Incident Library
  • Root cause
  • Component
  • Symptom
  • Contributing factor
  • Case

Cases2 cases

1 to 2 of 2

  1. Symptoms

    After Classi moved the search engine behind its Portfolio feature from Elasticsearch 6.8 to Amazon OpenSearch Service 1.3, the team saw in post-migration metrics that search responses were sometimes much slower. Most searches returned normally, but latency jumped at a certain frequency.

    Trigger

    The migration from Elasticsearch 6.8 to OpenSearch 1.3, with indices that had no explicit refresh_interval.

    Root cause
    Component
    Contributing factor
    Detection

    The team noticed latency spikes in metrics checked after the migration and compared the maximum search latency before and after the migration in Datadog APM.

    Remediation
    • Set refresh_interval explicitly to 3s, which sharply reduced the latency spikes
    • Set refresh_interval explicitly to 1s in production
    Lessons

    The took value in OpenSearch's responses was already large, which could have narrowed the investigation to OpenSearch sooner. The slow log and the Profile API alone did not find the cause; asking when searches became slow, instead of where, led to the fix. The same setting can behave differently between versions, so defaults have to be checked as well.

  2. Symptoms

    On Thursday 24 January 2013 GitHub's exception tracking and monitoring systems detected a large spike in exceptions. Most of them were timeouts in code search queries and in the background jobs that update the code search indexes with data from new pushes, and the outage of the newly launched Code Search lasted into Friday 25 January.

    Trigger

    On 17 January GitHub upgraded the code search cluster from Elasticsearch 0.19.9 to 0.20.2 before launching Code Search publicly; the outage began on 24 January, after the public launch.

    Root cause
    Component
    Contributing factor
    Detection

    GitHub's exception tracking and monitoring systems detected a large spike in exceptions, mostly timeouts from code search queries and from the indexing background jobs.

    Remediation
    • Stopped the whole cluster and brought it back in maintenance mode, with shard allocation and rebalancing disabled
    • Brought in engineers from elasticsearch, the company, to review the cluster and its unallocatable shards
    • Upgraded Java and Elasticsearch and applied the recommended configuration through a Puppet topic branch and environment
    Action items
    • Provision a staging environment for the code search cluster to test infrastructure changes
    • Have the elasticsearch team audit the code search cluster configuration again in future
    Lessons

    The Elasticsearch upgrade was not tested enough on GitHub's infrastructure before it reached the code search cluster, partly because the cluster had no proper staging environment. For large clusters the elasticsearch team recommended equal minimum and maximum JVM heap through ES_HEAP_SIZE, at 50% of system RAM, starting recovery immediately instead of after recover_after_time, and configuring minimum_master_nodes so that paused nodes do not form their own clusters.

Check your cluster for this

These 2 cases involve Elasticsearch or OpenSearch and Language runtime settings. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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