ES學習筆記九-地理位置(geolocation)

geolocation

Elasticsearch offers two ways of representing geolocations: latitude-longitude points using the geo_point field type, and complex shapes defined in GeoJSON, using the geo_shape field type.

ES用兩種方式代表geolocation,數據類型爲geo_point的經緯座標點,或者數據類型爲geo_shape定義的geoJSON複雜的圖形

PUT /attractions
{
  "mappings": {
    "restaurant": {
      "properties": {
        "name": {
          "type": "string"
        },
        "location": {
          "type": "geo_point"
        }
      }
    }
  }
}

geo-points不能被自動識別。所以必須通過mapping指定

PUT /attractions/restaurant/1
{
  "name":     "Chipotle Mexican Grill",
  "location": "40.715, -74.011"  location的格式化順序爲維度,經度
}

PUT /attractions/restaurant/2
{
  "name":     "Pala Pizza",
  "location": {  推薦使用這個，不容易弄混啊～～～
    "lat":     40.722, 
    "lon":    -73.989
  }
}

PUT /attractions/restaurant/3
{
  "name":     "Mini Munchies Pizza",
  "location": [ -73.983, 40.719 ]  經度，緯度
}

filtering by geo-point

Four geo-point filters can be used to include or exclude documents by geolocation:

geo_bounding_box Find geo-points that fall within the specified rectangle. 找出geo-points落在置頂的矩形中 geo_distance Find geo-points within the specified distance of a central point. 找出落在以distance爲半徑，已centeral point爲原點的圓內的geo-points geo_distance_range Find geo-points within a specified minimum and maximum distance from a central point. geo_polygon Find geo-points that fall within the specified polygon. This filter is very expensive. If you find yourself wanting to use it, you should be looking at geo-shapes instead.這個效率有點慢，如果你有使用此filter的需求，請參考geo-shapes.Geo-filters are expensive — they should be used on as few documents as possible. First remove as many documents as you can with cheaper filters, like term or rangefilters, and apply the geo-filters last.

geo-filter效率有點慢，所以把geo-filters放在其他filter之後

geo_bounding_box filter

GET /attractions/restaurant/_search
{
  "query": {
    "filtered": {
      "filter": {
        "geo_bounding_box": {
          "location": { 
            "top_left": {
              "lat":  40.8,
              "lon": -74.0
            },
            "bottom_right": {
              "lat":  40.7,
              "lon": -73.0
            }
          }
        }
      }
    }
  }
}

optimizing bounding boxes

PUT /attractions
{
  "mappings": {
    "restaurant": {
      "properties": {
        "name": {
          "type": "string"
        },
        "location": {
          "type":    "geo_point",
          "lat_lon": true 
        }
      }
    }
  }
}

The location.lat and location.lon fields will be indexed separately. These fields can be used for searching, but their values cannot be retrieved.

GET /attractions/restaurant/_search
{
  "query": {
    "filtered": {
      "filter": {
        "geo_bounding_box": {
          "type":    "indexed", 
          "location": {
            "top_left": {
              "lat":  40.8,
              "lon": -74.0
            },
            "bottom_right": {
              "lat":  40.7,
              "lon":  -73.0
            }
          }
        }
      }
    }
  }
}

Setting the type parameter to indexed (instead of the default memory) tells Elasticsearch to use the inverted index for this filter.

While a geo_point field can contain multiple geo-points, the lat_lon optimization can be used only on fields that contain a single geo-point. lat_lon優化只能用在只有一個值的字段上

geo_distance filter

{
  "query": {
    "filtered": {
      "filter": {
        "geo_distance": {
          "distance": "1km", 
          "location": { 
            "lat":  40.715,
            "lon": -73.988
          }
        }
      }
    }
  }
}

geo_distance_range filter

The only difference between the geo_distance and geo_distance_range filters is that the latter has a doughnut shape and excludes documents within the central hole.

Instead of specifying a single distance from the center, you specify a minimum distance (with gt or gte) and maximum distance (with lt or lte), just like a range filter:

GET /attractions/restaurant/_search
{
  "query": {
    "filtered": {
      "filter": {
        "geo_distance_range": {
          "gte":    "1km", 
          "lt":     "2km", 
          "location": {
            "lat":  40.715,
            "lon": -73.988
          }
        }
      }
    }
  }
}

Matches locations that are at least 1km from the center, and less than 2km from the center.

caching geo-filters

The results of geo-filters are not cached by default, for two reasons:

• Geo-filters are usually used to find entities that are near to a user’s current location. The problem is that users move, and no two users are in exactly the same location. A cached filter would have little chance of being reused.
• Filters are cached as bitsets that represent all documents in a segment. Imagine that our query excludes all documents but one in a particular segment. An uncached geo-filter just needs to check the one remaining document, but a cached geo-filter would need to check all of the documents in the segment.

This setting can be changed on a live index with the update-mapping API:

POST /attractions/_mapping/restaurant
{
  "location": {
    "type": "geo_point",
    "fielddata": {
      "format":    "compressed",
      "precision": "1km" 
    }
  }
}

sorting by distance

GET /attractions/restaurant/_search
{
  "query": {
    "filtered": {
      "filter": {
        "geo_bounding_box": {
          "type":       "indexed",
          "location": {
            "top_left": {
              "lat":  40,8,
              "lon": -74.0
            },
            "bottom_right": {
              "lat":  40.4,
              "lon": -73.0
            }
          }
        }
      }
    }
  },
  "sort": [
    {
      "_geo_distance": {
        "location": { 
          "lat":  40.715,
          "lon": -73.998
        },
        "order":         "asc",
        "unit":          "km", 
        "distance_type": "plane" 
      }
    }
  ]
}