In Elasticsearch, a tokenizer is a component of the analyzer that is responsible for breaking up text into individual tokens. The tokenizer uses a set of rules to determine how to split the text into tokens.
There are several built-in tokenizers available in Elasticsearch, including:
1. Whitespace Tokenizer: This tokenizer splits text into tokens based on whitespace characters (spaces, tabs, etc.).
2. Standard Tokenizer: This tokenizer splits text into tokens based on word boundaries, using Unicode Text Segmentation rules to determine those boundaries.
3. Keyword Tokenizer: This tokenizer indexes the entire input string as a single token.
4. Path Hierarchy Tokenizer: This tokenizer splits text into tokens based on a hierarchy of paths, using a specified delimiter to separate levels in the hierarchy.
In addition to these built-in tokenizers, Elasticsearch also allows you to create your own custom tokenizers using regular expressions or custom code. This can be useful when dealing with text in specific formats or languages that require more complex tokenization rules.
After the text has been tokenized, the resulting tokens are passed through one or more token filters to modify them further. These filters can be used to remove stopwords, apply stemming, or perform other transformations that can improve search results. The final tokens are then indexed and stored in Elasticsearch, making them available for searching.