Sql-server – Poor query performance

You say

Above plan is for subid = 11 or 7 in @t table variable

I think you may be under a misapprehension here. SQL Server does not look at the contents of the table variable and choose a plan based upon the values it contains.

The statement is compiled before the table variable contains any rows at all and you will get the same plan (that assumes a single row) regardless of whether it eventually contains 2 (and would match 95.5% of the rows) or 1 (and would match only 0.0008%).

The table variable may of course also contain multiple rows but SQL Server will not take account of that except if you use the OPTION (RECOMPILE) hint and even then there are no statistics on table variables so it cannot take any account of actual values.

Some alternate plans are below

These require finding all matching rows and sorting them.

Because NCx_1 is not declared as a unique index the include(QueueItemID) is ignored (as explained in More About Nonclustered Index Keys) and QueueItemID gets added as an index key column instead. This means that SQL Server can seek on IsProcessed, QCode and the matching rows will be ordered by QueueItemID.

The plan in your question therefore avoids a sort operation but performance is entirely reliant upon how many rows in practice need to be evaluated before the first one matching the SubID IN (SELECT SubID FROM @t) predicate is found and the range seek can stop.

Of course this can vary wildly depending on how common the SubID values contained in @t are and whether there is any skew in the distribution of these values with respect to QueueItemID (You say that both around 350k rows match the seek predicate and that around 350k end up being seeked so for SubID = 7 it sounds like these are all at the end or perhaps no rows match at all - which would be the worse case for this plan).

It would be interesting to know what the estimated number of rows coming out of the seek is. Presumably this is much less than 350,000 and thus SQL Server chooses the plan you see based on this estimated cost.

If the table variable will always just have few rows you might find this rewrite works better for you.

SELECT TOP 1 QueueItemID
FROM   @t
       CROSS APPLY (SELECT TOP 1 t.QueueItemID
                    FROM   QueueTable t
                    WHERE  t.IsProcessed = 0
                           AND t.QCode = 'USA'
                           AND SubID = [@t].SubID
                    ORDER  BY t.QueueItemID) CA 
ORDER BY QueueItemID   

For me it gives the plan below where it seeks into the index on subid,isprocessed,qcode,queueitemid as many times as you have rows in the table variable. It is similar to the first plan shown but may be slightly more efficient as each seek stops after the first row is returned.

There is a simple solution to this:

Drop all of the _dta_... statistics and stop blindly applying DTA recommendations.

More information

The particular problem was that there were multiple sets of statistics for the column in question. The extra dta statistics were created by sampling the data (the default behaviour for statistics not associated with an index).

As is often the case with sampled statistics, the resulting histograms did not cover the full range of the unerlying data. The query in the question happened to choose a value that was outside the histogram, resulting in a 1-row estimate.

The exact behaviour of the query optimizer when multiple sets of statistics exist for the same column is not fully documented. It does tend to prefer 'full scan' statistics over sampled, but it also prefers more recently-updated statistics to older ones.