atoti.cube module

class atoti.cube.Cube(java_api, name, base_store, session)

Bases: atoti._local_cube.LocalCube[atoti.hierarchies.Hierarchies, atoti.levels.Levels, atoti.measures.Measures]

Cube of a Session.

property aggregates_cache

Aggregates cache of the cube.

Return type

AggregatesCache

create_static_parameter_hierarchy(name, members, *, data_type=None, index_measure=None, indices=None, store_name=None)

Create an arbitrary single-level static hierarchy with the given members.

It can be used as a parameter hierarchy in advanced analyses.

Parameters

  • name (str) – The name of hierarchy and its single level.

  • members (Sequence[Any]) – The members of the hierarchy.

  • data_type (Optional[DataType]) – The type with which the members will be stored. Automatically inferred by default.

  • index_measure (Optional[str]) – The name of the indexing measure to create for this hierarchy, if any.

  • indices (Optional[Sequence[int]]) – The custom indices for each member in the new hierarchy. They are used when accessing a member through the index_measure. Defaults to range(len(members)).

  • store_name (Optional[str]) – The name of the store backing the parameter hierarchy. Defaults to the passed name argument.

create_store_column_parameter_hierarchy(name, column)

Create a single level static hierarchy which takes its members from a column.

explain_query(*measures, condition=None, include_totals=False, levels=None, scenario='Base', timeout=30)

Run the query but return an explanation of the query instead of the result.

The explanation contains a summary, global timings and the query plan with all the retrievals.

Parameters

  • measures (Union[NamedMeasure, QueryMeasure]) – The measures to query. If None, all the measures are queried.

  • condition (Union[LevelCondition, MultiCondition, LevelIsInCondition, HierarchyIsInCondition, None]) –

    The filtering condition. Only conditions on level equality with a string are supported. For instance:

    • lvl["Country"] == "France"

    • (lvl["Country"] == "USA") & (lvl["Currency"] == "USD")

    • h["Geography"].isin(("Asia",), ("Europe", "France"))

  • include_totals (bool) – Whether the returned DataFrame should include 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.

  • levels (Union[~_Level, Sequence[~_Level], None]) – The levels to split on. If None, the value of the measures at the top of the cube is returned.

  • scenario (str) – The scenario to query.

  • timeout (int) – The query timeout in seconds.

Return type

QueryAnalysis

property hierarchies

Hierarchies of the cube.

Return type

~_LocalHierarchies

property levels

Levels of the cube.

Return type

~_Levels

property measures

Measures of the cube.

Return type

~_LocalMeasures

property name

Name of the cube.

Return type

str

query(*measures, condition=None, include_totals=False, levels=None, mode='pretty', scenario='Base', timeout=30)

Query the cube to retrieve the value of the passed measures on the given levels.

In JupyterLab with the atoti-jupyterlab plugin installed, query results can be converted to interactive widgets with the Convert to Widget Below action available in the command palette or by right clicking on the representation of the returned Dataframe.

Parameters

  • measures (Union[NamedMeasure, QueryMeasure]) – The measures to query. If None, all the measures are queried.

  • condition (Union[LevelCondition, MultiCondition, LevelIsInCondition, HierarchyIsInCondition, None]) –

    The filtering condition. Only conditions on level equality with a string are supported. For instance:

    • lvl["Country"] == "France"

    • (lvl["Country"] == "USA") & (lvl["Currency"] == "USD")

    • h["Geography"].isin(("Asia",), ("Europe", "France"))

  • include_totals (bool) – Whether the returned DataFrame should include 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.

  • levels (Union[~_Level, Sequence[~_Level], None]) – The levels to split on. If None, the value of the measures at the top of the cube is returned.

  • scenario (str) – The scenario to query.

  • timeout (int) – The query timeout in seconds.

  • 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 comparators.

    • "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.

Return type

Union[QueryResult, DataFrame]

property schema

Schema of the cube’s stores as an SVG graph.

Note

Graphviz is required to display the graph. It can be installed with Conda: conda install graphviz or by following the download instructions.

Return type

Any

Returns

An SVG image in IPython and a Path to the SVG file otherwise.

setup_simulation(name, *, base_scenario='Base', levels=None, multiply=None, replace=None, add=None)

Create a simulation store for the given measures.

Simulations can have as many scenarios as desired.

The same measure cannot be passed in several methods.

Parameters
Return type

Simulation

Returns

The simulation on which scenarios can be made.

property shared_context

Context values shared by all the users.

Context values can also be set at query time, and per user, directly from the UI. The values in the shared context are the default ones for all the users.

  • queriesTimeLimit

    The number of seconds after which a running query is cancelled and its resources reclaimed. Set to -1 to remove the limit. Defaults to 30s.

  • queriesResultLimit.intermediateSize

    The limit number of point locations for a single intermediate result. This works as a safe-guard to prevent queries from consuming too much memory, which is especially useful when going to production with several simulatenous users on the same server. Set to -1 to use the maximum limit. In atoti, the maximum limit is the default while in Atoti+ it defaults to 1000000.

  • queriesResultLimit.tansientResultSize

    Similar to intermediateSize but across all the intermediate results of the same query. Set to -1 to use the maximum limit. In atoti, the maximum limit is the default while in Atoti+ it defaults to 10000000.

Example

>>> df = pd.DataFrame(
...     columns=["City", "Price"],
...     data=[
...         ("London", 240.0),
...         ("New York", 270.0),
...         ("Paris", 200.0),
...     ],
... )
>>> store = session.read_pandas(
...     df, keys=["City"], store_name="shared_context example"
... )
>>> cube = session.create_cube(store)
>>> cube.shared_context["queriesTimeLimit"] = 60
>>> cube.shared_context["queriesResultLimit.intermediateSize"] = 1000000
>>> cube.shared_context["queriesResultLimit.transientSize"] = 10000000
>>> cube.shared_context
{'queriesTimeLimit': '60', 'queriesResultLimit.intermediateSize': '1000000', 'queriesResultLimit.transientSize': '10000000'}
Return type

CubeContext

property simulations

Simulations of the cube.

Return type

Simulations

class atoti.cube.CubeContext(_java_api, _cube)

Bases: MutableMapping[str, str], atoti._repr_utils.ReprJsonable

clear() → None. Remove all items from D.
get(k[, d]) → D[k] if k in D, else d. d defaults to None.
items() → a set-like object providing a view on D’s items
keys() → a set-like object providing a view on D’s keys
pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D
update([E, ]**F) → None. Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D’s values