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CHOOSING THE RIGHT NUMBER OF BUCKETS
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Recall that 254 buckets is (currently) the maximum number of buckets you can request for a histogram. The histogram will be a frequency histogram when the number of buckets exceeds the number of different values. If a histogram on a column is going to be beneficial, you may as well ask for the maximum bucket count as a first guess at the best number of buckets the marginal cost of the extra buckets (the default is 75) is not significant for the potential benefit you could gain from the extra precision.
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Since I had only 100 different values in the columns, the histograms with 254 buckets actually collapsed to become high-precision frequency histograms with 100 endpoints and an exact picture of the current data content so the optimizer was able to make very good use of them, even in the join. The histograms with 85 buckets were the more common height balanced histograms, and the critical feature was that one of them (in fact the one at the t2 end of the join) did show a popular value. It seems that if You are running 9i or 10g, And you have histograms at both ends of an equality join, And at least one histogram either is a frequency histogram or shows a popular value, then the optimizer has some method (somehow comparing the histogram data to estimate the number of rows and distinct values in each table in the overlap) that makes it possible to allow for joins where there is only a partial overlap in the ranges of values in the columns being joined. Don t take this as a directive to build histograms all over the place, though. It is useful to know, however, so that you can test the effects, when you find those few critical pieces of SQL where you can see a join cardinality going badly wrong.
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CHAPTER 10 JOIN CARDINALITY
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FREQUENCY HISTOGRAMS AND DBMS_STATS
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There is a problem getting a frequency histogram out of the dbms_stats package. I have examples of data sets with only 100 distinct values, but the SQL used by Oracle in 9i and 10g (until 10.2) to generate the histogram failed to build a frequency histogram until I requested 134 buckets in the baseline test.
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There are a couple of side issues to consider. First, 8i also takes advantage of histograms in this situation, but the calculations used must be different because the results are much less accurate unless the histogram is the perfect frequency distribution histogram. Table 10-3, for example, shows the results you get from 8i when using the same 85-bucket histograms as we did previously for 9i and 10g.
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Table 10-3. Histograms in 8i Give Different Join Arithmetic
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t1.join1 Low Value
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100 50 25 10 0 25 50 75 99 100
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T1.join1 High Value
1 49 74 89 99 124 149 174 198 199
Computed Cardinality
1,000,000 147,201 350,499 497618 616,242 447,791 328,109 167,392 41,034 1,000,000
Actual Rows
0 486,318 737,270 895,925 999,920 758,631 513,404 262,170 10,560 0
The other issue is the problem of finding a histogram definition that just happens to work if you can t get a frequency distribution histogram. Basically, it is a question of choosing the right number of buckets to make a popular value visible if there is such a value. The optimizer seems to check for popular values by comparing the number of buckets defined against the number of endpoints stored. If you check view user_tab_histograms for a specific column, then the maximum value for column endpoint_number (assuming you haven t switched from a height balanced histogram to a frequency distribution histogram) will be one less than the number of rows stored if there are no popular values. To show how awkward it can be to find the right number of buckets for a histogram, look at Table 10-4, which was generated from the data used for the tests in join_card_06.sql. It lists a range of bucket counts and shows whether or not each bucket count managed to identify a popular value remember, all I am doing in this test is changing the number of buckets in the
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