Elasticsearch Elasticsearch _Score

By Opster Team

Updated: Jun 26, 2023

| 2 min read

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Understanding and Optimizing Elasticsearch _score

Elasticsearch _score is a crucial aspect of search results ranking, as it determines the relevance of each document in relation to the query. In this article, we will delve into the factors affecting _score and how to optimize it for better search performance.

Factors Affecting Elasticsearch _score

1. Term Frequency (TF): The number of times a term appears in a document. A higher frequency indicates a stronger relationship between the term and the document.

2. Inverse Document Frequency (IDF): Measures the importance of a term across all documents in the index. A term that appears in fewer documents will have a higher IDF, signifying its importance.

3. Field Length Norm: The length of the field in which the term appears. Shorter fields with matching terms are considered more relevant, as the term occupies a larger portion of the field.

4. Query-time Boosting: Allows you to assign a boost factor to specific fields or terms during query execution. This can be useful for emphasizing the importance of certain fields or terms in the search results.

Optimizing Elasticsearch _score

1. Customize Scoring with Function Score Query: Function Score Query allows you to modify the _score by applying various functions such as field value factor, decay functions, or custom script functions. This can help you tailor the scoring to your specific use case.

Example:

GET /_search
{
"query": {
"function_score": {
"query": {
"match": {
"title": "elasticsearch"
}
},
"field_value_factor": {
"field": "popularity",
"factor": 1.2,
"modifier": "sqrt"
}
}
}
}

2. Use Index-time Boosting: You can apply a boost factor to specific fields during indexing, which will affect the _score calculation. Even though this can be useful when you want to emphasize the importance of certain fields in your documents, this feature has been deprecated in 5.0.0 to promote query-time boosting which achieves the same effect without the need to reindex all documents when the boost factor should change.

Example:

PUT /my-index/_mapping
{
"properties": {
"title": {
"type": "text",
"boost": 2
},
"content": {
"type": "text"
}
}
}

3. Optimize Query-time Boosting: Make use of query-time boosting to prioritize specific fields or terms during search execution. This can help you fine-tune the relevance of your search results.

Example:

GET /_search
{
"query": {
"multi_match": {
"query": "elasticsearch",
"fields": ["title^3", "content"]
}
}
}

4. Use BM25 Similarity: Elasticsearch uses the BM25 similarity model by default, which is an improvement over the traditional TF-IDF model. You can fine-tune the parameters (k1 and b) of the BM25 model to optimize the _score calculation for your specific use case.

Example:

PUT /my-index
{
"settings": {
"index": {
"similarity": {
"my_bm25": {
"type": "BM25",
"k1": 1.2,
"b": 0.75
}
}
}
}
}

Conclusion 

By understanding the factors affecting Elasticsearch _score and applying the optimization techniques mentioned above, you can improve the relevance and performance of your search results.

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