The rank_feature field data type in Elasticsearch is specifically designed to boost the relevance score of documents based on a numeric feature. It's particularly useful for machine learning-based ranking models and scenarios where you want to incorporate additional factors into your search relevance calculations.
This data type is optimized for fast filtering and scoring, making it ideal for large-scale applications where performance is crucial. While alternatives like using script_score or function_score queries can achieve similar results, the rank_feature field offers better performance and easier integration into relevance models.
Example
PUT my-index
{
"mappings": {
"properties": {
"pageviews": {
"type": "rank_feature"
},
"popularity": {
"type": "rank_feature",
"positive_score_impact": false
}
}
}
}
In this example, "pageviews" is set up as a standard rank_feature field, where higher values will positively impact the relevance score. The "popularity" field is set up with "positive_score_impact": false, meaning lower values will have a positive impact on the score.
Common issues or misuses
- Overreliance on rank_feature fields without proper tuning can lead to unexpected search results.
- Using rank_feature for fields that don't actually impact relevance can unnecessarily complicate your index and queries.
- Forgetting to normalize values before indexing can result in one feature dominating the relevance calculation.
Frequently Asked Questions
Q: How does the rank_feature field differ from a regular numeric field?
A: The rank_feature field is optimized for fast filtering and scoring in relevance calculations, whereas regular numeric fields are designed for general-purpose numeric storage and range queries.
Q: Can I use rank_feature fields in sorting?
A: No, rank_feature fields are not sortable. They are designed specifically for influencing relevance scores, not for direct sorting or aggregations.
Q: How do I query a rank_feature field?
A: You can use the rank_feature query to include rank_feature fields in your search. For example: "rank_feature": { "field": "pageviews" }.
Q: What's the difference between rank_feature and rank_features fields?
A: rank_feature is for single-value fields, while rank_features is for multi-value fields where you have multiple named features that influence ranking.
Q: Can I update a rank_feature field value after indexing?
A: Yes, you can update rank_feature field values using the update API, just like other field types. However, keep in mind that this will trigger a reindex of the document.
