> Right. So you have a database with 2 000 000 rows that is 1.8GB > So your first 1 000 000 rows takes up about 1GB. > And your test case with just 1 000 000 rows in runs really fast. > > So what is happening is that most of the first 1 000 000 rows fits in memory. > Once the database gets bigger than that the application has to keep fetching > information from disk all the time, and that's far slower. >
i m affraid so ... but what it's will be with 50 000 000 rows ? i don't have 100 gigabytes of memory :( with 50 000 000 rows and btree index like where fiedA = yyy then result is imediate ! but why not also with rtree index ? thanks you by advance stéphane >> how to speed up this query : >> >> Select >> H1.ID >> from >> HASH1 H1 >> where >> x1_y1_min>= max((<#randomnumber> % 255)-10,0) and >> x1_y1_max<= min((<#randomnumber> % 255)+10,255) and >> x1_y2_min>= max((<#randomnumber> % 255)-10,0) and >> x1_y2_max<= min((<#randomnumber> % 255)+10,255) and >> x1_y3_min>= max((<#randomnumber> % 255)-10,0) and >> x1_y3_max<= min((<#randomnumber> % 255)+10,255) and >> x1_y4_min>= max((<#randomnumber> % 255)-10,0) and >> x1_y4_max<= min((<#randomnumber> % 255)+10,255) and >> x1_y5_min>= max((<#randomnumber> % 255)-10,0) and >> x1_y5_max<= min((<#randomnumber> % 255)+10,255); > If there is only one random number involved in all that, precalculate all the > max and mins: > > ll = max((<#randomnumber> % 255)-10,0) > mm = min((<#randomnumber> % 255)+10,255) in my test all the random are different ... _______________________________________________ sqlite-users mailing list sqlite-users@sqlite.org http://sqlite.org:8080/cgi-bin/mailman/listinfo/sqlite-users
