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Replace number-of-distinct-values estimator equation, per recent

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pghackers discussion.
This commit is contained in:
Tom Lane 2002-02-18 16:04:14 +00:00
parent b4a5fa4518
commit e4dd067398
1 changed files with 43 additions and 19 deletions
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62
src/backend/commands/analyze.c
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@ -8,7 +8,7 @@
*
*
* IDENTIFICATION
* $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.25 2002/01/06 00:37:44 tgl Exp $
* $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.26 2002/02/18 16:04:14 tgl Exp $
*
*-------------------------------------------------------------------------
*/
@ -1009,10 +1009,15 @@ compute_minimal_stats(VacAttrStats *stats,
{
/*----------
* Estimate the number of distinct values using the estimator
* proposed by Chaudhuri et al (see citation above). This is
* sqrt(n/r) * max(f1,1) + f2 + f3 + ...
* where fk is the number of distinct values that occurred
* exactly k times in our sample of r rows (from a total of n).
* proposed by Haas and Stokes in IBM Research Report RJ 10025:
* n*d / (n - f1 + f1*n/N)
* where f1 is the number of distinct values that occurred
* exactly once in our sample of n rows (from a total of N),
* and d is the total number of distinct values in the sample.
* This is their Duj1 estimator; the other estimators they
* recommend are considerably more complex, and are numerically
* very unstable when n is much smaller than N.
*
* We assume (not very reliably!) that all the multiply-occurring
* values are reflected in the final track[] list, and the other
* nonnull values all appeared but once. (XXX this usually
@ -1021,12 +1026,19 @@ compute_minimal_stats(VacAttrStats *stats,
*----------
*/
int f1 = nonnull_cnt - summultiple;
double term1;
int d = f1 + nmultiple;
double numer, denom, stadistinct;
if (f1 < 1)
f1 = 1;
term1 = sqrt(totalrows / (double) numrows) * f1;
stats->stadistinct = floor(term1 + nmultiple + 0.5);
numer = (double) numrows * (double) d;
denom = (double) (numrows - f1) +
(double) f1 * (double) numrows / totalrows;
stadistinct = numer / denom;
/* Clamp to sane range in case of roundoff error */
if (stadistinct < (double) d)
stadistinct = (double) d;
if (stadistinct > totalrows)
stadistinct = totalrows;
stats->stadistinct = floor(stadistinct + 0.5);
}
/*
@ -1313,20 +1325,32 @@ compute_scalar_stats(VacAttrStats *stats,
{
/*----------
* Estimate the number of distinct values using the estimator
* proposed by Chaudhuri et al (see citation above). This is
* sqrt(n/r) * max(f1,1) + f2 + f3 + ...
* where fk is the number of distinct values that occurred
* exactly k times in our sample of r rows (from a total of n).
* proposed by Haas and Stokes in IBM Research Report RJ 10025:
* n*d / (n - f1 + f1*n/N)
* where f1 is the number of distinct values that occurred
* exactly once in our sample of n rows (from a total of N),
* and d is the total number of distinct values in the sample.
* This is their Duj1 estimator; the other estimators they
* recommend are considerably more complex, and are numerically
* very unstable when n is much smaller than N.
*
* Overwidth values are assumed to have been distinct.
*----------
*/
int f1 = ndistinct - nmultiple + toowide_cnt;
double term1;
int d = f1 + nmultiple;
double numer, denom, stadistinct;
if (f1 < 1)
f1 = 1;
term1 = sqrt(totalrows / (double) numrows) * f1;
stats->stadistinct = floor(term1 + nmultiple + 0.5);
numer = (double) numrows * (double) d;
denom = (double) (numrows - f1) +
(double) f1 * (double) numrows / totalrows;
stadistinct = numer / denom;
/* Clamp to sane range in case of roundoff error */
if (stadistinct < (double) d)
stadistinct = (double) d;
if (stadistinct > totalrows)
stadistinct = totalrows;
stats->stadistinct = floor(stadistinct + 0.5);
}
/*