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Change patternsel() so that instead of switching from a pure
pattern-examination heuristic method to purely histogram-driven selectivity at histogram size 100, we compute both estimates and use a weighted average. The weight put on the heuristic estimate decreases linearly with histogram size, dropping to zero for 100 or more histogram entries. Likewise in ltreeparentsel(). After a patch by Greg Stark, though I reorganized the logic a bit to give the caller of histogram_selectivity() more control.
This commit is contained in:
Tom Lane
parent
422495d0da
commit
f4230d2937
@ -1,7 +1,7 @@
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/*
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* op function for ltree
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* Teodor Sigaev <teodor@stack.net>
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* $PostgreSQL: pgsql/contrib/ltree/ltree_op.c,v 1.16 2007/02/28 22:44:38 tgl Exp $
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* $PostgreSQL: pgsql/contrib/ltree/ltree_op.c,v 1.17 2008/03/09 00:32:09 tgl Exp $
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*/
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#include "ltree.h"
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@ -609,6 +609,7 @@ ltreeparentsel(PG_FUNCTION_ARGS)
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double mcvsum;
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double mcvsel;
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double nullfrac;
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int hist_size;
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fmgr_info(get_opcode(operator), &contproc);
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@ -626,21 +627,31 @@ ltreeparentsel(PG_FUNCTION_ARGS)
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*/
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selec = histogram_selectivity(&vardata, &contproc,
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constval, varonleft,
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100, 1);
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10, 1, &hist_size);
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if (selec < 0)
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{
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/* Nope, fall back on default */
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selec = DEFAULT_PARENT_SEL;
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}
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else
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else if (hist_size < 100)
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{
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/* Yes, but don't believe extremely small or large estimates. */
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if (selec < 0.0001)
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selec = 0.0001;
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else if (selec > 0.9999)
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selec = 0.9999;
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/*
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* For histogram sizes from 10 to 100, we combine the
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* histogram and default selectivities, putting increasingly
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* more trust in the histogram for larger sizes.
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*/
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double hist_weight = hist_size / 100.0;
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selec = selec * hist_weight +
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DEFAULT_PARENT_SEL * (1.0 - hist_weight);
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}
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/* In any case, don't believe extremely small or large estimates. */
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if (selec < 0.0001)
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selec = 0.0001;
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else if (selec > 0.9999)
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selec = 0.9999;
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if (HeapTupleIsValid(vardata.statsTuple))
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nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
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else
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@ -15,7 +15,7 @@
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*
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*
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* IDENTIFICATION
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* $PostgreSQL: pgsql/src/backend/utils/adt/selfuncs.c,v 1.244 2008/03/08 22:41:38 tgl Exp $
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* $PostgreSQL: pgsql/src/backend/utils/adt/selfuncs.c,v 1.245 2008/03/09 00:32:09 tgl Exp $
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*
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*-------------------------------------------------------------------------
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*/
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@ -567,17 +567,23 @@ mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
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* or not it has anything to do with the histogram sort operator. We are
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* essentially using the histogram just as a representative sample. However,
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* small histograms are unlikely to be all that representative, so the caller
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* should specify a minimum histogram size to use, and fall back on some
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* other approach if this routine fails.
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* should be prepared to fall back on some other estimation approach when the
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* histogram is missing or very small. It may also be prudent to combine this
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* approach with another one when the histogram is small.
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*
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* The caller also specifies n_skip, which causes us to ignore the first and
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* last n_skip histogram elements, on the grounds that they are outliers and
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* hence not very representative. If in doubt, min_hist_size = 100 and
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* n_skip = 1 are reasonable values.
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* If the actual histogram size is not at least min_hist_size, we won't bother
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* to do the calculation at all. Also, if the n_skip parameter is > 0, we
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* ignore the first and last n_skip histogram elements, on the grounds that
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* they are outliers and hence not very representative. Typical values for
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* these parameters are 10 and 1.
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*
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* The function result is the selectivity, or -1 if there is no histogram
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* or it's smaller than min_hist_size.
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*
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* The output parameter *hist_size receives the actual histogram size,
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* or zero if no histogram. Callers may use this number to decide how
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* much faith to put in the function result.
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*
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* Note that the result disregards both the most-common-values (if any) and
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* null entries. The caller is expected to combine this result with
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* statistics for those portions of the column population. It may also be
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@ -586,7 +592,8 @@ mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
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double
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histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
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Datum constval, bool varonleft,
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int min_hist_size, int n_skip)
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int min_hist_size, int n_skip,
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int *hist_size)
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{
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double result;
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Datum *values;
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@ -603,6 +610,7 @@ histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
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&values, &nvalues,
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NULL, NULL))
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{
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*hist_size = nvalues;
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if (nvalues >= min_hist_size)
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{
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int nmatch = 0;
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@ -626,7 +634,10 @@ histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
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free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
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}
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else
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{
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*hist_size = 0;
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result = -1;
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}
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return result;
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}
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@ -1117,13 +1128,16 @@ patternsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
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* selectivity of the fixed prefix and remainder of pattern
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* separately, then combine the two to get an estimate of the
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* selectivity for the part of the column population represented by
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* the histogram. We then add up data for any most-common-values
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* values; these are not in the histogram population, and we can get
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* exact answers for them by applying the pattern operator, so there's
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* no reason to approximate. (If the MCVs cover a significant part of
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* the total population, this gives us a big leg up in accuracy.)
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* the histogram. (For small histograms, we combine these approaches.)
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*
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* We then add up data for any most-common-values values; these are
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* not in the histogram population, and we can get exact answers for
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* them by applying the pattern operator, so there's no reason to
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* approximate. (If the MCVs cover a significant part of the total
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* population, this gives us a big leg up in accuracy.)
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*/
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Selectivity selec;
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int hist_size;
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FmgrInfo opproc;
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double nullfrac,
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mcv_selec,
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@ -1133,10 +1147,12 @@ patternsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
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fmgr_info(get_opcode(operator), &opproc);
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selec = histogram_selectivity(&vardata, &opproc, constval, true,
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100, 1);
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if (selec < 0)
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10, 1, &hist_size);
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/* If not at least 100 entries, use the heuristic method */
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if (hist_size < 100)
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{
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/* Nope, so fake it with the heuristic method */
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Selectivity heursel;
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Selectivity prefixsel;
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Selectivity restsel;
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@ -1146,17 +1162,29 @@ patternsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
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else
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prefixsel = 1.0;
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restsel = pattern_selectivity(rest, ptype);
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selec = prefixsel * restsel;
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}
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else
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{
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/* Yes, but don't believe extremely small or large estimates. */
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if (selec < 0.0001)
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selec = 0.0001;
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else if (selec > 0.9999)
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selec = 0.9999;
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heursel = prefixsel * restsel;
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if (selec < 0) /* fewer than 10 histogram entries? */
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selec = heursel;
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else
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{
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/*
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* For histogram sizes from 10 to 100, we combine the
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* histogram and heuristic selectivities, putting increasingly
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* more trust in the histogram for larger sizes.
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*/
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double hist_weight = hist_size / 100.0;
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selec = selec * hist_weight + heursel * (1.0 - hist_weight);
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}
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}
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/* In any case, don't believe extremely small or large estimates. */
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if (selec < 0.0001)
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selec = 0.0001;
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else if (selec > 0.9999)
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selec = 0.9999;
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/*
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* If we have most-common-values info, add up the fractions of the MCV
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* entries that satisfy MCV OP PATTERN. These fractions contribute
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@ -8,7 +8,7 @@
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* Portions Copyright (c) 1996-2008, PostgreSQL Global Development Group
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* Portions Copyright (c) 1994, Regents of the University of California
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*
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* $PostgreSQL: pgsql/src/include/utils/selfuncs.h,v 1.43 2008/01/01 19:45:59 momjian Exp $
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* $PostgreSQL: pgsql/src/include/utils/selfuncs.h,v 1.44 2008/03/09 00:32:09 tgl Exp $
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*
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*-------------------------------------------------------------------------
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*/
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@ -112,7 +112,8 @@ extern double mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
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double *sumcommonp);
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extern double histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
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Datum constval, bool varonleft,
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int min_hist_size, int n_skip);
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int min_hist_size, int n_skip,
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int *hist_size);
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extern Pattern_Prefix_Status pattern_fixed_prefix(Const *patt,
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Pattern_Type ptype,
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