Vector memory pressure

HNSW dense-vector graphs use off-heap RAM on the node. The cushion between available memory and that footprint is tight or shrinking, with elevated heap and other load signals.

Note

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

Field Value
Component Elasticsearch
Severity High
Scope Node
Domains performance, search, memory, vector

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.

HNSW off-heap vector footprint leaves little free RAM. Affected node(s): es-data-01 and es-data-02.

The node is near its off-heap memory limit.

Heap usage is elevated due to off-heap memory pressure spilling over.

Both search/index latency and disk read activity are elevated; the HNSW graph might start paging.

Note

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

Dense-vector fields that use HNSW keep approximate nearest-neighbor graphs primarily in physical memory, off the Java heap. A node can look healthy on heap charts while vector graphs still consume a large share of RAM.

When that off-heap footprint grows into a tight memory band, kNN and dense-vector workloads compete with indexing, search, and disk I/O on the same node. Latency can rise, thread pools can back up, and circuit breakers might trip if pressure continues.

Tune approximate kNN search