High memory-to-disk ratio on hot nodes
Hot-tier data nodes show a higher memory-to-disk ratio than the recommended range for that role. Hot tiers need balanced heap and disk for ingest, but RAM far above the ratio target is often unused capacity.
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 hot-tier 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 hot 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 hot nodes is 1:160.
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.
Hot tiers usually need more heap relative to disk than warm or cold tiers because they serve recent, frequently searched data. Even so, heap far above the target ratio rarely improves indexing or search enough to justify the cost.
If the condition persists, cloud or license cost stays elevated without a matching gain in search or indexing capacity.