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sp.createStreamProcessor()

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sp.createStreamProcessor()

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

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

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

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.

sp.createStreamProcessor() creates a persistent, named stream processor on the current stream processing instance. You can initialize this stream processor with sp.processor.start(). If you try to create a stream processor with the same name as an existing stream processor, mongosh will return an error.

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

The following example creates a stream processor named solarDemo 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.createStreamProcessor(
'solarDemo',
[
{
$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"]
}
}
]
)
← Atlas Stream Processing Methods