Highlighting in Queries in Elasticsearch

Elasticsearch is a powerful open-source search and analytics engine, widely used for full-text search. One of its most valuable features is highlighting, which allows you to emphasize the parts of search results that match a user’s query. Highlighting improves user experience by making it easier to spot relevant information in documents, especially in large datasets. In this post, we’ll dive into the ins and outs of highlighting in Elasticsearch and how to make your search results stand out.

What is Highlighting in Elasticsearch?

Highlighting is a feature that extracts matching query terms from your documents and surrounds them with HTML tags or other delimiters. This functionality is particularly useful in applications such as e-commerce search, document search, or anywhere users benefit from visually identifying relevant sections of content quickly.

For instance, when you search for "data analysis" in an Elasticsearch index, highlighting will identify and return the text snippets containing "data" and "analysis" with your preferred formatting. This makes it easy for users to see why a particular result was returned.

How Highlighting Works in Elasticsearch

Highlighting is achieved through a combination of the following components in an Elasticsearch query:

Query Context

Elasticsearch identifies which parts of a document match the query. Only the fields included in the query are eligible for highlighting.
Highlighting Context

You define highlighting parameters within the query to specify:
- Fields: Which fields should be highlighted.
- Tags: The HTML or other markers to wrap around highlighted terms.
- Fragment Size: The length of the text snippet to include around the highlighted term.
- Number of Fragments: The maximum number of snippets per document.
Analyzers and Tokenizers

Elasticsearch uses the same analyzers and tokenizers applied during indexing to break down the text for highlighting. This ensures consistency in how matching terms are identified.

Setting Up Highlighting in Elasticsearch

Here’s a step-by-step example of implementing highlighting in a search query:

1. Index Your Data

Before you can highlight, ensure that your data is indexed properly. Here's an example of a simple document:

PUT /example-index
{
  "mappings": {
    "properties": {
      "content": {
        "type": "text"
      }
    }
  }
}

POST /example-index/_doc
{
  "content": "Elasticsearch is a powerful search engine designed for full-text search and analytics."
}

2. Add a Highlight to Your Query

The highlight parameter can be added to a search query. Here’s an example:

POST /example-index/_search
{
  "query": {
    "match": {
      "content": "search engine"
    }
  },
  "highlight": {
    "fields": {
      "content": {}
    }
  }
}

3. Customize Highlighting

You can customize your highlighting behavior with additional options:

Tags: Define the markers for highlighted text:

"highlight": {
  "fields": {
    "content": {
      "pre_tags": ["<em>"],
      "post_tags": ["</em>"]
    }
  }
}

Fragment Size: Limit the length of the highlighted snippet:

"highlight": {
  "fields": {
    "content": {
      "fragment_size": 50,
      "number_of_fragments": 3
    }
  }
}

Order of Fragments: Ensure snippets are returned in a meaningful order:

"highlight": {
  "fields": {
    "content": {
      "fragment_size": 50,
      "number_of_fragments": 3,
      "order": "score"
    }
  }
}

Highlight Types in Elasticsearch

Elasticsearch supports multiple highlight types, each with its own trade-offs:

Plain Highlighter

This is the simplest and fastest highlighter. It works well for smaller datasets but does not support advanced features like custom token filters.
Unified Highlighter

A modern and versatile highlighter suitable for most use cases. It supports multiple fields and offsets for accurate highlighting.
Fast Vector Highlighter

This type uses term vectors for precise highlighting. It requires you to enable the term_vector mapping option during index setup.

Conclusion

Highlighting in Elasticsearch is a powerful tool for enhancing search result visibility. Whether you're building a search feature for a website or a complex analytics dashboard, using highlighting effectively can improve user satisfaction and engagement. By tailoring fragment sizes, customizing tags, and leveraging the right highlighter type, you can make your search application truly stand out.

With Elasticsearch’s flexible and scalable architecture, integrating highlighting into your search queries is straightforward. Start experimenting with this feature and transform the way users interact with your search results!