I'm working on some upgrades to an internal web analytics system we provide for our clients (in the absence of a preferred vendor or Google Analytics), and I'm working on the following query:
select
path as EntryPage,
count(Path) as [Count]
from
(
/* Sub-query 1 */
select
pv2.path
from
pageviews pv2
inner join
(
/* Sub-query 2 */
select
pv1.sessionid,
min(pv1.created) as created
from
pageviews pv1
inner join Sessions s1 on pv1.SessionID = s1.SessionID
inner join Visitors v1 on s1.VisitorID = v1.VisitorID
where
pv1.Domain = isnull(@Domain, pv1.Domain) and
v1.Campaign = @Campaign
group by
pv1.sessionid
) t1 on pv2.sessionid = t1.sessionid and pv2.created = t1.created
) t2
group by
Path;
I've tested this query with 2 million rows in the PageViews table and it takes about 20 seconds to run. I'm noticing a clustered index scan twice in the execution plan, both times it hits the PageViews table. There is a clustered index on the Created column in that table.
The problem is that in both cases it appears to iterate over all 2 million rows, which I believe is the performance bottleneck. Is there anything I can do to prevent this, or am I pretty much maxed out as far as optimization goes?
For reference, the purpose of the query is to find the first page view for each session.
EDIT: After much frustration, despite the help received here, I could not make this query work. Therefore, I decided to simply store a reference to the entry page (and now exit page) in the sessions table, which allows me to do the following:
select
pv.Path,
count(*)
from
PageViews pv
inner join Sessions s on pv.SessionID = s.SessionID
and pv.PageViewID = s.ExitPage
inner join Visitors v on s.VisitorID = v.VisitorID
where
(
@Domain is null or
pv.Domain = @Domain
) and
v.Campaign = @Campaign
group by pv.Path;
This query runs in 3 seconds or less. Now I either have to update the entry/exit page in real time as the page views are recorded (the optimal solution) or run a batch update at some interval. Either way, it solves the problem, but not like I'd intended.
Edit Edit: Adding a missing index (after cleaning up from last night) reduced the query to mere milliseconds). Woo hoo!