Table-driven Programming

Tagged programming, table-driven  Languages python

You can use if statements to run a function when the state of your application matches specific criteria.

However, if statements don’t scale as they are hard to understand and maintain:

if state_x and state_y and state_z:

if state_x and state_z:

if state_x and state_y and not state_z:

Table-driven programming is an alternative that sometimes is easier to maintain:

rules = (
    # x, y, z, function
    (True, True, True, do_xyz),
    (True, True, False, do_xy_not_z),
    (True, False, True, do_xz),
    (True, True, True, do_xz),
for rule in rules:
    rule_x = rule[0]
    rule_y = rule[1]
    rule_z = rule[2]
    doit = rule[3]
    if rule_x == state_x and rule_y == state_y and rule_z == state_z:

Or, more succinctly:

def matching_rules(rules, params):
    for criterion, func in rules:
        if all(params[ix] == criteria for ix, criteria in enumerate(criterion)):
            yield func

# The table of rules
rules = (
    # x, y, z, function
    ((True, True, True), do_xyz),
    ((True, True, False), do_xy_not_z),
    ((True, False, True), do_xz),
    (True, True, True, do_xz),
params = (state_x, state_y, state_z)
for func in matching_rules(rules, params):

In summary, a function is run only when the criteria match.

Pattern matching is also an alternative:

However, the first-to-match rule requires the order to be correct and prevents multiple function calls.

State machines and Prolog are also options…