.net qr code reader Ordered Covering Nonclustered Index Scan in .NET framework

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Ordered Covering Nonclustered Index Scan
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An ordered covering nonclustered index scan is similar in concept to an ordered clustered index scan, with the former performing the access method in a nonclustered indextypically when covering a query. The cost is of course lower than a clustered index scan because fewer pages are involved. For example, the PK_Orders index on our clustered Orders table happens to cover the following query, even though it might not seem so at first glance: SELECT orderid, orderdate FROM dbo.Orders ORDER BY orderid;
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Keep in mind that on a clustered table, nonclustered indexes will use clustering keys as row locators. In our case, the clustering keys contain the orderdate values, which can be used for covering purposes as well. Also, the first (and, in our case, the only) key column in the nonclustered index is the orderid column, which is the column specified in the ORDER BY clause of the query; therefore, an ordered index scan is a natural access method for the optimizer to choose. Figure 3-29 illustrates this access method, and Figure 3-30 shows the query's execution plan.
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Figure 3-29. Ordered covering nonclustered index scan
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Figure 3-30. Ordered covering nonclustered index scan (execution plan 1)
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Notice in the plan that the Ordered measure for the Index Scan operator in the yellow information box shows True . Here are the performance measures that I got for this query: Logical reads: 2848 Physical reads: 2 Read-ahead reads: 2841 CPU time: 370 ms Elapsed time: 13582 ms Estimated subtree cost: 3.20699 An ordered index scan is not used only when you explicitly request the data sorted; rather, it is also used when the plan uses an operator that can benefit from sorted input data. For example, check out the execution plan shown in Figure 3-31 for the following query: SELECT orderid, custid, empid, orderdate FROM dbo.Orders AS O1 WHERE orderid = (SELECT MAX(orderid) FROM dbo.Orders AS O2 WHERE O2.orderdate = O1.orderdate);
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Figure 3-31. Ordered covering nonclustered index scan (execution plan 2)
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The Segment operator arranges the data in groups and emits a group at a time to the next operator (Top in our case). Our query requests the orders with the maximum orderid per orderdate . Fortunately, we have a covering index for the task (idx_unc_od_oid_i_cid_eid ), with the key columns being (orderdate, orderid ), and included non-key columns are (custid, empid ). I'll elaborate on included non-key columns later in the chapter. The important point for our discussion is that the segment operator organizes the data by groups of orderdate values and emits the data, a group at a time, where the last row in each group is the maximum orderid in the group; because orderid is the second key column right after orderdate . Therefore, there's no need for the plan to sort the data; rather, the plan just collects it with an ordered scan from the covering index, which is already sorted by orderdate and orderid . The Top operator has a simple task of just collecting the last row (TOP 1 descending), which is the row of interest for the group. The number of rows reported by the Top operator is 1491, which is the number of unique groups (orderdate values), each of which got a single row from the operator. Because our nonclustered index covers the query by including in its leaf level all other columns that are mentioned in the query (custid, empid ), there's no need to look up the data rows; the query is satisfied by the index data alone. Here are the performance measures I got for this query: Logical reads: 4720 Physical reads: 3 Read-ahead reads: 4695 CPU time: 781 ms
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Elapsed time: 2128 ms Estimated subtree cost: 4.68047 The number of logical reads that you see is similar to the number of pages that the leaf level of the index holds.
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