The standard analyzer is the default analyzer that Elasticsearch uses. It is the best general choice for analyzing text that may be in any language. It splits the text on word boundaries, as defined by theUnicode Consortium, and removes most punctuation. Finally, it lowercases all terms. It would produce

set, the, shape, to, semi, transparent, by, calling, set_trans, 5

The simple analyzer splits the text on anything that isn’t a letter, and lowercases the terms. It would produce

set, the, shape, to, semi, transparent, by, calling, set, trans

The whitespace analyzer splits the text on whitespace. It doesn’t lowercase. It would produce

Set, the, shape, to, semi-transparent, by, calling, set_trans(5)

Language-specific analyzers are available for many languages. They are able to take the peculiarities of the specified language into account. For instance, the english analyzer comes with a set of Englishstopwords (common words like and or the that don’t have much impact on relevance), which it removes. This analyzer also is able to stem English words because it understands the rules of English grammar.

The english analyzer would produce the following:

set, shape, semi, transpar, call, set_tran, 5

Note how transparent, calling, and set_trans have been stemmed to their root form.

4种分词器有各自的特征，根据实际查询需要指定适合的分词器，否则会有意想不到的查询结果。当需要为字段指定分词器时，可以使用mapping。

When Elasticsearch detects a new string field in your documents, it automatically configures it as a full-textstring field and analyzes it with the standard analyzer.

You don’t always want this. Perhaps you want to apply a different analyzer that suits the language your data is in. And sometimes you want a string field to be just a string field—to index the exact value that you pass in, without any analysis, such as a string user ID or an internal status field or tag.

To achieve this, we have to configure these fields manually by specifying the mapping.