如何使用加权字段运行 Atlas Search 复合查询

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。

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。

转到 Search Tester（搜索测试器）。

单击要查询的索引右侧的 Query 按钮。

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。

使用 compound 操作符对 movies 集合运行以下 Atlas Search 查询。

将以下查询复制并粘贴到 Query Editor 中，然后点击 Query Editor 中的 Search 按钮。

以下示例使用带有子查询的 compound 操作符来搜索 2013 至 2015 年间且 title 字段中包含词语 snow 的电影。

[
  {
    $search: {
      index: "compound-query-custom-score-tutorial",
      compound: {
        filter: [{
          range: {
            path: "year",
            gte: 2013,
            lte: 2015
          }
        }],
        should: [{
          text: {
            query: "snow",
            path: "title",
            score: {constant: {value: 5}}
          }
        }]
      },
      highlight:{
        path: "title"
      }
    }
  }
]

SCORE: 5  _id:  "573a13d7f29313caabda38ad"
  Snow in Paradise
  Matching fields: title
SCORE: 5  _id:  "573a13e2f29313caabdbeded"
  Dead Snow 2: red vs.
  Matching fields: title
SCORE: 5  _id:  "573a13e6f29313caabdc66c4"
  The Snow White Murder Case
  Matching fields: title
SCORE: 5  _id:  "573a13edf29313caabdd37bd"
  Snow on the Blades
  Matching fields: title
SCORE: 0  _id:  "573a13acf29313caabd29366"
  No highlights found.
  Matching fields: unknown
SCORE: 0  _id:  "573a13adf29313caabd2b765"
  No highlights found.
  Matching fields: unknown
SCORE: 0  _id:  "573a13b0f29313caabd333e7"
  No highlights found.
  Matching fields: unknown
SCORE: 0  _id:  "573a13b0f29313caabd3486a"
  No highlights found.
  Matching fields: unknown
SCORE: 0  _id:  "573a13b1f29313caabd3719d"
  No highlights found.
  Matching fields: unknown
SCORE: 0  _id:  "573a13b2f29313caabd3abb9"
  No highlights found.
  Matching fields: unknown

[
  {
    $search: {
      index: "compound-query-custom-score-tutorial",
      compound: {
        must: [{
          range: {
            path: "year",
            gte: 2013,
            lte: 2015
          }
        }],
        should: [{
          text: {
            query: "snow",
            path: "title",
            score: {boost: {value: 2}}
          }
        }]
      },
      highlight:{
        path: "title"
      }
    }
  }
]

SCORE: 6.7722930908203125  _id:  "573a13d7f29313caabda38ad"
  Snow in Paradise
  Matching fields: title
SCORE: 6.063445568084717  _id:  "573a13edf29313caabdd37bd"
  Snow on the Blades
  Matching fields: title
SCORE: 5.509652137756348  _id:  "573a13e6f29313caabdc66c4"
  The Snow White Murder Case
  Matching fields: title
SCORE: 5.065053939819336  _id:  "573a13e2f29313caabdbeded"
  Dead Snow 2: Red vs.
  Matching fields: title
SCORE: 1  _id:  "573a13acf29313caabd29366"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13adf29313caabd2b765"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13b0f29313caabd333e7"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13b0f29313caabd3486a"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13b1f29313caabd3719d"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13b2f29313caabd3abb9"
  No highlights found.
  Matching fields: unknown

[
  {
    $search: {
      index: "compound-query-custom-score-tutorial",
      compound: {
        must: [{
          text: {
            query: "comedy",
            path: "genres",
            score: {boost: {value: 9}}
          }
        },
        {
          text: {
            query: "snow",
            path: "title",
            score: {boost: {value: 5}}
          }
        }],
        should: [{
          range: {
            path: "year",
            gte: 2013,
            lte: 2015,
            score: {boost: {value: 3}}
          }
        }]
      }
    }
  }
]

SCORE: 21.872983932495117  _id:  "573a13c2f29313caabd6874c"
  plot: "A ski vacation turns horrific for a group of medical students, as they…"
  genres: Array
  runtime: 91
SCORE: 21.043487548828125  _id:  "573a139ff29313caabcffff8"
  fullplot: "When an entire town in upstate New York is closed down by an unexpecte…"
  imdb: Object
  year: 2000
SCORE: 21.043487548828125  _id:  "573a13a6f29313caabd16b02"
  plot: "When a Miami dentist inherits a team of sled dogs, he's got to learn t…"
  genres: Array
  runtime: 99
SCORE: 19.523927688598633  _id:  "573a13a1f29313caabd06765"
  fullplot: "Our two young lovers meet on a series of snowy days in high school. Ye…"
  imdb: Object
  runtime: 1999
SCORE: 17.426334381103516  _id:  "573a13e2f29313caabdbeded"
  plot: "Still on the run from a group of Nazi zombies, a man seeks the aid of …"
  genres: Array
  runtime: 100
SCORE: 16.367326736450195  _id:  "573a13c2f29313caabd6688e"
  countries: Array
  genres: Array
  runtime: 108
SCORE: 15.537829399108887  _id:  "573a13b1f29313caabd36d7d"
  plot: "A love-struck Italian poet is stuck in Iraq at the onset of an America…"
  genres: Array
  runtime: 110
SCORE: 14.4263334274292  _id:  "573a1395f29313caabce1925"
  plot: "An ice-skating Snow White finds refuge from the Wicked Queen with the …"
  genres: Array
  runtime: 107

[
  {
    $search: {
      index: "compound-query-custom-score-tutorial",
      compound: {
        must: [{
          range: {
            path: "year",
            gte: 2013,
            lte: 2015,
          }
        }],
        should: [{
          text: {
            query: "snow",
            path: "title",
            score: {
              function: {
                add: [{
                  path: {
                    value: "imdb.rating",
                    undefined: 2
                  }
                },
                {
                  score: "relevance"
                }]
              }
            }
          }
        }]
      },
      highlight: {
        path: "title"
      }
    }
  }
]

SCORE: 10.454826354980469  _id:  "573a13e6f29313caabdc66c4"
  The Snow White Murder Case
  Matching fields: title
SCORE: 10.3317232131958  _id:  "573a13edf29313caabdd37bd"
  Snow on the Blades
  Matching fields: title
SCORE: 10.032526969909668  _id:  "573a13e2f29313caabdbeded"
  Dead Snow 2: Red vs.
  Matching fields: title
SCORE: 8.386146545410156  _id:  "573a13d7f29313caabda38ad"
  Snow in Paradise
  Matching fields: title
SCORE: 1  _id:  "573a13acf29313caabd29366"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13adf29313caabd2b765"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13b0f29313caabd333e7"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13b0f29313caabd3486a"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13b1f29313caabd3719d"
  No highlights found.
  Matching fields: unknown
SCORE: 1  _id:  "573a13b2f29313caabd3abb9"
  No highlights found.
  Matching fields: unknown

展开查询结果。

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。

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。

使用 sample_mflix 数据库。

在 mongosh 提示符下运行以下命令：

use sample_mflix

使用 compound 操作符对 movies 集合运行以下 Atlas Search 查询。

以下示例使用带有子查询的 compound 操作符来搜索 2013 至 2015 年间且 title 字段中包含词语 snow 的电影。

db.movies.aggregate([
  {
    "$search": {
      "index": "compound-query-custom-score-tutorial",
      "compound": {
        "filter": [{
          "range": {
            "path": "year",
            "gte": 2013,
            "lte": 2015
          }
        }],
        "should": [{
          "text": {
            "query": "snow",
            "path": "title",
            "score": {"constant": {"value": 5}}
          }
        }]
      },
      "highlight": {
        "path": "title"
      }
    }
  },
  {
    "$limit": 10
  },
  {
    "$project": {
      "_id": 0,
      "title": 1,
      "year": 1,
      "score": { "$meta": "searchScore" },
      "highlights": { "$meta": "searchHighlights" }
    }
  }
])

 [
  {
    title: 'Snow in Paradise',
    year: 2014,
    score: 5,
    highlights: [
      {
        score: 1.382846713066101,
        path: 'title',
        texts: [
          { value: 'Snow', type: 'hit' },
          { value: ' in Paradise', type: 'text' }
        ]
      }
    ]
  },
  {
    title: 'Dead Snow 2: Red vs. Dead',
    year: 2014,
    score: 5,
    highlights: [
      {
        score: 1.3924485445022583,
        path: 'title',
        texts: [
          { value: 'Dead ', type: 'text' },
          { value: 'Snow', type: 'hit' },
          { value: ' 2: Red vs. ', type: 'text' }
        ]
      }
    ]
  },
  {
    title: 'The Snow White Murder Case',
    year: 2014,
    score: 5,
    highlights: [
      {
        score: 1.3525336980819702,
        path: 'title',
        texts: [
          { value: 'The ', type: 'text' },
          { value: 'Snow', type: 'hit' },
          { value: ' White Murder Case', type: 'text' }
        ]
      }
    ]
  },
  {
    title: 'Snow on the Blades',
    year: 2014,
    score: 5,
    highlights: [
      {
        score: 1.3766303062438965,
        path: 'title',
        texts: [
          { value: 'Snow', type: 'hit' },
          { value: ' on the Blades', type: 'text' }
        ]
      }
    ]
  },
  { year: 2013, title: 'The Secret Life of Walter Mitty', score: 0, highlights: [] },
  { title: 'Jurassic World', year: 2015, score: 0, highlights: [] },
  { title: 'Action Jackson', year: 2014, score: 0, highlights: [] },
  { year: 2013, title: 'In Secret', score: 0, highlights: [] },
  { title: 'The Stanford Prison Experiment', year: 2015, score: 0, highlights: [] },
  { year: 2014, title: 'The Giver', score: 0, highlights: [] }
]

db.movies.aggregate([
  {
    "$search": {
      "index": "compound-query-custom-score-tutorial",
      "compound": {
        "must": [{
          "range": {
            "path": "year",
            "gte": 2013,
            "lte": 2015
          }
        }],
        "should": [{
          "text": {
            "query": "snow",
            "path": "title",
            "score": {"boost": {"value": 2}}
          }
        }]
      },
      "highlight": {
        "path": "title"
      }
    }
  },
  {
    "$limit": 10
  },
  {
    "$project": {
      "_id": 0,
      "title": 1,
      "year": 1,
      "score": { "$meta": "searchScore" },
      "highlights": { "$meta": "searchHighlights" }
    }
  }
])

[
  {
    title: 'Snow in Paradise',
    year: 2014,
    score: 6.7722930908203125,
    highlights: [
     {
       score: 1.382846713066101,
       path: 'title',
       texts: [
         { value: 'Snow', type: 'hit' },
         { value: ' in Paradise', type: 'text' }
       ]
     }
    ]
  },
  {
    title: 'Snow on the Blades',
    year: 2014,
    score: 6.063445568084717,
    highlights: [
      {
        score: 1.3766303062438965,
        path: 'title',
        texts: [
          { value: 'Snow', type: 'hit' },
          { value: ' on the Blades', type: 'text' }
        ]
      }
    ]
  },
  {
    title: 'The Snow White Murder Case',
    year: 2014,
    score: 5.509652137756348,
    highlights: [
      {
        score: 1.3525336980819702,
        path: 'title',
        texts: [
          { value: 'The ', type: 'text' },
          { value: 'Snow', type: 'hit' },
          { value: ' White Murder Case', type: 'text' }
        ]
      }
    ]
  },
  {
    title: 'Dead Snow 2: Red vs. Dead',
    year: 2014,
    score: 5.065053939819336,
    highlights: [
      {
        score: 1.3924485445022583,
        path: 'title',
        texts: [
          { value: 'Dead ', type: 'text' },
          { value: 'Snow', type: 'hit' },
          { value: ' 2: Red vs. ', type: 'text' }
        ]
      }
    ]
  },
  { year: 2013, title: 'The Secret Life of Walter Mitty', score: 1, highlights: [] },
  { title: 'Jurassic World', year: 2015, score: 1, highlights: [] },
  { title: 'Action Jackson', year: 2014, score: 1, highlights: [] },
  { year: 2013, title: 'In Secret', score: 1, highlights: [] },
  { title: 'The Stanford Prison Experiment', year: 2015, score: 1,  highlights: [] },
  { year: 2014, title: 'The Giver', score: 1, highlights: [] }
]

db.movies.aggregate([
    {
      "$search": {
        "index": "compound-query-custom-score-tutorial",
        "compound": {
          "must": [{
            "text": {
              "path": "genres",
              "query": "comedy",
              "score": {"boost": {"value": 9}}
            }
          },
          {
            "text": {
              "path": "title",
              "query": "snow",
              "score": {"boost": {"value": 5}}
            }
          }],
          "should": [{
            "range": {
              "path": "year",
              "gte": 2013,
              "lte": 2015,
              "score": {"boost": {"value": 3}}
            }
          }]
        }
      }
    },
    {
      "$limit": 10
    },
    {
      "$project": {
        "_id": 0,
        "title": 1,
        "year": 1,
        "genres": 1,
        "score": { "$meta": "searchScore" }
      }
    }
])

  [
    {
      genres: [ 'Comedy', 'Horror' ],
      title: 'Dead Snow',
      year: 2009,
      score: 21.872983932495117
    },
    {
      year: 2000,
      genres: [ 'Adventure', 'Comedy', 'Family' ],
      title: 'Snow Day',
      score: 21.043487548828125
    },
    {
      genres: [ 'Adventure', 'Comedy', 'Family' ],
      title: 'Snow Dogs',
      year: 2002,
      score: 21.043487548828125
    },
    {
      year: 1999,
      genres: [ 'Comedy', 'Romance' ],
      title: 'Let It Snow',
      score: 19.523927688598633
    },
    {
      genres: [ 'Action', 'Comedy', 'Horror' ],
      title: 'Dead Snow 2: Red vs. Dead',
      year: 2014,
      score: 17.426334381103516
    },
    {
      genres: [ 'Comedy', 'Drama' ],
      title: 'Snow White and Russian Red',
      year: 2009,
      score: 16.367326736450195
    },
    {
      genres: [ 'Comedy', 'Drama', 'Romance' ],
      title: 'The Tiger and the Snow',
      year: 2005,
      score: 15.537829399108887
    },
    {
      genres: [ 'Adventure', 'Comedy', 'Family' ],
      title: 'Snow White and the Three Stooges',
      year: 1961,
      score: 14.4263334274292
    }
  ]

db.movies.aggregate([
  {
    "$search": {
      "index": "compound-query-custom-score-tutorial",
      "compound": {
        "must": [{
          "range": {
            "path": "year",
            "gte": 2013,
            "lte": 2015
          }
        }],
        "should": [{
          "text": {
            "query": "snow",
            "path": "title",
            "score": {
              "function": {
                "add": [{
                  "path": {
                    "value": "imdb.rating",
                    "undefined": 2
                  }
                },
                {
                  "score": "relevance"
                }]
              }
            }
          }
        }]
      },
      "highlight": {
        "path": "title"
      }
    }
  },
  {
    "$limit": 10
  },
  {
    "$project": {
      "_id": 0,
      "title": 1,
      "year": 1,
      "score": { "$meta": "searchScore" },
      "highlights": { "$meta": "searchHighlights" }
    }
  }
])

 [
  {
    title: 'The Snow White Murder Case',
    year: 2014,
    score: 10.454826354980469,
    highlights: [
      {
        score: 1.3525336980819702,
        path: 'title',
        texts: [
          { value: 'The ', type: 'text' },
          { value: 'Snow', type: 'hit' },
          { value: ' White Murder Case', type: 'text' }
        ]
      }
    ]
  },
  {
    title: 'Snow on the Blades',
    year: 2014,
    score: 10.3317232131958,
    highlights: [
      {
        score: 1.3766303062438965,
        path: 'title',
        texts: [
          { value: 'Snow', type: 'hit' },
          { value: ' on the Blades', type: 'text' }
        ]
      }
    ]
  },
  {
    title: 'Dead Snow 2: Red vs. Dead',
    year: 2014,
    score: 10.032526969909668,
    highlights: [
      {
        score: 1.3924485445022583,
        path: 'title',
        texts: [
          { value: 'Dead ', type: 'text' },
          { value: 'Snow', type: 'hit' },
          { value: ' 2: Red vs. ', type: 'text' }
        ]
      }
    ]
  },
  {
    title: 'Snow in Paradise',
    year: 2014,
    score: 8.386146545410156,
    highlights: [
      {
        score: 1.382846713066101,
        path: 'title',
        texts: [
          { value: 'Snow', type: 'hit' },
          { value: ' in Paradise', type: 'text' }
        ]
      }
    ]
  },
  { year: 2013, title: 'The Secret Life of Walter Mitty', score: 1, highlights: [] },
  { title: 'Jurassic World', year: 2015, score: 1, highlights: [] },
  { title: 'Action Jackson', year: 2014, score: 1, highlights: [] },
  { year: 2013, title: 'In Secret', score: 1, highlights: [] },
  { title: 'The Stanford Prison Experiment', year: 2015, score: 1, highlights: [] },
  { year: 2014, title: 'The Giver', score: 1, highlights: [] }
]

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。

使用 sample_mflix 数据库中的 movies 集合。

在 Database 屏幕上，依次单击 sample_mflix 数据库和 movies 集合。

运行 Atlas Search 复合查询，使用 constant 选项更改分数。

该查询将执行以下任务：

• 使用以下 compound 操作符子句：

  • filter 带有范围操作符的子句，用于搜索 2013 到 2015 年之间的电影。

  • should 子句和 text 操作符，以在 title 字段中查询词语 snow，并使用 constant 选项更改 score。constant 选项将搜索词的所有得分结果替换为 5。

• 指定突出显示选项，从 title 字段返回与查询匹配的代码段。这些代码段包含使用 type: 'hit' 指定的匹配文本以及使用 type: 'text' 指定的相邻文本。

• 使用以下管道阶段：

  • $limit 阶段将输出限制为 10 个结果。

  • $project 阶段：

    • 排除 title 和 year 之外的所有字段

    • 添加两个名为 score 和 highlights 的字段

若要在 MongoDB Compass 中运行此查询：

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": "compound-query-custom-score-tutorial", "compound": { "filter": [{ "range": { "path": "year", "gte": 2013, "lte": 2015 } }], "should": [{ "text": { "query": "snow", "path": "title", "score": {"constant": {"value": 5}} } }] }, "highlight": { "path": "title" } }
   $limit	10
   $project	{ "_id": 0, "title": 1, "year": 1, "score": { "$meta": "searchScore" }, "highlights": { "$meta": "searchHighlights" } }

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

   {
     title: 'Snow in Paradise',
     year: 2014,
     score: 5,
     highlights: [{
       score: 1.382846713066101,
       path: 'title',
       texts: [
         { value: 'Snow', type: 'hit' },
         { value: ' in Paradise', type: 'text' }
       ]
     }]
   },
   {
     title: 'Dead Snow 2: Red vs. Dead',
     year: 2014,
     score: 5,
     highlights: [{
       score: 1.3924485445022583,
       path: 'title',
       texts: [
         { value: 'Dead ', type: 'text' },
         { value: 'Snow', type: 'hit' },
         { value: ' 2: Red vs. ', type: 'text' }
       ]
     }]
   },
   {
     title: 'The Snow White Murder Case',
     year: 2014,
     score: 5,
     highlights: [{
       score: 1.3525336980819702,
       path: 'title',
       texts: [
         { value: 'The ', type: 'text' },
         { value: 'Snow', type: 'hit' },
         { value: ' White Murder Case', type: 'text' }
       ]
     }]
   },
   {
     title: 'Snow on the Blades',
     year: 2014,
     score: 5,
     highlights: [{
       score: 1.3766303062438965,
       path: 'title',
       texts: [
         { value: 'Snow', type: 'hit' },
         { value: ' on the Blades', type: 'text' }
       ]
     }]
   },
   { year: 2013, title: 'The Secret Life of Walter Mitty', score: 0, highlights: [] },
   { title: 'Jurassic World', year: 2015, score: 0, highlights: [] },
   { title: 'Action Jackson', year: 2014, score: 0, highlights: [] },
   { year: 2013, title: 'In Secret', score: 0, highlights: [] },
   { title: 'The Stanford Prison Experiment', year: 2015, score: 0, highlights: [] },
   { year: 2014, title: 'The Giver', score: 0, highlights: [] }

   结果中列出的前四个文档评分较高，因为查询中的 should 子句将标题中包含 snow 的文档指定为首选项。should 子句还使用 constant 选项更改了查询词 snow 的分数。

运行 Atlas Search 复合查询，使用 boost 选项更改分数。

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。

   简单示例

   多个权重示例

   查询使用了下列管道阶段：

   • $search 执行以下任务：

     • 查询使用以下 compound 操作符子句：

       • must 带有范围操作符的子句，用于搜索 2013 到 2015 年之间的电影。

       • should 子句和 text 操作符，以在 title 字段中查询词语 snow，并使用 boost 选项更改 score。boost 选项将搜索词结果中的基本分数乘以 2。

     • 指定突出显示选项，从 title 字段返回与查询匹配的代码段。这些代码段包含使用 type: 'hit' 指定的匹配文本以及使用 type: 'text' 指定的相邻文本。

   • $limit 阶段将输出限制为 10 个结果。

   • $project 阶段：

     • 排除 title 和 year 之外的所有字段

     • 添加两个名为 score 和 highlights 的字段

   管道阶段	查询
   $search	{ "index": "compound-query-custom-score-tutorial", "compound": { "must": [{ "range": { "path": "year", "gte": 2013, "lte": 2015 } }], "should": [{ "text": { "query": "snow", "path": "title", "score": {"boost": {"value": 2}} } }] }, "highlight": { "path": "title" } }
   $limit	10
   $project	{ "_id": 0, "title": 1, "year": 1, "score": { "$meta": "searchScore" }, "highlights": { "$meta": "searchHighlights" } }

   此查询使用以下管道阶段：

   • $search 执行以下任务：

     • 查询使用 compound 操作符子句和 boost 选项，使某些字段的优先级高于其他字段：

       • must 带文本操作符的子句优先考虑 comedy 类型，然后是 title 字段中的术语 snow。boost 选项将权重应用于字段。

       • should 带有范围操作符的子句，用于搜索 2013 到 2015 年之间的电影。

       注意

       boost选项会对字段应用不同的权重，以确定字段的优先级。

   • $limit 阶段将输出限制为 10 个结果。

   • $project 阶段：

     • 排除 title 和 year 之外的所有字段

     • 添加两个名为 score 和 highlights 的字段

   • must 带文本操作符的子句优先考虑 comedy 类型，然后是 title 字段中的术语 snow。boost 选项将权重应用于字段。

   • should 带有范围操作符的子句，用于搜索 2013 到 2015 年之间的电影。

   注意

   boost选项会对字段应用不同的权重，以确定字段的优先级。

   管道阶段	查询
   $search	{ "index": "compound-query-custom-score-tutorial", "compound": { "must": [{ "text": { "path": "genres", "query": "comedy", "score": {"boost": {"value": 9}} } }, { "text": { "path": "title", "query": "snow", "score": {"boost": {"value": 5}} } }], "should": [{ "range": { "path": "year", "gte": 2013, "lte": 2015, "score": {"boost": {"value": 3}} } }] } }
   $limit	10
   $project	{ "_id": 0, "title": 1, "year": 1, "genres": 1, "score": { "$meta": "searchScore" } }

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

   简单示例

   多个权重示例

   {
     title: 'Snow in Paradise',
     year: 2014,
     score: 6.7722930908203125,
     highlights: [{
       score: 1.382846713066101,
       path: 'title',
       texts: [
         { value: 'Snow', type: 'hit' },
         { value: ' in Paradise', type: 'text' }
       ]
     }]
   },
   {
     title: 'Snow on the Blades',
     year: 2014,
     score: 6.063445568084717,
     highlights: [{
       score: 1.3766303062438965,
       path: 'title',
       texts: [
         { value: 'Snow', type: 'hit' },
         { value: ' on the Blades', type: 'text' }
       ]
     }]
   },
   {
     title: 'The Snow White Murder Case',
     year: 2014,
     score: 5.509652137756348,
     highlights: [{
       score: 1.3525336980819702,
       path: 'title',
       texts: [
         { value: 'The ', type: 'text' },
         { value: 'Snow', type: 'hit' },
         { value: ' White Murder Case', type: 'text' }
       ]
     }]
   },
   {
     title: 'Dead Snow 2: Red vs. Dead',
     year: 2014,
     score: 5.065053939819336,
     highlights: [{
       score: 1.3924485445022583,
       path: 'title',
       texts: [
         { value: 'Dead ', type: 'text' },
         { value: 'Snow', type: 'hit' },
         { value: ' 2: Red vs. ', type: 'text' }
       ]
     }]
   },
   { year: 2013, title: 'The Secret Life of Walter Mitty',score: 1, highlights: [] },
   { title: 'Jurassic World', year: 2015, score: 1, highlights: [] },
   { title: 'Action Jackson', year: 2014, score: 1, highlights: [] },
   { year: 2013, title: 'In Secret', score: 1, highlights: [] },
   { title: 'The Stanford Prison Experiment', year: 2015, score: 1,  highlights: [] },
   { year: 2014, title: 'The Giver', score: 1, highlights: [] }

   结果中列出的前四个文档评分较高，因为查询中的 should 子句将标题中包含 snow 的文档指定为首选项。should 子句还使用 boost 选项更改了查询词 snow 的分数。

     [
       {
         genres: [ 'Comedy', 'Horror' ],
         title: 'Dead Snow',
         year: 2009,
         score: 21.872983932495117
       },
       {
         year: 2000,
         genres: [ 'Adventure', 'Comedy', 'Family' ],
         title: 'Snow Day',
         score: 21.043487548828125
       },
       {
         genres: [ 'Adventure', 'Comedy', 'Family' ],
         title: 'Snow Dogs',
         year: 2002,
         score: 21.043487548828125
       },
       {
         year: 1999,
         genres: [ 'Comedy', 'Romance' ],
         title: 'Let It Snow',
         score: 19.523927688598633
       },
       {
         genres: [ 'Action', 'Comedy', 'Horror' ],
         title: 'Dead Snow 2: Red vs. Dead',
         year: 2014,
         score: 17.426334381103516
       },
       {
         genres: [ 'Comedy', 'Drama' ],
         title: 'Snow White and Russian Red',
         year: 2009,
         score: 16.367326736450195
       },
       {
         genres: [ 'Comedy', 'Drama', 'Romance' ],
         title: 'The Tiger and the Snow',
         year: 2005,
         score: 15.537829399108887
       },
       {
         genres: [ 'Adventure', 'Comedy', 'Family' ],
         title: 'Snow White and the Three Stooges',
         year: 1961,
         score: 14.4263334274292
       }
     ]

运行 Atlas Search 复合查询，使用 function 选项更改分数。

查询使用了下列管道阶段：

• $search 阶段将执行以下任务：

  • 查询使用以下 compound 操作符子句：

    • must 带有范围操作符的子句，用于搜索 2013 到 2015 年之间的电影。

    • should 子句和 text 操作符，以在 title 字段中查询词语 snow，并使用 function 选项更改 score。function 选项使用算术表达式添加以下内容：

      • 搜索词查询的相关性评分

      • 名为 imdb.rating 的数字字段的值，或者对于没有 imdb.rating 字段的文档，数字 2 的值。

  • 指定突出显示选项，从 title 字段返回与查询匹配的代码段。这些代码段包含使用 type: 'hit' 指定的匹配文本以及使用 type: 'text' 指定的相邻文本。

• $limit 阶段将输出限制为 10 个结果。

• $project 阶段：

  • 排除 title 和 year 之外的所有字段

  • 添加两个名为 score 和 highlights 的字段

若要在 MongoDB Compass 中运行此查询：

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": "compound-query-custom-score-tutorial", "compound": { "must": [{ "range": { "path": "year", "gte": 2013, "lte": 2015 } }], "should": [{ "text": { "query": "snow", "path": "title", "score": { "function": { "add": [{ "path": { "value": "imdb.rating", "undefined": 2 } }, { "score": "relevance" }] } } } }]}, "highlight":{ "path": "title" } }
   $limit	10
   $project	{ "_id": 0, "title": 1, "year": 1, "score": { "$meta": "searchScore" }, "highlights": { "$meta": "searchHighlights" } }

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

   {
     title: 'The Snow White Murder Case',
     year: 2014,
     score: 10.454826354980469,
     highlights: [{
         score: 1.3525336980819702,
         path: 'title',
         texts: [
           { value: 'The ', type: 'text' },
           { value: 'Snow', type: 'hit' },
           { value: ' White Murder Case', type: 'text' }
         ]
     }]
   },
   {
     title: 'Snow on the Blades',
     year: 2014,
     score: 10.3317232131958,
     highlights: [{
       score: 1.3766303062438965,
       path: 'title',
       texts: [
         { value: 'Snow', type: 'hit' },
         { value: ' on the Blades', type: 'text' }
       ]
     }]
   },
   {
     title: 'Dead Snow 2: Red vs. Dead',
     year: 2014,
     score: 10.032526969909668,
     highlights: [{
       score: 1.3924485445022583,
       path: 'title',
       texts: [
         { value: 'Dead ', type: 'text' },
         { value: 'Snow', type: 'hit' },
         { value: ' 2: Red vs. ', type: 'text' }
       ]
     }]
   },
   {
     title: 'Snow in Paradise',
     year: 2014,
     score: 8.386146545410156,
     highlights: [{
       score: 1.382846713066101,
       path: 'title',
       texts: [
         { value: 'Snow', type: 'hit' },
         { value: ' in Paradise', type: 'text' }
       ]
     }]
   },
   { year: 2013, title: 'The Secret Life of Walter Mitty', score: 1, highlights: [] },
   { title: 'Jurassic World', year: 2015, score: 1, highlights: [] },
   { title: 'Action Jackson', year: 2014, score: 1, highlights: [] },
   { year: 2013, title: 'In Secret', score: 1, highlights: [] },
   { title: 'The Stanford Prison Experiment', year: 2015, score: 1, highlights: [] },
   { year: 2014, title: 'The Giver', score: 1, highlights: [] }

   结果中列出的前四个文档评分较高，因为查询中的 should 子句将标题中包含 snow 的文档指定为首选项。should 子句还使用 function 选项更改了查询词 snow 的分数。

运行 Atlas Search 复合查询，使用 constant 选项更改分数。

1. 创建一个名为 compound-constant-example 的新目录，并使用 dotnet new 命令初始化项目。

   mkdir compound-constant-example
   cd compound-constant-example
   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

3. 将Program.cs文件的内容替换为以下代码。

   此代码示例将执行以下任务：

   • 导入mongodb包和依赖项。

   • 建立与您的 Atlas 集群的连接。

   • 使用以下复合子句查询集合：

     • filter 带有范围操作符的子句，用于搜索 2013 到 2015 年之间的电影。

     • should 子句和 text 操作符，以在 title 字段中查询词语 snow，并使用 constant 选项更改 score。constant 选项将搜索词的所有得分结果替换为 5。

   • 指定突出显示选项，从 title 字段返回与查询匹配的代码段。这些代码段包含使用 type: 'hit' 指定的匹配文本以及使用 type: 'text' 指定的相邻文本。

   • 使用以下管道阶段：

     • $limit 阶段将输出限制为 10 个结果。

     • $project 阶段：

       • 排除 title 和 year 之外的所有字段

       • 添加两个名为 score 和 highlights 的字段

   • 遍历游标以打印与查询匹配的文档。

   1
   using MongoDB.Bson;
   2
   using MongoDB.Bson.Serialization.Attributes;
   3
   using MongoDB.Bson.Serialization.Conventions;
   4
   using MongoDB.Driver;
   5
   using MongoDB.Driver.Search;
   6
   7
   public class CompoundConstantExample
   8
   {
   9
       private const string MongoConnectionString = "<connection-string>";
   10
   11
       public static void Main(string[] args)
   12
       {
   13
           // allow automapping of the camelCase database fields to our MovieDocument
   14
           var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
   15
           ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
   16
   17
           // connect to your Atlas cluster
   18
           var mongoClient = new MongoClient(MongoConnectionString);
   19
           var mflixDatabase = mongoClient.GetDatabase("sample_mflix");
   20
           var moviesCollection = mflixDatabase.GetCollection<MovieDocument>("movies");
   21
   22
           // define and run pipeline
   23
           var results = moviesCollection.Aggregate()
   24
               .Search(Builders<MovieDocument>.Search.Compound()
   25
                   .Filter(Builders<MovieDocument>.Search.Range(movie => movie.Year, SearchRangeBuilder.Gte(2013).Lte(2015)))
   26
                   .Should(Builders<MovieDocument>.Search.Text(movie => movie.Title, "snow", score: new SearchScoreDefinitionBuilder<MovieDocument>().Constant(5))), 
   27
                   new SearchHighlightOptions<MovieDocument>(movie => movie.Title),
   28
                   indexName: "compound-query-custom-score-tutorial")
   29
               .Project<MovieDocument>(Builders<MovieDocument>.Projection
   30
                   .Include(movie => movie.Title)
   31
                   .Include(movie => movie.Year)
   32
                   .Exclude(movie => movie.Id)
   33
                   .MetaSearchScore(movie => movie.Score)
   34
                   .MetaSearchHighlights("highlights"))
   35
               .Limit(10)
   36
               .ToList();
   37
   38
           // print results
   39
           foreach (var movie in results)
   40
           {
   41
               Console.WriteLine(movie.ToJson());
   42
           }
   43
       }
   44
   }
   45
   46
   [BsonIgnoreExtraElements]
   47
   public class MovieDocument
   48
   {
   49
       [BsonIgnoreIfDefault]
   50
       public ObjectId Id { get; set; }
   51
       public string Title { get; set; }
   52
       public int Year { get; set; }
   53
       [BsonElement("highlights")]
   54
       public List<SearchHighlight> Highlights { get; set; }
   55
       public double Score { get; set; }
   56
   }

4. 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Program.cs文件。

   dotnet run compound-constant-example.csproj

   隐藏输出

   {
     "title" : "Snow in Paradise",
     "year" : 2014,
     "highlights" : [{
       "path" : "title",
       "score" : 1.3828467130661011,
       "texts" : [{ "type" : "Hit", "value" : "Snow" }, { "type" : "Text", "value" : " in Paradise" }]
     }],
     "score" : 5.0
   }
   {
     "title" : "Dead Snow 2: Red vs. Dead",
     "year" : 2014,
     "highlights" : [{
       "path" : "title",
       "score" : 1.3924485445022583,
       "texts" : [{ "type" : "Text", "value" : "Dead " }, { "type" : "Hit", "value" : "Snow" }, { "type" : "Text", "value" : " 2: Red vs. " }]
     }],
     "score" : 5.0
   }
   {
     "title" : "The Snow White Murder Case",
     "year" : 2014,
     "highlights" : [{
       "path" : "title",
       "score" : 1.3525336980819702,
       "texts" : [{ "type" : "Text", "value" : "The " }, { "type" : "Hit", "value" : "Snow" }, { "type" : "Text", "value" : " White Murder Case" }]
     }],
     "score" : 5.0
   }
   {
     "title" : "Snow on the Blades",
     "year" : 2014,
     "highlights" : [{
       "path" : "title",
       "score" : 1.3766303062438965,
       "texts" : [{ "type" : "Hit", "value" : "Snow" }, { "type" : "Text", "value" : " on the Blades" }]
     }],
     "score" : 5.0
   }
   { "title" : "The Secret Life of Walter Mitty", "year" : 2013, "highlights" : [], "score" : 0.0 }
   { "title" : "Jurassic World", "year" : 2015, "highlights" : [], "score" : 0.0 }
   { "title" : "Action Jackson", "year" : 2014, "highlights" : [], "score" : 0.0 }
   { "title" : "In Secret", "year" : 2013, "highlights" : [], "score" : 0.0 }
   { "title" : "The Stanford Prison Experiment", "year" : 2015, "highlights" : [], "score" : 0.0 }
   { "title" : "The Giver", "year" : 2014, "highlights" : [], "score" : 0.0 }

   结果中列出的前四个文档评分较高，因为查询中的 should 子句将标题中包含 snow 的文档指定为首选项。should 子句还使用 constant 选项更改了查询词 snow 的分数。

运行 Atlas Search 复合查询，使用 boost 选项更改分数。

1. 创建一个名为 compound-boost-example 的新目录，并使用 dotnet new 命令初始化项目。

   mkdir compound-boost-example
   cd compound-boost-example
   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

3. 将Program.cs文件的内容替换为以下代码。

   此代码示例将执行以下任务：

   • 导入mongodb包和依赖项。

   • 建立与您的 Atlas 集群的连接。

   • 遍历游标以打印与查询匹配的文档。

   简单示例

   多个权重示例

   查询使用了下列管道阶段：

   • $search 查询集合阶段。查询：

     • 使用以下 compound 操作符子句：

       • must 带有范围操作符的子句，用于搜索 2013 到 2015 年之间的电影。

       • should 子句和 text 操作符，以在 title 字段中查询词语 snow，并使用 boost 选项更改 score。boost 选项将搜索词结果中的基本分数乘以 2。

     指定突出显示选项，从 title 字段返回与查询匹配的代码段。这些代码段包含使用 type: 'hit' 指定的匹配文本以及使用 type: 'text' 指定的相邻文本。

   • $limit 阶段将输出限制为 10 个结果。

   • $project 阶段：

     • 排除 title 和 year 之外的所有字段

     • 添加两个名为 score 和 highlights 的字段

   1
   using MongoDB.Bson;
   2
   using MongoDB.Bson.Serialization.Attributes;
   3
   using MongoDB.Bson.Serialization.Conventions;
   4
   using MongoDB.Driver;
   5
   using MongoDB.Driver.Search;
   6
   7
   public class CompoundBoostSingleExample
   8
   {
   9
       private const string MongoConnectionString = "<connection-string>";
   10
   11
       public static void Main(string[] args)
   12
       {
   13
           // allow automapping of the camelCase database fields to our MovieDocument
   14
           var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
   15
           ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
   16
   17
           // connect to your Atlas cluster
   18
           var mongoClient = new MongoClient(MongoConnectionString);
   19
           var mflixDatabase = mongoClient.GetDatabase("sample_mflix");
   20
           var moviesCollection = mflixDatabase.GetCollection<MovieDocument>("movies");
   21
   22
           // define and run pipeline
   23
           var results = moviesCollection.Aggregate()
   24
               .Search(Builders<MovieDocument>.Search.Compound()
   25
                   .Must(Builders<MovieDocument>.Search.Range(movie => movie.Year, SearchRangeBuilder.Gte(2013).Lte(2015)))
   26
                   .Should(Builders<MovieDocument>.Search.Text(movie => movie.Title, "snow", score: new SearchScoreDefinitionBuilder<MovieDocument>().Boost(2))), 
   27
                   new SearchHighlightOptions<MovieDocument>(movie => movie.Title),
   28
                   indexName: "compound-query-custom-score-tutorial")
   29
               .Project<MovieDocument>(Builders<MovieDocument>.Projection
   30
                   .Include(movie => movie.Title)
   31
                   .Include(movie => movie.Year)
   32
                   .Exclude(movie => movie.Id)
   33
                   .MetaSearchScore(movie => movie.Score)
   34
                   .MetaSearchHighlights("highlights"))
   35
               .Limit(10)
   36
               .ToList();
   37
   38
           // print results
   39
           foreach (var movie in results)
   40
           {
   41
               Console.WriteLine(movie.ToJson());
   42
           }
   43
       }
   44
   }
   45
   46
   [BsonIgnoreExtraElements]
   47
   public class MovieDocument
   48
   {
   49
       [BsonIgnoreIfDefault]
   50
       public ObjectId Id { get; set; }
   51
       public string Title { get; set; }
   52
       public int Year { get; set; }
   53
       [BsonElement("highlights")]
   54
       public List<SearchHighlight> Highlights { get; set; }
   55
       public double Score { get; set; }
   56
   }

   此查询使用以下带有boost选项的compound操作符子句，使某些字段的优先级高于其他字段：

   • must 带文本操作符的子句优先考虑 comedy 类型，然后是 title 字段中的术语 snow。boost 选项将权重应用于字段。

   • should 带有范围操作符的子句，用于搜索 2013 到 2015 年之间的电影。

   注意

   boost选项会对字段应用不同的权重，以确定字段的优先级。

   1
   using MongoDB.Bson;
   2
   using MongoDB.Bson.Serialization.Attributes;
   3
   using MongoDB.Bson.Serialization.Conventions;
   4
   using MongoDB.Driver;
   5
   using MongoDB.Driver.Search;
   6
   7
   public class CompoundBoostMultipleExample
   8
   {
   9
       private const string MongoConnectionString = "<connection-string>";
   10
   11
       public static void Main(string[] args)
   12
       {
   13
           // allow automapping of the camelCase database fields to our MovieDocument
   14
           var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
   15
           ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);
   16
   17
           // connect to your Atlas cluster
   18
           var mongoClient = new MongoClient(MongoConnectionString);
   19
           var mflixDatabase = mongoClient.GetDatabase("sample_mflix");
   20
           var moviesCollection = mflixDatabase.GetCollection<MovieDocument>("movies");
   21
   22
           // define and run pipeline
   23
           var results = moviesCollection.Aggregate()
   24
               .Search(Builders<MovieDocument>.Search.Compound()
   25
                   .Must(Builders<MovieDocument>.Search.Text(movie => movie.Genres, "comedy", score: new SearchScoreDefinitionBuilder<MovieDocument>().Boost(9)))
   26
                   .Must(Builders<MovieDocument>.Search.Text(movie => movie.Title, "snow", score: new SearchScoreDefinitionBuilder<MovieDocument>().Boost(5)))
   27
                   .Should(Builders<MovieDocument>.Search.Range(movie => movie.Year, SearchRangeBuilder.Gte(2013).Lte(2015), score: new SearchScoreDefinitionBuilder<MovieDocument>().Boost(3))),
   28
                   indexName: "compound-query-custom-score-tutorial")
   29
               .Project<MovieDocument>(Builders<MovieDocument>.Projection
   30
                   .Include(movie => movie.Genres)
   31
                   .Include(movie => movie.Title)
   32
                   .Include(movie => movie.Year)
   33
                   .Exclude(movie => movie.Id)
   34
                   .MetaSearchScore(movie => movie.Score))
   35
               .Limit(10)
   36
               .ToList();
   37
           
   38
           // print results
   39
           foreach (var movie in results)
   40
           {
   41
               Console.WriteLine(movie.ToJson());
   42
           }
   43
       }
   44
   }
   45
   46
   [BsonIgnoreExtraElements]
   47
   public class MovieDocument
   48
   {
   49
       [BsonIgnoreIfDefault]
   50
       public ObjectId Id { get; set; }
   51
       public string [] Genres { get; set; }
   52
       public string Title { get; set; }
   53
       public int Year { get; set; }
   54
       public double Score { get; set; }
   55
   }

4. 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Program.cs文件。

   简单示例

   多个权重示例

   dotnet run compound-boost-example.csproj

   隐藏输出

   {
     "title" : "Snow in Paradise",
     "year" : 2014,
     "highlights" : [{
       "path" : "title",
       "score" : 1.3828467130661011,
       "texts" : [{ "type" : "Hit", "value" : "Snow" }, { "type" : "Text", "value" : " in Paradise" }]
     }],
     "score" : 6.7722930908203125
   }
   {
     "title" : "Snow on the Blades",
     "year" : 2014,
     "highlights" : [{
       "path" : "title",
       "score" : 1.3766303062438965,
       "texts" : [{ "type" : "Hit", "value" : "Snow" }, { "type" : "Text", "value" : " on the Blades" }]
     }],
     "score" : 6.0634455680847168
   }
   {
     "title" : "The Snow White Murder Case",
     "year" : 2014,
     "highlights" : [{
       "path" : "title",
       "score" : 1.3525336980819702,
       "texts" : [{ "type" : "Text", "value" : "The " }, { "type" : "Hit", "value" : "Snow" }, { "type" : "Text", "value" : " White Murder Case" }]
     }],
     "score" : 5.5096521377563477
   }
   {
     "title" : "Dead Snow 2: Red vs. Dead",
     "year" : 2014,
     "highlights" : [{
       "path" : "title",
       "score" : 1.3924485445022583,
       "texts" : [{ "type" : "Text", "value" : "Dead " }, { "type" : "Hit", "value" : "Snow" }, { "type" : "Text", "value" : " 2: Red vs. " }]
     }],
     "score" : 5.0650539398193359
   }
   { "title" : "The Secret Life of Walter Mitty", "year" : 2013, "highlights" : [], "score" : 1.0 }
   { "title" : "Jurassic World", "year" : 2015, "highlights" : [], "score" : 1.0 }
   { "title" : "Action Jackson", "year" : 2014, "highlights" : [], "score" : 1.0 }
   { "title" : "In Secret", "year" : 2013, "highlights" : [], "score" : 1.0 }
   { "title" : "The Stanford Prison Experiment", "year" : 2015, "highlights" : [], "score" : 1.0 }
   { "title" : "The Giver", "year" : 2014, "highlights" : [], "score" : 1.0 }

   结果中列出的前四个文档评分较高，因为查询中的 should 子句将标题中包含 snow 的文档指定为首选项。should 子句还使用 boost 选项更改了查询词 snow 的分数。

   dotnet run compound-boost-example.csproj

   隐藏输出

   { "genres" : ["Comedy", "Horror"], "title" : "Dead Snow", "year" : 2009, "score" : 21.872983932495117 }
   { "genres" : ["Adventure", "Comedy", "Family"], "title" : "Snow Day", "year" : 2000, "score" : 21.043487548828125 }
   { "genres" : ["Adventure", "Comedy", "Family"], "title" : "Snow Dogs", "year" : 2002, "score" : 21.043487548828125 }
   { "genres" : ["Comedy", "Romance"], "title" : "Let It Snow", "year" : 1999, "score" : 19.523927688598633 }
   { "genres" : ["Action", "Comedy", "Horror"], "title" : "Dead Snow 2: Red vs. Dead", "year" : 2014, "score" : 17.426334381103516 }
   { "genres" : ["Comedy", "Drama"], "title" : "Snow White and Russian Red", "year" : 2009, "score" : 16.367326736450195 }
   { "genres" : ["Comedy", "Drama", "Romance"], "title" : "The Tiger and the Snow", "year" : 2005, "score" : 15.537829399108887 }
   { "genres" : ["Adventure", "Comedy", "Family"], "title" : "Snow White and the Three Stooges", "year" : 1961, "score" : 14.426333427429199 }