Tune approximate kNN search

Elasticsearch supports approximate k-nearest neighbor search for efficiently finding the k nearest vectors to a query vector. Since approximate kNN search works differently from other queries, there are special considerations around its performance.

Many of these recommendations help improve search speed. With approximate kNN, the indexing algorithm runs searches under the hood to create the vector index structures. So these same recommendations also help with indexing speed.

The default element_type is float. But this can be automatically quantized during index time through quantization. Quantization will reduce the required memory by 4x, 8x, or as much as 32x, but it will also reduce the precision of the vectors and increase disk usage for the field (by up to 25%, 12.5%, or 3.125%, respectively). Increased disk usage is a result of Elasticsearch storing both the quantized and the unquantized vectors. For example, when int8 quantizing 40GB of floating point vectors an extra 10GB of data will be stored for the quantized vectors. The total disk usage amounts to 50GB, but the memory usage for fast search will be reduced to 10GB.

For float vectors with dim greater than or equal to 384, using a quantized index is highly recommended.

The speed of kNN search scales linearly with the number of vector dimensions, because each similarity computation considers each element in the two vectors. Whenever possible, it’s better to use vectors with a lower dimension. Some embedding models come in different "sizes", with both lower and higher dimensional options available. You could also experiment with dimensionality reduction techniques like PCA. When experimenting with different approaches, it’s important to measure the impact on relevance to ensure the search quality is still acceptable.

Elasticsearch stores the original JSON document that was passed at index time in the _source field. By default, each hit in the search results contains the full document _source. When the documents contain high-dimensional dense_vector fields, the _source can be quite large and expensive to load. This could significantly slow down the speed of kNN search.

You can disable storing dense_vector fields in the _source through the excludes mapping parameter. This prevents loading and returning large vectors during search, and also cuts down on the index size. Vectors that have been omitted from _source can still be used in kNN search, since it relies on separate data structures to perform the search. Before using the excludes parameter, make sure to review the downsides of omitting fields from _source.

Another option is to use synthetic _source.

Elasticsearch uses the HNSW algorithm for approximate kNN search. HNSW is a graph-based algorithm which only works efficiently when most vector data is held in memory. You should ensure that data nodes have at least enough RAM to hold the vector data and index structures. To check the size of the vector data, you can use the Analyze index disk usage API.

Here are estimates for different element types and quantization levels:

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element_type: float: num_vectors * num_dimensions * 4 element_type: float with quantization: int8: num_vectors * (num_dimensions + 4) element_type: float with quantization: int4: num_vectors * (num_dimensions/2 + 4) element_type: float with quantization: bbq: num_vectors * (num_dimensions/8 + 12) element_type: byte: num_vectors * num_dimensions element_type: bit: num_vectors * (num_dimensions/8)

If utilizing HNSW, the graph must also be in memory, to estimate the required bytes use num_vectors * 4 * HNSW.m. The default value for HNSW.m is 16, so by default num_vectors * 4 * 16.

Note that the required RAM is for the filesystem cache, which is separate from the Java heap.

The data nodes should also leave a buffer for other ways that RAM is needed. For example your index might also include text fields and numerics, which also benefit from using filesystem cache. It’s recommended to run benchmarks with your specific dataset to ensure there’s a sufficient amount of memory to give good search performance. You can find here and here some examples of datasets and configurations that we use for our nightly benchmarks.

If the machine running Elasticsearch is restarted, the filesystem cache will be empty, so it will take some time before the operating system loads hot regions of the index into memory so that search operations are fast. You can explicitly tell the operating system which files should be loaded into memory eagerly depending on the file extension using the index.store.preload setting.

The following file extensions are used for the approximate kNN search:

Elasticsearch shards are composed of segments, which are internal storage elements in the index. For approximate kNN search, Elasticsearch stores the vector values of each segment as a separate HNSW graph, so kNN search must check each segment. The recent parallelization of kNN search made it much faster to search across multiple segments, but still kNN search can be up to several times faster if there are fewer segments. By default, Elasticsearch periodically merges smaller segments into larger ones through a background merge process. If this isn’t sufficient, you can take explicit steps to reduce the number of index segments.

Elasticsearch provides many tunable settings for controlling the merge process. One important setting is index.merge.policy.max_merged_segment. This controls the maximum size of the segments that are created during the merge process. By increasing the value, you can reduce the number of segments in the index. The default value is 5GB, but that might be too small for larger dimensional vectors. Consider increasing this value to 10GB or 20GB can help reduce the number of segments.

A common pattern is to first perform an initial bulk upload, then make an index available for searches. Instead of force merging, you can adjust the index settings to encourage Elasticsearch to create larger initial segments:

Actively indexing documents can have a negative impact on approximate kNN search performance, since indexing threads steal compute resources from search. When indexing and searching at the same time, Elasticsearch also refreshes frequently, which creates several small segments. This also hurts search performance, since approximate kNN search is slower when there are more segments.

When possible, it’s best to avoid heavy indexing during approximate kNN search. If you need to reindex all the data, perhaps because the vector embedding model changed, then it’s better to reindex the new documents into a separate index rather than update them in-place. This helps avoid the slowdown mentioned above, and prevents expensive merge operations due to frequent document updates.

Search can cause a lot of randomized read I/O. When the underlying block device has a high readahead value, there may be a lot of unnecessary read I/O done, especially when files are accessed using memory mapping (see storage types).

Most Linux distributions use a sensible readahead value of 128KiB for a single plain device, however, when using software raid, LVM or dm-crypt the resulting block device (backing Elasticsearch path.data) may end up having a very large readahead value (in the range of several MiB). This usually results in severe page (filesystem) cache thrashing adversely affecting search (or update) performance.

You can check the current value in KiB using lsblk -o NAME,RA,MOUNTPOINT,TYPE,SIZE. Consult the documentation of your distribution on how to alter this value (for example with a udev rule to persist across reboots, or via blockdev --setra as a transient setting). We recommend a value of 128KiB for readahead.