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Inside Microsoft SQL Server 2008: T-SQL Querying
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qty INT NOT NULL, PRIMARY KEY(empid, ordmonth) ); GO INSERT INTO dbo.EmpOrders(empid, ordmonth, qty) SELECT O.empid, DATEADD(month, DATEDIFF(month, 0, O.orderdate), 0) AS ordmonth, SUM(qty) AS qty FROM InsideTSQL2008.Sales.Orders AS O JOIN InsideTSQL2008.Sales.OrderDetails AS OD ON O.orderid = OD.orderid GROUP BY empid, DATEADD(month, DATEDIFF(month, 0, O.orderdate), 0);
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Tip I will represent each month by its start date stored as a DATE. This allows exible manipulation
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of the data using date-related functions. Of course, I ll ignore the day part of the value in my calculations.
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Run the following query to get the contents of the EmpOrders table:
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SELECT empid, CONVERT(VARCHAR(7), ordmonth, 121) AS ordmonth, qty FROM dbo.EmpOrders ORDER BY empid, ordmonth;
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This query generates the following output, shown here in abbreviated form:
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empid ----------1 1 1 1 1 1 1 1 1 1 ... 2 2 2 2 2 2 2 2 2 2 ... ordmonth -------2006-07 2006-08 2006-09 2006-10 2006-11 2006-12 2007-01 2007-02 2007-03 2007-04 2006-07 2006-08 2006-09 2006-10 2006-11 2006-12 2007-01 2007-02 2007-03 2007-04 qty ----------121 247 255 143 318 536 304 168 275 20 50 94 137 248 237 319 230 36 151 468
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I ll discuss three types of running aggregation problems: cumulative, sliding, and year-to-date (YTD).
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Aggregating and Pivoting Data
Cumulative Aggregations
Cumulative aggregations accumulate data from the rst element within the sequence up to the current point. For example, imagine the following request: for each employee and month, return the total quantity and average monthly quantity from the beginning of the employee s activity through the month in question. Recall the techniques for calculating row numbers without using the built-in ROW_NUMBER function; using these techniques, you scan the same rows we need here to calculate the total quantities. The difference is that for row numbers you used the aggregate COUNT, and here you ll use the aggregates SUM and AVG. I demonstrated two set-based solutions to calculate row numbers without the ROW_NUMBER function one using subqueries and one using joins. In the solution using joins, I applied what I called an expand-collapse technique. To me, the subquery solution is much more intuitive than the join solution, with its arti cial expand-collapse technique. So, when there s no performance difference, I d rather use subqueries. Typically, you won t see a performance difference when only one aggregate is involved because the plans would be similar. However, when you request multiple aggregates, the subquery solution might result in a plan that scans the data separately for each aggregate. Compare this to the plan for the join solution, which typically calculates all aggregates during a single scan of the source data. So my choice is usually simple use a subquery for one aggregate and use a join for multiple aggregates. The following query applies the expand-collapse approach to produce the desired result:
SELECT O1.empid, CONVERT(VARCHAR(7), O1.ordmonth, 121) AS ordmonth, O1.qty AS qtythismonth, SUM(O2.qty) AS totalqty, CAST(AVG(1.*O2.qty) AS NUMERIC(12, 2)) AS avgqty FROM dbo.EmpOrders AS O1 JOIN dbo.EmpOrders AS O2 ON O2.empid = O1.empid AND O2.ordmonth <= O1.ordmonth GROUP BY O1.empid, O1.ordmonth, O1.qty ORDER BY O1.empid, O1.ordmonth;
This query generates the following output, shown here in abbreviated form:
empid ----------1 1 1 1 1 1 1 1 1 1 ... ordmonth -------2006-07 2006-08 2006-09 2006-10 2006-11 2006-12 2007-01 2007-02 2007-03 2007-04 qtythismonth -----------121 247 255 143 318 536 304 168 275 20 totalqty ----------121 368 623 766 1084 1620 1924 2092 2367 2387 avgqty ---------121.00 184.00 207.67 191.50 216.80 270.00 274.86 261.50 263.00 238.70
Inside Microsoft SQL Server 2008: T-SQL Querying
2 2 2 2 2 2 2 2 2 2 ... 2006-07 2006-08 2006-09 2006-10 2006-11 2006-12 2007-01 2007-02 2007-03 2007-04 50 94 137 248 237 319 230 36 151 468 50 144 281 529 766 1085 1315 1351 1502 1970 50.00 72.00 93.67 132.25 153.20 180.83 187.86 168.88 166.89 197.00
Now let s say that you are asked to return only one aggregate (say, total quantity). You can safely use the subquery approach:
SELECT O1.empid, CONVERT(VARCHAR(7), O1.ordmonth, 121) AS ordmonth, O1.qty AS qtythismonth, (SELECT SUM(O2.qty) FROM dbo.EmpOrders AS O2 WHERE O2.empid = O1.empid AND O2.ordmonth <= O1.ordmonth) AS totalqty FROM dbo.EmpOrders AS O1 GROUP BY O1.empid, O1.ordmonth, O1.qty;
As was the case for calculating row numbers based on subqueries or joins, when calculating running aggregates based on similar techniques, the N2 performance issues I discussed before apply once again. Because running aggregates typically are calculated on a fairly small number of rows per group, you won t be adversely affected by performance issues, assuming you have appropriate indexes (keyed on grouping columns, then sort columns, and including covering columns). Let p be the number of partitions involved (employees in our case), let n be the average number of rows per partition (months in our case), and let a be the number of aggregates involved. The total number of rows scanned using the join approach can be expressed as pn + p(n+n2)/2 and as pn + ap(n+n2)/2 using the subquery approach because with subqueries the optimizer uses a separate scan per subquery. It s important to note that the N2 complexity is relevant to the partition size and not the table size. If the number of rows in the table grows by a factor of f but the partition size doesn t change, the run time increases by a factor of f as well. If, on the other hand, the average partition size grows by a factor of f, the run time increases by a factor of f2. With small partitions (say, up to several dozen rows), this set-based solution provides reasonable performance. With large partitions, a cursor solution would be faster despite the overhead associated with row-by-row manipulation because a cursor scans the rows only once, and the per-row overhead is constant.
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