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Elasticsearch & Full Text Search Systems · Lesson

Highlighting Search Results

Add highlighting to your search results to visually emphasize the matching terms within the returned documents, improving user experience.

Highlighting Search Results is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Elasticsearch & Full Text Search Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Highlight Results?

Imagine searching for a recipe and seeing 'chicken' highlighted exactly where it appears in the ingredients and steps. That's highlighting!

It visually emphasizes the matching terms in your search results, making it much easier for users to quickly scan and find relevant information.

  • Improved User Experience: Users quickly spot why a document is relevant.
  • Contextual Clues: Provides snippets of text around the match, giving context.
  • Faster Information Retrieval: Reduces time spent reading irrelevant parts.

Your First Highlight

Adding basic highlighting to your Elasticsearch search is straightforward. You just need to include a highlight block in your search request.

This block specifies which fields you want to highlight. By default, Elasticsearch wraps the matching terms with <em> and </em> tags.

curl -X GET "localhost:9200/products/_search?pretty" \
  -H 'Content-Type: application/json' \
  -d'
{
  "query": {
    "match": {
      "description": "lightweight laptop"
    }
  },
  "highlight": {
    "fields": {
      "description": {}
    }
  }
}'

Highlighting Specific Fields

In the previous example, we told Elasticsearch to highlight matches found in the description field.

The highlight.fields object is where you list all the fields you want to apply highlighting to. For each field, you can provide an empty object {} for default behavior, or specify custom options.

Only fields that are indexed for full-text search (like text fields) can be highlighted effectively.

Highlighting Multiple Fields

Often, you'll want to highlight matching terms across several fields within the same document, such as a product's title and its content.

Simply add more fields to the fields object in your highlight section. Elasticsearch will process each field independently.

curl -X GET "localhost:9200/articles/_search?pretty" \
  -H 'Content-Type: application/json' \
  -d'
{
  "query": {
    "match": {
      "text": "Elasticsearch indexing"
    }
  },
  "highlight": {
    "fields": {
      "title": {},
      "content": {}
    }
  }
}'

Customizing Highlight Tags

The default <em> tags are fine, but you might want to use different HTML tags or CSS classes for styling your highlighted terms.

You can change these using the pre_tags and post_tags parameters within your highlight block. These parameters accept arrays of strings.

curl -X GET "localhost:9200/products/_search?pretty" \
  -H 'Content-Type: application/json' \
  -d'
{
  "query": {
    "match": {
      "name": "wireless headphones"
    }
  },
  "highlight": {
    "pre_tags": ["<span class=\"highlight\">"],
    "post_tags": ["</span>"],
    "fields": {
      "name": {}
    }
  }
}'

Controlling Snippet Length (fragment_size)

When a document is very long, you usually don't want to return the entire field with highlights. Instead, you want short, relevant snippets.

The fragment_size parameter controls the maximum length (in characters) of the highlighted fragments. Elasticsearch tries to break fragments at sentence boundaries or natural breaks.

curl -X GET "localhost:9200/blogs/_search?pretty" \
  -H 'Content-Type: application/json' \
  -d'
{
  "query": {
    "match": {
      "body": "data analytics"
    }
  },
  "highlight": {
    "fragment_size": 100,
    "fields": {
      "body": {}
    }
  }
}'

Multiple Fragments & No Matches

You can also control the number of snippets returned per field using number_of_fragments. Set it to 0 to return the entire field content as a single fragment (if fragment_size is also 0).

What if there are no matches in a field, but you still want to see its content? Use no_match_size to specify the length of the fragment to return if no matches are found. By default, if there's no match, no fragment is returned for that field.

Advanced Fragment Boundaries

For even more precise control over how fragments are generated, you can use boundary_scanner, boundary_chars, and boundary_max_scan.

  • boundary_scanner: Defines how fragments are split (e.g., sentence, word, chars).
  • boundary_chars: Custom characters to use as fragment boundaries when boundary_scanner is chars.
  • boundary_max_scan: How far to scan for boundary characters.

These are useful for languages without clear sentence structures or for specific content types.

Highlighting from Matched Fields

Sometimes, your search query matches in one field (e.g., content), but you want to highlight the corresponding terms in another field (e.g., a shorter summary or title) for display.

The matched_fields parameter allows you to specify a list of fields that will be used to generate highlights for the current field. This is powerful for showing concise, highlighted snippets from a related, more prominent field.

curl -X GET "localhost:9200/documents/_search?pretty" \
  -H 'Content-Type: application/json' \
  -d'
{
  "query": {
    "match": {
      "full_text": "distributed systems"
    }
  },
  "highlight": {
    "fields": {
      "abstract": {
        "matched_fields": ["full_text"],
        "fragment_size": 100
      }
    }
  }
}'

Highlighting Options Quiz

Which of the following parameters can be used to customize how Elasticsearch generates search result highlights?

Recap: Emphasize Key Finds

You've learned how to bring your search results to life with highlighting! This powerful feature is crucial for improving user experience by visually emphasizing matching terms.

  • We started with basic highlighting using the highlight block.
  • You can specify fields to highlight and customize pre_tags/post_tags.
  • fragment_size and number_of_fragments give you control over snippet length and count.
  • Advanced options like boundary_scanner and matched_fields provide fine-grained control for complex scenarios.

Go forth and make your search results sparkle!

Frequently asked questions

Is the “Highlighting Search Results” lesson free?

Yes — the full text of “Highlighting Search Results” is free to read here on the web, and the Elasticsearch & Full Text Search Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Elasticsearch & Full Text Search Systems course, upgrade to CoddyKit PRO.

What will I learn in “Highlighting Search Results”?

Add highlighting to your search results to visually emphasize the matching terms within the returned documents, improving user experience. You practise Elasticsearch & Full Text Search Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Elasticsearch & Full Text Search Systems?

No prior experience is required. Elasticsearch & Full Text Search Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Highlighting Search Results” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Elasticsearch & Full Text Search Systems lesson?

Yes. Every Elasticsearch & Full Text Search Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Phrase and Proximity Searches
  2. Fuzzy and Wildcard Queries
  3. Highlighting Search Results
  4. Aggregations for Faceted Search
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