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api-clients-generation-pipeline[bot]ci.datadog-api-spec
api-clients-generation-pipeline[bot]
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ci.datadog-api-spec
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Add ci_pipelines enum to FormulaAndFunctionEventsDataSource (#1363)
Co-authored-by: ci.datadog-api-spec <[email protected]>
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‎.apigentools-info

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@@ -4,13 +4,13 @@
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"spec_versions": {
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"v1": {
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"apigentools_version": "1.6.4",
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"regenerated": "2023-03-02 15:38:14.789500",
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"spec_repo_commit": "0ec7fe9d"
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"regenerated": "2023-03-02 16:19:50.759538",
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"spec_repo_commit": "e71b6cea"
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},
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"v2": {
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"apigentools_version": "1.6.4",
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"regenerated": "2023-03-02 15:38:14.801240",
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"spec_repo_commit": "0ec7fe9d"
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"regenerated": "2023-03-02 16:19:50.770722",
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"spec_repo_commit": "e71b6cea"
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}
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}
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}

‎.generator/schemas/v1/openapi.yaml

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@@ -2518,6 +2518,7 @@ components:
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- audit
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- events
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- ci_tests
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- ci_pipelines
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example: logs
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type: string
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x-enum-varnames:
@@ -2530,6 +2531,7 @@ components:
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- AUDIT
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- EVENTS
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- CI_TESTS
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- CI_PIPELINES
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FormulaAndFunctionMetricAggregation:
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description: The aggregation methods available for metrics queries.
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enum:

‎examples/v1/dashboards/CreateDashboard_2800096921.py

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"""
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Create a new timeseries widget with ci_pipelines data source
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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.dashboards_api import DashboardsApi
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from datadog_api_client.v1.model.dashboard import Dashboard
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from datadog_api_client.v1.model.dashboard_layout_type import DashboardLayoutType
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from datadog_api_client.v1.model.dashboard_reflow_type import DashboardReflowType
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from datadog_api_client.v1.model.formula_and_function_event_aggregation import FormulaAndFunctionEventAggregation
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from datadog_api_client.v1.model.formula_and_function_event_query_definition import (
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FormulaAndFunctionEventQueryDefinition,
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)
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from datadog_api_client.v1.model.formula_and_function_event_query_definition_compute import (
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FormulaAndFunctionEventQueryDefinitionCompute,
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)
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from datadog_api_client.v1.model.formula_and_function_event_query_definition_search import (
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FormulaAndFunctionEventQueryDefinitionSearch,
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)
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from datadog_api_client.v1.model.formula_and_function_events_data_source import FormulaAndFunctionEventsDataSource
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from datadog_api_client.v1.model.formula_and_function_response_format import FormulaAndFunctionResponseFormat
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from datadog_api_client.v1.model.timeseries_widget_definition import TimeseriesWidgetDefinition
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from datadog_api_client.v1.model.timeseries_widget_definition_type import TimeseriesWidgetDefinitionType
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from datadog_api_client.v1.model.timeseries_widget_legend_column import TimeseriesWidgetLegendColumn
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from datadog_api_client.v1.model.timeseries_widget_legend_layout import TimeseriesWidgetLegendLayout
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from datadog_api_client.v1.model.timeseries_widget_request import TimeseriesWidgetRequest
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from datadog_api_client.v1.model.widget import Widget
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from datadog_api_client.v1.model.widget_display_type import WidgetDisplayType
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from datadog_api_client.v1.model.widget_formula import WidgetFormula
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from datadog_api_client.v1.model.widget_line_type import WidgetLineType
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from datadog_api_client.v1.model.widget_line_width import WidgetLineWidth
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from datadog_api_client.v1.model.widget_request_style import WidgetRequestStyle
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from datadog_api_client.v1.model.widget_time import WidgetTime
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body = Dashboard(
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title="Example-Create_a_new_timeseries_widget_with_ci_pipelines_data_source with ci_pipelines datasource",
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widgets=[
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Widget(
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definition=TimeseriesWidgetDefinition(
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title="",
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show_legend=True,
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legend_layout=TimeseriesWidgetLegendLayout.AUTO,
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legend_columns=[
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TimeseriesWidgetLegendColumn.AVG,
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TimeseriesWidgetLegendColumn.MIN,
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TimeseriesWidgetLegendColumn.MAX,
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TimeseriesWidgetLegendColumn.VALUE,
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TimeseriesWidgetLegendColumn.SUM,
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],
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time=WidgetTime(),
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type=TimeseriesWidgetDefinitionType.TIMESERIES,
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requests=[
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TimeseriesWidgetRequest(
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formulas=[
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WidgetFormula(
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formula="query1",
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),
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],
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queries=[
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FormulaAndFunctionEventQueryDefinition(
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data_source=FormulaAndFunctionEventsDataSource.CI_PIPELINES,
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name="query1",
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search=FormulaAndFunctionEventQueryDefinitionSearch(
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query="ci_level:job",
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),
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indexes=[
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"*",
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],
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compute=FormulaAndFunctionEventQueryDefinitionCompute(
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aggregation=FormulaAndFunctionEventAggregation.COUNT,
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metric="@ci.queue_time",
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),
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group_by=[],
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),
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],
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response_format=FormulaAndFunctionResponseFormat.TIMESERIES,
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style=WidgetRequestStyle(
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palette="dog_classic",
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line_type=WidgetLineType.SOLID,
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line_width=WidgetLineWidth.NORMAL,
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),
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display_type=WidgetDisplayType.LINE,
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),
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],
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),
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),
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],
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layout_type=DashboardLayoutType.ORDERED,
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reflow_type=DashboardReflowType.AUTO,
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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 = DashboardsApi(api_client)
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response = api_instance.create_dashboard(body=body)
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print(response)

‎src/datadog_api_client/v1/model/formula_and_function_events_data_source.py

Lines changed: 4 additions & 1 deletion
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@@ -16,7 +16,7 @@ class FormulaAndFunctionEventsDataSource(ModelSimple):
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"""
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Data source for event platform-based queries.
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:param value: Must be one of ["logs", "spans", "network", "rum", "security_signals", "profiles", "audit", "events", "ci_tests"].
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:param value: Must be one of ["logs", "spans", "network", "rum", "security_signals", "profiles", "audit", "events", "ci_tests", "ci_pipelines"].
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:type value: str
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"""
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@@ -30,6 +30,7 @@ class FormulaAndFunctionEventsDataSource(ModelSimple):
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"audit",
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"events",
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"ci_tests",
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"ci_pipelines",
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}
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LOGS: ClassVar["FormulaAndFunctionEventsDataSource"]
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SPANS: ClassVar["FormulaAndFunctionEventsDataSource"]
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AUDIT: ClassVar["FormulaAndFunctionEventsDataSource"]
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EVENTS: ClassVar["FormulaAndFunctionEventsDataSource"]
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CI_TESTS: ClassVar["FormulaAndFunctionEventsDataSource"]
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CI_PIPELINES: ClassVar["FormulaAndFunctionEventsDataSource"]
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@cached_property
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def openapi_types(_):
@@ -57,3 +59,4 @@ def openapi_types(_):
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FormulaAndFunctionEventsDataSource.AUDIT = FormulaAndFunctionEventsDataSource("audit")
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FormulaAndFunctionEventsDataSource.EVENTS = FormulaAndFunctionEventsDataSource("events")
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FormulaAndFunctionEventsDataSource.CI_TESTS = FormulaAndFunctionEventsDataSource("ci_tests")
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FormulaAndFunctionEventsDataSource.CI_PIPELINES = FormulaAndFunctionEventsDataSource("ci_pipelines")

‎tests/v1/cassettes/test_scenarios/test_create_a_new_timeseries_widget_with_ci_pipelines_data_source.frozen

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2023-03-02T15:41:16.500Z

‎tests/v1/cassettes/test_scenarios/test_create_a_new_timeseries_widget_with_ci_pipelines_data_source.yaml

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interactions:
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- request:
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body: '{"layout_type":"ordered","reflow_type":"auto","title":"Test-Create_a_new_timeseries_widget_with_ci_pipelines_data_source-1677771676
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with ci_pipelines datasource","widgets":[{"definition":{"legend_columns":["avg","min","max","value","sum"],"legend_layout":"auto","requests":[{"display_type":"line","formulas":[{"formula":"query1"}],"queries":[{"compute":{"aggregation":"count","metric":"@ci.queue_time"},"data_source":"ci_pipelines","group_by":[],"indexes":["*"],"name":"query1","search":{"query":"ci_level:job"}}],"response_format":"timeseries","style":{"line_type":"solid","line_width":"normal","palette":"dog_classic"}}],"show_legend":true,"time":{},"title":"","type":"timeseries"}}]}'
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headers:
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accept:
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- application/json
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content-type:
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- application/json
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method: POST
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uri: https://api.datadoghq.com/api/v1/dashboard
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response:
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body:
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string: '{"id":"iu9-viw-buz","title":"Test-Create_a_new_timeseries_widget_with_ci_pipelines_data_source-1677771676
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with ci_pipelines datasource","description":null,"author_handle":"[email protected]","author_name":null,"layout_type":"ordered","url":"/dashboard/iu9-viw-buz/test-createanewtimeserieswidgetwithcipipelinesdatasource-1677771676-with-cipipel","is_read_only":false,"template_variables":null,"widgets":[{"definition":{"legend_columns":["avg","min","max","value","sum"],"legend_layout":"auto","requests":[{"display_type":"line","formulas":[{"formula":"query1"}],"queries":[{"compute":{"aggregation":"count","metric":"@ci.queue_time"},"data_source":"ci_pipelines","group_by":[],"indexes":["*"],"name":"query1","search":{"query":"ci_level:job"}}],"response_format":"timeseries","style":{"line_type":"solid","line_width":"normal","palette":"dog_classic"}}],"show_legend":true,"time":{},"title":"","type":"timeseries"},"id":2105687409669278}],"notify_list":null,"created_at":"2023-03-02T15:41:16.697612+00:00","modified_at":"2023-03-02T15:41:16.697612+00:00","reflow_type":"auto","restricted_roles":[]}
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'
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headers:
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content-type:
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- application/json
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status:
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code: 200
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message: OK
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- request:
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body: null
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headers:
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accept:
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- application/json
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method: DELETE
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uri: https://api.datadoghq.com/api/v1/dashboard/iu9-viw-buz
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response:
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body:
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string: '{"deleted_dashboard_id":"iu9-viw-buz"}
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'
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headers:
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content-type:
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- application/json
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status:
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code: 200
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message: OK
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version: 1

‎tests/v1/features/dashboards.feature

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And the response "widgets[0].definition.type" is equal to "trace_service"
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And the response "widgets[0].definition.env" is equal to "none"
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@team:DataDog/dashboards
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Scenario: Create a new timeseries widget with ci_pipelines data source
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Given new "CreateDashboard" request
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And body with value {"title":"{{ unique }} with ci_pipelines datasource","widgets":[{"definition":{"title":"","show_legend":true,"legend_layout":"auto","legend_columns":["avg","min","max","value","sum"],"time":{},"type":"timeseries","requests":[{"formulas":[{"formula":"query1"}],"queries":[{"data_source":"ci_pipelines","name":"query1","search":{"query":"ci_level:job"},"indexes":["*"],"compute":{"aggregation":"count", "metric": "@ci.queue_time"},"group_by":[]}],"response_format":"timeseries","style":{"palette":"dog_classic","line_type":"solid","line_width":"normal"},"display_type":"line"}]}}],"layout_type":"ordered","reflow_type":"auto"}
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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 "widgets[0].definition.requests[0].queries[0].data_source" is equal to "ci_pipelines"
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And the response "widgets[0].definition.requests[0].queries[0].search.query" is equal to "ci_level:job"
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@team:DataDog/dashboards
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Scenario: Create a new timeseries widget with ci_tests data source
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Given new "CreateDashboard" request

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