The purpose of this document is to introduce a new Open Distro for Elasticsearch KNN plugin and to request comments from the community (RFC). This RFC covers only the high-level functionality of the KNN plugin and does not cover architecture and implementation details.
Customers with similarity-based search use cases like facial recognition, fraud detection and recommendation systems have a need for finding the K-Nearest Neighbor (KNN) of vectors in high dimensional space. The existing solutions for KNN search do not operate well at scale, are difficult to use, and are generally too slow for large-scale real-time search applications.
The Open Distro for Elasticsearch KNN plugin enables you to run an Approximate K Nearest Neighbor search on billions of documents across thousands of dimensions with the added usability and reliability of Elasticsearch. You can use aggregations and filter clauses to further refine your similarity search operations. Powerful use-cases include product recommendations, fraud detection, image and video search, related document search, and more.
The KNN Plugin uses the Non-Metric Space Library (NMSLIB), a highly efficient implementation of K-ANNS (K Approximate Nearest Neighbor Search), which has consistently out-performed most of the other solutions as per the ANN-Benchmarks published here. In order to integrate this powerful library into Elasticsearch, the KNN Plugin extends an Apache Lucene codec to introduce a separate file format for storing k-NN indices to deliver high efficiency k-NN search operations.
- Essentially, the KNN feature is powered by 4 customizations to Elasticsearch:
-
Mapper plugin to support new field type,
knn_vectorto represent the vector fields in a document. -
Lucene Codec named ‘KNNCodec’ which adds a new Lucene index file format for storing and retrieving the vectors in nmslib using a JNI layer.
-
Search plugin which introduces a query clause called
knnfor processing the KNN query elements. -
Action plugin to utilize Elasticsearch ResourceWatcher service for effective garbage collection management of hnsw indices.
-
####Highly scalable
This plugin will enable the user to easily leverage Elasticsearch’s distributed architecture to run high-scale k-NN search operations. Unlike many of the common k-NN solutions, this plugin does not plan to use a brute-force approach to compute k-NN during a search operation. This approach causes exponential degradation in performance with scale. Instead, the solution uses approximation methods to index the k-NN data efficiently, enabling the user to attain low latency even at high scale.
####Run k-NN using familiar Elasticsearch constructs
k-NN on Open Distro for Elasticsearch uses the familiar mapping and query syntax of Elasticsearch. To designate a field as a k-NN vector, you simply need to map it to the new k-NN field type provided by the mapper plugin. You can then invoke k-NN search operations using the search plugin with the familiar Elasticsearch query syntax.
####Combine with other Elasticsearch features
k-NN functionality integrates seamlessly with other Elasticsearch features. This provides you the flexibility to use Elasticsearch’s extensive search features such as aggregations and filtering with k-NN to further slice and dice your data to increase the precision of your searches.
KNN indices need the KNNCodec to read and write hnsw indices, which are created as part of each segment for a shard. Please find below example to create knn index.
PUT /my_index
{
"settings" : {
"index": {
"knn": true,
"knn.algo_param.m":90,
"knn.algo_param.ef_construction":200,
"knn.algo_param.ef_search":300
}
},
"mappings": {
"properties": {
"my_vector1": {
"type": "knn_vector",
"dimension": 2
},
"my_vector2": {
"type": "knn_vector",
"dimension": 3
},
"my_vector3": {
"type": "knn_vector",
"dimension": 4
}
}
}
}In the above example, we are creating K-NN index with 3 knn_vector fields namely my_vector1, my_vector2, my_vector3. We could index different category of embedding into these fields and query the nearest neighbors for each field independently.
POST my_index/_doc/1
{
"my_vector1" : [1.5, 2.5]
}
POST my_index/_doc/2
{
"my_vector1" : [2.5, 3.5]
}
POST my_index/_doc/3
{
"my_vector1" : [4.5, 5.5]
}
POST my_index/_doc/4
{
"my_vector2" : [6.5, 7.5, 8.5]
}
POST my_index/_doc/5
{
"my_vector2" : [8.5, 9.5, 0.5]
}
POST my_index/_doc/6
{
"my_vector3" : [8.5, 9.5, 10.5, 14.5]
}In the above examples, we have indexed 3 vectors of field my_vector1, 2 vectors of field my_vector2, 1 vector of field my_vector3
POST my_index/_doc/7
{
"my_vector1" : [2.5, 4.5],
"my_vector2" : [13.5, 14.5, 15.5],
"my_vector3" : [8.5, 9.5, 10.5, 7,8]
}POST my_index/_doc/8
{
"my_vector2" : [8.5, 9.5, 0.5],
"price": 10
}Use the new knn clause in the query DSL and specify the point of interest as ‘vector’ and the number of
nearest neighbors to fetch as k.
POST my_index/_search
{
"size" : 2,
"query": {
"knn": {
"my_vector1": {
"vector": [3, 4],
"k": 2
}
}
}
}{
"took": 967067,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 4,
"relation": "eq"
},
"max_score": 1.4142135,
"hits": [
{
"_index": "myindex",
"_type": "_doc",
"_id": "2",
"_score": 1.4142135,
"_source": {
"my_vector1": [
2.5,
3.5
]
}
},
{
"_index": "myindex",
"_type": "_doc",
"_id": "7",
"_score": 1.4142135,
"_source": {
"my_vector1": [
2.5,
4.5
]
}
}
]
}
}If you have any comments or feedback on the proposed design for the K-NN plugin, please create GitHub issue on this project to discuss.