Sql-server – Query with Variable not estimating right

I would have guessed that when a query includes TOP n the database engine would run the query ignoring the the TOP clause, and then at the end just shrink that result set down to the n number of rows that was requested. The graphical execution plan seems to indicate this is the case -- TOP is the "last" step. But it appears there is more going on.

The way the above is phrased makes me think you may have an incorrect mental picture of how a query executes. An operator in a query plan is not a step (where the full result set of a previous step is evaluated by the next one.

SQL Server uses a pipelined execution model, where each operator exposes methods like Init(), GetRow(), and Close(). As the GetRow() name suggests, an operator produces one row at a time on demand (as required by its parent operator). This is documented in the Books Online Logical and Physical Operators reference, with more detail in my blog post Why Query Plans Run Backwards. This row-at-a-time model is essential in forming a sound intuition for query execution.

My question is, how (and why) does a TOP n clause impact the execution plan of a query?

Some logical operations like TOP, semi joins and the FAST n query hint affect the way the query optimizer costs execution plan alternatives. The basic idea is that one possible plan shape might return the first n rows more quickly than a different plan that was optimized to return all rows.

For example, indexed nested loops join is often the fastest way to return a small number of rows, though hash or merge join with scans might be more efficient on larger sets. The way the query optimizer reasons about these choices is by setting a Row Goal at a particular point in the logical tree of operations.

A row goal modifies the way query plan alternatives are costed. The essence of it is that the optimizer starts by costing each operator as if the full result set were required, sets a row goal at the appropriate point, and then works back down the plan tree estimating the number of rows it expects to need to examine to meet the row goal.

For example, a logical TOP(10) sets a row goal of 10 at a particular point in the logical query tree. The costs of operators leading up to the row goal are modified to estimate how many rows they need to produce to meet the row goal. This calculation can become complex, so it is easier to understand all this with a fully worked example and annotated execution plans. Row goals can affect more than the choice of join type or whether seeks and lookups are preferred to scans. More details on that here.

As always, an execution plan selected on the basis of a row goal is subject to the optimizer's reasoning abilities and the quality of information provided to it. Not every plan with a row goal will produce the required number of rows faster in practice, but according to the costing model it will.

Where a row goal plan proves not to be faster, there are usually ways to modify the query or provide better information to the optimizer such that the naturally selected plan is best. Which option is appropriate in your case depends on the details of course. The row goal feature is generally very effective (though there is a bug to watch out for when used in parallel execution plans).

Your particular query and plan may not be suitable for detailed analysis here (by all means provide an actual execution plan if you wish) but hopefully the ideas outlined here will allow you to make forward progress.

The query takes a lot of time because it finds no rows.

The plan first is setup top pick one row from tabSparePart where sparepartname = '1270-3132'. It then moves along to the index scan of tabData. For each row fetched from tabData there is a clustered index seek in locSL followed by a clustered index seek in tabDataDetail which also checks for not null on gsp_part_price and finally returns the values of fiSparePart and GSP_Part_Price. fiSparePart is then compared against the idSparePart returned from the Index Seek in tabSparePart. If they are the same the query immediately terminates and return the gsp_part_price. If they are not the same the index scan in tabData moves to the next record and all the checks are executed again. That keeps happening until there is a row returned or all rows in tabData have been processed.

As it turns out you get a totally different query plan when you do top(1000).

You can use the optimize for hint to make SQL Server think you actually want 1000 rows when you only need one.

Your scalar valued function would then look something like this:

CREATE FUNCTION [dbo].[FunctionName]()
RETURNS INT
AS
BEGIN

  DECLARE @TOP INT
  DECLARE @RET INT

  SET @TOP = 1

  SELECT TOP (@TOP) @RET = dd.gsp_part_price 
  FROM  tabdata 
          INNER JOIN locsl 
                  ON tabdata.fisl = locsl.idsl 
          INNER JOIN locgsp 
                  ON locsl.figsp = locgsp.idgsp 
          INNER JOIN tabdatadetail dd
                  ON dd.fidata = tabdata.iddata 
          INNER JOIN tabsparepart sp 
                  ON dd.fisparepart = sp.idsparepart 
  WHERE  sp.sparepartname = '1270-3132' 
          AND dd.gsp_part_price IS NOT NULL 
  ORDER BY Claim_Submitted_Date DESC 
  OPTION (OPTIMIZE FOR(@TOP = 1000))

  RETURN @RET

END