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如何运行 Atlas Search 复合地理 JSON 查询

在此页面上

  • 创建 Atlas Search 索引
  • 运行地理、数字和文本字段的组合查询

本教程介绍如何在sample_airbnb数据库中的listingsAndReviewscollection上创建索引，并运行查询以返回指定索引范围内每个property具有name 、 address和property_type的文档。使用coordinates定义的多边形。

本教程将指导您完成以下步骤：

  1. 在 sample_airbnb.listingsAndReviews 集合的 address 字段上设置 Atlas Search 索引。

  2. 运行查询，返回 10 个文档，其中每个属性的name 、 address和property_type在指定地理coordinates内。 Atlas Search 结果反映了对condominium类型的属性偏好，并且为结果中的每个文档分配了相关性score ，按从高到低的顺序返回。

在开始之前，请确保您的 Atlas 集群满足先决条件中所述的要求。

要创建 Atlas Search 索引，您必须拥有 Project Data Access Admin 或更高的项目访问权限。

创建 Atlas Search 索引

在本部分中，您将在sample_airbnb.listingsAndReviews collection中的address字段上创建 Atlas Search 索引。

1

AtlasGoClusters在Atlas中，Go项目的 页面。

  1. 如果尚未显示，请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。

  2. 如果尚未显示，请从导航栏的Projects菜单中选择所需的项目。

  3. 如果尚未出现，请单击侧边栏中的 Clusters（集群）。

    显示 集群页面。

2

转到集群的 Atlas Search 页面。

您可以从侧边栏、 Data Explorer 或集群详细信息页面转到 Atlas Search 页面。

  1. 在侧边栏中，单击 Services 标题下的 Atlas Search。

  2. 从 Select data source 下拉菜单中选择您的集群并单击 Go to Atlas Search。

    将显示 Atlas Search 页面。

  1. 单击集群的对应 Browse Collections 按钮。

  2. 展开数据库并选择集合。

  3. 单击该集合的 Search Indexes 标签页。

    将显示 Atlas Search 页面。

  1. 单击集群的名称。

  2. 单击 Atlas Search 标签页。

    将显示 Atlas Search 页面。

3

单击 Create Search Index（保存并关闭）。

4

选择某一 Atlas Search Configuration Method（配置方法）并单击 Next（下一步）。

  • 要获得引导式体验，请选择 Atlas Search Visual Editor 。

  • 要编辑原始索引定义，请选择 Atlas SearchJSON Editor 。

5

输入 Index Name（索引名称），然后设置 Database and Collection（数据库和集合）。

  1. 在 Index Name 字段中输入 geo-json-tutorial。

    如果将索引命名为 default，则在使用 $search 管道阶段时无需指定 index 参数。如果您为索引指定了自定义名称，则必须在 index 参数中指定此名称。

  2. 在 Database and Collection（数据库和集合）部分中找到 sample_airbnb 数据库，然后选择 listingsAndReviews 集合。

6

在 address 字段上定义索引。

您可以使用 Atlas 用户界面中的 Atlas Search Visual Editor或 Atlas Search JSON Editor来创建索引。以下索引定义指定 Atlas Search 必须索引：

  • collection中的所有字段都会自动生成。

  • address.locationdocument作为类型geo 的 的字段。

  1. 单击 Next（连接）。

  2. 单击 Refine Your Index（连接）。

  3. 在 Field Mappings 部分中，单击 Add Field 打开 Add Field Mapping > Customized Configuration 标签页。

  4. 从 Field Name 下拉列表中选择 address.location。

  5. 单击 Data Type（添加数据）下拉列表并选择 Geo（插入文档）。

  6. 单击 Add（连接）。

  7. 单击 Save Changes（连接）。

  1. 将默认索引定义替换为以下示例索引定义。

    {
      "mappings": {
        "dynamic": true,
        "fields": {
          "address": {
            "fields": {
              "location": {
                "type": "geo"
              }
            },
            "type": "document"
          }
        }
      }
    }

  2. 单击 Next（连接）。

7

单击 Create Search Index（保存并关闭）。

8

关闭 You're All Set!（一切就绪！）模态窗口。

此时将显示一个模态窗口，让您知道索引正在构建中。点击 Close 按钮。

9

等待索引完成构建。

构建索引大约需要一分钟时间。在构建时，Status 列显示 Build in Progress。构建完成后，Status 列显示 Active。

运行地理、数字和文本字段的组合查询

➤ 使用本页的“选择语言”下拉菜单设置本节示例的语言。

在本部分中，您将运行一个查询，为指定地理name addressproperty_type内的每个属性返回 10 个文档，其中每个属性具有 、 和coordinates 。还返回指定每个文档score的字段，并优先考虑类型condominium的属性对结果进行排序。

1

AtlasGoClusters在Atlas中，Go项目的 页面。

  1. 如果尚未显示，请从导航栏上的 Organizations 菜单中选择包含所需项目的组织。

  2. 如果尚未显示，请从导航栏的Projects菜单中选择所需的项目。

  3. 如果尚未出现，请单击侧边栏中的 Clusters（集群）。

    会显示集群页面。

2

转到集群的 Atlas Search 页面。

您可以从侧边栏、 Data Explorer 或集群详细信息页面转到 Atlas Search 页面。

  1. 在侧边栏中，单击 Services 标题下的 Atlas Search。

  2. 从 Select data source 下拉菜单中选择您的集群并单击 Go to Atlas Search。

    将显示 Atlas Search 页面。

  1. 单击集群的对应 Browse Collections 按钮。

  2. 展开数据库并选择集合。

  3. 单击该集合的 Search Indexes 标签页。

    将显示 Atlas Search 页面。

  1. 单击集群的名称。

  2. 单击 Atlas Search 标签页。

    将显示 Atlas Search 页面。

3

转到 Search Tester（搜索测试器）。

单击要查询的索引右侧的 Query 按钮。

4

查看和编辑查询语法。

单击Edit Query查看 JSON格式的默认查询语法示例。

5

使用 geoWithin 操作符对 sample_airbnb.listingsAndReviews 集合运行 Atlas Search 复合查询。

以下 Atlas Search 查询使用复合操作符执行以下操作：

  • 指定结果must Polygon位于由 集定义的coordinates 内。

  • 优先显示condominium类型属性的结果。

注意

Search Tester 可能不会显示其所返回文档的所有字段。要查看所有字段，包括在查询路径中指定的字段，请展开结果中的文档。

[
  {
    "$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

通过 mongosh 连接到您的集群。

在终端窗口中打开mongosh并连接到集群。 有关连接的详细说明，请参阅通过mongosh连接。

2

使用 sample_airbnb 数据库。

在 mongosh 提示符下运行以下命令：

use sample_airbnb
3

在 mongosh 中运行合并的 Atlas Search 查询。

以下 Atlas Search 搜索查询：

  • 使用复合$search阶段可以：

    • 指定结果must Polygon位于由 集定义的coordinates 内。

    • 优先显示condominium类型属性的结果。

  • 使用 $project 阶段以：

    • 排除除name 、 address和property_type之外的所有字段。

    • 为每个返回的文档添加相关性score 。

查询如下：

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

在 MongoDB Compass 中连接到您的集群。

打开 MongoDB Compass 并连接到您的集群。有关连接的详细说明，请参阅通过 Compass 连接。

2

使用 sample_airbnb 数据库中的 listingsAndReviews 集合。

在 Database 屏幕上，依次单击 sample_airbnb 数据库和 listingsAndReviews 集合。

3

对 listingsAndReviews 集合运行 Atlas Search 查询。

以下查询：

  • 使用复合$search阶段可以：

    • 指定结果must Polygon位于由 集定义的coordinates 内。

    • 优先显示condominium类型属性的结果。

  • 使用 $project 阶段以：

    • 排除除name 、 address和property_type之外的所有字段。

    • 为每个返回的文档添加相关性score 。

要在 MongoDB Compass 中运行此 Atlas Search 查询，请执行以下操作：

  1. 单击 Aggregations 标签页。

  2. 单击 Select...，然后从下拉菜单中选择阶段并为该阶段添加查询，以配置以下每个管道阶段。单击 Add Stage 以添加其他阶段。

    管道阶段
    查询

    $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'
     }
    }

如果启用了 Auto Preview，MongoDB Compass 将在 $project 管道阶段旁边显示以下文档：

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}

要了解有关 $search 管道阶段的更多信息，请参阅其参考页面。有关聚合管道的完整文档，请参阅 MongoDB 服务器手册。

1

为查询设置并初始化 .NET/C# 项目。

  1. 创建一个名为 combined-geo-query 的新目录，并使用 dotnet new 命令初始化项目。

    mkdir combined-geo-query
    cd combined-geo-query
    dotnet new console

  2. 将 .NET/C# 驱动程序作为依赖项添加到项目中。

    dotnet add package MongoDB.Driver
2

在 Program.cs 文件中创建查询。

  1. 将 Program.cs 文件的内容替换为以下代码。

    以下 Atlas Search 搜索查询：

    • 使用复合$search阶段可以：

      • 指定结果must Polygon位于由 集定义的coordinates 内。

      • 优先显示condominium类型属性的结果。

    • 使用 $project 阶段以：

      • 排除除name 、 address和property_type之外的所有字段。

      • 为每个返回的文档添加相关性score 。

    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. 在运行示例之前，请将 <connection-string> 替换为 Atlas 连接字符串。确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

3

编译并运行 Program.cs 文件。

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

创建一个名为 run-geo-query.go 的文件。

2

将代码示例复制并粘贴到 run-geo-query.go 文件中。

以下代码示例：

  • 导入mongodb包和依赖项。

  • 建立与您的 Atlas 集群的连接。

  • 使用复合$search阶段可以：

    • 指定结果must Polygon位于由 集定义的coordinates 内。

    • 优先显示condominium类型属性的结果。

  • 使用 $project 阶段以：

    • 排除除name 、 address和property_type之外的所有字段。

    • 为每个返回的文档添加相关性score 。

  • 遍历游标以打印与查询匹配的文档。

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

请将 <connection-string> 替换为 Atlas 连接字符串。

在运行示例之前，请将 <connection-string> 替换为 Atlas 连接字符串。确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

4

编译并运行 run-geo-query.go 文件。

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

确保 CLASSPATH 包含以下库。

junit

4.11 或更高版本

mongodb-driver-sync

4.3.0 或更高版本

slf4j-log4j12

1.7.30 或更高版本

2

创建一个名为 GeoQuery.java 的文件。

3

将代码示例复制并粘贴到 GeoQuery.java 文件中。

以下代码示例：

  • 导入mongodb包和依赖项。

  • 建立与您的 Atlas 集群的连接。

  • 使用复合$search阶段可以：

    • 指定结果must Polygon位于由 集定义的coordinates 内。

    • 优先显示condominium类型属性的结果。

  • 使用 $project 阶段以：

    • 排除除name 、 address和property_type之外的所有字段。

    • 为每个返回的文档添加相关性score 。

  • 遍历游标以打印与以下内容匹配的文档
    查询。
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}

注意

要在 Maven 环境中运行示例代码，请在文件中的 import 语句上方添加以下内容。

package com.mongodb.drivers;
4

请将 <connection-string> 替换为 Atlas 连接字符串。

在运行示例之前，请将 <connection-string> 替换为 Atlas 连接字符串。确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

5

编译并运行 GeoQuery.java 文件。

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

确保将以下依赖项添加到项目中。

mongodb-driver-kotlin-coroutine

4.10.0 或更高版本

2

创建一个名为 GeoQuery.kt 的文件。

3

将代码示例复制并粘贴到 GeoQuery.kt 文件中。

以下代码示例：

  • 导入mongodb包和依赖项。

  • 建立与您的 Atlas 集群的连接。

  • 使用复合$search阶段可以：

    • 指定结果must Polygon位于由 集定义的coordinates 内。

    • 优先显示condominium类型属性的结果。

  • 使用 $project 阶段以：

    • 排除除name 、 address和property_type之外的所有字段。

    • 为每个返回的文档添加相关性score 。

  • 打印与 AggregateFlow 实例中的查询相匹配的文档。

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

请将 <connection-string> 替换为 Atlas 连接字符串。

在运行示例之前，请将 <connection-string> 替换为 Atlas 连接字符串。确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

5

运行 GeoQuery.kt 文件。

当你在 IDE 中运行 GeoQuery.kt 程序时，它会打印以下文档：

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

创建一个名为 run-geo-query.js 的文件。

2

将以下代码复制并粘贴到 run-geo-query.js 文件。

以下代码示例：

  • 导入 mongodb，即 MongoDB 的 Node.js 驱动程序。

  • 创建一个 MongoClient 类实例，以建立与 Atlas 集群的连接。

    • 使用复合$search阶段可以：

      • 指定结果must Polygon位于由 集定义的coordinates 内。

      • 优先显示condominium类型属性的结果。

    • 使用 $project 阶段以：

      • 排除除name 、 address和property_type之外的所有字段。

      • 为每个返回的文档添加相关性score 。

  • 遍历游标以打印与查询匹配的文档。

    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

请将 <connection-string> 替换为 Atlas 连接字符串。

在运行示例之前，请将 <connection-string> 替换为 Atlas 连接字符串。确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

4

查询您的集合。

运行以下命令来查询您的集合：

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

创建一个名为 run-geo-query.py 的文件。

2

将以下代码复制并粘贴到 run-geo-query.py 文件。

以下代码示例：

  • 导入 pymongo 、MongoDB 的 Python 驱动程序和 dns 模块，这是使用 DNS 种子列表连接字符串将 pymongo 连接到 Atlas 所必需的。

  • 创建一个 MongoClient 类实例，以建立与 Atlas 集群的连接。

    • 使用复合$search阶段可以：

      • 指定结果must Polygon位于由 集定义的coordinates 内。

      • 优先显示condominium类型属性的结果。

    • 使用 $project 阶段以：

      • 排除除name 、 address和property_type之外的所有字段。

      • 为每个返回的文档添加相关性score 。

  • 遍历游标以打印与查询匹配的文档。

    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

请将 <connection-string> 替换为 Atlas 连接字符串。

在运行示例之前，请将 <connection-string> 替换为 Atlas 连接字符串。确保您的连接字符串包含数据库用户的档案。要了解详情，请参阅通过驱动程序连接。

4

查询您的集合。

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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