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Be a little smarter about deciding how many most-common values to save.

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REL7_2_STABLE
Tom Lane 25 years ago
parent bf9e01d950
commit b67fc0079c
1 changed files with 104 additions and 13 deletions
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  1. 117
      src/backend/commands/analyze.c

117
src/backend/commands/analyze.c
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@ -1,14 +1,14 @@
/*-------------------------------------------------------------------------
*
* analyze.c
* the postgres optimizer analyzer
* the postgres statistics generator
*
* Portions Copyright (c) 1996-2001, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
* $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.18 2001/06/02 19:01:53 tgl Exp $
* $Header: /cvsroot/pgsql/src/backend/commands/analyze.c,v 1.19 2001/06/06 21:29:17 tgl Exp $
*
*-------------------------------------------------------------------------
*/
@ -63,7 +63,7 @@ typedef struct
/* These fields are set up by examine_attribute */
int attnum; /* attribute number */
AlgCode algcode; /* Which algorithm to use for this column */
int minrows; /* Minimum # of rows needed for stats */
int minrows; /* Minimum # of rows wanted for stats */
Form_pg_attribute attr; /* copy of pg_attribute row for column */
Form_pg_type attrtype; /* copy of pg_type row for column */
Oid eqopr; /* '=' operator for datatype, if any */
@ -990,7 +990,9 @@ compute_minimal_stats(VacAttrStats *stats,
* exactly k times in our sample of r rows (from a total of 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.
* nonnull values all appeared but once. (XXX this usually
* results in a drastic overestimate of ndistinct. Can we do
* any better?)
*----------
*/
int f1 = nonnull_cnt - summultiple;
@ -1011,9 +1013,49 @@ compute_minimal_stats(VacAttrStats *stats,
if (stats->stadistinct > 0.1 * totalrows)
stats->stadistinct = - (stats->stadistinct / totalrows);
/* Generate an MCV slot entry, only if we found multiples */
if (nmultiple < num_mcv)
num_mcv = nmultiple;
/*
* Decide how many values are worth storing as most-common values.
* If we are able to generate a complete MCV list (all the values
* in the sample will fit, and we think these are all the ones in
* the table), then do so. Otherwise, store only those values
* that are significantly more common than the (estimated) average.
* We set the threshold rather arbitrarily at 25% more than average,
* with at least 2 instances in the sample.
*/
if (track_cnt < track_max && toowide_cnt == 0 &&
stats->stadistinct > 0 &&
track_cnt <= num_mcv)
{
/* Track list includes all values seen, and all will fit */
num_mcv = track_cnt;
}
else
{
double ndistinct = stats->stadistinct;
double avgcount,
mincount;
if (ndistinct < 0)
ndistinct = - ndistinct * totalrows;
/* estimate # of occurrences in sample of a typical value */
avgcount = (double) numrows / ndistinct;
/* set minimum threshold count to store a value */
mincount = avgcount * 1.25;
if (mincount < 2)
mincount = 2;
if (num_mcv > track_cnt)
num_mcv = track_cnt;
for (i = 0; i < num_mcv; i++)
{
if (track[i].count < mincount)
{
num_mcv = i;
break;
}
}
}
/* Generate MCV slot entry */
if (num_mcv > 0)
{
MemoryContext old_context;
@ -1080,6 +1122,7 @@ compute_scalar_stats(VacAttrStats *stats,
ScalarMCVItem *track;
int track_cnt = 0;
int num_mcv = stats->attr->attstattarget;
int num_bins = stats->attr->attstattarget;
values = (ScalarItem *) palloc(numrows * sizeof(ScalarItem));
tupnoLink = (int *) palloc(numrows * sizeof(int));
@ -1266,10 +1309,57 @@ compute_scalar_stats(VacAttrStats *stats,
if (stats->stadistinct > 0.1 * totalrows)
stats->stadistinct = - (stats->stadistinct / totalrows);
/* Generate an MCV slot entry, only if we found multiples */
if (nmultiple < num_mcv)
num_mcv = nmultiple;
Assert(track_cnt >= num_mcv);
/*
* Decide how many values are worth storing as most-common values.
* If we are able to generate a complete MCV list (all the values
* in the sample will fit, and we think these are all the ones in
* the table), then do so. Otherwise, store only those values
* that are significantly more common than the (estimated) average.
* We set the threshold rather arbitrarily at 25% more than average,
* with at least 2 instances in the sample. Also, we won't suppress
* values that have a frequency of at least 1/K where K is the
* intended number of histogram bins; such values might otherwise
* cause us to emit duplicate histogram bin boundaries.
*/
if (track_cnt == ndistinct && toowide_cnt == 0 &&
stats->stadistinct > 0 &&
track_cnt <= num_mcv)
{
/* Track list includes all values seen, and all will fit */
num_mcv = track_cnt;
}
else
{
double ndistinct = stats->stadistinct;
double avgcount,
mincount,
maxmincount;
if (ndistinct < 0)
ndistinct = - ndistinct * totalrows;
/* estimate # of occurrences in sample of a typical value */
avgcount = (double) numrows / ndistinct;
/* set minimum threshold count to store a value */
mincount = avgcount * 1.25;
if (mincount < 2)
mincount = 2;
/* don't let threshold exceed 1/K, however */
maxmincount = (double) numrows / (double) num_bins;
if (mincount > maxmincount)
mincount = maxmincount;
if (num_mcv > track_cnt)
num_mcv = track_cnt;
for (i = 0; i < num_mcv; i++)
{
if (track[i].count < mincount)
{
num_mcv = i;
break;
}
}
}
/* Generate MCV slot entry */
if (num_mcv > 0)
{
MemoryContext old_context;
@ -1304,8 +1394,8 @@ compute_scalar_stats(VacAttrStats *stats,
* ensures the histogram won't collapse to empty or a singleton.)
*/
num_hist = ndistinct - num_mcv;
if (num_hist > stats->attr->attstattarget)
num_hist = stats->attr->attstattarget + 1;
if (num_hist > num_bins)
num_hist = num_bins + 1;
if (num_hist >= 2)
{
MemoryContext old_context;
@ -1321,6 +1411,7 @@ compute_scalar_stats(VacAttrStats *stats,
*
* Note we destroy the values[] array here... but we don't need
* it for anything more. We do, however, still need values_cnt.
* nvals will be the number of remaining entries in values[].
*/
if (num_mcv > 0)
{

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