Mappingedit

Mapping is the process of defining how a document, and the fields it contains, are stored and indexed. For instance, use mappings to define:

  • which string fields should be treated as full text fields.
  • which fields contain numbers, dates, or geolocations.
  • whether the values of all fields in the document should be indexed into the catch-all _all field.
  • the format of date values.
  • custom rules to control the mapping for dynamically added fields.

Mapping Typeedit

Each index has one mapping type which determines how the document will be indexed.

[6.0.0] Deprecated in 6.0.0. See Removal of mapping types .

A mapping type has:

Meta-fields
Meta-fields are used to customize how a document’s metadata associated is treated. Examples of meta-fields include the document’s _index, _type, _id, and _source fields.
Fields or properties
A mapping type contains a list of fields or properties pertinent to the document.

Field datatypesedit

Each field has a data type which can be:

It is often useful to index the same field in different ways for different purposes. For instance, a string field could be indexed as a text field for full-text search, and as a keyword field for sorting or aggregations. Alternatively, you could index a string field with the standard analyzer, the english analyzer, and the french analyzer.

This is the purpose of multi-fields. Most datatypes support multi-fields via the fields parameter.

Settings to prevent mappings explosionedit

Defining too many fields in an index is a condition that can lead to a mapping explosion, which can cause out of memory errors and difficult situations to recover from. This problem may be more common than expected. As an example, consider a situation in which every new document inserted introduces new fields. This is quite common with dynamic mappings. Every time a document contains new fields, those will end up in the index’s mappings. This isn’t worrying for a small amount of data, but it can become a problem as the mapping grows. The following settings allow you to limit the number of field mappings that can be created manually or dynamically, in order to prevent bad documents from causing a mapping explosion:

index.mapping.total_fields.limit
The maximum number of fields in an index. The default value is 1000.
index.mapping.depth.limit
The maximum depth for a field, which is measured as the number of inner objects. For instance, if all fields are defined at the root object level, then the depth is 1. If there is one object mapping, then the depth is 2, etc. The default is 20.
index.mapping.nested_fields.limit
The maximum number of nested fields in an index, defaults to 50. Indexing 1 document with 100 nested fields actually indexes 101 documents as each nested document is indexed as a separate hidden document.

Dynamic mappingedit

Fields and mapping types do not need to be defined before being used. Thanks to dynamic mapping, new field names will be added automatically, just by indexing a document. New fields can be added both to the top-level mapping type, and to inner object and nested fields.

The dynamic mapping rules can be configured to customise the mapping that is used for new fields.

Explicit mappingsedit

You know more about your data than Elasticsearch can guess, so while dynamic mapping can be useful to get started, at some point you will want to specify your own explicit mappings.

You can create field mappings when you create an index, and you can add fields to an existing index with the PUT mapping API.

Updating existing field mappingsedit

Other than where documented, existing field mappings cannot be updated. Changing the mapping would mean invalidating already indexed documents. Instead, you should create a new index with the correct mappings and reindex your data into that index.

Example mappingedit

A mapping could be specified when creating an index, as follows:

PUT my_index 
{
  "mappings": {
    "doc": { 
      "properties": { 
        "title":    { "type": "text"  }, 
        "name":     { "type": "text"  }, 
        "age":      { "type": "integer" },  
        "created":  {
          "type":   "date", 
          "format": "strict_date_optional_time||epoch_millis"
        }
      }
    }
  }
}

Create an index called my_index.

Add a mapping type called doc.

Specify fields or properties.

Specify the data type and mapping for each field.