At 02:14 AM 12/14/2005, Liam Clarke wrote:
>Hi all,
>
>Just contemplating.
>
>If in Python I were organising a data index along the lines of -
>
>j = {
>
>"k_word1" : ["rec1","rec2","rec3","rec4"],
>...
>"k_wordn" :["recX","rec4"]
>
>}
>
>and I was going to find records that matched by seeing what record
>occurred in the most lists (via set intersections or similar; going to
>have a play see what works faster) selected by searching keywords...
>
>how easily does that translate to a SQL table and query format?
Data modeling looks for relationships between objects. Relationships
can be 1-1 1-many or many-many. Your case is a many-many
(each keyword may appear in one or more records, and each record may
contain one or more keywords.) The customary way to represent this in
a relational database 3 tables. One with one row per keyword, one
with one row per record and one "junction" or "association" table
with one row for each keyword-record pair.
KEYWORD TABLE
kid keyword
1 cat
2 dog
3 mouse
4 bird
5 banana
RECORD TABLE
rid record
1 rexX
2 rec4
3 recAB
4 rec99
5 recFoo
KEYWORD-RECORD TABLE
kid rid
1 1
1 3
1 4
2 2
3 5
4 1
5 3
For processing things like this nothing IMHO beats a relational
database and SQL. With many databases accessible from Python I
strongly suggest this approach. SQLite is especially attractive.
[snip]
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