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- Release notes
- Elasticsearch version 8.17.1
- Elasticsearch version 8.17.0
- Elasticsearch version 8.16.2
- Elasticsearch version 8.16.1
- Elasticsearch version 8.16.0
- Elasticsearch version 8.15.5
- Elasticsearch version 8.15.4
- Elasticsearch version 8.15.3
- Elasticsearch version 8.15.2
- Elasticsearch version 8.15.1
- Elasticsearch version 8.15.0
- Elasticsearch version 8.14.3
- Elasticsearch version 8.14.2
- Elasticsearch version 8.14.1
- Elasticsearch version 8.14.0
- Elasticsearch version 8.13.4
- Elasticsearch version 8.13.3
- Elasticsearch version 8.13.2
- Elasticsearch version 8.13.1
- Elasticsearch version 8.13.0
- Elasticsearch version 8.12.2
- Elasticsearch version 8.12.1
- Elasticsearch version 8.12.0
- Elasticsearch version 8.11.4
- Elasticsearch version 8.11.3
- Elasticsearch version 8.11.2
- Elasticsearch version 8.11.1
- Elasticsearch version 8.11.0
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- Elasticsearch version 8.2.3
- Elasticsearch version 8.2.2
- Elasticsearch version 8.2.1
- Elasticsearch version 8.2.0
- Elasticsearch version 8.1.3
- Elasticsearch version 8.1.2
- Elasticsearch version 8.1.1
- Elasticsearch version 8.1.0
- Elasticsearch version 8.0.1
- Elasticsearch version 8.0.0
- Elasticsearch version 8.0.0-rc2
- Elasticsearch version 8.0.0-rc1
- Elasticsearch version 8.0.0-beta1
- Elasticsearch version 8.0.0-alpha2
- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Tune approximate kNN search
editTune approximate kNN search
editElasticsearch 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.
Reduce vector memory foot-print
editThe 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.
Reduce vector dimensionality
editThe 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.
Exclude vector fields from _source
editElasticsearch 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.
reindex, update,
and update by query operations generally
require the _source
field. Disabling _source
for a field might result in
unexpected behavior for these operations. For example, reindex might not actually
contain the dense_vector
field in the new index.
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
.
Ensure data nodes have enough memory
editElasticsearch 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:
-
element_type: float
:num_vectors * num_dimensions * 4
-
element_type: float
withquantization: int8
:num_vectors * (num_dimensions + 4)
-
element_type: float
withquantization: int4
:num_vectors * (num_dimensions/2 + 4)
-
element_type: float
withquantization: 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.
Warm up the filesystem cache
editIf 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.
Loading data into the filesystem cache eagerly on too many indices or too many files will make search slower if the filesystem cache is not large enough to hold all the data. Use with caution.
The following file extensions are used for the approximate kNN search: Each extension is broken down by the quantization types.
-
vex
for the HNSW graph -
vec
for all non-quantized vector values. This includes all element types:float
,byte
, andbit
. -
veq
for quantized vectors indexed withquantization
:int4
orint8
-
veb
for binary vectors indexed withquantization
:bbq
-
vem
,vemf
,vemq
, andvemb
for metadata, usually small and not a concern for preloading
Generally, if you are using a quantized index, you should only preload the relevant quantized values and the HNSW graph. Preloading the raw vectors is not necessary and might be counterproductive.
Reduce the number of index segments
editElasticsearch 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.
Increase maximum segment size
editElasticsearch 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.
Create large segments during bulk indexing
editA 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:
-
Ensure there are no searches during the bulk upload and disable
index.refresh_interval
by setting it to-1
. This prevents refresh operations and avoids creating extra segments. -
Give Elasticsearch a large indexing buffer so it can accept more documents before
flushing. By default, the
indices.memory.index_buffer_size
is set to 10% of the heap size. With a substantial heap size like 32GB, this is often enough. To allow the full indexing buffer to be used, you should also increase the limitindex.translog.flush_threshold_size
.
Avoid heavy indexing during searches
editActively 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.
Avoid page cache thrashing by using modest readahead values on Linux
editSearch 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.
blockdev
expects values in 512 byte sectors whereas lsblk
reports
values in KiB
. As an example, to temporarily set readahead to 128KiB
for /dev/nvme0n1
, specify blockdev --setra 256 /dev/nvme0n1
.
On this page
- Reduce vector memory foot-print
- Reduce vector dimensionality
- Exclude vector fields from
_source
- Ensure data nodes have enough memory
- Warm up the filesystem cache
- Reduce the number of index segments
- Increase maximum segment size
- Create large segments during bulk indexing
- Avoid heavy indexing during searches
- Avoid page cache thrashing by using modest readahead values on Linux