Type to search.

Rank feature field type

Serverless

Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Serverless interfaces and procedures might differ from classic Elastic Stack deployments.

Stack

The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments.

This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.

A rank_feature field can index numbers so that they can later be used to boost documents in queries with a rank_feature query.

JSON 
PUT my-index-000001
{
  "mappings": {
    "properties": {
      "pagerank": {
        "type": "rank_feature"
      },
      "url_length": {
        "type": "rank_feature",
        "positive_score_impact": false
      }
    }
  }
}
PUT my-index-000001/_doc/1
{
  "pagerank": 8,
  "url_length": 22
}
GET my-index-000001/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank"
    }
  }
}
  1. Rank feature fields must use the rank_feature field type
  2. Rank features that correlate negatively with the score need to declare it
Note

rank_feature fields only support single-valued fields and strictly positive values. Multi-valued fields and negative values will be rejected.

Note

rank_feature fields do not support querying, sorting or aggregating. They may only be used within rank_feature queries.

Note

rank_feature fields only preserve 9 significant bits for the precision, which translates to a relative error of about 0.4%.

Rank features that correlate negatively with the score should set positive_score_impact to false (defaults to true). This will be used by the rank_feature query to modify the scoring formula in such a way that the score decreases with the value of the feature instead of increasing. For instance in web search, the url length is a commonly used feature which correlates negatively with scores.