[[complex-core-fields]] === Complex core field types

Besides the simple scalar datatypes that we mentioned above, JSON also has null values, arrays and objects, all of which are supported by Elasticsearch:

==== Multi-value fields

It is quite possible that we want our tag field to contain more than one tag. Instead of a single string, we could index an array of tags:

{ "tag": [ "search", "nosql" ]}

There is no special mapping required for arrays. Any field can contain zero, one or more values, in the same way as a full text field is analyzed to produce multiple terms.

By implication, this means that all of the values of an array must be of the same datatype. You can't mix dates with strings. If you create a new field by indexing an array, Elasticsearch will use the datatype of the first value in the array to determine the type of the new field.


The elements inside an array are not ordered. You cannot refer to the first element'' orthe last element''. Rather think of an array as a bag of values.


==== Empty fields

Arrays can, of course, be empty. This is the equivalent of having zero values. In fact, there is no way of storing a null value in Lucene, so a field with a null value is also considered to be an empty field.

These four fields would all be considered to be empty, and would not be indexed:

"empty_string":             "",
"null_value":               null,
"empty_array":              [],
"array_with_null_value":    [ null ]

==== Multi-level objects

The last native JSON datatype that we need to discuss is the object -- known in other languages as hashes, hashmaps, dictionaries or associative arrays.

Inner objects are often used to embed one entity or object inside another. For instance, instead of having fields called user_name and user_id inside our tweet document, we could write it as:


{
    "tweet":            "Elasticsearch is very flexible",
    "user": {
        "id":           "@johnsmith",
        "gender":       "male",
        "age":          26,
        "name": {
            "full":     "John Smith",
            "first":    "John",
            "last":     "Smith"
        }
    }
}
--------------------------------------------------

==== Mapping for inner objects

Elasticsearch will detect new object fields dynamically and map them as
type `object`, with each inner field listed under `properties`:

[source,js]
--------------------------------------------------
{
  "gb": {
    "tweet": { <1>
      "properties": {
        "tweet":            { "type": "string" },
        "user": { <2>
          "type":             "object",
          "properties": {
            "id":           { "type": "string" },
            "gender":       { "type": "string" },
            "age":          { "type": "long"   },
            "name":   { <2>
              "type":         "object",
              "properties": {
                "full":     { "type": "string" },
                "first":    { "type": "string" },
                "last":     { "type": "string" }
              }
            }
          }
        }
      }
    }
  }
}
--------------------------------------------------
<1> Root object.
<2> Inner objects.

The mapping for the `user` and `name` fields have a similar structure
to the mapping for the `tweet` type itself.  In fact, the `type` mapping
is just a special type of `object` mapping, which we refer to as the
_root object_.  It is just the same as any other object, except that it has
some special top-level fields for document metadata, like `_source`,
the `_all` field etc.

==== How inner objects are indexed

Lucene doesn't understand inner objects. A Lucene document consists of a flat
list of key-value pairs.  In order for Elasticsearch to index inner objects
usefully, it converts our document into something like this:

[source,js]
--------------------------------------------------
{
    "tweet":            [elasticsearch, flexible, very],
    "user.id":          [@johnsmith],
    "user.gender":      [male],
    "user.age":         [26],
    "user.name.full":   [john, smith],
    "user.name.first":  [john],
    "user.name.last":   [smith]
}
--------------------------------------------------

_Inner fields_ can be referred to by name, eg `"first"`. To distinguish
between two fields that have the same name we can use the full _path_,
eg `"user.name.first"` or even the `type` name plus
the path: `"tweet.user.name.first"`.

NOTE: In the simple flattened document above, there is no field called `user`
and no field called `user.name`.  Lucene only indexes scalar or simple values,
not complex datastructures.

==== Arrays of inner objects

Finally, consider how an array containing inner objects would be indexed.
Let's say we have a `followers` array which looks like this:

[source,js]
--------------------------------------------------
{
    "followers": [
        { "age": 35, "name": "Mary White"},
        { "age": 26, "name": "Alex Jones"},
        { "age": 19, "name": "Lisa Smith"}
    ]
}
--------------------------------------------------

This document will be flattened as we described above, but the
result will look like this:

[source,js]
--------------------------------------------------
{
    "followers.age":    [19, 26, 35],
    "followers.name":   [alex, jones, lisa, smith, mary, white]
}
--------------------------------------------------

The correlation between `{age: 35}` and `{name: Mary White}` has been lost as
each multi-value field is just a bag of values, not an ordered array.  This is
sufficient for us to ask:

* _Is there a follower who is 26 years old?_

but we can't get an accurate answer to:

* _Is there a follower who is 26 years old **and who is called Alex Jones?**_

Correlated inner objects, which are able to answer queries like these,
are called _nested_ objects, and we will discuss them later on in
<<relations>>.