Elasticsearch dense_vector Field Data Type

The dense_vector field type in Elasticsearch is designed to store dense vector data, which are fixed-length arrays of floating-point numbers. This data type is particularly useful for machine learning applications, similarity search, and recommendation systems. It allows for efficient storage and retrieval of high-dimensional vector data, enabling vector search capabilities within Elasticsearch.

Dense vectors are preferred when dealing with fixed-length, continuous numerical representations of data, such as word embeddings, image features, or user preferences. An alternative to dense_vector is the `sparse_vector` type, which is more suitable for high-dimensional data with many zero values.

Example

PUT my-index
{
  "mappings": {
    "properties": {
      "product_embedding": {
        "type": "dense_vector",
        "dims": 128
      }
    }
  }
}

PUT my-index/_doc/1
{
  "product_embedding": [0.5, 10.0, -0.3, ...]
}

Common issues or misuses

1. Incorrect dimensionality: Ensure that the number of dimensions specified in the mapping matches the actual vector size in your data.
2. Performance impact: Large vector dimensions can significantly impact indexing and search performance.
3. Scaling issues: As the number of vectors grows, consider using approximate nearest neighbor (ANN) algorithms for better search performance.
4. Limited query support: Not all query types are supported for dense_vector fields.

Dense vector workloads place unique demands on Elasticsearch clusters, from high memory consumption to increased indexing overhead as vector dimensions grow. Pulse monitors your cluster's resource utilization and provides tailored recommendations, helping you ensure that your vector search infrastructure stays performant as your embeddings data scales.

Frequently Asked Questions

Q: What is the maximum number of dimensions supported by the dense_vector field?
A: As of Elasticsearch 7.x, the maximum number of dimensions for a dense_vector field is 2048.

Q: Can I update a dense_vector field after indexing?
A: Yes, you can update a dense_vector field using the update API, but the entire vector must be provided in the update operation.

Q: How does Elasticsearch handle similarity search for dense vectors?
A: Elasticsearch uses cosine similarity or Euclidean distance to measure the similarity between dense vectors during search operations.

Q: Can I use dense_vector fields with aggregations?
A: Dense vector fields have limited support for aggregations. As of Elasticsearch 7.x, you can use them with the vector_tile aggregation for geospatial use cases.

Q: Is it possible to combine dense_vector search with text search?
A: Yes, you can combine dense_vector similarity search with traditional text search using a function score query or a custom script.