Object Type

JSON documents are hierarchical in nature, allowing them to define inner "objects" within the actual JSON. Elasticsearch completely understands the nature of these inner objects and can map them easily, providing query support for their inner fields. Because each document can have objects with different fields each time, objects mapped this way are known as "dynamic". Dynamic mapping is enabled by default. Let’s take the following JSON as an example:

{
    "tweet" : {
        "person" : {
            "name" : {
                "first_name" : "Shay",
                "last_name" : "Banon"
            },
            "sid" : "12345"
        },
        "message" : "This is a tweet!"
    }
}

The above shows an example where a tweet includes the actual person details. A person is an object, with a sid, and a name object which has first_name and last_name. It’s important to note that tweet is also an object, although it is a special root object type which allows for additional mapping definitions.

The following is an example of explicit mapping for the above JSON:

{
    "tweet" : {
        "properties" : {
            "person" : {
                "type" : "object",
                "properties" : {
                    "name" : {
                        "type" : "object",
                        "properties" : {
                            "first_name" : {"type" : "string"},
                            "last_name" : {"type" : "string"}
                        }
                    },
                    "sid" : {"type" : "string", "index" : "not_analyzed"}
                }
            },
            "message" : {"type" : "string"}
        }
    }
}

In order to mark a mapping of type object, set the type to object. This is an optional step, since if there are properties defined for it, it will automatically be identified as an object mapping.

An object mapping can optionally define one or more properties using the properties tag for a field. Each property can be either another object, or one of the core_types.

One of the most important features of Elasticsearch is its ability to be schema-less. This means that, in our example above, the person object can be indexed later with a new property — age, for example — and it will automatically be added to the mapping definitions. Same goes for the tweet root object.

This feature is by default turned on, and it’s the dynamic nature of each object mapped. Each object mapped is automatically dynamic, though it can be explicitly turned off:

{
    "tweet" : {
        "properties" : {
            "person" : {
                "type" : "object",
                "properties" : {
                    "name" : {
                        "dynamic" : false,
                        "properties" : {
                            "first_name" : {"type" : "string"},
                            "last_name" : {"type" : "string"}
                        }
                    },
                    "sid" : {"type" : "string", "index" : "not_analyzed"}
                }
            },
            "message" : {"type" : "string"}
        }
    }
}

In the above example, the name object mapped is not dynamic, meaning that if, in the future, we try to index JSON with a middle_name within the name object, it will get discarded and not added.

There is no performance overhead if an object is dynamic, the ability to turn it off is provided as a safety mechanism so "malformed" objects won’t, by mistake, index data that we do not wish to be indexed.

If a dynamic object contains yet another inner object, it will be automatically added to the index and mapped as well.

When processing dynamic new fields, their type is automatically derived. For example, if it is a number, it will automatically be treated as number core_type. Dynamic fields default to their default attributes, for example, they are not stored and they are always indexed.

Date fields are special since they are represented as a string. Date fields are detected if they can be parsed as a date when they are first introduced into the system. The set of date formats that are tested against can be configured using the dynamic_date_formats on the root object, which is explained later.

Note, once a field has been added, its type can not change. For example, if we added age and its value is a number, then it can’t be treated as a string.

The dynamic parameter can also be set to strict, meaning that not only will new fields not be introduced into the mapping, but also that parsing (indexing) docs with such new fields will fail.

The enabled flag allows to disable parsing and indexing a named object completely. This is handy when a portion of the JSON document contains arbitrary JSON which should not be indexed, nor added to the mapping. For example:

{
    "tweet" : {
        "properties" : {
            "person" : {
                "type" : "object",
                "properties" : {
                    "name" : {
                        "type" : "object",
                        "enabled" : false
                    },
                    "sid" : {"type" : "string", "index" : "not_analyzed"}
                }
            },
            "message" : {"type" : "string"}
        }
    }
}

In the above, name and its content will not be indexed at all.

include_in_all can be set on the object type level. When set, it propagates down to all the inner mappings defined within the object that do not explicitly set it.

In the core_types section, a field can have a index_name associated with it in order to control the name of the field that will be stored within the index. When that field exists within an object(s) that are not the root object, the name of the field of the index can either include the full "path" to the field with its index_name, or just the index_name. For example (under mapping of type person, removed the tweet type for clarity):

{
    "person" : {
        "properties" : {
            "name1" : {
                "type" : "object",
                "path" : "just_name",
                "properties" : {
                    "first1" : {"type" : "string"},
                    "last1" : {"type" : "string", "index_name" : "i_last_1"}
                }
            },
            "name2" : {
                "type" : "object",
                "path" : "full",
                "properties" : {
                    "first2" : {"type" : "string"},
                    "last2" : {"type" : "string", "index_name" : "i_last_2"}
                }
            }
        }
    }
}

In the above example, the name1 and name2 objects within the person object have different combination of path and index_name. The document fields that will be stored in the index as a result of that are:

Note, when querying or using a field name in any of the APIs provided (search, query, selective loading, …​), there is an automatic detection from logical full path and into the index_name and vice versa. For example, even though name1/last1 defines that it is stored with just_name and a different index_name, it can either be referred to using name1.last1 (logical name), or its actual indexed name of i_last_1.

More over, where applicable, for example, in queries, the full path including the type can be used such as person.name.last1, in this case, both the actual indexed name will be resolved to match against the index, and an automatic query filter will be added to only match person types.