Custom Analyzers in Elasticsearch

Introduction

In Elasticsearch, an analyzer is used to break down text into terms or tokens. This process is essential for effective searching and indexing. A custom analyzer allows you to define a specific set of rules and components (tokenizers and token filters) to handle text in a way that suits your requirements.

Components of an Analyzer

An analyzer in Elasticsearch is composed of three main parts:

• Character Filters: These preprocess the text.
• Tokenizer: This breaks the text into individual terms.
• Token Filters: These process the tokens generated by the tokenizer.

Creating a Custom Analyzer

To create a custom analyzer, you need to define it in the index settings. Here is an example:

PUT /my_index
{
  "settings": {
    "analysis": {
      "char_filter": {
        "my_char_filter": {
          "type": "mapping",
          "mappings": ["ß => ss"]
        }
      },
      "tokenizer": {
        "my_tokenizer": {
          "type": "standard"
        }
      },
      "filter": {
        "my_token_filter": {
          "type": "lowercase"
        }
      },
      "analyzer": {
        "my_custom_analyzer": {
          "type": "custom",
          "char_filter": ["my_char_filter"],
          "tokenizer": "my_tokenizer",
          "filter": ["my_token_filter"]
        }
      }
    }
  }
}
                

This example defines a custom analyzer named my_custom_analyzer with a character filter, a tokenizer, and a token filter.

Testing the Custom Analyzer

You can test the custom analyzer using the following API:

POST /my_index/_analyze
{
  "analyzer": "my_custom_analyzer",
  "text": "Elasticsearch ß custom analyzer"
}
                

This request will return the tokens generated by your custom analyzer:

{
  "tokens": [
    {
      "token": "elasticsearch",
      "start_offset": 0,
      "end_offset": 13,
      "type": "",
      "position": 0
    },
    {
      "token": "ss",
      "start_offset": 14,
      "end_offset": 15,
      "type": "",
      "position": 1
    },
    {
      "token": "custom",
      "start_offset": 16,
      "end_offset": 22,
      "type": "",
      "position": 2
    },
    {
      "token": "analyzer",
      "start_offset": 23,
      "end_offset": 31,
      "type": "",
      "position": 3
    }
  ]
}

Using the Custom Analyzer in Mappings

To use the custom analyzer in your index mappings, you need to specify it in the field definition:

PUT /my_index
{
  "mappings": {
    "properties": {
      "content": {
        "type": "text",
        "analyzer": "my_custom_analyzer"
      }
    }
  }
}
                

This will ensure that the content field uses the custom analyzer during indexing and searching.

Conclusion

Custom analyzers in Elasticsearch provide powerful ways to tailor text analysis to your specific needs. By understanding and utilizing character filters, tokenizers, and token filters, you can create sophisticated text processing pipelines that improve the accuracy and relevance of your search results.