mirror of
https://git.postgresql.org/git/postgresql.git
synced 2024-12-27 08:39:28 +08:00
Be a little smarter about deciding how many most-common values to save.
This commit is contained in:
Tom Lane
parent
bf9e01d950
commit
b67fc0079c
@ -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)
|
||||
{
|
||||
|
||||
Loading…
Reference in New Issue
Block a user