Control Break ReportingΒΆ

date:2006-02-06 23:49:14
category:Building Skills

Control Break Reporting is a design pattern that has been around since the earliest days of business applications. It solves the problem of producing a report on nested (or hierarchical) data, the kind often found in a chart of accounts.

The result of control break reporting is a properly nested set of reports, each of which has localized subtotals. The details add up to a deeply nested subtotal. The subtotals add up to higher and higher level totals, and the top-level totals add to a grand total.

The classical algorithm for control break reporting, however, tends to hide the basic hierarchy under a welter of details about keys and totals and subtotals. It can’t produce “heading” totals or counts, only footing totals or counts. As soon as you want additional features, you may as well ditch the classical algorithm.

The Problem is the Sort

The most important thing I dislike about the classical control break algorithm is the sort that’s required. Sorting is an expensive operation. Rarely does a control break report show all of the detail with all of the nested totals, so why should I sort all that data only to produce higher-level subtotals?

The sorting seems so logical and necessary. Often our data isn’t in the desired order, so sorting it makes superficial sense. More to the point, however, is that no one wants all the data in a single report. Even if I did produce a PDF file with all 21,000 accounts in the general ledger, people only want their little section of details, or they want a slice down to a depth of 3 from where their ownership of the finances starts.

The CFO wants the top-most totals. The manager of production wants her costs, and the next few levels of summary data. Her shift supervisors want the details for their specific times and production areas. No one wants ALL of the data. If no one wants all of the data, why sort all of it?

The Data is Dimensional

Most of the interesting reporting problems have a combination of two features: dimensions and hierarchies. The basic numeric measurements (dollars, hours, pounds, pallets, and the like) are the facts on which we are reporting. Each fact has a number of relevant dimensions along which that fact is measured. We might, for example, have sales dollars by product, by fiscal period, by sales person; this is a three-dimensional analysis. We could group these three dimensions in any of 6 different orders, and produce a number of hierarchies with different kinds of totals.

In additional to the independent dimensions of a fact, each dimension may be a hierarchical grouping of data. Time, for example, has groupings of days, weeks, months, quarters and years. Sales people may be organized into territories, regions and countries. Products may be organized into lines and families.

In the Cookbook example from ASPN, they have one dimension: the sales organization. This is broken into branches and sales people. This forms a tidy hierarchy, good for a simple example.

In most kinds of reporting, however, there are often a large number of dimensions. Worse, there may be complex relationships within a dimension. For example, the calendar has weeks and months, but months don’t fall on nice weekly boundaries. Similarly, our USA office may have many regions in a single country, but our European office may combine several countries in a region.

The Solution is a Mapping

The right way to handle Control Break reporting in Python is through a design pattern that is a variation on the Index or the Inverted Database. I prefer to call it the Dimensional Map, because that’s a better clue as to how it works.

Let’s look at the data we have in the ASPN example:

records = [("branch1",  "sales1", 100),
           ("branch1",  "sales1", 50),
           ("branch1",  "sales2", 10),
           ("branch2",  "sales1", 104),
           ("branch2",  "sales2", 56),
           ("branch2",  "sales2", 156)]

In this case, we have two keys (branch and sales person), and one fact (the sales dollars). What we will make is a Map of the branches. Each entry in the branch-level Map is a Map of the sales people. Each entry in the person-level map is a list of their detailed sales dollars. We can then traverse these nested maps to write the report we want to see, correctly labeled with headers and footers. We can, without too much extra work. have totals in the header as well as the footer. I’ll leave that as an exercise.

It works like this:

branchMap= {}
for branch, person, dollars in records:
    branchMap.setdefault( branch, {} )
    personMap= branchMap[ branch ]
    personMap.setdefault( person, 0 )
    personMap[person].append( dollars )

branchList= branchMap.keys()
for branch in branchList:
    print "header Branch", branch
    personMap= branchMap[branch]
    personList= personMap.keys()
    for person in personList:
        print "header Person", person
        for data in personMap[person]
            print data
        print "footer Person"
    print "footer Branch"
print "grand Total"

This little script is essentially hard-wired for this simple two-dimensional analysis. It doesn’t take too much cut-and-paste to expand this to the desired number of levels. It isn’t, however, the most general solution. For that, we need a better class design.

Expanding On The Pattern

The real problem with Control Break reporting is the recursion. Any level of the report (except the numeric facts) is a recursive structure: it contains a Map of the next lower level of detail. We can define a class, Dimension, which does two things for us.

  • Dimension carries the data elements for that Dimension, the key and the next lower level Dimension object with the details. A Dimension’s key may contains a Fact object which has a simple unkeyed list of values.
  • Dimension handles the recursive structure implied by the hierarchy. We have methods which process data recursively, treating each subsidiary Dimension (or Fact) in a uniform way.

A simple tail recursion technique assures that each Dimension contains subsidiary Dimensions, and the most deeply-nested item is the basic Fact. This leads to programs that fit the recursive model of a number of dimensions, terminated by a single fact.

To keep the classes polymorphic, both Dimension and Fact must implement an append() method that loads data and a report() method that produces the final report on the data.

Further, to keep this example simple, we’ll make each object a combination of data and meta-data. The data is the mapping of key to details or the list of facts. The metadata is the column name and the relationship with the lower-level dimensions. The metadata is a universal truth about the data.

We have multiple instances of each object: there are multiple branches and multiple people. We’ll need to create additional collections to hold the data. We’ll do this by cloning the object definition. There’s a better way to do this by separating the metadata from the actual detailed numeric data, but that is a more complex solution, not a simple recipe.

import copy

class Fact( object ):
    """A Fact is a measurable quantity."""
    def __init__ ( self, name ): name [] 0
    def append( self, item ): item[0] ) += item[0]
    def values( self ):
    def report( self, depth=0 ):
        for d in
            print depth*' ', d

class Dimension( object ):
    """A Dimension is a value to group Facts or Dimensions."""
    def __init__( self, name, child=None ): name {}
        self.child= child 0
    def append( self, row ):
        """The first value is the key for this dimension.
        The remaining values are other dimension keys or the fact value.""
        key= row[0]
        values= row[1:] key, copy.deepcopy(self.child) )[key].append( values )
    def keys( self ):
        return keyList
    def get( self, value ):
        return value )
    def report( self, depth=0 ):
        """Report this dimension, relying on other Dimensions or Facts.""" 0
        for k in self.keys():
            print depth*' ',, k, 'header'
  [k].report( depth+1 )
            print depth*' ', k, 'total',[k].total

Loading this structure with data is pleasantly simple. We define the nested structure of our Dimensions and the Fact which they contain. This same recursive structure can then be used to break up each record into a key and the data associated with that key.

analysis= Dimension( "branch", Dimension( "person", Fact( "dollars" ) ) )

for row in records:
    analysis.append( row )

Reporting, similarly, relies on the recursive structure of Dimension objects nested within Dimension objects.

More Generalization

Since some people are uncomfortable with the recursion, and would prefer this to use a flat list of Dimension and Fact objects. This flat list can be used with explicit for-loops to parse the input and assign an appropriate structure. We’ll post this solution in the future, perhaps.

Additionally, it would be nice to allow for multiple Facts and not force the file to be kept with the columns in order from most general to most specific. The first improvement (multiple facts for reporting) is a pretty simple generalization. The second, however, is a matter of a simple map to switch the order in which the columns are examined to create the various levels of detail.

Finally, the separation of meta-data from the real application data would shift the complexity around. It would make some of this simpler, but it would introduce more classes into the solution.

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