.net qr code reader Figure 3-48. Execution plan with unordered covering nonclustered index scan in VS .NET

Print QR-Code in VS .NET Figure 3-48. Execution plan with unordered covering nonclustered index scan

Figure 3-48. Execution plan with unordered covering nonclustered index scan
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The row size in the covering index is about a fifth of the size of a full data row, and this would be reflected in the query's cost and run time. Here are the performance measures I got for this query: Logical reads: 5095 CPU time: 170 ms Elapsed time: 1128 ms Estimated subtree cost: 4.86328 As with the previous plan, this plan is also trivial and not dependent on selectivity.
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The run times you will get for your queries will vary based on what portion of the data is cached. If you want to make credible performance comparisons in terms of run times, make sure that the caching environment in both cases reflect what you would have in your production environment. That is, if you expect most pages to reside in cache in your production environment (warm cache), run each query twice, and measure the run time of the second run. If you expect most pages not to reside in cache (cold cache), in your tests clear the cache before you run each query.
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Before you proceed, drop the index that you just created: DROP INDEX dbo.Orders.idx_nc_od_i_oid_cid_eid_sid;
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Unordered Nonclustered Index Scan + Lookups
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The next step in our index optimization scale is to create a smaller nonclustered index that would not cover the query and that contains the filtered column (orderid ), but not as the first key column: CREATE NONCLUSTERED INDEX idx_nc_od_i_oid ON dbo.Orders(orderdate) INCLUDE(orderid);
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You would get an unordered nonclustered index scan + lookups as shown in Figure 3-49 :
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Figure 3-49. Execution plan with unordered nonclustered index scan + lookups
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Note that the efficiency of this plan compared to the previous one will depend on the selectivity of the query. As the selectivity of the query grows larger, the more substantial the cost is of the lookups here. In our case, the query is fairly selective, so this plan is more efficient than the previous two; however, with low selectivity, this plan will be less efficient than the previous two. Here are the performance measures that I got for this query: Logical reads: 5923 CPU time: 100 ms Elapsed time: 379 ms Estimated subtree cost: 7.02136 Note that even though the number of logical reads and the query cost seem higher than in the previous plan, you can see that the run times are lower. Remember that the lookup operations here traverse the clustered index, and the nonleaf levels of the clustered index are most likely to reside in cache. Before you continue, drop the new index: DROP INDEX dbo.Orders.idx_nc_od_i_oid;
Nonclustered Index Seek + Ordered Partial Scan + Lookups
You can get the next level of optimization in the scale by creating a nonclustered noncovering index on orderid : CREATE UNIQUE NONCLUSTERED INDEX idx_unc_oid ON dbo.Orders(orderid);
This index would yield a nonclustered index seek + ordered partial scan + lookups as shown in Figure 3-50 .
Figure 3-50. Execution plan with nonclustered index seek + ordered partial scan + lookups
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Instead of performing the full index scan as the previous plan did, this plan performs a seek to the first key in the sought range, followed by an ordered partial scan of only the relevant range. Still, you get as many lookups as previously, which in our case amounts to a big chunk of the query cost. As the range grows larger, the contribution of the lookups to the query's cost becomes more substantial, and the costs of these two plans would become closer and closer. Here are the performance measures for this query: Logical reads: 3077 CPU time: 0 ms Elapsed time: 53 ms Estimated subtree cost: 3.22852
Determining the Selectivity Point
Allow me to digress a bit to expand on a subject I started discussing earlierplans that are dependent on the selectivity of the query. The efficiency of the last plan is dependent on selectivity because you get one whole lookup per sought row. At some selectivity point, the optimizer would realize that a table scan is more efficient than using this plan. You might find it surprising, but that selectivity point is a pretty small percentage. Even if you have no clue about how to calculate this point, you can practice a trial-and-error approach, where you apply a binary algorithm, shifting the selectivity point to the left or right based on the plan that you get. Remember that high selectivity means a low percentage of rows, so going to the left of a selectivity point (lowering the percentage) would mean getting higher selectivity. You can invoke a range query, where you start with 50 percent selectivity by invoking the following query: SELECT orderid, custid, empid, shipperid, orderdate FROM dbo.Orders WHERE orderid >= 500001;
Examine the estimated (no need for actual here) execution plan, and determine whether to proceed in the next step to the left or to the right of this point, based on whether you got a table scan
(clustered index scan) or an index seek. With the median key, you get the plan shown in Figure 3-51 , showing a table scan:
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