|From:||"Mischa Sandberg" <mischa_sandberg(at)telus(dot)net>|
|Subject:||Range query optimization|
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I'm trying to make a (qua-technical, qua-business) case for switching from
MS SQL, and one of the types of query that really doesn't sit well with MS
-- All fields integers or equivalent.
-- Table T(k, x: nonkey fields...)
-- Table U(k, a, z: m) -- for each value of (k) a set of non-intersecting
ranges [a,z) that map to (m) values.
select T.*, U.m from T join U on T.k=U.k and T.x >= U.a and T.x < U.z
Typically there are are about 1000-2000 U rows per value of (k), about 100K
values of (k) and about 50M
values of T.
By itself, this type of query grinds the CPU to dust. A clustered index on
fields of U (take your pick) barely halves the problem of the loop through
1000-2000 rows of U for each row of T. Hash join likewise.
The current workaround is a 'manual' radix index on top of the range table,
but it's something of a hack.
Would the geometric of extensions handle such queries efficiently? I'm not
familiar with applying R-trees to linear range problems.
"Dreams come true, not free." -- S.Sondheim
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