Elasticsearch sparse_vector Field Data Type

The sparse_vector data type in Elasticsearch is used to store sparse vectors, which are arrays where most elements are zero. This data type is particularly useful for machine learning applications and vector search scenarios where the data is high-dimensional but contains many zero values.

Unlike the dense_vector type, sparse_vector only stores non-zero values and their corresponding dimensions, making it more efficient for sparse data. Use this type when your vectors have a large number of dimensions but relatively few non-zero values.

Example

PUT my-index
{
  "mappings": {
    "properties": {
      "my_sparse_vector": {
        "type": "sparse_vector"
      }
    }
  }
}

PUT my-index/_doc/1
{
  "my_sparse_vector": {"1": 0.5, "5": -0.1, "100": 0.75}
}

In this example, we define a field my_sparse_vector of type sparse_vector and then index a document with a sparse vector containing non-zero values at dimensions 1, 5, and 100.

Common Issues and Misuses

1. Indexing dense data: Using sparse_vector for data that isn't actually sparse can lead to inefficient storage and slower queries.
2. Exceeding dimension limits: Elasticsearch has a limit on the maximum dimension allowed for sparse vectors (typically 1024).
3. Using with unsupported queries: Not all query types support sparse_vector fields.
4. Incorrect normalization: Failing to properly normalize vectors before indexing can lead to unexpected results in similarity searches.

Frequently Asked Questions

Q: What's the difference between sparse_vector and dense_vector?
A: sparse_vector stores only non-zero values and their dimensions, while dense_vector stores all values including zeros. sparse_vector is more efficient for high-dimensional data with many zero values.

Q: Can I use sparse_vector fields in all types of queries?
A: No, sparse_vector fields are primarily used with specialized vector similarity queries and are not supported in traditional text or numeric queries.

Q: Is there a limit to the number of dimensions in a sparse_vector?
A: Yes, Elasticsearch typically limits sparse vectors to 1024 dimensions, but this can vary based on your configuration and Elasticsearch version.

Q: How do I perform similarity searches with sparse_vector fields?
A: You can use the script_score query with a vector similarity function like cosine similarity to perform searches on sparse_vector fields.

Q: Can I update individual dimensions of a sparse_vector field?
A: No, you cannot partially update a sparse_vector field. You need to reindex the entire vector if you want to make changes.