[[one-lang-docs]] === One Language per Document A single predominant language per document ((("languages", "one language per document")))((("indices", "documents in different languages")))requires a relatively simple setup. Documents from different languages can be stored in separate indices&#x2014;`blogs-en`, `blogs-fr`, and so forth&#x2014;that use the same type and the same fields for each index, just with different analyzers: [source,js] -------------------------------------------------- PUT /blogs-en { "mappings": { "post": { "properties": { "title": { "type": "string", <1> "fields": { "stemmed": { "type": "string", "analyzer": "english" <2> } }}}}}} PUT /blogs-fr { "mappings": { "post": { "properties": { "title": { "type": "string", <1> "fields": { "stemmed": { "type": "string", "analyzer": "french" <2> } }}}}}} -------------------------------------------------- <1> Both `blogs-en` and `blogs-fr` have a type called `post` that contains the field `title`. <2> The `title.stemmed` subfield uses a language-specific analyzer. This approach is clean and flexible. New languages are easy to add--just create a new index--and because each language is completely separate, we don't suffer from the term-frequency and stemming problems described in <<language-pitfalls>>. The documents of a single language can be queried independently, or queries can target multiple languages by querying multiple indices. We can even specify a preference((("indices_boost parameter", "specifying preference for a specific language"))) for particular languages with the `indices_boost` parameter: [source,js] -------------------------------------------------- GET /blogs-*/post/_search <1> { "query": { "multi_match": { "query": "deja vu", "fields": [ "title", "title.stemmed" ] <2> "type": "most_fields" } }, "indices_boost": { <3> "blogs-en": 3, "blogs-fr": 2 } } -------------------------------------------------- <1> This search is performed on any index beginning with `blogs-`. <2> The `title.stemmed` fields are queried using the analyzer specified in each index. <3> Perhaps the user's `accept-language` headers showed a preference for English, and then French, so we boost results from each index accordingly. Any other languages will have a neutral boost of `1`. ==== Foreign Words Of course, these documents may contain words or sentences in other languages, and these words are unlikely to be stemmed correctly. With predominant-language documents, this is not usually a major problem. The user will often search for the exact words--for instance, of a quotation from another language--rather than for inflections of a word. Recall can be improved by using techniques explained in <<token-normalization>>. Perhaps some words like place names should be queryable in the predominant language and in the original language, such as _Munich_ and _München_. These words are effectively synonyms, which we discuss in <<synonyms>>. .Don't Use Types for Languages ************************************************* You may be tempted to use a separate type for each language,((("types", "not using for languages")))((("languages", "not using types for"))) instead of a separate index. For best results, you should avoid using types for this purpose. As explained in <<mapping>>, fields from different types but with the same field name are indexed into the _same inverted index_. This means that the term frequencies from each type (and thus each language) are mixed together. To ensure that the term frequencies of one language don't pollute those of another, either use a separate index for each language, or a separate field, as explained in the next section. *************************************************