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postgres/src/backend/utils/adt/selfuncs.c
Tom Lane 8555586707 Fix an Assert that turns out to be reachable after all. 
estimate_num_groups() gets unhappy with
	create table empty();
	select * from empty except select * from empty e2;
I can't see any actual use-case for such a query (and the table is illegal
per SQL spec), but it seems like a good idea that it not cause an assert
failure.
2012-04-09 11:59:11 -04:00

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/*-------------------------------------------------------------------------
*
* selfuncs.c
* Selectivity functions and index cost estimation functions for
* standard operators and index access methods.
*
* Selectivity routines are registered in the pg_operator catalog
* in the "oprrest" and "oprjoin" attributes.
*
* Index cost functions are registered in the pg_am catalog
* in the "amcostestimate" attribute.
*
* Portions Copyright (c) 1996-2009, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
* $PostgreSQL: pgsql/src/backend/utils/adt/selfuncs.c,v 1.261 2009/06/11 14:49:04 momjian Exp $
*
*-------------------------------------------------------------------------
*/
/*----------
* Operator selectivity estimation functions are called to estimate the
* selectivity of WHERE clauses whose top-level operator is their operator.
* We divide the problem into two cases:
* Restriction clause estimation: the clause involves vars of just
* one relation.
* Join clause estimation: the clause involves vars of multiple rels.
* Join selectivity estimation is far more difficult and usually less accurate
* than restriction estimation.
*
* When dealing with the inner scan of a nestloop join, we consider the
* join's joinclauses as restriction clauses for the inner relation, and
* treat vars of the outer relation as parameters (a/k/a constants of unknown
* values). So, restriction estimators need to be able to accept an argument
* telling which relation is to be treated as the variable.
*
* The call convention for a restriction estimator (oprrest function) is
*
* Selectivity oprrest (PlannerInfo *root,
* Oid operator,
* List *args,
* int varRelid);
*
* root: general information about the query (rtable and RelOptInfo lists
* are particularly important for the estimator).
* operator: OID of the specific operator in question.
* args: argument list from the operator clause.
* varRelid: if not zero, the relid (rtable index) of the relation to
* be treated as the variable relation. May be zero if the args list
* is known to contain vars of only one relation.
*
* This is represented at the SQL level (in pg_proc) as
*
* float8 oprrest (internal, oid, internal, int4);
*
* The result is a selectivity, that is, a fraction (0 to 1) of the rows
* of the relation that are expected to produce a TRUE result for the
* given operator.
*
* The call convention for a join estimator (oprjoin function) is similar
* except that varRelid is not needed, and instead join information is
* supplied:
*
* Selectivity oprjoin (PlannerInfo *root,
* Oid operator,
* List *args,
* JoinType jointype,
* SpecialJoinInfo *sjinfo);
*
* float8 oprjoin (internal, oid, internal, int2, internal);
*
* (Before Postgres 8.4, join estimators had only the first four of these
* parameters. That signature is still allowed, but deprecated.) The
* relationship between jointype and sjinfo is explained in the comments for
* clause_selectivity() --- the short version is that jointype is usually
* best ignored in favor of examining sjinfo.
*
* Join selectivity for regular inner and outer joins is defined as the
* fraction (0 to 1) of the cross product of the relations that is expected
* to produce a TRUE result for the given operator. For both semi and anti
* joins, however, the selectivity is defined as the fraction of the left-hand
* side relation's rows that are expected to have a match (ie, at least one
* row with a TRUE result) in the right-hand side.
*----------
*/
#include "postgres.h"
#include <ctype.h>
#include <math.h>
#include "access/sysattr.h"
#include "catalog/pg_opfamily.h"
#include "catalog/pg_statistic.h"
#include "catalog/pg_type.h"
#include "mb/pg_wchar.h"
#include "nodes/makefuncs.h"
#include "nodes/nodeFuncs.h"
#include "optimizer/clauses.h"
#include "optimizer/cost.h"
#include "optimizer/pathnode.h"
#include "optimizer/paths.h"
#include "optimizer/plancat.h"
#include "optimizer/predtest.h"
#include "optimizer/restrictinfo.h"
#include "optimizer/var.h"
#include "parser/parse_coerce.h"
#include "parser/parsetree.h"
#include "utils/builtins.h"
#include "utils/date.h"
#include "utils/datum.h"
#include "utils/fmgroids.h"
#include "utils/lsyscache.h"
#include "utils/nabstime.h"
#include "utils/pg_locale.h"
#include "utils/selfuncs.h"
#include "utils/syscache.h"
/* Hooks for plugins to get control when we ask for stats */
get_relation_stats_hook_type get_relation_stats_hook = NULL;
get_index_stats_hook_type get_index_stats_hook = NULL;
static double var_eq_const(VariableStatData *vardata, Oid operator,
Datum constval, bool constisnull,
bool varonleft);
static double var_eq_non_const(VariableStatData *vardata, Oid operator,
Node *other,
bool varonleft);
static double ineq_histogram_selectivity(VariableStatData *vardata,
FmgrInfo *opproc, bool isgt,
Datum constval, Oid consttype);
static double eqjoinsel_inner(Oid operator,
VariableStatData *vardata1, VariableStatData *vardata2);
static double eqjoinsel_semi(Oid operator,
VariableStatData *vardata1, VariableStatData *vardata2,
RelOptInfo *inner_rel);
static bool convert_to_scalar(Datum value, Oid valuetypid, double *scaledvalue,
Datum lobound, Datum hibound, Oid boundstypid,
double *scaledlobound, double *scaledhibound);
static double convert_numeric_to_scalar(Datum value, Oid typid);
static void convert_string_to_scalar(char *value,
double *scaledvalue,
char *lobound,
double *scaledlobound,
char *hibound,
double *scaledhibound);
static void convert_bytea_to_scalar(Datum value,
double *scaledvalue,
Datum lobound,
double *scaledlobound,
Datum hibound,
double *scaledhibound);
static double convert_one_string_to_scalar(char *value,
int rangelo, int rangehi);
static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
int rangelo, int rangehi);
static char *convert_string_datum(Datum value, Oid typid);
static double convert_timevalue_to_scalar(Datum value, Oid typid);
static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
Oid sortop, Datum *min, Datum *max);
static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
static Selectivity prefix_selectivity(VariableStatData *vardata,
Oid vartype, Oid opfamily, Const *prefixcon);
static Selectivity pattern_selectivity(Const *patt, Pattern_Type ptype);
static Datum string_to_datum(const char *str, Oid datatype);
static Const *string_to_const(const char *str, Oid datatype);
static Const *string_to_bytea_const(const char *str, size_t str_len);
/*
* eqsel - Selectivity of "=" for any data types.
*
* Note: this routine is also used to estimate selectivity for some
* operators that are not "=" but have comparable selectivity behavior,
* such as "~=" (geometric approximate-match). Even for "=", we must
* keep in mind that the left and right datatypes may differ.
*/
Datum
eqsel(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_EQ_SEL);
/*
* We can do a lot better if the something is a constant. (Note: the
* Const might result from estimation rather than being a simple constant
* in the query.)
*/
if (IsA(other, Const))
selec = var_eq_const(&vardata, operator,
((Const *) other)->constvalue,
((Const *) other)->constisnull,
varonleft);
else
selec = var_eq_non_const(&vardata, operator, other,
varonleft);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* var_eq_const --- eqsel for var = const case
*
* This is split out so that some other estimation functions can use it.
*/
static double
var_eq_const(VariableStatData *vardata, Oid operator,
Datum constval, bool constisnull,
bool varonleft)
{
double selec;
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE.
*/
if (constisnull)
return 0.0;
/*
* If we matched the var to a unique index, assume there is exactly one
* match regardless of anything else. (This is slightly bogus, since the
* index's equality operator might be different from ours, but it's more
* likely to be right than ignoring the information.)
*/
if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
return 1.0 / vardata->rel->tuples;
if (HeapTupleIsValid(vardata->statsTuple))
{
Form_pg_statistic stats;
Datum *values;
int nvalues;
float4 *numbers;
int nnumbers;
bool match = false;
int i;
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
/*
* Is the constant "=" to any of the column's most common values?
* (Although the given operator may not really be "=", we will assume
* that seeing whether it returns TRUE is an appropriate test. If you
* don't like this, maybe you shouldn't be using eqsel for your
* operator...)
*/
if (get_attstatsslot(vardata->statsTuple,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
&values, &nvalues,
&numbers, &nnumbers))
{
FmgrInfo eqproc;
fmgr_info(get_opcode(operator), &eqproc);
for (i = 0; i < nvalues; i++)
{
/* be careful to apply operator right way 'round */
if (varonleft)
match = DatumGetBool(FunctionCall2(&eqproc,
values[i],
constval));
else
match = DatumGetBool(FunctionCall2(&eqproc,
constval,
values[i]));
if (match)
break;
}
}
else
{
/* no most-common-value info available */
values = NULL;
numbers = NULL;
i = nvalues = nnumbers = 0;
}
if (match)
{
/*
* Constant is "=" to this common value. We know selectivity
* exactly (or as exactly as ANALYZE could calculate it, anyway).
*/
selec = numbers[i];
}
else
{
/*
* Comparison is against a constant that is neither NULL nor any
* of the common values. Its selectivity cannot be more than
* this:
*/
double sumcommon = 0.0;
double otherdistinct;
for (i = 0; i < nnumbers; i++)
sumcommon += numbers[i];
selec = 1.0 - sumcommon - stats->stanullfrac;
CLAMP_PROBABILITY(selec);
/*
* and in fact it's probably a good deal less. We approximate that
* all the not-common values share this remaining fraction
* equally, so we divide by the number of other distinct values.
*/
otherdistinct = get_variable_numdistinct(vardata) - nnumbers;
if (otherdistinct > 1)
selec /= otherdistinct;
/*
* Another cross-check: selectivity shouldn't be estimated as more
* than the least common "most common value".
*/
if (nnumbers > 0 && selec > numbers[nnumbers - 1])
selec = numbers[nnumbers - 1];
}
free_attstatsslot(vardata->atttype, values, nvalues,
numbers, nnumbers);
}
else
{
/*
* No ANALYZE stats available, so make a guess using estimated number
* of distinct values and assuming they are equally common. (The guess
* is unlikely to be very good, but we do know a few special cases.)
*/
selec = 1.0 / get_variable_numdistinct(vardata);
}
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return selec;
}
/*
* var_eq_non_const --- eqsel for var = something-other-than-const case
*/
static double
var_eq_non_const(VariableStatData *vardata, Oid operator,
Node *other,
bool varonleft)
{
double selec;
/*
* If we matched the var to a unique index, assume there is exactly one
* match regardless of anything else. (This is slightly bogus, since the
* index's equality operator might be different from ours, but it's more
* likely to be right than ignoring the information.)
*/
if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
return 1.0 / vardata->rel->tuples;
if (HeapTupleIsValid(vardata->statsTuple))
{
Form_pg_statistic stats;
double ndistinct;
float4 *numbers;
int nnumbers;
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
/*
* Search is for a value that we do not know a priori, but we will
* assume it is not NULL. Estimate the selectivity as non-null
* fraction divided by number of distinct values, so that we get a
* result averaged over all possible values whether common or
* uncommon. (Essentially, we are assuming that the not-yet-known
* comparison value is equally likely to be any of the possible
* values, regardless of their frequency in the table. Is that a good
* idea?)
*/
selec = 1.0 - stats->stanullfrac;
ndistinct = get_variable_numdistinct(vardata);
if (ndistinct > 1)
selec /= ndistinct;
/*
* Cross-check: selectivity should never be estimated as more than the
* most common value's.
*/
if (get_attstatsslot(vardata->statsTuple,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
NULL, NULL,
&numbers, &nnumbers))
{
if (nnumbers > 0 && selec > numbers[0])
selec = numbers[0];
free_attstatsslot(vardata->atttype, NULL, 0, numbers, nnumbers);
}
}
else
{
/*
* No ANALYZE stats available, so make a guess using estimated number
* of distinct values and assuming they are equally common. (The guess
* is unlikely to be very good, but we do know a few special cases.)
*/
selec = 1.0 / get_variable_numdistinct(vardata);
}
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return selec;
}
/*
* neqsel - Selectivity of "!=" for any data types.
*
* This routine is also used for some operators that are not "!="
* but have comparable selectivity behavior. See above comments
* for eqsel().
*/
Datum
neqsel(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);
Oid eqop;
float8 result;
/*
* We want 1 - eqsel() where the equality operator is the one associated
* with this != operator, that is, its negator.
*/
eqop = get_negator(operator);
if (eqop)
{
result = DatumGetFloat8(DirectFunctionCall4(eqsel,
PointerGetDatum(root),
ObjectIdGetDatum(eqop),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
}
else
{
/* Use default selectivity (should we raise an error instead?) */
result = DEFAULT_EQ_SEL;
}
result = 1.0 - result;
PG_RETURN_FLOAT8(result);
}
/*
* scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
*
* This is the guts of both scalarltsel and scalargtsel. The caller has
* commuted the clause, if necessary, so that we can treat the variable as
* being on the left. The caller must also make sure that the other side
* of the clause is a non-null Const, and dissect same into a value and
* datatype.
*
* This routine works for any datatype (or pair of datatypes) known to
* convert_to_scalar(). If it is applied to some other datatype,
* it will return a default estimate.
*/
static double
scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
VariableStatData *vardata, Datum constval, Oid consttype)
{
Form_pg_statistic stats;
FmgrInfo opproc;
double mcv_selec,
hist_selec,
sumcommon;
double selec;
if (!HeapTupleIsValid(vardata->statsTuple))
{
/* no stats available, so default result */
return DEFAULT_INEQ_SEL;
}
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
fmgr_info(get_opcode(operator), &opproc);
/*
* 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.
*/
mcv_selec = mcv_selectivity(vardata, &opproc, constval, true,
&sumcommon);
/*
* If there is a histogram, determine which bin the constant falls in, and
* compute the resulting contribution to selectivity.
*/
hist_selec = ineq_histogram_selectivity(vardata, &opproc, isgt,
constval, consttype);
/*
* 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 - stats->stanullfrac - sumcommon;
if (hist_selec > 0.0)
selec *= hist_selec;
else
{
/*
* If no histogram but there are values not accounted for by MCV,
* arbitrarily assume half of them will match.
*/
selec *= 0.5;
}
selec += mcv_selec;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return selec;
}
/*
* mcv_selectivity - Examine the MCV list for selectivity estimates
*
* Determine the fraction of the variable's MCV population that satisfies
* the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
* compute the fraction of the total column population represented by the MCV
* list. This code will work for any boolean-returning predicate operator.
*
* The function result is the MCV selectivity, and the fraction of the
* total population is returned into *sumcommonp. Zeroes are returned
* if there is no MCV list.
*/
double
mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
Datum constval, bool varonleft,
double *sumcommonp)
{
double mcv_selec,
sumcommon;
Datum *values;
int nvalues;
float4 *numbers;
int nnumbers;
int i;
mcv_selec = 0.0;
sumcommon = 0.0;
if (HeapTupleIsValid(vardata->statsTuple) &&
get_attstatsslot(vardata->statsTuple,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
&values, &nvalues,
&numbers, &nnumbers))
{
for (i = 0; i < nvalues; i++)
{
if (varonleft ?
DatumGetBool(FunctionCall2(opproc,
values[i],
constval)) :
DatumGetBool(FunctionCall2(opproc,
constval,
values[i])))
mcv_selec += numbers[i];
sumcommon += numbers[i];
}
free_attstatsslot(vardata->atttype, values, nvalues,
numbers, nnumbers);
}
*sumcommonp = sumcommon;
return mcv_selec;
}
/*
* histogram_selectivity - Examine the histogram for selectivity estimates
*
* Determine the fraction of the variable's histogram entries that satisfy
* the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
*
* This code will work for any boolean-returning predicate operator, whether
* 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 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.
*
* 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
* prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
*/
double
histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
Datum constval, bool varonleft,
int min_hist_size, int n_skip,
int *hist_size)
{
double result;
Datum *values;
int nvalues;
/* check sanity of parameters */
Assert(n_skip >= 0);
Assert(min_hist_size > 2 * n_skip);
if (HeapTupleIsValid(vardata->statsTuple) &&
get_attstatsslot(vardata->statsTuple,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
&values, &nvalues,
NULL, NULL))
{
*hist_size = nvalues;
if (nvalues >= min_hist_size)
{
int nmatch = 0;
int i;
for (i = n_skip; i < nvalues - n_skip; i++)
{
if (varonleft ?
DatumGetBool(FunctionCall2(opproc,
values[i],
constval)) :
DatumGetBool(FunctionCall2(opproc,
constval,
values[i])))
nmatch++;
}
result = ((double) nmatch) / ((double) (nvalues - 2 * n_skip));
}
else
result = -1;
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
else
{
*hist_size = 0;
result = -1;
}
return result;
}
/*
* ineq_histogram_selectivity - Examine the histogram for scalarineqsel
*
* Determine the fraction of the variable's histogram population that
* satisfies the inequality condition, ie, VAR < CONST or VAR > CONST.
*
* Returns zero if there is no histogram (valid results will always be
* greater than zero).
*
* 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.
*/
static double
ineq_histogram_selectivity(VariableStatData *vardata,
FmgrInfo *opproc, bool isgt,
Datum constval, Oid consttype)
{
double hist_selec;
Datum *values;
int nvalues;
hist_selec = 0.0;
/*
* Someday, ANALYZE might store more than one histogram per rel/att,
* corresponding to more than one possible sort ordering defined for the
* column type. However, to make that work we will need to figure out
* which staop to search for --- it's not necessarily the one we have at
* hand! (For example, we might have a '<=' operator rather than the '<'
* operator that will appear in staop.) For now, assume that whatever
* appears in pg_statistic is sorted the same way our operator sorts, or
* the reverse way if isgt is TRUE.
*/
if (HeapTupleIsValid(vardata->statsTuple) &&
get_attstatsslot(vardata->statsTuple,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
&values, &nvalues,
NULL, NULL))
{
if (nvalues > 1)
{
/*
* Use binary search to find proper location, ie, the first slot
* at which the comparison fails. (If the given operator isn't
* actually sort-compatible with the histogram, you'll get garbage
* results ... but probably not any more garbage-y than you would
* from the old linear search.)
*/
double histfrac;
int lobound = 0; /* first possible slot to search */
int hibound = nvalues; /* last+1 slot to search */
while (lobound < hibound)
{
int probe = (lobound + hibound) / 2;
bool ltcmp;
ltcmp = DatumGetBool(FunctionCall2(opproc,
values[probe],
constval));
if (isgt)
ltcmp = !ltcmp;
if (ltcmp)
lobound = probe + 1;
else
hibound = probe;
}
if (lobound <= 0)
{
/* Constant is below lower histogram boundary. */
histfrac = 0.0;
}
else if (lobound >= nvalues)
{
/* Constant is above upper histogram boundary. */
histfrac = 1.0;
}
else
{
int i = lobound;
double val,
high,
low;
double binfrac;
/*
* We have values[i-1] < constant < values[i].
*
* Convert the constant and the two nearest bin boundary
* values to a uniform comparison scale, and do a linear
* interpolation within this bin.
*/
if (convert_to_scalar(constval, consttype, &val,
values[i - 1], values[i],
vardata->vartype,
&low, &high))
{
if (high <= low)
{
/* cope if bin boundaries appear identical */
binfrac = 0.5;
}
else if (val <= low)
binfrac = 0.0;
else if (val >= high)
binfrac = 1.0;
else
{
binfrac = (val - low) / (high - low);
/*
* Watch out for the possibility that we got a NaN or
* Infinity from the division. This can happen
* despite the previous checks, if for example "low"
* is -Infinity.
*/
if (isnan(binfrac) ||
binfrac < 0.0 || binfrac > 1.0)
binfrac = 0.5;
}
}
else
{
/*
* Ideally we'd produce an error here, on the grounds that
* the given operator shouldn't have scalarXXsel
* registered as its selectivity func unless we can deal
* with its operand types. But currently, all manner of
* stuff is invoking scalarXXsel, so give a default
* estimate until that can be fixed.
*/
binfrac = 0.5;
}
/*
* Now, compute the overall selectivity across the values
* represented by the histogram. We have i-1 full bins and
* binfrac partial bin below the constant.
*/
histfrac = (double) (i - 1) + binfrac;
histfrac /= (double) (nvalues - 1);
}
/*
* Now histfrac = fraction of histogram entries below the
* constant.
*
* Account for "<" vs ">"
*/
hist_selec = isgt ? (1.0 - histfrac) : histfrac;
/*
* The histogram boundaries are only approximate to begin with,
* and may well be out of date anyway. Therefore, don't believe
* extremely small or large selectivity estimates.
*/
if (hist_selec < 0.0001)
hist_selec = 0.0001;
else if (hist_selec > 0.9999)
hist_selec = 0.9999;
}
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
return hist_selec;
}
/*
* scalarltsel - Selectivity of "<" (also "<=") for scalars.
*/
Datum
scalarltsel(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;
Datum constval;
Oid consttype;
bool isgt;
double selec;
/*
* 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_INEQ_SEL);
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE.
*/
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
constval = ((Const *) other)->constvalue;
consttype = ((Const *) other)->consttype;
/*
* Force the var to be on the left to simplify logic in scalarineqsel.
*/
if (varonleft)
{
/* we have var < other */
isgt = false;
}
else
{
/* we have other < var, commute to make var > other */
operator = get_commutator(operator);
if (!operator)
{
/* Use default selectivity (should we raise an error instead?) */
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
isgt = true;
}
selec = scalarineqsel(root, operator, isgt, &vardata, constval, consttype);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* scalargtsel - Selectivity of ">" (also ">=") for integers.
*/
Datum
scalargtsel(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;
Datum constval;
Oid consttype;
bool isgt;
double selec;
/*
* 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_INEQ_SEL);
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE.
*/
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
constval = ((Const *) other)->constvalue;
consttype = ((Const *) other)->consttype;
/*
* Force the var to be on the left to simplify logic in scalarineqsel.
*/
if (varonleft)
{
/* we have var > other */
isgt = true;
}
else
{
/* we have other > var, commute to make var < other */
operator = get_commutator(operator);
if (!operator)
{
/* Use default selectivity (should we raise an error instead?) */
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
isgt = false;
}
selec = scalarineqsel(root, operator, isgt, &vardata, constval, consttype);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* patternsel - Generic code for pattern-match selectivity.
*/
static double
patternsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
{
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 *variable;
Node *other;
bool varonleft;
Datum constval;
Oid consttype;
Oid vartype;
Oid opfamily;
Pattern_Prefix_Status pstatus;
Const *patt = NULL;
Const *prefix = NULL;
Const *rest = NULL;
double result;
/*
* If this is for a NOT LIKE or similar operator, get the corresponding
* positive-match operator and work with that. Set result to the correct
* default estimate, too.
*/
if (negate)
{
operator = get_negator(operator);
if (!OidIsValid(operator))
elog(ERROR, "patternsel called for operator without a negator");
result = 1.0 - DEFAULT_MATCH_SEL;
}
else
{
result = DEFAULT_MATCH_SEL;
}
/*
* If expression is not variable op constant, then punt and return a
* default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
return result;
if (!varonleft || !IsA(other, Const))
{
ReleaseVariableStats(vardata);
return result;
}
variable = (Node *) linitial(args);
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE. (It's zero even for a negator op.)
*/
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
return 0.0;
}
constval = ((Const *) other)->constvalue;
consttype = ((Const *) other)->consttype;
/*
* The right-hand const is type text or bytea for all supported operators.
* We do not expect to see binary-compatible types here, since
* const-folding should have relabeled the const to exactly match the
* operator's declared type.
*/
if (consttype != TEXTOID && consttype != BYTEAOID)
{
ReleaseVariableStats(vardata);
return result;
}
/*
* Similarly, the exposed type of the left-hand side should be one of
* those we know. (Do not look at vardata.atttype, which might be
* something binary-compatible but different.) We can use it to choose
* the index opfamily from which we must draw the comparison operators.
*
* NOTE: It would be more correct to use the PATTERN opfamilies than the
* simple ones, but at the moment ANALYZE will not generate statistics for
* the PATTERN operators. But our results are so approximate anyway that
* it probably hardly matters.
*/
vartype = vardata.vartype;
switch (vartype)
{
case TEXTOID:
opfamily = TEXT_BTREE_FAM_OID;
break;
case BPCHAROID:
opfamily = BPCHAR_BTREE_FAM_OID;
break;
case NAMEOID:
opfamily = NAME_BTREE_FAM_OID;
break;
case BYTEAOID:
opfamily = BYTEA_BTREE_FAM_OID;
break;
default:
ReleaseVariableStats(vardata);
return result;
}
/* divide pattern into fixed prefix and remainder */
patt = (Const *) other;
pstatus = pattern_fixed_prefix(patt, ptype, &prefix, &rest);
/*
* If necessary, coerce the prefix constant to the right type. (The "rest"
* constant need not be changed.)
*/
if (prefix && prefix->consttype != vartype)
{
char *prefixstr;
switch (prefix->consttype)
{
case TEXTOID:
prefixstr = TextDatumGetCString(prefix->constvalue);
break;
case BYTEAOID:
prefixstr = DatumGetCString(DirectFunctionCall1(byteaout,
prefix->constvalue));
break;
default:
elog(ERROR, "unrecognized consttype: %u",
prefix->consttype);
ReleaseVariableStats(vardata);
return result;
}
prefix = string_to_const(prefixstr, vartype);
pfree(prefixstr);
}
if (pstatus == Pattern_Prefix_Exact)
{
/*
* Pattern specifies an exact match, so pretend operator is '='
*/
Oid eqopr = get_opfamily_member(opfamily, vartype, vartype,
BTEqualStrategyNumber);
if (eqopr == InvalidOid)
elog(ERROR, "no = operator for opfamily %u", opfamily);
result = var_eq_const(&vardata, eqopr, prefix->constvalue,
false, true);
}
else
{
/*
* Not exact-match pattern. If we have a sufficiently large
* histogram, estimate selectivity for the histogram part of the
* population by counting matches in the histogram. If not, estimate
* 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. (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,
sumcommon;
/* Try to use the histogram entries to get selectivity */
fmgr_info(get_opcode(operator), &opproc);
selec = histogram_selectivity(&vardata, &opproc, constval, true,
10, 1, &hist_size);
/* If not at least 100 entries, use the heuristic method */
if (hist_size < 100)
{
Selectivity heursel;
Selectivity prefixsel;
Selectivity restsel;
if (pstatus == Pattern_Prefix_Partial)
prefixsel = prefix_selectivity(&vardata, vartype,
opfamily, prefix);
else
prefixsel = 1.0;
restsel = pattern_selectivity(rest, ptype);
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
* directly to the result selectivity. Also add up the total fraction
* represented by MCV entries.
*/
mcv_selec = mcv_selectivity(&vardata, &opproc, constval, true,
&sumcommon);
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 - sumcommon;
selec += mcv_selec;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
result = selec;
}
if (prefix)
{
pfree(DatumGetPointer(prefix->constvalue));
pfree(prefix);
}
ReleaseVariableStats(vardata);
return negate ? (1.0 - result) : result;
}
/*
* regexeqsel - Selectivity of regular-expression pattern match.
*/
Datum
regexeqsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex, false));
}
/*
* icregexeqsel - Selectivity of case-insensitive regex match.
*/
Datum
icregexeqsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex_IC, false));
}
/*
* likesel - Selectivity of LIKE pattern match.
*/
Datum
likesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like, false));
}
/*
* iclikesel - Selectivity of ILIKE pattern match.
*/
Datum
iclikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like_IC, false));
}
/*
* regexnesel - Selectivity of regular-expression pattern non-match.
*/
Datum
regexnesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex, true));
}
/*
* icregexnesel - Selectivity of case-insensitive regex non-match.
*/
Datum
icregexnesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex_IC, true));
}
/*
* nlikesel - Selectivity of LIKE pattern non-match.
*/
Datum
nlikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like, true));
}
/*
* icnlikesel - Selectivity of ILIKE pattern non-match.
*/
Datum
icnlikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like_IC, true));
}
/*
* booltestsel - Selectivity of BooleanTest Node.
*/
Selectivity
booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
{
VariableStatData vardata;
double selec;
examine_variable(root, arg, varRelid, &vardata);
if (HeapTupleIsValid(vardata.statsTuple))
{
Form_pg_statistic stats;
double freq_null;
Datum *values;
int nvalues;
float4 *numbers;
int nnumbers;
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
freq_null = stats->stanullfrac;
if (get_attstatsslot(vardata.statsTuple,
vardata.atttype, vardata.atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
&values, &nvalues,
&numbers, &nnumbers)
&& nnumbers > 0)
{
double freq_true;
double freq_false;
/*
* Get first MCV frequency and derive frequency for true.
*/
if (DatumGetBool(values[0]))
freq_true = numbers[0];
else
freq_true = 1.0 - numbers[0] - freq_null;
/*
* Next derive frequency for false. Then use these as appropriate
* to derive frequency for each case.
*/
freq_false = 1.0 - freq_true - freq_null;
switch (booltesttype)
{
case IS_UNKNOWN:
/* select only NULL values */
selec = freq_null;
break;
case IS_NOT_UNKNOWN:
/* select non-NULL values */
selec = 1.0 - freq_null;
break;
case IS_TRUE:
/* select only TRUE values */
selec = freq_true;
break;
case IS_NOT_TRUE:
/* select non-TRUE values */
selec = 1.0 - freq_true;
break;
case IS_FALSE:
/* select only FALSE values */
selec = freq_false;
break;
case IS_NOT_FALSE:
/* select non-FALSE values */
selec = 1.0 - freq_false;
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
free_attstatsslot(vardata.atttype, values, nvalues,
numbers, nnumbers);
}
else
{
/*
* No most-common-value info available. Still have null fraction
* information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
* for null fraction and assume an even split for boolean tests.
*/
switch (booltesttype)
{
case IS_UNKNOWN:
/*
* Use freq_null directly.
*/
selec = freq_null;
break;
case IS_NOT_UNKNOWN:
/*
* Select not unknown (not null) values. Calculate from
* freq_null.
*/
selec = 1.0 - freq_null;
break;
case IS_TRUE:
case IS_NOT_TRUE:
case IS_FALSE:
case IS_NOT_FALSE:
selec = (1.0 - freq_null) / 2.0;
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
}
}
else
{
/*
* If we can't get variable statistics for the argument, perhaps
* clause_selectivity can do something with it. We ignore the
* possibility of a NULL value when using clause_selectivity, and just
* assume the value is either TRUE or FALSE.
*/
switch (booltesttype)
{
case IS_UNKNOWN:
selec = DEFAULT_UNK_SEL;
break;
case IS_NOT_UNKNOWN:
selec = DEFAULT_NOT_UNK_SEL;
break;
case IS_TRUE:
case IS_NOT_FALSE:
selec = (double) clause_selectivity(root, arg,
varRelid,
jointype, sjinfo);
break;
case IS_FALSE:
case IS_NOT_TRUE:
selec = 1.0 - (double) clause_selectivity(root, arg,
varRelid,
jointype, sjinfo);
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
}
ReleaseVariableStats(vardata);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return (Selectivity) selec;
}
/*
* nulltestsel - Selectivity of NullTest Node.
*/
Selectivity
nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
{
VariableStatData vardata;
double selec;
examine_variable(root, arg, varRelid, &vardata);
if (HeapTupleIsValid(vardata.statsTuple))
{
Form_pg_statistic stats;
double freq_null;
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
freq_null = stats->stanullfrac;
switch (nulltesttype)
{
case IS_NULL:
/*
* Use freq_null directly.
*/
selec = freq_null;
break;
case IS_NOT_NULL:
/*
* Select not unknown (not null) values. Calculate from
* freq_null.
*/
selec = 1.0 - freq_null;
break;
default:
elog(ERROR, "unrecognized nulltesttype: %d",
(int) nulltesttype);
return (Selectivity) 0; /* keep compiler quiet */
}
}
else
{
/*
* No ANALYZE stats available, so make a guess
*/
switch (nulltesttype)
{
case IS_NULL:
selec = DEFAULT_UNK_SEL;
break;
case IS_NOT_NULL:
selec = DEFAULT_NOT_UNK_SEL;
break;
default:
elog(ERROR, "unrecognized nulltesttype: %d",
(int) nulltesttype);
return (Selectivity) 0; /* keep compiler quiet */
}
}
ReleaseVariableStats(vardata);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return (Selectivity) selec;
}
/*
* strip_array_coercion - strip binary-compatible relabeling from an array expr
*
* For array values, the parser normally generates ArrayCoerceExpr conversions,
* but it seems possible that RelabelType might show up. Also, the planner
* is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
* so we need to be ready to deal with more than one level.
*/
static Node *
strip_array_coercion(Node *node)
{
for (;;)
{
if (node && IsA(node, ArrayCoerceExpr) &&
((ArrayCoerceExpr *) node)->elemfuncid == InvalidOid)
{
node = (Node *) ((ArrayCoerceExpr *) node)->arg;
}
else if (node && IsA(node, RelabelType))
{
/* We don't really expect this case, but may as well cope */
node = (Node *) ((RelabelType *) node)->arg;
}
else
break;
}
return node;
}
/*
* scalararraysel - Selectivity of ScalarArrayOpExpr Node.
*/
Selectivity
scalararraysel(PlannerInfo *root,
ScalarArrayOpExpr *clause,
bool is_join_clause,
int varRelid,
JoinType jointype,
SpecialJoinInfo *sjinfo)
{
Oid operator = clause->opno;
bool useOr = clause->useOr;
Node *leftop;
Node *rightop;
Oid nominal_element_type;
RegProcedure oprsel;
FmgrInfo oprselproc;
Selectivity s1;
/*
* First, look up the underlying operator's selectivity estimator. Punt if
* it hasn't got one.
*/
if (is_join_clause)
oprsel = get_oprjoin(operator);
else
oprsel = get_oprrest(operator);
if (!oprsel)
return (Selectivity) 0.5;
fmgr_info(oprsel, &oprselproc);
/* deconstruct the expression */
Assert(list_length(clause->args) == 2);
leftop = (Node *) linitial(clause->args);
rightop = (Node *) lsecond(clause->args);
/* get nominal (after relabeling) element type of rightop */
nominal_element_type = get_element_type(exprType(rightop));
if (!OidIsValid(nominal_element_type))
return (Selectivity) 0.5; /* probably shouldn't happen */
/* look through any binary-compatible relabeling of rightop */
rightop = strip_array_coercion(rightop);
/*
* We consider three cases:
*
* 1. rightop is an Array constant: deconstruct the array, apply the
* operator's selectivity function for each array element, and merge the
* results in the same way that clausesel.c does for AND/OR combinations.
*
* 2. rightop is an ARRAY[] construct: apply the operator's selectivity
* function for each element of the ARRAY[] construct, and merge.
*
* 3. otherwise, make a guess ...
*/
if (rightop && IsA(rightop, Const))
{
Datum arraydatum = ((Const *) rightop)->constvalue;
bool arrayisnull = ((Const *) rightop)->constisnull;
ArrayType *arrayval;
int16 elmlen;
bool elmbyval;
char elmalign;
int num_elems;
Datum *elem_values;
bool *elem_nulls;
int i;
if (arrayisnull) /* qual can't succeed if null array */
return (Selectivity) 0.0;
arrayval = DatumGetArrayTypeP(arraydatum);
get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
&elmlen, &elmbyval, &elmalign);
deconstruct_array(arrayval,
ARR_ELEMTYPE(arrayval),
elmlen, elmbyval, elmalign,
&elem_values, &elem_nulls, &num_elems);
s1 = useOr ? 0.0 : 1.0;
for (i = 0; i < num_elems; i++)
{
List *args;
Selectivity s2;
args = list_make2(leftop,
makeConst(nominal_element_type,
-1,
elmlen,
elem_values[i],
elem_nulls[i],
elmbyval));
if (is_join_clause)
s2 = DatumGetFloat8(FunctionCall5(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
if (useOr)
s1 = s1 + s2 - s1 * s2;
else
s1 = s1 * s2;
}
}
else if (rightop && IsA(rightop, ArrayExpr) &&
!((ArrayExpr *) rightop)->multidims)
{
ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
int16 elmlen;
bool elmbyval;
ListCell *l;
get_typlenbyval(arrayexpr->element_typeid,
&elmlen, &elmbyval);
s1 = useOr ? 0.0 : 1.0;
foreach(l, arrayexpr->elements)
{
Node *elem = (Node *) lfirst(l);
List *args;
Selectivity s2;
/*
* Theoretically, if elem isn't of nominal_element_type we should
* insert a RelabelType, but it seems unlikely that any operator
* estimation function would really care ...
*/
args = list_make2(leftop, elem);
if (is_join_clause)
s2 = DatumGetFloat8(FunctionCall5(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
if (useOr)
s1 = s1 + s2 - s1 * s2;
else
s1 = s1 * s2;
}
}
else
{
CaseTestExpr *dummyexpr;
List *args;
Selectivity s2;
int i;
/*
* We need a dummy rightop to pass to the operator selectivity
* routine. It can be pretty much anything that doesn't look like a
* constant; CaseTestExpr is a convenient choice.
*/
dummyexpr = makeNode(CaseTestExpr);
dummyexpr->typeId = nominal_element_type;
dummyexpr->typeMod = -1;
args = list_make2(leftop, dummyexpr);
if (is_join_clause)
s2 = DatumGetFloat8(FunctionCall5(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
s1 = useOr ? 0.0 : 1.0;
/*
* Arbitrarily assume 10 elements in the eventual array value (see
* also estimate_array_length)
*/
for (i = 0; i < 10; i++)
{
if (useOr)
s1 = s1 + s2 - s1 * s2;
else
s1 = s1 * s2;
}
}
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(s1);
return s1;
}
/*
* Estimate number of elements in the array yielded by an expression.
*
* It's important that this agree with scalararraysel.
*/
int
estimate_array_length(Node *arrayexpr)
{
/* look through any binary-compatible relabeling of arrayexpr */
arrayexpr = strip_array_coercion(arrayexpr);
if (arrayexpr && IsA(arrayexpr, Const))
{
Datum arraydatum = ((Const *) arrayexpr)->constvalue;
bool arrayisnull = ((Const *) arrayexpr)->constisnull;
ArrayType *arrayval;
if (arrayisnull)
return 0;
arrayval = DatumGetArrayTypeP(arraydatum);
return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
}
else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
!((ArrayExpr *) arrayexpr)->multidims)
{
return list_length(((ArrayExpr *) arrayexpr)->elements);
}
else
{
/* default guess --- see also scalararraysel */
return 10;
}
}
/*
* rowcomparesel - Selectivity of RowCompareExpr Node.
*
* We estimate RowCompare selectivity by considering just the first (high
* order) columns, which makes it equivalent to an ordinary OpExpr. While
* this estimate could be refined by considering additional columns, it
* seems unlikely that we could do a lot better without multi-column
* statistics.
*/
Selectivity
rowcomparesel(PlannerInfo *root,
RowCompareExpr *clause,
int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
{
Selectivity s1;
Oid opno = linitial_oid(clause->opnos);
List *opargs;
bool is_join_clause;
/* Build equivalent arg list for single operator */
opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
/*
* Decide if it's a join clause. This should match clausesel.c's
* treat_as_join_clause(), except that we intentionally consider only the
* leading columns and not the rest of the clause.
*/
if (varRelid != 0)
{
/*
* Caller is forcing restriction mode (eg, because we are examining an
* inner indexscan qual).
*/
is_join_clause = false;
}
else if (sjinfo == NULL)
{
/*
* It must be a restriction clause, since it's being evaluated at a
* scan node.
*/
is_join_clause = false;
}
else
{
/*
* Otherwise, it's a join if there's more than one relation used.
*/
is_join_clause = (NumRelids((Node *) opargs) > 1);
}
if (is_join_clause)
{
/* Estimate selectivity for a join clause. */
s1 = join_selectivity(root, opno,
opargs,
jointype,
sjinfo);
}
else
{
/* Estimate selectivity for a restriction clause. */
s1 = restriction_selectivity(root, opno,
opargs,
varRelid);
}
return s1;
}
/*
* eqjoinsel - Join selectivity of "="
*/
Datum
eqjoinsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
#ifdef NOT_USED
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
#endif
SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
double selec;
VariableStatData vardata1;
VariableStatData vardata2;
bool join_is_reversed;
RelOptInfo *inner_rel;
get_join_variables(root, args, sjinfo,
&vardata1, &vardata2, &join_is_reversed);
switch (sjinfo->jointype)
{
case JOIN_INNER:
case JOIN_LEFT:
case JOIN_FULL:
selec = eqjoinsel_inner(operator, &vardata1, &vardata2);
break;
case JOIN_SEMI:
case JOIN_ANTI:
/*
* Look up the join's inner relation. min_righthand is sufficient
* information because neither SEMI nor ANTI joins permit any
* reassociation into or out of their RHS, so the righthand will
* always be exactly that set of rels.
*/
inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
if (!join_is_reversed)
selec = eqjoinsel_semi(operator, &vardata1, &vardata2,
inner_rel);
else
selec = eqjoinsel_semi(get_commutator(operator),
&vardata2, &vardata1,
inner_rel);
break;
default:
/* other values not expected here */
elog(ERROR, "unrecognized join type: %d",
(int) sjinfo->jointype);
selec = 0; /* keep compiler quiet */
break;
}
ReleaseVariableStats(vardata1);
ReleaseVariableStats(vardata2);
CLAMP_PROBABILITY(selec);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* eqjoinsel_inner --- eqjoinsel for normal inner join
*
* We also use this for LEFT/FULL outer joins; it's not presently clear
* that it's worth trying to distinguish them here.
*/
static double
eqjoinsel_inner(Oid operator,
VariableStatData *vardata1, VariableStatData *vardata2)
{
double selec;
double nd1;
double nd2;
Form_pg_statistic stats1 = NULL;
Form_pg_statistic stats2 = NULL;
bool have_mcvs1 = false;
Datum *values1 = NULL;
int nvalues1 = 0;
float4 *numbers1 = NULL;
int nnumbers1 = 0;
bool have_mcvs2 = false;
Datum *values2 = NULL;
int nvalues2 = 0;
float4 *numbers2 = NULL;
int nnumbers2 = 0;
nd1 = get_variable_numdistinct(vardata1);
nd2 = get_variable_numdistinct(vardata2);
if (HeapTupleIsValid(vardata1->statsTuple))
{
stats1 = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple);
have_mcvs1 = get_attstatsslot(vardata1->statsTuple,
vardata1->atttype,
vardata1->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
&values1, &nvalues1,
&numbers1, &nnumbers1);
}
if (HeapTupleIsValid(vardata2->statsTuple))
{
stats2 = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple);
have_mcvs2 = get_attstatsslot(vardata2->statsTuple,
vardata2->atttype,
vardata2->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
&values2, &nvalues2,
&numbers2, &nnumbers2);
}
if (have_mcvs1 && have_mcvs2)
{
/*
* We have most-common-value lists for both relations. Run through
* the lists to see which MCVs actually join to each other with the
* given operator. This allows us to determine the exact join
* selectivity for the portion of the relations represented by the MCV
* lists. We still have to estimate for the remaining population, but
* in a skewed distribution this gives us a big leg up in accuracy.
* For motivation see the analysis in Y. Ioannidis and S.
* Christodoulakis, "On the propagation of errors in the size of join
* results", Technical Report 1018, Computer Science Dept., University
* of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
*/
FmgrInfo eqproc;
bool *hasmatch1;
bool *hasmatch2;
double nullfrac1 = stats1->stanullfrac;
double nullfrac2 = stats2->stanullfrac;
double matchprodfreq,
matchfreq1,
matchfreq2,
unmatchfreq1,
unmatchfreq2,
otherfreq1,
otherfreq2,
totalsel1,
totalsel2;
int i,
nmatches;
fmgr_info(get_opcode(operator), &eqproc);
hasmatch1 = (bool *) palloc0(nvalues1 * sizeof(bool));
hasmatch2 = (bool *) palloc0(nvalues2 * sizeof(bool));
/*
* Note we assume that each MCV will match at most one member of the
* other MCV list. If the operator isn't really equality, there could
* be multiple matches --- but we don't look for them, both for speed
* and because the math wouldn't add up...
*/
matchprodfreq = 0.0;
nmatches = 0;
for (i = 0; i < nvalues1; i++)
{
int j;
for (j = 0; j < nvalues2; j++)
{
if (hasmatch2[j])
continue;
if (DatumGetBool(FunctionCall2(&eqproc,
values1[i],
values2[j])))
{
hasmatch1[i] = hasmatch2[j] = true;
matchprodfreq += numbers1[i] * numbers2[j];
nmatches++;
break;
}
}
}
CLAMP_PROBABILITY(matchprodfreq);
/* Sum up frequencies of matched and unmatched MCVs */
matchfreq1 = unmatchfreq1 = 0.0;
for (i = 0; i < nvalues1; i++)
{
if (hasmatch1[i])
matchfreq1 += numbers1[i];
else
unmatchfreq1 += numbers1[i];
}
CLAMP_PROBABILITY(matchfreq1);
CLAMP_PROBABILITY(unmatchfreq1);
matchfreq2 = unmatchfreq2 = 0.0;
for (i = 0; i < nvalues2; i++)
{
if (hasmatch2[i])
matchfreq2 += numbers2[i];
else
unmatchfreq2 += numbers2[i];
}
CLAMP_PROBABILITY(matchfreq2);
CLAMP_PROBABILITY(unmatchfreq2);
pfree(hasmatch1);
pfree(hasmatch2);
/*
* Compute total frequency of non-null values that are not in the MCV
* lists.
*/
otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
CLAMP_PROBABILITY(otherfreq1);
CLAMP_PROBABILITY(otherfreq2);
/*
* We can estimate the total selectivity from the point of view of
* relation 1 as: the known selectivity for matched MCVs, plus
* unmatched MCVs that are assumed to match against random members of
* relation 2's non-MCV population, plus non-MCV values that are
* assumed to match against random members of relation 2's unmatched
* MCVs plus non-MCV values.
*/
totalsel1 = matchprodfreq;
if (nd2 > nvalues2)
totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - nvalues2);
if (nd2 > nmatches)
totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
(nd2 - nmatches);
/* Same estimate from the point of view of relation 2. */
totalsel2 = matchprodfreq;
if (nd1 > nvalues1)
totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - nvalues1);
if (nd1 > nmatches)
totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
(nd1 - nmatches);
/*
* Use the smaller of the two estimates. This can be justified in
* essentially the same terms as given below for the no-stats case: to
* a first approximation, we are estimating from the point of view of
* the relation with smaller nd.
*/
selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
}
else
{
/*
* We do not have MCV lists for both sides. Estimate the join
* selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
* is plausible if we assume that the join operator is strict and the
* non-null values are about equally distributed: a given non-null
* tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
* of rel2, so total join rows are at most
* N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
* not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
* is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
* with MIN() is an upper bound. Using the MIN() means we estimate
* from the point of view of the relation with smaller nd (since the
* larger nd is determining the MIN). It is reasonable to assume that
* most tuples in this rel will have join partners, so the bound is
* probably reasonably tight and should be taken as-is.
*
* XXX Can we be smarter if we have an MCV list for just one side? It
* seems that if we assume equal distribution for the other side, we
* end up with the same answer anyway.
*/
double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
if (nd1 > nd2)
selec /= nd1;
else
selec /= nd2;
}
if (have_mcvs1)
free_attstatsslot(vardata1->atttype, values1, nvalues1,
numbers1, nnumbers1);
if (have_mcvs2)
free_attstatsslot(vardata2->atttype, values2, nvalues2,
numbers2, nnumbers2);
return selec;
}
/*
* eqjoinsel_semi --- eqjoinsel for semi join
*
* (Also used for anti join, which we are supposed to estimate the same way.)
* Caller has ensured that vardata1 is the LHS variable.
*/
static double
eqjoinsel_semi(Oid operator,
VariableStatData *vardata1, VariableStatData *vardata2,
RelOptInfo *inner_rel)
{
double selec;
double nd1;
double nd2;
Form_pg_statistic stats1 = NULL;
Form_pg_statistic stats2 = NULL;
bool have_mcvs1 = false;
Datum *values1 = NULL;
int nvalues1 = 0;
float4 *numbers1 = NULL;
int nnumbers1 = 0;
bool have_mcvs2 = false;
Datum *values2 = NULL;
int nvalues2 = 0;
float4 *numbers2 = NULL;
int nnumbers2 = 0;
nd1 = get_variable_numdistinct(vardata1);
nd2 = get_variable_numdistinct(vardata2);
/*
* We clamp nd2 to be not more than what we estimate the inner relation's
* size to be. This is intuitively somewhat reasonable since obviously
* there can't be more than that many distinct values coming from the
* inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
* likewise) is that this is the only pathway by which restriction clauses
* applied to the inner rel will affect the join result size estimate,
* since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
* only the outer rel's size. If we clamped nd1 we'd be double-counting
* the selectivity of outer-rel restrictions.
*
* We can apply this clamping both with respect to the base relation from
* which the join variable comes (if there is just one), and to the
* immediate inner input relation of the current join.
*/
if (vardata2->rel)
nd2 = Min(nd2, vardata2->rel->rows);
nd2 = Min(nd2, inner_rel->rows);
if (HeapTupleIsValid(vardata1->statsTuple))
{
stats1 = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple);
have_mcvs1 = get_attstatsslot(vardata1->statsTuple,
vardata1->atttype,
vardata1->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
&values1, &nvalues1,
&numbers1, &nnumbers1);
}
if (HeapTupleIsValid(vardata2->statsTuple))
{
stats2 = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple);
have_mcvs2 = get_attstatsslot(vardata2->statsTuple,
vardata2->atttype,
vardata2->atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
&values2, &nvalues2,
&numbers2, &nnumbers2);
}
if (have_mcvs1 && have_mcvs2 && OidIsValid(operator))
{
/*
* We have most-common-value lists for both relations. Run through
* the lists to see which MCVs actually join to each other with the
* given operator. This allows us to determine the exact join
* selectivity for the portion of the relations represented by the MCV
* lists. We still have to estimate for the remaining population, but
* in a skewed distribution this gives us a big leg up in accuracy.
*/
FmgrInfo eqproc;
bool *hasmatch1;
bool *hasmatch2;
double nullfrac1 = stats1->stanullfrac;
double matchfreq1,
uncertainfrac,
uncertain;
int i,
nmatches,
clamped_nvalues2;
/*
* The clamping above could have resulted in nd2 being less than
* nvalues2; in which case, we assume that precisely the nd2 most
* common values in the relation will appear in the join input, and so
* compare to only the first nd2 members of the MCV list. Of course
* this is frequently wrong, but it's the best bet we can make.
*/
clamped_nvalues2 = Min(nvalues2, nd2);
fmgr_info(get_opcode(operator), &eqproc);
hasmatch1 = (bool *) palloc0(nvalues1 * sizeof(bool));
hasmatch2 = (bool *) palloc0(clamped_nvalues2 * sizeof(bool));
/*
* Note we assume that each MCV will match at most one member of the
* other MCV list. If the operator isn't really equality, there could
* be multiple matches --- but we don't look for them, both for speed
* and because the math wouldn't add up...
*/
nmatches = 0;
for (i = 0; i < nvalues1; i++)
{
int j;
for (j = 0; j < clamped_nvalues2; j++)
{
if (hasmatch2[j])
continue;
if (DatumGetBool(FunctionCall2(&eqproc,
values1[i],
values2[j])))
{
hasmatch1[i] = hasmatch2[j] = true;
nmatches++;
break;
}
}
}
/* Sum up frequencies of matched MCVs */
matchfreq1 = 0.0;
for (i = 0; i < nvalues1; i++)
{
if (hasmatch1[i])
matchfreq1 += numbers1[i];
}
CLAMP_PROBABILITY(matchfreq1);
pfree(hasmatch1);
pfree(hasmatch2);
/*
* Now we need to estimate the fraction of relation 1 that has at
* least one join partner. We know for certain that the matched MCVs
* do, so that gives us a lower bound, but we're really in the dark
* about everything else. Our crude approach is: if nd1 <= nd2 then
* assume all non-null rel1 rows have join partners, else assume for
* the uncertain rows that a fraction nd2/nd1 have join partners. We
* can discount the known-matched MCVs from the distinct-values counts
* before doing the division.
*
* Crude as the above is, it's completely useless if we don't have
* reliable ndistinct values for both sides. Hence, if either nd1
* or nd2 is default, punt and assume half of the uncertain rows
* have join partners.
*/
if (nd1 != DEFAULT_NUM_DISTINCT && nd2 != DEFAULT_NUM_DISTINCT)
{
nd1 -= nmatches;
nd2 -= nmatches;
if (nd1 <= nd2 || nd2 < 0)
uncertainfrac = 1.0;
else
uncertainfrac = nd2 / nd1;
}
else
uncertainfrac = 0.5;
uncertain = 1.0 - matchfreq1 - nullfrac1;
CLAMP_PROBABILITY(uncertain);
selec = matchfreq1 + uncertainfrac * uncertain;
}
else
{
/*
* Without MCV lists for both sides, we can only use the heuristic
* about nd1 vs nd2.
*/
double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
if (nd1 != DEFAULT_NUM_DISTINCT && nd2 != DEFAULT_NUM_DISTINCT)
{
if (nd1 <= nd2 || nd2 < 0)
selec = 1.0 - nullfrac1;
else
selec = (nd2 / nd1) * (1.0 - nullfrac1);
}
else
selec = 0.5 * (1.0 - nullfrac1);
}
if (have_mcvs1)
free_attstatsslot(vardata1->atttype, values1, nvalues1,
numbers1, nnumbers1);
if (have_mcvs2)
free_attstatsslot(vardata2->atttype, values2, nvalues2,
numbers2, nnumbers2);
return selec;
}
/*
* neqjoinsel - Join selectivity of "!="
*/
Datum
neqjoinsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
Oid eqop;
float8 result;
/*
* We want 1 - eqjoinsel() where the equality operator is the one
* associated with this != operator, that is, its negator.
*/
eqop = get_negator(operator);
if (eqop)
{
result = DatumGetFloat8(DirectFunctionCall5(eqjoinsel,
PointerGetDatum(root),
ObjectIdGetDatum(eqop),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
}
else
{
/* Use default selectivity (should we raise an error instead?) */
result = DEFAULT_EQ_SEL;
}
result = 1.0 - result;
PG_RETURN_FLOAT8(result);
}
/*
* scalarltjoinsel - Join selectivity of "<" and "<=" for scalars
*/
Datum
scalarltjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalargtjoinsel - Join selectivity of ">" and ">=" for scalars
*/
Datum
scalargtjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* patternjoinsel - Generic code for pattern-match join selectivity.
*/
static double
patternjoinsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
{
/* For the moment we just punt. */
return negate ? (1.0 - DEFAULT_MATCH_SEL) : DEFAULT_MATCH_SEL;
}
/*
* regexeqjoinsel - Join selectivity of regular-expression pattern match.
*/
Datum
regexeqjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex, false));
}
/*
* icregexeqjoinsel - Join selectivity of case-insensitive regex match.
*/
Datum
icregexeqjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex_IC, false));
}
/*
* likejoinsel - Join selectivity of LIKE pattern match.
*/
Datum
likejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like, false));
}
/*
* iclikejoinsel - Join selectivity of ILIKE pattern match.
*/
Datum
iclikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like_IC, false));
}
/*
* regexnejoinsel - Join selectivity of regex non-match.
*/
Datum
regexnejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex, true));
}
/*
* icregexnejoinsel - Join selectivity of case-insensitive regex non-match.
*/
Datum
icregexnejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex_IC, true));
}
/*
* nlikejoinsel - Join selectivity of LIKE pattern non-match.
*/
Datum
nlikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like, true));
}
/*
* icnlikejoinsel - Join selectivity of ILIKE pattern non-match.
*/
Datum
icnlikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like_IC, true));
}
/*
* mergejoinscansel - Scan selectivity of merge join.
*
* A merge join will stop as soon as it exhausts either input stream.
* Therefore, if we can estimate the ranges of both input variables,
* we can estimate how much of the input will actually be read. This
* can have a considerable impact on the cost when using indexscans.
*
* Also, we can estimate how much of each input has to be read before the
* first join pair is found, which will affect the join's startup time.
*
* clause should be a clause already known to be mergejoinable. opfamily,
* strategy, and nulls_first specify the sort ordering being used.
*
* The outputs are:
* *leftstart is set to the fraction of the left-hand variable expected
* to be scanned before the first join pair is found (0 to 1).
* *leftend is set to the fraction of the left-hand variable expected
* to be scanned before the join terminates (0 to 1).
* *rightstart, *rightend similarly for the right-hand variable.
*/
void
mergejoinscansel(PlannerInfo *root, Node *clause,
Oid opfamily, int strategy, bool nulls_first,
Selectivity *leftstart, Selectivity *leftend,
Selectivity *rightstart, Selectivity *rightend)
{
Node *left,
*right;
VariableStatData leftvar,
rightvar;
int op_strategy;
Oid op_lefttype;
Oid op_righttype;
Oid opno,
lsortop,
rsortop,
lstatop,
rstatop,
ltop,
leop,
revltop,
revleop;
bool isgt;
Datum leftmin,
leftmax,
rightmin,
rightmax;
double selec;
/* Set default results if we can't figure anything out. */
/* XXX should default "start" fraction be a bit more than 0? */
*leftstart = *rightstart = 0.0;
*leftend = *rightend = 1.0;
/* Deconstruct the merge clause */
if (!is_opclause(clause))
return; /* shouldn't happen */
opno = ((OpExpr *) clause)->opno;
left = get_leftop((Expr *) clause);
right = get_rightop((Expr *) clause);
if (!right)
return; /* shouldn't happen */
/* Look for stats for the inputs */
examine_variable(root, left, 0, &leftvar);
examine_variable(root, right, 0, &rightvar);
/* Extract the operator's declared left/right datatypes */
get_op_opfamily_properties(opno, opfamily,
&op_strategy,
&op_lefttype,
&op_righttype);
Assert(op_strategy == BTEqualStrategyNumber);
/*
* Look up the various operators we need. If we don't find them all, it
* probably means the opfamily is broken, but we just fail silently.
*
* Note: we expect that pg_statistic histograms will be sorted by the '<'
* operator, regardless of which sort direction we are considering.
*/
switch (strategy)
{
case BTLessStrategyNumber:
isgt = false;
if (op_lefttype == op_righttype)
{
/* easy case */
ltop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTLessStrategyNumber);
leop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTLessEqualStrategyNumber);
lsortop = ltop;
rsortop = ltop;
lstatop = lsortop;
rstatop = rsortop;
revltop = ltop;
revleop = leop;
}
else
{
ltop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTLessStrategyNumber);
leop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTLessEqualStrategyNumber);
lsortop = get_opfamily_member(opfamily,
op_lefttype, op_lefttype,
BTLessStrategyNumber);
rsortop = get_opfamily_member(opfamily,
op_righttype, op_righttype,
BTLessStrategyNumber);
lstatop = lsortop;
rstatop = rsortop;
revltop = get_opfamily_member(opfamily,
op_righttype, op_lefttype,
BTLessStrategyNumber);
revleop = get_opfamily_member(opfamily,
op_righttype, op_lefttype,
BTLessEqualStrategyNumber);
}
break;
case BTGreaterStrategyNumber:
/* descending-order case */
isgt = true;
if (op_lefttype == op_righttype)
{
/* easy case */
ltop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTGreaterStrategyNumber);
leop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTGreaterEqualStrategyNumber);
lsortop = ltop;
rsortop = ltop;
lstatop = get_opfamily_member(opfamily,
op_lefttype, op_lefttype,
BTLessStrategyNumber);
rstatop = lstatop;
revltop = ltop;
revleop = leop;
}
else
{
ltop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTGreaterStrategyNumber);
leop = get_opfamily_member(opfamily,
op_lefttype, op_righttype,
BTGreaterEqualStrategyNumber);
lsortop = get_opfamily_member(opfamily,
op_lefttype, op_lefttype,
BTGreaterStrategyNumber);
rsortop = get_opfamily_member(opfamily,
op_righttype, op_righttype,
BTGreaterStrategyNumber);
lstatop = get_opfamily_member(opfamily,
op_lefttype, op_lefttype,
BTLessStrategyNumber);
rstatop = get_opfamily_member(opfamily,
op_righttype, op_righttype,
BTLessStrategyNumber);
revltop = get_opfamily_member(opfamily,
op_righttype, op_lefttype,
BTGreaterStrategyNumber);
revleop = get_opfamily_member(opfamily,
op_righttype, op_lefttype,
BTGreaterEqualStrategyNumber);
}
break;
default:
goto fail; /* shouldn't get here */
}
if (!OidIsValid(lsortop) ||
!OidIsValid(rsortop) ||
!OidIsValid(lstatop) ||
!OidIsValid(rstatop) ||
!OidIsValid(ltop) ||
!OidIsValid(leop) ||
!OidIsValid(revltop) ||
!OidIsValid(revleop))
goto fail; /* insufficient info in catalogs */
/* Try to get ranges of both inputs */
if (!isgt)
{
if (!get_variable_range(root, &leftvar, lstatop,
&leftmin, &leftmax))
goto fail; /* no range available from stats */
if (!get_variable_range(root, &rightvar, rstatop,
&rightmin, &rightmax))
goto fail; /* no range available from stats */
}
else
{
/* need to swap the max and min */
if (!get_variable_range(root, &leftvar, lstatop,
&leftmax, &leftmin))
goto fail; /* no range available from stats */
if (!get_variable_range(root, &rightvar, rstatop,
&rightmax, &rightmin))
goto fail; /* no range available from stats */
}
/*
* Now, the fraction of the left variable that will be scanned is the
* fraction that's <= the right-side maximum value. But only believe
* non-default estimates, else stick with our 1.0.
*/
selec = scalarineqsel(root, leop, isgt, &leftvar,
rightmax, op_righttype);
if (selec != DEFAULT_INEQ_SEL)
*leftend = selec;
/* And similarly for the right variable. */
selec = scalarineqsel(root, revleop, isgt, &rightvar,
leftmax, op_lefttype);
if (selec != DEFAULT_INEQ_SEL)
*rightend = selec;
/*
* Only one of the two "end" fractions can really be less than 1.0;
* believe the smaller estimate and reset the other one to exactly 1.0. If
* we get exactly equal estimates (as can easily happen with self-joins),
* believe neither.
*/
if (*leftend > *rightend)
*leftend = 1.0;
else if (*leftend < *rightend)
*rightend = 1.0;
else
*leftend = *rightend = 1.0;
/*
* Also, the fraction of the left variable that will be scanned before the
* first join pair is found is the fraction that's < the right-side
* minimum value. But only believe non-default estimates, else stick with
* our own default.
*/
selec = scalarineqsel(root, ltop, isgt, &leftvar,
rightmin, op_righttype);
if (selec != DEFAULT_INEQ_SEL)
*leftstart = selec;
/* And similarly for the right variable. */
selec = scalarineqsel(root, revltop, isgt, &rightvar,
leftmin, op_lefttype);
if (selec != DEFAULT_INEQ_SEL)
*rightstart = selec;
/*
* Only one of the two "start" fractions can really be more than zero;
* believe the larger estimate and reset the other one to exactly 0.0. If
* we get exactly equal estimates (as can easily happen with self-joins),
* believe neither.
*/
if (*leftstart < *rightstart)
*leftstart = 0.0;
else if (*leftstart > *rightstart)
*rightstart = 0.0;
else
*leftstart = *rightstart = 0.0;
/*
* If the sort order is nulls-first, we're going to have to skip over any
* nulls too. These would not have been counted by scalarineqsel, and we
* can safely add in this fraction regardless of whether we believe
* scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
*/
if (nulls_first)
{
Form_pg_statistic stats;
if (HeapTupleIsValid(leftvar.statsTuple))
{
stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
*leftstart += stats->stanullfrac;
CLAMP_PROBABILITY(*leftstart);
*leftend += stats->stanullfrac;
CLAMP_PROBABILITY(*leftend);
}
if (HeapTupleIsValid(rightvar.statsTuple))
{
stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple);
*rightstart += stats->stanullfrac;
CLAMP_PROBABILITY(*rightstart);
*rightend += stats->stanullfrac;
CLAMP_PROBABILITY(*rightend);
}
}
/* Disbelieve start >= end, just in case that can happen */
if (*leftstart >= *leftend)
{
*leftstart = 0.0;
*leftend = 1.0;
}
if (*rightstart >= *rightend)
{
*rightstart = 0.0;
*rightend = 1.0;
}
fail:
ReleaseVariableStats(leftvar);
ReleaseVariableStats(rightvar);
}
/*
* Helper routine for estimate_num_groups: add an item to a list of
* GroupVarInfos, but only if it's not known equal to any of the existing
* entries.
*/
typedef struct
{
Node *var; /* might be an expression, not just a Var */
RelOptInfo *rel; /* relation it belongs to */
double ndistinct; /* # distinct values */
} GroupVarInfo;
static List *
add_unique_group_var(PlannerInfo *root, List *varinfos,
Node *var, VariableStatData *vardata)
{
GroupVarInfo *varinfo;
double ndistinct;
ListCell *lc;
ndistinct = get_variable_numdistinct(vardata);
/* cannot use foreach here because of possible list_delete */
lc = list_head(varinfos);
while (lc)
{
varinfo = (GroupVarInfo *) lfirst(lc);
/* must advance lc before list_delete possibly pfree's it */
lc = lnext(lc);
/* Drop exact duplicates */
if (equal(var, varinfo->var))
return varinfos;
/*
* Drop known-equal vars, but only if they belong to different
* relations (see comments for estimate_num_groups)
*/
if (vardata->rel != varinfo->rel &&
exprs_known_equal(root, var, varinfo->var))
{
if (varinfo->ndistinct <= ndistinct)
{
/* Keep older item, forget new one */
return varinfos;
}
else
{
/* Delete the older item */
varinfos = list_delete_ptr(varinfos, varinfo);
}
}
}
varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
varinfo->var = var;
varinfo->rel = vardata->rel;
varinfo->ndistinct = ndistinct;
varinfos = lappend(varinfos, varinfo);
return varinfos;
}
/*
* estimate_num_groups - Estimate number of groups in a grouped query
*
* Given a query having a GROUP BY clause, estimate how many groups there
* will be --- ie, the number of distinct combinations of the GROUP BY
* expressions.
*
* This routine is also used to estimate the number of rows emitted by
* a DISTINCT filtering step; that is an isomorphic problem. (Note:
* actually, we only use it for DISTINCT when there's no grouping or
* aggregation ahead of the DISTINCT.)
*
* Inputs:
* root - the query
* groupExprs - list of expressions being grouped by
* input_rows - number of rows estimated to arrive at the group/unique
* filter step
*
* Given the lack of any cross-correlation statistics in the system, it's
* impossible to do anything really trustworthy with GROUP BY conditions
* involving multiple Vars. We should however avoid assuming the worst
* case (all possible cross-product terms actually appear as groups) since
* very often the grouped-by Vars are highly correlated. Our current approach
* is as follows:
* 1. Expressions yielding boolean are assumed to contribute two groups,
* independently of their content, and are ignored in the subsequent
* steps. This is mainly because tests like "col IS NULL" break the
* heuristic used in step 2 especially badly.
* 2. Reduce the given expressions to a list of unique Vars used. For
* example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
* It is clearly correct not to count the same Var more than once.
* It is also reasonable to treat f(x) the same as x: f() cannot
* increase the number of distinct values (unless it is volatile,
* which we consider unlikely for grouping), but it probably won't
* reduce the number of distinct values much either.
* As a special case, if a GROUP BY expression can be matched to an
* expressional index for which we have statistics, then we treat the
* whole expression as though it were just a Var.
* 3. If the list contains Vars of different relations that are known equal
* due to equivalence classes, then drop all but one of the Vars from each
* known-equal set, keeping the one with smallest estimated # of values
* (since the extra values of the others can't appear in joined rows).
* Note the reason we only consider Vars of different relations is that
* if we considered ones of the same rel, we'd be double-counting the
* restriction selectivity of the equality in the next step.
* 4. For Vars within a single source rel, we multiply together the numbers
* of values, clamp to the number of rows in the rel (divided by 10 if
* more than one Var), and then multiply by the selectivity of the
* restriction clauses for that rel. When there's more than one Var,
* the initial product is probably too high (it's the worst case) but
* clamping to a fraction of the rel's rows seems to be a helpful
* heuristic for not letting the estimate get out of hand. (The factor
* of 10 is derived from pre-Postgres-7.4 practice.) Multiplying
* by the restriction selectivity is effectively assuming that the
* restriction clauses are independent of the grouping, which is a crummy
* assumption, but it's hard to do better.
* 5. If there are Vars from multiple rels, we repeat step 4 for each such
* rel, and multiply the results together.
* Note that rels not containing grouped Vars are ignored completely, as are
* join clauses. Such rels cannot increase the number of groups, and we
* assume such clauses do not reduce the number either (somewhat bogus,
* but we don't have the info to do better).
*/
double
estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows)
{
List *varinfos = NIL;
double numdistinct;
ListCell *l;
/*
* If no grouping columns, there's exactly one group. (This can't happen
* for normal cases with GROUP BY or DISTINCT, but it is possible for
* corner cases with set operations.)
*/
if (groupExprs == NIL)
return 1.0;
/*
* Count groups derived from boolean grouping expressions. For other
* expressions, find the unique Vars used, treating an expression as a Var
* if we can find stats for it. For each one, record the statistical
* estimate of number of distinct values (total in its table, without
* regard for filtering).
*/
numdistinct = 1.0;
foreach(l, groupExprs)
{
Node *groupexpr = (Node *) lfirst(l);
VariableStatData vardata;
List *varshere;
ListCell *l2;
/* Short-circuit for expressions returning boolean */
if (exprType(groupexpr) == BOOLOID)
{
numdistinct *= 2.0;
continue;
}
/*
* If examine_variable is able to deduce anything about the GROUP BY
* expression, treat it as a single variable even if it's really more
* complicated.
*/
examine_variable(root, groupexpr, 0, &vardata);
if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
{
varinfos = add_unique_group_var(root, varinfos,
groupexpr, &vardata);
ReleaseVariableStats(vardata);
continue;
}
ReleaseVariableStats(vardata);
/*
* Else pull out the component Vars. Handle PlaceHolderVars by
* recursing into their arguments (effectively assuming that the
* PlaceHolderVar doesn't change the number of groups, which boils
* down to ignoring the possible addition of nulls to the result set).
*/
varshere = pull_var_clause(groupexpr, PVC_RECURSE_PLACEHOLDERS);
/*
* If we find any variable-free GROUP BY item, then either it is a
* constant (and we can ignore it) or it contains a volatile function;
* in the latter case we punt and assume that each input row will
* yield a distinct group.
*/
if (varshere == NIL)
{
if (contain_volatile_functions(groupexpr))
return input_rows;
continue;
}
/*
* Else add variables to varinfos list
*/
foreach(l2, varshere)
{
Node *var = (Node *) lfirst(l2);
examine_variable(root, var, 0, &vardata);
varinfos = add_unique_group_var(root, varinfos, var, &vardata);
ReleaseVariableStats(vardata);
}
}
/*
* If now no Vars, we must have an all-constant or all-boolean GROUP BY
* list.
*/
if (varinfos == NIL)
{
/* Guard against out-of-range answers */
if (numdistinct > input_rows)
numdistinct = input_rows;
return numdistinct;
}
/*
* Group Vars by relation and estimate total numdistinct.
*
* For each iteration of the outer loop, we process the frontmost Var in
* varinfos, plus all other Vars in the same relation. We remove these
* Vars from the newvarinfos list for the next iteration. This is the
* easiest way to group Vars of same rel together.
*/
do
{
GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
RelOptInfo *rel = varinfo1->rel;
double reldistinct = varinfo1->ndistinct;
double relmaxndistinct = reldistinct;
int relvarcount = 1;
List *newvarinfos = NIL;
/*
* Get the product of numdistinct estimates of the Vars for this rel.
* Also, construct new varinfos list of remaining Vars.
*/
for_each_cell(l, lnext(list_head(varinfos)))
{
GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
if (varinfo2->rel == varinfo1->rel)
{
reldistinct *= varinfo2->ndistinct;
if (relmaxndistinct < varinfo2->ndistinct)
relmaxndistinct = varinfo2->ndistinct;
relvarcount++;
}
else
{
/* not time to process varinfo2 yet */
newvarinfos = lcons(varinfo2, newvarinfos);
}
}
/*
* Sanity check --- don't divide by zero if empty relation.
*/
Assert(rel->reloptkind == RELOPT_BASEREL);
if (rel->tuples > 0)
{
/*
* Clamp to size of rel, or size of rel / 10 if multiple Vars. The
* fudge factor is because the Vars are probably correlated but we
* don't know by how much. We should never clamp to less than the
* largest ndistinct value for any of the Vars, though, since
* there will surely be at least that many groups.
*/
double clamp = rel->tuples;
if (relvarcount > 1)
{
clamp *= 0.1;
if (clamp < relmaxndistinct)
{
clamp = relmaxndistinct;
/* for sanity in case some ndistinct is too large: */
if (clamp > rel->tuples)
clamp = rel->tuples;
}
}
if (reldistinct > clamp)
reldistinct = clamp;
/*
* Multiply by restriction selectivity.
*/
reldistinct *= rel->rows / rel->tuples;
/*
* Update estimate of total distinct groups.
*/
numdistinct *= reldistinct;
}
varinfos = newvarinfos;
} while (varinfos != NIL);
numdistinct = ceil(numdistinct);
/* Guard against out-of-range answers */
if (numdistinct > input_rows)
numdistinct = input_rows;
if (numdistinct < 1.0)
numdistinct = 1.0;
return numdistinct;
}
/*
* Estimate hash bucketsize fraction (ie, number of entries in a bucket
* divided by total tuples in relation) if the specified expression is used
* as a hash key.
*
* XXX This is really pretty bogus since we're effectively assuming that the
* distribution of hash keys will be the same after applying restriction
* clauses as it was in the underlying relation. However, we are not nearly
* smart enough to figure out how the restrict clauses might change the
* distribution, so this will have to do for now.
*
* We are passed the number of buckets the executor will use for the given
* input relation. If the data were perfectly distributed, with the same
* number of tuples going into each available bucket, then the bucketsize
* fraction would be 1/nbuckets. But this happy state of affairs will occur
* only if (a) there are at least nbuckets distinct data values, and (b)
* we have a not-too-skewed data distribution. Otherwise the buckets will
* be nonuniformly occupied. If the other relation in the join has a key
* distribution similar to this one's, then the most-loaded buckets are
* exactly those that will be probed most often. Therefore, the "average"
* bucket size for costing purposes should really be taken as something close
* to the "worst case" bucket size. We try to estimate this by adjusting the
* fraction if there are too few distinct data values, and then scaling up
* by the ratio of the most common value's frequency to the average frequency.
*
* If no statistics are available, use a default estimate of 0.1. This will
* discourage use of a hash rather strongly if the inner relation is large,
* which is what we want. We do not want to hash unless we know that the
* inner rel is well-dispersed (or the alternatives seem much worse).
*/
Selectivity
estimate_hash_bucketsize(PlannerInfo *root, Node *hashkey, double nbuckets)
{
VariableStatData vardata;
double estfract,
ndistinct,
stanullfrac,
mcvfreq,
avgfreq;
float4 *numbers;
int nnumbers;
examine_variable(root, hashkey, 0, &vardata);
/* Get number of distinct values and fraction that are null */
ndistinct = get_variable_numdistinct(&vardata);
if (HeapTupleIsValid(vardata.statsTuple))
{
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
stanullfrac = stats->stanullfrac;
}
else
{
/*
* Believe a default ndistinct only if it came from stats. Otherwise
* punt and return 0.1, per comments above.
*/
if (ndistinct == DEFAULT_NUM_DISTINCT)
{
ReleaseVariableStats(vardata);
return (Selectivity) 0.1;
}
stanullfrac = 0.0;
}
/* Compute avg freq of all distinct data values in raw relation */
avgfreq = (1.0 - stanullfrac) / ndistinct;
/*
* Adjust ndistinct to account for restriction clauses. Observe we are
* assuming that the data distribution is affected uniformly by the
* restriction clauses!
*
* XXX Possibly better way, but much more expensive: multiply by
* selectivity of rel's restriction clauses that mention the target Var.
*/
if (vardata.rel)
ndistinct *= vardata.rel->rows / vardata.rel->tuples;
/*
* Initial estimate of bucketsize fraction is 1/nbuckets as long as the
* number of buckets is less than the expected number of distinct values;
* otherwise it is 1/ndistinct.
*/
if (ndistinct > nbuckets)
estfract = 1.0 / nbuckets;
else
estfract = 1.0 / ndistinct;
/*
* Look up the frequency of the most common value, if available.
*/
mcvfreq = 0.0;
if (HeapTupleIsValid(vardata.statsTuple))
{
if (get_attstatsslot(vardata.statsTuple,
vardata.atttype, vardata.atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
NULL, NULL, &numbers, &nnumbers))
{
/*
* The first MCV stat is for the most common value.
*/
if (nnumbers > 0)
mcvfreq = numbers[0];
free_attstatsslot(vardata.atttype, NULL, 0,
numbers, nnumbers);
}
}
/*
* Adjust estimated bucketsize upward to account for skewed distribution.
*/
if (avgfreq > 0.0 && mcvfreq > avgfreq)
estfract *= mcvfreq / avgfreq;
/*
* Clamp bucketsize to sane range (the above adjustment could easily
* produce an out-of-range result). We set the lower bound a little above
* zero, since zero isn't a very sane result.
*/
if (estfract < 1.0e-6)
estfract = 1.0e-6;
else if (estfract > 1.0)
estfract = 1.0;
ReleaseVariableStats(vardata);
return (Selectivity) estfract;
}
/*-------------------------------------------------------------------------
*
* Support routines
*
*-------------------------------------------------------------------------
*/
/*
* convert_to_scalar
* Convert non-NULL values of the indicated types to the comparison
* scale needed by scalarineqsel().
* Returns "true" if successful.
*
* XXX this routine is a hack: ideally we should look up the conversion
* subroutines in pg_type.
*
* All numeric datatypes are simply converted to their equivalent
* "double" values. (NUMERIC values that are outside the range of "double"
* are clamped to +/- HUGE_VAL.)
*
* String datatypes are converted by convert_string_to_scalar(),
* which is explained below. The reason why this routine deals with
* three values at a time, not just one, is that we need it for strings.
*
* The bytea datatype is just enough different from strings that it has
* to be treated separately.
*
* The several datatypes representing absolute times are all converted
* to Timestamp, which is actually a double, and then we just use that
* double value. Note this will give correct results even for the "special"
* values of Timestamp, since those are chosen to compare correctly;
* see timestamp_cmp.
*
* The several datatypes representing relative times (intervals) are all
* converted to measurements expressed in seconds.
*/
static bool
convert_to_scalar(Datum value, Oid valuetypid, double *scaledvalue,
Datum lobound, Datum hibound, Oid boundstypid,
double *scaledlobound, double *scaledhibound)
{
/*
* Both the valuetypid and the boundstypid should exactly match the
* declared input type(s) of the operator we are invoked for, so we just
* error out if either is not recognized.
*
* XXX The histogram we are interpolating between points of could belong
* to a column that's only binary-compatible with the declared type. In
* essence we are assuming that the semantics of binary-compatible types
* are enough alike that we can use a histogram generated with one type's
* operators to estimate selectivity for the other's. This is outright
* wrong in some cases --- in particular signed versus unsigned
* interpretation could trip us up. But it's useful enough in the
* majority of cases that we do it anyway. Should think about more
* rigorous ways to do it.
*/
switch (valuetypid)
{
/*
* Built-in numeric types
*/
case BOOLOID:
case INT2OID:
case INT4OID:
case INT8OID:
case FLOAT4OID:
case FLOAT8OID:
case NUMERICOID:
case OIDOID:
case REGPROCOID:
case REGPROCEDUREOID:
case REGOPEROID:
case REGOPERATOROID:
case REGCLASSOID:
case REGTYPEOID:
case REGCONFIGOID:
case REGDICTIONARYOID:
*scaledvalue = convert_numeric_to_scalar(value, valuetypid);
*scaledlobound = convert_numeric_to_scalar(lobound, boundstypid);
*scaledhibound = convert_numeric_to_scalar(hibound, boundstypid);
return true;
/*
* Built-in string types
*/
case CHAROID:
case BPCHAROID:
case VARCHAROID:
case TEXTOID:
case NAMEOID:
{
char *valstr = convert_string_datum(value, valuetypid);
char *lostr = convert_string_datum(lobound, boundstypid);
char *histr = convert_string_datum(hibound, boundstypid);
convert_string_to_scalar(valstr, scaledvalue,
lostr, scaledlobound,
histr, scaledhibound);
pfree(valstr);
pfree(lostr);
pfree(histr);
return true;
}
/*
* Built-in bytea type
*/
case BYTEAOID:
{
convert_bytea_to_scalar(value, scaledvalue,
lobound, scaledlobound,
hibound, scaledhibound);
return true;
}
/*
* Built-in time types
*/
case TIMESTAMPOID:
case TIMESTAMPTZOID:
case ABSTIMEOID:
case DATEOID:
case INTERVALOID:
case RELTIMEOID:
case TINTERVALOID:
case TIMEOID:
case TIMETZOID:
*scaledvalue = convert_timevalue_to_scalar(value, valuetypid);
*scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid);
*scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid);
return true;
/*
* Built-in network types
*/
case INETOID:
case CIDROID:
case MACADDROID:
*scaledvalue = convert_network_to_scalar(value, valuetypid);
*scaledlobound = convert_network_to_scalar(lobound, boundstypid);
*scaledhibound = convert_network_to_scalar(hibound, boundstypid);
return true;
}
/* Don't know how to convert */
*scaledvalue = *scaledlobound = *scaledhibound = 0;
return false;
}
/*
* Do convert_to_scalar()'s work for any numeric data type.
*/
static double
convert_numeric_to_scalar(Datum value, Oid typid)
{
switch (typid)
{
case BOOLOID:
return (double) DatumGetBool(value);
case INT2OID:
return (double) DatumGetInt16(value);
case INT4OID:
return (double) DatumGetInt32(value);
case INT8OID:
return (double) DatumGetInt64(value);
case FLOAT4OID:
return (double) DatumGetFloat4(value);
case FLOAT8OID:
return (double) DatumGetFloat8(value);
case NUMERICOID:
/* Note: out-of-range values will be clamped to +-HUGE_VAL */
return (double)
DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
value));
case OIDOID:
case REGPROCOID:
case REGPROCEDUREOID:
case REGOPEROID:
case REGOPERATOROID:
case REGCLASSOID:
case REGTYPEOID:
case REGCONFIGOID:
case REGDICTIONARYOID:
/* we can treat OIDs as integers... */
return (double) DatumGetObjectId(value);
}
/*
* Can't get here unless someone tries to use scalarltsel/scalargtsel on
* an operator with one numeric and one non-numeric operand.
*/
elog(ERROR, "unsupported type: %u", typid);
return 0;
}
/*
* Do convert_to_scalar()'s work for any character-string data type.
*
* String datatypes are converted to a scale that ranges from 0 to 1,
* where we visualize the bytes of the string as fractional digits.
*
* We do not want the base to be 256, however, since that tends to
* generate inflated selectivity estimates; few databases will have
* occurrences of all 256 possible byte values at each position.
* Instead, use the smallest and largest byte values seen in the bounds
* as the estimated range for each byte, after some fudging to deal with
* the fact that we probably aren't going to see the full range that way.
*
* An additional refinement is that we discard any common prefix of the
* three strings before computing the scaled values. This allows us to
* "zoom in" when we encounter a narrow data range. An example is a phone
* number database where all the values begin with the same area code.
* (Actually, the bounds will be adjacent histogram-bin-boundary values,
* so this is more likely to happen than you might think.)
*/
static void
convert_string_to_scalar(char *value,
double *scaledvalue,
char *lobound,
double *scaledlobound,
char *hibound,
double *scaledhibound)
{
int rangelo,
rangehi;
char *sptr;
rangelo = rangehi = (unsigned char) hibound[0];
for (sptr = lobound; *sptr; sptr++)
{
if (rangelo > (unsigned char) *sptr)
rangelo = (unsigned char) *sptr;
if (rangehi < (unsigned char) *sptr)
rangehi = (unsigned char) *sptr;
}
for (sptr = hibound; *sptr; sptr++)
{
if (rangelo > (unsigned char) *sptr)
rangelo = (unsigned char) *sptr;
if (rangehi < (unsigned char) *sptr)
rangehi = (unsigned char) *sptr;
}
/* If range includes any upper-case ASCII chars, make it include all */
if (rangelo <= 'Z' && rangehi >= 'A')
{
if (rangelo > 'A')
rangelo = 'A';
if (rangehi < 'Z')
rangehi = 'Z';
}
/* Ditto lower-case */
if (rangelo <= 'z' && rangehi >= 'a')
{
if (rangelo > 'a')
rangelo = 'a';
if (rangehi < 'z')
rangehi = 'z';
}
/* Ditto digits */
if (rangelo <= '9' && rangehi >= '0')
{
if (rangelo > '0')
rangelo = '0';
if (rangehi < '9')
rangehi = '9';
}
/*
* If range includes less than 10 chars, assume we have not got enough
* data, and make it include regular ASCII set.
*/
if (rangehi - rangelo < 9)
{
rangelo = ' ';
rangehi = 127;
}
/*
* Now strip any common prefix of the three strings.
*/
while (*lobound)
{
if (*lobound != *hibound || *lobound != *value)
break;
lobound++, hibound++, value++;
}
/*
* Now we can do the conversions.
*/
*scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
*scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
*scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
}
static double
convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
{
int slen = strlen(value);
double num,
denom,
base;
if (slen <= 0)
return 0.0; /* empty string has scalar value 0 */
/*
* Since base is at least 10, need not consider more than about 20 chars
*/
if (slen > 20)
slen = 20;
/* Convert initial characters to fraction */
base = rangehi - rangelo + 1;
num = 0.0;
denom = base;
while (slen-- > 0)
{
int ch = (unsigned char) *value++;
if (ch < rangelo)
ch = rangelo - 1;
else if (ch > rangehi)
ch = rangehi + 1;
num += ((double) (ch - rangelo)) / denom;
denom *= base;
}
return num;
}
/*
* Convert a string-type Datum into a palloc'd, null-terminated string.
*
* When using a non-C locale, we must pass the string through strxfrm()
* before continuing, so as to generate correct locale-specific results.
*/
static char *
convert_string_datum(Datum value, Oid typid)
{
char *val;
switch (typid)
{
case CHAROID:
val = (char *) palloc(2);
val[0] = DatumGetChar(value);
val[1] = '\0';
break;
case BPCHAROID:
case VARCHAROID:
case TEXTOID:
val = TextDatumGetCString(value);
break;
case NAMEOID:
{
NameData *nm = (NameData *) DatumGetPointer(value);
val = pstrdup(NameStr(*nm));
break;
}
default:
/*
* Can't get here unless someone tries to use scalarltsel on an
* operator with one string and one non-string operand.
*/
elog(ERROR, "unsupported type: %u", typid);
return NULL;
}
if (!lc_collate_is_c())
{
char *xfrmstr;
size_t xfrmlen;
size_t xfrmlen2;
/*
* Note: originally we guessed at a suitable output buffer size, and
* only needed to call strxfrm twice if our guess was too small.
* However, it seems that some versions of Solaris have buggy strxfrm
* that can write past the specified buffer length in that scenario.
* So, do it the dumb way for portability.
*
* Yet other systems (e.g., glibc) sometimes return a smaller value
* from the second call than the first; thus the Assert must be <= not
* == as you'd expect. Can't any of these people program their way
* out of a paper bag?
*
* XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
* bogus data or set an error. This is not really a problem unless it
* crashes since it will only give an estimation error and nothing
* fatal.
*/
#if _MSC_VER == 1400 /* VS.Net 2005 */
/*
*
* http://connect.microsoft.com/VisualStudio/feedback/ViewFeedback.aspx?
* FeedbackID=99694
*/
{
char x[1];
xfrmlen = strxfrm(x, val, 0);
}
#else
xfrmlen = strxfrm(NULL, val, 0);
#endif
#ifdef WIN32
/*
* On Windows, strxfrm returns INT_MAX when an error occurs. Instead
* of trying to allocate this much memory (and fail), just return the
* original string unmodified as if we were in the C locale.
*/
if (xfrmlen == INT_MAX)
return val;
#endif
xfrmstr = (char *) palloc(xfrmlen + 1);
xfrmlen2 = strxfrm(xfrmstr, val, xfrmlen + 1);
Assert(xfrmlen2 <= xfrmlen);
pfree(val);
val = xfrmstr;
}
return val;
}
/*
* Do convert_to_scalar()'s work for any bytea data type.
*
* Very similar to convert_string_to_scalar except we can't assume
* null-termination and therefore pass explicit lengths around.
*
* Also, assumptions about likely "normal" ranges of characters have been
* removed - a data range of 0..255 is always used, for now. (Perhaps
* someday we will add information about actual byte data range to
* pg_statistic.)
*/
static void
convert_bytea_to_scalar(Datum value,
double *scaledvalue,
Datum lobound,
double *scaledlobound,
Datum hibound,
double *scaledhibound)
{
int rangelo,
rangehi,
valuelen = VARSIZE(DatumGetPointer(value)) - VARHDRSZ,
loboundlen = VARSIZE(DatumGetPointer(lobound)) - VARHDRSZ,
hiboundlen = VARSIZE(DatumGetPointer(hibound)) - VARHDRSZ,
i,
minlen;
unsigned char *valstr = (unsigned char *) VARDATA(DatumGetPointer(value)),
*lostr = (unsigned char *) VARDATA(DatumGetPointer(lobound)),
*histr = (unsigned char *) VARDATA(DatumGetPointer(hibound));
/*
* Assume bytea data is uniformly distributed across all byte values.
*/
rangelo = 0;
rangehi = 255;
/*
* Now strip any common prefix of the three strings.
*/
minlen = Min(Min(valuelen, loboundlen), hiboundlen);
for (i = 0; i < minlen; i++)
{
if (*lostr != *histr || *lostr != *valstr)
break;
lostr++, histr++, valstr++;
loboundlen--, hiboundlen--, valuelen--;
}
/*
* Now we can do the conversions.
*/
*scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
*scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
*scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
}
static double
convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
int rangelo, int rangehi)
{
double num,
denom,
base;
if (valuelen <= 0)
return 0.0; /* empty string has scalar value 0 */
/*
* Since base is 256, need not consider more than about 10 chars (even
* this many seems like overkill)
*/
if (valuelen > 10)
valuelen = 10;
/* Convert initial characters to fraction */
base = rangehi - rangelo + 1;
num = 0.0;
denom = base;
while (valuelen-- > 0)
{
int ch = *value++;
if (ch < rangelo)
ch = rangelo - 1;
else if (ch > rangehi)
ch = rangehi + 1;
num += ((double) (ch - rangelo)) / denom;
denom *= base;
}
return num;
}
/*
* Do convert_to_scalar()'s work for any timevalue data type.
*/
static double
convert_timevalue_to_scalar(Datum value, Oid typid)
{
switch (typid)
{
case TIMESTAMPOID:
return DatumGetTimestamp(value);
case TIMESTAMPTZOID:
return DatumGetTimestampTz(value);
case ABSTIMEOID:
return DatumGetTimestamp(DirectFunctionCall1(abstime_timestamp,
value));
case DATEOID:
return date2timestamp_no_overflow(DatumGetDateADT(value));
case INTERVALOID:
{
Interval *interval = DatumGetIntervalP(value);
/*
* Convert the month part of Interval to days using assumed
* average month length of 365.25/12.0 days. Not too
* accurate, but plenty good enough for our purposes.
*/
#ifdef HAVE_INT64_TIMESTAMP
return interval->time + interval->day * (double) USECS_PER_DAY +
interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
#else
return interval->time + interval->day * SECS_PER_DAY +
interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * (double) SECS_PER_DAY);
#endif
}
case RELTIMEOID:
#ifdef HAVE_INT64_TIMESTAMP
return (DatumGetRelativeTime(value) * 1000000.0);
#else
return DatumGetRelativeTime(value);
#endif
case TINTERVALOID:
{
TimeInterval tinterval = DatumGetTimeInterval(value);
#ifdef HAVE_INT64_TIMESTAMP
if (tinterval->status != 0)
return ((tinterval->data[1] - tinterval->data[0]) * 1000000.0);
#else
if (tinterval->status != 0)
return tinterval->data[1] - tinterval->data[0];
#endif
return 0; /* for lack of a better idea */
}
case TIMEOID:
return DatumGetTimeADT(value);
case TIMETZOID:
{
TimeTzADT *timetz = DatumGetTimeTzADTP(value);
/* use GMT-equivalent time */
#ifdef HAVE_INT64_TIMESTAMP
return (double) (timetz->time + (timetz->zone * 1000000.0));
#else
return (double) (timetz->time + timetz->zone);
#endif
}
}
/*
* Can't get here unless someone tries to use scalarltsel/scalargtsel on
* an operator with one timevalue and one non-timevalue operand.
*/
elog(ERROR, "unsupported type: %u", typid);
return 0;
}
/*
* get_restriction_variable
* Examine the args of a restriction clause to see if it's of the
* form (variable op pseudoconstant) or (pseudoconstant op variable),
* where "variable" could be either a Var or an expression in vars of a
* single relation. If so, extract information about the variable,
* and also indicate which side it was on and the other argument.
*
* Inputs:
* root: the planner info
* args: clause argument list
* varRelid: see specs for restriction selectivity functions
*
* Outputs: (these are valid only if TRUE is returned)
* *vardata: gets information about variable (see examine_variable)
* *other: gets other clause argument, aggressively reduced to a constant
* *varonleft: set TRUE if variable is on the left, FALSE if on the right
*
* Returns TRUE if a variable is identified, otherwise FALSE.
*
* Note: if there are Vars on both sides of the clause, we must fail, because
* callers are expecting that the other side will act like a pseudoconstant.
*/
bool
get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
VariableStatData *vardata, Node **other,
bool *varonleft)
{
Node *left,
*right;
VariableStatData rdata;
/* Fail if not a binary opclause (probably shouldn't happen) */
if (list_length(args) != 2)
return false;
left = (Node *) linitial(args);
right = (Node *) lsecond(args);
/*
* Examine both sides. Note that when varRelid is nonzero, Vars of other
* relations will be treated as pseudoconstants.
*/
examine_variable(root, left, varRelid, vardata);
examine_variable(root, right, varRelid, &rdata);
/*
* If one side is a variable and the other not, we win.
*/
if (vardata->rel && rdata.rel == NULL)
{
*varonleft = true;
*other = estimate_expression_value(root, rdata.var);
/* Assume we need no ReleaseVariableStats(rdata) here */
return true;
}
if (vardata->rel == NULL && rdata.rel)
{
*varonleft = false;
*other = estimate_expression_value(root, vardata->var);
/* Assume we need no ReleaseVariableStats(*vardata) here */
*vardata = rdata;
return true;
}
/* Ooops, clause has wrong structure (probably var op var) */
ReleaseVariableStats(*vardata);
ReleaseVariableStats(rdata);
return false;
}
/*
* get_join_variables
* Apply examine_variable() to each side of a join clause.
* Also, attempt to identify whether the join clause has the same
* or reversed sense compared to the SpecialJoinInfo.
*
* We consider the join clause "normal" if it is "lhs_var OP rhs_var",
* or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
* where we can't tell for sure, we default to assuming it's normal.
*/
void
get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
VariableStatData *vardata1, VariableStatData *vardata2,
bool *join_is_reversed)
{
Node *left,
*right;
if (list_length(args) != 2)
elog(ERROR, "join operator should take two arguments");
left = (Node *) linitial(args);
right = (Node *) lsecond(args);
examine_variable(root, left, 0, vardata1);
examine_variable(root, right, 0, vardata2);
if (vardata1->rel &&
bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
*join_is_reversed = true; /* var1 is on RHS */
else if (vardata2->rel &&
bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
*join_is_reversed = true; /* var2 is on LHS */
else
*join_is_reversed = false;
}
/*
* examine_variable
* Try to look up statistical data about an expression.
* Fill in a VariableStatData struct to describe the expression.
*
* Inputs:
* root: the planner info
* node: the expression tree to examine
* varRelid: see specs for restriction selectivity functions
*
* Outputs: *vardata is filled as follows:
* var: the input expression (with any binary relabeling stripped, if
* it is or contains a variable; but otherwise the type is preserved)
* rel: RelOptInfo for relation containing variable; NULL if expression
* contains no Vars (NOTE this could point to a RelOptInfo of a
* subquery, not one in the current query).
* statsTuple: the pg_statistic entry for the variable, if one exists;
* otherwise NULL.
* freefunc: pointer to a function to release statsTuple with.
* vartype: exposed type of the expression; this should always match
* the declared input type of the operator we are estimating for.
* atttype, atttypmod: type data to pass to get_attstatsslot(). This is
* commonly the same as the exposed type of the variable argument,
* but can be different in binary-compatible-type cases.
* isunique: TRUE if we were able to match the var to a unique index,
* implying its values are unique for this query.
*
* Caller is responsible for doing ReleaseVariableStats() before exiting.
*/
void
examine_variable(PlannerInfo *root, Node *node, int varRelid,
VariableStatData *vardata)
{
Node *basenode;
Relids varnos;
RelOptInfo *onerel;
/* Make sure we don't return dangling pointers in vardata */
MemSet(vardata, 0, sizeof(VariableStatData));
/* Save the exposed type of the expression */
vardata->vartype = exprType(node);
/* Look inside any binary-compatible relabeling */
if (IsA(node, RelabelType))
basenode = (Node *) ((RelabelType *) node)->arg;
else
basenode = node;
/* Fast path for a simple Var */
if (IsA(basenode, Var) &&
(varRelid == 0 || varRelid == ((Var *) basenode)->varno))
{
Var *var = (Var *) basenode;
RangeTblEntry *rte;
vardata->var = basenode; /* return Var without relabeling */
vardata->rel = find_base_rel(root, var->varno);
vardata->atttype = var->vartype;
vardata->atttypmod = var->vartypmod;
vardata->isunique = has_unique_index(vardata->rel, var->varattno);
rte = root->simple_rte_array[var->varno];
if (get_relation_stats_hook &&
(*get_relation_stats_hook) (root, rte, var->varattno, vardata))
{
/*
* The hook took control of acquiring a stats tuple. If it did
* supply a tuple, it'd better have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata->statsTuple) &&
!vardata->freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else if (rte->inh)
{
/*
* XXX This means the Var represents a column of an append
* relation. Later add code to look at the member relations and
* try to derive some kind of combined statistics?
*/
}
else if (rte->rtekind == RTE_RELATION)
{
vardata->statsTuple = SearchSysCache(STATRELATT,
ObjectIdGetDatum(rte->relid),
Int16GetDatum(var->varattno),
0, 0);
vardata->freefunc = ReleaseSysCache;
}
else
{
/*
* XXX This means the Var comes from a JOIN or sub-SELECT. Later
* add code to dig down into the join etc and see if we can trace
* the variable to something with stats. (But beware of
* sub-SELECTs with DISTINCT/GROUP BY/etc. Perhaps there are no
* cases where this would really be useful, because we'd have
* flattened the subselect if it is??)
*/
}
return;
}
/*
* Okay, it's a more complicated expression. Determine variable
* membership. Note that when varRelid isn't zero, only vars of that
* relation are considered "real" vars.
*/
varnos = pull_varnos(basenode);
onerel = NULL;
switch (bms_membership(varnos))
{
case BMS_EMPTY_SET:
/* No Vars at all ... must be pseudo-constant clause */
break;
case BMS_SINGLETON:
if (varRelid == 0 || bms_is_member(varRelid, varnos))
{
onerel = find_base_rel(root,
(varRelid ? varRelid : bms_singleton_member(varnos)));
vardata->rel = onerel;
node = basenode; /* strip any relabeling */
}
/* else treat it as a constant */
break;
case BMS_MULTIPLE:
if (varRelid == 0)
{
/* treat it as a variable of a join relation */
vardata->rel = find_join_rel(root, varnos);
node = basenode; /* strip any relabeling */
}
else if (bms_is_member(varRelid, varnos))
{
/* ignore the vars belonging to other relations */
vardata->rel = find_base_rel(root, varRelid);
node = basenode; /* strip any relabeling */
/* note: no point in expressional-index search here */
}
/* else treat it as a constant */
break;
}
bms_free(varnos);
vardata->var = node;
vardata->atttype = exprType(node);
vardata->atttypmod = exprTypmod(node);
if (onerel)
{
/*
* We have an expression in vars of a single relation. Try to match
* it to expressional index columns, in hopes of finding some
* statistics.
*
* XXX it's conceivable that there are multiple matches with different
* index opfamilies; if so, we need to pick one that matches the
* operator we are estimating for. FIXME later.
*/
ListCell *ilist;
foreach(ilist, onerel->indexlist)
{
IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
ListCell *indexpr_item;
int pos;
indexpr_item = list_head(index->indexprs);
if (indexpr_item == NULL)
continue; /* no expressions here... */
for (pos = 0; pos < index->ncolumns; pos++)
{
if (index->indexkeys[pos] == 0)
{
Node *indexkey;
if (indexpr_item == NULL)
elog(ERROR, "too few entries in indexprs list");
indexkey = (Node *) lfirst(indexpr_item);
if (indexkey && IsA(indexkey, RelabelType))
indexkey = (Node *) ((RelabelType *) indexkey)->arg;
if (equal(node, indexkey))
{
/*
* Found a match ... is it a unique index? Tests here
* should match has_unique_index().
*/
if (index->unique &&
index->ncolumns == 1 &&
(index->indpred == NIL || index->predOK))
vardata->isunique = true;
/*
* Has it got stats? We only consider stats for
* non-partial indexes, since partial indexes probably
* don't reflect whole-relation statistics; the above
* check for uniqueness is the only info we take from
* a partial index.
*
* An index stats hook, however, must make its own
* decisions about what to do with partial indexes.
*/
if (get_index_stats_hook &&
(*get_index_stats_hook) (root, index->indexoid,
pos + 1, vardata))
{
/*
* The hook took control of acquiring a stats
* tuple. If it did supply a tuple, it'd better
* have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata->statsTuple) &&
!vardata->freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else if (index->indpred == NIL)
{
vardata->statsTuple =
SearchSysCache(STATRELATT,
ObjectIdGetDatum(index->indexoid),
Int16GetDatum(pos + 1),
0, 0);
vardata->freefunc = ReleaseSysCache;
}
if (vardata->statsTuple)
break;
}
indexpr_item = lnext(indexpr_item);
}
}
if (vardata->statsTuple)
break;
}
}
}
/*
* get_variable_numdistinct
* Estimate the number of distinct values of a variable.
*
* vardata: results of examine_variable
*
* NB: be careful to produce an integral result, since callers may compare
* the result to exact integer counts.
*/
double
get_variable_numdistinct(VariableStatData *vardata)
{
double stadistinct;
double ntuples;
/*
* Determine the stadistinct value to use. There are cases where we can
* get an estimate even without a pg_statistic entry, or can get a better
* value than is in pg_statistic.
*/
if (HeapTupleIsValid(vardata->statsTuple))
{
/* Use the pg_statistic entry */
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
stadistinct = stats->stadistinct;
}
else if (vardata->vartype == BOOLOID)
{
/*
* Special-case boolean columns: presumably, two distinct values.
*
* Are there any other datatypes we should wire in special estimates
* for?
*/
stadistinct = 2.0;
}
else
{
/*
* We don't keep statistics for system columns, but in some cases we
* can infer distinctness anyway.
*/
if (vardata->var && IsA(vardata->var, Var))
{
switch (((Var *) vardata->var)->varattno)
{
case ObjectIdAttributeNumber:
case SelfItemPointerAttributeNumber:
stadistinct = -1.0; /* unique */
break;
case TableOidAttributeNumber:
stadistinct = 1.0; /* only 1 value */
break;
default:
stadistinct = 0.0; /* means "unknown" */
break;
}
}
else
stadistinct = 0.0; /* means "unknown" */
/*
* XXX consider using estimate_num_groups on expressions?
*/
}
/*
* If there is a unique index for the variable, assume it is unique no
* matter what pg_statistic says; the statistics could be out of date, or
* we might have found a partial unique index that proves the var is
* unique for this query.
*/
if (vardata->isunique)
stadistinct = -1.0;
/*
* If we had an absolute estimate, use that.
*/
if (stadistinct > 0.0)
return stadistinct;
/*
* Otherwise we need to get the relation size; punt if not available.
*/
if (vardata->rel == NULL)
return DEFAULT_NUM_DISTINCT;
ntuples = vardata->rel->tuples;
if (ntuples <= 0.0)
return DEFAULT_NUM_DISTINCT;
/*
* If we had a relative estimate, use that.
*/
if (stadistinct < 0.0)
return floor((-stadistinct * ntuples) + 0.5);
/*
* With no data, estimate ndistinct = ntuples if the table is small, else
* use default.
*/
if (ntuples < DEFAULT_NUM_DISTINCT)
return ntuples;
return DEFAULT_NUM_DISTINCT;
}
/*
* get_variable_range
* Estimate the minimum and maximum value of the specified variable.
* If successful, store values in *min and *max, and return TRUE.
* If no data available, return FALSE.
*
* sortop is the "<" comparison operator to use. This should generally
* be "<" not ">", as only the former is likely to be found in pg_statistic.
*/
static bool
get_variable_range(PlannerInfo *root, VariableStatData *vardata, Oid sortop,
Datum *min, Datum *max)
{
Datum tmin = 0;
Datum tmax = 0;
bool have_data = false;
Form_pg_statistic stats;
int16 typLen;
bool typByVal;
Datum *values;
int nvalues;
int i;
if (!HeapTupleIsValid(vardata->statsTuple))
{
/* no stats available, so default result */
return false;
}
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
get_typlenbyval(vardata->atttype, &typLen, &typByVal);
/*
* If there is a histogram, grab the first and last values.
*
* If there is a histogram that is sorted with some other operator than
* the one we want, fail --- this suggests that there is data we can't
* use.
*/
if (get_attstatsslot(vardata->statsTuple,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, sortop,
&values, &nvalues,
NULL, NULL))
{
if (nvalues > 0)
{
tmin = datumCopy(values[0], typByVal, typLen);
tmax = datumCopy(values[nvalues - 1], typByVal, typLen);
have_data = true;
}
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
else if (get_attstatsslot(vardata->statsTuple,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
&values, &nvalues,
NULL, NULL))
{
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
return false;
}
/*
* If we have most-common-values info, look for extreme MCVs. This is
* needed even if we also have a histogram, since the histogram excludes
* the MCVs. However, usually the MCVs will not be the extreme values, so
* avoid unnecessary data copying.
*/
if (get_attstatsslot(vardata->statsTuple,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
&values, &nvalues,
NULL, NULL))
{
bool tmin_is_mcv = false;
bool tmax_is_mcv = false;
FmgrInfo opproc;
fmgr_info(get_opcode(sortop), &opproc);
for (i = 0; i < nvalues; i++)
{
if (!have_data)
{
tmin = tmax = values[i];
tmin_is_mcv = tmax_is_mcv = have_data = true;
continue;
}
if (DatumGetBool(FunctionCall2(&opproc, values[i], tmin)))
{
tmin = values[i];
tmin_is_mcv = true;
}
if (DatumGetBool(FunctionCall2(&opproc, tmax, values[i])))
{
tmax = values[i];
tmax_is_mcv = true;
}
}
if (tmin_is_mcv)
tmin = datumCopy(tmin, typByVal, typLen);
if (tmax_is_mcv)
tmax = datumCopy(tmax, typByVal, typLen);
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
*min = tmin;
*max = tmax;
return have_data;
}
/*
* find_join_input_rel
* Look up the input relation for a join.
*
* We assume that the input relation's RelOptInfo must have been constructed
* already.
*/
static RelOptInfo *
find_join_input_rel(PlannerInfo *root, Relids relids)
{
RelOptInfo *rel = NULL;
switch (bms_membership(relids))
{
case BMS_EMPTY_SET:
/* should not happen */
break;
case BMS_SINGLETON:
rel = find_base_rel(root, bms_singleton_member(relids));
break;
case BMS_MULTIPLE:
rel = find_join_rel(root, relids);
break;
}
if (rel == NULL)
elog(ERROR, "could not find RelOptInfo for given relids");
return rel;
}
/*-------------------------------------------------------------------------
*
* Pattern analysis functions
*
* These routines support analysis of LIKE and regular-expression patterns
* by the planner/optimizer. It's important that they agree with the
* regular-expression code in backend/regex/ and the LIKE code in
* backend/utils/adt/like.c. Also, the computation of the fixed prefix
* must be conservative: if we report a string longer than the true fixed
* prefix, the query may produce actually wrong answers, rather than just
* getting a bad selectivity estimate!
*
* Note that the prefix-analysis functions are called from
* backend/optimizer/path/indxpath.c as well as from routines in this file.
*
*-------------------------------------------------------------------------
*/
/*
* Extract the fixed prefix, if any, for a pattern.
*
* *prefix is set to a palloc'd prefix string (in the form of a Const node),
* or to NULL if no fixed prefix exists for the pattern.
* *rest is set to a palloc'd Const representing the remainder of the pattern
* after the portion describing the fixed prefix.
* Each of these has the same type (TEXT or BYTEA) as the given pattern Const.
*
* The return value distinguishes no fixed prefix, a partial prefix,
* or an exact-match-only pattern.
*/
static Pattern_Prefix_Status
like_fixed_prefix(Const *patt_const, bool case_insensitive,
Const **prefix_const, Const **rest_const)
{
char *match;
char *patt;
int pattlen;
char *rest;
Oid typeid = patt_const->consttype;
int pos,
match_pos;
bool is_multibyte = (pg_database_encoding_max_length() > 1);
/* the right-hand const is type text or bytea */
Assert(typeid == BYTEAOID || typeid == TEXTOID);
if (typeid == BYTEAOID && case_insensitive)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("case insensitive matching not supported on type bytea")));
if (typeid != BYTEAOID)
{
patt = TextDatumGetCString(patt_const->constvalue);
pattlen = strlen(patt);
}
else
{
bytea *bstr = DatumGetByteaP(patt_const->constvalue);
pattlen = VARSIZE(bstr) - VARHDRSZ;
patt = (char *) palloc(pattlen);
memcpy(patt, VARDATA(bstr), pattlen);
if ((Pointer) bstr != DatumGetPointer(patt_const->constvalue))
pfree(bstr);
}
match = palloc(pattlen + 1);
match_pos = 0;
for (pos = 0; pos < pattlen; pos++)
{
/* % and _ are wildcard characters in LIKE */
if (patt[pos] == '%' ||
patt[pos] == '_')
break;
/* Backslash escapes the next character */
if (patt[pos] == '\\')
{
pos++;
if (pos >= pattlen)
break;
}
/*
* XXX In multibyte character sets, we can't trust isalpha, so assume
* any multibyte char is potentially case-varying.
*/
if (case_insensitive)
{
if (is_multibyte && (unsigned char) patt[pos] >= 0x80)
break;
if (isalpha((unsigned char) patt[pos]))
break;
}
/*
* NOTE: this code used to think that %% meant a literal %, but
* textlike() itself does not think that, and the SQL92 spec doesn't
* say any such thing either.
*/
match[match_pos++] = patt[pos];
}
match[match_pos] = '\0';
rest = &patt[pos];
if (typeid != BYTEAOID)
{
*prefix_const = string_to_const(match, typeid);
*rest_const = string_to_const(rest, typeid);
}
else
{
*prefix_const = string_to_bytea_const(match, match_pos);
*rest_const = string_to_bytea_const(rest, pattlen - pos);
}
pfree(patt);
pfree(match);
/* in LIKE, an empty pattern is an exact match! */
if (pos == pattlen)
return Pattern_Prefix_Exact; /* reached end of pattern, so exact */
if (match_pos > 0)
return Pattern_Prefix_Partial;
return Pattern_Prefix_None;
}
static Pattern_Prefix_Status
regex_fixed_prefix(Const *patt_const, bool case_insensitive,
Const **prefix_const, Const **rest_const)
{
char *match;
int pos,
match_pos,
prev_pos,
prev_match_pos;
bool have_leading_paren;
char *patt;
char *rest;
Oid typeid = patt_const->consttype;
bool is_basic = regex_flavor_is_basic();
bool is_multibyte = (pg_database_encoding_max_length() > 1);
/*
* Should be unnecessary, there are no bytea regex operators defined. As
* such, it should be noted that the rest of this function has *not* been
* made safe for binary (possibly NULL containing) strings.
*/
if (typeid == BYTEAOID)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("regular-expression matching not supported on type bytea")));
/* the right-hand const is type text for all of these */
patt = TextDatumGetCString(patt_const->constvalue);
/*
* Check for ARE director prefix. It's worth our trouble to recognize
* this because similar_escape() uses it.
*/
pos = 0;
if (strncmp(patt, "***:", 4) == 0)
{
pos = 4;
is_basic = false;
}
/* Pattern must be anchored left */
if (patt[pos] != '^')
{
rest = patt;
*prefix_const = NULL;
*rest_const = string_to_const(rest, typeid);
return Pattern_Prefix_None;
}
pos++;
/*
* If '|' is present in pattern, then there may be multiple alternatives
* for the start of the string. (There are cases where this isn't so, for
* instance if the '|' is inside parens, but detecting that reliably is
* too hard.)
*/
if (strchr(patt + pos, '|') != NULL)
{
rest = patt;
*prefix_const = NULL;
*rest_const = string_to_const(rest, typeid);
return Pattern_Prefix_None;
}
/* OK, allocate space for pattern */
match = palloc(strlen(patt) + 1);
prev_match_pos = match_pos = 0;
/*
* We special-case the syntax '^(...)$' because psql uses it. But beware:
* in BRE mode these parentheses are just ordinary characters. Also,
* sequences beginning "(?" are not what they seem, unless they're "(?:".
* (We should recognize that, too, because of similar_escape().)
*
* Note: it's a bit bogus to be depending on the current regex_flavor
* setting here, because the setting could change before the pattern is
* used. We minimize the risk by trusting the flavor as little as we can,
* but perhaps it would be a good idea to get rid of the "basic" setting.
*/
have_leading_paren = false;
if (patt[pos] == '(' && !is_basic &&
(patt[pos + 1] != '?' || patt[pos + 2] == ':'))
{
have_leading_paren = true;
pos += (patt[pos + 1] != '?' ? 1 : 3);
}
/* Scan remainder of pattern */
prev_pos = pos;
while (patt[pos])
{
int len;
/*
* Check for characters that indicate multiple possible matches here.
* Also, drop out at ')' or '$' so the termination test works right.
*/
if (patt[pos] == '.' ||
patt[pos] == '(' ||
patt[pos] == ')' ||
patt[pos] == '[' ||
patt[pos] == '^' ||
patt[pos] == '$')
break;
/*
* XXX In multibyte character sets, we can't trust isalpha, so assume
* any multibyte char is potentially case-varying.
*/
if (case_insensitive)
{
if (is_multibyte && (unsigned char) patt[pos] >= 0x80)
break;
if (isalpha((unsigned char) patt[pos]))
break;
}
/*
* Check for quantifiers. Except for +, this means the preceding
* character is optional, so we must remove it from the prefix too!
* Note: in BREs, \{ is a quantifier.
*/
if (patt[pos] == '*' ||
patt[pos] == '?' ||
patt[pos] == '{' ||
(patt[pos] == '\\' && patt[pos + 1] == '{'))
{
match_pos = prev_match_pos;
pos = prev_pos;
break;
}
if (patt[pos] == '+')
{
pos = prev_pos;
break;
}
/*
* Normally, backslash quotes the next character. But in AREs,
* backslash followed by alphanumeric is an escape, not a quoted
* character. Must treat it as having multiple possible matches. In
* BREs, \( is a parenthesis, so don't trust that either. Note: since
* only ASCII alphanumerics are escapes, we don't have to be paranoid
* about multibyte here.
*/
if (patt[pos] == '\\')
{
if (isalnum((unsigned char) patt[pos + 1]) || patt[pos + 1] == '(')
break;
pos++;
if (patt[pos] == '\0')
break;
}
/* save position in case we need to back up on next loop cycle */
prev_match_pos = match_pos;
prev_pos = pos;
/* must use encoding-aware processing here */
len = pg_mblen(&patt[pos]);
memcpy(&match[match_pos], &patt[pos], len);
match_pos += len;
pos += len;
}
match[match_pos] = '\0';
rest = &patt[pos];
if (have_leading_paren && patt[pos] == ')')
pos++;
if (patt[pos] == '$' && patt[pos + 1] == '\0')
{
rest = &patt[pos + 1];
*prefix_const = string_to_const(match, typeid);
*rest_const = string_to_const(rest, typeid);
pfree(patt);
pfree(match);
return Pattern_Prefix_Exact; /* pattern specifies exact match */
}
*prefix_const = string_to_const(match, typeid);
*rest_const = string_to_const(rest, typeid);
pfree(patt);
pfree(match);
if (match_pos > 0)
return Pattern_Prefix_Partial;
return Pattern_Prefix_None;
}
Pattern_Prefix_Status
pattern_fixed_prefix(Const *patt, Pattern_Type ptype,
Const **prefix, Const **rest)
{
Pattern_Prefix_Status result;
switch (ptype)
{
case Pattern_Type_Like:
result = like_fixed_prefix(patt, false, prefix, rest);
break;
case Pattern_Type_Like_IC:
result = like_fixed_prefix(patt, true, prefix, rest);
break;
case Pattern_Type_Regex:
result = regex_fixed_prefix(patt, false, prefix, rest);
break;
case Pattern_Type_Regex_IC:
result = regex_fixed_prefix(patt, true, prefix, rest);
break;
default:
elog(ERROR, "unrecognized ptype: %d", (int) ptype);
result = Pattern_Prefix_None; /* keep compiler quiet */
break;
}
return result;
}
/*
* Estimate the selectivity of a fixed prefix for a pattern match.
*
* A fixed prefix "foo" is estimated as the selectivity of the expression
* "variable >= 'foo' AND variable < 'fop'" (see also indxpath.c).
*
* The selectivity estimate is with respect to the portion of the column
* population represented by the histogram --- the caller must fold this
* together with info about MCVs and NULLs.
*
* We use the >= and < operators from the specified btree opfamily to do the
* estimation. The given variable and Const must be of the associated
* datatype.
*
* XXX Note: we make use of the upper bound to estimate operator selectivity
* even if the locale is such that we cannot rely on the upper-bound string.
* The selectivity only needs to be approximately right anyway, so it seems
* more useful to use the upper-bound code than not.
*/
static Selectivity
prefix_selectivity(VariableStatData *vardata,
Oid vartype, Oid opfamily, Const *prefixcon)
{
Selectivity prefixsel;
Oid cmpopr;
FmgrInfo opproc;
Const *greaterstrcon;
Selectivity eq_sel;
cmpopr = get_opfamily_member(opfamily, vartype, vartype,
BTGreaterEqualStrategyNumber);
if (cmpopr == InvalidOid)
elog(ERROR, "no >= operator for opfamily %u", opfamily);
fmgr_info(get_opcode(cmpopr), &opproc);
prefixsel = ineq_histogram_selectivity(vardata, &opproc, true,
prefixcon->constvalue,
prefixcon->consttype);
if (prefixsel <= 0.0)
{
/* No histogram is present ... return a suitable default estimate */
return DEFAULT_MATCH_SEL;
}
/*-------
* If we can create a string larger than the prefix, say
* "x < greaterstr".
*-------
*/
cmpopr = get_opfamily_member(opfamily, vartype, vartype,
BTLessStrategyNumber);
if (cmpopr == InvalidOid)
elog(ERROR, "no < operator for opfamily %u", opfamily);
fmgr_info(get_opcode(cmpopr), &opproc);
greaterstrcon = make_greater_string(prefixcon, &opproc);
if (greaterstrcon)
{
Selectivity topsel;
topsel = ineq_histogram_selectivity(vardata, &opproc, false,
greaterstrcon->constvalue,
greaterstrcon->consttype);
/* ineq_histogram_selectivity worked before, it shouldn't fail now */
Assert(topsel > 0.0);
/*
* Merge the two selectivities in the same way as for a range query
* (see clauselist_selectivity()). Note that we don't need to worry
* about double-exclusion of nulls, since ineq_histogram_selectivity
* doesn't count those anyway.
*/
prefixsel = topsel + prefixsel - 1.0;
}
/*
* If the prefix is long then the two bounding values might be too close
* together for the histogram to distinguish them usefully, resulting in a
* zero estimate (plus or minus roundoff error). To avoid returning a
* ridiculously small estimate, compute the estimated selectivity for
* "variable = 'foo'", and clamp to that. (Obviously, the resultant
* estimate should be at least that.)
*
* We apply this even if we couldn't make a greater string. That case
* suggests that the prefix is near the maximum possible, and thus
* probably off the end of the histogram, and thus we probably got a very
* small estimate from the >= condition; so we still need to clamp.
*/
cmpopr = get_opfamily_member(opfamily, vartype, vartype,
BTEqualStrategyNumber);
if (cmpopr == InvalidOid)
elog(ERROR, "no = operator for opfamily %u", opfamily);
eq_sel = var_eq_const(vardata, cmpopr, prefixcon->constvalue,
false, true);
prefixsel = Max(prefixsel, eq_sel);
return prefixsel;
}
/*
* Estimate the selectivity of a pattern of the specified type.
* Note that any fixed prefix of the pattern will have been removed already.
*
* For now, we use a very simplistic approach: fixed characters reduce the
* selectivity a good deal, character ranges reduce it a little,
* wildcards (such as % for LIKE or .* for regex) increase it.
*/
#define FIXED_CHAR_SEL 0.20 /* about 1/5 */
#define CHAR_RANGE_SEL 0.25
#define ANY_CHAR_SEL 0.9 /* not 1, since it won't match end-of-string */
#define FULL_WILDCARD_SEL 5.0
#define PARTIAL_WILDCARD_SEL 2.0
static Selectivity
like_selectivity(Const *patt_const, bool case_insensitive)
{
Selectivity sel = 1.0;
int pos;
Oid typeid = patt_const->consttype;
char *patt;
int pattlen;
/* the right-hand const is type text or bytea */
Assert(typeid == BYTEAOID || typeid == TEXTOID);
if (typeid == BYTEAOID && case_insensitive)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("case insensitive matching not supported on type bytea")));
if (typeid != BYTEAOID)
{
patt = TextDatumGetCString(patt_const->constvalue);
pattlen = strlen(patt);
}
else
{
bytea *bstr = DatumGetByteaP(patt_const->constvalue);
pattlen = VARSIZE(bstr) - VARHDRSZ;
patt = (char *) palloc(pattlen);
memcpy(patt, VARDATA(bstr), pattlen);
if ((Pointer) bstr != DatumGetPointer(patt_const->constvalue))
pfree(bstr);
}
/* Skip any leading wildcard; it's already factored into initial sel */
for (pos = 0; pos < pattlen; pos++)
{
if (patt[pos] != '%' && patt[pos] != '_')
break;
}
for (; pos < pattlen; pos++)
{
/* % and _ are wildcard characters in LIKE */
if (patt[pos] == '%')
sel *= FULL_WILDCARD_SEL;
else if (patt[pos] == '_')
sel *= ANY_CHAR_SEL;
else if (patt[pos] == '\\')
{
/* Backslash quotes the next character */
pos++;
if (pos >= pattlen)
break;
sel *= FIXED_CHAR_SEL;
}
else
sel *= FIXED_CHAR_SEL;
}
/* Could get sel > 1 if multiple wildcards */
if (sel > 1.0)
sel = 1.0;
pfree(patt);
return sel;
}
static Selectivity
regex_selectivity_sub(char *patt, int pattlen, bool case_insensitive)
{
Selectivity sel = 1.0;
int paren_depth = 0;
int paren_pos = 0; /* dummy init to keep compiler quiet */
int pos;
for (pos = 0; pos < pattlen; pos++)
{
if (patt[pos] == '(')
{
if (paren_depth == 0)
paren_pos = pos; /* remember start of parenthesized item */
paren_depth++;
}
else if (patt[pos] == ')' && paren_depth > 0)
{
paren_depth--;
if (paren_depth == 0)
sel *= regex_selectivity_sub(patt + (paren_pos + 1),
pos - (paren_pos + 1),
case_insensitive);
}
else if (patt[pos] == '|' && paren_depth == 0)
{
/*
* If unquoted | is present at paren level 0 in pattern, we have
* multiple alternatives; sum their probabilities.
*/
sel += regex_selectivity_sub(patt + (pos + 1),
pattlen - (pos + 1),
case_insensitive);
break; /* rest of pattern is now processed */
}
else if (patt[pos] == '[')
{
bool negclass = false;
if (patt[++pos] == '^')
{
negclass = true;
pos++;
}
if (patt[pos] == ']') /* ']' at start of class is not
* special */
pos++;
while (pos < pattlen && patt[pos] != ']')
pos++;
if (paren_depth == 0)
sel *= (negclass ? (1.0 - CHAR_RANGE_SEL) : CHAR_RANGE_SEL);
}
else if (patt[pos] == '.')
{
if (paren_depth == 0)
sel *= ANY_CHAR_SEL;
}
else if (patt[pos] == '*' ||
patt[pos] == '?' ||
patt[pos] == '+')
{
/* Ought to be smarter about quantifiers... */
if (paren_depth == 0)
sel *= PARTIAL_WILDCARD_SEL;
}
else if (patt[pos] == '{')
{
while (pos < pattlen && patt[pos] != '}')
pos++;
if (paren_depth == 0)
sel *= PARTIAL_WILDCARD_SEL;
}
else if (patt[pos] == '\\')
{
/* backslash quotes the next character */
pos++;
if (pos >= pattlen)
break;
if (paren_depth == 0)
sel *= FIXED_CHAR_SEL;
}
else
{
if (paren_depth == 0)
sel *= FIXED_CHAR_SEL;
}
}
/* Could get sel > 1 if multiple wildcards */
if (sel > 1.0)
sel = 1.0;
return sel;
}
static Selectivity
regex_selectivity(Const *patt_const, bool case_insensitive)
{
Selectivity sel;
char *patt;
int pattlen;
Oid typeid = patt_const->consttype;
/*
* Should be unnecessary, there are no bytea regex operators defined. As
* such, it should be noted that the rest of this function has *not* been
* made safe for binary (possibly NULL containing) strings.
*/
if (typeid == BYTEAOID)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("regular-expression matching not supported on type bytea")));
/* the right-hand const is type text for all of these */
patt = TextDatumGetCString(patt_const->constvalue);
pattlen = strlen(patt);
/* If patt doesn't end with $, consider it to have a trailing wildcard */
if (pattlen > 0 && patt[pattlen - 1] == '$' &&
(pattlen == 1 || patt[pattlen - 2] != '\\'))
{
/* has trailing $ */
sel = regex_selectivity_sub(patt, pattlen - 1, case_insensitive);
}
else
{
/* no trailing $ */
sel = regex_selectivity_sub(patt, pattlen, case_insensitive);
sel *= FULL_WILDCARD_SEL;
if (sel > 1.0)
sel = 1.0;
}
return sel;
}
static Selectivity
pattern_selectivity(Const *patt, Pattern_Type ptype)
{
Selectivity result;
switch (ptype)
{
case Pattern_Type_Like:
result = like_selectivity(patt, false);
break;
case Pattern_Type_Like_IC:
result = like_selectivity(patt, true);
break;
case Pattern_Type_Regex:
result = regex_selectivity(patt, false);
break;
case Pattern_Type_Regex_IC:
result = regex_selectivity(patt, true);
break;
default:
elog(ERROR, "unrecognized ptype: %d", (int) ptype);
result = 1.0; /* keep compiler quiet */
break;
}
return result;
}
/*
* Try to generate a string greater than the given string or any
* string it is a prefix of. If successful, return a palloc'd string
* in the form of a Const node; else return NULL.
*
* The caller must provide the appropriate "less than" comparison function
* for testing the strings.
*
* The key requirement here is that given a prefix string, say "foo",
* we must be able to generate another string "fop" that is greater than
* all strings "foobar" starting with "foo". We can test that we have
* generated a string greater than the prefix string, but in non-C locales
* that is not a bulletproof guarantee that an extension of the string might
* not sort after it; an example is that "foo " is less than "foo!", but it
* is not clear that a "dictionary" sort ordering will consider "foo!" less
* than "foo bar". CAUTION: Therefore, this function should be used only for
* estimation purposes when working in a non-C locale.
*
* To try to catch most cases where an extended string might otherwise sort
* before the result value, we determine which of the strings "Z", "z", "y",
* and "9" is seen as largest by the locale, and append that to the given
* prefix before trying to find a string that compares as larger.
*
* If we max out the righthand byte, truncate off the last character
* and start incrementing the next. For example, if "z" were the last
* character in the sort order, then we could produce "foo" as a
* string greater than "fonz".
*
* This could be rather slow in the worst case, but in most cases we
* won't have to try more than one or two strings before succeeding.
*/
Const *
make_greater_string(const Const *str_const, FmgrInfo *ltproc)
{
Oid datatype = str_const->consttype;
char *workstr;
int len;
Datum cmpstr;
text *cmptxt = NULL;
/*
* Get a modifiable copy of the prefix string in C-string format, and set
* up the string we will compare to as a Datum. In C locale this can just
* be the given prefix string, otherwise we need to add a suffix. Types
* NAME and BYTEA sort bytewise so they don't need a suffix either.
*/
if (datatype == NAMEOID)
{
workstr = DatumGetCString(DirectFunctionCall1(nameout,
str_const->constvalue));
len = strlen(workstr);
cmpstr = str_const->constvalue;
}
else if (datatype == BYTEAOID)
{
bytea *bstr = DatumGetByteaP(str_const->constvalue);
len = VARSIZE(bstr) - VARHDRSZ;
workstr = (char *) palloc(len);
memcpy(workstr, VARDATA(bstr), len);
if ((Pointer) bstr != DatumGetPointer(str_const->constvalue))
pfree(bstr);
cmpstr = str_const->constvalue;
}
else
{
workstr = TextDatumGetCString(str_const->constvalue);
len = strlen(workstr);
if (lc_collate_is_c() || len == 0)
cmpstr = str_const->constvalue;
else
{
/* If first time through, determine the suffix to use */
static char suffixchar = 0;
if (!suffixchar)
{
char *best;
best = "Z";
if (varstr_cmp(best, 1, "z", 1) < 0)
best = "z";
if (varstr_cmp(best, 1, "y", 1) < 0)
best = "y";
if (varstr_cmp(best, 1, "9", 1) < 0)
best = "9";
suffixchar = *best;
}
/* And build the string to compare to */
cmptxt = (text *) palloc(VARHDRSZ + len + 1);
SET_VARSIZE(cmptxt, VARHDRSZ + len + 1);
memcpy(VARDATA(cmptxt), workstr, len);
*(VARDATA(cmptxt) + len) = suffixchar;
cmpstr = PointerGetDatum(cmptxt);
}
}
while (len > 0)
{
unsigned char *lastchar = (unsigned char *) (workstr + len - 1);
unsigned char savelastchar = *lastchar;
/*
* Try to generate a larger string by incrementing the last byte.
*/
while (*lastchar < (unsigned char) 255)
{
Const *workstr_const;
(*lastchar)++;
if (datatype != BYTEAOID)
{
/* do not generate invalid encoding sequences */
if (!pg_verifymbstr(workstr, len, true))
continue;
workstr_const = string_to_const(workstr, datatype);
}
else
workstr_const = string_to_bytea_const(workstr, len);
if (DatumGetBool(FunctionCall2(ltproc,
cmpstr,
workstr_const->constvalue)))
{
/* Successfully made a string larger than cmpstr */
if (cmptxt)
pfree(cmptxt);
pfree(workstr);
return workstr_const;
}
/* No good, release unusable value and try again */
pfree(DatumGetPointer(workstr_const->constvalue));
pfree(workstr_const);
}
/* restore last byte so we don't confuse pg_mbcliplen */
*lastchar = savelastchar;
/*
* Truncate off the last character, which might be more than 1 byte,
* depending on the character encoding.
*/
if (datatype != BYTEAOID && pg_database_encoding_max_length() > 1)
len = pg_mbcliplen(workstr, len, len - 1);
else
len -= 1;
if (datatype != BYTEAOID)
workstr[len] = '\0';
}
/* Failed... */
if (cmptxt)
pfree(cmptxt);
pfree(workstr);
return NULL;
}
/*
* Generate a Datum of the appropriate type from a C string.
* Note that all of the supported types are pass-by-ref, so the
* returned value should be pfree'd if no longer needed.
*/
static Datum
string_to_datum(const char *str, Oid datatype)
{
Assert(str != NULL);
/*
* We cheat a little by assuming that CStringGetTextDatum() will do for
* bpchar and varchar constants too...
*/
if (datatype == NAMEOID)
return DirectFunctionCall1(namein, CStringGetDatum(str));
else if (datatype == BYTEAOID)
return DirectFunctionCall1(byteain, CStringGetDatum(str));
else
return CStringGetTextDatum(str);
}
/*
* Generate a Const node of the appropriate type from a C string.
*/
static Const *
string_to_const(const char *str, Oid datatype)
{
Datum conval = string_to_datum(str, datatype);
return makeConst(datatype, -1,
((datatype == NAMEOID) ? NAMEDATALEN : -1),
conval, false, false);
}
/*
* Generate a Const node of bytea type from a binary C string and a length.
*/
static Const *
string_to_bytea_const(const char *str, size_t str_len)
{
bytea *bstr = palloc(VARHDRSZ + str_len);
Datum conval;
memcpy(VARDATA(bstr), str, str_len);
SET_VARSIZE(bstr, VARHDRSZ + str_len);
conval = PointerGetDatum(bstr);
return makeConst(BYTEAOID, -1, -1, conval, false, false);
}
/*-------------------------------------------------------------------------
*
* Index cost estimation functions
*
* genericcostestimate is a general-purpose estimator for use when we
* don't have any better idea about how to estimate. Index-type-specific
* knowledge can be incorporated in the type-specific routines.
*
* One bit of index-type-specific knowledge we can relatively easily use
* in genericcostestimate is the estimate of the number of index tuples
* visited. If numIndexTuples is not 0 then it is used as the estimate,
* otherwise we compute a generic estimate.
*
*-------------------------------------------------------------------------
*/
static void
genericcostestimate(PlannerInfo *root,
IndexOptInfo *index, List *indexQuals,
RelOptInfo *outer_rel,
double numIndexTuples,
Cost *indexStartupCost,
Cost *indexTotalCost,
Selectivity *indexSelectivity,
double *indexCorrelation)
{
double numIndexPages;
double num_sa_scans;
double num_outer_scans;
double num_scans;
QualCost index_qual_cost;
double qual_op_cost;
double qual_arg_cost;
List *selectivityQuals;
ListCell *l;
/*----------
* If the index is partial, AND the index predicate with the explicitly
* given indexquals to produce a more accurate idea of the index
* selectivity. However, we need to be careful not to insert redundant
* clauses, because clauselist_selectivity() is easily fooled into
* computing a too-low selectivity estimate. Our approach is to add
* only the index predicate clause(s) that cannot be proven to be implied
* by the given indexquals. This successfully handles cases such as a
* qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
* There are many other cases where we won't detect redundancy, leading
* to a too-low selectivity estimate, which will bias the system in favor
* of using partial indexes where possible. That is not necessarily bad
* though.
*
* Note that indexQuals contains RestrictInfo nodes while the indpred
* does not. This is OK for both predicate_implied_by() and
* clauselist_selectivity().
*----------
*/
if (index->indpred != NIL)
{
List *predExtraQuals = NIL;
foreach(l, index->indpred)
{
Node *predQual = (Node *) lfirst(l);
List *oneQual = list_make1(predQual);
if (!predicate_implied_by(oneQual, indexQuals))
predExtraQuals = list_concat(predExtraQuals, oneQual);
}
/* list_concat avoids modifying the passed-in indexQuals list */
selectivityQuals = list_concat(predExtraQuals, indexQuals);
}
else
selectivityQuals = indexQuals;
/*
* Check for ScalarArrayOpExpr index quals, and estimate the number of
* index scans that will be performed.
*/
num_sa_scans = 1;
foreach(l, indexQuals)
{
RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
if (IsA(rinfo->clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
int alength = estimate_array_length(lsecond(saop->args));
if (alength > 1)
num_sa_scans *= alength;
}
}
/* Estimate the fraction of main-table tuples that will be visited */
*indexSelectivity = clauselist_selectivity(root, selectivityQuals,
index->rel->relid,
JOIN_INNER,
NULL);
/*
* If caller didn't give us an estimate, estimate the number of index
* tuples that will be visited. We do it in this rather peculiar-looking
* way in order to get the right answer for partial indexes.
*/
if (numIndexTuples <= 0.0)
{
numIndexTuples = *indexSelectivity * index->rel->tuples;
/*
* The above calculation counts all the tuples visited across all
* scans induced by ScalarArrayOpExpr nodes. We want to consider the
* average per-indexscan number, so adjust. This is a handy place to
* round to integer, too. (If caller supplied tuple estimate, it's
* responsible for handling these considerations.)
*/
numIndexTuples = rint(numIndexTuples / num_sa_scans);
}
/*
* We can bound the number of tuples by the index size in any case. Also,
* always estimate at least one tuple is touched, even when
* indexSelectivity estimate is tiny.
*/
if (numIndexTuples > index->tuples)
numIndexTuples = index->tuples;
if (numIndexTuples < 1.0)
numIndexTuples = 1.0;
/*
* Estimate the number of index pages that will be retrieved.
*
* We use the simplistic method of taking a pro-rata fraction of the total
* number of index pages. In effect, this counts only leaf pages and not
* any overhead such as index metapage or upper tree levels. In practice
* this seems a better approximation than charging for access to the upper
* levels, perhaps because those tend to stay in cache under load.
*/
if (index->pages > 1 && index->tuples > 1)
numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
else
numIndexPages = 1.0;
/*
* Now compute the disk access costs.
*
* The above calculations are all per-index-scan. However, if we are in a
* nestloop inner scan, we can expect the scan to be repeated (with
* different search keys) for each row of the outer relation. Likewise,
* ScalarArrayOpExpr quals result in multiple index scans. This creates
* the potential for cache effects to reduce the number of disk page
* fetches needed. We want to estimate the average per-scan I/O cost in
* the presence of caching.
*
* We use the Mackert-Lohman formula (see costsize.c for details) to
* estimate the total number of page fetches that occur. While this
* wasn't what it was designed for, it seems a reasonable model anyway.
* Note that we are counting pages not tuples anymore, so we take N = T =
* index size, as if there were one "tuple" per page.
*/
if (outer_rel != NULL && outer_rel->rows > 1)
{
num_outer_scans = outer_rel->rows;
num_scans = num_sa_scans * num_outer_scans;
}
else
{
num_outer_scans = 1;
num_scans = num_sa_scans;
}
if (num_scans > 1)
{
double pages_fetched;
/* total page fetches ignoring cache effects */
pages_fetched = numIndexPages * num_scans;
/* use Mackert and Lohman formula to adjust for cache effects */
pages_fetched = index_pages_fetched(pages_fetched,
index->pages,
(double) index->pages,
root);
/*
* Now compute the total disk access cost, and then report a pro-rated
* share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
* since that's internal to the indexscan.)
*/
*indexTotalCost = (pages_fetched * random_page_cost) / num_outer_scans;
}
else
{
/*
* For a single index scan, we just charge random_page_cost per page
* touched.
*/
*indexTotalCost = numIndexPages * random_page_cost;
}
/*
* A difficulty with the leaf-pages-only cost approach is that for small
* selectivities (eg, single index tuple fetched) all indexes will look
* equally attractive because we will estimate exactly 1 leaf page to be
* fetched. All else being equal, we should prefer physically smaller
* indexes over larger ones. (An index might be smaller because it is
* partial or because it contains fewer columns; presumably the other
* columns in the larger index aren't useful to the query, or the larger
* index would have better selectivity.)
*
* We can deal with this by adding a very small "fudge factor" that
* depends on the index size. The fudge factor used here is one
* random_page_cost per 100000 index pages, which should be small enough
* to not alter index-vs-seqscan decisions, but will prevent indexes of
* different sizes from looking exactly equally attractive.
*/
*indexTotalCost += index->pages * random_page_cost / 100000.0;
/*
* CPU cost: any complex expressions in the indexquals will need to be
* evaluated once at the start of the scan to reduce them to runtime keys
* to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
* CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
* indexqual operator. Because we have numIndexTuples as a per-scan
* number, we have to multiply by num_sa_scans to get the correct result
* for ScalarArrayOpExpr cases.
*
* Note: this neglects the possible costs of rechecking lossy operators
* and OR-clause expressions. Detecting that that might be needed seems
* more expensive than it's worth, though, considering all the other
* inaccuracies here ...
*/
cost_qual_eval(&index_qual_cost, indexQuals, root);
qual_op_cost = cpu_operator_cost * list_length(indexQuals);
qual_arg_cost = index_qual_cost.startup +
index_qual_cost.per_tuple - qual_op_cost;
if (qual_arg_cost < 0) /* just in case... */
qual_arg_cost = 0;
*indexStartupCost = qual_arg_cost;
*indexTotalCost += qual_arg_cost;
*indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
/*
* We also add a CPU-cost component to represent the general costs of
* starting an indexscan, such as analysis of btree index keys and initial
* tree descent. This is estimated at 100x cpu_operator_cost, which is a
* bit arbitrary but seems the right order of magnitude. (As noted above,
* we don't charge any I/O for touching upper tree levels, but charging
* nothing at all has been found too optimistic.)
*
* Although this is startup cost with respect to any one scan, we add it
* to the "total" cost component because it's only very interesting in the
* many-ScalarArrayOpExpr-scan case, and there it will be paid over the
* life of the scan node.
*/
*indexTotalCost += num_sa_scans * 100.0 * cpu_operator_cost;
/*
* Generic assumption about index correlation: there isn't any.
*/
*indexCorrelation = 0.0;
}
Datum
btcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
Oid relid;
AttrNumber colnum;
VariableStatData vardata;
double numIndexTuples;
List *indexBoundQuals;
int indexcol;
bool eqQualHere;
bool found_saop;
bool found_null_op;
double num_sa_scans;
ListCell *l;
/*
* For a btree scan, only leading '=' quals plus inequality quals for the
* immediately next attribute contribute to index selectivity (these are
* the "boundary quals" that determine the starting and stopping points of
* the index scan). Additional quals can suppress visits to the heap, so
* it's OK to count them in indexSelectivity, but they should not count
* for estimating numIndexTuples. So we must examine the given indexQuals
* to find out which ones count as boundary quals. We rely on the
* knowledge that they are given in index column order.
*
* For a RowCompareExpr, we consider only the first column, just as
* rowcomparesel() does.
*
* If there's a ScalarArrayOpExpr in the quals, we'll actually perform N
* index scans not one, but the ScalarArrayOpExpr's operator can be
* considered to act the same as it normally does.
*/
indexBoundQuals = NIL;
indexcol = 0;
eqQualHere = false;
found_saop = false;
found_null_op = false;
num_sa_scans = 1;
foreach(l, indexQuals)
{
RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
Expr *clause;
Node *leftop,
*rightop;
Oid clause_op;
int op_strategy;
bool is_null_op = false;
Assert(IsA(rinfo, RestrictInfo));
clause = rinfo->clause;
if (IsA(clause, OpExpr))
{
leftop = get_leftop(clause);
rightop = get_rightop(clause);
clause_op = ((OpExpr *) clause)->opno;
}
else if (IsA(clause, RowCompareExpr))
{
RowCompareExpr *rc = (RowCompareExpr *) clause;
leftop = (Node *) linitial(rc->largs);
rightop = (Node *) linitial(rc->rargs);
clause_op = linitial_oid(rc->opnos);
}
else if (IsA(clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
leftop = (Node *) linitial(saop->args);
rightop = (Node *) lsecond(saop->args);
clause_op = saop->opno;
found_saop = true;
}
else if (IsA(clause, NullTest))
{
NullTest *nt = (NullTest *) clause;
Assert(nt->nulltesttype == IS_NULL);
leftop = (Node *) nt->arg;
rightop = NULL;
clause_op = InvalidOid;
found_null_op = true;
is_null_op = true;
}
else
{
elog(ERROR, "unsupported indexqual type: %d",
(int) nodeTag(clause));
continue; /* keep compiler quiet */
}
if (match_index_to_operand(leftop, indexcol, index))
{
/* clause_op is correct */
}
else if (match_index_to_operand(rightop, indexcol, index))
{
/* Must flip operator to get the opfamily member */
clause_op = get_commutator(clause_op);
}
else
{
/* Must be past the end of quals for indexcol, try next */
if (!eqQualHere)
break; /* done if no '=' qual for indexcol */
indexcol++;
eqQualHere = false;
if (match_index_to_operand(leftop, indexcol, index))
{
/* clause_op is correct */
}
else if (match_index_to_operand(rightop, indexcol, index))
{
/* Must flip operator to get the opfamily member */
clause_op = get_commutator(clause_op);
}
else
{
/* No quals for new indexcol, so we are done */
break;
}
}
/* check for equality operator */
if (is_null_op)
{
/* IS NULL is like = for purposes of selectivity determination */
eqQualHere = true;
}
else
{
op_strategy = get_op_opfamily_strategy(clause_op,
index->opfamily[indexcol]);
Assert(op_strategy != 0); /* not a member of opfamily?? */
if (op_strategy == BTEqualStrategyNumber)
eqQualHere = true;
}
/* count up number of SA scans induced by indexBoundQuals only */
if (IsA(clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
int alength = estimate_array_length(lsecond(saop->args));
if (alength > 1)
num_sa_scans *= alength;
}
indexBoundQuals = lappend(indexBoundQuals, rinfo);
}
/*
* If index is unique and we found an '=' clause for each column, we can
* just assume numIndexTuples = 1 and skip the expensive
* clauselist_selectivity calculations. However, a ScalarArrayOp or
* NullTest invalidates that theory, even though it sets eqQualHere.
*/
if (index->unique &&
indexcol == index->ncolumns - 1 &&
eqQualHere &&
!found_saop &&
!found_null_op)
numIndexTuples = 1.0;
else
{
Selectivity btreeSelectivity;
btreeSelectivity = clauselist_selectivity(root, indexBoundQuals,
index->rel->relid,
JOIN_INNER,
NULL);
numIndexTuples = btreeSelectivity * index->rel->tuples;
/*
* As in genericcostestimate(), we have to adjust for any
* ScalarArrayOpExpr quals included in indexBoundQuals, and then round
* to integer.
*/
numIndexTuples = rint(numIndexTuples / num_sa_scans);
}
genericcostestimate(root, index, indexQuals, outer_rel, numIndexTuples,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
/*
* If we can get an estimate of the first column's ordering correlation C
* from pg_statistic, estimate the index correlation as C for a
* single-column index, or C * 0.75 for multiple columns. (The idea here
* is that multiple columns dilute the importance of the first column's
* ordering, but don't negate it entirely. Before 8.0 we divided the
* correlation by the number of columns, but that seems too strong.)
*
* We can skip all this if we found a ScalarArrayOpExpr, because then the
* call must be for a bitmap index scan, and the caller isn't going to
* care what the index correlation is.
*/
if (found_saop)
PG_RETURN_VOID();
MemSet(&vardata, 0, sizeof(vardata));
if (index->indexkeys[0] != 0)
{
/* Simple variable --- look to stats for the underlying table */
RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
Assert(rte->rtekind == RTE_RELATION);
relid = rte->relid;
Assert(relid != InvalidOid);
colnum = index->indexkeys[0];
if (get_relation_stats_hook &&
(*get_relation_stats_hook) (root, rte, colnum, &vardata))
{
/*
* The hook took control of acquiring a stats tuple. If it did
* supply a tuple, it'd better have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata.statsTuple) &&
!vardata.freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else
{
vardata.statsTuple = SearchSysCache(STATRELATT,
ObjectIdGetDatum(relid),
Int16GetDatum(colnum),
0, 0);
vardata.freefunc = ReleaseSysCache;
}
}
else
{
/* Expression --- maybe there are stats for the index itself */
relid = index->indexoid;
colnum = 1;
if (get_index_stats_hook &&
(*get_index_stats_hook) (root, relid, colnum, &vardata))
{
/*
* The hook took control of acquiring a stats tuple. If it did
* supply a tuple, it'd better have supplied a freefunc.
*/
if (HeapTupleIsValid(vardata.statsTuple) &&
!vardata.freefunc)
elog(ERROR, "no function provided to release variable stats with");
}
else
{
vardata.statsTuple = SearchSysCache(STATRELATT,
ObjectIdGetDatum(relid),
Int16GetDatum(colnum),
0, 0);
vardata.freefunc = ReleaseSysCache;
}
}
if (HeapTupleIsValid(vardata.statsTuple))
{
float4 *numbers;
int nnumbers;
if (get_attstatsslot(vardata.statsTuple, InvalidOid, 0,
STATISTIC_KIND_CORRELATION,
index->fwdsortop[0],
NULL, NULL, &numbers, &nnumbers))
{
double varCorrelation;
Assert(nnumbers == 1);
varCorrelation = numbers[0];
if (index->ncolumns > 1)
*indexCorrelation = varCorrelation * 0.75;
else
*indexCorrelation = varCorrelation;
free_attstatsslot(InvalidOid, NULL, 0, numbers, nnumbers);
}
else if (get_attstatsslot(vardata.statsTuple, InvalidOid, 0,
STATISTIC_KIND_CORRELATION,
index->revsortop[0],
NULL, NULL, &numbers, &nnumbers))
{
double varCorrelation;
Assert(nnumbers == 1);
varCorrelation = numbers[0];
if (index->ncolumns > 1)
*indexCorrelation = -varCorrelation * 0.75;
else
*indexCorrelation = -varCorrelation;
free_attstatsslot(InvalidOid, NULL, 0, numbers, nnumbers);
}
}
ReleaseVariableStats(vardata);
PG_RETURN_VOID();
}
Datum
hashcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
genericcostestimate(root, index, indexQuals, outer_rel, 0.0,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
PG_RETURN_VOID();
}
Datum
gistcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
genericcostestimate(root, index, indexQuals, outer_rel, 0.0,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
PG_RETURN_VOID();
}
Datum
gincostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
genericcostestimate(root, index, indexQuals, outer_rel, 0.0,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
PG_RETURN_VOID();
}
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