Elasticsearch Optimizing Elasticsearch Sort by Text Field

By Opster Team

Updated: Jul 23, 2023

| 2 min read

Introduction

Sorting search results is a crucial aspect of any search engine, and Elasticsearch is no exception. In this article, we will discuss how to optimize Elasticsearch when sorting by text fields, which can be a challenging task due to the nature of text data. We will cover the following topics:

1. Understanding the challenges of sorting by text fields
2. Using keyword fields for sorting
3. Utilizing multi-fields for efficient sorting
4. Implementing custom analyzers for better sorting performance

Understanding the Challenges of Sorting by Text Fields

Sorting by text fields in Elasticsearch can be problematic because text fields are analyzed, meaning they are broken down into individual tokens. This process makes it difficult to sort the data in a meaningful way, as the tokens are not stored in their original order. Additionally, sorting by text fields can be resource-intensive, leading to slower query performance.

Using Keyword Fields for Sorting

One solution to the challenges of sorting by text fields is to use keyword fields instead. Keyword fields are not analyzed, so they maintain the original order of the terms. This makes them more suitable for sorting purposes. To use keyword fields for sorting, you can define a field as a keyword type in your index mapping:

PUT /my_index
{
  "mappings": {
    "properties": {
      "title": {
        "type": "keyword"
      }
    }
  }
}

Now, you can sort your search results by the “title” field:

GET /my_index/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    {
      "title": {
        "order": "asc"
      }
    }
  ]
}

Utilizing Multi-Fields for Efficient Sorting

In some cases, you may want to use a text field for both full-text search and sorting. To achieve this, you can use multi-fields, which allow you to index a single field in multiple ways. For example, you can index a field as both a text and keyword type:

PUT /my_index
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword"
          }
        }
      }
    }
  }
}

Now, you can perform a full-text search on the “title” field and sort the results using the “title.keyword” field:

GET /my_index/_search
{
  "query": {
    "match": {
      "title": "example"
    }
  },
  "sort": [
    {
      "title.keyword": {
        "order": "asc"
      }
    }
  ]
}

Implementing Custom Analyzers for Better Sorting Performance

In some cases, the default keyword analyzer may not be suitable for your sorting needs. For example, you may want to sort text fields in a case-insensitive manner. To achieve this, you can create a custom analyzer that uses the “lowercase” token filter:

PUT /my_index
{
  "settings": {
    "analysis": {
      "normalizer": {
        "lowercase_keyword": {
          "type": "custom",
          "filter": ["lowercase", "trim"]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "fields": {
          "lowercase": {
            "type": "keyword",
            "normalizer": "lowercase_keyword"
          }
        }
      }
    }
  }
}

Now, you can sort your search results using the “title.lowercase” field, which will be case-insensitive:

GET /my_index/_search
{
  "query": {
    "match": {
      "title": "example"
    }
  },
  "sort": [
    {
      "title.lowercase": {
        "order": "asc"
      }
    }
  ]
}

Conclusion

In conclusion, optimizing Elasticsearch sort by text field can be achieved by using keyword fields, multi-fields, and custom analyzers. By implementing these techniques, you can improve the performance and accuracy of your search results, providing a better user experience for your application.