Dashboards - Add semantic_mode support to FormulaAndFunctionMetricQueryDefinition

.generator/schemas/v1/openapi.yaml

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

examples/v1_dashboards_CreateDashboard_3685886950.rs

@@ -0,0 +1,55 @@
+// Create a new dashboard with a timeseries widget using formulas and functions
+// metrics query with native semantic_mode
+use datadog_api_client::datadog;
+use datadog_api_client::datadogV1::api_dashboards::DashboardsAPI;
+use datadog_api_client::datadogV1::model::Dashboard;
+use datadog_api_client::datadogV1::model::DashboardLayoutType;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionMetricDataSource;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionMetricQueryDefinition;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionMetricSemanticMode;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionQueryDefinition;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionResponseFormat;
+use datadog_api_client::datadogV1::model::TimeseriesWidgetDefinition;
+use datadog_api_client::datadogV1::model::TimeseriesWidgetDefinitionType;
+use datadog_api_client::datadogV1::model::TimeseriesWidgetRequest;
+use datadog_api_client::datadogV1::model::Widget;
+use datadog_api_client::datadogV1::model::WidgetDefinition;
+use datadog_api_client::datadogV1::model::WidgetDisplayType;
+use datadog_api_client::datadogV1::model::WidgetFormula;
+
+#[tokio::main]
+async fn main() {
+    let body = Dashboard::new(
+        DashboardLayoutType::ORDERED,
+        "Example-Dashboard with native semantic_mode".to_string(),
+        vec![Widget::new(WidgetDefinition::TimeseriesWidgetDefinition(
+            Box::new(TimeseriesWidgetDefinition::new(
+                vec![TimeseriesWidgetRequest::new()
+                    .display_type(WidgetDisplayType::LINE)
+                    .formulas(vec![WidgetFormula::new("query1".to_string())])
+                    .queries(vec![
+                        FormulaAndFunctionQueryDefinition::FormulaAndFunctionMetricQueryDefinition(
+                            Box::new(
+                                FormulaAndFunctionMetricQueryDefinition::new(
+                                    FormulaAndFunctionMetricDataSource::METRICS,
+                                    "query1".to_string(),
+                                    "avg:system.cpu.user{*}".to_string(),
+                                )
+                                .semantic_mode(FormulaAndFunctionMetricSemanticMode::NATIVE),
+                            ),
+                        ),
+                    ])
+                    .response_format(FormulaAndFunctionResponseFormat::TIMESERIES)],
+                TimeseriesWidgetDefinitionType::TIMESERIES,
+            )),
+        ))],
+    );
+    let configuration = datadog::Configuration::new();
+    let api = DashboardsAPI::with_config(configuration);
+    let resp = api.create_dashboard(body).await;
+    if let Ok(value) = resp {
+        println!("{:#?}", value);
+    } else {
+        println!("{:#?}", resp.unwrap_err());
+    }
+}

examples/v1_dashboards_CreateDashboard_865807520.rs

@@ -0,0 +1,55 @@
+// Create a new dashboard with a timeseries widget using formulas and functions
+// metrics query with combined semantic_mode
+use datadog_api_client::datadog;
+use datadog_api_client::datadogV1::api_dashboards::DashboardsAPI;
+use datadog_api_client::datadogV1::model::Dashboard;
+use datadog_api_client::datadogV1::model::DashboardLayoutType;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionMetricDataSource;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionMetricQueryDefinition;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionMetricSemanticMode;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionQueryDefinition;
+use datadog_api_client::datadogV1::model::FormulaAndFunctionResponseFormat;
+use datadog_api_client::datadogV1::model::TimeseriesWidgetDefinition;
+use datadog_api_client::datadogV1::model::TimeseriesWidgetDefinitionType;
+use datadog_api_client::datadogV1::model::TimeseriesWidgetRequest;
+use datadog_api_client::datadogV1::model::Widget;
+use datadog_api_client::datadogV1::model::WidgetDefinition;
+use datadog_api_client::datadogV1::model::WidgetDisplayType;
+use datadog_api_client::datadogV1::model::WidgetFormula;
+
+#[tokio::main]
+async fn main() {
+    let body = Dashboard::new(
+        DashboardLayoutType::ORDERED,
+        "Example-Dashboard with combined semantic_mode".to_string(),
+        vec![Widget::new(WidgetDefinition::TimeseriesWidgetDefinition(
+            Box::new(TimeseriesWidgetDefinition::new(
+                vec![TimeseriesWidgetRequest::new()
+                    .display_type(WidgetDisplayType::LINE)
+                    .formulas(vec![WidgetFormula::new("query1".to_string())])
+                    .queries(vec![
+                        FormulaAndFunctionQueryDefinition::FormulaAndFunctionMetricQueryDefinition(
+                            Box::new(
+                                FormulaAndFunctionMetricQueryDefinition::new(
+                                    FormulaAndFunctionMetricDataSource::METRICS,
+                                    "query1".to_string(),
+                                    "avg:system.cpu.user{*}".to_string(),
+                                )
+                                .semantic_mode(FormulaAndFunctionMetricSemanticMode::COMBINED),
+                            ),
+                        ),
+                    ])
+                    .response_format(FormulaAndFunctionResponseFormat::TIMESERIES)],
+                TimeseriesWidgetDefinitionType::TIMESERIES,
+            )),
+        ))],
+    );
+    let configuration = datadog::Configuration::new();
+    let api = DashboardsAPI::with_config(configuration);
+    let resp = api.create_dashboard(body).await;
+    if let Ok(value) = resp {
+        println!("{:#?}", value);
+    } else {
+        println!("{:#?}", resp.unwrap_err());
+    }
+}

src/datadogV1/model/mod.rs

@@ -194,6 +194,8 @@ pub mod model_formula_and_function_metric_aggregation;
 pub use self::model_formula_and_function_metric_aggregation::FormulaAndFunctionMetricAggregation;
 pub mod model_formula_and_function_metric_data_source;
 pub use self::model_formula_and_function_metric_data_source::FormulaAndFunctionMetricDataSource;
+pub mod model_formula_and_function_metric_semantic_mode;
+pub use self::model_formula_and_function_metric_semantic_mode::FormulaAndFunctionMetricSemanticMode;
 pub mod model_formula_and_function_event_query_definition;
 pub use self::model_formula_and_function_event_query_definition::FormulaAndFunctionEventQueryDefinition;
 pub mod model_formula_and_function_event_query_definition_compute;

src/datadogV1/model/model_formula_and_function_metric_query_definition.rs

@@ -26,6 +26,9 @@ pub struct FormulaAndFunctionMetricQueryDefinition {
     /// Metrics query definition.
     #[serde(rename = "query")]
     pub query: String,
+    /// Semantic mode for metrics queries. This determines how metrics from different sources are combined or displayed.
+    #[serde(rename = "semantic_mode")]
+    pub semantic_mode: Option<crate::datadogV1::model::FormulaAndFunctionMetricSemanticMode>,
     #[serde(flatten)]
     pub additional_properties: std::collections::BTreeMap<String, serde_json::Value>,
     #[serde(skip)]
@@ -45,6 +48,7 @@ impl FormulaAndFunctionMetricQueryDefinition {
             data_source,
             name,
             query,
+            semantic_mode: None,
             additional_properties: std::collections::BTreeMap::new(),
             _unparsed: false,
         }
@@ -63,6 +67,14 @@ impl FormulaAndFunctionMetricQueryDefinition {
         self
     }
 
+    pub fn semantic_mode(
+        mut self,
+        value: crate::datadogV1::model::FormulaAndFunctionMetricSemanticMode,
+    ) -> Self {
+        self.semantic_mode = Some(value);
+        self
+    }
+
     pub fn additional_properties(
         mut self,
         value: std::collections::BTreeMap<String, serde_json::Value>,
@@ -98,6 +110,9 @@ impl<'de> Deserialize<'de> for FormulaAndFunctionMetricQueryDefinition {
                 > = None;
                 let mut name: Option<String> = None;
                 let mut query: Option<String> = None;
+                let mut semantic_mode: Option<
+                    crate::datadogV1::model::FormulaAndFunctionMetricSemanticMode,
+                > = None;
                 let mut additional_properties: std::collections::BTreeMap<
                     String,
                     serde_json::Value,
@@ -145,6 +160,21 @@ impl<'de> Deserialize<'de> for FormulaAndFunctionMetricQueryDefinition {
                         "query" => {
                             query = Some(serde_json::from_value(v).map_err(M::Error::custom)?);
                         }
+                        "semantic_mode" => {
+                            if v.is_null() {
+                                continue;
+                            }
+                            semantic_mode =
+                                Some(serde_json::from_value(v).map_err(M::Error::custom)?);
+                            if let Some(ref _semantic_mode) = semantic_mode {
+                                match _semantic_mode {
+                                    crate::datadogV1::model::FormulaAndFunctionMetricSemanticMode::UnparsedObject(_semantic_mode) => {
+                                        _unparsed = true;
+                                    },
+                                    _ => {}
+                                }
+                            }
+                        }
                         &_ => {
                             if let Ok(value) = serde_json::from_value(v.clone()) {
                                 additional_properties.insert(k, value);
@@ -163,6 +193,7 @@ impl<'de> Deserialize<'de> for FormulaAndFunctionMetricQueryDefinition {
                     data_source,
                     name,
                     query,
+                    semantic_mode,
                     additional_properties,
                     _unparsed,
                 };

src/datadogV1/model/model_formula_and_function_metric_semantic_mode.rs

@@ -0,0 +1,51 @@
+// Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License.
+// This product includes software developed at Datadog (https://www.datadoghq.com/).
+// Copyright 2019-Present Datadog, Inc.
+
+use serde::{Deserialize, Deserializer, Serialize, Serializer};
+
+#[non_exhaustive]
+#[derive(Clone, Debug, Eq, PartialEq)]
+pub enum FormulaAndFunctionMetricSemanticMode {
+    COMBINED,
+    NATIVE,
+    UnparsedObject(crate::datadog::UnparsedObject),
+}
+
+impl ToString for FormulaAndFunctionMetricSemanticMode {
+    fn to_string(&self) -> String {
+        match self {
+            Self::COMBINED => String::from("combined"),
+            Self::NATIVE => String::from("native"),
+            Self::UnparsedObject(v) => v.value.to_string(),
+        }
+    }
+}
+
+impl Serialize for FormulaAndFunctionMetricSemanticMode {
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
+    where
+        S: Serializer,
+    {
+        match self {
+            Self::UnparsedObject(v) => v.serialize(serializer),
+            _ => serializer.serialize_str(self.to_string().as_str()),
+        }
+    }
+}
+
+impl<'de> Deserialize<'de> for FormulaAndFunctionMetricSemanticMode {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+        let s: String = String::deserialize(deserializer)?;
+        Ok(match s.as_str() {
+            "combined" => Self::COMBINED,
+            "native" => Self::NATIVE,
+            _ => Self::UnparsedObject(crate::datadog::UnparsedObject {
+                value: serde_json::Value::String(s.into()),
+            }),
+        })
+    }
+}

tests/scenarios/cassettes/v1/dashboards/Create-a-new-dashboard-with-a-timeseries-widget-using-formulas-and-functions-metrics-query-with-combined-semantic-mode.frozen

@@ -0,0 +1 @@
+2025-12-08T18:40:10.047Z

tests/scenarios/cassettes/v1/dashboards/Create-a-new-dashboard-with-a-timeseries-widget-using-formulas-and-functions-metrics-query-with-combined-semantic-mode.json

@@ -0,0 +1,67 @@
+{
+  "http_interactions": [
+    {
+      "request": {
+        "body": {
+          "string": "{\"layout_type\":\"ordered\",\"title\":\"Test-Create_a_new_dashboard_with_a_timeseries_widget_using_formulas_and_functions_metrics_query_with_comb-1765219210 with combined semantic_mode\",\"widgets\":[{\"definition\":{\"requests\":[{\"display_type\":\"line\",\"formulas\":[{\"formula\":\"query1\"}],\"queries\":[{\"data_source\":\"metrics\",\"name\":\"query1\",\"query\":\"avg:system.cpu.user{*}\",\"semantic_mode\":\"combined\"}],\"response_format\":\"timeseries\"}],\"type\":\"timeseries\"}}]}",
+          "encoding": null
+        },
+        "headers": {
+          "Accept": [
+            "application/json"
+          ],
+          "Content-Type": [
+            "application/json"
+          ]
+        },
+        "method": "post",
+        "uri": "https://api.datadoghq.com/api/v1/dashboard"
+      },
+      "response": {
+        "body": {
+          "string": "{\"id\":\"bpt-wdw-b9x\",\"title\":\"Test-Create_a_new_dashboard_with_a_timeseries_widget_using_formulas_and_functions_metrics_query_with_comb-1765219210 with combined semantic_mode\",\"description\":null,\"author_handle\":\"[email protected]\",\"author_name\":\"frog\",\"layout_type\":\"ordered\",\"url\":\"/dashboard/bpt-wdw-b9x/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\":\"combined\"}],\"response_format\":\"timeseries\"}],\"type\":\"timeseries\"},\"id\":7196642548461969}],\"notify_list\":null,\"created_at\":\"2025-12-08T18:40:10.214467+00:00\",\"modified_at\":\"2025-12-08T18:40:10.214467+00:00\",\"restricted_roles\":[]}",
+          "encoding": null
+        },
+        "headers": {
+          "Content-Type": [
+            "application/json"
+          ]
+        },
+        "status": {
+          "code": 200,
+          "message": "OK"
+        }
+      },
+      "recorded_at": "Mon, 08 Dec 2025 18:40:10 GMT"
+    },
+    {
+      "request": {
+        "body": "",
+        "headers": {
+          "Accept": [
+            "application/json"
+          ]
+        },
+        "method": "delete",
+        "uri": "https://api.datadoghq.com/api/v1/dashboard/bpt-wdw-b9x"
+      },
+      "response": {
+        "body": {
+          "string": "{\"deleted_dashboard_id\":\"bpt-wdw-b9x\"}",
+          "encoding": null
+        },
+        "headers": {
+          "Content-Type": [
+            "application/json"
+          ]
+        },
+        "status": {
+          "code": 200,
+          "message": "OK"
+        }
+      },
+      "recorded_at": "Mon, 08 Dec 2025 18:40:10 GMT"
+    }
+  ],
+  "recorded_with": "VCR 6.0.0"
+}

tests/scenarios/cassettes/v1/dashboards/Create-a-new-dashboard-with-a-timeseries-widget-using-formulas-and-functions-metrics-query-with-native-semantic-mode.frozen

@@ -0,0 +1 @@
+2025-12-08T18:32:38.191Z