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Mapping

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Mapping

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In order to be able to treat date fields as dates, numeric fields as numbers, and string fields as full-text or exact-value strings, Elasticsearch needs to know what type of data each field contains. This information is contained in the mapping.

As explained in Data In, Data Out, each document in an index has a type. Every type has its own mapping, or schema definition. A mapping defines the fields within a type, the datatype for each field, and how the field should be handled by Elasticsearch. A mapping is also used to configure metadata associated with the type.

We discuss mappings in detail in Types and Mappings. In this section, we’re going to look at just enough to get you started.

Core Simple Field Types

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Elasticsearch supports the following simple field types:

String: string
Whole number: byte, short, integer, long
Floating-point: float, double
Boolean: boolean
Date: date

When you index a document that contains a new field—one previously not seen—Elasticsearch will use dynamic mapping to try to guess the field type from the basic datatypes available in JSON, using the following rules:

JSON type	Field type
Boolean: `true` or `false`	`boolean`
Whole number: `123`	`long`
Floating point: `123.45`	`double`
String, valid date: `2014-09-15`	`date`
String: `foo bar`	`string`

This means that if you index a number in quotes ("123"), it will be mapped as type string, not type long. However, if the field is already mapped as type long, then Elasticsearch will try to convert the string into a long, and throw an exception if it can’t.

Viewing the Mapping

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We can view the mapping that Elasticsearch has for one or more types in one or more indices by using the /_mapping endpoint. At the start of this chapter, we already retrieved the mapping for type tweet in index gb:

GET /gb/_mapping/tweet

This shows us the mapping for the fields (called properties) that Elasticsearch generated dynamically from the documents that we indexed:

{
   "gb": {
      "mappings": {
         "tweet": {
            "properties": {
               "date": {
                  "type": "date",
                  "format": "strict_date_optional_time||epoch_millis"
               },
               "name": {
                  "type": "string"
               },
               "tweet": {
                  "type": "string"
               },
               "user_id": {
                  "type": "long"
               }
            }
         }
      }
   }
}

Incorrect mappings, such as having an age field mapped as type string instead of integer, can produce confusing results to your queries.

Instead of assuming that your mapping is correct, check it!

Customizing Field Mappings

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While the basic field datatypes are sufficient for many cases, you will often need to customize the mapping for individual fields, especially string fields. Custom mappings allow you to do the following:

Distinguish between full-text string fields and exact value string fields
Use language-specific analyzers
Optimize a field for partial matching
Specify custom date formats
And much more

The most important attribute of a field is the type. For fields other than string fields, you will seldom need to map anything other than type:

{
    "number_of_clicks": {
        "type": "integer"
    }
}

Fields of type string are, by default, considered to contain full text. That is, their value will be passed through an analyzer before being indexed, and a full-text query on the field will pass the query string through an analyzer before searching.

The two most important mapping attributes for string fields are index and analyzer.

index

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The index attribute controls how the string will be indexed. It can contain one of three values:

analyzed: First analyze the string and then index it. In other words, index this field as full text.
not_analyzed: Index this field, so it is searchable, but index the value exactly as specified. Do not analyze it.
no: Don’t index this field at all. This field will not be searchable.

The default value of index for a string field is analyzed. If we want to map the field as an exact value, we need to set it to not_analyzed:

{
    "tag": {
        "type":     "string",
        "index":    "not_analyzed"
    }
}

The other simple types (such as long, double, date etc) also accept the index parameter, but the only relevant values are no and not_analyzed, as their values are never analyzed.

analyzer

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For analyzed string fields, use the analyzer attribute to specify which analyzer to apply both at search time and at index time. By default, Elasticsearch uses the standard analyzer, but you can change this by specifying one of the built-in analyzers, such as whitespace, simple, or english:

{
    "tweet": {
        "type":     "string",
        "analyzer": "english"
    }
}

In Custom Analyzers, we show you how to define and use custom analyzers as well.

Updating a Mapping

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You can specify the mapping for a type when you first create an index. Alternatively, you can add the mapping for a new type (or update the mapping for an existing type) later, using the /_mapping endpoint.

Although you can add to an existing mapping, you can’t change existing field mappings. If a mapping already exists for a field, data from that field has probably been indexed. If you were to change the field mapping, the indexed data would be wrong and would not be properly searchable.

We can update a mapping to add a new field, but we can’t change an existing field from analyzed to not_analyzed.

To demonstrate both ways of specifying mappings, let’s first delete the gb index:

DELETE /gb

Then create a new index, specifying that the tweet field should use the english analyzer:

PUT /gb 
{
  "mappings": {
    "tweet" : {
      "properties" : {
        "tweet" : {
          "type" :    "string",
          "analyzer": "english"
        },
        "date" : {
          "type" :   "date"
        },
        "name" : {
          "type" :   "string"
        },
        "user_id" : {
          "type" :   "long"
        }
      }
    }
  }
}

This creates the index with the mappings specified in the body.

Later on, we decide to add a new not_analyzed text field called tag to the tweet mapping, using the _mapping endpoint:

PUT /gb/_mapping/tweet
{
  "properties" : {
    "tag" : {
      "type" :    "string",
      "index":    "not_analyzed"
    }
  }
}

Note that we didn’t need to list all of the existing fields again, as we can’t change them anyway. Our new field has been merged into the existing mapping.

Testing the Mapping

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You can use the analyze API to test the mapping for string fields by name. Compare the output of these two requests:

GET /gb/_analyze
{
  "field": "tweet",
  "text": "Black-cats" 
}

GET /gb/_analyze
{
  "field": "tag",
  "text": "Black-cats" 
}

The text we want to analyze is passed in the body.

The tweet field produces the two terms black and cat, while the tag field produces the single term Black-cats. In other words, our mapping is working correctly.

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