$densify (aggregation stage)（聚合阶段）

The C# examples on this page use the sample_weatherdata.data collection from the Atlas sample datasets. 本页上的C#示例使用Atlas示例数据集中的sample_weatherdata.data集合。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 Weather and Point classes model the documents in the sample_weatherdata.data collection:以下Weather和Point类对sample_weatherdata.data集合中的文档进行建模：

public class Weather
{
    public Guid Id { get; set; }
    
    public Point Position { get; set; }
  
    [BsonElement("ts")]
    public DateTime Timestamp { get; set; }
}

public class Point
{
    public float[] Coordinates { get; set; }
}

The sample_weatherdata.data collection contains the following documents, which contain measurements for the same position field, one hour apart:sample_weatherdata.data集合包含以下文档，其中包含相隔一小时的同一position字段的测量值：

Document{{ _id=5553a..., position=Document{{type=Point, coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:00:00 EST 1984, ... }}
Document{{ _id=5553b..., position=Document{{type=Point, coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 09:00:00 EST 1984, ... }}

To use the MongoDB .NET/C# driver to add a $densify stage to an aggregation pipeline, call the Densify() method on a PipelineDefinition object.要使用MongoDB NET/C#驱动程序向聚合管道添加$density阶段，请在PipelineDefinition对象上调用Densify()方法。

The following example creates a pipeline stage that adds a document at every 15-minute interval between the previous two documents. The code then groups these documents by the values of their Position.Coordinates field.以下示例创建了一个管道阶段，该阶段在前两个文档之间每隔15分钟添加一个文档。然后，代码将这些文档按其Position.Coordinates（位置坐标）字段的值进行分组。

var densifyTimeRange = new DensifyDateTimeRange(
    new DensifyLowerUpperDateTimeBounds(
        lowerBound: new DateTime(1984, 3, 5, 8, 0, 0),
        upperBound: new DateTime(1984, 3, 5, 9, 0, 0)
    ),
    step: 15,
    unit: DensifyDateTimeUnit.Minutes
);

var pipeline = new EmptyPipelineDefinition<Weather>()
    .Densify(
        field: w => w.Timestamp,
        range: densifyTimeRange,
        partitionByFields: [w => w.Position.Coordinates]);

The previous aggregation stage generates the following highlighted documents in the collection:前一个聚合阶段在集合中生成以下突出显示的文档：

Document{{ _id=5553a..., position=Document{{type=Point, coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:00:00 EST 1984, ... }}
Document{{ position=Document{{coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:15:00 EST 1984 }}
Document{{ position=Document{{coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:30:00 EST 1984 }}
Document{{ position=Document{{coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 08:45:00 EST 1984 }}
Document{{ _id=5553b..., position=Document{{type=Point, coordinates=[-47.9, 47.6]}}, ts=Mon Mar 05 09:00:00 EST 1984, ... }}

The Node.js examples on this page use the sample_weatherdata.data collection from the Atlas sample datasets. 此页面上的Node.js示例使用Atlas示例数据集中的sample_weatherdata.data集合。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。

The sample_weatherdata.data collection contains the following documents, which contain measurements for the same position field, one hour apart:sample_weatherdata.data集合包含以下文档，其中包含相隔一小时的同一position（位置）字段的测量值：

{_id: new ObjectId(...), ts: 1984-03-05T13:00:00.000Z, position: {type: 'Point', coordinates: [-47.9, 47.6]}, ... },
{_id: new ObjectId(...), ts: 1984-03-05T14:00:00.000Z, position: {type: 'Point', coordinates: [-47.9, 47.6]}, ... }

To use the MongoDB Node.js driver to add a $densify stage to an aggregation pipeline, use the $densify operator in a pipeline object.要使用MongoDB Node.js驱动程序向聚合管道添加$density阶段，请在管道对象中使用$density运算符。

The following example creates a pipeline stage that adds a document at every 15-minute interval between the previous two documents. The code then groups these documents by the values of their position.coordinates field. The example then runs the aggregation pipeline:以下示例创建了一个管道阶段，该阶段在前两个文档之间每隔15分钟添加一个文档。然后，代码根据position.coordinates（位置坐标）字段的值对这些文档进行分组。然后，该示例运行聚合管道：

const pipeline = [
  {
    $densify: {
      field: "ts",
      partitionByFields: ["position.coordinates"],
      range: {
        step: 15,
        unit: "minute",
        bounds: [new Date(1984, 3, 5, 8, 0, 0), new Date(1984, 3, 5, 9, 0, 0)]
      }
    }
  }
];

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

The previous aggregation stage generates the following highlighted documents in the collection:前一个聚合阶段在集合中生成以下突出显示的文档：

{ _id: new ObjectId(...), ts: 1984-03-05T13:00:00.000Z, position: {type: 'Point', coordinates: [-47.9, 47.6]}, ... },
{ position: { coordinates: [-47.9, 47.6] }, ts: 1984-03-05T13:15:00.000Z },
{ position: { coordinates: [-47.9, 47.6] }, ts: 1984-03-05T13:30:00.000Z },
{ position: { coordinates: [-47.9, 47.6] }, ts: 1984-03-05T13:45:00.000Z },
{ _id: new ObjectId(...), ts: 1984-03-05T14:00:00.000Z, position: {type: 'Point', coordinates: [-47.9, 47.6]}, ... }