Map-Reduce, Python and Named Tuples
Map-Reduce, Python and Named Tuples
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A year and change back, I wrote this on "Exploratory Programming".It turns out that it was a mistake. While the subclass-expansion technique is a cool way to bang out a program incrementally, in the long run, the subclassing is ill-advised.
The more I look at Python generator functions and the idea of using Map-Reduce, the more I realize that Visitors and Subclass Extension are not the best design pattern. The same kind of exploration can be done with map-reduce techniques, and the resulting application is slightly simpler.
The problem with design-by-subclass is that the map and reduce operations are often defined relatively informally. After all, they're just method invocations within the same class. You can, unthinkingly, create obscure dependencies and assumptions. This can make the Visitor rather complex or make subclass features hard to refactor into the Visitor.
As a concrete example, we have an application that processes directories of files and file archives (ZIP and TAR) of workbooks with multiple sheets. All of this nesting merely wraps a source of spreadsheet rows. This should be a collection of simple nested map operations to transform directories, files, archives of files, etc., into a spreadsheet row source.
Part way through our class hierarchy, however, a subclass introduced a stateful object that other method functions could use. However, when we tried to refactor things into a simple Visitor to visit all of the workbooks (ignoring all the directory, archive and file structure), we worked around the hidden stateful object without realizing it.
Named Tuples and Immutability
A much cleaner solution is to make use of Python's namedtuple constructor and write generator functions which map one kind of namedtuple to another kind of namedtuple. This has the advantage that -- unless you've done something really bad -- you should be able to pickle and unpickle each tuple, easily seeding a multi-processing pipeline. Maximal concurrency, minimal work.
Each stage in a map-reduce pipeline can have the following form.
SomeResult = namedtuple('SomeResult',['a','b','c']) for x in someSource(): assert isinstance( x, SomeSource ) yield SomeResult( <i>some transformation of x</i> )
The assertion should be obvious from inspection of the someSource function. It's provided here because it's essential to creating map-reduce pipelines that work. It's better to prove that the assertion true through code inspection and comment out the assert statement.
What pops out of this are stateless objects. Since named tuples are immutable, it appears that we've done some purely functional programming without really breaking a sweat.
Further, we can see our way toward encapsulating a generator function and it's resulting namedtuple as a single MapReduce object. The assertion can then be plucked out of the loop and refactored into a pipeline manager.
This might give us several benefits.
- A way to specify a pipeline as a connected series of generator functions. E.g., Pipeline( generator_1, map_2, map_3, reduce_4 ).
- Inspection of the pipeline to be sure that constraints will be met. The head of the pipeline has a resulting type, all other stages have an required source type and a result type. Since they're named tuples we only care that the required attributes are a subset of the previous stage's result attributes.
- Implementation through injection of a pickle/unpickle wrapper around each stage.
- Distribution through a "runner" that forks a subprocess pipeline. This should yield map-reduce operations that swamp the OS by having multiple concurrent stages.
Generally, our goal is to get the CPU 100% committed to the right task. Either 100% doing web services, or 100% doing database queries or 100% doing batch processing of massive flat-file datasets.
Currently, we can't get much past 66%: one core is close to 100%, but the other is only lightly involved. By growing to multi-processing, we should be able to red-line a processor with any number of cores.
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