[Tutor] value range checker
alan.gauld at btinternet.com
Wed Aug 26 18:29:08 CEST 2015
On 26/08/15 14:19, Albert-Jan Roskam wrote:
> I have a written a function checks the validity of values.
> The ranges of valid values are stored in a database table.
That's an unusual choice because:
1) using a database normally only makes sense in the case
where you are already using the database to store the
other data. But in that case you would normally get
validation done using a database constraint.
2) For small amounts of data the database introduces
a significant overhead. Databases are good for handling
large amounts of data.
3) A database is rather inflexible since you need to
initialise it, create it, etc. Which limits the number
of environments where it can be used.
> Such a table contains three columns: category, min and max. ...
> a category may be spread out over multiple records.
And searching multiple rows is even less efficient.
> Would yaml be a better choice? Some of the tables are close to 200 records.
Mostly I wouldn't use a data format per-se (except for
persistence between sessions). I'd load the limits into
a Python set and let the validation be a simple member-of check.
Unless you are dealing with large ranges rather than sets
of small ranges. Even with complex options I'd still
opt for a two tier data structure. But mostly I'd query
any design that requires a lot of standalone data validation.
(Unless its function is to be a bulk data loader or similar.)
I'd probably be looking to having the data stored as
objects that did their own validation at creation/modification
If I was doing a bulk data loader/checker I'd probably create
a validation function for each category and add it to a
dictionary. So I'd write a make_validator() function that
took the validation data and created a specific validator
function for that category. Very simple example:
def make_validator(min, max, *values):
return (min <= value <= max) or value in *values)
for category in categories:
lookup[category] = make_validator(min,max, valueList)
# process valid value
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