added workspace monitoring functions

src/sempy_labs/_authentication.py

@@ -1,6 +1,5 @@
 from typing import Literal
 from sempy.fabric._token_provider import TokenProvider
-import notebookutils
 from azure.identity import ClientSecretCredential
 
 
@@ -72,6 +71,8 @@ def with_azure_key_vault(
         sempy.fabric.TokenProvider
             Token provider to be used with FabricRestClient or PowerBIRestClient.
         """
+        import notebookutils
+
         tenant_id = notebookutils.credentials.getSecret(
             key_vault_uri, key_vault_tenant_id
         )

src/sempy_labs/_helper_functions.py

@@ -149,6 +149,51 @@ def resolve_report_name(report_id: UUID, workspace: Optional[str] = None) -> str
     return obj
 
 
+def resolve_item_name_and_id(
+    item: str | UUID, type: Optional[str] = None, workspace: Optional[str | UUID] = None
+) -> Tuple[str, UUID]:
+
+    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+
+    if _is_valid_uuid(item):
+        item_id = item
+        item_name = fabric.resolve_item_name(
+            item_id=item_id, type=type, workspace=workspace_id
+        )
+    else:
+        if type is None:
+            raise ValueError(
+                f"{icons.warning} Must specify a 'type' if specifying a name as the 'item'."
+            )
+        item_name = item
+        item_id = fabric.resolve_item_id(
+            item_name=item, type=type, workspace=workspace_id
+        )
+
+    return item_name, item_id
+
+
+def resolve_lakehouse_name_and_id(lakehouse: Optional[str | UUID] = None, workspace: Optional[str | UUID] = None) -> Tuple[str, UUID]:
+
+    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+
+    if lakehouse is None:
+        lakehouse_id = fabric.get_lakehouse_id()
+        lakehouse_name = fabric.resolve_item_name(item_id=lakehouse_id, type="Lakehouse", workspace=workspace_id)
+    elif _is_valid_uuid(lakehouse):
+        lakehouse_id = lakehouse
+        lakehouse_name = fabric.resolve_item_name(
+            item_id=lakehouse_id, type="Lakehouse", workspace=workspace_id
+        )
+    else:
+        lakehouse_name = lakehouse
+        lakehouse_id = fabric.resolve_item_id(
+            item_name=lakehouse, type="Lakehouse", workspace=workspace_id
+        )
+
+    return lakehouse_name, lakehouse_id
+
+
 def resolve_dataset_id(dataset: str, workspace: Optional[str] = None) -> UUID:
     """
     Obtains the ID of the semantic model.
@@ -395,8 +440,8 @@ def save_as_delta_table(
     write_mode: str,
     merge_schema: bool = False,
     schema: Optional[dict] = None,
-    lakehouse: Optional[str] = None,
-    workspace: Optional[str] = None,
+    lakehouse: Optional[str | UUID] = None,
+    workspace: Optional[str | UUID] = None,
 ):
     """
     Saves a pandas dataframe as a delta table in a Fabric lakehouse.
@@ -413,11 +458,11 @@ def save_as_delta_table(
         Merges the schemas of the dataframe to the delta table.
     schema : dict, default=None
         A dictionary showing the schema of the columns and their data types.
-    lakehouse : str, default=None
-        The Fabric lakehouse used by the Direct Lake semantic model.
+    lakehouse : str | uuid.UUID, default=None
+        The Fabric lakehouse name or ID.
         Defaults to None which resolves to the lakehouse attached to the notebook.
-    workspace : str, default=None
-        The Fabric workspace name.
+    workspace : str | uuid.UUID, default=None
+        The Fabric workspace name or ID.
         Defaults to None which resolves to the workspace of the attached lakehouse
         or if no lakehouse attached, resolves to the workspace of the notebook.
     """
@@ -436,26 +481,15 @@ def save_as_delta_table(
         TimestampType,
     )
 
-    if workspace is None:
-        workspace_id = fabric.get_workspace_id()
-        workspace = fabric.resolve_workspace_name(workspace_id)
-    else:
-        workspace_id = fabric.resolve_workspace_id(workspace)
-
-    if lakehouse is None:
-        lakehouse_id = fabric.get_lakehouse_id()
-        lakehouse = resolve_lakehouse_name(
-            lakehouse_id=lakehouse_id, workspace=workspace
-        )
-    else:
-        lakehouse_id = resolve_lakehouse_id(lakehouse, workspace)
+    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+    (lakehouse_name, lakehouse_id) = resolve_lakehouse_name_and_id(lakehouse=lakehouse, workspace=workspace_id)
 
-    writeModes = ["append", "overwrite"]
+    write_modes = ["append", "overwrite"]
     write_mode = write_mode.lower()
 
-    if write_mode not in writeModes:
+    if write_mode not in write_modes:
         raise ValueError(
-            f"{icons.red_dot} Invalid 'write_type' parameter. Choose from one of the following values: {writeModes}."
+            f"{icons.red_dot} Invalid 'write_type' parameter. Choose from one of the following values: {write_modes}."
         )
 
     if " " in delta_table_name:
@@ -480,16 +514,19 @@ def save_as_delta_table(
         "timestamp": TimestampType(),
     }
 
-    if schema is None:
-        spark_df = spark.createDataFrame(dataframe)
+    if isinstance(dataframe, pd.DataFrame):
+        if schema is None:
+            spark_df = spark.createDataFrame(dataframe)
+        else:
+            schema_map = StructType(
+                [
+                    StructField(column_name, type_mapping[data_type], True)
+                    for column_name, data_type in schema.items()
+                ]
+            )
+            spark_df = spark.createDataFrame(dataframe, schema_map)
     else:
-        schema_map = StructType(
-            [
-                StructField(column_name, type_mapping[data_type], True)
-                for column_name, data_type in schema.items()
-            ]
-        )
-        spark_df = spark.createDataFrame(dataframe, schema_map)
+        spark_df = dataframe
 
     filePath = create_abfss_path(
         lakehouse_id=lakehouse_id,
@@ -504,7 +541,7 @@ def save_as_delta_table(
     else:
         spark_df.write.mode(write_mode).format("delta").save(filePath)
     print(
-        f"{icons.green_dot} The dataframe has been saved as the '{delta_table_name}' table in the '{lakehouse}' lakehouse within the '{workspace}' workspace."
+        f"{icons.green_dot} The dataframe has been saved as the '{delta_table_name}' table in the '{lakehouse_name}' lakehouse within the '{workspace_name}' workspace."
     )
 
 

src/sempy_labs/_kql_databases.py

@@ -8,6 +8,7 @@
     pagination,
 )
 from sempy.fabric.exceptions import FabricHTTPException
+from uuid import UUID
 
 
 def list_kql_databases(workspace: Optional[str] = None) -> pd.DataFrame:
@@ -138,3 +139,15 @@ def delete_kql_database(name: str, workspace: Optional[str] = None):
     print(
         f"{icons.green_dot} The '{name}' KQL database within the '{workspace}' workspace has been deleted."
     )
+
+
+def _resolve_cluster_uri(workspace: Optional[str | UUID] = None) -> str:
+
+    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+    dfK = list_kql_databases(workspace=workspace_id)
+    dfK_filt = dfK[dfK["KQL Database Name"] == "Monitoring KQL database"]
+    if len(dfK_filt) == 0:
+        raise ValueError(
+            f"{icons.red_dot} Workspace monitoring is not set up for the '{workspace_name}' workspace."
+        )
+    return dfK_filt["Query Service URI"].iloc[0]