This link has been bookmarked by 31 people . It was first bookmarked on 05 Sep 2006, by James Houx.
-
13 Feb 12
-
11 Feb 12
-
20 Oct 11
-
02 Sep 11
samuel sungll table scans to index accesses – For large data sets full table scans are often faster than range scans and other t
-
10 Jul 11
-
15 Sep 10
-
14 Jan 09
-
05 Dec 08
-
07 Nov 08
-
17 Jun 08
-
25 Sep 07
-
make sure your data fits in memory as good as possible. This could be done by data partitioning (ie old and rarely accessed data stored in different servers), multi-server partitioning to use combined memory
-
normalized data normally becomes smaller, but dramatically increased number of index lookups which could be random accesses.
-
if you have star join with dimention tables being small it would not slow things down too much
-
MySQL can't do hash join or sort merge join - it only can do nested loops method which requires a lot of index lookups which may be random
-
In MySQL single query runs as single thread (with exeption of MySQL Cluster) and MySQL issues IO requests one by one for query execution, which means if single query execution time is your concern many hard drives and large number of CPUs will not help
-
Prefer full table scans to index accesses - For large data sets full table scans are often faster than range scans
-
tables and your working set do not fit in memory. This is the case then full table scan will actually require less IO than using indexes.
-
Using SQL_BIG_RESULT helps to make it use sort instead.
-
-
19 Jun 07
-
Avoid joins to large tables Joining of large data sets using nested loops is very expensive.
-
-
05 Sep 06
-
05 Aug 06
Page Comments
Would you like to comment?
Join Diigo for a free account, or sign in if you are already a member.