/*
* ltreeparentsel - Selectivity of parent relationship for ltree data types.
*/
Datum
ltreeparentsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
VariableStatData vardata;
Node *other;
bool varonleft;
double selec;
/*
* If expression is not variable <@ something or something <@ variable,
* then punt and return a default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
PG_RETURN_FLOAT8(DEFAULT_PARENT_SEL);
/*
* If the something is a NULL constant, assume operator is strict and
* return zero, ie, operator will never return TRUE.
*/
if (IsA(other, Const) &&
((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
if (IsA(other, Const))
{
/* Variable is being compared to a known non-null constant */
Datum constval = ((Const *) other)->constvalue;
FmgrInfo contproc;
double mcvsum;
double mcvsel;
double nullfrac;
int hist_size;
fmgr_info(get_opcode(operator), &contproc);
/*
* Is the constant "<@" to any of the column's most common values?
*/
mcvsel = mcv_selectivity(&vardata, &contproc, constval, varonleft,
&mcvsum);
/*
* If the histogram is large enough, see what fraction of it the
* constant is "<@" to, and assume that's representative of the
* non-MCV population. Otherwise use the default selectivity for the
* non-MCV population.
*/
selec = histogram_selectivity(&vardata, &contproc,
constval, varonleft,
10, 1, &hist_size);
if (selec < 0)
{
/* Nope, fall back on default */
selec = DEFAULT_PARENT_SEL;
}
else if (hist_size < 100)
{
/*
* 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
nullfrac = 0.0;
/*
* Now merge the results from the MCV and histogram calculations,
* realizing that the histogram covers only the non-null values that
* are not listed in MCV.
*/
selec *= 1.0 - nullfrac - mcvsum;
selec += mcvsel;
}
else
selec = DEFAULT_PARENT_SEL;
ReleaseVariableStats(vardata);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* Selectivity estimation for the subnet inclusion/overlap operators
*/
Datum
networksel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
VariableStatData vardata;
Node *other;
bool varonleft;
Selectivity selec,
mcv_selec,
non_mcv_selec;
Datum constvalue,
*hist_values;
int hist_nvalues;
Form_pg_statistic stats;
double sumcommon,
nullfrac;
FmgrInfo proc;
/*
* If expression is not (variable op something) or (something op
* variable), then punt and return a default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
PG_RETURN_FLOAT8(DEFAULT_SEL(operator));
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_SEL(operator));
}
/* All of the operators handled here are strict. */
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
constvalue = ((Const *) other)->constvalue;
/* Otherwise, we need stats in order to produce a non-default estimate. */
if (!HeapTupleIsValid(vardata.statsTuple))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_SEL(operator));
}
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
nullfrac = stats->stanullfrac;
/*
* If we have most-common-values info, add up the fractions of the MCV
* entries that satisfy MCV OP CONST. These fractions contribute directly
* to the result selectivity. Also add up the total fraction represented
* by MCV entries.
*/
fmgr_info(get_opcode(operator), &proc);
mcv_selec = mcv_selectivity(&vardata, &proc, constvalue, varonleft,
&sumcommon);
/*
* If we have a histogram, use it to estimate the proportion of the
* non-MCV population that satisfies the clause. If we don't, apply the
* default selectivity to that population.
*/
if (get_attstatsslot(vardata.statsTuple,
vardata.atttype, vardata.atttypmod,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
NULL,
&hist_values, &hist_nvalues,
NULL, NULL))
{
int opr_codenum = inet_opr_codenum(operator);
/* Commute if needed, so we can consider histogram to be on the left */
if (!varonleft)
opr_codenum = -opr_codenum;
non_mcv_selec = inet_hist_value_sel(hist_values, hist_nvalues,
constvalue, opr_codenum);
free_attstatsslot(vardata.atttype, hist_values, hist_nvalues, NULL, 0);
}
else
non_mcv_selec = DEFAULT_SEL(operator);
/* Combine selectivities for MCV and non-MCV populations */
selec = mcv_selec + (1.0 - nullfrac - sumcommon) * non_mcv_selec;
/* Result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(selec);
}
