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
mostof 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