OpenSearch vector memory is controlled by several different levers: vector encoding and compression, the HNSW graph, native-index cache retention, and the native-memory circuit breaker. To reduce memory without blindly sacrificing search quality, first identify what is consuming memory, then test storage mode and compression against representative latency and recall requirements. The right values depend on OpenSearch version, engine, index, and workload.
Which OpenSearch k-NN settings affect memory?
It helps to separate the memory footprint of the vector index from the amount of native memory OpenSearch permits it to use. Representation and graph settings affect how much memory an index needs; cache and breaker settings affect how native indexes are retained and budgeted on a node.
| Setting or choice | What it controls | Memory and performance implication |
|---|---|---|
mode and compression_level |
Vector search mode and quantization encoder on a knn_vector field |
on_disk and compression can reduce memory use, with latency and recall tradeoffs that should be tested. |
HNSW m |
Number of bidirectional graph links created per element | Can significantly affect graph memory; graph behavior and available options depend on engine and version. |
ef_construction |
Construction-time search list | Affects graph accuracy and indexing speed rather than serving as a node memory budget. |
ef_search |
Search-time candidate exploration for applicable engines | Increasing it can improve recall at the cost of query latency. Lucene ignores this setting and dynamically uses request k. |
knn.memory.circuit_breaker.limit |
Native-memory limit for native library indexes | Sets the permitted budget; exceeding it leads to least-recently-used native index eviction. Raising it does not shrink the graph. |
knn.cache.item.expiry.enabled and knn.cache.item.expiry.minutes |
Whether idle native indexes expire, and after what idle period | Can release idle cache entries when enabled; it is separate from the breaker budget. |
How do the circuit breaker and cache expiry work?
The cluster setting knn.memory.circuit_breaker.limit defines the native-memory limit for native library indexes. OpenSearch documents a default of 50%. Its example calculates that as 34 GB on a node with 100 GB of memory and a 32 GB JVM allocation: 50% of the remaining 68 GB. When usage exceeds the configured limit, the plugin evicts native library indexes used least recently. The circuit breaker is enabled by default. See OpenSearch vector search settings.
For nodes with different roles, OpenSearch supports tier-specific breaker settings. Set node.attr.knn_cb_tier in opensearch.yml, then configure knn.memory.circuit_breaker.limit.<tier-name> through cluster settings. A node uses its tier’s limit when configured; otherwise it inherits the cluster-wide setting.
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Idle-cache expiry is controlled separately. knn.cache.item.expiry.enabled defaults to false. knn.cache.item.expiry.minutes specifies the idle interval and is documented with a default of 3h; that interval only applies when expiry is enabled. Expiry removes entries after inactivity, whereas the circuit breaker enforces a memory ceiling.
Can on-disk search or compression reduce vector memory?
The knn_vector mapping’s mode supports in_memory and on_disk. OpenSearch positions in_memory for low latency and on_disk for lower cost and memory use, with higher search latency as a tradeoff. The compression_level chooses a quantization encoder, but available levels and engine combinations vary. Check the documentation for the OpenSearch release and engine in use before selecting a combination: k-NN vector mappings and memory-optimized vectors.
Disk-based vector search uses a compressed index to find candidates, then rescoring against full-precision vectors loaded from disk. OpenSearch says rescoring is enabled by default to preserve recall. Its documented on_disk support covers float and half_float vector types. Treat recall and latency as workload-dependent: validate both with representative queries rather than assuming a compression level will preserve your application’s search quality. See disk-based vector search.
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There is also version-specific behavior: OpenSearch documents that starting with 3.1, on_disk with 1x compression activates memory-optimized search, loading data on demand rather than loading all data into memory at once. Confirm that behavior for your deployed release.
How do vector type and HNSW parameters change the footprint?
Uncompressed float vectors use 4 bytes per dimension. OpenSearch’s memory-optimized vector guide gives this HNSW planning estimate: 1.1 * (dimension + 8 * m) bytes per vector. It is an estimate, not a measured prediction for a particular index. Actual use also depends on implementation, metadata, segment count, cache state, and other cluster activity.
For HNSW, m is the number of bidirectional links per element and can significantly change graph memory. ef_construction controls the construction search list, influencing graph accuracy and indexing speed. ef_search controls how many vectors are examined for applicable engines; a larger value can improve recall while increasing query latency. Engine behavior matters: Lucene ignores ef_search and dynamically uses the query’s k. Consult OpenSearch methods and engines and the k-NN query documentation before applying engine-specific tuning.
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Do not confuse storage savings with native graph-memory savings. index.knn.derived_source.enabled prevents vectors from being stored in _source and reduces disk use; it is not a direct control for native graph memory. index.knn.memory_optimized_search is a static index setting. To enable it on an existing index, the documented procedure is to close the index, update the setting, and reopen it. Details are in memory-optimized search.
How should you tune memory without making search too slow?
- Record the deployment details. Note the exact OpenSearch version, vector engine and method, dimensions, vector type, and current mapping and settings. Defaults, capabilities, and updateability vary by release and engine.
- Measure current behavior. Use the k-NN stats API to inspect per-index native library index counts and
graph_memory_usage, as well ascache_capacity_reached,load_success_count, andload_exception_count. Compare them with representative workload behavior and the configured breaker limit. - Set the priority. If low latency is essential, retain an in-memory-oriented design unless measurements show room to trade latency for memory. If memory or cost is the constraint, evaluate
on_diskand supported compression choices. - Test search quality and latency. Run representative queries against the candidate configuration and compare recall and query latency with the current index. Include application-level relevance checks, not only infrastructure metrics.
- Review graph settings for the selected engine. Evaluate
m, construction parameters, and engine-specific query behavior. OpenSearch’s method tables mark some parameters as not updatable after index creation, so check before assuming a live change is possible; creating a new index may be necessary. - Set retention and budget controls deliberately. Configure the breaker for the native-memory budget and consider expiry only if idle cache entries should be removed after an interval. Neither control reduces the underlying graph representation.
- Measure again after each change. Recheck stats, cache behavior, latency, and application search quality so you can attribute any change to the setting that caused it.
OpenSearch documentation describes the mechanisms and defaults, but it does not establish one optimal combination for every dataset or workload. Choose settings from measured behavior on the version, engine, and query mix you actually run.
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