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There is a problem built into the implementation. So far we ve been using well-behaved data, and I m about to change that in one important respect. The nice feature of our data is that the number of distinct values in the join columns has matched up quite well, and the range (low/high values) of the joins columns has also matched up quite nicely. In other words, our two tables are supposed to join properly.
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CHAPTER 10 JOIN CARDINALITY
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Let s see what happens to the join cardinality when the overlap is less convenient. The example (see join_card_06.sql in the online code suite) creates two tables of 10,000 rows each, and then executes a select statement with a single, simple, predicate: t1.join1 = t2.join1 The join1 columns both have 100 distinct values, and at the start of the test their values range from 0 to 99. But we run the test time and time again, rebuilding table t1 each time and changing the low/high values for t1.join1 by a different offset from the t2.join1 value each time so that we can report tests where t1.join1 ranging from 100 to 1, or from 100 to 199. Table 10-1 shows the effects on the cardinality from running a few tests on 9i or 10g. Remember that column t2.join1 is always built with values from 0 to 99.
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Table 10-1. Join Cardinality Problems
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t1.join1 Low Value
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100 50 25 0 25 50 75 99 100
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t1.join1 High Value
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1 49 74 99 124 149 174 198 199
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1 1,000,000 1,000,000 1,000,000 1,000,000 1,000,000 1,000,000 1,000,000 1
Actual Rows
0 486,318 737,270 999,920 758,631 513,404 262,170 10,560 0
Spot the issue until the point where the tables fail to overlap at all, the computed cardinality does not change from assuming that the overlap is 100%. (In fact, 8i is even worse it doesn t even notice that the overlap has disappeared completely and keeps reporting a cardinality of 1,000,000.) If you want a graphic image of what is happening, Figure 10-1 represents the 0, 50, and 100 state of the two tables visually it is obvious that the join is likely to be less successful as the two tables slide past each other, but the arithmetic simply doesn t change. If you are joining two tables with the expectation of eliminating rows because rows in one table are not supposed to exist in the other table, then the optimizer may well produce an inappropriate execution plan because its rules of calculation do not match your knowledge of the data.
CHAPTER 10 JOIN CARDINALITY
Figure 10-1. Join cardinality anomaly
Histograms
I haven t been entirely truthful about overlaps, because sometimes the optimizer will use alternative arithmetic to calculate the join selectivity, and the alternative seems to recognize the effect of the overlapping ranges. Consider the results shown in Table 10-2, again from 9i/10g, based on exactly the same data as we used in the previous section, but with a carefully selected histogram built on the two columns at either end of the join.
Table 10-2. Histograms Helping Join Cardinality
t1.join1 Low Value
100 50 25 22 0 25 50 75 99 100
t1.join1 High Value
1 49 74 79 99 124 149 174 198 199
Computed Cardinality
1,000,000 1,000,000 1,000,000 622,025 1,007,560 767,036 538,364 286,825 1,000,000 (10,615) 1,000,000
Actual Rows
0 486,318 737,270 767,437 999,920 758,631 513,404 262,170 10,560 0
CHAPTER 10 JOIN CARDINALITY
With the histograms in place, I achieved reasonable, though not fantastic, accuracy over a wide range of overlaps. With the histograms I chose initially, the computed cardinality went back to 1,000,000 when the low value on t1 went below 22 or above 77. You will notice that in the 99 row, I have shown a join cardinality of 10,615 in parentheses. When I changed my histogram definitions, the optimizer got accurate figures, as t1.join1 varied all the way from 99 to +99, before flipping back to a computed cardinality of 1,000,000 when t1.join1 hit +/ 100. Histograms look helpful although they do introduce a new problem when the data sets don t overlap at all. So what was special about my final choice of histograms, and why did one set of histogram definitions give better results than another After all, given the way I have been generating my data using the dbms_random.value() procedure, there aren t likely to be any extreme values. The answer is that one set of histograms was built with 85 buckets, the other was built with a nominal 254.
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