In my experience, Python is an effective rule engine. Thanks to Python's readability, you can store business rules as snippets of Python code - in textfiles, a database table, or wherever you prefer - and business users should be able to read them comfortably. After that, a very lightweight Python program can load the rules and the relevant data and use exec() or eval() to apply the rules to it.
One of my main projects is an example of this. It's program that synchronizes data between two Oracle databases. That sounds easy, but business details complicate it enormously:
- Table and column naming, structure, and normalization differ
- Only some rows are transferred, according to a complex set of business rules
- Only some columns are transferred. Column values are combined, split, truncated, have functions applied, etc. Again, governed by a jungle of business rules
- The business rules change continuallyRules must be documented. Letting documentation get out of synch with implemented rules is very bad.
- Users may demand explanations for each decision made by the program, down to the row and column level
My first take on the problem was a large hard-coded PL/SQL procedure. What a nightmare!
Later, I rewrote the rules as snippets of Python. Each rule is stored a database table along with the dates it takes effect and expires, the person authorizing the rule, and a justification. This readable, self-documenting set of rules can also answer questions like, "Why did things change since last month?".
Unfortunately, I didn't know much about object-relational mappers when I wrote it, so the program has clunky data-fetching code. I'm currently working on a third version of the program that uses SQLAlchemy; the resulting program is very short. Broadly, here's what it does:
- Queries the row-level and column-level rules from their respective tables
- Fetches a row from the local database (ours) and the corresponding row from the remote database (theirs)
- The heart of the engine:
data = {'ours': ours, 'theirs': theirs}
for row_rule in row_rules:
if not eval(row_rule, data):
return
for column_rule in column_rules:
exec(column_rule, data)
Actually, the data dict also includes definitions of a few functions that some of the rules invoke. The function names are chosen to be self-explanatory to business users. For example:
def fiscalYear(inDate):
if inDate.month > 9:
result = inDate.year + 1
else:
result = inDate.year
return result
data = {'ours': ours, 'theirs': theirs, 'fiscalYear': fiscalYear}
I suppose it wouldn't be too hard to put the function definitions themselves in among the rules, then include locals() in with data, as long as execution order is controlled (easily done by putting execution_order columns in the rules tables). It hasn't been necessary for my project.
- Now row_rules has eval()-able entries like::
id start end code authorized reason
1 1/1/06 7/1/09 ours.funded == "Y" Bob I said so
and column_rules has exec()-able entries like::
id start end code authorized reason
1 2/2/08 if (ours.value > 1000): Steve to annoy Bob
theirs.value = ours.value
- Add some logging and a "test-run" capacity which reports on the changes without actually performing them. (It uses sqlalchemy.orm.attributes.get_history() for this; make sure to set autoflush=False if you use this, or intermediate flushes might clear the history.)
I suppose I could try writing a sample rules-engine implementation in simple, general terms, that people could crib from for their own "rules engine" applications. I wonder if that would be helpful, or if just the general idea is enough guidance.
