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There are many ways to solve this problem, some of which I ll present here . But most solutions benefit from the following index on custid, orderdate as the keys and empid, orderid as included columns:

Of course, you could use techniques I showed earlier to calculate running percents and lter query patterns based on those.

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Keep in mind that to calculate an aggregate product you have to scan all values in the group. So the performance potential your solution can reach is to achieve the calculation by scanning the data only once, using a set-based query. In the case of an aggregate product, this can be achieved using mathematical manipulation based on logarithms. I'll rely on the following logarithmic equations: Equation 1: loga(b) = x if and only if ax = b Equation 2: loga(v1 * v2 * ... * vn) = log a(v1) + loga(v2) + ... + loga(vn) Basically, what you're going to do here is a transformation of calculations. You have support in T-SQL for LOG, POWER, and SUM functions. Using those, you can generate the missing product. Group the data by the groupid column, as you would with any built-in aggregate. The expression SUM(LOG10(val)) corresponds to the right side of Equation 2, where the base a is equal to 10 in our case, because you used the LOG10 function. To get the product of the elements, all you have left to do is raise the base (10) to the power of the right side of the equation. In other words, the expression POWER(10., SUM(LOG10(val))) gives you the product of elements within the group. Here's what the full query looks like: SELECT groupid, POWER(10., SUM(LOG10(val))) AS product FROM dbo.Groups GROUP BY groupid;

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Now that you know which patterns you need to tune, you can start with a more focused query-tuning process. The process might involve index tuning or query code revisions, and we will practice it thoroughly throughout the book. Or you might realize that the queries are already tuned pretty well, in which case you would need to inspect other aspects of the system (for example, hardware, database layout, and so on). In our case, the tuning process is fairly simple. You need to create a clustered index on the orderdate column:

CREATE INDEX idx_cid_od_i_eid_oid ON Sales.Orders(custid, orderdate) INCLUDE(empid, orderid);

CREATE CLUSTERED INDEX idx_cl_od ON dbo.Orders(orderdate);

The first solution I ll present is one where I use a CTE to calculate the maximum order date per customer, and then in the outer query join the Orders table with the CTE to return the orders with the maximum order date for each customer, like so:

This is the final solution if you're dealing only with positive values. However, the logarithm function is undefined for zero and negative numbers. You can use pivoting techniques to identify and deal with zeros and negatives as follows: SELECT groupid, CASE WHEN MAX(CASE WHEN val = 0 THEN 1 END) = 1 THEN 0 ELSE

Later in the chapter, I ll cover index tuning and explain why a clustered index is adequate for query patterns such as the ones that our tuning process isolated.

WITH CustMax AS ( SELECT custid, MAX(orderdate) AS mx FROM Sales.Orders GROUP BY custid ) SELECT O.orderid, O.orderdate, O.custid, O.empid FROM Sales.Orders AS O JOIN CustMax AS M ON O.custid = M.custid AND O.orderdate = M.mx;

To see the effect of adding the index, run the following code to start a new trace:

CASE WHEN COUNT(CASE WHEN val < 0 THEN 1 END) % 2 = 0 THEN 1 ELSE -1 END * POWER(10., SUM(LOG10(NULLIF(ABS(val), 0)))) END AS product FROM dbo.Groups GROUP BY groupid;

DECLARE @dbid AS INT, @traceid AS INT; SET @dbid = DB_ID('Performance'); EXEC dbo.PerfworkloadTraceStart @dbid = @dbid, @tracefile = 'c:\temp\Perfworkload 20090212 Tuned', @traceid = @traceid OUTPUT;

Here the fact that a table expression is not materialized rather its definition gets expanded is an advantage . You might expect SQL Server to scan the data twice once to process the inner reference to the Orders table in the CTE query, and another for the outer reference to the Orders table . But the optimizer figured out a way to handle this query by scanning the data only once, which is truly admirable . Figure 7-6 shows the execution plan the optimizer produced for this query .

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