atoti.Cube.restrictions

property Cube.restrictions : MutableMapping[str, MembershipCondition[LevelIdentifier, ‘IN’, bool | int | float | date | datetime | time | str] | RelationalCondition[LevelIdentifier, ‘EQ’, bool | int | float | date | datetime | time | str] | LogicalCondition[MembershipCondition[LevelIdentifier, ‘IN’, bool | int | float | date | datetime | time | str] | RelationalCondition[LevelIdentifier, ‘EQ’, bool | int | float | date | datetime | time | str], ‘AND’]]

Mapping from role to the corresponding restriction. Restrictions limit the data accessible to users based on their roles.

Restrictions on different hierarchies are intersected.
Restrictions on the same hierarchy are unioned.

These restrictions do not secure tables, consider configuring atoti.tables.Tables.restrictions or changing atoti.tables.Tables.readers instead.
A QueryCube only enforces its own restrictions, it ignores the ones of the contributing data cubes.

Example

>>> session_config = tt.SessionConfig(security=tt.SecurityConfig())
>>> session = tt.Session.start(session_config)
>>> df = pd.DataFrame(
...     [
...         ("Asia", "Korea", "KRW"),
...         ("Asia", "Japan", "JPY"),
...         ("Europe", "France", "EUR"),
...         ("Europe", "Germany", "EUR"),
...         ("Europe", "Norway", "NOK"),
...         ("Europe", "Sweden", "SEK"),
...     ],
...     columns=["Continent", "Country", "Currency"],
... )
>>> table = session.read_pandas(
...     df,
...     keys={"Continent", "Country", "Currency"},
...     table_name="Restrictions example",
... )
>>> cube = session.create_cube(table)
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> cube.hierarchies["Geography"] = [
...     table["Continent"],
...     table["Country"],
... ]
>>> for level_name in cube.hierarchies["Geography"]:
...     del cube.hierarchies[level_name]

Adding a user to the session:

>>> session.security.individual_roles["Rose"] = {"ROLE_USER"}
>>> password = "abcdef123456"
>>> session.security.basic_authentication.credentials["Rose"] = password
>>> rose_session = tt.Session.connect(
...     session.url,
...     authentication=tt.BasicAuthentication("Rose", password),
... )
>>> rose_cube = rose_session.cubes[cube.name]

ROLE_USER has no restrictions so all the countries and currencies are accessible from the cube:

>>> rose_cube.query(
...     m["contributors.COUNT"], levels=[l["Country"], l["Currency"]]
... )
                           contributors.COUNT
Continent Country Currency
Asia      Japan   JPY                       1
          Korea   KRW                       1
Europe    France  EUR                       1
          Germany EUR                       1
          Norway  NOK                       1
          Sweden  SEK                       1

Assigning a role to Rose to limit her access to France only:

>>> cube.restrictions["ROLE_FRANCE"] = l["Country"] == "France"
>>> cube.restrictions["ROLE_FRANCE"]
l['Restrictions example', 'Geography', 'Country'] == 'France'
>>> session.security.individual_roles["Rose"] |= {"ROLE_FRANCE"}
>>> rose_cube.query(
...     m["contributors.COUNT"], include_totals=True, levels=[l["Country"]]
... )
                  contributors.COUNT
Continent Country
Total                              1
Europe                             1
          France                   1

Unlike atoti.tables.Tables.restrictions, cube restrictions have no impact on tables:

>>> rose_table = rose_session.tables[table.name]
>>> rose_table.query().set_index(["Continent", "Country"]).sort_index()
                  Currency
Continent Country
Asia      Japan        JPY
          Korea        KRW
Europe    France       EUR
          Germany      EUR
          Norway       NOK
          Sweden       SEK

Adding Lena with ROLE_GERMANY limiting her access to Germany only:

>>> cube.restrictions["ROLE_GERMANY"] = l["Country"] == "Germany"
>>> session.security.basic_authentication.credentials["Lena"] = password
>>> session.security.individual_roles["Lena"] = {
...     "ROLE_GERMANY",
...     "ROLE_USER",
... }
>>> lena_session = tt.Session.connect(
...     session.url,
...     authentication=tt.BasicAuthentication("Lena", password),
... )
>>> lena_cube = lena_session.cubes[cube.name]
>>> lena_cube.query(m["contributors.COUNT"], levels=[l["Country"]])
                  contributors.COUNT
Continent Country
Europe    Germany                  1

Assigning ROLE_GERMANY to Rose lets her access the union of the restricted countries:

>>> session.security.individual_roles["Rose"] |= {"ROLE_GERMANY"}
>>> cube.restrictions
{'ROLE_FRANCE': l['Restrictions example', 'Geography', 'Country'] == 'France', 'ROLE_GERMANY': l['Restrictions example', 'Geography', 'Country'] == 'Germany'}
>>> rose_cube.query(m["contributors.COUNT"], levels=[l["Country"]])
                  contributors.COUNT
Continent Country
Europe    France                   1
          Germany                  1

Restrictions can include multiple elements:

>>> cube.restrictions["ROLE_NORDIC"] = l["Country"].isin("Norway", "Sweden")
>>> session.security.individual_roles["Rose"] |= {"ROLE_NORDIC"}
>>> rose_cube.query(m["contributors.COUNT"], levels=[l["Country"]])
                  contributors.COUNT
Continent Country
Europe    France                   1
          Germany                  1
          Norway                   1
          Sweden                   1

Since Country and Continent are part of the same Geography hierarchy, restrictions on these two levels are unioned:

>>> cube.restrictions["ROLE_ASIA"] = l["Continent"] == "Asia"
>>> session.security.individual_roles["Rose"] |= {"ROLE_ASIA"}
>>> rose_cube.query(m["contributors.COUNT"], levels=[l["Country"]])
                  contributors.COUNT
Continent Country
Asia      Japan                    1
          Korea                    1
Europe    France                   1
          Germany                  1
          Norway                   1
          Sweden                   1

Currency is part of a different hierarchy so restrictions on it are intersected with the ones from Geography:

>>> cube.restrictions["ROLE_EUR"] = l["Currency"] == "EUR"
>>> session.security.individual_roles["Rose"] |= {"ROLE_EUR"}
>>> rose_cube.query(
...     m["contributors.COUNT"], levels=[l["Country"], l["Currency"]]
... )
                           contributors.COUNT
Continent Country Currency
Europe    France  EUR                       1
          Germany EUR                       1

Removing the ROLE_FRANCE and ROLE_GERMANY roles leaves no remaining accessible countries:

>>> session.security.individual_roles["Rose"] -= {
...     "ROLE_FRANCE",
...     "ROLE_GERMANY",
... }
>>> rose_cube.query(m["contributors.COUNT"], levels=[l["Country"]])
Empty DataFrame
Columns: [contributors.COUNT]
Index: []

A QuerySession has cubes but no tables so there is nothing to merge cube restrictions with. However, data cubes have their restrictions merged with the ones from the session’s tables:

>>> session.tables.restrictions.update(
...     {
...         "ROLE_SEK": table["Currency"] == "SEK",
...         "ROLE_JPY": table["Currency"] == "JPY",
...     }
... )
>>> session.security.individual_roles["Rose"] = {
...     "ROLE_ASIA",  # Cube restriction
...     "ROLE_NORDIC",  # Cube restriction
...     "ROLE_SEK",  # Tables restriction
...     "ROLE_JPY",  # Tables restriction
...     "ROLE_USER",
... }
>>> rose_table.query().set_index(["Continent", "Country"]).sort_index()
                  Currency
Continent Country
Asia      Japan        JPY
Europe    Sweden       SEK
>>> rose_cube.query(
...     m["contributors.COUNT"], levels=[l["Country"], l["Currency"]]
... )
                           contributors.COUNT
Continent Country Currency
Asia      Japan   JPY                       1
Europe    Sweden  SEK                       1

>>> cube.restrictions.clear()
>>> cube.restrictions
{}

See also: atoti.tables.Tables.restrictions.

​Example

Example