Compound GeoSpatial Index in MongoDB is not working as intended

Question

the collection nodesWays has the following indexes:

> db.nodesWays.getIndexes()                          
[                                                    
        {                                            
                "v" : 1,                             
                "key" : {                            
                        "_id" : 1                    
                },                                   
                "name" : "_id_",                     
                "ns" : "h595.nodesWays"              
        },                                           
        {                                            
                "v" : 1,                             
                "key" : {                            
                        "amenity" : 1,               
                        "geo" : "2dsphere"           
                },                                   
                "name" : "amenity_1_geo_2dsphere",   
                "ns" : "h595.nodesWays",             
                "2dsphereIndexVersion" : 2           
        },                                           
        {                                            
                "v" : 1,                             
                "key" : {                            
                        "geo" : "2dsphere"           
                },                                   
                "name" : "geo_2dsphere",             
                "ns" : "h595.nodesWays",             
                "2dsphereIndexVersion" : 2           
        }                                            
] 

Now the following two queries should return the same result, but they don't. I want the nearest 10 restaurants to the specified point. The first query is working how it should be, the second is not working like intended.

The only difference between these two queries is that the first one uses the geo_2dsphere-Index and the second query the amenity_1_geo_2dsphere-Index.

> db.nodesWays.find(
{
    geo:{
        $nearSphere:{
            $geometry:{
                type: "Point", coordinates:  [9.7399777,52.3715156]
            }
        }
    }, "amenity":"restaurant", 
        name: {$exists: true}
}, {id:1, name:1}).hint( "geo_2dsphere" ).limit(10)

{ "_id" : ObjectId("53884860e552e471be2b7192"), "id" : "321256694", "name" : "Masa" }
{ "_id" : ObjectId("53884860e552e471be2b7495"), "id" : "323101271", "name" : "Bavarium" }
{ "_id" : ObjectId("53884862e552e471be2ba605"), "id" : "442496282", "name" : "Naxos" }
{ "_id" : ObjectId("53884860e552e471be2b7488"), "id" : "323101189", "name" : "Block House" }
{ "_id" : ObjectId("53884878e552e471be2d1a41"), "id" : "2453236451", "name" : "Maestro" }
{ "_id" : ObjectId("53884870e552e471be2c8aab"), "id" : "1992166428", "name" : "Weinstube Leonardo Ristorante" }
{ "_id" : ObjectId("53884869e552e471be2c168b"), "id" : "1440320284", "name" : "Altdeutsche küche" }
{ "_id" : ObjectId("53884861e552e471be2b88f7"), "id" : "353119010", "name" : "Mövenpick" }
{ "_id" : ObjectId("5388485de552e471be2b2c86"), "id" : "265546900", "name" : "Miles" }
{ "_id" : ObjectId("53884863e552e471be2bb5d3"), "id" : "532304135", "name" : "Globetrotter" }

> db.nodesWays.find(
{
    geo:{
        $nearSphere:{
            $geometry:{
                type: "Point", coordinates:  [9.7399777,52.3715156]
            }
        }
    }, "amenity":"restaurant", 
        name: {$exists: true}
}, {id:1, name:1}).hint( "amenity_1_geo_2dsphere" ).limit(10)

{ "_id" : ObjectId("53884875e552e471be2cf4a8"), "id" : "2110027373", "name" : "Schloßhof Salder" }
{ "_id" : ObjectId("5388485be552e471be2aff19"), "id" : "129985174", "name" : "Balkan Paradies" }
{ "_id" : ObjectId("5388485be552e471be2afeb4"), "id" : "129951134", "name" : "Asia Dragon" }
{ "_id" : ObjectId("53884863e552e471be2ba811"), "id" : "450130115", "name" : "Kings Palast" }
{ "_id" : ObjectId("53884863e552e471be2ba823"), "id" : "450130135", "name" : "Restaurant Montenegro" }
{ "_id" : ObjectId("53884877e552e471be2d053a"), "id" : "2298722569", "name" : "Pizzaria Da-Lucia" }
{ "_id" : ObjectId("53884869e552e471be2c152e"), "id" : "1420101752", "name" : "Napoli" }
{ "_id" : ObjectId("5388485be552e471be2b0028"), "id" : "136710095", "name" : "Europa" }
{ "_id" : ObjectId("53884862e552e471be2ba5bc"), "id" : "442136241", "name" : "Syrtaki" }
{ "_id" : ObjectId("53884863e552e471be2ba763"), "id" : "447972565", "name" : "Pamukkale" }

My goal with the second index is to: select all restaurants then use the nearSphere-Operator to sort them in regards to the distance from the specified point

Auf Wiedersehen

Answer 1

I think you should try to put the geolocation first in the index.