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
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