Mapping is the process of defining how a document should be mapped to the Search Engine, including its searchable characteristics such as which fields are searchable and if/how they are tokenized. In ElasticSearch, an index may store documents of different “mapping types”. ElasticSearch allows one to associate multiple mapping definitions for each mapping type.
Explicit mapping is defined on an index/type level. By default, there isn’t a need to define an explicit mapping, since one is automatically created and registered when a new type or new field is introduced (with no performance overhead) and have sensible defaults. Only when the defaults need to be overridden must a mapping definition be provided.
Mapping types are a way to divide the documents in an index into logical groups. Think of it as tables in a database. Though there is separation between types, it’s not a full separation (all end up as a document within the same Lucene index).
Field names with the same name across types are highly recommended to have the same type and same mapping characteristics (analysis settings for example). There is an effort to allow to explicitly “choose” which field to use by using type prefix (
my_type.my_field), but it’s not complete, and there are places where it will never work (like faceting on the field).
In practice though, this restriction is almost never an issue. The field name usually ends up being a good indication to its “typeness” (e.g. “first_name” will always be a string). Note also, that this does not apply to the cross index case.
index.mapping.ignore_malformed global setting can be set on the index level to allow to ignore malformed content globally across all mapping types (malformed content example is trying to index a string value as a numeric type).