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feat - Add data-quality alert monitor type support (#3266)
Co-authored-by: ci.datadog-api-spec <packages@datadoghq.com>
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‎.generator/schemas/v1/openapi.yaml‎

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@@ -7534,6 +7534,104 @@ components:
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- data_source
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- query
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type: object
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MonitorFormulaAndFunctionDataQualityDataSource:
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description: Data source for data quality queries.
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enum:
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- data_quality_metrics
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example: data_quality_metrics
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type: string
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x-enum-varnames:
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- DATA_QUALITY_METRICS
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MonitorFormulaAndFunctionDataQualityMeasure:
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description: 'The data quality measure to query. Common values include:
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`bytes`, `cardinality`, `custom`, `freshness`, `max`, `mean`, `min`,
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`nullness`, `percent_negative`, `percent_zero`, `row_count`, `stddev`,
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`sum`, `uniqueness`. Additional values may be supported.'
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example: row_count
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type: string
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MonitorFormulaAndFunctionDataQualityModelTypeOverride:
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description: Override for the model type used in anomaly detection.
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enum:
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- freshness
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- percentage
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- any
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type: string
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x-enum-varnames:
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- FRESHNESS
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- PERCENTAGE
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- ANY
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MonitorFormulaAndFunctionDataQualityMonitorOptions:
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description: Monitor configuration options for data quality queries.
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properties:
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crontab_override:
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description: Crontab expression to override the default schedule.
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example: '* * * 10'
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type: string
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custom_sql:
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description: Custom SQL query for the monitor.
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example: SELECT COUNT(*) FROM users AS dd_value
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type: string
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custom_where:
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description: Custom WHERE clause for the query.
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example: USER_ID = 123
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type: string
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group_by_columns:
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description: Columns to group results by.
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example:
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- col1
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- col2
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items:
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type: string
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type: array
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model_type_override:
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$ref: '#/components/schemas/MonitorFormulaAndFunctionDataQualityModelTypeOverride'
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type: object
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MonitorFormulaAndFunctionDataQualityQueryDefinition:
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description: A formula and functions data quality query.
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properties:
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data_source:
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$ref: '#/components/schemas/MonitorFormulaAndFunctionDataQualityDataSource'
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filter:
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description: Filter expression used to match on data entities. Uses Aastra
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query syntax.
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example: search for column where `database:production AND table:users`
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type: string
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group_by:
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description: Optional grouping fields for aggregation.
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example:
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- entity_id
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items:
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type: string
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type: array
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measure:
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$ref: '#/components/schemas/MonitorFormulaAndFunctionDataQualityMeasure'
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monitor_options:
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$ref: '#/components/schemas/MonitorFormulaAndFunctionDataQualityMonitorOptions'
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name:
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description: Name of the query for use in formulas.
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example: query1
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type: string
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schema_version:
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description: Schema version for the data quality query.
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example: 0.0.1
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type: string
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scope:
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description: 'Optional scoping expression to further filter metrics. Uses
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metrics filter syntax.
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This is useful when an entity has been configured to emit metrics with
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additional tags.'
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example: env:production
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type: string
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required:
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- name
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- data_source
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- measure
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- filter
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type: object
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MonitorFormulaAndFunctionEventAggregation:
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description: Aggregation methods for event platform queries.
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enum:
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oneOf:
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- $ref: '#/components/schemas/MonitorFormulaAndFunctionEventQueryDefinition'
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- $ref: '#/components/schemas/MonitorFormulaAndFunctionCostQueryDefinition'
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- $ref: '#/components/schemas/MonitorFormulaAndFunctionDataQualityQueryDefinition'
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MonitorGroupSearchResponse:
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description: The response of a monitor group search.
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example:
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- database-monitoring alert
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- network-performance alert
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- cost alert
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- data-quality alert
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example: query alert
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type: string
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x-enum-varnames:
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- DATABASE_MONITORING_ALERT
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- NETWORK_PERFORMANCE_ALERT
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- COST_ALERT
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- DATA_QUALITY_ALERT
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MonitorUpdateRequest:
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description: Object describing a monitor update request.
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properties:

‎cassettes/v1/Monitors_4022238206/Create-a-Data-Quality-monitor-returns-OK-response_2192049983/frozen.json‎

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"2026-01-12T17:23:49.629Z"

‎cassettes/v1/Monitors_4022238206/Create-a-Data-Quality-monitor-returns-OK-response_2192049983/recording.har‎

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{
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"log": {
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"_recordingName": "Monitors/Create a Data Quality monitor returns \"OK\" response",
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"creator": {
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"comment": "persister:fs",
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"name": "Polly.JS",
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"version": "6.0.5"
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},
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"entries": [
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{
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"_id": "09dbbe87ffa480b059d2f4f4bb6a2d62",
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"_order": 0,
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"cache": {},
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"request": {
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"bodySize": 520,
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"cookies": [],
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"headers": [
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{
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"_fromType": "array",
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"name": "accept",
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},
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{
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"_fromType": "array",
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"name": "content-type",
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"value": "application/json"
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}
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],
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"headersSize": 555,
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"httpVersion": "HTTP/1.1",
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"method": "POST",
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"postData": {
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"mimeType": "application/json",
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"params": [],
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"text": "{\"message\":\"Data quality alert triggered\",\"name\":\"Test-Create_a_Data_Quality_monitor_returns_OK_response-1768238629\",\"options\":{\"thresholds\":{\"critical\":100},\"variables\":[{\"data_source\":\"data_quality_metrics\",\"filter\":\"search for column where `database:production AND table:users`\",\"group_by\":[\"entity_id\"],\"measure\":\"row_count\",\"name\":\"query1\"}]},\"priority\":3,\"query\":\"formula(\\\"query1\\\").last(\\\"5m\\\") > 100\",\"tags\":[\"test:testcreateadataqualitymonitorreturnsokresponse1768238629\",\"env:ci\"],\"type\":\"data-quality alert\"}"
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},
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"queryString": [],
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"url": "https://api.datadoghq.com/api/v1/monitor"
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},
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"response": {
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"bodySize": 1088,
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"content": {
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"mimeType": "application/json",
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"size": 1088,
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"text": "{\"id\":250323411,\"org_id\":321813,\"type\":\"data-quality alert\",\"name\":\"Test-Create_a_Data_Quality_monitor_returns_OK_response-1768238629\",\"message\":\"Data quality alert triggered\",\"tags\":[\"test:testcreateadataqualitymonitorreturnsokresponse1768238629\",\"env:ci\"],\"query\":\"formula(\\\"query1\\\").last(\\\"5m\\\") > 100\",\"options\":{\"thresholds\":{\"critical\":100.0},\"variables\":[{\"data_source\":\"data_quality_metrics\",\"filter\":\"search for column where `database:production AND table:users`\",\"group_by\":[\"entity_id\"],\"measure\":\"row_count\",\"name\":\"query1\"}],\"notify_no_data\":false,\"notify_audit\":false,\"new_host_delay\":300,\"include_tags\":true,\"silenced\":{}},\"multi\":false,\"created_at\":1768238629000,\"created\":\"2026-01-12T17:23:49.782143+00:00\",\"modified\":\"2026-01-12T17:23:49.782143+00:00\",\"deleted\":null,\"priority\":3,\"restricted_roles\":null,\"restriction_policy\":null,\"draft_status\":\"published\",\"assets\":[],\"overall_state_modified\":null,\"overall_state\":\"No Data\",\"creator\":{\"name\":\"CI Account\",\"handle\":\"9919ec9b-ebc7-49ee-8dc8-03626e717cca\",\"email\":\"team-intg-tools-libs-spam@datadoghq.com\",\"id\":2320499}}\n"
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},
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{
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"name": "content-type",
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"value": "application/json"
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}
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],
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"headersSize": 680,
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"httpVersion": "HTTP/1.1",
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"redirectURL": "",
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"status": 200,
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"statusText": "OK"
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},
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"startedDateTime": "2026-01-12T17:23:49.637Z",
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"time": 205
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},
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{
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"_id": "8aa5bcb8a1681f4e66bc94bc158d0c77",
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"_order": 0,
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"cache": {},
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"request": {
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"bodySize": 0,
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"cookies": [],
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"headers": [
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{
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"_fromType": "array",
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"name": "accept",
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"value": "application/json"
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}
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],
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"headersSize": 513,
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"httpVersion": "HTTP/1.1",
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"method": "DELETE",
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"queryString": [],
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"url": "https://api.datadoghq.com/api/v1/monitor/250323411"
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},
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"response": {
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"bodySize": 33,
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"content": {
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"mimeType": "application/json",
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"size": 33,
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"text": "{\"deleted_monitor_id\":250323411}\n"
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},
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"cookies": [],
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"headers": [
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{
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"name": "content-type",
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"value": "application/json"
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}
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],
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"headersSize": 547,
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"httpVersion": "HTTP/1.1",
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"redirectURL": "",
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"status": 200,
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"statusText": "OK"
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},
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"startedDateTime": "2026-01-12T17:23:49.850Z",
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"time": 279
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}
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],
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"pages": [],
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"version": "1.2"
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}
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}

‎examples/v1/monitors/CreateMonitor_3626832481.ts‎

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/**
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* Create a Data Quality monitor returns "OK" response
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*/
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import { client, v1 } from "@datadog/datadog-api-client";
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const configuration = client.createConfiguration();
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const apiInstance = new v1.MonitorsApi(configuration);
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const params: v1.MonitorsApiCreateMonitorRequest = {
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body: {
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name: "Example-Monitor",
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type: "data-quality alert",
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query: `formula("query1").last("5m") > 100`,
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message: "Data quality alert triggered",
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tags: ["test:examplemonitor", "env:ci"],
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priority: 3,
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options: {
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thresholds: {
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critical: 100,
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},
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variables: [
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{
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name: "query1",
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dataSource: "data_quality_metrics",
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measure: "row_count",
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filter:
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`search for column where ` +
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"`" +
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`database:production AND table:users` +
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"`",
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groupBy: ["entity_id"],
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},
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],
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},
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},
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};
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apiInstance
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.createMonitor(params)
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.then((data: v1.Monitor) => {
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console.log(
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"API called successfully. Returned data: " + JSON.stringify(data)
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);
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})
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.catch((error: any) => console.error(error));

‎features/v1/monitors.feature‎

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When the request is sent
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Then the response status is 200 OK
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@team:DataDog/monitor-app
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Scenario: Create a Data Quality monitor returns "OK" response
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Given new "CreateMonitor" request
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And body with value {"name": "{{ unique }}", "type": "data-quality alert", "query": "formula(\"query1\").last(\"5m\") > 100", "message": "Data quality alert triggered", "tags": ["test:{{ unique_lower_alnum }}", "env:ci"], "priority": 3, "options": {"thresholds": {"critical": 100}, "variables": [{"name": "query1", "data_source": "data_quality_metrics", "measure": "row_count", "filter": "search for column where `database:production AND table:users`", "group_by": ["entity_id"]}]}}
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When the request is sent
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Then the response status is 200 OK
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And the response "name" is equal to "{{ unique }}"
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And the response "type" is equal to "data-quality alert"
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@team:DataDog/monitor-app
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Scenario: Create a RUM formula and functions monitor returns "OK" response
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Given new "CreateMonitor" request

‎packages/datadog-api-client-v1/index.ts‎

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export { MonitorFormulaAndFunctionCostAggregator } from "./models/MonitorFormulaAndFunctionCostAggregator";
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export { MonitorFormulaAndFunctionCostDataSource } from "./models/MonitorFormulaAndFunctionCostDataSource";
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export { MonitorFormulaAndFunctionCostQueryDefinition } from "./models/MonitorFormulaAndFunctionCostQueryDefinition";
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export { MonitorFormulaAndFunctionDataQualityDataSource } from "./models/MonitorFormulaAndFunctionDataQualityDataSource";
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export { MonitorFormulaAndFunctionDataQualityModelTypeOverride } from "./models/MonitorFormulaAndFunctionDataQualityModelTypeOverride";
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export { MonitorFormulaAndFunctionDataQualityMonitorOptions } from "./models/MonitorFormulaAndFunctionDataQualityMonitorOptions";
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export { MonitorFormulaAndFunctionDataQualityQueryDefinition } from "./models/MonitorFormulaAndFunctionDataQualityQueryDefinition";
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export { MonitorFormulaAndFunctionEventAggregation } from "./models/MonitorFormulaAndFunctionEventAggregation";
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export { MonitorFormulaAndFunctionEventQueryDefinition } from "./models/MonitorFormulaAndFunctionEventQueryDefinition";
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export { MonitorFormulaAndFunctionEventQueryDefinitionCompute } from "./models/MonitorFormulaAndFunctionEventQueryDefinitionCompute";

‎packages/datadog-api-client-v1/models/MonitorFormulaAndFunctionDataQualityDataSource.ts‎

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/**
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* Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License.
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* This product includes software developed at Datadog (https://www.datadoghq.com/).
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* Copyright 2020-Present Datadog, Inc.
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*/
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import { UnparsedObject } from "../../datadog-api-client-common/util";
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/**
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* Data source for data quality queries.
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*/
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export type MonitorFormulaAndFunctionDataQualityDataSource =
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| typeof DATA_QUALITY_METRICS
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| UnparsedObject;
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export const DATA_QUALITY_METRICS = "data_quality_metrics";

‎packages/datadog-api-client-v1/models/MonitorFormulaAndFunctionDataQualityModelTypeOverride.ts‎

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/**
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* Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License.
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* This product includes software developed at Datadog (https://www.datadoghq.com/).
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* Copyright 2020-Present Datadog, Inc.
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*/
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import { UnparsedObject } from "../../datadog-api-client-common/util";
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/**
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* Override for the model type used in anomaly detection.
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*/
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export type MonitorFormulaAndFunctionDataQualityModelTypeOverride =
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| typeof FRESHNESS
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| typeof PERCENTAGE
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| typeof ANY
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| UnparsedObject;
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export const FRESHNESS = "freshness";
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export const PERCENTAGE = "percentage";
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export const ANY = "any";

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