Hello,
For a big table with more than 10 Million records, may I know which update is quicker please? (1) update t1 set c1 = a.c1 from a where pk and t1.c1 <> a.c1; ...... update t1 set c_N = a.c_N from a where pk and t1.c_N <> a.c_N;(2) update t1 set c1 = a.c1 , c2 = a.c2, ... c_N = a.c_N from a where pk AND (t1.c1, c2...c_N) <> (a.c1, c2... c_N)
Probably (2). <> is not indexable, so each update will have to perform a sequential scan of the table. With (2), you only need to scan it once, with (1) you have to scan it N times. Also, method (1) will update the same row multiple times, if it needs to have more than one column updated.
Or other quicker way for update action?
If a large percentage of the table needs to be updated, it can be faster to create a new table, insert all the rows with the right values, drop the old table and rename the new one in its place. All in one transaction. The situation is: (t1.c1, c2, ... c_N) <> (a.c1, c2...c_N) won't return too many diff records. So, the calculation will only be query most of the case. But if truncate/delete and copy will cause definitely write all more than 10 million data. If for situation like this, will it still be quicker to delete/insert quicker? Thank you Emi -- Sent via pgsql-performance mailing list (pgsql-performance@postgresql.org) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-performance
