The geohash_cell query simply translates a lat/lon location into a geohash with the specified precision and finds all locations that contain that geohash—​a very efficient query indeed.

GET /attractions/restaurant/_search
{
  "query": {
    "constant_score": {
      "filter": {
        "geohash_cell": {
          "location": {
            "lat":  40.718,
            "lon": -73.983
          },
          "precision": "2km" 
        }
      }
    }
  }
}

This query translates the lat/lon point into a geohash of the appropriate length—​in this example dr5rsk—and looks for all locations that contain that exact term.

However, the query as written in the preceding example may not return all restaurants within 5km of the specified point. Remember that a geohash is just a rectangle, and the point may fall anywhere within that rectangle. If the point happens to fall near the edge of a geohash cell, the filter may well exclude any restaurants in the adjacent cell.

To fix that, we can tell the query to include the neigboring cells, by setting neighbors to true:

GET /attractions/restaurant/_search
{
  "query": {
    "constant_score": {
      "filter": {
        "geohash_cell": {
          "location": {
            "lat":  40.718,
            "lon": -73.983
          },
          "neighbors": true, 
          "precision": "2km"
        }
      }
    }
  }
}

Clearly, looking for a geohash with precision 2km plus all the neighboring cells results in quite a large search area. This query is not built for accuracy, but it is very efficient and can be used as a prefiltering step before applying a more accurate geo-filter.

The other advantage that this query has over a geo_bounding_box query is that it supports multiple locations per field. The lat_lon option that we discussed in Optimizing Bounding Boxes is efficient, but only when there is a single lat/lon point per field.