Improve patternsel() by applying the operator itself to each value
listed in the column's most-common-values statistics entry. This gives us an exact selectivity result for the portion of the column population represented by the MCV list, which can be a big leg up in accuracy if that's a large fraction of the population. The heuristics involving pattern contents and prefix are applied only to the part of the population not included in the MCV list.
This commit is contained in:
Tom Lane
parent
ad24b8e6ee
commit
ce8fd39e15
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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 1.194 2005/11/25 19:47:49 tgl Exp $
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* $PostgreSQL: pgsql/src/backend/utils/adt/selfuncs.c,v 1.195 2006/01/10 17:35:52 tgl Exp $
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*
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*-------------------------------------------------------------------------
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*/
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@ -131,6 +131,11 @@ typedef struct
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} while(0)
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static double mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
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Datum constval, double *sumcommonp);
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static double ineq_histogram_selectivity(VariableStatData *vardata,
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FmgrInfo *opproc, bool isgt,
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Datum constval, Oid consttype);
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static bool convert_to_scalar(Datum value, Oid valuetypid, double *scaledvalue,
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Datum lobound, Datum hibound, Oid boundstypid,
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double *scaledlobound, double *scaledhibound);
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@ -164,8 +169,8 @@ static void examine_variable(PlannerInfo *root, Node *node, int varRelid,
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static double get_variable_numdistinct(VariableStatData *vardata);
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static bool get_variable_maximum(PlannerInfo *root, VariableStatData *vardata,
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Oid sortop, Datum *max);
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static Selectivity prefix_selectivity(PlannerInfo *root, Node *variable,
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Oid opclass, Const *prefix);
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static Selectivity prefix_selectivity(VariableStatData *vardata,
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Oid opclass, Const *prefixcon);
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static Selectivity pattern_selectivity(Const *patt, Pattern_Type ptype);
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static Datum string_to_datum(const char *str, Oid datatype);
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static Const *string_to_const(const char *str, Oid datatype);
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@ -426,15 +431,10 @@ scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
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{
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Form_pg_statistic stats;
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FmgrInfo opproc;
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Datum *values;
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int nvalues;
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float4 *numbers;
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int nnumbers;
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double mcv_selec,
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hist_selec,
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sumcommon;
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double selec;
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int i;
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if (!HeapTupleIsValid(vardata->statsTuple))
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{
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@ -451,10 +451,76 @@ scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
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* to the result selectivity. Also add up the total fraction represented
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* by MCV entries.
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*/
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mcv_selec = mcv_selectivity(vardata, &opproc, constval,
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&sumcommon);
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/*
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* If there is a histogram, determine which bin the constant falls in, and
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* compute the resulting contribution to selectivity.
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*/
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hist_selec = ineq_histogram_selectivity(vardata, &opproc, isgt,
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constval, consttype);
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/*
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* Now merge the results from the MCV and histogram calculations,
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* realizing that the histogram covers only the non-null values that are
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* not listed in MCV.
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*/
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selec = 1.0 - stats->stanullfrac - sumcommon;
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if (hist_selec > 0.0)
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selec *= hist_selec;
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else
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{
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/*
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* If no histogram but there are values not accounted for by MCV,
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* arbitrarily assume half of them will match.
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*/
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selec *= 0.5;
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}
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selec += mcv_selec;
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/* result should be in range, but make sure... */
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CLAMP_PROBABILITY(selec);
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return selec;
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}
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/*
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* mcv_selectivity - Examine the MCV list for scalarineqsel
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*
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* Determine the fraction of the variable's MCV population that satisfies
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* the predicate (VAR OP CONST), as well as the fraction of the total column
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* population represented by the MCV list. This code will work for any
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* boolean-returning predicate operator.
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*
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* The function result is the MCV selectivity, and the fraction of the
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* total population is returned into *sumcommonp. Zeroes are returned
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* if there is no MCV list.
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*/
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static double
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mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc, Datum constval,
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double *sumcommonp)
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{
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double mcv_selec,
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sumcommon;
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Datum *values;
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int nvalues;
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float4 *numbers;
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int nnumbers;
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int i;
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mcv_selec = 0.0;
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sumcommon = 0.0;
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if (get_attstatsslot(vardata->statsTuple,
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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 CONST. Also add up the total fraction
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* represented by MCV entries.
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*/
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if (HeapTupleIsValid(vardata->statsTuple) &&
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get_attstatsslot(vardata->statsTuple,
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vardata->atttype, vardata->atttypmod,
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STATISTIC_KIND_MCV, InvalidOid,
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&values, &nvalues,
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@ -462,7 +528,7 @@ scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
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{
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for (i = 0; i < nvalues; i++)
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{
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if (DatumGetBool(FunctionCall2(&opproc,
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if (DatumGetBool(FunctionCall2(opproc,
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values[i],
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constval)))
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mcv_selec += numbers[i];
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@ -472,10 +538,36 @@ scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
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numbers, nnumbers);
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}
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*sumcommonp = sumcommon;
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return mcv_selec;
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}
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/*
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* ineq_histogram_selectivity - Examine the histogram for scalarineqsel
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*
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* Determine the fraction of the variable's histogram population that
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* satisfies the inequality condition, ie, VAR < CONST or VAR > CONST.
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*
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* Returns zero if there is no histogram (valid results will always be
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* greater than zero).
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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.
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*/
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static double
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ineq_histogram_selectivity(VariableStatData *vardata,
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FmgrInfo *opproc, bool isgt,
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Datum constval, Oid consttype)
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{
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double hist_selec;
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Datum *values;
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int nvalues;
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int i;
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hist_selec = 0.0;
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/*
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* If there is a histogram, determine which bin the constant falls in, and
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* compute the resulting contribution to selectivity.
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*
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* Someday, VACUUM might store more than one histogram per rel/att,
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* corresponding to more than one possible sort ordering defined for the
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* column type. However, to make that work we will need to figure out
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@ -485,9 +577,8 @@ scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
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* appears in pg_statistic is sorted the same way our operator sorts, or
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* the reverse way if isgt is TRUE.
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*/
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hist_selec = 0.0;
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if (get_attstatsslot(vardata->statsTuple,
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if (HeapTupleIsValid(vardata->statsTuple) &&
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get_attstatsslot(vardata->statsTuple,
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vardata->atttype, vardata->atttypmod,
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STATISTIC_KIND_HISTOGRAM, InvalidOid,
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&values, &nvalues,
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@ -498,7 +589,7 @@ scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
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double histfrac;
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bool ltcmp;
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ltcmp = DatumGetBool(FunctionCall2(&opproc,
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ltcmp = DatumGetBool(FunctionCall2(opproc,
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values[0],
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constval));
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if (isgt)
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@ -517,7 +608,7 @@ scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
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*/
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for (i = 1; i < nvalues; i++)
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{
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ltcmp = DatumGetBool(FunctionCall2(&opproc,
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ltcmp = DatumGetBool(FunctionCall2(opproc,
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values[i],
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constval));
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if (isgt)
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@ -618,30 +709,7 @@ scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
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free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
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}
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/*
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* Now merge the results from the MCV and histogram calculations,
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* realizing that the histogram covers only the non-null values that are
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* not listed in MCV.
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*/
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selec = 1.0 - stats->stanullfrac - sumcommon;
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if (hist_selec > 0.0)
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selec *= hist_selec;
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else
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{
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/*
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* If no histogram but there are values not accounted for by MCV,
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* arbitrarily assume half of them will match.
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*/
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selec *= 0.5;
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}
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selec += mcv_selec;
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/* result should be in range, but make sure... */
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CLAMP_PROBABILITY(selec);
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return selec;
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return hist_selec;
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}
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/*
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@ -801,10 +869,7 @@ static double
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patternsel(PG_FUNCTION_ARGS, Pattern_Type ptype)
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{
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PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
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#ifdef NOT_USED
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Oid operator = PG_GETARG_OID(1);
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#endif
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List *args = (List *) PG_GETARG_POINTER(2);
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int varRelid = PG_GETARG_INT32(3);
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VariableStatData vardata;
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@ -948,18 +1013,53 @@ patternsel(PG_FUNCTION_ARGS, Pattern_Type ptype)
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{
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/*
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* Not exact-match pattern. We estimate selectivity of the fixed
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* prefix and remainder of pattern separately, then combine the two.
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* prefix and remainder of pattern separately, then combine the two
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* to get an estimate of the selectivity for the part of the column
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* population represented by the histogram. We then add up data for
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* any most-common-values values; these are not in the histogram
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* population, and we can get exact answers for them by applying
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* the pattern operator, so there's no reason to approximate.
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* (If the MCVs cover a significant part of the total population,
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* this gives us a big leg up in accuracy.)
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*/
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Selectivity prefixsel;
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Selectivity restsel;
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Selectivity selec;
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FmgrInfo opproc;
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double nullfrac,
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mcv_selec,
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sumcommon;
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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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nullfrac = 0.0;
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if (pstatus == Pattern_Prefix_Partial)
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prefixsel = prefix_selectivity(root, variable, opclass, prefix);
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prefixsel = prefix_selectivity(&vardata, opclass, prefix);
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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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* 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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* directly to the result selectivity. Also add up the total fraction
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* represented by MCV entries.
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*/
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fmgr_info(get_opcode(operator), &opproc);
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mcv_selec = mcv_selectivity(&vardata, &opproc, constval,
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&sumcommon);
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/*
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* Now merge the results from the MCV and histogram calculations,
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* realizing that the histogram covers only the non-null values that
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* are not listed in MCV.
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*/
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selec *= 1.0 - nullfrac - sumcommon;
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selec += mcv_selec;
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/* result should be in range, but make sure... */
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CLAMP_PROBABILITY(selec);
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result = selec;
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@ -2427,7 +2527,7 @@ estimate_hash_bucketsize(PlannerInfo *root, Node *hashkey, double nbuckets)
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/*
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* convert_to_scalar
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* Convert non-NULL values of the indicated types to the comparison
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* scale needed by scalarltsel()/scalargtsel().
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* scale needed by scalarineqsel().
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* Returns "true" if successful.
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*
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* XXX this routine is a hack: ideally we should look up the conversion
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@ -3841,6 +3941,10 @@ pattern_fixed_prefix(Const *patt, Pattern_Type ptype,
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* A fixed prefix "foo" is estimated as the selectivity of the expression
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* "variable >= 'foo' AND variable < 'fop'" (see also indxpath.c).
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*
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* The selectivity estimate is with respect to the portion of the column
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* population represented by the histogram --- the caller must fold this
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* together with info about MCVs and NULLs.
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*
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* We use the >= and < operators from the specified btree opclass to do the
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* estimation. The given variable and Const must be of the associated
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* datatype.
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@ -3851,25 +3955,28 @@ pattern_fixed_prefix(Const *patt, Pattern_Type ptype,
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* more useful to use the upper-bound code than not.
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*/
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static Selectivity
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prefix_selectivity(PlannerInfo *root, Node *variable,
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Oid opclass, Const *prefixcon)
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prefix_selectivity(VariableStatData *vardata, Oid opclass, Const *prefixcon)
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{
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Selectivity prefixsel;
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Oid cmpopr;
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List *cmpargs;
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FmgrInfo opproc;
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Const *greaterstrcon;
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cmpopr = get_opclass_member(opclass, InvalidOid,
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BTGreaterEqualStrategyNumber);
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if (cmpopr == InvalidOid)
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elog(ERROR, "no >= operator for opclass %u", opclass);
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cmpargs = list_make2(variable, prefixcon);
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/* Assume scalargtsel is appropriate for all supported types */
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prefixsel = DatumGetFloat8(DirectFunctionCall4(scalargtsel,
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PointerGetDatum(root),
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ObjectIdGetDatum(cmpopr),
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PointerGetDatum(cmpargs),
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Int32GetDatum(0)));
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fmgr_info(get_opcode(cmpopr), &opproc);
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prefixsel = ineq_histogram_selectivity(vardata, &opproc, true,
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prefixcon->constvalue,
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prefixcon->consttype);
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if (prefixsel <= 0.0)
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{
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/* No histogram is present ... return a suitable default estimate */
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return 0.005;
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}
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/*-------
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* If we can create a string larger than the prefix, say
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@ -3885,49 +3992,30 @@ prefix_selectivity(PlannerInfo *root, Node *variable,
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BTLessStrategyNumber);
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if (cmpopr == InvalidOid)
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elog(ERROR, "no < operator for opclass %u", opclass);
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cmpargs = list_make2(variable, greaterstrcon);
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/* Assume scalarltsel is appropriate for all supported types */
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topsel = DatumGetFloat8(DirectFunctionCall4(scalarltsel,
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PointerGetDatum(root),
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ObjectIdGetDatum(cmpopr),
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PointerGetDatum(cmpargs),
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Int32GetDatum(0)));
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fmgr_info(get_opcode(cmpopr), &opproc);
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topsel = ineq_histogram_selectivity(vardata, &opproc, false,
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greaterstrcon->constvalue,
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greaterstrcon->consttype);
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/* ineq_histogram_selectivity worked before, it shouldn't fail now */
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Assert(topsel > 0.0);
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/*
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* Merge the two selectivities in the same way as for a range query
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* (see clauselist_selectivity()).
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* (see clauselist_selectivity()). Note that we don't need to worry
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* about double-exclusion of nulls, since ineq_histogram_selectivity
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* doesn't count those anyway.
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*/
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prefixsel = topsel + prefixsel - 1.0;
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/* Adjust for double-exclusion of NULLs */
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prefixsel += nulltestsel(root, IS_NULL, variable, 0);
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/*
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* A zero or slightly negative prefixsel should be converted into a
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* small positive value; we probably are dealing with a very tight
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* range and got a bogus result due to roundoff errors. However, if
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* prefixsel is very negative, then we probably have default
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* selectivity estimates on one or both sides of the range. In that
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* case, insert a not-so-wildly-optimistic default estimate.
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* A zero or negative prefixsel should be converted into a small
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* positive value; we probably are dealing with a very tight range
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* and got a bogus result due to roundoff errors.
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*/
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if (prefixsel <= 0.0)
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{
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if (prefixsel < -0.01)
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{
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/*
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* No data available --- use a default estimate that is small,
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* but not real small.
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*/
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prefixsel = 0.005;
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}
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else
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{
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/*
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* It's just roundoff error; use a small positive value
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*/
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prefixsel = 1.0e-10;
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}
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}
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prefixsel = 1.0e-10;
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}
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return prefixsel;
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