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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.

Create the Atlas Search Index

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

1

In Atlas, go to the Clusters page for your project.

  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 it's not already displayed, click Clusters in the sidebar.

    The Clusters page displays.

2

Go to the Atlas Search page for your cluster.

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.

    The Atlas Search page displays.

  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.

    The Atlas Search page displays.

  1. Click the cluster's name.

  2. Click the Atlas Search tab.

    The Atlas Search page displays.

3

Click Create Search Index.

4

Select an Atlas Search Configuration Method and click Next.

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

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

5

Enter the Index Name, and set the Database and Collection.

  1. In the Index Name field, enter geo-json-tutorial.

    If you name your index default, you don't need to specify an index parameter in the $search pipeline stage. If you give a custom name to your index, you must specify this name in the index parameter.

  2. In the Database and Collection section, find the sample_airbnb database, and select the listingsAndReviews collection.

6

Define an index on the address field.

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 to open the Add Field Mapping > Customized Configuration tab.

  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

Click Create Search Index.

8

Close the You're All Set! Modal Window.

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

9

Wait for the index to finish building.

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.

Run a Combined Geo, Number, and Text Fields Query

➤ 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

In Atlas, go to the Clusters page for your project.

  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 it's not already displayed, click Clusters in the sidebar.

    The Clusters page displays.

2

Go to the Atlas Search page for your cluster.

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.

    The Atlas Search page displays.

  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.

    The Atlas Search page displays.

  1. Click the cluster's name.

  2. Click the Atlas Search tab.

    The Atlas Search page displays.

3

Go to the Search Tester.

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

4

View and edit the query syntax.

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

5

Run an Atlas Search compound query with the geoWithin operator on the sample_airbnb.listingsAndReviews collection.

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

Connect to your cluster in mongosh.

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

2

Use the sample_airbnb database.

Run the following command at mongosh prompt:

use sample_airbnb
3

Run the combined Atlas Search query in the mongosh.

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

Connect to your cluster in MongoDB Compass.

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

2

Use the listingsAndReviews collection in the sample_airbnb database.

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

3

Run an Atlas Search query on the listingsAndReviews collection.

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

Set up and initialize the .NET/C# project for the query.

  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

Create the query in the Program.cs file.

  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

Compile and run the Program.cs file.

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

Create a file named run-geo-query.go.

2

Copy and paste the code example into the run-geo-query.go file.

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

Replace the <connection-string> with your Atlas connection string.

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

Compile and run the run-geo-query.go file.

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

Ensure that your CLASSPATH contains the following libraries.

junit
4.11 or higher version
mongodb-driver-sync
4.3.0 or higher version
slf4j-log4j12
1.7.30 or higher version
2

Create a file named GeoQuery.java.

3

Copy and paste the code example into the GeoQuery.java file.

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

Replace the <connection-string> with your Atlas connection string.

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

Compile and run the GeoQuery.java file.

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

Ensure that you add the following dependency to your project.

mongodb-driver-kotlin-coroutine
4.10.0 or higher version
2

Create a file named GeoQuery.kt.

3

Copy and paste the code example into the GeoQuery.kt file.

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

Replace the <connection-string> with your Atlas connection string.

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

Run the GeoQuery.kt file.

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

Create a file named run-geo-query.js.

2

Copy and paste the following code into the run-geo-query.js file.

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

Replace the <connection-string> with your Atlas connection string.

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

Query your collection.

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

Create a file named run-geo-query.py.

2

Copy and paste the following code into the run-geo-query.py file.

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

Replace the <connection-string> with your Atlas connection string.

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

Query your collection.

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
}

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