Drill-down Tree

Goal: Create a tree by aggregating every level of a dimension.

Level: Advanced.

Drill-down

Drill-down is an action that will provide more details about data. Drilling down through a dimension hierarchy will expand next level of the dimension. It can be compared to browsing through your directory structure.

We create a function that will recursively traverse a dimension hierarchy and will print-out aggregations (count of records in this example) at the actual browsed location.

Attributes

• cell - cube cell to drill-down
• dimension - dimension to be traversed through all levels
• path - current path of the dimension

Path is list of dimension points (keys) at each level. It is like file-system path.

def drill_down(cell, dimension, path=[]):

Get dimension’s default hierarchy. Cubes supports multiple hierarchies, for example for date you might have year-month-day or year-quarter-month-day. Most dimensions will have one hierarchy, thought.

hierarchy = dimension.hierarchy()

Base path is path to the most detailed element, to the leaf of a tree, to the fact. Can we go deeper in the hierarchy?

if hierarchy.path_is_base(path):
    return

Get the next level in the hierarchy. levels_for_path returns list of levels according to provided path. When drilldown is set to True then one more level is returned.

levels = hierarchy.levels_for_path(path,drilldown=True)
current_level = levels[-1]

We need to know name of the level key attribute which contains a path component. If the model does not explicitly specify key attribute for the level, then first attribute will be used:

level_key = dimension.attribute_reference(current_level.key)

For prettier display, we get name of attribute which contains label to be displayed for the current level. If there is no label attribute, then key attribute is used.

level_label = dimension.attribute_reference(current_level.label_attribute)

We do the aggregation of the cell...

Note

Shell analogy: Think of ls $CELL command in commandline, where $CELL is a directory name. In this function we can think of $CELL to be same as current working directory (pwd)

result = browser.aggregate(cell, drilldown=[dimension])

for record in result.drilldown:
    print "%s%s: %d" % (indent, record[level_label], record["record_count"])
    ...

And now the drill-down magic. First, construct new path by key attribute value appended to the current path:

drill_path = path[:] + [record[level_key]]

Then get a new cell slice for current path:

drill_down_cell = cell.slice(dimension, drill_path)

And do recursive drill-down:

drill_down(drill_down_cell, dimension, drill_path)

The whole recursive drill down function looks like this:

Recursive drill-down explained

Whole working example can be found in the tutorial sources.

Get the full cube (or any part of the cube you like):

cell = browser.full_cube()

And do the drill-down through the item dimension:

drill_down(cell, cube.dimension("item"))

The output should look like this:

a: 32
    da: 8
        Borrowings: 2
        Client operations: 2
        Investments: 2
        Other: 2
    dfb: 4
        Currencies subject to restriction: 2
        Unrestricted currencies: 2
    i: 2
        Trading: 2
    lo: 2
        Net loans outstanding: 2
    nn: 2
        Nonnegotiable, nonintrest-bearing demand obligations on account of subscribed capital: 2
    oa: 6
        Assets under retirement benefit plans: 2
        Miscellaneous: 2
        Premises and equipment (net): 2

Note that because we have changed our source data, we see level codes instead of level names. We will fix that later. Now focus on the drill-down.

See that nice hierarchy tree?

Now if you slice the cell through year 2010 and do the exact same drill-down:

cell = cell.slice("year", [2010])
drill_down(cell, cube.dimension("item"))

you will get similar tree, but only for year 2010 (obviously).

Level Labels and Details

Codes and ids are good for machines and programmers, they are short, might follow some scheme, easy to handle in scripts. Report users have no much use of them, as they look cryptic and have no meaning for the first sight.

Our source data contains two columns for category and for subcategory: column with code and column with label for user interfaces. Both columns belong to the same dimension and to the same level. The key column is used by the analytical system to refer to the dimension point and the label is just decoration.

Levels can have any number of detail attributes. The detail attributes have no analytical meaning and are just ignored during aggregations. If you want to do analysis based on an attribute, make it a separate dimension instead.

So now we fix our model by specifying detail attributes for the levels:

Attribute details.

The model description is:

"levels": [
        {
            "name":"category",
            "label":"Category",
            "label_attribute": "category_label",
            "attributes": ["category", "category_label"]
        },
        {
            "name":"subcategory",
            "label":"Sub-category",
            "label_attribute": "subcategory_label",
            "attributes": ["subcategory", "subcategory_label"]
        },
        {
            "name":"line_item",
            "label":"Line Item",
            "attributes": ["line_item"]
        }
    ]
}

Note the label_attribute keys. They specify which attribute contains label to be displayed. Key attribute is by-default the first attribute in the list. If one wants to use some other attribute it can be specified in key_attribute.

Because we added two new attributes, we have to add mappings for them:

"mappings": { "item.line_item": "line_item",
              "item.subcategory": "subcategory",
              "item.subcategory_label": "subcategory_label",
              "item.category": "category",
              "item.category_label": "category_label"
             }

Now the result will be with labels instead of codes:

Assets: 32
    Derivative Assets: 8
        Borrowings: 2
        Client operations: 2
        Investments: 2
        Other: 2
    Due from Banks: 4
        Currencies subject to restriction: 2
        Unrestricted currencies: 2
    Investments: 2
        Trading: 2
    Loans Outstanding: 2
        Net loans outstanding: 2
    Nonnegotiable: 2
        Nonnegotiable, nonintrest-bearing demand obligations on account of subscribed capital: 2
    Other Assets: 6
        Assets under retirement benefit plans: 2
        Miscellaneous: 2
        Premises and equipment (net): 2