Prateek <[EMAIL PROTECTED]> writes: > The big set does stay around for a while - I've implemented an LRU > based caching algorithm on the code that does the I/O. Since the db is > transactioned, I keep one copy in the current transaction cache (which > is a simple dictionary) and one in the main read cache (LRU based) > (which obviously survives across transactions). Since the largest sets > also tend to be the most frequently used, this basically solves my I/O > caching issue.
The best approach varies from instance to instance. Some big databases often will do stuff like statistically sample the sets from a given query, try a few different strategies on the samples in order to figure out which one works best on them, then use the apparent best strategy on the full sets. -- http://mail.python.org/mailman/listinfo/python-list
