Core Types

Each JSON field can be mapped to a specific core type. JSON itself already provides us with some typing, with its support for string, integer/long, float/double, boolean, and null.

The following sample tweet JSON document will be used to explain the core types:

{
    "tweet" {
        "user" : "kimchy",
        "message" : "This is a tweet!",
        "postDate" : "2009-11-15T14:12:12",
        "priority" : 4,
        "rank" : 12.3
    }
}

Explicit mapping for the above JSON tweet can be:

{
    "tweet" : {
        "properties" : {
            "user" : {"type" : "string", "index" : "not_analyzed"},
            "message" : {"type" : "string", "null_value" : "na"},
            "postDate" : {"type" : "date"},
            "priority" : {"type" : "integer"},
            "rank" : {"type" : "float"}
        }
    }
}

The text based string type is the most basic type, and contains one or more characters. An example mapping can be:

{
    "tweet" : {
        "properties" : {
            "message" : {
                "type" : "string",
                "store" : true,
                "index" : "analyzed",
                "null_value" : "na"
            },
            "user" : {
                "type" : "string",
                "index" : "not_analyzed",
                "norms" : {
                    "enabled" : false
                }
            }
        }
    }
}

The above mapping defines a string message property/field within the tweet type. The field is stored in the index (so it can later be retrieved using selective loading when searching), and it gets analyzed (broken down into searchable terms). If the message has a null value, then the value that will be stored is na. There is also a string user which is indexed as-is (not broken down into tokens) and has norms disabled (so that matching this field is a binary decision, no match is better than another one).

The following table lists all the attributes that can be used with the string type:

The string type also support custom indexing parameters associated with the indexed value. For example:

{
    "message" : {
        "_value":  "boosted value",
        "_boost":  2.0
    }
}

The mapping is required to disambiguate the meaning of the document. Otherwise, the structure would interpret "message" as a value of type "object". The key _value (or value) in the inner document specifies the real string content that should eventually be indexed. The _boost (or boost) key specifies the per field document boost (here 2.0).

Norms store various normalization factors that are later used (at query time) in order to compute the score of a document relatively to a query.

Although useful for scoring, norms also require quite a lot of memory (typically in the order of one byte per document per field in your index, even for documents that don’t have this specific field). As a consequence, if you don’t need scoring on a specific field, it is highly recommended to disable norms on it. In particular, this is the case for fields that are used solely for filtering or aggregations.

In case you would like to disable norms after the fact, it is possible to do so by using the PUT mapping API, like this:

PUT my_index/_mapping/my_type
{
  "properties": {
    "title": {
      "type": "string",
      "norms": {
        "enabled": false
      }
    }
  }
}

Please however note that norms won’t be removed instantly, but will be removed as old segments are merged into new segments as you continue indexing new documents. Any score computation on a field that has had norms removed might return inconsistent results since some documents won’t have norms anymore while other documents might still have norms.

A number based type supporting float, double, byte, short, integer, and long. It uses specific constructs within Lucene in order to support numeric values. The number types have the same ranges as corresponding Java types. An example mapping can be:

{
    "tweet" : {
        "properties" : {
            "rank" : {
                "type" : "float",
                "null_value" : 1.0
            }
        }
    }
}

The following table lists all the attributes that can be used with a numbered type:

The token_count type maps to the JSON string type but indexes and stores the number of tokens in the string rather than the string itself. For example:

{
    "tweet" : {
        "properties" : {
            "name" : {
                "type" : "string",
                "fields" : {
                    "word_count": {
                        "type" : "token_count",
                        "store" : "yes",
                        "analyzer" : "standard"
                    }
                }
            }
        }
    }
}

All the configuration that can be specified for a number can be specified for a token_count. The only extra configuration is the required analyzer field which specifies which analyzer to use to break the string into tokens. For best performance, use an analyzer with no token filters.

The date type is a special type which maps to JSON string type. It follows a specific format that can be explicitly set. All dates are UTC. Internally, a date maps to a number type long, with the added parsing stage from string to long and from long to string. An example mapping:

{
    "tweet" : {
        "properties" : {
            "postDate" : {
                "type" : "date",
                "format" : "YYYY-MM-dd"
            }
        }
    }
}

The date type will also accept a long number representing UTC milliseconds since the epoch, regardless of the format it can handle.

The following table lists all the attributes that can be used with a date type:

The boolean type Maps to the JSON boolean type. It ends up storing within the index either T or F, with automatic translation to true and false respectively.

{
    "tweet" : {
        "properties" : {
            "hes_my_special_tweet" : {
                "type" : "boolean"
            }
        }
    }
}

The boolean type also supports passing the value as a number or a string (in this case 0, an empty string, false, off and no are false, all other values are true).

The following table lists all the attributes that can be used with the boolean type:

The binary type is a base64 representation of binary data that can be stored in the index. The field is not stored by default and not indexed at all.

{
    "tweet" : {
        "properties" : {
            "image" : {
                "type" : "binary"
            }
        }
    }
}

The following table lists all the attributes that can be used with the binary type:

It is possible to control which field values are loaded into memory, which is particularly useful for aggregating on string fields, using fielddata filters, which are explained in detail in the Fielddata section.

Fielddata filters can exclude terms which do not match a regex, or which don’t fall between a min and max frequency range:

{
    tweet: {
        type:      "string",
        analyzer:  "whitespace"
        fielddata: {
            filter: {
                regex: {
                    "pattern":        "^#.*"
                },
                frequency: {
                    min:              0.001,
                    max:              0.1,
                    min_segment_size: 500
                }
            }
        }
    }
}

These filters can be updated on an existing field mapping and will take effect the next time the fielddata for a segment is loaded. Use the Clear Cache API to reload the fielddata using the new filters.

Elasticsearch allows you to configure a similarity (scoring algorithm) per field. The similarity setting provides a simple way of choosing a similarity algorithm other than the default TF/IDF, such as BM25.

You can configure similarities via the similarity module

Defining the Similarity for a field is done via the similarity mapping property, as this example shows:

{
   "book":{
      "properties":{
         "title":{
            "type":"string", "similarity":"BM25"
         }
      }
   }
}

The following Similarities are configured out-of-box:

Adding copy_to parameter to any field mapping will cause all values of this field to be copied to fields specified in the parameter. In the following example all values from fields title and abstract will be copied to the field meta_data.

{
  "book" : {
    "properties" : {
      "title" : { "type" : "string", "copy_to" : "meta_data" },
      "abstract" : { "type" : "string", "copy_to" : "meta_data" },
      "meta_data" : { "type" : "string" }
    }
}

Multiple fields are also supported:

{
  "book" : {
    "properties" : {
      "title" : { "type" : "string", "copy_to" : ["meta_data", "article_info"] }
    }
}

The fields options allows to map several core types fields into a single json source field. This can be useful if a single field need to be used in different ways. For example a single field is to be used for both free text search and sorting.

{
  "tweet" : {
    "properties" : {
      "name" : {
        "type" : "string",
        "index" : "analyzed",
        "fields" : {
          "raw" : {"type" : "string", "index" : "not_analyzed"}
        }
      }
    }
  }
}

In the above example the field name gets processed twice. The first time it gets processed as an analyzed string and this version is accessible under the field name name, this is the main field and is in fact just like any other field. The second time it gets processed as a not analyzed string and is accessible under the name name.raw.

The include_in_all setting is ignored on any field that is defined in the fields options. Setting the include_in_all only makes sense on the main field, since the raw field value is copied to the _all field, the tokens aren’t copied.

In the essence a field can’t be updated. However multi fields can be added to existing fields. This allows for example to have a different index_analyzer configuration in addition to the already configured index_analyzer configuration specified in the main and other multi fields.

Also the new multi field will only be applied on document that have been added after the multi field has been added and in fact the new multi field doesn’t exist in existing documents.

Another important note is that new multi fields will be merged into the list of existing multi fields, so when adding new multi fields for a field previous added multi fields don’t need to be specified.

The multi fields defined in the fields are prefixed with the name of the main field and can be accessed by their full path using the navigation notation: name.raw, or using the typed navigation notation tweet.name.raw.

In older releases, the path option allows to control how fields are accessed. If the path option is set to full, then the full path of the main field is prefixed, but if the path option is set to just_name the actual multi field name without any prefix is used. The default value for the path option is full.

The just_name setting, among other things, allows indexing content of multiple fields under the same name (commonly used to set up custom _all fields in the past). In the example below the content of both fields first_name and last_name can be accessed by using any_name or tweet.any_name.

{
  "tweet" : {
    "properties": {
      "first_name": {
        "type": "string",
        "index": "analyzed",
        "path": "just_name",
        "fields": {
          "any_name": {"type": "string","index": "analyzed"}
        }
      },
      "last_name": {
        "type": "string",
        "index": "analyzed",
        "path": "just_name",
        "fields": {
          "any_name": {"type": "string","index": "analyzed"}
        }
      }
    }
  }
}