OpenSearch vector memory is shaped by the vector representation, the ANN graph, and how native indexes are retained in cache. To reduce memory without making search unacceptably slow, first measure graph and cache behavior, then test mapping and HNSW changes against your own recall and latency targets. The circuit breaker limits memory use; it does not shrink an index.
Which settings affect OpenSearch vector memory?
There is no single k-NN memory setting. A useful distinction is between settings that affect the index’s footprint and settings that govern how much native memory the plugin may use or retain.
| Setting or choice | What it controls | Memory and performance implications |
|---|---|---|
knn_vector type, dimension, and compression_level |
Vector representation and quantization | Smaller representations can reduce memory. Supported combinations vary by OpenSearch version and engine. |
Mapping mode: in_memory or on_disk |
Search and storage approach | on_disk prioritizes lower cost and memory use, with potentially higher search latency; in_memory prioritizes low latency. OpenSearch documents the mode options. |
HNSW m |
Number of bidirectional graph links per element | Can significantly affect graph memory and should be evaluated alongside search quality. |
knn.memory.circuit_breaker.limit |
Native-memory budget for native library indexes | Constrains permitted use and can cause least-recently-used indexes to be evicted; it does not reduce their underlying footprint. |
knn.cache.item.expiry.enabled and knn.cache.item.expiry.minutes |
Whether idle native indexes expire and after how long | Can release idle cache entries when enabled; it is separate from breaker enforcement. |
Choose a memory strategy before tuning parameters
Use on-disk search when lower memory or cost matters more than minimum latency
OpenSearch’s disk-based vector search searches a compressed index first, then rescores candidate results with full-precision vectors loaded from disk. Rescoring is documented as enabled by default to preserve recall. This two-stage process can reduce memory needs, but the added disk access can increase latency. Benchmark representative queries and compare recall as well as response time. The documented on_disk mode supports float and half_float vector types. See disk-based vector search.
Select compression for the deployed engine and version
The mapping’s compression_level selects a quantization encoder. Its available levels and engine combinations are version-dependent, so check the compatibility table for the exact release and engine rather than assuming a setting transfers between them. Higher compression can reduce representation size, but test its effect on search quality and latency. The memory-optimized vectors documentation says that, starting with OpenSearch 3.1, on_disk with 1x compression activates memory-optimized search, which loads data on demand rather than loading all data into memory at once. Confirm the behavior in your release.
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Size vectors and graph parameters with the right expectations
Uncompressed float vectors use 4 bytes per dimension. For HNSW, OpenSearch gives the planning estimate 1.1 * (dimension + 8 * m) bytes per vector. It is an estimate, not a prediction of total node memory: implementation details, metadata, segment count, cache state, and other cluster activity also contribute. Consult the methods and engines documentation for engine-specific behavior.
mcontrols the number of bidirectional links created per element and can materially change graph memory.ef_constructioncontrols the construction search list; it affects indexing effort and graph accuracy rather than acting as a simple runtime memory cap.ef_searchcontrols how many vectors are examined at query time for applicable engines. A larger value can improve recall at the cost of latency.
Do not apply a Faiss or NMSLIB ef_search tuning recipe to Lucene: OpenSearch documents that Lucene ignores this parameter and dynamically uses the request’s k. Also check whether the chosen method parameters can be updated after index creation; some method settings are not updatable, which may require building a new index.
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Set the native-memory budget and cache behavior
Configure the circuit breaker as a budget, not a footprint optimization
knn.memory.circuit_breaker.limit sets the native-memory limit for native library indexes. OpenSearch documents a default of 50%; when native memory exceeds the configured limit, the plugin evicts the least-recently-used native indexes. Its example says a node with 100 GB of memory and a 32 GB JVM allocation has 68 GB remaining, so the default limit corresponds to 34 GB. That is an illustration of the documented calculation, not a recommended node size. The breaker is enabled by default. Details are in Vector search settings.
For clusters with different node roles, OpenSearch supports tier-specific limits. 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 present and otherwise inherits the cluster-wide limit. Raising the limit permits more native index memory; it does not make vectors or graphs smaller.
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Use idle expiry only when it suits the access pattern
knn.cache.item.expiry.enabled defaults to false. knn.cache.item.expiry.minutes specifies the idle period and is documented with a default of 3h, but that period only applies when expiry is enabled. Expiry removes entries after they have been idle for the configured time; the circuit breaker instead enforces a memory budget when use exceeds its limit. If frequently queried indexes are repeatedly unloaded and loaded, expiry may work against the access pattern.
Measure memory and cache pressure on the cluster
Use the k-NN stats API to inspect native index and cache behavior, then compare it with the configured breaker limit and the application’s search workload. The k-NN API documentation describes the available statistics.
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graph_memory_usagehelps track memory attributed to the graph.cache_capacity_reachedindicates whether cache capacity has been reached.load_success_countandload_exception_counthelp reveal index loading activity and failures.
High graph memory points toward representation or graph choices; repeated loads, capacity signals, or load exceptions can point to cache pressure or operational issues. Interpret the counters in context with traffic and the breaker configuration rather than treating any one statistic as a complete measure of total node memory.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical tuning sequence
- Record the baseline. Note the exact OpenSearch version, engine and method, vector dimension and type, mapping, and relevant index and cluster settings. Defaults and supported combinations differ across releases.
- Observe representative traffic. Capture k-NN statistics, including graph memory and cache behavior, while the cluster handles a representative workload.
- Set the latency-versus-memory goal. If memory or cost is the priority, test
on_diskand supported compression choices; compare recall and latency on representative queries. - Review HNSW parameters. Evaluate
mfor graph footprint and quality,ef_constructionfor indexing effort and graph quality, and query-time behavior for the selected engine. Check whether changes require a new index. - Adjust the budget and expiry separately. Set the breaker to an appropriate native-memory budget. Enable idle expiry only if removing idle indexes fits the workload.
- Re-measure after each change. Check statistics and application-level search quality so that memory savings are not purchased with unacceptable latency or recall loss.
Settings that help disk use but do not directly control graph memory
index.knn.derived_source.enabled prevents vectors from being stored in _source and reduces disk use; it is not a direct native graph-memory control. index.knn.memory_optimized_search is a static index setting. For an existing index, the documented procedure requires closing the index, updating the setting, and reopening it. See Memory-optimized search for the applicable version’s procedure and requirements.
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