Docs Menu
Docs Home
/
MongoDB Atlas
/ /

How to Run an Atlas Search Compound Geo JSON Query

On this page

  • Create the Atlas Search Index
  • Run a Combined Geo, Number, and Text Fields Query

This tutorial describes how to create an index on the listingsAndReviews collection in the sample_airbnb database and run a query that returns documents with the name, address, and property_type for each property within the specified polygon defined using coordinates.

This tutorial takes you through the following steps:

  1. Set up an Atlas Search index on the address field in the sample_airbnb.listingsAndReviews collection.

  2. Run a query that returns 10 documents with the name, address, and property_type of each property within the specified geographic coordinates. Atlas Search results reflect a preference for properties of type condominium, and each document in the result is assigned a relevance score, returned in order from highest to lowest.

Before you begin, ensure that your Atlas cluster meets the requirements described in the Prerequisites.

To create an Atlas Search index, you must have Project Data Access Admin or higher access to the project.

In this section, you will create an Atlas Search index on the address field in the sample_airbnb.listingsAndReviews collection.

1
  1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

  3. If the Clusters page is not already displayed, click Database in the sidebar.

2

You can go the Atlas Search page from the sidebar, the Data Explorer, or your cluster details page.

  1. In the sidebar, click Atlas Search under the Services heading.

  2. From the Select data source dropdown, select your cluster and click Go to Atlas Search.

  1. Click the Browse Collections button for your cluster.

  2. Expand the database and select the collection.

  3. Click the Search Indexes tab for the collection.

  1. Click the cluster's name.

  2. Click the Atlas Search tab.

3
4
  • For a guided experience, select the Atlas Search Visual Editor.

  • To edit the raw index definition, select the Atlas Search JSON Editor.

5
  1. In the Index Name field, enter default.

    Note

    If you name your index default, you don't need to specify an index parameter when using the $search pipeline stage. Otherwise, you must specify the index name using the index parameter.

  2. In the Database and Collection section, find the sample_mflix database, and select the movies collection.

6

You can use the Atlas Search Visual Editor or the Atlas Search JSON Editor in the Atlas user interface to create the index. The following index definition specifies that Atlas Search must index:

  • All of the fields in the collection automatically.

  • The address.location field of a document as type geo.

  1. Click Next.

  2. Click Refine Your Index.

  3. In the Field Mappings section, click Add Field.

  4. Select address.location from the Field Name dropdown.

  5. Click the Data Type dropdown and select Geo.

  6. Click Add.

  7. Click Save Changes.

  1. Replace the default index definition with the following example index definition.

    {
    "mappings": {
    "dynamic": true,
    "fields": {
    "address": {
    "fields": {
    "location": {
    "type": "geo"
    }
    },
    "type": "document"
    }
    }
    }
    }
  2. Click Next.

7
8

A modal window appears to let you know your index is building. Click the Close button.

9

The index should take about one minute to build. While it is building, the Status column reads Build in Progress. When it is finished building, the Status column reads Active.


Use the Select your language drop-down menu on this page to set the language of the examples in this section.


In this section, you will run a query that returns 10 documents with the name, address, and property_type for each property within the specified geographic coordinates. A field specifying each documents score is also returned, and results are ordered with a preference for properties of type condominium.

1
  1. If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it's not already displayed, select your desired project from the Projects menu in the navigation bar.

  3. If the Clusters page is not already displayed, click Database in the sidebar.

2

You can go the Atlas Search page from the sidebar, the Data Explorer, or your cluster details page.

  1. In the sidebar, click Atlas Search under the Services heading.

  2. From the Select data source dropdown, select your cluster and click Go to Atlas Search.

  1. Click the Browse Collections button for your cluster.

  2. Expand the database and select the collection.

  3. Click the Search Indexes tab for the collection.

  1. Click the cluster's name.

  2. Click the Atlas Search tab.

3

Click the Query button to the right of the index to query.

4

Click Edit Query to view a default query syntax sample in JSON format.

5

The following Atlas Search query uses the compound operator to:

  • Specify that results must be within a Polygon defined by a set of coordinates.

  • Give preference to results for properties of type condominium.

Note

The Search Tester might not display all the fields in the documents it returns. To view all the fields, including the field that you specify in the query path, expand the document in the results.

[
{
"$search": {
"index": "geo-json-tutorial",
"compound": {
"must": [{
"geoWithin": {
"geometry": {
"type": "Polygon",
"coordinates": [[[ -161.323242, 22.512557 ],
[ -152.446289, 22.065278 ],
[ -156.09375, 17.811456 ],
[ -161.323242, 22.512557 ]]]
},
"path": "address.location"
}
}],
"should": [{
"text": {
"path": "property_type",
"query": "Condominium"
}
}]
}
}
}
]
1SCORE: 2.238388776779175 _id: "1001265"
2 listing_url: "https://www.airbnb.com/rooms/1001265"
3 name: "Ocean View Waikiki Marina w/prkg"
4 summary: "A short distance from Honolulu's billion dollar mall,
5 and the same dis…"
6 ...
7 property_type: "Condominium"
8 ...
9 address: Object
10 street: "Honolulu, HI, United States"
11 suburb: "Oʻahu"
12 government_area: "Primary Urban Center"
13 market: "Oahu"
14 country: "United States"
15 country_code: "US"
16 location: Object
17 type: "Point"
18 coordinates: Array
19 0: -157.83919
20 1: 21.28634
21 is_location_exact: true
22 ...
23
24SCORE: 2.238388776779175 _id: "10227000"
25 listing_url: "https://www.airbnb.com/rooms/10227000"
26 name: "LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!"
27 summary: "THIS IS A VERY SPACIOUS 1 BEDROOM FULL CONDO (SLEEPS 4) AT THE BEAUTIF…"
28 ...
29 property_type: "Condominium"
30 ...
31 address: Object
32 street: "Lahaina, HI, United States"
33 suburb: "Maui"
34 government_area: "Lahaina"
35 market: "Maui"
36 country: "United States"
37 country_code: "US"
38 location: Object
39 type: "Point"
40 coordinates: Array
41 0: -156.68012
42 1: 20.96996
43 is_location_exact: true
44 ...
45
46SCORE: 2.238388776779175 _id: "10266175"
47 listing_url: "https://www.airbnb.com/rooms/10266175"
48 name: "Makaha Valley Paradise with OceanView"
49 summary: "A beautiful and comfortable 1 Bedroom Air Conditioned Condo in Makaha …"
50 ...
51 property_type: "Condominium"
52 ...
53 address: Object
54 street: "Waianae, HI, United States"
55 suburb: "Leeward Side"
56 government_area: "Waianae"
57 market: "Oahu"
58 country: "United States"
59 country_code: "US"
60 location: Object
61 type: "Point"
62 coordinates: Array
63 0: -158.20291
64 1: 21.4818
65 is_location_exact: true
66 ...
67
68SCORE: 2.238388776779175 _id: "1042446"
69 listing_url: "https://www.airbnb.com/rooms/1042446"
70 name: "March 2019 availability! Oceanview on Sugar Beach!"
71 summary: ""
72 ...
73 property_type: "Condominium"
74 ...
75 address: Object
76 street: "Kihei, HI, United States"
77 suburb: "Maui"
78 government_area: "Kihei-Makena"
79 market: "Maui"
80 country: "United States"
81 country_code: "US"
82 location: Object
83 type: "Point"
84 coordinates: Array
85 0: -156.46881
86 1: 20.78621
87 is_location_exact: true
88 ...
89
90SCORE: 2.238388776779175 _id: "10527243"
91 listing_url: "https://www.airbnb.com/rooms/10527243"
92 name: "Tropical Jungle Oasis"
93 summary: "2 bedrooms, one with a queen sized bed, one with 2 single beds. 1 and …"
94 ...
95 property_type: "Condominium"
96 ...
97 address: Object
98 street: "Hilo, HI, United States"
99 suburb: "Island of Hawaiʻi"
100 government_area: "South Hilo"
101 market: "The Big Island"
102 country: "United States"
103 country_code: "US"
104 location: Object
105 type: "Point"
106 coordinates: Array
107 0: -155.09259
108 1: 19.73108
109 is_location_exact: true
110 ...
111
112SCORE: 2.238388776779175 _id: "1104768"
113 listing_url: "https://www.airbnb.com/rooms/1104768"
114 name: "2 Bdrm/2 Bath Family Suite Ocean View"
115 summary: "This breathtaking 180 degree view of Waikiki is one of a kind. You wil…"
116 ...
117 property_type: "Condominium"
118 ...
119 address: Object
120 street: "Honolulu, HI, United States"
121 suburb: "Waikiki"
122 government_area: "Primary Urban Center"
123 market: "Oahu"
124 country: "United States"
125 country_code: "US"
126 location: Object
127 type: "Point"
128 coordinates: Array
129 0: -157.82696
130 1: 21.27971
131 is_location_exact: true
132 ...
133
134SCORE: 2.238388776779175 _id: "11207193"
135 listing_url: "https://www.airbnb.com/rooms/11207193"
136 name: "302 Kanai A Nalu Ocean front/view"
137 summary: "Welcome to Kana'i A Nalu a quiet resort that sits on the ocean away fr…"
138 ...
139 property_type: "Condominium"
140 ...
141 address: Object
142 street: "Wailuku, HI, United States"
143 suburb: "Maui"
144 government_area: "Kihei-Makena"
145 market: "Maui"
146 country: "United States"
147 country_code: "US"
148 location: Object
149 type: "Point"
150 coordinates: Array
151 0: -156.5039
152 1: 20.79664
153 is_location_exact: true
154 ...
155
156SCORE: 2.238388776779175 _id: "11319047"
157 listing_url: "https://www.airbnb.com/rooms/11319047"
158 name: "Sugar Beach Resort 1BR Ground Floor Condo !"
159 summary: "The Sugar Beach Resort enjoys a beachfront setting fit for a postcard."
160 ...
161 property_type: "Condominium"
162 ...
163 address: Object
164 street: "Kihei, HI, United States"
165 suburb: "Maui"
166 government_area: "Kihei-Makena"
167 market: "Maui"
168 country: "United States"
169 country_code: "US"
170 location: Object
171 type: "Point"
172 coordinates: Array
173 0: -156.46697
174 1: 20.78484
175 is_location_exact: true
176 ...
177
178SCORE: 2.238388776779175 _id: "11695887"
179 listing_url: "https://www.airbnb.com/rooms/11695887"
180 name: "2 BR Oceanview - Great Location!"
181 summary: "Location, location, location... This is a great 2 bed, 2 bath condo is…"
182 ...
183 property_type: "Condominium"
184 ...
185 address: Object
186 street: "Kihei, HI, United States"
187 suburb: "Kihei/Wailea"
188 government_area: "Kihei-Makena"
189 market: "Maui"
190 country: "United States"
191 country_code: "US"
192 location: Object
193 type: "Point"
194 coordinates: Array
195 0: -156.44917
196 1: 20.73013
197 is_location_exact: true
198 ...
199
200SCORE: 2.238388776779175 _id: "11817249"
201 listing_url: "https://www.airbnb.com/rooms/11817249"
202 name: "PALMS AT WAILEA #905-2BR-REMODELED-LARGE LANAI-AC"
203 summary: "Book with confidence this stunning 2 bedroom, 2 bathroom condo at the …"
204 ...
205 property_type: "Condominium"
206 ...
207 address: Object
208 street: "Kihei, HI, United States"
209 suburb: "Maui"
210 government_area: "Kihei-Makena"
211 market: "Maui"
212 country: "United States"
213 country_code: "US"
214 location: Object
215 type: "Point"
216 coordinates: Array
217 0: -156.4409
218 1: 20.69735
219 is_location_exact: true
220 ...
1

Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect via mongosh.

2

Run the following command at mongosh prompt:

use sample_airbnb
3

The following Atlas Search query:

  • Uses a compound $search stage to:

    • Specify that results must be within a Polygon defined by a set of coordinates.

    • Give preference to results for properties of type condominium.

  • Uses a $project stage to:

    • Exclude all fields except name, address and property_type.

    • Add a relevance score to each returned document.

The query is as follows:

db.listingsAndReviews.aggregate([
{
"$search": {
"index": "geo-json-tutorial",
"compound": {
"must": [{
"geoWithin": {
"geometry": {
"type": "Polygon",
"coordinates": [[[ -161.323242, 22.512557 ],
[ -152.446289, 22.065278 ],
[ -156.09375, 17.811456 ],
[ -161.323242, 22.512557 ]]]
},
"path": "address.location"
}
}],
"should": [{
"text": {
"path": "property_type",
"query": "Condominium"
}
}]
}
}
},
{
"$limit": 10
},
{
$project: {
"_id": 0,
"name": 1,
"address": 1,
"property_type": 1,
score: { $meta: "searchScore" }
}
}
])
[
{
name: 'Ocean View Waikiki Marina w/prkg',
property_type: 'Condominium',
address: {
street: 'Honolulu, HI, United States',
suburb: 'Oʻahu',
government_area: 'Primary Urban Center',
market: 'Oahu',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -157.83919, 21.28634 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!',
property_type: 'Condominium',
address: {
street: 'Lahaina, HI, United States',
suburb: 'Maui',
government_area: 'Lahaina',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.68012, 20.96996 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'Makaha Valley Paradise with OceanView',
property_type: 'Condominium',
address: {
street: 'Waianae, HI, United States',
suburb: 'Leeward Side',
government_area: 'Waianae',
market: 'Oahu',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -158.20291, 21.4818 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'March 2019 availability! Oceanview on Sugar Beach!',
property_type: 'Condominium',
address: {
street: 'Kihei, HI, United States',
suburb: 'Maui',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.46881, 20.78621 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'Tropical Jungle Oasis',
property_type: 'Condominium',
address: {
street: 'Hilo, HI, United States',
suburb: 'Island of Hawaiʻi',
government_area: 'South Hilo',
market: 'The Big Island',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -155.09259, 19.73108 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: '2 Bdrm/2 Bath Family Suite Ocean View',
property_type: 'Condominium',
address: {
street: 'Honolulu, HI, United States',
suburb: 'Waikiki',
government_area: 'Primary Urban Center',
market: 'Oahu',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -157.82696, 21.27971 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: '302 Kanai A Nalu Ocean front/view',
property_type: 'Condominium',
address: {
street: 'Wailuku, HI, United States',
suburb: 'Maui',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.5039, 20.79664 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'Sugar Beach Resort 1BR Ground Floor Condo !',
property_type: 'Condominium',
address: {
street: 'Kihei, HI, United States',
suburb: 'Maui',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.46697, 20.78484 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: '2 BR Oceanview - Great Location!',
property_type: 'Condominium',
address: {
street: 'Kihei, HI, United States',
suburb: 'Kihei/Wailea',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.44917, 20.73013 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'PALMS AT WAILEA #905-2BR-REMODELED-LARGE LANAI-AC',
property_type: 'Condominium',
address: {
street: 'Kihei, HI, United States',
suburb: 'Maui',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.4409, 20.69735 ],
is_location_exact: true
}
},
score: 2.238388776779175
}
]
1

Open MongoDB Compass and connect to your cluster. For detailed instructions on connecting, see Connect via Compass.

2

On the Database screen, click the sample_airbnb database, then click the listingsAndReviews collection.

3

The following query:

  • Uses a compound $search stage to:

    • Specify that results must be within a Polygon defined by a set of coordinates.

    • Give preference to results for properties of type condominium.

  • Uses a $project stage to:

    • Exclude all fields except name, address and property_type.

    • Add a relevance score to each returned document.

To run this Atlas Search query in MongoDB Compass:

  1. Click the Aggregations tab.

  2. Click Select..., then configure each of the following pipeline stages by selecting the stage from the dropdown and adding the query for that stage. Click Add Stage to add additional stages.

    Pipeline Stage
    Query
    $search
    {
    'index': 'geo-json-tutorial',
    'compound': {
    'must': [
    {
    'geoWithin': {
    'geometry': {
    'type': 'Polygon',
    'coordinates': [
    [
    [
    -161.323242, 22.512557
    ], [
    -152.446289, 22.065278
    ], [
    -156.09375, 17.811456
    ], [
    -161.323242, 22.512557
    ]
    ]
    ]
    },
    'path': 'address.location'
    }
    }
    ],
    'should': [
    {
    'text': {
    'path': 'property_type',
    'query': 'Condominium'
    }
    }
    ]
    }
    }
    $limit
    10
    $project
    {
    '_id': 0,
    'name': 1,
    'address': 1,
    'property_type': 1,
    'score': {
    '$meta': 'searchScore'
    }
    }

If you enabled Auto Preview, MongoDB Compass displays the following documents next to the $project pipeline stage:

1{
2 name: 'Ocean View Waikiki Marina w/prkg',
3 property_type: 'Condominium',
4 address: {
5 street: 'Honolulu, HI, United States',
6 suburb: 'Oʻahu',
7 government_area: 'Primary Urban Center',
8 market: 'Oahu',
9 country: 'United States',
10 country_code: 'US',
11 location: {
12 type: 'Point',
13 coordinates: [ -157.83919, 21.28634 ],
14 is_location_exact: true
15 }
16 },
17 score: 2.238388776779175
18},
19{
20 name: 'LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!',
21 property_type: 'Condominium',
22 address: {
23 street: 'Lahaina, HI, United States',
24 suburb: 'Maui',
25 government_area: 'Lahaina',
26 market: 'Maui',
27 country: 'United States',
28 country_code: 'US',
29 location: {
30 type: 'Point',
31 coordinates: [ -156.68012, 20.96996 ],
32 is_location_exact: true
33 }
34 },
35 score: 2.238388776779175
36},
37{
38 name: 'Makaha Valley Paradise with OceanView',
39 property_type: 'Condominium',
40 address: {
41 street: 'Waianae, HI, United States',
42 suburb: 'Leeward Side',
43 government_area: 'Waianae',
44 market: 'Oahu',
45 country: 'United States',
46 country_code: 'US',
47 location: {
48 type: 'Point',
49 coordinates: [ -158.20291, 21.4818 ],
50 is_location_exact: true
51 }
52 },
53 score: 2.238388776779175
54},
55{
56 name: 'March 2019 availability! Oceanview on Sugar Beach!',
57 property_type: 'Condominium',
58 address: {
59 street: 'Kihei, HI, United States',
60 suburb: 'Maui',
61 government_area: 'Kihei-Makena',
62 market: 'Maui',
63 country: 'United States',
64 country_code: 'US',
65 location: {
66 type: 'Point',
67 coordinates: [ -156.46881, 20.78621 ],
68 is_location_exact: true
69 }
70 },
71 score: 2.238388776779175
72},
73{
74 name: 'Tropical Jungle Oasis',
75 property_type: 'Condominium',
76 address: {
77 street: 'Hilo, HI, United States',
78 suburb: 'Island of Hawaiʻi',
79 government_area: 'South Hilo',
80 market: 'The Big Island',
81 country: 'United States',
82 country_code: 'US',
83 location: {
84 type: 'Point',
85 coordinates: [ -155.09259, 19.73108 ],
86 is_location_exact: true
87 }
88 },
89 score: 2.238388776779175
90},
91{
92 name: '2 Bdrm/2 Bath Family Suite Ocean View',
93 property_type: 'Condominium',
94 address: {
95 street: 'Honolulu, HI, United States',
96 suburb: 'Waikiki',
97 government_area: 'Primary Urban Center',
98 market: 'Oahu',
99 country: 'United States',
100 country_code: 'US',
101 location: {
102 type: 'Point',
103 coordinates: [ -157.82696, 21.27971 ],
104 is_location_exact: true
105 }
106 },
107 score: 2.238388776779175
108},
109{
110 name: '302 Kanai A Nalu Ocean front/view',
111 property_type: 'Condominium',
112 address: {
113 street: 'Wailuku, HI, United States',
114 suburb: 'Maui',
115 government_area: 'Kihei-Makena',
116 market: 'Maui',
117 country: 'United States',
118 country_code: 'US',
119 location: {
120 type: 'Point',
121 coordinates: [ -156.5039, 20.79664 ],
122 is_location_exact: true
123 }
124 },
125 score: 2.238388776779175
126},
127{
128 name: 'Sugar Beach Resort 1BR Ground Floor Condo !',
129 property_type: 'Condominium',
130 address: {
131 street: 'Kihei, HI, United States',
132 suburb: 'Maui',
133 government_area: 'Kihei-Makena',
134 market: 'Maui',
135 country: 'United States',
136 country_code: 'US',
137 location: {
138 type: 'Point',
139 coordinates: [ -156.46697, 20.78484 ],
140 is_location_exact: true
141 }
142 },
143 score: 2.238388776779175
144},
145{
146 name: '2 BR Oceanview - Great Location!',
147 property_type: 'Condominium',
148 address: {
149 street: 'Kihei, HI, United States',
150 suburb: 'Kihei/Wailea',
151 government_area: 'Kihei-Makena',
152 market: 'Maui',
153 country: 'United States',
154 country_code: 'US',
155 location: {
156 type: 'Point',
157 coordinates: [ -156.44917, 20.73013 ],
158 is_location_exact: true
159 }
160 },
161 score: 2.238388776779175
162},
163{
164 name: 'PALMS AT WAILEA #905-2BR-REMODELED-LARGE LANAI-AC',
165 property_type: 'Condominium',
166 address: {
167 street: 'Kihei, HI, United States',
168 suburb: 'Maui',
169 government_area: 'Kihei-Makena',
170 market: 'Maui',
171 country: 'United States',
172 country_code: 'US',
173 location: {
174 type: 'Point',
175 coordinates: [ -156.4409, 20.69735 ],
176 is_location_exact: true
177 }
178 },
179 score: 2.238388776779175
180}

For more information about the $search pipeline stage, see its reference page. For complete aggregation pipeline documentation, see the MongoDB Server Manual.

1
  1. Create a new directory called combined-geo-query and initialize your project with the dotnet new command.

    mkdir combined-geo-query
    cd combined-geo-query
    dotnet new console
  2. Add the .NET/C# Driver to your project as a dependency.

    dotnet add package MongoDB.Driver
2
  1. Replace the contents of the the Program.cs file with the following code.

    The following Atlas Search query:

    • Uses a compound $search stage to:

      • Specify that results must be within a Polygon defined by a set of coordinates.

      • Give preference to results for properties of type condominium.

    • Uses a $project stage to:

      • Exclude all fields except name, address and property_type.

      • Add a relevance score to each returned document.

    1using MongoDB.Bson;
    2using MongoDB.Bson.IO;
    3using MongoDB.Bson.Serialization;
    4using MongoDB.Bson.Serialization.Attributes;
    5using MongoDB.Bson.Serialization.Conventions;
    6using MongoDB.Driver;
    7using MongoDB.Driver.GeoJsonObjectModel;
    8using MongoDB.Driver.Search;
    9using System;
    10
    11public class GeoQuery
    12{
    13 private const string MongoConnectionString = "<connection-string>";
    14
    15 public static void Main(string[] args)
    16 {
    17 // allow automapping of the camelCase database fields to our AirbnbDocument
    18 var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
    19 ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
    20
    21 // connect to your Atlas cluster
    22 var mongoClient = new MongoClient(MongoConnectionString);
    23 var airbnbDatabase = mongoClient.GetDatabase("sample_airbnb");
    24 var airbnbCollection = airbnbDatabase.GetCollection<AirbnbDocument>("listingsAndReviews");
    25
    26 // declare data for the compound query
    27 string property_type = "Condominium";
    28 var coordinates = new GeoJson2DCoordinates[]
    29 {
    30 new(-161.323242, 22.512557),
    31 new(-152.446289, 22.065278),
    32 new(-156.09375, 17.811456),
    33 new(-161.323242, 22.512557)
    34 };
    35 var polygon = GeoJson.Polygon(coordinates);
    36
    37 // define and run pipeline
    38 var results = airbnbCollection.Aggregate()
    39 .Search(Builders<AirbnbDocument>.Search.Compound()
    40 .Must(Builders<AirbnbDocument>.Search.GeoWithin(airbnb => airbnb.Address.Location, polygon))
    41 .Should((Builders<AirbnbDocument>.Search.Text(airbnb => airbnb.PropertyType, property_type))),
    42 indexName: "geo-json-tutorial")
    43 .Limit (10)
    44 .Project<AirbnbDocument>(Builders<AirbnbDocument>.Projection
    45 .Include(airbnb => airbnb.PropertyType)
    46 .Include(airbnb => airbnb.Address.Location)
    47 .Include(airbnb => airbnb.Name)
    48 .Exclude(airbnb => airbnb.Id)
    49 .MetaSearchScore(airbnb => airbnb.Score))
    50 .ToList();
    51
    52 // print results
    53 foreach (var x in results) {
    54 Console.WriteLine(x.ToJson());
    55 }
    56 }
    57}
    58[BsonIgnoreExtraElements]
    59public class AirbnbDocument
    60{
    61 [BsonIgnoreIfDefault]
    62 public ObjectId Id { get; set; }
    63 public String Name { get; set; }
    64 [BsonElement("property_type")]
    65 public string PropertyType { get; set; }
    66 public Address Address { get; set; }
    67 public double Score { get; set; }
    68}
    69[BsonIgnoreExtraElements]
    70public class Address
    71{
    72 public GeoJsonPoint<GeoJson2DCoordinates> Location { get; set; }
    73}
  2. Before you run the sample, replace <connection-string> with your Atlas connection string. Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.

3
dotnet run combined-geo-query.csproj
{
"name" : "Ocean View Waikiki Marina w/prkg",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-157.83919, 21.286339999999999],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-156.68011999999999, 20.96996],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "Makaha Valley Paradise with OceanView",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-158.20291, 21.4818],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "March 2019 availability! Oceanview on Sugar Beach!",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-156.46880999999999, 20.786210000000001],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "Tropical Jungle Oasis",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-155.09259, 19.731079999999999],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "2 Bdrm/2 Bath Family Suite Ocean View",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-157.82696000000001, 21.279710000000001],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "302 Kanai A Nalu Ocean front/view",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-156.50389999999999, 20.79664],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "Sugar Beach Resort 1BR Ground Floor Condo !",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-156.46697, 20.784839999999999],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "2 BR Oceanview - Great Location!",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-156.44917000000001, 20.730129999999999],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
{
"name" : "PALMS AT WAILEA #905-2BR-REMODELED-LARGE LANAI-AC",
"property_type" : "Condominium",
"address" : {
"location" : {
"type" : "Point",
"coordinates" : [-156.4409, 20.69735],
"is_location_exact" : true
}
},
"score" : 2.2383887767791748
}
1
2

The following code example:

  • Imports mongodb packages and dependencies.

  • Establishes a connection to your Atlas cluster.

  • Uses a compound $search stage to:

    • Specify that results must be within a Polygon defined by a set of coordinates.

    • Give preference to results for properties of type condominium.

  • Uses a $project stage to:

    • Exclude all fields except name, address and property_type.

    • Add a relevance score to each returned document.

  • Iterates over the cursor to print the documents that match the query.

1package main
2
3import (
4 "context"
5 "fmt"
6
7 "go.mongodb.org/mongo-driver/bson"
8 "go.mongodb.org/mongo-driver/mongo"
9 "go.mongodb.org/mongo-driver/mongo/options"
10)
11
12func main() {
13 // connect to your Atlas cluster
14 client, err := mongo.Connect(context.TODO(), options.Client().ApplyURI("<connection-string>"))
15 if err != nil {
16 panic(err)
17 }
18 defer client.Disconnect(context.TODO())
19
20 // set namespace
21 collection := client.Database("sample_airbnb").Collection("listingsAndReviews")
22
23 // define polygon
24 polygon := [][][]float64{{
25 {-161.323242, 22.512557},
26 {-152.446289, 22.065278},
27 {-156.09375, 17.811456},
28 {-161.323242, 22.512557},
29 }}
30
31 // define pipeline
32 searchStage := bson.D{{"$search", bson.M{
33 "index": "geo-json-tutorial",
34 "compound": bson.M{
35 "must": bson.M{
36 "geoWithin": bson.M{
37 "geometry": bson.M{
38 "type": "Polygon",
39 "coordinates": polygon,
40 },
41 "path": "address.location",
42 },
43 },
44 "should": bson.M{
45 "text": bson.M{
46 "path": "property_type",
47 "query": "Condominium",
48 }},
49 },
50 },
51 }}
52 limitStage := bson.D{{"$limit", 10}}
53 projectStage := bson.D{{"$project", bson.D{{"name", 1}, {"address", 1}, {"property_type", 1}, {"_id", 0}, {"score", bson.D{{"$meta", "searchScore"}}}}}}
54
55 // run pipeline
56 cursor, err := collection.Aggregate(context.TODO(), mongo.Pipeline{searchStage, limitStage, projectStage})
57 if err != nil {
58 panic(err)
59 }
60
61 // print results
62 var results []bson.D
63 if err = cursor.All(context.TODO(), &results); err != nil {
64 panic(err)
65 }
66 for _, result := range results {
67 fmt.Println(result)
68 }
69}
3

Before you run the sample, replace <connection-string> with your Atlas connection string. Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.

4
go run run-geo-query.go
[{name Ocean View Waikiki Marina w/prkg} {property_type Condominium} {address [{street Honolulu, HI, United States} {suburb Oʻahu} {government_area Primary Urban Center} {market Oahu} {country United States} {country_code US} {location [{type Point} {coordinates [-157.83919 21.28634]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!} {property_type Condominium} {address [{street Lahaina, HI, United States} {suburb Maui} {government_area Lahaina} {market Maui} {country United States} {country_code US} {location [{type Point} {coordinates [-156.68012 20.96996]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name Makaha Valley Paradise with OceanView} {property_type Condominium} {address [{street Waianae, HI, United States} {suburb Leeward Side} {government_area Waianae} {market Oahu} {country United States} {country_code US} {location [{type Point} {coordinates [-158.20291 21.4818]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name March 2019 availability! Oceanview on Sugar Beach!} {property_type Condominium} {address [{street Kihei, HI, United States} {suburb Maui} {government_area Kihei-Makena} {market Maui} {country United States} {country_code US} {location [{type Point} {coordinates [-156.46881 20.78621]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name Tropical Jungle Oasis} {property_type Condominium} {address [{street Hilo, HI, United States} {suburb Island of Hawaiʻi} {government_area South Hilo} {market The Big Island} {country United States} {country_code US} {location [{type Point} {coordinates [-155.09259 19.73108]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name 2 Bdrm/2 Bath Family Suite Ocean View} {property_type Condominium} {address [{street Honolulu, HI, United States} {suburb Waikiki} {government_area Primary Urban Center} {market Oahu} {country United States} {country_code US} {location [{type Point} {coordinates [-157.82696 21.27971]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name 302 Kanai A Nalu Ocean front/view} {property_type Condominium} {address [{street Wailuku, HI, United States} {suburb Maui} {government_area Kihei-Makena} {market Maui} {country United States} {country_code US} {location [{type Point} {coordinates [-156.5039 20.79664]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name Sugar Beach Resort 1BR Ground Floor Condo !} {property_type Condominium} {address [{street Kihei, HI, United States} {suburb Maui} {government_area Kihei-Makena} {market Maui} {country United States} {country_code US} {location [{type Point} {coordinates [-156.46697 20.78484]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name 2 BR Oceanview - Great Location!} {property_type Condominium} {address [{street Kihei, HI, United States} {suburb Kihei/Wailea} {government_area Kihei-Makena} {market Maui} {country United States} {country_code US} {location [{type Point} {coordinates [-156.44917 20.73013]} {is_location_exact true}]}]} {score 2.238388776779175}]
[{name PALMS AT WAILEA #905-2BR-REMODELED-LARGE LANAI-AC} {property_type Condominium} {address [{street Kihei, HI, United States} {suburb Maui} {government_area Kihei-Makena} {market Maui} {country United States} {country_code US} {location [{type Point} {coordinates [-156.4409 20.69735]} {is_location_exact true}]}]} {score 2.238388776779175}]
1
junit
4.11 or higher version
mongodb-driver-sync
4.3.0 or higher version
slf4j-log4j12
1.7.30 or higher version
2
3

The following code example:

  • Imports mongodb packages and dependencies.

  • Establishes a connection to your Atlas cluster.

  • Uses a compound $search stage to:

    • Specify that results must be within a Polygon defined by a set of coordinates.

    • Give preference to results for properties of type condominium.

  • Uses a $project stage to:

    • Exclude all fields except name, address and property_type.

    • Add a relevance score to each returned document.

  • Iterates over the cursor to print the documents that match the
    query.
1import java.util.Arrays;
2import static com.mongodb.client.model.Filters.eq;
3import static com.mongodb.client.model.Aggregates.limit;
4import static com.mongodb.client.model.Aggregates.project;
5import static com.mongodb.client.model.Projections.computed;
6import static com.mongodb.client.model.Projections.excludeId;
7import static com.mongodb.client.model.Projections.fields;
8import static com.mongodb.client.model.Projections.include;
9import com.mongodb.client.MongoClient;
10import com.mongodb.client.MongoClients;
11import com.mongodb.client.MongoCollection;
12import com.mongodb.client.MongoDatabase;
13import org.bson.Document;
14
15public class GeoQuery {
16 public static void main( String[] args ) {
17 Document agg = new Document( "$search",
18 new Document( "index", "geo-json-tutorial")
19 .append("compound",
20 new Document("must", Arrays.asList(new Document("geoWithin",
21 new Document("geometry",
22 new Document("type", "Polygon")
23 .append("coordinates", Arrays.asList(Arrays.asList(Arrays.asList(-161.323242d, 22.512557d), Arrays.asList(-152.446289d, 22.065278d), Arrays.asList(-156.09375d, 17.811456d), Arrays.asList(-161.323242d, 22.512557d)))))
24 .append("path", "address.location"))))
25 .append("should", Arrays.asList(new Document("text",
26 new Document("path", "property_type")
27 .append("query", "Condominium"))))));
28
29 String uri = "<connection-string>";
30
31 try (MongoClient mongoClient = MongoClients.create(uri)) {
32 MongoDatabase database = mongoClient.getDatabase("sample_airbnb");
33 MongoCollection<Document> collection = database.getCollection("listingsAndReviews");
34
35 collection.aggregate(Arrays.asList(agg,
36 limit(10),
37 project(fields(excludeId(), include("name", "address", "property_type"), computed("score", new Document("$meta", "searchScore"))))))
38 .forEach(doc -> System.out.println(doc.toJson() + "\n"));
39 }
40 }
41}

Note

To run the sample code in your Maven environment, add the following above the import statements in your file.

package com.mongodb.drivers;
4

Before you run the sample, replace <connection-string> with your Atlas connection string. Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.

5
javac GeoQuery.java
java GeoQuery
{"name": "Ocean View Waikiki Marina w/prkg", "property_type": "Condominium", "address": {"street": "Honolulu, HI, United States", "suburb": "O\u02bbahu", "government_area": "Primary Urban Center", "market": "Oahu", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-157.83919, 21.28634], "is_location_exact": true}}, "score": 1.0},
{"name": "Kailua-Kona, Kona Coast II 2b condo", "property_type": "Apartment", "address": {"street": "Kailua-Kona, HI, United States", "suburb": "Kailua/Kona", "government_area": "North Kona", "market": "The Big Island", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-155.96445, 19.5702], "is_location_exact": true}}, "score": 1.0},
{"name": "LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!", "property_type": "Condominium", "address": {"street": "Lahaina, HI, United States", "suburb": "Maui", "government_area": "Lahaina", "market": "Maui", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-156.68012, 20.96996], "is_location_exact": true}}, "score": 1.0},
{"name": "Makaha Valley Paradise with OceanView", "property_type": "Condominium", "address": {"street": "Waianae, HI, United States", "suburb": "Leeward Side", "government_area": "Waianae", "market": "Oahu", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-158.20291, 21.4818], "is_location_exact": true}}, "score": 1.0},
{"name": "~Ao Lele~ Flying Cloud", "property_type": "Treehouse", "address": {"street": "Volcano, HI, United States", "suburb": "Island of Hawai\u02bbi", "government_area": "Puna", "market": "The Big Island", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-155.21763, 19.42151], "is_location_exact": false}}, "score": 1.0},
{"name": "Private OceanFront - Bathtub Beach. Spacious House", "property_type": "House", "address": {"street": "Laie, HI, United States", "suburb": "Ko'olauloa", "government_area": "Koolauloa", "market": "Oahu", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-157.91952, 21.63549], "is_location_exact": true}}, "score": 1.0},
{"name": "Banyan Bungalow", "property_type": "Bungalow", "address": {"street": "Waialua, HI, United States", "suburb": "O\u02bbahu", "government_area": "North Shore Oahu", "market": "Oahu", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-158.1602, 21.57561], "is_location_exact": false}}, "score": 1.0},
{"name": "March 2019 availability! Oceanview on Sugar Beach!", "property_type": "Condominium", "address": {"street": "Kihei, HI, United States", "suburb": "Maui", "government_area": "Kihei-Makena", "market": "Maui", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-156.46881, 20.78621], "is_location_exact": true}}, "score": 1.0},
{"name": "Tropical Jungle Oasis", "property_type": "Condominium", "address": {"street": "Hilo, HI, United States", "suburb": "Island of Hawai\u02bbi", "government_area": "South Hilo", "market": "The Big Island", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-155.09259, 19.73108], "is_location_exact": true}}, "score": 1.0},
{"name": "Jubilee By The Sea (Ocean Views)", "property_type": "House", "address": {"street": "Kailua-Kona, HI, United States", "suburb": "Island of Hawai\u02bbi", "government_area": "North Kona", "market": "The Big Island", "country": "United States", "country_code": "US", "location": {"type": "Point", "coordinates": [-155.97349, 19.61318], "is_location_exact": false}}, "score": 1.0}
1
mongodb-driver-kotlin-coroutine
4.10.0 or higher version
2
3

The following code example:

  • Imports mongodb packages and dependencies.

  • Establishes a connection to your Atlas cluster.

  • Uses a compound $search stage to:

    • Specify that results must be within a Polygon defined by a set of coordinates.

    • Give preference to results for properties of type condominium.

  • Uses a $project stage to:

    • Exclude all fields except name, address and property_type.

    • Add a relevance score to each returned document.

  • Prints the documents that match the query from the AggregateFlow instance.

1import com.mongodb.client.model.Aggregates.limit
2import com.mongodb.client.model.Aggregates.project
3import com.mongodb.client.model.Projections.*
4import com.mongodb.kotlin.client.coroutine.MongoClient
5import kotlinx.coroutines.runBlocking
6import org.bson.Document
7
8fun main() {
9 // connect to your Atlas cluster
10 val uri = "<connection-string>"
11 val mongoClient = MongoClient.create(uri)
12
13 // set namespace
14 val database = mongoClient.getDatabase("sample_airbnb")
15 val collection = database.getCollection<Document>("listingsAndReviews")
16
17 runBlocking {
18 // define pipeline
19 val agg = Document(
20 "\$search",
21 Document("index", "geo-json-tutorial")
22 .append(
23 "compound",
24 Document(
25 "must", listOf(
26 Document(
27 "geoWithin",
28 Document(
29 "geometry",
30 Document("type", "Polygon")
31 .append(
32 "coordinates",
33 listOf(
34 listOf(
35 listOf(-161.323242, 22.512557),
36 listOf(-152.446289, 22.065278),
37 listOf(-156.09375, 17.811456),
38 listOf(-161.323242, 22.512557)
39 )
40 )
41 )
42 )
43 .append("path", "address.location")
44 )
45 )
46 )
47 .append(
48 "should", listOf(
49 Document(
50 "text",
51 Document("path", "property_type")
52 .append("query", "Condominium")
53 )
54 )
55 )
56 )
57 )
58
59 // run pipeline and print results
60 val resultsFlow = collection.aggregate<Document>(
61 listOf(
62 agg,
63 limit(10),
64 project(fields(
65 excludeId(),
66 include("name", "address", "property_type"),
67 computed("score", Document("\$meta", "searchScore"))
68 ))
69 )
70 )
71 resultsFlow.collect { println(it) }
72 }
73 mongoClient.close()
74}
4

Before you run the sample, replace <connection-string> with your Atlas connection string. Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.

5

When you run the GeoQuery.kt program in your IDE, it prints the following documents:

Document{{name=Ocean View Waikiki Marina w/prkg, property_type=Condominium, address=Document{{street=Honolulu, HI, United States, suburb=Oʻahu, government_area=Primary Urban Center, market=Oahu, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-157.83919, 21.28634], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!, property_type=Condominium, address=Document{{street=Lahaina, HI, United States, suburb=Maui, government_area=Lahaina, market=Maui, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-156.68012, 20.96996], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=Makaha Valley Paradise with OceanView, property_type=Condominium, address=Document{{street=Waianae, HI, United States, suburb=Leeward Side, government_area=Waianae, market=Oahu, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-158.20291, 21.4818], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=March 2019 availability! Oceanview on Sugar Beach!, property_type=Condominium, address=Document{{street=Kihei, HI, United States, suburb=Maui, government_area=Kihei-Makena, market=Maui, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-156.46881, 20.78621], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=Tropical Jungle Oasis, property_type=Condominium, address=Document{{street=Hilo, HI, United States, suburb=Island of Hawaiʻi, government_area=South Hilo, market=The Big Island, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-155.09259, 19.73108], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=2 Bdrm/2 Bath Family Suite Ocean View, property_type=Condominium, address=Document{{street=Honolulu, HI, United States, suburb=Waikiki, government_area=Primary Urban Center, market=Oahu, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-157.82696, 21.27971], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=302 Kanai A Nalu Ocean front/view, property_type=Condominium, address=Document{{street=Wailuku, HI, United States, suburb=Maui, government_area=Kihei-Makena, market=Maui, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-156.5039, 20.79664], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=Sugar Beach Resort 1BR Ground Floor Condo !, property_type=Condominium, address=Document{{street=Kihei, HI, United States, suburb=Maui, government_area=Kihei-Makena, market=Maui, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-156.46697, 20.78484], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=2 BR Oceanview - Great Location!, property_type=Condominium, address=Document{{street=Kihei, HI, United States, suburb=Kihei/Wailea, government_area=Kihei-Makena, market=Maui, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-156.44917, 20.73013], is_location_exact=true}}}}, score=2.238388776779175}}
Document{{name=PALMS AT WAILEA #905-2BR-REMODELED-LARGE LANAI-AC, property_type=Condominium, address=Document{{street=Kihei, HI, United States, suburb=Maui, government_area=Kihei-Makena, market=Maui, country=United States, country_code=US, location=Document{{type=Point, coordinates=[-156.4409, 20.69735], is_location_exact=true}}}}, score=2.238388776779175}}
1
2

The following code example:

  • Imports mongodb, MongoDB's Node.js driver.

  • Creates an instance of the MongoClient class to establish a connection to your Atlas cluster.

    • Uses a compound $search stage to:

      • Specify that results must be within a Polygon defined by a set of coordinates.

      • Give preference to results for properties of type condominium.

    • Uses a $project stage to:

      • Exclude all fields except name, address and property_type.

      • Add a relevance score to each returned document.

  • Iterates over the cursor to print the documents that match the query.

    1const { MongoClient } = require("mongodb");
    2
    3// connect to your Atlas cluster
    4const uri ="<connection-string>";
    5
    6const client = new MongoClient(uri);
    7
    8async function run() {
    9 try {
    10 await client.connect();
    11
    12 // set namespace
    13 const database = client.db("sample_airbnb");
    14 const coll = database.collection("listingsAndReviews");
    15
    16 // define pipeline
    17 const agg = [
    18 {
    19 '$search': {
    20 'index': 'geo-json-tutorial',
    21 'compound': {
    22 'must': [
    23 {
    24 'geoWithin': {
    25 'geometry': {
    26 'type': 'Polygon',
    27 'coordinates': [
    28 [
    29 [
    30 -161.323242, 22.512557
    31 ], [
    32 -152.446289, 22.065278
    33 ], [
    34 -156.09375, 17.811456
    35 ], [
    36 -161.323242, 22.512557
    37 ]
    38 ]
    39 ]
    40 },
    41 'path': 'address.location'
    42 }
    43 }
    44 ],
    45 'should': [
    46 {
    47 'text': {
    48 'path': 'property_type',
    49 'query': 'Condominium'
    50 }
    51 }
    52 ]
    53 }
    54 }
    55 }, {
    56 '$limit': 10
    57 }, {
    58 '$project': {
    59 '_id': 0,
    60 'name': 1,
    61 'address': 1,
    62 'property_type': 1,
    63 'score': {
    64 '$meta': 'searchScore'
    65 }
    66 }
    67 }
    68 ];
    69 // run pipeline
    70 const result = await coll.aggregate(agg);
    71
    72 // print results
    73 await result.forEach((doc) => console.log(doc));
    74 } finally {
    75 await client.close();
    76 }
    77}
    78run().catch(console.dir);
3

Before you run the sample, replace <connection-string> with your Atlas connection string. Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.

4

Run the following command to query your collection:

node run-geo-query.js
{
name: 'Ocean View Waikiki Marina w/prkg',
property_type: 'Condominium',
address: {
street: 'Honolulu, HI, United States',
suburb: 'Oʻahu',
government_area: 'Primary Urban Center',
market: 'Oahu',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -157.83919, 21.28634 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!',
property_type: 'Condominium',
address: {
street: 'Lahaina, HI, United States',
suburb: 'Maui',
government_area: 'Lahaina',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.68012, 20.96996 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'Makaha Valley Paradise with OceanView',
property_type: 'Condominium',
address: {
street: 'Waianae, HI, United States',
suburb: 'Leeward Side',
government_area: 'Waianae',
market: 'Oahu',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -158.20291, 21.4818 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'March 2019 availability! Oceanview on Sugar Beach!',
property_type: 'Condominium',
address: {
street: 'Kihei, HI, United States',
suburb: 'Maui',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.46881, 20.78621 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'Tropical Jungle Oasis',
property_type: 'Condominium',
address: {
street: 'Hilo, HI, United States',
suburb: 'Island of Hawaiʻi',
government_area: 'South Hilo',
market: 'The Big Island',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -155.09259, 19.73108 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: '2 Bdrm/2 Bath Family Suite Ocean View',
property_type: 'Condominium',
address: {
street: 'Honolulu, HI, United States',
suburb: 'Waikiki',
government_area: 'Primary Urban Center',
market: 'Oahu',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -157.82696, 21.27971 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: '302 Kanai A Nalu Ocean front/view',
property_type: 'Condominium',
address: {
street: 'Wailuku, HI, United States',
suburb: 'Maui',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.5039, 20.79664 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'Sugar Beach Resort 1BR Ground Floor Condo !',
property_type: 'Condominium',
address: {
street: 'Kihei, HI, United States',
suburb: 'Maui',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.46697, 20.78484 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: '2 BR Oceanview - Great Location!',
property_type: 'Condominium',
address: {
street: 'Kihei, HI, United States',
suburb: 'Kihei/Wailea',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.44917, 20.73013 ],
is_location_exact: true
}
},
score: 2.238388776779175
},
{
name: 'PALMS AT WAILEA #905-2BR-REMODELED-LARGE LANAI-AC',
property_type: 'Condominium',
address: {
street: 'Kihei, HI, United States',
suburb: 'Maui',
government_area: 'Kihei-Makena',
market: 'Maui',
country: 'United States',
country_code: 'US',
location: {
type: 'Point',
coordinates: [ -156.4409, 20.69735 ],
is_location_exact: true
}
},
score: 2.238388776779175
}
1
2

The following code example:

  • Imports pymongo, MongoDB's Python driver, and the dns module, which is required to connect pymongo to Atlas using a DNS seed list connection string.

  • Creates an instance of the MongoClient class to establish a connection to your Atlas cluster.

    • Uses a compound $search stage to:

      • Specify that results must be within a Polygon defined by a set of coordinates.

      • Give preference to results for properties of type condominium.

    • Uses a $project stage to:

      • Exclude all fields except name, address and property_type.

      • Add a relevance score to each returned document.

  • Iterates over the cursor to print the documents that match the query.

    1import pymongo
    2
    3# connect to your Atlas cluster
    4client = pymongo.MongoClient('<connection-string>')
    5
    6# define pipeline
    7pipeline = [
    8 {
    9 '$search': {
    10 'index': 'geo-json-tutorial',
    11 'compound': {
    12 'must': [
    13 {
    14 'geoWithin': {
    15 'geometry': {
    16 'type': 'Polygon',
    17 'coordinates': [
    18 [
    19 [
    20 -161.323242, 22.512557
    21 ], [
    22 -152.446289, 22.065278
    23 ], [
    24 -156.09375, 17.811456
    25 ], [
    26 -161.323242, 22.512557
    27 ]
    28 ]
    29 ]
    30 },
    31 'path': 'address.location'
    32 }
    33 }
    34 ],
    35 'should': [
    36 {
    37 'text': {
    38 'path': 'property_type',
    39 'query': 'Condominium'
    40 }
    41 }
    42 ]
    43 }
    44 }
    45 }, {
    46 '$limit': 10
    47 }, {
    48 '$project': {
    49 '_id': 0,
    50 'name': 1,
    51 'address': 1,
    52 'property_type': 1,
    53 'score': {
    54 '$meta': 'searchScore'
    55 }
    56 }
    57 }
    58]
    59# run pipeline
    60result = client["sample_airbnb"]["listingsAndReviews"].aggregate(pipeline)
    61
    62# print results
    63for i in result:
    64 print(i)
3

Before you run the sample, replace <connection-string> with your Atlas connection string. Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.

4
python run-geo-query.py
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Primary Urban Center",
"location": {
"coordinates": [
-157.83919,
21.28634
],
"is_location_exact": true,
"type": "Point"
},
"market": "Oahu",
"street": "Honolulu, HI, United States",
"suburb": "O\u02bbahu"
},
"name": "Ocean View Waikiki Marina w/prkg",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Lahaina",
"location": {
"coordinates": [
-156.68012,
20.96996
],
"is_location_exact": true,
"type": "Point"
},
"market": "Maui",
"street": "Lahaina, HI, United States",
"suburb": "Maui"
},
"name": "LAHAINA, MAUI! RESORT/CONDO BEACHFRONT!! SLEEPS 4!",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Waianae",
"location": {
"coordinates": [
-158.20291,
21.4818
],
"is_location_exact": true,
"type": "Point"
},
"market": "Oahu",
"street": "Waianae, HI, United States",
"suburb": "Leeward Side"
},
"name": "Makaha Valley Paradise with OceanView",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Kihei-Makena",
"location": {
"coordinates": [
-156.46881,
20.78621
],
"is_location_exact": true,
"type": "Point"
},
"market": "Maui",
"street": "Kihei, HI, United States",
"suburb": "Maui"
},
"name": "March 2019 availability! Oceanview on Sugar Beach!",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "South Hilo",
"location": {
"coordinates": [
-155.09259,
19.73108
],
"is_location_exact": true,
"type": "Point"
},
"market": "The Big Island",
"street": "Hilo, HI, United States",
"suburb": "Island of Hawai\u02bbi"
},
"name": "Tropical Jungle Oasis",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Primary Urban Center",
"location": {
"coordinates": [
-157.82696,
21.27971
],
"is_location_exact": true,
"type": "Point"
},
"market": "Oahu",
"street": "Honolulu, HI, United States",
"suburb": "Waikiki"
},
"name": "2 Bdrm/2 Bath Family Suite Ocean View",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Kihei-Makena",
"location": {
"coordinates": [
-156.5039,
20.79664
],
"is_location_exact": true,
"type": "Point"
},
"market": "Maui",
"street": "Wailuku, HI, United States",
"suburb": "Maui"
},
"name": "302 Kanai A Nalu Ocean front/view",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Kihei-Makena",
"location": {
"coordinates": [
-156.46697,
20.78484
],
"is_location_exact": true,
"type": "Point"
},
"market": "Maui",
"street": "Kihei, HI, United States",
"suburb": "Maui"
},
"name": "Sugar Beach Resort 1BR Ground Floor Condo !",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Kihei-Makena",
"location": {
"coordinates": [
-156.44917,
20.73013
],
"is_location_exact": true,
"type": "Point"
},
"market": "Maui",
"street": "Kihei, HI, United States",
"suburb": "Kihei/Wailea"
},
"name": "2 BR Oceanview - Great Location!",
"property_type": "Condominium",
"score": 2.238388776779175
}
{
"address": {
"country": "United States",
"country_code": "US",
"government_area": "Kihei-Makena",
"location": {
"coordinates": [
-156.4409,
20.69735
],
"is_location_exact": true,
"type": "Point"
},
"market": "Maui",
"street": "Kihei, HI, United States",
"suburb": "Maui"
},
"name": "PALMS AT WAILEA #905-2BR-REMODELED-LARGE LANAI-AC",
"property_type": "Condominium",
"score": 2.238388776779175
}

Back

Diacritic-Insensitive

Next

Compound