ElasticsearchHeapUsageWarning

ElasticsearchHeapUsageWarning #

The heap usage is over 80%

Alert Rule
alert: ElasticsearchHeapUsageWarning
annotations:
  description: |-
    The heap usage is over 80%
      VALUE = {{ $value }}
      LABELS = {{ $labels }}    
  runbook: https://srerun.github.io/prometheus-alerts/runbooks/prometheus-community-elasticsearch-exporter/elasticsearchheapusagewarning/
  summary: Elasticsearch Heap Usage warning (instance {{ $labels.instance }})
expr: (elasticsearch_jvm_memory_used_bytes{area="heap"} / elasticsearch_jvm_memory_max_bytes{area="heap"})
  * 100 > 80
for: 2m
labels:
  severity: warning

Here is the runbook for the ElasticsearchHeapUsageWarning alert:

Meaning #

The ElasticsearchHeapUsageWarning alert is triggered when the heap usage of an Elasticsearch node exceeds 80% for more than 2 minutes. This indicates that the node is experiencing high memory usage, which can lead to performance issues, slow query responses, and even node crashes.

Impact #

If left unaddressed, high heap usage can cause:

  • Slow query responses and timeouts
  • Node crashes and restarts, leading to cluster instability
  • Data loss and inconsistencies
  • Increased latency and decreased overall system performance

Diagnosis #

To diagnose the root cause of the high heap usage, follow these steps:

  1. Check the Elasticsearch cluster’s overall health and node statuses using the Elasticsearch API or a monitoring tool like Kibana.
  2. Investigate the heap usage trend over time to identify any patterns or spikes.
  3. Check the node’s JVM settings, such as the heap size and garbage collection configuration.
  4. Analyze the node’s CPU and memory usage to identify any resource bottlenecks.
  5. Review the Elasticsearch logs for any errors or warnings related to memory usage.

Mitigation #

To mitigate the high heap usage, follow these steps:

  1. Increase the heap size: Adjust the JVM heap size settings to provide more memory to the Elasticsearch node.
  2. Tune garbage collection: Optimize the garbage collection configuration to reduce pause times and improve heap usage.
  3. Reduce indexing rate: Temporarily reduce the indexing rate to decrease memory usage.
  4. Add more nodes: Scale out the Elasticsearch cluster by adding more nodes to distribute the load and reduce memory pressure.
  5. Optimize data storage: Review and optimize data storage settings, such as compression and caching, to reduce memory usage.

Remember to monitor the heap usage and system performance after implementing these mitigations to ensure the issue is resolved.

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