Added SQLDabatase connectivity

src/sempy_labs/_sql.py

@@ -49,6 +49,11 @@ def __init__(
             (resource_id, resource_name) = resolve_item_name_and_id(
                 item=item, type=endpoint_type.capitalize(), workspace=workspace_id
             )
+        if endpoint_type == "sqldatabase":
+            # SQLDatabase is has special case for resolving the name and id
+            (resource_id, resource_name) = resolve_item_name_and_id(
+                item=item, type="SQLDatabase", workspace=workspace_id
+            )
         else:
             (resource_id, resource_name) = resolve_lakehouse_name_and_id(
                 lakehouse=item, workspace=workspace_id
@@ -61,6 +66,8 @@ def __init__(
 
         if endpoint_type == "warehouse":
             tds_endpoint = response.json().get("properties", {}).get("connectionString")
+        if endpoint_type == "sqldatabase":
+            tds_endpoint = response.json().get("properties", {}).get("serverFqdn")
         else:
             tds_endpoint = (
                 response.json()
@@ -72,7 +79,10 @@ def __init__(
         # Set up the connection string
         access_token = SynapseTokenProvider()()
         tokenstruct = _bytes2mswin_bstr(access_token.encode())
-        conn_str = f"DRIVER={{ODBC Driver 18 for SQL Server}};SERVER={tds_endpoint};DATABASE={resource_name};Encrypt=Yes;"
+        if endpoint_type == "sqldatabase":
+            conn_str = f"DRIVER={{ODBC Driver 18 for SQL Server}};SERVER={tds_endpoint};DATABASE={resource_name}-{resource_id};Encrypt=Yes;"
+        else:
+            conn_str = f"DRIVER={{ODBC Driver 18 for SQL Server}};SERVER={tds_endpoint};DATABASE={resource_name};Encrypt=Yes;"
 
         if timeout is not None:
             conn_str += f"Connect Timeout={timeout};"
@@ -166,3 +176,17 @@ def __init__(
             timeout=timeout,
             endpoint_type="lakehouse",
         )
+
+class ConnectSQLDatabase(ConnectBase):
+    def __init__(
+        self,
+        sqldatabase: str,
+        workspace: Optional[Union[str, UUID]] = None,
+        timeout: Optional[int] = None,
+    ):
+        super().__init__(
+            name=sqldatabase,
+            workspace=workspace,
+            timeout=timeout,
+            endpoint_type="sqldatabase",
+        )

src/sempy_labs/_sqldatabase.py

@@ -0,0 +1,218 @@
+import sempy.fabric as fabric
+from sempy_labs._helper_functions import (
+    resolve_workspace_name_and_id,
+    _base_api,
+    _create_dataframe,
+    _update_dataframe_datatypes,
+)
+import pandas as pd
+from typing import Optional
+import sempy_labs._icons as icons
+from uuid import UUID
+
+## Still debugging the creation of an sql database
+# def create_warehouse(
+#     warehouse: str,
+#     description: Optional[str] = None,
+#     case_insensitive_collation: bool = False,
+#     workspace: Optional[str | UUID] = None,
+# ):
+#     """
+#     Creates a Fabric warehouse.
+
+#     This is a wrapper function for the following API: `Items - Create Warehouse <https://learn.microsoft.com/rest/api/fabric/warehouse/items/create-warehouse>`_.
+
+#     Parameters
+#     ----------
+#     warehouse: str
+#         Name of the warehouse.
+#     description : str, default=None
+#         A description of the warehouse.
+#     case_insensitive_collation: bool, default=False
+#         If True, creates the warehouse with case-insensitive collation.
+#     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.
+#     """
+
+#     (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+
+#     payload = {"displayName": warehouse}
+
+#     if description:
+#         payload["description"] = description
+#     if case_insensitive_collation:
+#         payload.setdefault("creationPayload", {})
+#         payload["creationPayload"][
+#             "defaultCollation"
+#         ] = "Latin1_General_100_CI_AS_KS_WS_SC_UTF8"
+
+#     _base_api(
+#         request=f"/v1/workspaces/{workspace_id}/warehouses",
+#         payload=payload,
+#         method="post",
+#         lro_return_status_code=True,
+#         status_codes=[201, 202],
+#     )
+
+#     print(
+#         f"{icons.green_dot} The '{warehouse}' warehouse has been created within the '{workspace_name}' workspace."
+#     )
+
+
+def list_sqldatabses(workspace: Optional[str | UUID] = None) -> pd.DataFrame:
+    """
+    Shows the databses within a workspace.
+
+
+    Parameters
+    ----------
+    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.
+
+    Returns
+    -------
+    pandas.DataFrame
+        A pandas dataframe showing the SQLDabatases within a workspace.
+    """
+
+    columns = {
+        "SQLDatabase Name": "string",
+        "SQLDatabase Id": "string",
+        "Description": "string",
+        "Connection Info": "string",
+        "Created Date": "datetime",
+        "Last Updated Time": "datetime",
+    }
+    df = _create_dataframe(columns=columns)
+
+    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+
+    responses = _base_api(
+        reqeust=f"/v1/workspaces/{workspace_id}/sqldatabases", uses_pagination=True
+    )
+
+    for r in responses:
+        for v in r.get("value", []):
+            prop = v.get("properties", {})
+
+            new_data = {
+                "Warehouse Name": v.get("displayName"),
+                "Warehouse Id": v.get("id"),
+                "Description": v.get("description"),
+                "Connection Info": prop.get("connectionInfo"),
+                "Created Date": prop.get("createdDate"),
+                "Last Updated Time": prop.get("lastUpdatedTime"),
+            }
+            df = pd.concat([df, pd.DataFrame(new_data, index=[0])], ignore_index=True)
+
+    _update_dataframe_datatypes(dataframe=df, column_map=columns)
+
+    return df
+
+## Still debugging the deletion of an sql database
+# def delete_warehouse(name: str, workspace: Optional[str | UUID] = None):
+#     """
+#     Deletes a Fabric warehouse.
+
+#     This is a wrapper function for the following API: `Items - Delete Warehouse <https://learn.microsoft.com/rest/api/fabric/warehouse/items/delete-warehouse>`_.
+
+#     Parameters
+#     ----------
+#     name: str
+#         Name of the warehouse.
+#     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.
+#     """
+
+#     (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+
+#     item_id = fabric.resolve_item_id(
+#         item_name=name, type="Warehouse", workspace=workspace_id
+#     )
+
+#     _base_api(
+#         request=f"/v1/workspaces/{workspace_id}/warehouses/{item_id}", method="delete"
+#     )
+
+#     print(
+#         f"{icons.green_dot} The '{name}' warehouse within the '{workspace_name}' workspace has been deleted."
+#     )
+
+
+def get_sqldatabase_tables(
+    sqldatabase: str | UUID, workspace: Optional[str | UUID] = None
+) -> pd.DataFrame:
+    """
+    Shows a list of the tables in the Fabric SQLDabatse. This function is based on INFORMATION_SCHEMA.TABLES.
+
+    Parameters
+    ----------
+    sqldatabase : str | uuid.UUID
+        Name or ID of the Fabric SQLDabatase.
+    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.
+
+    Returns
+    -------
+    pandas.DataFrame
+        A pandas dataframe showing a list of the tables in the Fabric SQLDabatase.
+    """
+
+    from sempy_labs._sql import ConnectSQLDatabase
+
+    with ConnectSQLDatabase(sqldatabase=sqldatabase, workspace=workspace) as sql:
+        df = sql.query(
+            """
+        SELECT TABLE_SCHEMA AS [Schema], TABLE_NAME AS [Table Name], TABLE_TYPE AS [Table Type]
+        FROM INFORMATION_SCHEMA.TABLES
+        WHERE TABLE_TYPE = 'BASE TABLE'
+        """
+        )
+
+    return df
+
+
+def get_sqldatabase_columns(
+    sqldatabase: str | UUID, workspace: Optional[str | UUID] = None
+) -> pd.DataFrame:
+    """
+    Shows a list of the columns in each table within the Fabric SQLDabatase. This function is based on INFORMATION_SCHEMA.COLUMNS.
+
+    Parameters
+    ----------
+    sqldatabase : str | uuid.UUID
+        Name or ID of the Fabric SQLDabatase.
+    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.
+
+    Returns
+    -------
+    pandas.DataFrame
+        A pandas dataframe showing a list of the columns in each table within the Fabric SQLDabatase.
+    """
+
+    from sempy_labs._sql import ConnectSQLDatabase
+
+    with ConnectSQLDatabase(sqldatabase=sqldatabase, workspace=workspace) as sql:
+        df = sql.query(
+            """
+        SELECT t.TABLE_SCHEMA AS [Schema], t.TABLE_NAME AS [Table Name], c.COLUMN_NAME AS [Column Name], c.DATA_TYPE AS [Data Type], c.IS_NULLABLE AS [Is Nullable], c.CHARACTER_MAXIMUM_LENGTH AS [Character Max Length]
+        FROM INFORMATION_SCHEMA.TABLES AS t
+        LEFT JOIN INFORMATION_SCHEMA.COLUMNS AS c
+        ON t.TABLE_NAME = c.TABLE_NAME
+        AND t.TABLE_SCHEMA = c.TABLE_SCHEMA
+        WHERE t.TABLE_TYPE = 'BASE TABLE'
+        """
+        )
+
+    return df