Elasticsearch Mapping - elasticsearch - elasticsearch tutorial - elastic search



 Mapping

Elasticsearch Mapping

  • Mapping is the outline of the documents stored in an index. It defines the data type like geo_point or string and format of the fields present in the documents and rules to control the mapping of dynamically added fields
  • 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.
 Map

For example:

POST http://localhost:9200/bankaccountdetails
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Request Body

{
   "mappings":{
      "report":{
         "_all":{
            "enabled":true
         },
			
         "properties":{
            "name":{ "type":"string"}, "date":{ "type":"date"},
            "balance":{ "type":"double"}, "liability":{ "type":"double"}
         }
      }
   }
}
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Response

{"acknowledged":true}
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Field Data Types

  • Elasticsearch supports a number of different datatypes for the fields in a document. The following data types are used to store fields in Elasticsearch .

Core Data Types

  • These are the basic data types supported by almost all the systems like integer, long, double, short, byte, double, float, string, date, Boolean and binary.

Complex Data Types

  • These data types are a combination of core data types. Like array, JSON object and nested data type. Following is the example of nested data type −
POST http://localhost:9200/tabletennis/team/1
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Request Body

{
   "group" : "players",
   "user" : [
      {
         "first" : "dave", "last" : "jones"
      },
			
      {
         "first" : "kevin", "last" : "morris"
      }
   ]
}
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Response

{
   "_index":"tabletennis", "_type":"team", "_id":"1", "_version":1, 
   "_shards":{"total":2, "successful":1, "failed":0}, "created":true
}
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Geo Data Types

  • These data types are used for defining geographic properties. For instance, geo_point is used for defining longitude and latitude, and geo_shape for defining different geometric shapes like rectangle.

Specialized Data Types

  • These data types are used for special purposes like IPv4 (“ip”) accepts IP address, completion data type is used to support auto-complete suggestions and token_count for counting the number of tokens in a string.
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Mapping Types

  • Each index has one or more mapping types, which are used to divide the documents of an index into logical groups.
  • Mapping can be different from each other on the basis of the following parameters .

Meta-Fields

  • These fields provide information about the mappings and the other objects associated with it. Like _index, _type, _id, and _source fields.

Fields

  • Different mapping contains different number of fields and fields with different data types.
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Dynamic Mapping

  • Elasticsearch provides a user-friendly mechanism for the automatic creation of mapping.
  • A user can post the data directly to any undefined mapping and Elasticsearch will automatically create the mapping, which is called dynamic mapping. For example,
POST http://localhost:9200/accountdetails/tansferreport
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Request Body

{
   "from_acc":"7056443341", "to_acc":"7032460534",
   "date":"11/1/2016", "amount":10000
}
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Response

{
   "_index":"accountdetails", "_type":"tansferreport",
   "_id":"AVI3FeH0icjGpNBI4ake", "_version":1,
   "_shards":{"total":2, "successful":1, "failed":0},
   "created":true
}
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Mapping Parameters

  • The mapping parameters define the structure of mapping, information about fields and about storage and how the mapped data will be analyzed at the time of searching.
    These are the following mapping parameters −
    • analyzer
    • boost
    • coerce
    • copy_to
    • doc_values
    • dynamic
    • enabled
    • fielddata
    • geohash
    • geohash_precision
    • geohash_prefix
    • format
    • ignore_above
    • ignore_malformed
    • include_in_all
    • index_options
    • lat_lon
    • index
    • fields
    • norms
    • null_value
    • position_increment_gap
    • properties
    • search_analyzer
    • similarity
    • store
    • term_vector

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