Elasticsearch Optimizing Query String Searches in Elasticsearch

By Opster Team

Updated: Jun 22, 2023

| 2 min read

Introduction 

Elasticsearch is a widely used search and analytics engine that provides powerful and flexible search capabilities. One of the essential features of Elasticsearch is the ability to perform full-text searches using the Query String Query. This article will discuss how to optimize query string searches in Elasticsearch to improve performance and relevance. If you want to learn about the combined fields query type in Elasticsearch, check out this guide.

How to optimize query string searches in Elasticsearch

1. Use the `default_field` parameter

By default, Elasticsearch searches across all fields in the index when a query string is provided. This can lead to slower search performance and less relevant results. To optimize your query string searches, specify the `default_field` parameter to limit the search to specific fields. This will improve performance and ensure that the results are more relevant to the user’s query.

Example:

GET /_search
{
  "query": {
    "query_string": {
      "query": "quick brown fox",
      "default_field": "content"
    }
  }
}

Additionally, it is also possible to specify the default fields on which to run query string queries directly in the index settings. When doing so, there’s no need to specify the default_field parameter in the query.

Example:

PUT /my_index/_settings
{
  "index.query.default_field": "content"
}

2. Utilize field boosting

Field boosting allows you to assign different weights to different fields, making some fields more important than others when calculating the relevance score. This can be useful when you want to prioritize specific fields in your search results.

Example:

GET /_search
{
  "query": {
    "query_string": {
      "query": "quick brown fox",
      "fields": ["title^3", "content"]
    }
  }
}

In this example, the `title` field is given three times more weight than the `content` field when calculating the relevance score.

3. Use the `minimum_should_match` parameter

Example:

The `minimum_should_match` parameter allows you to specify the minimum number of optional clauses that must match for a document to be considered relevant. This can help to filter out less relevant results and improve the overall quality of the search results.

GET /_search
{
  "query": {
    "query_string": {
      "query": "quick brown fox",
      "default_field": "content",
      "minimum_should_match": "75%"
    }
  }
}

In this example, at least 75% of the terms in the query string must match for a document to be considered relevant.

4. Use the `fuzziness` parameter

The `fuzziness` parameter allows you to include documents in the search results that contain terms similar to the ones in the query string. This can be useful for handling typos and misspellings in the user’s query.

Example:

GET /_search
{
  "query": {
    "query_string": {
      "query": "quikc brown fox",
      "default_field": "content",
      "fuzziness": "AUTO"
    }
  }
}

In this example, the `fuzziness` parameter is set to “AUTO”, which automatically calculates the appropriate fuzziness level based on the length of the terms in the query string.

5. Use the `analyze_wildcard` parameter

By default, Elasticsearch does not analyze wildcard terms in query string searches. This can lead to less relevant results when using wildcards. To improve the quality of the search results, set the `analyze_wildcard` parameter to `true`.

Example:

GET /_search
{
  "query": {
    "query_string": {
      "query": "qui* brown fox",
      "default_field": "content",
      "analyze_wildcard": true
    }
  }
}

6. Optimize your index

Optimizing your index can significantly improve the performance of query string searches. Some best practices for index optimization include:

• Use the appropriate field data types and analyzers for your data.
• Regularly update your index settings and mappings as your data and search requirements evolve.
• Use index templates to ensure consistent settings and mappings across multiple indices.
• Monitor and optimize the performance of your index using tools like the Index Management API and the Cluster Health API.

Conclusion 

In conclusion, optimizing query string searches in Elasticsearch involves a combination of configuring search parameters, boosting specific fields, and maintaining a well-structured index. By following these best practices, you can improve the performance and relevance of your Elasticsearch query string searches, providing a better search experience for your users.