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api-clients-generation-pipeline[bot]ci.datadog-api-spec
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Regenerate client from commit 914e068 of spec repo (#3965)
Co-authored-by: ci.datadog-api-spec <packages@datadoghq.com>
1 parent f26db57 commit a04142e

16 files changed

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‎.generator/schemas/v1/openapi.yaml‎

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@@ -8997,6 +8997,61 @@ components:
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type: string
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x-enum-varnames:
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- DATA_QUALITY_METRICS
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MonitorFormulaAndFunctionDataQualityDiffFunction:
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description: |-
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Function applied to the measure before it is compared against the predicted bounds.
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enum:
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- DIFF
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- DIFF_PERCENT
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example: "DIFF"
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type: string
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x-enum-varnames:
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- DIFF
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- DIFF_PERCENT
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MonitorFormulaAndFunctionDataQualityDiffType:
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description: |-
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How the difference between the source and target measures is computed.
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`absolute` subtracts the two values, `diff_percent` expresses the difference
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as a percentage of the source value.
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enum:
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- absolute
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- diff_percent
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example: "absolute"
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type: string
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x-enum-varnames:
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- ABSOLUTE
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- DIFF_PERCENT
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MonitorFormulaAndFunctionDataQualityEntityMetricConfig:
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description: |-
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Measure configuration for one side of a source to target comparison.
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properties:
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custom_sql:
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description: Custom SQL query used to compute the measure for this entity.
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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 applied when computing the measure for this entity.
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example: "USER_ID = 123"
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type: string
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entity_id:
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description: Identifier of the data entity to measure.
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example: "01234567-89ab-cdef-0123-456789abcdef"
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type: string
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entity_type:
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description: Type of the data entity to measure.
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example: "table"
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type: string
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group_by_columns:
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description: Columns to group results by when computing the measure for this entity.
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example: ["col1", "col2"]
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items:
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description: A column name to group results by.
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type: string
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type: array
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required:
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- entity_id
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- entity_type
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type: object
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MonitorFormulaAndFunctionDataQualityMeasure:
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description: |-
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The data quality measure to query. Common values include:
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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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MonitorFormulaAndFunctionDataQualityModelBoundsOverride:
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description: |-
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Restricts which predicted bound the monitor alerts on. `UPPER_ONLY` alerts only when
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the measure rises above the upper bound, `LOWER_ONLY` only when it falls below the
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lower bound. When unset, the monitor alerts on both.
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enum:
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- UPPER_ONLY
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- LOWER_ONLY
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example: "UPPER_ONLY"
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type: string
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x-enum-varnames:
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- UPPER_ONLY
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- LOWER_ONLY
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MonitorFormulaAndFunctionDataQualityModelConfiguration:
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description: |-
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Tuning options for the anomaly detection model used by the monitor.
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properties:
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auto_resolve_days:
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description: |-
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Number of days after which an open alert is automatically resolved.
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When unset, alerts stay open until the measure returns within bounds.
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example: 7
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format: int32
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maximum: 365
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minimum: 1
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type: integer
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enable_flatline_detection:
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description: |-
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Whether to alert when the measure stops changing entirely.
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Defaults to `true`.
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example: true
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type: boolean
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function:
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$ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualityDiffFunction"
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min_lower_bound_size:
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description: |-
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Minimum distance between the predicted value and the lower bound. Widening the
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lower bound to at least this size suppresses alerts on small downward deviations.
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When unset, no minimum is enforced.
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example: 10.0
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format: double
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type: number
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min_upper_bound_size:
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description: |-
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Minimum distance between the predicted value and the upper bound. Widening the
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upper bound to at least this size suppresses alerts on small upward deviations.
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When unset, no minimum is enforced.
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example: 10.0
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format: double
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type: number
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model_bounds_override:
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$ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualityModelBoundsOverride"
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type: object
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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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description: A column name to group results by.
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type: string
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type: array
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model_configuration:
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$ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualityModelConfiguration"
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model_type_override:
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$ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualityModelTypeOverride"
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sensitivity:
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example: 3.0
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format: double
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type: number
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source_to_target_config:
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$ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualitySourceToTargetConfig"
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type: object
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MonitorFormulaAndFunctionDataQualityQueryDefinition:
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description: A formula and functions data quality query.
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- measure
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- filter
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type: object
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MonitorFormulaAndFunctionDataQualitySourceToTargetConfig:
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description: |-
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Configuration for a source to target monitor, which compares the same measure
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across two data entities and alerts on the difference between them.
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properties:
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diff_type:
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$ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualityDiffType"
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entity_type:
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description: Type of the data entities being compared.
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example: "table"
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type: string
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source:
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$ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualityEntityMetricConfig"
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target:
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$ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualityEntityMetricConfig"
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required:
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- source
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- target
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- diff_type
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- entity_type
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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:

‎examples/v1/monitors/CreateMonitor_1553931399.py‎

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"""
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Create a Data Quality monitor with a model configuration returns "OK" response
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"""
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from datadog_api_client import ApiClient, Configuration
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from datadog_api_client.v1.api.monitors_api import MonitorsApi
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from datadog_api_client.v1.model.monitor import Monitor
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_data_source import (
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MonitorFormulaAndFunctionDataQualityDataSource,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_diff_function import (
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MonitorFormulaAndFunctionDataQualityDiffFunction,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_model_bounds_override import (
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MonitorFormulaAndFunctionDataQualityModelBoundsOverride,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_model_configuration import (
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MonitorFormulaAndFunctionDataQualityModelConfiguration,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_monitor_options import (
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MonitorFormulaAndFunctionDataQualityMonitorOptions,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_query_definition import (
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MonitorFormulaAndFunctionDataQualityQueryDefinition,
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)
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from datadog_api_client.v1.model.monitor_options import MonitorOptions
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from datadog_api_client.v1.model.monitor_thresholds import MonitorThresholds
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from datadog_api_client.v1.model.monitor_type import MonitorType
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body = Monitor(
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name="Example-Monitor",
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type=MonitorType.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=[
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"test:examplemonitor",
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"env:ci",
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],
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priority=3,
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options=MonitorOptions(
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thresholds=MonitorThresholds(
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critical=100.0,
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),
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variables=[
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MonitorFormulaAndFunctionDataQualityQueryDefinition(
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name="query1",
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data_source=MonitorFormulaAndFunctionDataQualityDataSource.DATA_QUALITY_METRICS,
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measure="row_count",
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filter="search for column where `database:production AND table:users`",
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group_by=[
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"entity_id",
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],
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monitor_options=MonitorFormulaAndFunctionDataQualityMonitorOptions(
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model_configuration=MonitorFormulaAndFunctionDataQualityModelConfiguration(
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auto_resolve_days=7,
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enable_flatline_detection=True,
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function=MonitorFormulaAndFunctionDataQualityDiffFunction.DIFF,
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min_lower_bound_size=10.0,
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min_upper_bound_size=10.0,
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model_bounds_override=MonitorFormulaAndFunctionDataQualityModelBoundsOverride.UPPER_ONLY,
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),
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),
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),
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],
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),
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)
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configuration = Configuration()
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with ApiClient(configuration) as api_client:
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api_instance = MonitorsApi(api_client)
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response = api_instance.create_monitor(body=body)
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print(response)

‎examples/v1/monitors/CreateMonitor_2986418353.py‎

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"""
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Create a Data Quality monitor with a source to target configuration returns "OK" response
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"""
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from datadog_api_client import ApiClient, Configuration
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from datadog_api_client.v1.api.monitors_api import MonitorsApi
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from datadog_api_client.v1.model.monitor import Monitor
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_data_source import (
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MonitorFormulaAndFunctionDataQualityDataSource,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_diff_type import (
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MonitorFormulaAndFunctionDataQualityDiffType,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_entity_metric_config import (
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MonitorFormulaAndFunctionDataQualityEntityMetricConfig,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_monitor_options import (
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MonitorFormulaAndFunctionDataQualityMonitorOptions,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_query_definition import (
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MonitorFormulaAndFunctionDataQualityQueryDefinition,
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)
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from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_source_to_target_config import (
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MonitorFormulaAndFunctionDataQualitySourceToTargetConfig,
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)
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from datadog_api_client.v1.model.monitor_options import MonitorOptions
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from datadog_api_client.v1.model.monitor_thresholds import MonitorThresholds
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from datadog_api_client.v1.model.monitor_type import MonitorType
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body = Monitor(
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name="Example-Monitor",
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type=MonitorType.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=[
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"test:examplemonitor",
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"env:ci",
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],
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priority=3,
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options=MonitorOptions(
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thresholds=MonitorThresholds(
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critical=100.0,
43+
),
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variables=[
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MonitorFormulaAndFunctionDataQualityQueryDefinition(
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name="query1",
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data_source=MonitorFormulaAndFunctionDataQualityDataSource.DATA_QUALITY_METRICS,
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measure="row_count",
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filter="search for column where `database:production AND table:users`",
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group_by=[
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"entity_id",
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],
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monitor_options=MonitorFormulaAndFunctionDataQualityMonitorOptions(
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source_to_target_config=MonitorFormulaAndFunctionDataQualitySourceToTargetConfig(
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source=MonitorFormulaAndFunctionDataQualityEntityMetricConfig(
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entity_id="source-entity-id",
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entity_type="table",
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),
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target=MonitorFormulaAndFunctionDataQualityEntityMetricConfig(
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entity_id="target-entity-id",
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entity_type="table",
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),
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diff_type=MonitorFormulaAndFunctionDataQualityDiffType.ABSOLUTE,
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entity_type="table",
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),
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),
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),
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],
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),
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)
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configuration = Configuration()
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with ApiClient(configuration) as api_client:
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api_instance = MonitorsApi(api_client)
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response = api_instance.create_monitor(body=body)
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print(response)

‎src/datadog_api_client/v1/model/monitor_formula_and_function_data_quality_diff_function.py‎

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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 2019-Present Datadog, Inc.
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from __future__ import annotations
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from datadog_api_client.model_utils import (
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ModelSimple,
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cached_property,
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)
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from typing import ClassVar
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class MonitorFormulaAndFunctionDataQualityDiffFunction(ModelSimple):
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"""
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Function applied to the measure before it is compared against the predicted bounds.
18+
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:param value: Must be one of ["DIFF", "DIFF_PERCENT"].
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:type value: str
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"""
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allowed_values = {
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"DIFF",
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"DIFF_PERCENT",
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}
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DIFF: ClassVar["MonitorFormulaAndFunctionDataQualityDiffFunction"]
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DIFF_PERCENT: ClassVar["MonitorFormulaAndFunctionDataQualityDiffFunction"]
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@cached_property
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def openapi_types(_):
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return {
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"value": (str,),
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}
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MonitorFormulaAndFunctionDataQualityDiffFunction.DIFF = MonitorFormulaAndFunctionDataQualityDiffFunction("DIFF")
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MonitorFormulaAndFunctionDataQualityDiffFunction.DIFF_PERCENT = MonitorFormulaAndFunctionDataQualityDiffFunction(
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"DIFF_PERCENT"
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)

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