Hello,
It always depends on the dataset but you should try an explain analyze
on each query. It will tell you which one is more efficient for your
particular data.
Sincerely,
Joshua D. Drake
>
> Here's the join:
>
> # explain select child_pid from ssv_product_children, nv_products where
> nv_products.id = ssv_product_children.child_pid and
> ssv_product_children.pid = 1 and nv_products.isrel = 't';
> QUERY PLAN
> --------------------------------------------------------------------------
> Hash Join (cost=1.22..2.47 rows=2 width=8)
> Hash Cond: ("outer".child_pid = "inner".id)
> -> Seq Scan on ssv_product_children (cost=0.00..1.18 rows=9 width=4)
> Filter: (pid = 1)
> -> Hash (cost=1.21..1.21 rows=4 width=4)
> -> Seq Scan on nv_products (cost=0.00..1.21 rows=4 width=4)
> Filter: (isrel = true)
> (7 rows)
>
>
> Here's the correlated subquery:
>
>
> # explain select child_pid from ssv_product_children where pid = 1 and
> child_pid = (select nv_products.id from nv_products where nv_products.id
> = child_pid and isrel = 't');
> QUERY PLAN
> ---------------------------------------------------------------------
> Seq Scan on ssv_product_children (cost=0.00..18.78 rows=1 width=4)
> Filter: ((pid = 1) AND (child_pid = (subplan)))
> SubPlan
> -> Seq Scan on nv_products (cost=0.00..1.26 rows=1 width=4)
> Filter: ((id = $0) AND (isrel = true))
> (5 rows)
>
>
> Thanks for any advice.
>
> ---------------------------(end of broadcast)---------------------------
> TIP 9: the planner will ignore your desire to choose an index scan if your
> joining column's datatypes do not match
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