Slicing and Dicing


Examples are in Python and in Slicer HTTP requests.


The aggregation, slicing, dicing, browsing of the multi-dimensional data is being done by an AggregationBrowser.

from cubes import Workspace

workspace = Workspace("slicer.ini")
browser = workspace.browser()

Cell and Cuts

Cell defines a point of interest – portion of the cube to be aggergated or browsed.


There are three types of cells: point – defines a single point in a dimension at a particular level; range – defines all points of an ordered dimension (such as date) within the range and set – collection of points:


Points are defined as dimension paths – list of dimension level keys. For example a date path for 24th of December 2010 would be: [2010, 12, 24]. For December 2010, regardless of day: [2010, 12] and for the whole year: it would be a single item list [2010]. Similar for other dimensions: ["sk", "Bratislava"] for city Bratislava in Slovakia (code sk).

In Python the cuts for “sales in Slovakia between June 2010 and June 2012” are defined as:

cuts = [
    PointCut("geography", ["sk"]),
    PointCut("date", [2010, 6], [2012, 6])

Same cuts for Slicer: cut=geography:sk|date:2010,6-2012,6.

If a different hierarchy than default is desired – “from the second quartal of 2010 to the second quartal of 2012”:

cuts = [
    PointCut("date", [2010, 2], [2012, 2], hierarchy="yqmd")

Slicer: cut=date@yqmd:2010,2-2012,2.

Ranges and sets might have unequal depths: from [2010] to [2012,12,24] means “from the beginning of the year 2010 to December 24th 2012”.

cuts = [
    PointCut("date", [2010], [2012, 12, 24])

Slicer: cut=date:2010-2012,12,24.

Ranges might be open, such as “everything until Dec 24 2012”:

cuts = [
    PointCut("date", None, [2012, 12, 24])

Slicer: cut=date:-2012,12,24.


browser = workspace.browser("sales")
result = browser.aggregate()

print result.summary

Slicer: /cube/sales/aggregate

Aggregate of a cell:

cuts = [
    PointCut("geography", ["sk"])
    PointCut("date", [2010, 6], [2012, 6]),
cell = Cell(cube, cuts)
result = browser.aggregate(cell)

Slicer: /cube/sales/aggregate?cut=geography:sk|date:2010,6-2012,6

It is possible to select only specific aggregates to be aggregated:

result = browser.aggregate(cell, aggregates=["amount"])

Slicer: /cube/sales/aggregate?aggregates=amount


Drill-down – get more details, group the aggregation by dimension members.

For example “sales by month in 2010”:

cut = PointCut("date", [2010])
cell = Cell(cube, [cut])
result = browser.aggregate(cell, drilldown=["date"])

for row in result:
    print "%s: %s" % (row["date.year"], row["amount_sum"])

Slicer: /cube/sales/aggregate?cut=date:2010&drilldown=date


If not stated otherwise, the cubes drills-down to the next level of the drilled dimension. For example, if there is no cell constraint and the drilldown is [“date”], that means to use the first level of dimension date, usually year. If there is already a cut by year: PointCut(“date”, [2010]) then the next level is by month.

The “next level” is determined as the next level after the deepest level used in a cut. Consider hierarchies for date: year, month and day, for geography: region, country, city. The implicit drilldown will be as follows:

Drilldown Cut Result levels
date date:year
date date point [2010] date:month
date date point [2010, 4, 1] error
country, date date range [2010, 1] - [2010, 4] date:day, geo:region

If the cut is at its deepest level, then it is not possible to drill-down deeper which results in an error.


If the implicit behavior is not satisfying, then the drill-down levels might be specified explicitly. In this case, the cut is not considered for the drilldown level.

You might want to specify drill-down levels explicitly for example if a cut range spans between multiple months and you don’t want to have the next level to be day, but month. Another use is whe you want to use another hierarchy for drill-don than the default hierarchy.

Drilldown Python Server
by year ("date", None, "year") drilldown=date:year
by month and city ("date", None, "month"), ("geo", None, "city") drilldown=date:month,geo:city
by month but with quarter included ("date", "yqmd", "month") drilldown=date@yqmd:month



  • aggregate(cell, drilldown, split)


To get list of facts within a cell use cubes.AggregationBrowser.facts():

facts = browser.facts(cell)

Server: /cube/sales/facts?cell=...

You can also paginate the result as in the aggregation.

Note that not all backends might support fact listing. Please refer to the backend’s documentation for more information.


A single fact can be fetched using cubes.AggregationBrowser.fact() as in fact(123) or with the server as /cube/sales/fact/123.

Note that not all backends might support fact listing. Please refer to the backend’s documentation for more information.


Getting dimension members might be useful for example for populating drill-downs or for providing an information to the user what he can use for slicing and dicing. In python tehre is cubes.AggregationBrowser.members().

For example to get all countries present in a cell:

members = browser.members(cell, "country")

Same query with the server would be: /cube/sales/dimension/country?cut=...

It is also possible to specify hierarchy and level depth for the dimension members.

Cell Details

When we are browsing a cube, the cell provides current browsing context. For aggregations and selections to happen, only keys and some other internal attributes are necessary. Those can not be presented to the user though. For example we have geography path (country, region) as ['sk', 'ba'], however we want to display to the user Slovakia for the country and Bratislava for the region. We need to fetch those values from the data store. Cell details is basically a human readable description of the current cell.

For applications where it is possible to store state between aggregation calls, we can use values from previous aggregations or value listings. Problem is with web applications - sometimes it is not desirable or possible to store whole browsing context with all details. This is exact the situation where fetching cell details explicitly might come handy.

The cell details are provided by method cubes.AggregationBrowser.cell_details() which has Slicer HTTP equivalent /cell or {"query":"detail", ...} in /report request (see the server documentation for more information).

For point cuts, the detail is a list of dictionaries for each level. For example our previously mentioned path ['sk', 'ba'] would have details described as:

        "geography.country_code": "sk",
        "geography.country_name": "Slovakia",
        "geography.something_more": "..."
        "_key": "sk",
        "_label": "Slovakia"
        "geography.region_code": "ba",
        "geography.region_name": "Bratislava",
        "geography.something_even_more": "...",
        "_key": "ba",
        "_label": "Bratislava"

You might have noticed the two redundant keys: _key and _label - those contain values of a level key attribute and level label attribute respectively. It is there to simplify the use of the details in presentation layer, such as templates. Take for example doing only one-dimensional browsing and compare presentation of “breadcrumbs”:

labels = [detail["_label"] for detail in cut_details]

Which is equivalent to:

levels = dimension.hierarchy().levels()
labels = []
for i, detail in enumerate(cut_details):

Note that this might change a bit: either full detail will be returned or just key and label, depending on an option argument (not yet decided).

Supported Methods

Not all browsers might provide full functionality. For example a browser, such as Google Analytics, might provide aggregations, but might not provide fact details.

To learn what features are provided by the browser for particular cube use the cubes.AggregationBrowser.features() method which returns a dictionary with more detailed description of what can be done with the cube.