Loaded frozen tier data nodes

Some frozen-tier data nodes show materially higher CPU utilization than their peers on the same tier. Frozen searches can be latency-sensitive even at low volume when a few nodes absorb most of the work.

Note

For a complete list of insights, refer to AutoOps insights.

Field Value
Component Elasticsearch
Severity Medium
Scope Node
Domains performance, resource-utilization

You can customize these settings to adjust when AutoOps detects this event and presents the insight. Refer to AutoOps event settings for details.

The default customization settings are:

Setting Type Default
CPU percent above peer baseline for frozen-tier data nodes Integer 180
Consecutive samples meeting the imbalance conditions on frozen-tier data nodes Integer 3
Minimum CPU utilization to consider a frozen-tier data node loaded Integer 1
Search queue threshold Integer 5
Write queue threshold Integer 1
Tip

Raising these thresholds reduces noise but delays detection. Lowering them triggers the insight sooner but can increase alerts during minor blips.

The following is an example of what you might see when this insight is triggered. Real insights use live data and links from your deployment or cluster.

CPU utilization on es-data-01 and es-data-02 is sustained above the peer baseline for frozen-tier data nodes.

  • Indices with high search activity: logs-prod-000045
Note

AutoOps shows different recommendations depending on how their conditions match your deployment or cluster.

When load is uneven across frozen-tier data nodes, busier nodes become bottlenecks for searchable-snapshot traffic while quieter peers sit underused. Common causes include clients that do not spread requests across the tier, and hot indices whose shards sit on only a few frozen nodes.

If the imbalance persists, latency tends to concentrate on the overloaded frozen nodes first.