A geo polygon query is a query type in Elasticsearch that is used to search for documents within a polygonal geographic area defined by a series of latitude-longitude points. This query is used to find documents that are located within a specific geographic region with arbitrary polygonal boundaries.
When a geo polygon query is executed, Elasticsearch searches for documents that fall within the specified polygonal area defined by a series of latitude-longitude pairs. The query calculates whether each document’s location field, which is typically stored as a geo_point data type, falls within the polygon.
Here’s an example of a geo polygon query in Elasticsearch:
GET /my_index/_search
{
"query": {
"bool": {
"must": {
"match_all": {}
},
"filter": {
"geo_polygon": {
"location": {
"points": [
{"lat": 37.8025, "lon": -122.405},
{"lat": 37.7895, "lon": -122.422},
{"lat": 37.7755, "lon": -122.415},
{"lat": 37.7765, "lon": -122.398}
]
}
}
}
}
}
}
In this example, we are searching the `my_index` index for documents that are within a polygonal geographic area defined by the series of latitude-longitude pairs specified in the `location` field. The `geo_polygon` query specifies the polygon using the `points` parameter.
The `geo_polygon` query also supports a variety of other features, such as specifying different coordinate formats, using different indexing strategies, and filtering documents by a minimum and maximum distance.
The `geo_polygon` query provides a powerful and flexible way to search for documents within a specific geographic region with arbitrary polygonal boundaries in Elasticsearch. However, it’s important to note that geo polygon queries can be computationally expensive and may not be suitable for large datasets or high-traffic applications. Additionally, the accuracy of geo polygon queries can be impacted by the quality and completeness of the location data. Therefore, it’s important to carefully consider the use case and performance implications before using geo polygon queries in Elasticsearch.