atoti_query.query_session module#

class atoti_query.QuerySession#

Used to query a remote atoti session (or a classic ActivePivot >= 5.7 server).

Note

Query sessions are immutable: the structure of their underlying cubes is not expected to change.

__init__(url, *, auth=None, certificate_authority=None, client_certificate=None, **kwargs)#

Create a QuerySession.

Parameters

url (str) – The base URL of the session. The endpoint f"{url}/versions/rest" is expected to exist.
auth (Optional[Auth]) – The authentication to use to access the session.
certificate_authority (Optional[Union[str, Path]]) – Path to the custom certificate authority file to use to verify the HTTPS connection. Required when the session has been configured with a certificate that is not signed by some trusted public certificate authority.
client_certificate (Optional[ClientCertificate]) – The client certificate to authenticate against the session.

property cubes: QueryCubes#: Cubes of the session.

link(*, path='')#

Display a link to this session.

Clicking on the link will open it in a new browser tab.

Note

This method requires the atoti-jupyterlab plugin.

The extension will try to access the session through (in that order):

Jupyter Server Proxy if it is enabled.
f"{session_protocol}//{jupyter_server_hostname}:{session.port}" for atoti.Session and session.url for atoti_query.QuerySession.

Parameters: path (str) – The path to append to the session base URL. Defaults to the session home page.
Return type: object

Example

Pointing directly to an existing dashboard:

dashboard_id = "92i"
session.link(path=f"#/dashboard/{dashboard_id}")

query_mdx(mdx, *, keep_totals=False, timeout=datetime.timedelta(seconds=30), mode='pretty', context={}, **kwargs)#

Execute an MDX query and return its result as a pandas DataFrame.

Parameters

mdx (str) –

The MDX SELECT query to execute.

Regardless of the axes on which levels and measures appear in the MDX, the returned DataFrame will have all levels on rows and measures on columns.

Example

>>> from datetime import date
>>> df = pd.DataFrame(
...     columns=["Country", "Date", "Price"],
...     data=[
...         ("China", date(2020, 3, 3), 410.0),
...         ("France", date(2020, 1, 1), 480.0),
...         ("France", date(2020, 2, 2), 500.0),
...         ("France", date(2020, 3, 3), 400.0),
...         ("India", date(2020, 1, 1), 360.0),
...         ("India", date(2020, 2, 2), 400.0),
...         ("UK", date(2020, 2, 2), 960.0),
...     ],
... )
>>> table = session.read_pandas(
...     df, keys=["Country", "Date"], table_name="Prices"
... )
>>> cube = session.create_cube(table)

This MDX:

>>> mdx = (
...     "SELECT"
...     "  NON EMPTY Hierarchize("
...     "    DrilldownLevel("
...     "      [Prices].[Country].[ALL].[AllMember]"
...     "    )"
...     "  ) ON ROWS,"
...     "  NON EMPTY Crossjoin("
...     "    [Measures].[Price.SUM],"
...     "    Hierarchize("
...     "      DrilldownLevel("
...     "        [Prices].[Date].[ALL].[AllMember]"
...     "      )"
...     "    )"
...     "  ) ON COLUMNS"
...     "  FROM [Prices]"
... )

Returns this DataFrame:

>>> session.query_mdx(mdx, keep_totals=True)
                   Price.SUM
Date       Country
Total               3,510.00
2020-01-01            840.00
2020-02-02          1,860.00
2020-03-03            810.00
           China      410.00
2020-01-01 China
2020-02-02 China
2020-03-03 China      410.00
           France   1,380.00
2020-01-01 France     480.00
2020-02-02 France     500.00
2020-03-03 France     400.00
           India      760.00
2020-01-01 India      360.00
2020-02-02 India      400.00
2020-03-03 India
           UK         960.00
2020-01-01 UK
2020-02-02 UK         960.00
2020-03-03 UK

But, if it was displayed into a pivot table, would look like this:

Country	Price.sum
Country	Total	2020-01-01	2020-02-02	2020-03-03
Total	3,510.00	840.00	1,860.00	810.00
China	410.00			410.00
France	1,380.00	480.00	500.00	400.00
India	760.00	360.00	400.00
UK	960.00		960.00

keep_totals (bool) – Whether the resulting DataFrame should contain, if they are present in the query result, the grand total and subtotals. Totals can be useful but they make the DataFrame harder to work with since its index will have some empty values.
timeout (timedelta) – The amount of time the query execution can take before aborting it.
mode (Literal['pretty', 'raw']) –
The query mode.
- "pretty" is best for queries returning small results:
  - A QueryResult will be returned and its rows will be sorted according to the level order.
- "raw" is best for benchmarks or large exports:
  - A faster and more efficient endpoint reducing the data transfer from Java to Python will be used.
  - A classic pandas.DataFrame will be returned.
  - include_totals="True" will not be allowed.
  - The Convert to Widget Below action provided by the atoti-jupyterlab plugin will not be available.
context (Mapping[str, Any]) – Context values to use when executing the query.

Return type

DataFrame

property url: str#: URL of the session.

visualize(name=None)#

Display an atoti widget to explore the session interactively.

Note

This method requires the atoti-jupyterlab plugin.

The widget state will be stored in the cell metadata. This state should not have to be edited but, if desired, it can be found in JupyterLab by opening the “Notebook tools” sidebar and expanding the “Advanced Tools” section.

Parameters: name (Optional[str]) – The name to give to the widget.