How can I implement fuzzy matching for email addresses and telephone numbers in Elasticsearch?

Fuzzy Matching for Email and Telephone in Elasticsearch

Elasticsearch provides robust capabilities for implementing fuzzy matching, allowing you to search for email addresses or telephone numbers that partially match a given value. Here's how to achieve this goal efficiently:

1. Employ Custom Analyzers

To optimize performance, create custom analyzers for email addresses (index_email_analyzer, search_email_analyzer) and telephone numbers (index_phone_analyzer, search_phone_analyzer). These analyzers use specific tokenizers and filters to break down input values into relevant tokens.

2. Index Data with Index Analyzers

When indexing data, utilize the custom index analyzers to process email and telephone values. This ensures that the data is stored in a tokenized form suitable for fuzzy matching.

3. Search with Search Analyzers

During search operations, employ the custom search analyzers to tokenize input search parameters. This allows Elasticsearch to compare the tokenized search parameters against the tokenized data, identifying even partial matches.

4. Example Index Definition

Here's an example of an index definition with the necessary analyzers for fuzzy matching of email and telephone numbers:

<code class="json">{
  "settings": {
    "analysis": {
      "analyzer": {
        "email_url_analyzer": {
          "type": "custom",
          "tokenizer": "uax_url_email",
          "filter": [ "trim" ]
        },
        "index_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "digit_edge_ngram_tokenizer",
          "filter": [ "trim" ]
        },
        "search_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "keyword",
          "filter": [ "trim" ]
        },
        "index_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "name_ngram_filter", "trim" ]
        },
        "search_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "trim" ]
        }
      },
      "char_filter": {
        "digit_only": {
          "type": "pattern_replace",
          "pattern": "\D+",
          "replacement": ""
        }
      },
      "tokenizer": {
        "digit_edge_ngram_tokenizer": {
          "type": "edgeNGram",
          "min_gram": "1",
          "max_gram": "15",
          "token_chars": [ "digit" ]
        }
      },
      "filter": {
        "name_ngram_filter": {
          "type": "ngram",
          "min_gram": "1",
          "max_gram": "20"
        }
      }
    }
  },
  "mappings": {
    "your_type": {
      "properties": {
        "email": {
          "type": "string",
          "analyzer": "index_email_analyzer",
          "search_analyzer": "search_email_analyzer"
        },
        "phone": {
          "type": "string",
          "analyzer": "index_phone_analyzer",
          "search_analyzer": "search_phone_analyzer"
        }
      }
    }
  }
}</code>

5. Example Queries

To perform fuzzy matches, utilize the term query:

<code class="json">{ 
    "query": {
        "term": 
            { "phone": "136" }
    }
}</code>

<code class="json">{ 
    "query": {
        "term": 
            { "email": "@gmail.com" }
    }
}</code>

This solution offers efficient and accurate fuzzy matching for email addresses and telephone numbers, empowering you to easily retrieve data based on partial or incomplete input.

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