

   Node NotReadyLoad imbalanceHigh latencyCredentials unavailable

   Trigger

   A network subsystem upgrade in the datacenter OpenStack infrastructure disrupted connectivity for several hours and made Kafka intermittently available.

   Root cause

   Dependency failure

   Component

   Container runtimeService discoveryTelemetry agentCredential broker for pods

   Contributing factor

   Excessive blast radiusAutomation amplificationSingle point of failureUnpatched known bug

   Remediation

   • enabled gossip encryption on Consul so messages from the poisoned mesh were rejected
   • stabilized the deployment engine and redeployed the development environment with encryption, terminating instances without it
   • upgraded Consul in place to 1.2.3 and then to 1.3.0

   Action items

   • Reduce how much of an outage a single Docker daemon can cause, together with running smaller and more numerous clusters

   Lessons

   Smaller and more numerous clusters limited the damage: the smaller development clusters and production were not hit the same way as the one large cluster, and dev and prod on one cluster would have been far worse. A Docker daemon shared by many workloads is a brittle failure point. Per-workload logging and metrics sidecars were judged the right design; a shared cluster-level agent would almost certainly have made the problem worse and harder to fix.

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