Link Search Menu Expand Document Documentation Menu

k-NN index

Preview

The k-NN plugin introduces a custom data type, the knn_vector, that allows users to ingest their k-NN vectors into an Lucenia index and perform different kinds of k-NN search. The knn_vector field is highly configurable and can serve many different k-NN workloads. For more information, see k-NN vector.

To create a k-NN index, set the settings.index.knn parameter to true:

PUT /test-index
{
  "settings": {
    "index": {
      "knn": true
    }
  },
  "mappings": {
    "properties": {
      "my_vector1": {
        "type": "knn_vector",
        "dimension": 3,
        "method": {
          "name": "hnsw",
          "space_type": "l2",
          "engine": "lucene",
          "parameters": {
            "ef_construction": 128,
            "m": 24
          }
        }
      }
    }
  }
}

Lucene byte vector

Starting with k-NN plugin version 2.9, you can use byte vectors with the lucene engine to reduce the amount of storage space needed. For more information, see Lucene byte vector.

SIMD optimization for the Faiss engine

Starting with version 2.13, the k-NN plugin supports Single Instruction Multiple Data (SIMD) processing if the underlying hardware supports SIMD instructions (AVX2 on x64 architecture and Neon on ARM64 architecture). SIMD is supported by default on Linux machines only for the Faiss engine. SIMD architecture helps boost overall performance by improving indexing throughput and reducing search latency.

SIMD optimization is applicable only if the vector dimension is a multiple of 8.

x64 architecture

For the x64 architecture, two different versions of the Faiss library are built and shipped with the artifact:

  • libluceniaknn_faiss.so: The non-optimized Faiss library without SIMD instructions.
  • libluceniaknn_faiss_avx2.so: The Faiss library that contains AVX2 SIMD instructions.

If your hardware supports AVX2, the k-NN plugin loads the libluceniaknn_faiss_avx2.so library at runtime.

To disable AVX2 and load the non-optimized Faiss library (libluceniaknn_faiss.so), specify the knn.faiss.avx2.disabled static setting as true in lucenia.yml (default is false). Note that to update a static setting, you must stop the cluster, change the setting, and restart the cluster. For more information, see Static settings.

ARM64 architecture

For the ARM64 architecture, only one performance-boosting Faiss library (libluceniaknn_faiss.so) is built and shipped. The library contains Neon SIMD instructions and cannot be disabled.

Method definitions

A method definition refers to the underlying configuration of the approximate k-NN algorithm you want to use. Method definitions are used to either create a knn_vector field (when the method does not require training) or create a model during training that can then be used to create a knn_vector field.

A method definition will always contain the name of the method, the space_type the method is built for, the engine (the library) to use, and a map of parameters.

Mapping parameter Required Default Updatable Description
name true n/a false The identifier for the nearest neighbor method.
space_type false l2 false The vector space used to calculate the distance between vectors.
engine false nmslib false The approximate k-NN library to use for indexing and search. The available libraries are faiss, nmslib, and Lucene.
parameters false null false The parameters used for the nearest neighbor method.

Supported nmslib methods

Method name Requires training Supported spaces Description
hnsw false l2, innerproduct, cosinesimil, l1, linf Hierarchical proximity graph approach to approximate k-NN search. For more details on the algorithm, see this abstract.

HNSW parameters

Parameter name Required Default Updatable Description
ef_construction false 100 false The size of the dynamic list used during k-NN graph creation. Higher values result in a more accurate graph but slower indexing speed.
m false 16 false The number of bidirectional links that the plugin creates for each new element. Increasing and decreasing this value can have a large impact on memory consumption. Keep this value between 2 and 100.

For nmslib, ef_search is set in the index settings.

Supported Faiss methods

Method name Requires training Supported spaces Description
hnsw false l2, innerproduct Hierarchical proximity graph approach to approximate k-NN search.
ivf true l2, innerproduct Stands for inverted file index. Bucketing approach where vectors are assigned different buckets based on clustering and, during search, only a subset of the buckets is searched.

For hnsw, “innerproduct” is not available when PQ is used.

HNSW parameters

Parameter name Required Default Updatable Description
ef_search false 100 false The size of the dynamic list used during k-NN searches. Higher values result in more accurate but slower searches.
ef_construction false 100 false The size of the dynamic list used during k-NN graph creation. Higher values result in a more accurate graph but slower indexing speed.
m false 16 false The number of bidirectional links that the plugin creates for each new element. Increasing and decreasing this value can have a large impact on memory consumption. Keep this value between 2 and 100.
encoder false flat false Encoder definition for encoding vectors. Encoders can reduce the memory footprint of your index, at the expense of search accuracy.

IVF parameters

Parameter name Required Default Updatable Description
nlist false 4 false Number of buckets to partition vectors into. Higher values may lead to more accurate searches at the expense of memory and training latency. For more information about choosing the right value, refer to Guidelines to choose an index.
nprobes false 1 false Number of buckets to search during query. Higher values lead to more accurate but slower searches.
encoder false flat false Encoder definition for encoding vectors. Encoders can reduce the memory footprint of your index, at the expense of search accuracy.

For more information about setting these parameters, refer to the Faiss documentation.

IVF training requirements

The IVF algorithm requires a training step. To create an index that uses IVF, you need to train a model with the Train API, passing the IVF method definition. IVF requires that, at a minimum, there are nlist training data points, but it is recommended that you use more than this. Training data can be composed of either the same data that is going to be ingested or a separate dataset.

Supported Lucene methods

Method name Requires training Supported spaces Description
hnsw false l2, cosinesimil, innerproduct Hierarchical proximity graph approach to approximate k-NN search.

HNSW parameters

Parameter name Required Default Updatable Description
ef_construction false 100 false The size of the dynamic list used during k-NN graph creation. Higher values result in a more accurate graph but slower indexing speed.
The Lucene engine uses the proprietary term “beam_width” to describe this function, which corresponds directly to “ef_construction”. To be consistent throughout the Lucenia documentation, we retain the term “ef_construction” for this parameter.
m false 16 false The number of bidirectional links that the plugin creates for each new element. Increasing and decreasing this value can have a large impact on memory consumption. Keep this value between 2 and 100.
The Lucene engine uses the proprietary term “max_connections” to describe this function, which corresponds directly to “m”. To be consistent throughout Lucenia documentation, we retain the term “m” to label this parameter.

Lucene HNSW implementation ignores ef_search and dynamically sets it to the value of “k” in the search request. Therefore, there is no need to make settings for ef_search when using the Lucene engine.

"method": {
    "name":"hnsw",
    "engine":"lucene",
    "space_type": "l2",
    "parameters":{
        "m":2048,
        "ef_construction": 245
    }
}

Supported Faiss encoders

You can use encoders to reduce the memory footprint of a k-NN index at the expense of search accuracy. The k-NN plugin currently supports the flat, pq, and sq encoders in the Faiss library.

The following example method definition specifies the hnsw method and a pq encoder:

"method": {
  "name":"hnsw",
  "engine":"faiss",
  "space_type": "l2",
  "parameters":{
    "encoder":{
      "name":"pq",
      "parameters":{
        "code_size": 8,
        "m": 8
      }
    }
  }
}

The hnsw method supports the pq encoder. The code_size parameter of a pq encoder with the hnsw method must be 8.

Encoder name Requires training Description
flat (Default) false Encode vectors as floating-point arrays. This encoding does not reduce memory footprint.
pq true An abbreviation for product quantization, it is a lossy compression technique that uses clustering to encode a vector into a fixed size of bytes, with the goal of minimizing the drop in k-NN search accuracy. At a high level, vectors are broken up into m subvectors, and then each subvector is represented by a code_size code obtained from a code book produced during training. For more information about product quantization, see this blog post.
sq false An abbreviation for scalar quantization. Starting with k-NN plugin version 2.13, you can use the sq encoder to quantize 32-bit floating-point vectors into 16-bit floats. In version 2.13, the built-in sq encoder is the SQFP16 Faiss encoder. The encoder reduces memory footprint with a minimal loss of precision and improves performance by using SIMD optimization (using AVX2 on x86 architecture or Neon on ARM64 architecture). For more information, see Faiss scalar quantization.

PQ parameters

Parameter name Required Default Updatable Description
m false 1 false Determines the number of subvectors into which to break the vector. Subvectors are encoded independently of each other. This vector dimension must be divisible by m. Maximum value is 1,024.
code_size false 8 false Determines the number of bits into which to encode a subvector. Maximum value is 8. For IVF, this value must be less than or equal to 8. For HNSW, this value can only be 8.

SQ parameters

Parameter name Required Default Updatable Description
type false fp16 false The type of scalar quantization to be used to encode 32-bit float vectors into the corresponding type. Only the fp16 encoder type is supported. For the fp16 encoder, vector values must be in the [-65504.0, 65504.0] range.
clip false false false If true, then any vector values outside of the supported range for the specified vector type are rounded so that they are in the range. If false, then the request is rejected if any vector values are outside of the supported range. Setting clip to true may decrease recall.

For more information and examples, see Using Faiss scalar quantization.

Examples

The following example uses the ivf method without specifying an encoder (by default, Lucenia uses the flat encoder):

"method": {
  "name":"ivf",
  "engine":"faiss",
  "space_type": "l2",
  "parameters":{
    "nlist": 4,
    "nprobes": 2
  }
}

The following example uses the ivf method with a pq encoder:

"method": {
  "name":"ivf",
  "engine":"faiss",
  "space_type": "l2",
  "parameters":{
    "encoder":{
      "name":"pq",
      "parameters":{
        "code_size": 8,
        "m": 8
      }
    }
  }
}

The following example uses the hnsw method without specifying an encoder (by default, Lucenia uses the flat encoder):

"method": {
  "name":"hnsw",
  "engine":"faiss",
  "space_type": "l2",
  "parameters":{
    "ef_construction": 256,
    "m": 8
  }
}

The following example uses the hnsw method with an sq encoder of type fp16 with clip enabled:

"method": {
  "name":"hnsw",
  "engine":"faiss",
  "space_type": "l2",
  "parameters":{
    "encoder": {
      "name": "sq",
      "parameters": {
        "type": "fp16",
        "clip": true
      }
    },
    "ef_construction": 256,
    "m": 8
  }
}

The following example uses the ivf method with an sq encoder of type fp16:

"method": {
  "name":"ivf",
  "engine":"faiss",
  "space_type": "l2",
  "parameters":{
    "encoder": {
      "name": "sq",
      "parameters": {
        "type": "fp16",
        "clip": false
      }
    },
    "nprobes": 2
  }
}

Choosing the right method

There are several options to choose from when building your knn_vector field. To determine the correct methods and parameters, you should first understand the requirements of your workload and what trade-offs you are willing to make. Factors to consider are (1) query latency, (2) query quality, (3) memory limits, and (4) indexing latency.

If memory is not a concern, HNSW offers a strong query latency/query quality trade-off.

If you want to use less memory and increase indexing speed as compared to HNSW while maintaining similar query quality, you should evaluate IVF.

If memory is a concern, consider adding a PQ encoder to your HNSW or IVF index. Because PQ is a lossy encoding, query quality will drop.

You can reduce the memory footprint by a factor of 2, with a minimal loss in search quality, by using the fp_16 encoder. If your vector dimensions are within the [-128, 127] byte range, we recommend using the byte quantizer to reduce the memory footprint by a factor of 4. To learn more about vector quantization options, see k-NN vector quantization.

Memory estimation

In a typical Lucenia cluster, a certain portion of RAM is reserved for the JVM heap. The k-NN plugin allocates native library indexes to a portion of the remaining RAM. This portion’s size is determined by the circuit_breaker_limit cluster setting. By default, the limit is set to 50%.

Having a replica doubles the total number of vectors.

For information about using memory estimation with vector quantization, see the vector quantization documentation.

HNSW memory estimation

The memory required for HNSW is estimated to be 1.1 * (4 * dimension + 8 * M) bytes/vector.

As an example, assume you have a million vectors with a dimension of 256 and M of 16. The memory requirement can be estimated as follows:

1.1 * (4 * 256 + 8 * 16) * 1,000,000 ~= 1.267 GB

IVF memory estimation

The memory required for IVF is estimated to be 1.1 * (((4 * dimension) * num_vectors) + (4 * nlist * d)) bytes.

As an example, assume you have a million vectors with a dimension of 256 and nlist of 128. The memory requirement can be estimated as follows:

1.1 * (((4 * 256) * 1,000,000) + (4 * 128 * 256))  ~= 1.126 GB

Index settings

Additionally, the k-NN plugin introduces several index settings that can be used to configure the k-NN structure as well.

At the moment, several parameters defined in the settings are in the deprecation process. Those parameters should be set in the mapping instead of the index settings. Parameters set in the mapping will override the parameters set in the index settings. Setting the parameters in the mapping allows an index to have multiple knn_vector fields with different parameters.

Setting Default Updatable Description
index.knn false false Whether the index should build native library indexes for the knn_vector fields. If set to false, the knn_vector fields will be stored in doc values, but approximate k-NN search functionality will be disabled.
index.knn.algo_param.ef_search 100 true The size of the dynamic list used during k-NN searches. Higher values result in more accurate but slower searches. Only available for NMSLIB.
index.knn.algo_param.ef_construction 100 false Deprecated in 1.0.0. Instead, use the mapping parameters to set this value.
index.knn.algo_param.m 16 false Deprecated in 1.0.0. Use the mapping parameters to set this value instead.
index.knn.space_type l2 false Deprecated in 1.0.0. Use the mapping parameters to set this value instead.