java code 39 reader TABLESCANS in Java

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CHAPTER 2 TABLESCANS
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where and and ; select /*+ cpu_costing ordered_predicates */ v1, n2, n1 from t1 where and and ; v1 = '1' n2 = 18 n1 = 998 n1 = 998 n2 = 18 v1 = 1
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As you would expect, the execution plan in all three cases is a full tablescan, and if you use autotrace to see what s going on, you will discover only that the cost of the query (in all three cases) is 6. But if you use a proper query against the plan_table, reporting the columns cpu_cost and filter_predicates (yet another column that appeared in 9i), you will see the results summarized in Table 2-4.
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Table 2-4. Predicate Order Can Affect the Cost
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Predicate Order
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v1 = 1 and n2 = 18 and n1 = 998 n1 = 998 and n2 = 18 and v1 = 1 v1 = '1' and n2 = 18 and n1 = 998
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CPU Cost
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1,070,604
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filter_predicates
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TO_NUMBER("T1"."V1")=1 AND "T1"."N2"=18 AND "T1"."N1"=998
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762,787
"T1"."N1"=998 AND "T1"."N2"=18 AND TO_NUMBER("T1"."V1")=1
770,604
"T1"."V1"='1' AND "T1"."N2"=18 AND "T1"."N1"=998
It is the column filter_predicates that tells you exactly what is going on, and why the CPU cost can change even though the plan structure is the same. Based on the information it has about the different columns, including the low, high, number of distinct values, and so on, the optimizer in 9i is capable of working out (to a reasonable degree of accuracy) that the first query will require the following operations:
CHAPTER 2 TABLESCANS
Convert column v1 to a number 3,000 times, and compare to produce 150 rows. For those 150 rows, compare n2 with a number to produce 8 (technically 7.5) rows. For those 8 rows, compare n1 with a number. That s 3,000 coercions and 3,158 numeric comparisons. On the other hand, the second query will require the following: Compare n1 to a numeric 3,000 times to produce just one row (probably). For that one row, compare n2 with a number to produce just one row (probably). For that one row, coerce v1 to a number and compare. That s 3,002 numeric comparisons and a single coercion which is a big saving in CPU. And if you take the ordered_predicates hint out of the first query, the optimizer automatically chooses to rearrange the predicates to match the order of the second query. (The default setting of true for parameter _pred_move_around gives you a hint that this can happen.) Just as a final detail, the third query repeats the predicate order of the first query, but uses the correct string comparison, instead of an implicit conversion. Eliminate those 3,000 conversions and the CPU cost of the query drops from 1,070,604 to 770,604, a difference of exactly 300,000. This tends to suggest that the to_number() function has been given a cost of 100 CPU operations.
The BCHR Is Dead! Long Live the BCHR!
The next big change isn t in production yet but the signs are there in 10g that something dramatic is still waiting in the wings. (The section heading isn t intended to be serious, by the way.) It has become common knowledge over the last few years that the buffer cache hit ratio (BCHR) isn t a useful performance target although plotting trend lines from regular snapshots can give you important clues that something is changing, or that a performance anomaly occurred at some point in time. One of the biggest problems of the buffer cache hit ratio is that it is a system-wide average, and one statistically bizarre object (or query) can render the value completely meaningless. (For a demonstration of this fact, you can visit Connor McDonald s web site, www.oracledba.co.uk, to download the Set Your Hit Ratio utility). But what if you track the cache hit ratio for every individual object in the cache updating the figures every 30 minutes, maintaining rolling averages and trend values Could you do anything useful with such a precisely targeted data collection One of the defects in early versions of the CBO is that the arithmetic works on the basis that every block visit goes to a data block that has never been visited before and therefore turns into a physical disk read. There were a couple of parameters introduced in 8i to work around the problems this caused (optimizer_index_cost_adj which you will meet in 4, and optimizer_index_caching which you will also see in 4, and again in 11). But in 9i, Oracle collects statistics about logical block requests and physical disk reads for every data segment. In fact, Oracle seems to have several ways of collecting cache-related statistics. 9i introduced the dynamic performance view v$segstat, and 10g expanded the range of statistics the
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