/*
* op function for ltree
* Teodor Sigaev <teodor@stack.net>
- * $PostgreSQL: pgsql/contrib/ltree/ltree_op.c,v 1.16 2007/02/28 22:44:38 tgl Exp $
+ * $PostgreSQL: pgsql/contrib/ltree/ltree_op.c,v 1.17 2008/03/09 00:32:09 tgl Exp $
*/
#include "ltree.h"
double mcvsum;
double mcvsel;
double nullfrac;
+ int hist_size;
fmgr_info(get_opcode(operator), &contproc);
*/
selec = histogram_selectivity(&vardata, &contproc,
constval, varonleft,
- 100, 1);
+ 10, 1, &hist_size);
if (selec < 0)
{
/* Nope, fall back on default */
selec = DEFAULT_PARENT_SEL;
}
- else
+ else if (hist_size < 100)
{
- /* Yes, but don't believe extremely small or large estimates. */
- if (selec < 0.0001)
- selec = 0.0001;
- else if (selec > 0.9999)
- selec = 0.9999;
+ /*
+ * For histogram sizes from 10 to 100, we combine the
+ * histogram and default selectivities, putting increasingly
+ * more trust in the histogram for larger sizes.
+ */
+ double hist_weight = hist_size / 100.0;
+
+ selec = selec * hist_weight +
+ DEFAULT_PARENT_SEL * (1.0 - hist_weight);
}
+ /* In any case, don't believe extremely small or large estimates. */
+ if (selec < 0.0001)
+ selec = 0.0001;
+ else if (selec > 0.9999)
+ selec = 0.9999;
+
if (HeapTupleIsValid(vardata.statsTuple))
nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
else
*
*
* IDENTIFICATION
- * $PostgreSQL: pgsql/src/backend/utils/adt/selfuncs.c,v 1.244 2008/03/08 22:41:38 tgl Exp $
+ * $PostgreSQL: pgsql/src/backend/utils/adt/selfuncs.c,v 1.245 2008/03/09 00:32:09 tgl Exp $
*
*-------------------------------------------------------------------------
*/
* or not it has anything to do with the histogram sort operator. We are
* essentially using the histogram just as a representative sample. However,
* small histograms are unlikely to be all that representative, so the caller
- * should specify a minimum histogram size to use, and fall back on some
- * other approach if this routine fails.
+ * should be prepared to fall back on some other estimation approach when the
+ * histogram is missing or very small. It may also be prudent to combine this
+ * approach with another one when the histogram is small.
*
- * The caller also specifies n_skip, which causes us to ignore the first and
- * last n_skip histogram elements, on the grounds that they are outliers and
- * hence not very representative. If in doubt, min_hist_size = 100 and
- * n_skip = 1 are reasonable values.
+ * If the actual histogram size is not at least min_hist_size, we won't bother
+ * to do the calculation at all. Also, if the n_skip parameter is > 0, we
+ * ignore the first and last n_skip histogram elements, on the grounds that
+ * they are outliers and hence not very representative. Typical values for
+ * these parameters are 10 and 1.
*
* The function result is the selectivity, or -1 if there is no histogram
* or it's smaller than min_hist_size.
*
+ * The output parameter *hist_size receives the actual histogram size,
+ * or zero if no histogram. Callers may use this number to decide how
+ * much faith to put in the function result.
+ *
* Note that the result disregards both the most-common-values (if any) and
* null entries. The caller is expected to combine this result with
* statistics for those portions of the column population. It may also be
double
histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
Datum constval, bool varonleft,
- int min_hist_size, int n_skip)
+ int min_hist_size, int n_skip,
+ int *hist_size)
{
double result;
Datum *values;
&values, &nvalues,
NULL, NULL))
{
+ *hist_size = nvalues;
if (nvalues >= min_hist_size)
{
int nmatch = 0;
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
else
+ {
+ *hist_size = 0;
result = -1;
+ }
return result;
}
* selectivity of the fixed prefix and remainder of pattern
* separately, then combine the two to get an estimate of the
* selectivity for the part of the column population represented by
- * the histogram. We then add up data for any most-common-values
- * values; these are not in the histogram population, and we can get
- * exact answers for them by applying the pattern operator, so there's
- * no reason to approximate. (If the MCVs cover a significant part of
- * the total population, this gives us a big leg up in accuracy.)
+ * the histogram. (For small histograms, we combine these approaches.)
+ *
+ * We then add up data for any most-common-values values; these are
+ * not in the histogram population, and we can get exact answers for
+ * them by applying the pattern operator, so there's no reason to
+ * approximate. (If the MCVs cover a significant part of the total
+ * population, this gives us a big leg up in accuracy.)
*/
Selectivity selec;
+ int hist_size;
FmgrInfo opproc;
double nullfrac,
mcv_selec,
fmgr_info(get_opcode(operator), &opproc);
selec = histogram_selectivity(&vardata, &opproc, constval, true,
- 100, 1);
- if (selec < 0)
+ 10, 1, &hist_size);
+
+ /* If not at least 100 entries, use the heuristic method */
+ if (hist_size < 100)
{
- /* Nope, so fake it with the heuristic method */
+ Selectivity heursel;
Selectivity prefixsel;
Selectivity restsel;
else
prefixsel = 1.0;
restsel = pattern_selectivity(rest, ptype);
- selec = prefixsel * restsel;
- }
- else
- {
- /* Yes, but don't believe extremely small or large estimates. */
- if (selec < 0.0001)
- selec = 0.0001;
- else if (selec > 0.9999)
- selec = 0.9999;
+ heursel = prefixsel * restsel;
+
+ if (selec < 0) /* fewer than 10 histogram entries? */
+ selec = heursel;
+ else
+ {
+ /*
+ * For histogram sizes from 10 to 100, we combine the
+ * histogram and heuristic selectivities, putting increasingly
+ * more trust in the histogram for larger sizes.
+ */
+ double hist_weight = hist_size / 100.0;
+
+ selec = selec * hist_weight + heursel * (1.0 - hist_weight);
+ }
}
+ /* In any case, don't believe extremely small or large estimates. */
+ if (selec < 0.0001)
+ selec = 0.0001;
+ else if (selec > 0.9999)
+ selec = 0.9999;
+
/*
* If we have most-common-values info, add up the fractions of the MCV
* entries that satisfy MCV OP PATTERN. These fractions contribute
projects / postgresql / commitdiff