WARNING: The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
Gone are the days when we wander around a city with paper maps. Thanks to smartphones, we now know exactly where we are all the time, and we expect websites to use that information. I’m not interested in restaurants in Greater London—I want to know about restaurants within a 5-minute walk of my current location.
But geolocation is only one part of the puzzle. The beauty of Elasticsearch is that it allows you to combine geolocation with full-text search, structured search, and analytics.
For instance: show me restaurants that mention vitello tonnato, are within a 5-minute walk, and are open at 11 p.m., and then rank them by a combination of user rating, distance, and price. Another example: show me a map of vacation rental properties available in August throughout the city, and calculate the average price per zone.
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-points allow you to find points within a certain distance of another point, to calculate distances between two points for sorting or relevance scoring, or to aggregate into a grid to display on a map. Geo-shapes, on the other hand, are used purely for filtering. They can be used to decide whether two shapes overlap, or whether one shape completely contains other shapes.
Intro to Kibana
ELK for Logs & Metrics