$sortByCount (aggregation stage)（聚合阶段）

Consider a collection exhibits with the following documents:考虑一个包含以下文件的集合exhibits（展品）：

db.exhibits.insertMany(
   [
      { _id: 1, title: "The Pillars of Society", artist: "Grosz", year: 1926, tags: [ "painting", "satire", "Expressionism", "caricature" ] },
      { _id: 2, title: "Melancholy III", artist: "Munch", year: 1902, tags: [ "woodcut", "Expressionism" ] },
      { _id: 3, title: "Dancer", artist: "Miro", year: 1925, tags: [ "oil", "Surrealism", "painting" ] },
      { _id: 4, title: "The Great Wave off Kanagawa", artist: "Hokusai", tags: [ "woodblock", "ukiyo-e" ] },
      { _id: 5, title: "The Persistence of Memory", artist: "Dali", year: 1931, tags: [ "Surrealism", "painting", "oil" ] },
      { _id: 6, title: "Composition VII", artist: "Kandinsky", year: 1913, tags: [ "oil", "painting", "abstract" ] },
      { _id: 7, title: "The Scream", artist: "Munch", year: 1893, tags: [ "Expressionism", "painting", "oil" ] },
      { _id: 8, title: "Blue Flower", artist: "O'Keefe", year: 1918, tags: [ "abstract", "painting" ] }
   ]
)

The following operation unwinds the tags array and uses the $sortByCount stage to count the number of documents associated with each tag:以下操作将用$unwind展开此tags数组，并使用$sortByCount阶段来计算与每个标签关联的文档数量：

db.exhibits.aggregate( [ { $unwind: "$tags" },  { $sortByCount: "$tags" } ] )

The operation returns the following documents, sorted in descending order by count:该操作返回以下文档，按计数降序排列：

[
   { _id: "painting", count: 6 },
   { _id: "oil", count: 4 },
   { _id: "Expressionism", count: 3 },
   { _id: "Surrealism", count: 2 },
   { _id: "abstract", count: 2 },
   { _id: "woodblock", count: 1 },
   { _id: "woodcut", count: 1 },
   { _id: "ukiyo-e", count: 1 },
   { _id: "satire", count: 1 },
   { _id: "caricature", count: 1 }
]

The C# examples on this page use the sample_mflix database from the Atlas sample datasets. To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see Get Started in the MongoDB .NET/C# Driver documentation.本页上的C#示例使用Atlas示例数据集中的sample_mflix数据库。要了解如何创建免费的MongoDB Atlas集群并加载示例数据集，请参阅MongoDB .NET/C#驱动程序文档中的入门。

The following Movie class models the documents in the sample_mflix.movies collection:以下Movie类对sample_mflix.movies集合中的文档进行建模：

public class Movie
{
    public ObjectId Id { get; set; }

    public int Runtime { get; set; }
    
    public string Title { get; set; }

    public string Rated { get; set; }

    public List<string> Genres { get; set; }

    public string Plot { get; set; }
    
    public ImdbData Imdb { get; set; }

    public int Year { get; set; }

    public int Index { get; set; }
    
    public string[] Comments { get; set; }
   
    [BsonElement("lastupdated")]
    public DateTime LastUpdated { get; set; }
}

var camelCaseConvention = new ConventionPack { new CamelCaseElementNameConvention() };
ConventionRegistry.Register("CamelCase", camelCaseConvention, type => true);

To use the MongoDB .NET/C# driver to add a $sortByCount stage to an aggregation pipeline, call the SortByCount() method on a PipelineDefinition object.要使用MongoDB .NET/C#驱动程序将$sortByCount阶段添加到聚合管道中，请在PipelineDefinition对象上调用SortByCount()方法。

The following example creates a pipeline stage that groups the incoming Movie documents by the value of their Rated field, and then returns the number of documents in each group:以下示例创建了一个管道阶段，该阶段根据Rated字段的值对传入的Movie文档进行分组，然后返回每个组中的文档数量：

var pipeline = new EmptyPipelineDefinition<Movie>()
    .SortByCount(m => m.Rated);

The Node.js examples on this page use the sample_mflix database from the Atlas sample datasets. To learn how to create a free MongoDB Atlas cluster and load the sample datasets, see Get Started in the MongoDB Node.js driver documentation.本页上的Node.js示例使用Atlas示例数据集中的sample_mflix数据库。要了解如何创建免费的MongoDB Atlas集群并加载示例数据集，请参阅MongoDB Node.js驱动程序文档中的入门。

To use the MongoDB Node.js driver to add a $sortByCount stage to an aggregation pipeline, use the $sortByCount operator in a pipeline object.要使用MongoDB Node.js驱动程序将$sortByCount阶段添加到聚合管道中，请在管道对象中使用$sortByCount运算符。

The following example creates a pipeline stage that groups the incoming movie documents by the value of their rated field, and then returns the number of documents in each group. The example then runs the aggregation pipeline:以下示例创建了一个管道阶段，该阶段根据其rated字段的值对传入的movie文档进行分组，然后返回每个组中的文档数量。然后，该示例运行聚合管道：

const pipeline = [{ $sortByCount: "$rated" }];

const cursor = collection.aggregate(pipeline);
return cursor;