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Merge branch 'm-kovalsky/warehouse-restore-points'
2 parents 705aa99 + 781dd11 commit 5e60692

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‎src/sempy_labs/_mirrored_databases.py‎

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -384,7 +384,9 @@ def update_mirrored_database_definition(
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item_id = resolve_item_id(
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item=mirrored_database, type="MirroredDatabase", workspace=workspace
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)
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payload = base64.b64encode(mirrored_database_content)
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payload = (
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base64.b64encode(mirrored_database_content).encode("utf-8").decode("utf-8")
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)
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request_body = {
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"displayName": mirrored_database,

‎src/sempy_labs/warehouse/__init__.py‎

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from ._restore_points import (
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create_restore_point,
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delete_restore_point,
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list_restore_points,
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update_restore_point,
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restore_to_restore_point,
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)
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from ._items import (
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create_warehouse,
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delete_warehouse,
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get_warehouse_tables,
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get_warehouse_columns,
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list_warehouses,
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)
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__all__ = [
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"create_restore_point",
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"delete_restore_point",
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"list_restore_points",
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"update_restore_point",
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"restore_to_restore_point",
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"create_warehouse",
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"delete_warehouse",
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"get_warehouse_tables",
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"get_warehouse_columns",
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"list_warehouses",
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]

‎src/sempy_labs/warehouse/_items.py‎

Lines changed: 234 additions & 0 deletions
Original file line numberDiff line numberDiff line change
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from sempy_labs._helper_functions import (
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resolve_workspace_name_and_id,
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_base_api,
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_create_dataframe,
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_update_dataframe_datatypes,
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delete_item,
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resolve_workspace_id,
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)
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import pandas as pd
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from typing import Optional
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import sempy_labs._icons as icons
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from uuid import UUID
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from sempy._utils._log import log
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@log
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def create_warehouse(
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warehouse: str,
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description: Optional[str] = None,
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case_insensitive_collation: bool = False,
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workspace: Optional[str | UUID] = None,
22+
) -> UUID:
23+
"""
24+
Creates a Fabric warehouse.
25+
26+
This is a wrapper function for the following API: `Items - Create Warehouse <https://learn.microsoft.com/rest/api/fabric/warehouse/items/create-warehouse>`_.
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Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
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Parameters
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----------
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warehouse: str
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Name of the warehouse.
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description : str, default=None
35+
A description of the warehouse.
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case_insensitive_collation: bool, default=False
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If True, creates the warehouse with case-insensitive collation.
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workspace : str | uuid.UUID, default=None
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The Fabric workspace name or ID.
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Defaults to None which resolves to the workspace of the attached lakehouse
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or if no lakehouse attached, resolves to the workspace of the notebook.
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Returns
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-------
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uuid.UUID
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The ID of the created warehouse.
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"""
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(workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
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payload = {"displayName": warehouse}
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if description:
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payload["description"] = description
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if case_insensitive_collation:
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payload.setdefault("creationPayload", {})
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payload["creationPayload"][
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"defaultCollation"
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] = "Latin1_General_100_CI_AS_KS_WS_SC_UTF8"
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result = _base_api(
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request=f"/v1/workspaces/{workspace_id}/warehouses",
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payload=payload,
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method="post",
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lro_return_json=True,
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status_codes=[201, 202],
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client="fabric_sp",
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)
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print(
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f"{icons.green_dot} The '{warehouse}' warehouse has been created within the '{workspace_name}' workspace."
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)
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return result.get("id")
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@log
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def list_warehouses(workspace: Optional[str | UUID] = None) -> pd.DataFrame:
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"""
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Shows the warehouses within a workspace.
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This is a wrapper function for the following API: `Items - List Warehouses <https://learn.microsoft.com/rest/api/fabric/warehouse/items/list-warehouses>`_.
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84+
Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
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86+
Parameters
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----------
88+
workspace : str | uuid.UUID, default=None
89+
The Fabric workspace name or ID.
90+
Defaults to None which resolves to the workspace of the attached lakehouse
91+
or if no lakehouse attached, resolves to the workspace of the notebook.
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93+
Returns
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-------
95+
pandas.DataFrame
96+
A pandas dataframe showing the warehouses within a workspace.
97+
"""
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99+
columns = {
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"Warehouse Name": "string",
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"Warehouse Id": "string",
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"Description": "string",
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"Connection Info": "string",
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"Created Date": "datetime",
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"Last Updated Time": "datetime",
106+
}
107+
df = _create_dataframe(columns=columns)
108+
109+
workspace_id = resolve_workspace_id(workspace)
110+
111+
responses = _base_api(
112+
request=f"/v1/workspaces/{workspace_id}/warehouses",
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uses_pagination=True,
114+
client="fabric_sp",
115+
)
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rows = []
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for r in responses:
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for v in r.get("value", []):
120+
prop = v.get("properties", {})
121+
122+
rows.append(
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{
124+
"Warehouse Name": v.get("displayName"),
125+
"Warehouse Id": v.get("id"),
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"Description": v.get("description"),
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"Connection Info": prop.get("connectionInfo"),
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"Created Date": prop.get("createdDate"),
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"Last Updated Time": prop.get("lastUpdatedTime"),
130+
}
131+
)
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if rows:
134+
df = pd.DataFrame(rows, columns=list(columns.keys()))
135+
_update_dataframe_datatypes(dataframe=df, column_map=columns)
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137+
return df
138+
139+
140+
@log
141+
def delete_warehouse(name: str | UUID, workspace: Optional[str | UUID] = None):
142+
"""
143+
Deletes a Fabric warehouse.
144+
145+
This is a wrapper function for the following API: `Items - Delete Warehouse <https://learn.microsoft.com/rest/api/fabric/warehouse/items/delete-warehouse>`_.
146+
147+
Service Principal Authentication is supported (see `here <https://github.com/microsoft/semantic-link-labs/blob/main/notebooks/Service%20Principal.ipynb>`_ for examples).
148+
149+
Parameters
150+
----------
151+
name: str | uuid.UUID
152+
Name or ID of the warehouse.
153+
workspace : str | uuid.UUID, default=None
154+
The Fabric workspace name or ID.
155+
Defaults to None which resolves to the workspace of the attached lakehouse
156+
or if no lakehouse attached, resolves to the workspace of the notebook.
157+
"""
158+
159+
delete_item(item=name, type="Warehouse", workspace=workspace)
160+
161+
162+
@log
163+
def get_warehouse_tables(
164+
warehouse: str | UUID, workspace: Optional[str | UUID] = None
165+
) -> pd.DataFrame:
166+
"""
167+
Shows a list of the tables in the Fabric warehouse. This function is based on INFORMATION_SCHEMA.TABLES.
168+
169+
Parameters
170+
----------
171+
warehouse : str | uuid.UUID
172+
Name or ID of the Fabric warehouse.
173+
workspace : str | uuid.UUID, default=None
174+
The Fabric workspace name or ID.
175+
Defaults to None which resolves to the workspace of the attached lakehouse
176+
or if no lakehouse attached, resolves to the workspace of the notebook.
177+
178+
Returns
179+
-------
180+
pandas.DataFrame
181+
A pandas dataframe showing a list of the tables in the Fabric warehouse.
182+
"""
183+
184+
from sempy_labs._sql import ConnectWarehouse
185+
186+
with ConnectWarehouse(warehouse=warehouse, workspace=workspace) as sql:
187+
df = sql.query(
188+
"""
189+
SELECT TABLE_SCHEMA AS [Schema], TABLE_NAME AS [Table Name], TABLE_TYPE AS [Table Type]
190+
FROM INFORMATION_SCHEMA.TABLES
191+
WHERE TABLE_TYPE = 'BASE TABLE'
192+
"""
193+
)
194+
195+
return df
196+
197+
198+
@log
199+
def get_warehouse_columns(
200+
warehouse: str | UUID, workspace: Optional[str | UUID] = None
201+
) -> pd.DataFrame:
202+
"""
203+
Shows a list of the columns in each table within the Fabric warehouse. This function is based on INFORMATION_SCHEMA.COLUMNS.
204+
205+
Parameters
206+
----------
207+
warehouse : str | uuid.UUID
208+
Name or ID of the Fabric warehouse.
209+
workspace : str | uuid.UUID, default=None
210+
The Fabric workspace name or ID.
211+
Defaults to None which resolves to the workspace of the attached lakehouse
212+
or if no lakehouse attached, resolves to the workspace of the notebook.
213+
214+
Returns
215+
-------
216+
pandas.DataFrame
217+
A pandas dataframe showing a list of the columns in each table within the Fabric warehouse.
218+
"""
219+
220+
from sempy_labs._sql import ConnectWarehouse
221+
222+
with ConnectWarehouse(warehouse=warehouse, workspace=workspace) as sql:
223+
df = sql.query(
224+
"""
225+
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]
226+
FROM INFORMATION_SCHEMA.TABLES AS t
227+
LEFT JOIN INFORMATION_SCHEMA.COLUMNS AS c
228+
ON t.TABLE_NAME = c.TABLE_NAME
229+
AND t.TABLE_SCHEMA = c.TABLE_SCHEMA
230+
WHERE t.TABLE_TYPE = 'BASE TABLE'
231+
"""
232+
)
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return df

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