Tom Lane <[EMAIL PROTECTED]> writes: > I think the problem may well be that we use hash buckets that are too > large (ie, whole pages). After we fetch the page, we have to grovel > through every tuple on it to find the one(s) that really match the > query, whereas btree has a much more intelligent strategy (viz binary > search) to do its intrapage searches. Smaller buckets would help make > up for this.
Hm, you would expect hash indexes to still be a win for very large indexes where you're worried more about i/o than cpu resources. > Another issue is that we don't store the raw hashcode in the index > tuples, so the only way to test a tuple is to actually invoke the > datatype equality function. If we stored the whole 32-bit hashcode > we could eliminate non-matching hashcodes cheaply. I'm not sure how > painful it'd be to do this though ... hash uses the same index tuple > layout as everybody else, and so there's no convenient place to put > the hashcode. I looked a while back and was suspicious about the actual hash functions too. It seemed like a lot of them were vastly suboptimal. That would mean we're often dealing with mostly empty and mostly full buckets instead of well distributed hash tables. -- Gregory Stark EnterpriseDB http://www.enterprisedb.com ---------------------------(end of broadcast)--------------------------- TIP 9: In versions below 8.0, the planner will ignore your desire to choose an index scan if your joining column's datatypes do not match
