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Dashboards - Add semantic_mode support to FormulaAndFunctionMetricQueryDefinition (#3088)
Co-authored-by: ci.datadog-api-spec <[email protected]>
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‎.generator/schemas/v1/openapi.yaml‎

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@@ -2952,11 +2952,24 @@ components:
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description: Metrics query definition.
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example: avg:system.cpu.user{*}
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type: string
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semantic_mode:
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$ref: '#/components/schemas/FormulaAndFunctionMetricSemanticMode'
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required:
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- data_source
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- query
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- name
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type: object
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FormulaAndFunctionMetricSemanticMode:
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description: Semantic mode for metrics queries. This determines how metrics
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from different sources are combined or displayed.
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enum:
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- combined
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- native
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example: combined
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type: string
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x-enum-varnames:
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- COMBINED
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- NATIVE
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FormulaAndFunctionProcessQueryDataSource:
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description: Data sources that rely on the process backend.
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enum:

‎cassettes/v1/Dashboards_1335235736/Create-a-new-dashboard-with-a-timeseries-widget-using-formulas-and-functions-metrics-quer_3988734830/frozen.json‎

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"2025-12-08T18:40:10.047Z"

‎cassettes/v1/Dashboards_1335235736/Create-a-new-dashboard-with-a-timeseries-widget-using-formulas-and-functions-metrics-quer_3988734830/recording.har‎

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{
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"log": {
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"_recordingName": "Dashboards/Create a new dashboard with a timeseries widget using formulas and functions metrics query with combined semantic_mode",
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"creator": {
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"comment": "persister:fs",
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"name": "Polly.JS",
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},
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"url": "https://api.datadoghq.com/api/v1/dashboard"
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},
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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/dashboard/bpt-wdw-b9x"
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},
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"text": "{\"deleted_dashboard_id\":\"bpt-wdw-b9x\"}"
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},
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"version": "1.2"
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}
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}

‎cassettes/v1/Dashboards_1335235736/Create-a-new-dashboard-with-a-timeseries-widget-using-formulas-and-functions-metrics-query_683231166/frozen.json‎

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"2025-12-08T18:32:38.191Z"

‎cassettes/v1/Dashboards_1335235736/Create-a-new-dashboard-with-a-timeseries-widget-using-formulas-and-functions-metrics-query_683231166/recording.har‎

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{
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"log": {
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"_recordingName": "Dashboards/Create a new dashboard with a timeseries widget using formulas and functions metrics query with native semantic_mode",
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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": "ba7f467c39d187aeca02ce0e6ebe3de8",
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"text": "{\"layout_type\":\"ordered\",\"title\":\"Test-Create_a_new_dashboard_with_a_timeseries_widget_using_formulas_and_functions_metrics_query_with_nati-1765218758 with native semantic_mode\",\"widgets\":[{\"definition\":{\"requests\":[{\"display_type\":\"line\",\"formulas\":[{\"formula\":\"query1\"}],\"queries\":[{\"data_source\":\"metrics\",\"name\":\"query1\",\"query\":\"avg:system.cpu.user{*}\",\"semantic_mode\":\"native\"}],\"response_format\":\"timeseries\"}],\"type\":\"timeseries\"}}]}"
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},
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"queryString": [],
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"url": "https://api.datadoghq.com/api/v1/dashboard"
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},
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"text": "{\"id\":\"ptr-h98-jx4\",\"title\":\"Test-Create_a_new_dashboard_with_a_timeseries_widget_using_formulas_and_functions_metrics_query_with_nati-1765218758 with native semantic_mode\",\"description\":null,\"author_handle\":\"[email protected]\",\"author_name\":\"frog\",\"layout_type\":\"ordered\",\"url\":\"/dashboard/ptr-h98-jx4/test-createanewdashboardwithatimeserieswidgetusingformulasandfunctionsmetricsque\",\"template_variables\":null,\"widgets\":[{\"definition\":{\"requests\":[{\"display_type\":\"line\",\"formulas\":[{\"formula\":\"query1\"}],\"queries\":[{\"data_source\":\"metrics\",\"name\":\"query1\",\"query\":\"avg:system.cpu.user{*}\",\"semantic_mode\":\"native\"}],\"response_format\":\"timeseries\"}],\"type\":\"timeseries\"},\"id\":7543625669678795}],\"notify_list\":null,\"created_at\":\"2025-12-08T18:32:38.359385+00:00\",\"modified_at\":\"2025-12-08T18:32:38.359385+00:00\",\"restricted_roles\":[]}"
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},
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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": "2025-12-08T18:32:38.194Z",
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"time": 191
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},
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{
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"_id": "e052b7cf63125b650a3cc6393245f8d4",
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}
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],
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"headersSize": 518,
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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/dashboard/ptr-h98-jx4"
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},
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"response": {
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"bodySize": 38,
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"content": {
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"text": "{\"deleted_dashboard_id\":\"ptr-h98-jx4\"}"
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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": 379,
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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": "2025-12-08T18:32:38.390Z",
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"time": 453
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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/dashboards/CreateDashboard_3685886950.ts‎

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/**
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* Create a new dashboard with a timeseries widget using formulas and functions metrics query with native semantic_mode
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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.DashboardsApi(configuration);
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const params: v1.DashboardsApiCreateDashboardRequest = {
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body: {
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layoutType: "ordered",
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title: "Example-Dashboard with native semantic_mode",
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widgets: [
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{
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definition: {
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type: "timeseries",
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requests: [
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{
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queries: [
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{
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dataSource: "metrics",
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name: "query1",
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query: "avg:system.cpu.user{*}",
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semanticMode: "native",
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},
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],
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responseFormat: "timeseries",
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formulas: [
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{
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formula: "query1",
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},
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],
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displayType: "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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},
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};
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apiInstance
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.createDashboard(params)
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.then((data: v1.Dashboard) => {
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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));

‎examples/v1/dashboards/CreateDashboard_865807520.ts‎

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/**
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* Create a new dashboard with a timeseries widget using formulas and functions metrics query with combined semantic_mode
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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.DashboardsApi(configuration);
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const params: v1.DashboardsApiCreateDashboardRequest = {
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body: {
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layoutType: "ordered",
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title: "Example-Dashboard with combined semantic_mode",
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widgets: [
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{
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definition: {
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type: "timeseries",
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requests: [
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{
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queries: [
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{
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dataSource: "metrics",
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name: "query1",
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query: "avg:system.cpu.user{*}",
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semanticMode: "combined",
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},
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],
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responseFormat: "timeseries",
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formulas: [
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{
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formula: "query1",
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},
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],
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displayType: "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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},
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};
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apiInstance
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.createDashboard(params)
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.then((data: v1.Dashboard) => {
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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/dashboards.feature‎

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And the response "widgets[0].definition.requests[0].formulas[0].formula" is equal to "query1"
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And the response "widgets[0].definition.time.live_span" is equal to "week_to_date"
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@team:DataDog/dashboards-backend
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Scenario: Create a new dashboard with a timeseries widget using formulas and functions metrics query with combined semantic_mode
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Given new "CreateDashboard" request
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And body with value {"layout_type": "ordered", "title": "{{ unique }} with combined semantic_mode", "widgets": [{"definition": {"type": "timeseries", "requests": [{"queries": [{"data_source": "metrics", "name": "query1", "query": "avg:system.cpu.user{*}", "semantic_mode": "combined"}], "response_format": "timeseries", "formulas": [{"formula": "query1"}], "display_type": "line"}]}}]}
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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].response_format" is equal to "timeseries"
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And the response "widgets[0].definition.requests[0].queries[0].data_source" is equal to "metrics"
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And the response "widgets[0].definition.requests[0].queries[0].name" is equal to "query1"
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And the response "widgets[0].definition.requests[0].queries[0].query" is equal to "avg:system.cpu.user{*}"
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And the response "widgets[0].definition.requests[0].queries[0].semantic_mode" is equal to "combined"
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@team:DataDog/dashboards-backend
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Scenario: Create a new dashboard with a timeseries widget using formulas and functions metrics query with native semantic_mode
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Given new "CreateDashboard" request
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And body with value {"layout_type": "ordered", "title": "{{ unique }} with native semantic_mode", "widgets": [{"definition": {"type": "timeseries", "requests": [{"queries": [{"data_source": "metrics", "name": "query1", "query": "avg:system.cpu.user{*}", "semantic_mode": "native"}], "response_format": "timeseries", "formulas": [{"formula": "query1"}], "display_type": "line"}]}}]}
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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].response_format" is equal to "timeseries"
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And the response "widgets[0].definition.requests[0].queries[0].data_source" is equal to "metrics"
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And the response "widgets[0].definition.requests[0].queries[0].name" is equal to "query1"
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And the response "widgets[0].definition.requests[0].queries[0].query" is equal to "avg:system.cpu.user{*}"
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And the response "widgets[0].definition.requests[0].queries[0].semantic_mode" is equal to "native"
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@team:DataDog/dashboards-backend
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Scenario: Create a new dashboard with a toplist widget sorted by group
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Given new "CreateDashboard" request

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

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@@ -457,6 +457,7 @@ export { FormulaAndFunctionEventsDataSource } from "./models/FormulaAndFunctionE
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export { FormulaAndFunctionMetricAggregation } from "./models/FormulaAndFunctionMetricAggregation";
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export { FormulaAndFunctionMetricDataSource } from "./models/FormulaAndFunctionMetricDataSource";
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export { FormulaAndFunctionMetricQueryDefinition } from "./models/FormulaAndFunctionMetricQueryDefinition";
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export { FormulaAndFunctionMetricSemanticMode } from "./models/FormulaAndFunctionMetricSemanticMode";
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export { FormulaAndFunctionProcessQueryDataSource } from "./models/FormulaAndFunctionProcessQueryDataSource";
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export { FormulaAndFunctionProcessQueryDefinition } from "./models/FormulaAndFunctionProcessQueryDefinition";
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export { FormulaAndFunctionQueryDefinition } from "./models/FormulaAndFunctionQueryDefinition";

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