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`$validate` Stage (Stream Processing)

Definition

The $validate stage checks streaming documents for conformity to a schema of expected ranges, values, or datatypes.

$validate

A $validate pipeline stage has the following prototype form:

{
  "$validate": {
    "validator": { <filter> },
    "validationAction" : "discard" | "dlq"
  }
}

Syntax

The $validate stage takes a document with the following fields:

Field	Type	Necessity	Description
`validator`	document	Required	Document of expressions used to validate incoming messages against a user-defined schema. You can use all but the following query operators to define validation expressions: `$near` `$nearSphere` `$text` `$where`
`validationAction`	string	Optional	Specifies the action to take when a message violates the user-defined schema. You can specify one of the following values: `discard`: Discards the message. If you do not specify a value for `validationAction`, this is the default behavior. `dlq`: Logs the violation to the collection defined in your Stream Processor configuration and performs a best-effort discard without transactional guarantees.

Behavior

You can use $validate at any point in a pipeline after the $source stage, and before the $emit or $merge stage.

Atlas Stream Processing supports only JSON Schema Draft 4 or earlier.

Validator Example

The following document shows an example validator expression using $and to perform a logical AND operation:

{
  $validate: {
    validator: {
      $and: [{
        $expr: {
          $ne: [
            "$Racer_Name",
            "Pace Car"
          ]
        }
      },
      {
        $jsonSchema: {
          required: [ "Racer_Num", "Racer_Name", "lap", "Corner_Num", "timestamp" ],
          properties: {
            Racer_Num: {
              bsonType: "int",
              description: "'Racer_Num' is the integer number of the race car and is required"
            },
            Racer_Name: {
              bsonType: "string",
              description: "'Racer_Name' must be a string and is required"
            },
            lap: {
              bsonType: "int",
              minimum: 1,
              description: "'lap' must be a int and is required"
            },
            Corner_Num: {
              bsonType: "int",
              minimum: 1,
              maximum: 4,
              description: "'Corner_Num' must be a int between 1 and 4 and is required"
            },
            timestamp: {
              bsonType: "string",
              pattern: "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{6}$",
              description: "'timestamp' must be a string matching iso date pattern and is required"
            }
          }
        }
      }]
    }, validationAction : "dlq"
  }
}

Examples

Consider a streaming data source that generates detailed weather reports from various locations. In the following example aggregation pipeline, you include a $validate stage to ensure the documents conform to the schema of the Sample Weather Dataset. The aggregation has four stages:

The $source stage establishes a connection with the Apache Kafka broker collecting these reports in a topic named my_weatherdata, passing each record as it is ingested to the subsequent aggregation stages.
The $validate stage checks whether a document has array values for the position.coordinates and sections fields, passing documents that do to the rest of the pipeline, and passing documents that don't to a DLQ.
The $match stage excludes documents that have a wind.speed.rate value of greater than or equal to 30 and passes along the documents with a wind.speed.rate value of less than 30.
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.

{
  '$source': {
    connectionName: 'sample_weatherdata',
    topic: 'my_weatherdata',
    tsFieldName: 'ingestionTime'
  }
},
{
  '$validate': {
    validator: {
      '$jsonSchema': { properties: { position: [Object], sections: [Object] } }
    },
    validationAction: 'dlq'
  }
},
{ '$match': { 'wind.speed.rate': { '$lt': 30 } } },
{
  '$merge': {
    into: {
      connectionName: 'weatherStreamOutput',
      db: 'sample_weatherstream',
      coll: 'stream'
    }
  }
}

To view the documents in the resulting sample_weatherstream.sample collection, you can connect to your Atlas cluster using mongosh to run the following command:

Note

The following is a representative example. Streaming data are not static, and each user sees distinct documents.

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

{
  _id: ObjectId('66b25302fe8bbac5f39dbdba'),
  airTemperature: { quality: '9', value: 3.5 },
  atmosphericPressureChange: {
    quantity24Hours: { quality: '9', value: 99.9 },
    quantity3Hours: { quality: '1', value: 10.9 },
    tendency: { code: '3', quality: '1' }
  },
  atmosphericPressureObservation: {
    altimeterSetting: { quality: '1', value: 1015.9 },
    stationPressure: { quality: '9', value: 1022.5 }
  },
  callLetters: 'JIVX',
  dataSource: '4',
  dewPoint: { quality: '9', value: 20.5 },
  elevation: 9999,
  extremeAirTemperature: { code: 'N', period: 99.9, quantity: '9', value: -30.4 },
  ingestionTime: ISODate('2024-08-06T16:44:50.322Z'),
  liquidPrecipitation: { condition: '9', depth: 7000, period: 12, quality: '9' },
  pastWeatherObservationManual: {
    atmosphericCondition: { quality: '1', value: '7' },
    period: { quality: '1', value: 3 }
  },
  position: { coordinates: [ 120.7, -98.2 ], type: 'Point' },
  precipitationEstimatedObservation: { discrepancy: '5', estimatedWaterDepth: 999 },
  presentWeatherObservationManual: { condition: '90', quality: '1' },
  pressure: { quality: '1', value: 1028.2 },
  qualityControlProcess: 'V020',
  seaSurfaceTemperature: { quality: '9', value: 11.1 },
  sections: [ 'UG1', 'MA1', 'GA3', 'KA1', 'UA1' ],
  skyCondition: {
    cavok: 'N',
    ceilingHeight: { determination: 'C', quality: '1', value: 390 }
  },
  skyConditionObservation: {
    highCloudGenus: { quality: '1', value: '06' },
    lowCloudGenus: { quality: '9', value: '07' },
    lowestCloudBaseHeight: { quality: '1', value: 800 },
    lowestCloudCoverage: { quality: '9', value: '06' },
    midCloudGenus: { quality: '9', value: '07' },
    totalCoverage: { opaque: '99', quality: '1', value: '99' }
  },
  skyCoverLayer: {
    baseHeight: { quality: '9', value: 1200 },
    cloudType: { quality: '9', value: '04' },
    coverage: { quality: '1', value: '09' }
  },
  st: 'x+36700+144300',
  type: 'FM-13',
  visibility: {
    distance: { quality: '9', value: 9000 },
    variability: { quality: '9', value: 'N' }
  },
  waveMeasurement: {
    method: 'I',
    seaState: { code: '00', quality: '9' },
    waves: { height: 9.5, period: 4, quality: '9' }
  },
  wind: {
    direction: { angle: 140, quality: '1' },
    speed: { quality: '2', rate: 15.9 },
    type: 'N'
  }
}

Observe that all documents in this collection have the expected array-type values for position.coordinates and sections. To view the documents that failed validation, given a dead letter queue named dlq, run the following command:

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

{
  _id: ObjectId('66b254d3a045fb1406047394'),
  errInfo: { reason: 'Input document found to be invalid in $validate stage' },
  operatorName: 'Validate',
  doc: {
    airTemperature: { quality: '9', value: 7.6 },
    atmosphericPressureChange: {
      quantity24Hours: { quality: '9', value: 99.9 },
      quantity3Hours: { quality: '1', value: 0.3 },
      tendency: { code: '8', quality: '1' }
    },
    atmosphericPressureObservation: {
      altimeterSetting: { quality: '9', value: 1015.9 },
      stationPressure: { quality: '1', value: 1017 }
    },
    callLetters: 'WRGL',
    dataSource: '4',
    dewPoint: { quality: '9', value: 25.3 },
    elevation: 9999,
    extremeAirTemperature: { code: 'M', period: 99.9, quantity: '1', value: -30.9 },
    liquidPrecipitation: { condition: '9', period: 99, quality: '9' },
    pastWeatherObservationManual: {
      atmosphericCondition: { quality: '1', value: '2' },
      period: { quality: '1', value: 6 }
    },
    position: { coordinates: -100.2, type: 'Point' },
    precipitationEstimatedObservation: { discrepancy: '5', estimatedWaterDepth: 17 },
    presentWeatherObservationManual: { condition: '08', quality: '1' },
    pressure: { quality: '9', value: 1001 },
    qualityControlProcess: 'V020',
    seaSurfaceTemperature: { quality: '9', value: 10.4 },
    sections: [ 'GA2', 'GA1', 'KA1', 'AA1', 'OA1' ],
    skyCondition: {
      cavok: 'N',
      ceilingHeight: { determination: 'C', quality: '1', value: 240 }
    },
    skyConditionObservation: {
      highCloudGenus: { quality: '1', value: '02' },
      lowCloudGenus: { quality: '9', value: '02' },
      lowestCloudBaseHeight: { quality: '1', value: 150 },
      lowestCloudCoverage: { quality: '1', value: '03' },
      midCloudGenus: { quality: '1', value: '06' },
      totalCoverage: { opaque: '99', quality: '1', value: '06' }
    },
    skyCoverLayer: {
      baseHeight: { quality: '9', value: 450 },
      cloudType: { quality: '9', value: '03' },
      coverage: { quality: '1', value: '07' }
    },
    st: 'x+20500-074300',
    type: 'SAO',
    visibility: {
      distance: { quality: '9', value: 3800 },
      variability: { quality: '9', value: 'N' }
    },
    waveMeasurement: {
      method: 'I',
      seaState: { code: '00', quality: '9' },
      waves: { height: 37.5, period: 7, quality: '9' }
    },
    wind: {
      direction: { angle: 230, quality: '1' },
      speed: { quality: '1', rate: 46.3 },
      type: 'N'
    },
    ingestionTime: ISODate('2024-08-06T16:52:35.287Z')
  },
  processorName: 'sampleWeather',
  dlqTime: ISODate('2024-08-06T16:52:35.291Z')
}

Observe that all documents in the dead letter queue have invalid values for either position.coordinates, sections, or both.

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$validate Stage (Stream Processing)