sp.process()

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Definition

Syntax
Command Fields
Behavior
Access Control
Example
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Definition

sp.process()

New in version 7.0: Creates an ephemeral Stream Processor on the current Stream Processing Instance.

Syntax

The sp.process() method has the following syntax:

sp.process(
  [
    <pipeline>
  ],
  {
    <options>
  }
)

Command Fields

sp.createStreamProcessor() takes these fields:

Field	Type	Necessity	Description
`name`	string	Required	Logical name for the stream processor. This must be unique within the stream processing instance.
`pipeline`	array	Required	Stream aggregation pipeline you want to apply to your streaming data.
`options`	object	Optional	Object defining various optional settings for your stream processor.
`options.dlq`	object	Conditional	Object assigning a dead letter queue for your stream processing instance. This field is necessary if you define the `options` field.
`options.dlq.connectionName`	string	Conditional	Label that identifies a connection in your connection registry. This connection must reference an Atlas cluster. This field is necessary if you define the `options.dlq` field.
`options.dlq.db`	string	Conditional	Name of an Atlas database on the cluster specified in `options.dlq.connectionName`. This field is necessary if you define the `options.dlq` field.
`options.dlq.coll`	string	Conditional	Name of a collection in the database specified in `options.dlq.db`. This field is necessary if you define the `options.dlq` field.

Behavior

sp.process() creates an ephemeral, unnamed stream processor on the current stream processing instance and immediately initializes it. This stream processor only persists as long as it runs. If you terminate an ephemeral stream processor, you must create it again in order to use it.

Access Control

The user running sp.process() must have the atlasAdmin role.

Example

The following example creates an ephemeral stream processor which ingests data from the sample_stream_solar connection. The processor excludes all documents where the value of the device_id field is device_8, passing the rest to a tumbling window with a 10-second duration. Each window groups the documents it receives, then returns various useful statistics of each group. The stream processor then merges these records to solar_db.solar_coll over the mongodb1 connection.

sp.process(
  [
    {
      $source: {
        connectionName: 'sample_stream_solar',
        timeField: {
          $dateFromString: {
            dateString: '$timestamp'
          }
        }
      }
    },
    {
      $match: {
        $expr: {
          $ne: [
            "$device_id",
            "device_8"
          ]
        }
      }
    },
    {
      $tumblingWindow: {
        interval: {
          size: NumberInt(10),
          unit: "second"
        },
        "pipeline": [
          {
            $group: {
              "_id": {  "device_id": "$device_id" },
              "max_temp": { $max: "$obs.temp" },
              "max_watts": { $max: "$obs.watts" },
              "min_watts": { $min: "$obs.watts" },
              "avg_watts": { $avg: "$obs.watts" },
              "median_watts": {
                                $median: {
                                  input: "$obs.watts",
                                  method: "approximate"
                                }
                              }
            }
          }
        ]
      }
    },
    {
      $merge: {
        into: {
          connectionName: "mongodb1",
          db: "solar_db",
          coll: "solar_coll"
        },
        on: ["_id"]
      }
    }
  ]
)

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