Define Field Mappings

Static Mappings

Use static mappings to configure index options for fields that you don't want indexed dynamically, or to configure a single field independently from others in an index.

For static mappings, set mappings.dynamic to false and specify the fields to index using mappings.fields. Atlas Search indexes only the specified fields with specific options. If an indexed field contains polymorphic data, Atlas Search indexes only documents that correspond to the mappings specified in the index definition for that field and ignores documents that contain values that aren't the data type specified in the index definition for that field.

You can't use the dot notation to statically index nested fields. When defining the index for a nested field, you must define the mappings for each parent field of that nested field. For an example, see the Examples on this page, which demonstrates the index syntax for a field named city that is nested inside a field named address.

Dynamic Mappings

Use dynamic mappings if your schema changes regularly or is unknown, or when experimenting with Atlas Search. You can configure an entire index to use dynamic mappings, or specify individual fields, such as fields of type document, to be dynamically mapped. Before using dynamic mappings, see the table for Data Types.

For dynamic mappings, set mappings.dynamic to true. Atlas Search automatically indexes the fields of supported types in each document. For fields of type string, Atlas Search stores the fields on mongot.

Limitations

By default, Atlas Search stops replicating changes for indexes larger than 2.1 billion index objects, on a replica set or single shard, where each indexed document or nested embeddedDocument counts as a single object. This means that your index is queryable, but you might get stale results.

If you plan to index fields that might exceed 2.1 billion objects, use numPartitions or shard your cluster.

You can't index fields that contain the dollar ($) sign at the start of the field name.

Index Field as Multiple Data Types

To index a field as multiple types, define the types in the field definition array for the field.

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{
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  ...
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  "mappings": {
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    "dynamic": <boolean>,
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    "fields": {
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      "<field-name>": [
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        {
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          "type": "<field-type>",
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          ...
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        },
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        {
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          "type": "<field-type>",
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          ...
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        },
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        ...
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      ],
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      ...
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    },
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    ...
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  }
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}

Static Mapping Example

The following index definition example uses static mappings.

• The default index analyzer is lucene.standard.

• The default search analyzer is lucene.standard. You can change the search analyzer if you want the query term to be parsed differently than how it is stored in your Atlas Search index.

• The index specifies static field mappings (dynamic: false), which means fields that are not explicitly mentioned are not indexed. So, the index definition includes:

  • The address field, which is of type document. It has two embedded sub-fields, city and state.

    The city sub-field uses the lucene.simple analyzer by default for queries. It uses the ignoreAbove option to ignore any string of more than 255 bytes in length.

    The state sub-field uses the lucene.english analyzer by default for queries.

  • The company field, which is of type string. It uses the lucene.whitespace analyzer by default for queries. It has a multi analyzer named mySecondaryAnalyzer which uses the lucene.french analyzer by default for queries.

    To learn more about multi analyzers, see Path Construction.

  • The employees field, which is an array of strings. It uses the lucene.standard analyzer by default for queries. For indexing arrays, Atlas Search only requires the data type of the array elements. You don't have to specify that the data is contained in an array in the index definition.

{
  "analyzer": "lucene.standard",
  "searchAnalyzer": "lucene.standard",
  "mappings": {
    "dynamic": false,
    "fields": {
      "address": {
        "type": "document",
        "fields": {
          "city": {
            "type": "string",
            "analyzer": "lucene.simple",
            "ignoreAbove": 255
          },
          "state": {
            "type": "string",
            "analyzer": "lucene.english"
          }
        }
      },
      "company": {
        "type": "string",
        "analyzer": "lucene.whitespace",
        "multi": {
          "mySecondaryAnalyzer": {
            "type": "string",
            "analyzer": "lucene.french"
          }
        }
      },
      "employees": {
        "type": "string",
        "analyzer": "lucene.standard"
      }
    }
  }
}

Combined Mapping Example

The following index definition example uses both static and dynamic mappings.

• The default index analyzer is lucene.standard.

• The default search analyzer is lucene.standard. You can change the search analyzer if you want the query term to be parsed differently than how it is stored in your Atlas Search index.

• The index specifies static field mappings (dynamic: false), which means fields that aren't explicitly mentioned aren't indexed. So, the index definition includes:

  • The company field, which is of type string. It uses the lucene.whitespace analyzer by default for queries. It has a multi analyzer named mySecondaryAnalyzer which uses the lucene.french analyzer by default for queries. To learn more about multi analyzers, see Path Construction.

  • The employees field, which is an array of strings. It uses the lucene.standard analyzer by default for queries.

  • The address field, which is of type document. It has two embedded sub-fields, city and state. Instead of explicitly mentioning each nested field in the document, the index definition enables dynamic mapping for all the sub-fields in the document. It uses the lucene.standard analyzer by default for queries.

{
  "analyzer": "lucene.standard",
  "searchAnalyzer": "lucene.standard",
  "mappings": {
    "dynamic": false,
    "fields": {
      "company": {
        "type": "string",
        "analyzer": "lucene.whitespace",
        "multi": {
          "mySecondaryAnalyzer": {
            "type": "string",
            "analyzer": "lucene.french"
          }
        }
      },
      "employees": {
        "type": "string",
        "analyzer": "lucene.standard"
      },
      "address": {
        "type": "document",
        "dynamic": true
      }
    }
  }
}