Geo-search in Elasticsearch allows you to perform queries based on geographical data. This is particularly useful for applications that need to handle location-based data, such as finding nearby points of interest, geofencing, and more.

1. Geo-Points: Represents a single point on the earth's surface, defined by latitude and longitude.
2. Geo-Shapes: Represents complex shapes like polygons, lines, and circles.
3. Geo-Queries: Queries that allow you to search and filter documents based on their geographical location.

Geo-points can be defined in your Elasticsearch index mapping. Here’s an example of how to define a geo-point field:

Once the mapping is set, you can index documents with geo-point data:

A geo-distance query allows you to find documents within a certain distance from a specific point. Here’s an example:

A geo-bounding box query finds documents within a specified rectangle. Here’s an example:

A geo-polygon query finds documents within a specified polygon. Here’s an example:

Geo-shapes can be defined in your Elasticsearch index mapping. Here’s an example of how to define a geo-shape field:

Once the mapping is set, you can index documents with geo-shape data:

1. Define a geo-point field in your index mapping.
2. Index a few documents with geo-point data.
3. Perform a geo-distance query to find documents within a 5km radius of a specific point.

Solution

1. Define a geo-point field:

   PUT /places
   {
     "mappings": {
       "properties": {
         "location": {
           "type": "geo_point"
         }
       }
     }
   }
   

2. Index documents:

   POST /places/_doc/1
   {
     "name": "Place A",
     "location": {
       "lat": 40.730610,
       "lon": -73.935242
     }
   }
   
   POST /places/_doc/2
   {
     "name": "Place B",
     "location": {
       "lat": 40.650002,
       "lon": -73.949997
     }
   }
   

3. Perform a geo-distance query:

   GET /places/_search
   {
     "query": {
       "geo_distance": {
         "distance": "5km",
         "location": {
           "lat": 40.730610,
           "lon": -73.935242
         }
       }
     }
   }
   

1. Define a geo-shape field in your index mapping.
2. Index a document with a polygon geo-shape.
3. Perform a geo-shape query to find documents within a specified polygon.

Solution

1. Define a geo-shape field:

   PUT /regions
   {
     "mappings": {
       "properties": {
         "location": {
           "type": "geo_shape"
         }
       }
     }
   }
   

2. Index a document:

   POST /regions/_doc/1
   {
     "name": "Region A",
     "location": {
       "type": "polygon",
       "coordinates": [
         [
           [-73.981, 40.768],
           [-73.958, 40.768],
           [-73.958, 40.785],
           [-73.981, 40.785],
           [-73.981, 40.768]
         ]
       ]
     }
   }
   

3. Perform a geo-shape query:

   GET /regions/_search
   {
     "query": {
       "geo_shape": {
         "location": {
           "shape": {
             "type": "polygon",
             "coordinates": [
               [
                 [-73.981, 40.768],
                 [-73.958, 40.768],
                 [-73.958, 40.785],
                 [-73.981, 40.785],
                 [-73.981, 40.768]
               ]
             ]
           },
           "relation": "within"
         }
       }
     }
   }
   

In this section, you learned about geo-search capabilities in Elasticsearch, including how to define and index geo-points and geo-shapes, and how to perform various geo-queries. These skills are essential for building applications that require location-based search functionality. In the next module, we will explore custom plugins and how to extend Elasticsearch's capabilities further.