$tumblingWindow 阶段（流处理）

定义

The $tumblingWindow stage specifies a tumbling window for aggregation of data. Atlas Stream Processing windows are stateful, can be recovered if interrupted, and have mechanisms for processing late-arriving data. You must apply all other aggregation queries to your streaming data within this window stage.

$tumblingWindow

$tumblingWindow 管道阶段采用以下原型形式：

{
  "$tumblingWindow": {
    "boundary": "eventTime" | "processingTime",
    "interval": {
      "size": <int>,
      "unit": "<unit-of-time>"
    },
    "pipeline" : [
      <aggregation-stage-array>
    ],
    "offset": {
      "offsetFromUtc": <int>,
      "unit": "<unit-of-time>"
    },
    "idleTimeout": {
      "size": <int>,
      "unit": "<unit-of-time>"
    },
    "allowedLateness": {
      size: <int>,
      unit: "<unit-of-time>"
    }
  }
}

或者，$tumblingWindow 管道阶段可以具有 allowedLateness 和 idleTimeout 字段，其整数值为 0，如下所示：

{
  "$tumblingWindow": {
    "boundary": "eventTime" | "processingTime",
    "interval": {
      "size": <int>,
      "unit": "<unit-of-time>"
    },
    "pipeline" : [
      <aggregation-stage-array>
    ],
    "offset": {
      "offsetFromUtc": <int>,
      "unit": "<unit-of-time>"
    },
    "idleTimeout": 0,
    "allowedLateness": 0
  }
}

语法

$tumblingWindow 阶段采用包含以下字段的文档：

行为

Atlas Stream Processing 仅支持每个管道一个窗口阶段。

When you apply the $group stage to your window stage, a single group key has a limit of 100 megabytes of RAM.

在 Windows 中，对某些聚合阶段的支持可能会受到限制或不可用。要了解更多信息，请参阅支持的聚合管道阶段。

如果服务中断，您可以从中断点的状态恢复窗口的内部管道。要了解更多信息，请参阅检查点。

示例

A streaming data source generates detailed weather reports from various locations, conformant to the schema of the Sample Weather Dataset. The following aggregation has three stages:

1. $source 阶段与 Apache Kafka 代理建立连接，在名为 my_weatherdata 的主题中收集这些报告，从而在将每条记录引入后续聚合阶段时将其公开。

2. The $tumblingWindow stage defines consecutive windows with 30-second duration. Each window executes an internal pipeline, which finds the average, median, maximum, and minimum atmosphericPressureObservation.altimeterSetting.value for the duration of that window. The pipeline then outputs a single document with an _id equivalent to the start timestamp of the window it represents and the specified values for that window.

3. The $merge stage writes the output to an Atlas collection named stream in the sample_weatherstream database. If no such database or collection exist, Atlas creates them.

{
  '$source': {
    connectionName: 'sample_weatherdata',
    topic: 'my_weatherdata',
    tsFieldName: 'ingestionTime'
  }
},
{
  '$tumblingWindow': {
    interval: {
      size: 30,
      unit: "second"
    },
    pipeline: [{
      $group: {
        _id:
  { $meta: "stream.window.start"},
        averagePressure: { $avg: "$atmosphericPressureObservation.altimeterSetting.value" },
        medianPressure: {
          $median: {
            input: "$atmosphericPressureObservation.altimeterSetting.value",
            method: "approximate"
          }
        },
        maxPressure: { $max: "$atmosphericPressureObservation.altimeterSetting.value" },
        minPressure: { $min: "$atmosphericPressureObservation.altimeterSetting.value" }
      }
    }]
  }
},
{
  '$merge': {
    into: {
      connectionName: 'weatherStreamOutput',
      db: 'sample_weatherstream',
      coll: 'stream'
    }
  }
}

要查看生成的 sample_weatherstream.stream 集合中的文档，请连接到您的 Atlas 集群并运行以下命令：

  db.getSiblingDB("sample_weatherstream").stream.find()

{
   _id: ISODate('2024-09-26T16:34:00.000Z'),
   averagePressure: 5271.47894736842,
   maxPressure: 9999.9,
   medianPressure: 1015.9,
   minPressure: 1015.9
 },
 {
   _id: ISODate('2024-09-26T16:34:30.000Z'),
   averagePressure: 5507.9,
   maxPressure: 9999.9,
   medianPressure: 1015.9,
   minPressure: 1015.9
 }