High memory-to-disk ratio on data nodes

Data nodes show a higher memory-to-disk ratio than the recommended range for that role. You might be paying for heap that indexing and search rarely need given the actual disk footprint.

Note

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

Field Value
Component Elasticsearch
Severity Low
Scope Node
Domains memory, 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
Disk-to-memory ratio threshold for data nodes Integer 30

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.

The memory-to-disk ratio is higher than needed on the following data nodes: es-data-01 and es-data-02.

For example, on es-data-01, allocated memory is 64 GB and allocated disk is 10240 GB, a memory-to-disk ratio of 1:160. A common target ratio for data nodes is 1:160.

Note

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

Reduce node memory capacity

Condition: Always shown for this insight.

To improve your memory-to-disk ratio, move hot nodes from 64 GB to 16 GB by selecting instances with less memory.

Reduce data node count

Condition: Always shown for this insight.

Reduce the number of data nodes and increase disk allocated per remaining node accordingly.

The memory-to-disk ratio compares heap size to disk capacity on a node. When the ratio is higher than the target for that role or tier, you pay for RAM that the disk-backed workload does not need.

Mixed data-node deployments still benefit from a target memory-to-disk ratio. Heap far above that ratio is capacity you pay for without matching disk-backed workload.

If the condition persists, cloud or license cost stays elevated without a matching gain in search or indexing capacity.