$bucketAuto (aggregation stage)（聚合阶段）

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

{ "_id" : 1, "title" : "The Pillars of Society", "artist" : "Grosz", "year" : 1926,
    "price" : Decimal128("199.99"),
    "dimensions" : { "height" : 39, "width" : 21, "units" : "in" } }
{ "_id" : 2, "title" : "Melancholy III", "artist" : "Munch", "year" : 1902,
    "price" : Decimal128("280.00"),
    "dimensions" : { "height" : 49, "width" : 32, "units" : "in" } }
{ "_id" : 3, "title" : "Dancer", "artist" : "Miro", "year" : 1925,
    "price" : Decimal128("76.04"),
    "dimensions" : { "height" : 25, "width" : 20, "units" : "in" } }
{ "_id" : 4, "title" : "The Great Wave off Kanagawa", "artist" : "Hokusai",
    "price" : Decimal128("167.30"),
    "dimensions" : { "height" : 24, "width" : 36, "units" : "in" } }
{ "_id" : 5, "title" : "The Persistence of Memory", "artist" : "Dali", "year" : 1931,
    "price" : Decimal128("483.00"),
    "dimensions" : { "height" : 20, "width" : 24, "units" : "in" } }
{ "_id" : 6, "title" : "Composition VII", "artist" : "Kandinsky", "year" : 1913,
    "price" : Decimal128("385.00"),
    "dimensions" : { "height" : 30, "width" : 46, "units" : "in" } }
{ "_id" : 7, "title" : "The Scream", "artist" : "Munch",
    "price" : Decimal128("159.00"),
    "dimensions" : { "height" : 24, "width" : 18, "units" : "in" } }
{ "_id" : 8, "title" : "Blue Flower", "artist" : "O'Keefe", "year" : 1918,
    "price" : Decimal128("118.42"),
    "dimensions" : { "height" : 24, "width" : 20, "units" : "in" } }

db.artwork.aggregate( [
  {
    $bucketAuto: {
        groupBy: "$price",
        buckets: 4
    }
  }
] )

{
  "_id" : {
    "min" : Decimal128("76.04"),
    "max" : Decimal128("159.00")
  },
  "count" : 2
}
{
  "_id" : {
    "min" : Decimal128("159.00"),
    "max" : Decimal128("199.99")
  },
  "count" : 2
}
{
  "_id" : {
    "min" : Decimal128("199.99"),
    "max" : Decimal128("385.00")
  },
  "count" : 2
}
{
  "_id" : {
    "min" : Decimal128("385.00"),
    "max" : Decimal128("483.00")
  },
  "count" : 2
}

db.artwork.aggregate( [
  {
    $facet: {
      "price": [
        {
          $bucketAuto: {
            groupBy: "$price",
            buckets: 4
          }
        }
      ],
      "year": [
        {
          $bucketAuto: {
            groupBy: "$year",
            buckets: 3,
            output: {
              "count": { $sum: 1 },
              "years": { $push: "$year" }
            }
          }
        }
      ],
      "area": [
        {
          $bucketAuto: {
            groupBy: {
              $multiply: [ "$dimensions.height", "$dimensions.width" ]
            },
            buckets: 4,
            output: {
              "count": { $sum: 1 },
              "titles": { $push: "$title" }
            }
          }
        }
      ]
    }
  }
] )

{
  "area" : [
    {
      "_id" : { "min" : 432, "max" : 500 },
      "count" : 3,
      "titles" : [
        "The Scream",
        "The Persistence of Memory",
        "Blue Flower"
      ]
    },
    {
      "_id" : { "min" : 500,   "max" : 864 },
      "count" : 2,
      "titles" : [
        "Dancer",
        "The Pillars of Society"
      ]
    },
    {
      "_id" : { "min" : 864, "max" : 1568 },
      "count" : 2,
      "titles" : [
        "The Great Wave off Kanagawa",
        "Composition VII"
      ]
    },
    {
      "_id" : { "min" : 1568, "max" : 1568 },
      "count" : 1,
      "titles" : [
        "Melancholy III"
      ]
    }
  ],
  "price" : [
    {
      "_id" : { "min" : Decimal128("76.04"), "max" : Decimal128("159.00") },
      "count" : 2
    },
    {
      "_id" : { "min" : Decimal128("159.00"), "max" : Decimal128("199.99") },
      "count" : 2
    },
    {
      "_id" : { "min" : Decimal128("199.99"), "max" : Decimal128("385.00") },
      "count" : 2 },
    {
      "_id" : { "min" : Decimal128("385.00"), "max" : Decimal128("483.00") },
      "count" : 2
    }
  ],
  "year" : [
    { "_id" : { "min" : null, "max" : 1913 }, "count" : 3, "years" : [ 1902 ] },
    { "_id" : { "min" : 1913, "max" : 1926 }, "count" : 3, "years" : [ 1913, 1918, 1925 ] },
    { "_id" : { "min" : 1926, "max" : 1931 }, "count" : 2, "years" : [ 1926, 1931 ] }
  ]
}

The C# examples on this page use the sample_mflix database from the Atlas sample datasets. 本页上的C#示例使用Atlas示例数据集中的sample_mflix数据库。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.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 $bucketAuto stage to an aggregation pipeline, call the BucketAuto() method on a PipelineDefinition object.要使用MongoDB .NET/C#驱动程序将BucketAuto()阶段添加到聚合管道中，请在PipelineDefinition对象上调用bucketAuto()方法。

The following example creates a pipeline stage that evenly distributes documents into five buckets by the value of their Runtime field:以下示例创建了一个管道阶段，该阶段根据其Runtime字段的值将文档平均分配到五个桶中：

var pipeline = new EmptyPipelineDefinition<Movie>()
    .BucketAuto(
        groupBy: m => m.Runtime,
        buckets: 5);

You can use an AggregateBucketAutoOptions object to specify a preferred number -based scheme to set boundary values. 您可以使用AggregateBucketAutoOptions对象指定基于首选数字的方案设置边界值。The following example performs the same $bucketAuto operation as the previous example, but also sets the bucket boundaries at powers of 2:以下示例执行与前一个示例相同的$bucketAuto操作，但也将桶边界设置为2的幂次方：

var bucketAutoOptions = new AggregateBucketAutoOptions()
{
    Granularity = new AggregateBucketAutoGranularity("POWERSOF2")
};

var pipeline = new EmptyPipelineDefinition<Movie>()
    .BucketAuto(
        groupBy: m => m.Runtime,
        buckets: 5,
        options: bucketAutoOptions);

The Node.js examples on this page use the sample_mflix database from the Atlas sample datasets. 本页上的Node.js示例使用Atlas示例数据集中的sample_mflix数据库。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.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 $bucketAuto stage to an aggregation pipeline, use the $bucketAuto operator in a pipeline object.要使用MongoDB Node.js驱动程序将$bucketAuto阶段添加到聚合管道中，请在管道对象中使用$bucketAuto运算符。

The following example creates a pipeline stage that evenly distributes documents into five buckets by the value of their runtime field. The example then runs the aggregation pipeline:以下示例创建了一个管道阶段，该阶段根据文档的runtime字段的值将文档平均分配到五个桶中。然后，该示例运行聚合管道：

const pipeline = [
  {
    $bucketAuto: {
      groupBy: "$runtime",
      buckets: 5
    }
  }
];

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

The following example performs the same $bucketAuto operation as the previous example, but sets the bucket boundaries as powers of 2 by using the granularity parameter:以下示例执行与前一个示例相同的$bucketAuto操作，但通过使用粒度参数将桶边界设置为2的幂：

const pipeline = [
  {
    $bucketAuto: {
      groupBy: "$runtime",
      buckets: 5,
      granularity: "POWERSOF2"
    }
  }
];

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