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About Mappings

You can define how documents and their fields are stored and indexed by creating a mapping.

If you’re just starting to build out your cluster and data, you may not know exactly how your data should be stored. In those cases, you can use dynamic mappings, which tell OpenSearch to dynamically add data and its fields. However, if you know exactly what types your data falls under and want to enforce that standard, then you can use explicit mappings.

For example, if you want to indicate that year should be of type text instead of an integer, and age should be an integer, you can do so with explicit mappings. Using dynamic mapping OpenSearch might interpret both year and age as integers.

This section provides an example for how to create an index mapping, and how to add a document to it that will get ip_range validated.

Table of contents

  1. Dynamic mapping
  2. Explicit mapping
    1. Response
  3. Mapping example usage
    1. Create an index with an ip_range mapping

Dynamic mapping

When you index a document, OpenSearch adds fields automatically with dynamic mapping. You can also explicitly add fields to an index mapping.

Dynamic mapping types

Type Description
null A null field can’t be indexed or searched. When a field is set to null, OpenSearch behaves as if that field has no values.
boolean OpenSearch accepts true and false as boolean values. An empty string is equal to false.
float A single-precision 32-bit floating point number.
double A double-precision 64-bit floating point number.
integer A signed 32-bit number.
object Objects are standard JSON objects, which can have fields and mappings of their own. For example, a movies object can have additional properties such as title, year, and director.
array Arrays in OpenSearch can only store values of one type, such as an array of just integers or strings. Empty arrays are treated as though they are fields with no values.
text A string sequence of characters that represent full-text values.
keyword A string sequence of structured characters, such as an email address or ZIP code.
date detection string Enabled by default, if new string fields match a date’s format, then the string is processed as a date field. For example, date: "2012/03/11" is processed as a date.
numeric detection string If disabled, OpenSearch may automatically process numeric values as strings when they should be processed as numbers. When enabled, OpenSearch can process strings into long, integer, short, byte, double, float, half_float, scaled_float, and unsigned_long. Default is disabled.

Explicit mapping

If you know exactly what your field data types need to be, you can specify them in your request body when creating your index.

{
  "mappings": {
    "properties": {
      "year":    { "type" : "text" },
      "age":     { "type" : "integer" },
      "director":{ "type" : "text" }
    }
  }
}

Response

{
    "acknowledged": true,
    "shards_acknowledged": true,
    "index": "sample-index1"
}

Mapping example usage

The following example shows how to create a mapping to specify that OpenSearch should ignore any documents with malformed ip addresses that do not conform to the ip_range data type. You accomplish this by setting the ignore_malformed parameter to true.

Create an index with an ip_range mapping

To create an index, use a PUT request:

PUT _index_ip
{
  "mappings": {
    "dynamic_templates": [
     {
        "ip_range": {
        "match": "*ip_range",
        "mapping": {
           "type": "ip_range",
           "ignore_malformed": true
      }
     }
    }
   ]
  }
}

You can add a document to your index that has an IP range specified:

PUT _index_ip/_doc/<id>
{
  "source_ip_range": "192.168.1.1/32"
}

This indexed ip_range does not throw an error because ignore_malformed is set to true.