Elasticsearch Elasticsearch Fuzzy Match: Advanced Techniques and Best Practices

By Opster Team

Updated: Nov 14, 2023

| 2 min read

Introduction

In this article, we will explore advanced techniques and best practices for implementing fuzzy matching in Elasticsearch.

Techniques and best practices for implementing fuzzy matching

1. Using the Fuzzy Query

The fuzzy query is the most straightforward way to perform fuzzy matching in Elasticsearch. It is based on the Damerau-Levenshtein distance, which calculates the number of single-character edits (insertions, deletions, substitutions, or transpositions) required to change one term into another. To use the fuzzy query, you can simply add the “fuzzy” parameter to your query:

json
{
  "query": {
    "fuzzy": {
      "field_name": {
        "value": "search_term",
        "fuzziness": "AUTO"
      }
    }
  }
}

The “fuzziness” parameter controls the allowed edit distance. You can set it to an integer value or use the “AUTO” option, which automatically adjusts the fuzziness based on the length of the search term.

2. Combining Fuzzy and Exact Matches

In some cases, you may want to prioritize exact matches over fuzzy matches. To achieve this, you can use the “bool” query to combine a “match” query (for exact matches) with a “fuzzy” query (for fuzzy matches):

json
{
  "query": {
    "bool": {
      "should": [
        {
          "match": {
            "field_name": "search_term"
          }
        },
        {
          "fuzzy": {
            "field_name": {
              "value": "search_term",
              "fuzziness": "AUTO"
            }
          }
        }
      ]
    }
  }
}

This query will return documents that either exactly match the search term or have a fuzzy match. The exact matches will be scored higher, so they will appear first in the search results.

3. Using N-Grams for Improved Fuzzy Matching

N-grams are a technique that can be used to improve the performance and accuracy of fuzzy matching. An n-gram is a contiguous sequence of n characters from a given string. By indexing n-grams of your text, you can efficiently search for terms that are similar to the query term, even if they have multiple character differences.

To use n-grams in Elasticsearch, you need to create a custom analyzer that includes the “ngram” token filter:

json
{
  "settings": {
    "analysis": {
      "analyzer": {
        "ngram_analyzer": {
          "tokenizer": "standard",
          "filter": ["lowercase", "ngram_filter"]
        }
      },
      "filter": {
        "ngram_filter": {
          "type": "ngram",
          "min_gram": 2,
          "max_gram": 3
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "field_name": {
        "type": "text",
        "analyzer": "ngram_analyzer",
        "search_analyzer": "standard"
      }
    }
  }
}

This configuration creates an n-gram analyzer with a minimum n-gram length of 2 and a maximum length of 3. You can adjust these values based on your specific use case and the level of fuzziness you want to allow.

Conclusion

In conclusion, Elasticsearch offers several advanced techniques for implementing fuzzy matching, including the fuzzy query, n-grams, and custom analyzers. By combining these techniques and following best practices, you can improve the relevance and accuracy of your search results, even when dealing with typos, misspellings, or synonyms.