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書名： ElasticSearch Cookbook
作者名： Alberto Paro
本章字數： 406字
更新時間： 2021-04-02 10:10:02

Mapping a GeoPoint field

ElasticSearch natively supports the use of geolocation types—special types that allow localization of your document in geographic coordinate (latitude and longitude) around the world.

There are two main types used in the geographic world: the point and the shape. In this recipe we'll see the GeoPoint—the base element of a geolocation.

Getting ready

You need a working ElasticSearch cluster.

How to do it...

The type of the field must be set to geo_point to define a GeoPoint.

We can extend the order example adding a new field that stores the location of a customer. The following code will be the result:

{
  "order": {
    "properties": {
      "id": {
        "type": "string",
        "store": "yes",
        "index": "not_analyzed"
      },
    "date": {
      "type": "date",
      "store": "no",
      "index": "not_analyzed"
    },
    "customer_id": {
      "type": "string",
      "store": "yes",
      "index": "not_analyzed"
    },
    "customer_ip": {
      "type": "ip",
      "store": "yes",
      "index": "yes"
    },
    "customer_location": {
      "type": "geo_point",
      "store": "yes"
    },
    "sent": {
      "type": "boolean",
      "store": "no",
      "index": "not_analyzed"
    }
  }
  }
}

How it works...

When ElasticSearch indexes a document with a GeoPoint field (such as lat, lon), it processes the latitude and longitude coordinates and creates a special accessory field data to fast query on these coordinates.

It depends on properties, given latitude and a longitude, it's possible to compute a geohash value (http://en.wikipedia.org/wiki/Geohash) and the index process also optimizes these values for special computation such as distance, ranges, and in-shape match.

GeoPoint has special parameters that allow storage of additional geographic data:

lat_lon (defaults to false): This allows storing the latitude and longitude as a .lat and .lon field. Storing these values improves the performance in many memory algorithms used in distance and in-shape calculus.

Note

It makes sense to be stored only if there is a single point value for field, for multiple values.
geohash (defaults to false): This allows storing the computed geohash value.
geohash_precision (defaults to 12): This defines the precision to be used in geohash calculus.

For example, given a GeoPoint value [45.61752, 9.08363], it will store:

customer_location = "45.61752, 9.08363"
customer_location.lat = 45.61752
customer_location.lon = 9.08363
customer_location.geohash = "u0n7w8qmrfj"

There's more...

GeoPoint is a special type and can accept several formats as input:

lat and lon as properties:

    "customer_location": {
        "lat": 45.61752,
        "lon": 9.08363
    },

lat and lon as strings:

"customer_location": "45.61752,9.08363",

Geohash string
```
"customer_location": "u0n7w8qmrfj",
```
As a GeoJSON array (note in it that lat and lon are reversed)
```
"customer_location": [9.08363, 45.61752]
```

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ElasticSearch Cookbook

Mapping a GeoPoint field

Getting ready

How to do it...

How it works...

Note

There's more...