/*-------------------------------------------------------------------------
*
* 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 $
*
*-------------------------------------------------------------------------
*/
/* 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 */
* 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;
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;
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));
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;
* 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;
*
* 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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