Text analysis enables Elasticsearch to perform full-text search, where the search returns all relevant results rather than just exact matches.

If you search for Quick fox jumps, you probably want the document that contains A quick brown fox jumps over the lazy dog, and you might also want documents that contain related words like fast fox or foxes leap.

Tokenizationedit

Analysis makes full-text search possible through tokenization: breaking a text down into smaller chunks, called tokens. In most cases, these tokens are individual words.

If you index the phrase the quick brown fox jumps as a single string and the user searches for quick fox, it isn’t considered a match. However, if you tokenize the phrase and index each word separately, the terms in the query string can be looked up individually. This means they can be matched by searches for quick fox, fox brown, or other variations.

Normalizationedit

Tokenization enables matching on individual terms, but each token is still matched literally. This means:

To solve these problems, text analysis can normalize these tokens into a standard format. This allows you to match tokens that are not exactly the same as the search terms, but similar enough to still be relevant. For example:

To ensure search terms match these words as intended, you can apply the same tokenization and normalization rules to the query string. For example, a search for Foxes leap can be normalized to a search for fox jump.

Customize text analysisedit

Text analysis is performed by an analyzer, a set of rules that govern the entire process.

Elasticsearch includes a default analyzer, called the standard analyzer, which works well for most use cases right out of the box.

If you want to tailor your search experience, you can choose a different built-in analyzer or even configure a custom one. A custom analyzer gives you control over each step of the analysis process, including: