atoti.experimental.distributed.session module¶

class atoti.experimental.distributed.session.DistributedSession(name, *, config, **kwargs)¶

Bases: atoti._local_session.LocalSession[atoti.experimental.distributed.cubes.DistributedCubes]

Holds a connection to the Java gateway.

close()¶

Close this session and free all the associated resources.

Return type: None

property closed¶

Return whether the session is closed or not.

Return type: bool

create_cube(name)¶

Create a distributed cube.

Parameters: name (str) – The name of the created cube.
Return type: DistributedCube

property cubes¶

Cubes of the session.

Return type: DistributedCubes

endpoint(route, *, method='GET')¶

Create a custom endpoint at f"{session.url}/atoti/pyapi/{route}".

The decorated function must take three arguments with types User, HttpRequest and Session and return a response body as a Python data structure that can be converted to JSON. DELETE, POST, and PUT requests can have a body but it must be JSON.

Path parameters can be configured by wrapping their name in curly braces in the route.

Example:

@session.endpoint("simple_get")
def callback(request: HttpRequest, user: User, session: Session):
    return "something that will be in response.data"


@session.endpoint(f"simple_post/{store_name}", method="POST")
def callback(request: HttpRequest, user: User, session: Session):
    return request.path_parameters.store_name

Parameters

route (str) – The path suffix after /atoti/pyapi/. For instance, if custom/search is passed, a request to /atoti/pyapi/custom/search?query=test#results will match. The route should not contain the query (?) or fragment (#).
method (Literal[‘POST’, ‘GET’, ‘PUT’, ‘DELETE’]) – The HTTP method the request must be using to trigger this endpoint.

Return type

Any

property excel_url¶

URL of the Excel endpoint.

To connect to the session in Excel, create a new connection to an Analysis Services. Use this URL for the server field and choose to connect with “User Name and Password”:

Without authentication, leave these fields blank.
With Basic authentication, fill them with your username and password.
Other authentication types (such as Auth0) are not supported by Excel.

Return type: str

explain_mdx_query(mdx, *, timeout=30)¶

Explain an MDX query.

Parameters

mdx (str) – The MDX SELECT query to execute.
keep_totals – Whether the returned DataFrame should contain, if they are present in the query result, the grand total and subtotals.
timeout (int) – The query timeout in seconds.

Return type

QueryAnalysis

export_translations_template(path)¶

Export a template containing all translatable values in the session’s cubes.

Parameters: path (Union[str, Path]) – The path at which to write the template.

property logs_path¶

Path to the session logs file.

Return type: Path

logs_tail(n=20)¶

Return the n last lines of the logs or all the lines if n <= 0.

Return type: Logs

property name¶

Name of the session.

Return type: str

property port¶

Port on which the session is exposed.

Can be set in SessionConfiguration.

Return type: int

query_mdx(mdx, *, keep_totals=False, timeout=30)¶

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

Parameters

mdx (str) – The MDX SELECT query to execute.
keep_totals (bool) – Whether the returned DataFrame should contain, if they are present in the query result, the grand total and subtotals.
timeout (int) – The query timeout in seconds.
keep_totals – 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.

Example

An MDX query that would be displayed as this pivot table:

Country	Total Price.SUM	2018-01-01	2019-01-01	2019-01-02	2019-01-05
Country	Total Price.SUM	Price.SUM	Price.SUM	Price.SUM	Price.SUM
Total Country	2,280.00	840.00	1,860.00	810.00	770.00
China	760.00			410.00	350.00
France	1,800.00	480.00	500.00	400.00	420.00
India	760.00	360.00	400.00
UK	960.00		960.00

will return this DataFrame:

Date	Country	Price.SUM
2019-01-02	China	410.0
2019-01-05	China	350.0
2018-01-01	France	480.0
2019-01-01	France	500.0
2019-01-02	France	400.0
2019-01-05	France	420.0
2018-01-01	India	360.0
2019-01-01	India	400.0
2019-01-01	UK	960.0

Return type: QueryResult

start_transaction()¶

Start a transaction to batch several store operations.

It is more efficient than doing each store operation one after the other.
It avoids possibly incorrect intermediate states (e.g. if loading some new data first requires to drop some existing one).

Note

Some operations are not allowed during a transaction:

Long-running operations such as load_kafka() or load_csv() where watch=True is used.
Operations changing the structure of the session’s stores such as join() or read_parquet().
Operations not related to data loading or dropping such as defining a new measure.

Example

>>> df = pd.DataFrame(
...     columns=["City", "Price"],
...     data=[
...         ("Berlin", 150.0),
...         ("London", 240.0),
...         ("New York", 270.0),
...         ("Paris", 200.0),
...     ],
... )
>>> store = session.read_pandas(
...     df, keys=["City"], store_name="start_transaction example"
... )
>>> cube = session.create_cube(store)
>>> extra_df = pd.DataFrame(
...     columns=["City", "Price"],
...     data=[
...         ("Singapore", 250.0),
...     ],
... )
>>> with session.start_transaction():
...     store += ("New York", 100.0)
...     store.drop({"City": "Paris"})
...     store.load_pandas(extra_df)
...
>>> store.head(10)
           Price
City
Berlin     150.0
London     240.0
New York   100.0
Singapore  250.0

Return type: Transaction

property url¶

Public URL of the session.

Can be set in SessionConfiguration.

Return type: str

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 the “Advanced Tools” section.

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

wait()¶

Wait for the underlying server subprocess to terminate.

This will prevent the Python process to exit.

Return type: None