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Commitb706172

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C comment: improve statistics computation comment example
Discussion:https://postgr.es/m/CAKFQuwbD672Sc0EXv0ifx3pzfQ5UAEpiAeaBGKz_Ox-4d2NGCA@mail.gmail.comAuthor: David G. JohnstonBackpatch-through: master
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‎doc/src/sgml/planstats.sgml

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@@ -389,18 +389,20 @@ tablename | null_frac | n_distinct | most_common_vals
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</programlisting>
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In this case there is no <acronym>MCV</acronym> information for
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<structfield>unique2</structfield> because all the values appear to be
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unique, so we use an algorithm that relies only on the number of
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distinct values for both relations together with their null fractions:
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<structname>unique2</structname> and all the values appear to be
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unique (n_distinct = -1), so we use an algorithm that relies on the row
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count estimates for both relations (num_rows, not shown, but "tenk")
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together with the column null fractions (zero for both):
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<programlisting>
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selectivity = (1 - null_frac1) * (1 - null_frac2)* min(1/num_distinct1, 1/num_distinct2)
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selectivity = (1 - null_frac1) * (1 - null_frac2)/ max(num_rows1, num_rows2)
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= (1 - 0) * (1 - 0) / max(10000, 10000)
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= 0.0001
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</programlisting>
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This is, subtract the null fraction from one for each of the relations,
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and divide by the maximum of the numbers of distinct values.
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and divide by the row count of the larger relation (this value does get
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scaled in the non-unique case).
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The number of rows
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that the join is likely to emit is calculated as the cardinality of the
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Cartesian product of the two inputs, multiplied by the

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