.net qr code reader Figure 3-54. Execution plan with covering nonclustered index seek + ordered partial scan in Visual Studio .NET

Draw QR-Code in Visual Studio .NET Figure 3-54. Execution plan with covering nonclustered index seek + ordered partial scan

Figure 3-54. Execution plan with covering nonclustered index seek + ordered partial scan
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And here are the performance measures I got for this query: Logical reads: 9 CPU time: 0 ms Elapsed time: 47 ms Estimated subtree cost: 0.008086 Again, this is a trivial plan. And also here, the performance of the ordered partial scan will vary depending on the fragmentation level of the index. As you can see, the cost of the query dropped from 19.621100 in the lowest level in the scale to 0.008086, and the elapsed time from more than
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16 seconds to 47 milliseconds. Such a drop in run time is common when tuning indexes in an environment with poor index design. When done, drop the last index you created: DROP INDEX dbo.Orders.idx_unc_oid_i_od_cid_eid_sid;
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Index Optimization Scale Summary and Analysis
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Remember that the efficiency of several plans in our index optimization scale was based on the selectivity of the query. If the selectivity of a query you're tuning varies significantly between invocations of the query, make sure that in your tuning process you take this into account. For example, you can prepare tables and graphs with the performance measurements vs. selectivity, and analyze such data before you make your index design choices. For example, Table 3-16 shows a summary of logical reads vs. selectivity of the different levels in the scale for the sample query pattern under discussion against the sample Orders table. Table Scan/Unordered Clustered Index Scan 25,080 25,080 25,080 25,080 25,080 25,080 25,080
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Unordered Covering Nonclustered Index Scan 5,095 5,095 5,095 5,095 5,095 5,095 5,095
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Unordered Covering Nonclustered Index Scan + Lookups 2,855
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5,923 33,486 309,111 615,361 1,534,111 3,065,442
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Nonclustered Index Seek + Ordered Partial Scan + Lookups 6 3,078 30,667 306,547 613,082 1,532,685 3,073,741
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Clustered Index Seek + Ordered Partial Scan 4 29 249 2,447 4,890 12,219 24,433
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Covering Nonclustered Index Seek + Ordered Partial Scan 4 9 54
512 1,021 2,546 5,087
Table 3-16. Logical Reads vs. Selectivity for Each Access Method
Access 1 1,000 Method 0.0001% 0.1% 10,000 1% 100,000 200,000 500,000 1,000,000 rows 10% 20% 50% 100% selectivity
Note
To apply a certain execution plan in a case where the optimizer would normally opt for another plan that is more efficient, I had to use a table hint to force using the relevant index.
Of course, logical reads shouldn't be the only indication you rely on. Remember that different I/O patterns have different performance, and that physical reads are much more expensive than logical reads. But when you see a significant difference in logical reads between two options, it is usually a good indication of which option is faster. Figure 3-55 has a graphical depiction of the information from Table 3-16 .
Figure 3-55. Graph of logical reads vs. selectivity
[View full size image]
You can observe many interesting things when analyzing the graph. For example, you can clearly see which plans are based on selectivity and which aren't. And also, you can see the selectivity point at which one plan becomes better than another. Similarly, Table 3-17 shows summary performance statistics of the query cost vs. selectivity. Table Scan/Unordered Clustered Index Scan 19.621100 19.621100 19.621100 19.621100 19.621100 19.621100 19.621100
Unordered Covering Nonclustered Index Scan 4.863280 4.863280 4.863280 4.863280
4.863280 4.863280 4.863280
Unordered Covering Nonclustered Index Scan + Lookups 3.690270 7.021360 30.665400 96.474000 113.966000 163.240000 244.092000
Nonclustered Index Seek + Ordered Partial Scan + Lookups 0.006570 3.228520 21.921600 100.881000 127.376000 204.329000 335.109000
Clustered Index Seek + Ordered Partial Scan 0.022160 0.022160 0.022160 0.022160 0.022160 0.022160 19.169900
Covering Nonclustered Index Seek + Ordered Partial Scan 0.008086 0.008086 0.008086 0.008086 0.008086 0.008086 4.862540
Table 3-17. Estimated Subtree Costs vs. Selectivity for Each Access Method
Access 1 1,000 Method 0.0001% 0.1% 10,000 1% 100,000 200,000 500,000 1,000,000 rows 10% 20% 50% 100% selectivity
Figure 3-56 shows a graph based on the data in Table 3-17 .
Figure 3-56. Graph of subtree cost vs. selectivity
[View full size image]
You can observe a striking resemblance between the two graphs; but when you think about it, it makes sense because most of the cost involved with our query pattern is because of I/O. Naturally, in plans where a more substantial portion of the cost is related to CPU, you will get different results. Of course, you would also want to generate similar statistics and graphs for the actual run times of the queries in your benchmarks. At the end of the day, run time is what the user cares about. I also find it valuable to visualize performance information in another graphical way as shown in Figure 3-57 .
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