Cases2 cases

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1. IL-0386 After a move to Google Cloud, Postgres connection timeouts and memcached i/o timeouts spiked on GKE nodes

   incident.io2023

   Symptoms

   Shortly after incident.io moved its infrastructure fully into Google Cloud, the application saw a larger-than-normal number of Postgres connection timeouts, and new database connections per second were in the hundreds, spiking up and down. At the same time, the memcached instance used for transient data and faster API responses had sporadic connection timeouts.

   Intermittent timeoutsPacket loss

   Trigger

   After the move to Google Cloud, the application opened hundreds of new Postgres connections per second, and an accidental database join made it send thousands of duplicate calls to an external third party in parallel.

   Root cause

   Capacity exhaustion

   Component

   CNI pluginDatabaseIn-memory cache

   Contributing factor

   Unreviewed defaultsOverlapping failures

   Detection

   An incident was declared when the number of Postgres connection timeouts rose above normal. The remaining cache timeouts were traced through the logs of the Kubernetes node that served an affected request, the request's trace and the node's metrics.

   Remediation

   • Doubled the maximum Postgres connection lifespan from 15m to 30m
   • Made the Postgres connection pools static so they always keep N connections open
   • Raised the memcache client's MaxIdleConns from 2 to 20
   • Moved temporarily from Google's hosted memcached to a memcached inside the Kubernetes cluster
   • Shipped the fix for the accidental database join that made thousands of duplicate external calls

   Action items

   • Set more sensible concurrency limits for outbound network calls

   Lessons

   Network timeouts are not always local to one request or pod: when they appear, the whole activity of the node, including other pods on it, needs to be checked. Staying within a provider's rate limits does not keep outbound calls from affecting a node, so outbound network calls need more sensible concurrency limits.

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2. IL-0430 Mackerel's Redis stopped answering during a replication setup, keepalived failed over by mistake, and Mackerel degraded

   Hatena2018

   Symptoms

   On 26 September 2018 at 10:51 JST the whole Mackerel monitoring service stopped working properly and its alive monitoring stopped. The application servers could not connect to Redis, which stores Mackerel's monitoring data, and could not return correct responses.

   Monitoring outageHigh latencyIntermittent timeouts

   Trigger

   Redis memory use had been growing, so operators set up replication in order to scale Redis up. Building the replication took a long time and Redis stopped answering for a while, and keepalived treated this as a node failure and started a failover nobody intended.

   Root cause

   Configuration change

   Component

   In-memory cacheDatabase replicationDatabase failover manager

   Contributing factor

   Automation amplificationOverlapping failures

   Remediation

   • Switched back to the correct Redis and briefly saw the application return
   • Restarted the application servers one after another, which did not improve the symptoms
   • Detected the organizations posting inappropriate metrics and blocked their requests
   • Adjusted the timeout interval of the application servers
   • Temporarily added capacity to Diamond, the TSDB
   • Increased the number of Kinesis shards and Redis Cluster shards
   • Scaled out the API application servers
   • Put Mackerel in maintenance mode, checked it internally and let outside requests back gradually

   Action items

   • Raise the number of keepalived health checks for Redis so a brief load increase does not trigger a failover
   • Scale Redis up
   • Add more application servers
   • Change the application to store only the monitoring data it needs in Redis, reducing Redis memory use
   • Build a way to detect inappropriate API requests quickly and show them on a dashboard
   • Build a function to block organizations making inappropriate requests quickly
   • Keep improving the accuracy of detecting inappropriate requests and consider adding features such as an API limit
   • Strengthen monitoring of the application servers' internal processing so problems are handled before they become outages

   Lessons

   Hatena did not find why recovery took so long, but expects that keeping failover from reacting to short load spikes and giving the application more performance headroom would prevent an outage this long. It also aims to keep problems contained to a small part of the service when a failure does happen.

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