Category: ElasticSearch

What is Custom Tokenizer Elasticsearch>

In Elasticsearch, a tokenizer is a component that breaks a string into a list of tokens, or terms, which are then used to build an inverted index. An inverted index is a data structure that allows Elasticsearch to efficiently search for and retrieve documents that match a given query.

A custom tokenizer is a user-defined tokenizer that can be used to split a string into tokens in a way that is specific to a particular use case. For example, you might create a custom tokenizer that is optimized for tokenizing technical documentation, or a custom tokenizer that is designed to handle a specific language or script.

To create a custom tokenizer in Elasticsearch, you will need to define a custom plugin that extends the TokenizerPlugin class and overrides the createTokenizer method. You will also need to implement the custom tokenizer class, which extends the Tokenizer class and overrides the reset and incrementToken methods.

Custom tokenizers can be useful when the built-in tokenizers provided by Elasticsearch do not meet the needs of your application. They can allow you to customize the way that Elasticsearch tokenizes strings, which can improve the accuracy and relevance of search results.

Usecase with Custom Tokenizer Elasticsearch

Here are a few examples of use cases where a custom tokenizer might be useful in Elasticsearch:

Tokenizing specialized content: If you are indexing documents that contain specialized content, such as technical documentation or legal documents, you may need to create a custom tokenizer that is optimized for handling this type of content. For example, you might create a custom tokenizer that is able to handle acronyms, abbreviations, or technical terms in a way that is specific to your domain.

Tokenizing non-Latin scripts: Elasticsearch includes tokenizers for several Latin-based languages, such as English and Spanish, but it does not include tokenizers for non-Latin scripts, such as Chinese or Arabic. If you need to index documents written in a non-Latin script, you may need to create a custom tokenizer that is able to handle the specific characteristics of that script.

Tokenizing proprietary formats: If you are indexing documents in a proprietary format, such as a custom XML or JSON schema, you may need to create a custom tokenizer that is able to extract relevant information from the documents and create tokens based on that information.

Tokenizing large documents: Elasticsearch has a default token size limit of 256 characters, which means that tokens larger than this size will be truncated. If you are indexing documents with large tokens, you may need to create a custom tokenizer that is able to handle these large tokens without truncating them.

Custom Tokenizer Elasticsearch code example with Java

To create a custom tokenizer in Elasticsearch, you will need to define a custom plugin that extends the TokenizerPlugin class and overrides the createTokenizer method.

Here’s an example of how you might implement a custom tokenizer plugin:

import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.env.Environment;
import org.elasticsearch.index.IndexSettings;
import org.elasticsearch.index.analysis.AbstractTokenizerFactory;
import org.elasticsearch.index.analysis.Tokenizer;

public class MyCustomTokenizer extends AbstractTokenizerFactory {

  public MyCustomTokenizer(IndexSettings indexSettings, Environment environment, String name, Settings settings) {
    super(indexSettings, name, settings);
  }

  @Override
  public Tokenizer create() {
    return new MyCustomTokenizer(settings);
  }
}

You will also need to implement the MyCustomTokenizer class, which extends the Tokenizer class and overrides the reset and incrementToken methods. The reset method is called when the tokenizer is reset, and the incrementToken method is called to advance the tokenizer to the next token.

Here’s an example of how you might implement the MyCustomTokenizer class:

import org.apache.lucene.analysis.Tokenizer;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.analysis.tokenattributes.OffsetAttribute;

public class MyCustomTokenizer extends Tokenizer {

  private final CharTermAttribute termAtt = addAttribute(CharTermAttribute.class);
  private final OffsetAttribute offsetAtt = addAttribute(OffsetAttribute.class);
  private int start = 0;
  private int end = 0;

  public MyCustomTokenizer() {
  }

  @Override
  public boolean incrementToken() {
    clearAttributes();
    if (start == end) {
      // Advance the tokenizer to the next token
      // ...
    }
    termAtt.setLength(end - start);
    offsetAtt.setOffset(start, end);
    return true;
  }

  @Override
  public void reset() {
    super.reset();
    // Reset the tokenizer
    // ...
  }
}