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postgresql/src/backend/utils/adt/selfuncs.c
Tom Lane 1a8d5afb0d Refactor the representation of indexable clauses in IndexPaths. 
In place of three separate but interrelated lists (indexclauses,
indexquals, and indexqualcols), an IndexPath now has one list
"indexclauses" of IndexClause nodes.  This holds basically the same
information as before, but in a more useful format: in particular, there
is now a clear connection between an indexclause (an original restriction
clause from WHERE or JOIN/ON) and the indexquals (directly usable index
conditions) derived from it.

We also change the ground rules a bit by mandating that clause commutation,
if needed, be done up-front so that what is stored in the indexquals list
is always directly usable as an index condition.  This gets rid of repeated
re-determination of which side of the clause is the indexkey during costing
and plan generation, as well as repeated lookups of the commutator
operator.  To minimize the added up-front cost, the typical case of
commuting a plain OpExpr is handled by a new special-purpose function
commute_restrictinfo().  For RowCompareExprs, generating the new clause
properly commuted to begin with is not really any more complex than before,
it's just different --- and we can save doing that work twice, as the
pretty-klugy original implementation did.

Tracking the connection between original and derived clauses lets us
also track explicitly whether the derived clauses are an exact or lossy
translation of the original.  This provides a cheap solution to getting
rid of unnecessary rechecks of boolean index clauses, which previously
seemed like it'd be more expensive than it was worth.

Another pleasant (IMO) side-effect is that EXPLAIN now always shows
index clauses with the indexkey on the left; this seems less confusing.

This commit leaves expand_indexqual_conditions() and some related
functions in a slightly messy state.  I didn't bother to change them
any more than minimally necessary to work with the new data structure,
because all that code is going to be refactored out of existence in
a follow-on patch.

Discussion: https://postgr.es/m/22182.1549124950@sss.pgh.pa.us
2019-02-09 17:30:43 -05:00

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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 located via the index AM's API struct,
* which is obtained from the handler function registered in pg_am.
*
* Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
* src/backend/utils/adt/selfuncs.c
*
*-------------------------------------------------------------------------
*/
/*----------
* 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.
*
* For both oprrest and oprjoin functions, the operator's input collation OID
* (if any) is passed using the standard fmgr mechanism, so that the estimator
* function can fetch it with PG_GET_COLLATION(). Note, however, that all
* statistics in pg_statistic are currently built using the relevant column's
* collation. Thus, in most cases where we are looking at statistics, we
* should ignore the operator collation and use the stats entry's collation.
* We expect that the error induced by doing this is usually not large enough
* to justify complicating matters. In any case, doing otherwise would yield
* entirely garbage results for ordered stats data such as histograms.
*----------
*/
#include "postgres.h"
#include <ctype.h>
#include <math.h>
#include "access/brin.h"
#include "access/gin.h"
#include "access/htup_details.h"
#include "access/sysattr.h"
#include "access/table.h"
#include "catalog/index.h"
#include "catalog/pg_am.h"
#include "catalog/pg_collation.h"
#include "catalog/pg_operator.h"
#include "catalog/pg_opfamily.h"
#include "catalog/pg_statistic.h"
#include "catalog/pg_statistic_ext.h"
#include "catalog/pg_type.h"
#include "executor/executor.h"
#include "mb/pg_wchar.h"
#include "miscadmin.h"
#include "nodes/makefuncs.h"
#include "nodes/nodeFuncs.h"
#include "optimizer/clauses.h"
#include "optimizer/cost.h"
#include "optimizer/optimizer.h"
#include "optimizer/pathnode.h"
#include "optimizer/paths.h"
#include "optimizer/plancat.h"
#include "optimizer/restrictinfo.h"
#include "parser/parse_clause.h"
#include "parser/parse_coerce.h"
#include "parser/parsetree.h"
#include "statistics/statistics.h"
#include "utils/acl.h"
#include "utils/builtins.h"
#include "utils/bytea.h"
#include "utils/date.h"
#include "utils/datum.h"
#include "utils/fmgroids.h"
#include "utils/index_selfuncs.h"
#include "utils/lsyscache.h"
#include "utils/pg_locale.h"
#include "utils/rel.h"
#include "utils/selfuncs.h"
#include "utils/snapmgr.h"
#include "utils/spccache.h"
#include "utils/syscache.h"
#include "utils/timestamp.h"
#include "utils/typcache.h"
#include "utils/varlena.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 eqsel_internal(PG_FUNCTION_ARGS, bool negate);
static double var_eq_const(VariableStatData *vardata, Oid operator,
Datum constval, bool constisnull,
bool varonleft, bool negate);
static double var_eq_non_const(VariableStatData *vardata, Oid operator,
Node *other,
bool varonleft, bool negate);
static double ineq_histogram_selectivity(PlannerInfo *root,
VariableStatData *vardata,
FmgrInfo *opproc, bool isgt, bool iseq,
Datum constval, Oid consttype);
static double eqjoinsel_inner(Oid opfuncoid,
VariableStatData *vardata1, VariableStatData *vardata2,
double nd1, double nd2,
bool isdefault1, bool isdefault2,
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
Form_pg_statistic stats1, Form_pg_statistic stats2,
bool have_mcvs1, bool have_mcvs2);
static double eqjoinsel_semi(Oid opfuncoid,
VariableStatData *vardata1, VariableStatData *vardata2,
double nd1, double nd2,
bool isdefault1, bool isdefault2,
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
Form_pg_statistic stats1, Form_pg_statistic stats2,
bool have_mcvs1, bool have_mcvs2,
RelOptInfo *inner_rel);
static bool estimate_multivariate_ndistinct(PlannerInfo *root,
RelOptInfo *rel, List **varinfos, double *ndistinct);
static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
double *scaledvalue,
Datum lobound, Datum hibound, Oid boundstypid,
double *scaledlobound, double *scaledhibound);
static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
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, Oid collid,
bool *failure);
static double convert_timevalue_to_scalar(Datum value, Oid typid,
bool *failure);
static void examine_simple_variable(PlannerInfo *root, Var *var,
VariableStatData *vardata);
static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
Oid sortop, Datum *min, Datum *max);
static bool get_actual_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(PlannerInfo *root,
VariableStatData *vardata,
Oid vartype, Oid opfamily, Const *prefixcon);
static Selectivity like_selectivity(const char *patt, int pattlen,
bool case_insensitive);
static Selectivity regex_selectivity(const char *patt, int pattlen,
bool case_insensitive,
int fixed_prefix_len);
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);
static IndexQualInfo *deconstruct_indexqual(RestrictInfo *rinfo,
IndexOptInfo *index, int indexcol);
static List *add_predicate_to_quals(IndexOptInfo *index, List *indexQuals);
/*
* 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)
{
PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
}
/*
* Common code for eqsel() and neqsel()
*/
static double
eqsel_internal(PG_FUNCTION_ARGS, 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 *other;
bool varonleft;
double selec;
/*
* When asked about <>, we do the estimation using the corresponding =
* operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
*/
if (negate)
{
operator = get_negator(operator);
if (!OidIsValid(operator))
{
/* Use default selectivity (should we raise an error instead?) */
return 1.0 - DEFAULT_EQ_SEL;
}
}
/*
* 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))
return negate ? (1.0 - DEFAULT_EQ_SEL) : 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, negate);
else
selec = var_eq_non_const(&vardata, operator, other,
varonleft, negate);
ReleaseVariableStats(vardata);
return 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, bool negate)
{
double selec;
double nullfrac = 0.0;
bool isdefault;
Oid opfuncoid;
/*
* 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 (constisnull)
return 0.0;
/*
* Grab the nullfrac for use below. Note we allow use of nullfrac
* regardless of security check.
*/
if (HeapTupleIsValid(vardata->statsTuple))
{
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
nullfrac = stats->stanullfrac;
}
/*
* If we matched the var to a unique index or DISTINCT clause, assume
* there is exactly one match regardless of anything else. (This is
* slightly bogus, since the index or clause's equality operator might be
* different from ours, but it's much more likely to be right than
* ignoring the information.)
*/
if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
{
selec = 1.0 / vardata->rel->tuples;
}
else if (HeapTupleIsValid(vardata->statsTuple) &&
statistic_proc_security_check(vardata,
(opfuncoid = get_opcode(operator))))
{
AttStatsSlot sslot;
bool match = false;
int i;
/*
* 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(&sslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
{
FmgrInfo eqproc;
fmgr_info(opfuncoid, &eqproc);
for (i = 0; i < sslot.nvalues; i++)
{
/* be careful to apply operator right way 'round */
if (varonleft)
match = DatumGetBool(FunctionCall2Coll(&eqproc,
sslot.stacoll,
sslot.values[i],
constval));
else
match = DatumGetBool(FunctionCall2Coll(&eqproc,
sslot.stacoll,
constval,
sslot.values[i]));
if (match)
break;
}
}
else
{
/* no most-common-value info available */
i = 0; /* keep compiler quiet */
}
if (match)
{
/*
* Constant is "=" to this common value. We know selectivity
* exactly (or as exactly as ANALYZE could calculate it, anyway).
*/
selec = sslot.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 < sslot.nnumbers; i++)
sumcommon += sslot.numbers[i];
selec = 1.0 - sumcommon - nullfrac;
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, &isdefault) -
sslot.nnumbers;
if (otherdistinct > 1)
selec /= otherdistinct;
/*
* Another cross-check: selectivity shouldn't be estimated as more
* than the least common "most common value".
*/
if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
selec = sslot.numbers[sslot.nnumbers - 1];
}
free_attstatsslot(&sslot);
}
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, &isdefault);
}
/* now adjust if we wanted <> rather than = */
if (negate)
selec = 1.0 - selec - nullfrac;
/* 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, bool negate)
{
double selec;
double nullfrac = 0.0;
bool isdefault;
/*
* Grab the nullfrac for use below.
*/
if (HeapTupleIsValid(vardata->statsTuple))
{
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
nullfrac = stats->stanullfrac;
}
/*
* If we matched the var to a unique index or DISTINCT clause, assume
* there is exactly one match regardless of anything else. (This is
* slightly bogus, since the index or clause's equality operator might be
* different from ours, but it's much more likely to be right than
* ignoring the information.)
*/
if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
{
selec = 1.0 / vardata->rel->tuples;
}
else if (HeapTupleIsValid(vardata->statsTuple))
{
double ndistinct;
AttStatsSlot sslot;
/*
* 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 - nullfrac;
ndistinct = get_variable_numdistinct(vardata, &isdefault);
if (ndistinct > 1)
selec /= ndistinct;
/*
* Cross-check: selectivity should never be estimated as more than the
* most common value's.
*/
if (get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_NUMBERS))
{
if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
selec = sslot.numbers[0];
free_attstatsslot(&sslot);
}
}
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, &isdefault);
}
/* now adjust if we wanted <> rather than = */
if (negate)
selec = 1.0 - selec - nullfrac;
/* 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)
{
PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
}
/*
* scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
*
* This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
* The isgt and iseq flags distinguish which of the four cases apply.
*
* 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 that into
* a value and datatype. (This definition simplifies some callers that
* want to estimate against a computed value instead of a Const node.)
*
* 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 an approximate estimate based on assuming that the constant
* value falls in the middle of the bin identified by binary search.
*/
static double
scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
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(root, vardata,
&opproc, isgt, iseq,
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;
AttStatsSlot sslot;
int i;
mcv_selec = 0.0;
sumcommon = 0.0;
if (HeapTupleIsValid(vardata->statsTuple) &&
statistic_proc_security_check(vardata, opproc->fn_oid) &&
get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
{
for (i = 0; i < sslot.nvalues; i++)
{
if (varonleft ?
DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
sslot.values[i],
constval)) :
DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
constval,
sslot.values[i])))
mcv_selec += sslot.numbers[i];
sumcommon += sslot.numbers[i];
}
free_attstatsslot(&sslot);
}
*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;
AttStatsSlot sslot;
/* check sanity of parameters */
Assert(n_skip >= 0);
Assert(min_hist_size > 2 * n_skip);
if (HeapTupleIsValid(vardata->statsTuple) &&
statistic_proc_security_check(vardata, opproc->fn_oid) &&
get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES))
{
*hist_size = sslot.nvalues;
if (sslot.nvalues >= min_hist_size)
{
int nmatch = 0;
int i;
for (i = n_skip; i < sslot.nvalues - n_skip; i++)
{
if (varonleft ?
DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
sslot.values[i],
constval)) :
DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
constval,
sslot.values[i])))
nmatch++;
}
result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
}
else
result = -1;
free_attstatsslot(&sslot);
}
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 < (or <=, >, >=) CONST.
* The isgt and iseq flags distinguish which of the four cases apply.
*
* Returns -1 if there is no histogram (valid results will always be >= 0).
*
* 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(PlannerInfo *root,
VariableStatData *vardata,
FmgrInfo *opproc, bool isgt, bool iseq,
Datum constval, Oid consttype)
{
double hist_selec;
AttStatsSlot sslot;
hist_selec = -1.0;
/*
* 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) &&
statistic_proc_security_check(vardata, opproc->fn_oid) &&
get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
ATTSTATSSLOT_VALUES))
{
if (sslot.nvalues > 1)
{
/*
* Use binary search to find the desired location, namely the
* right end of the histogram bin containing the comparison value,
* which is the leftmost entry for which the comparison operator
* succeeds (if isgt) or fails (if !isgt). (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 have from the old linear search.)
*
* In this loop, we pay no attention to whether the operator iseq
* or not; that detail will be mopped up below. (We cannot tell,
* anyway, whether the operator thinks the values are equal.)
*
* If the binary search accesses the first or last histogram
* entry, we try to replace that endpoint with the true column min
* or max as found by get_actual_variable_range(). This
* ameliorates misestimates when the min or max is moving as a
* result of changes since the last ANALYZE. Note that this could
* result in effectively including MCVs into the histogram that
* weren't there before, but we don't try to correct for that.
*/
double histfrac;
int lobound = 0; /* first possible slot to search */
int hibound = sslot.nvalues; /* last+1 slot to search */
bool have_end = false;
/*
* If there are only two histogram entries, we'll want up-to-date
* values for both. (If there are more than two, we need at most
* one of them to be updated, so we deal with that within the
* loop.)
*/
if (sslot.nvalues == 2)
have_end = get_actual_variable_range(root,
vardata,
sslot.staop,
&sslot.values[0],
&sslot.values[1]);
while (lobound < hibound)
{
int probe = (lobound + hibound) / 2;
bool ltcmp;
/*
* If we find ourselves about to compare to the first or last
* histogram entry, first try to replace it with the actual
* current min or max (unless we already did so above).
*/
if (probe == 0 && sslot.nvalues > 2)
have_end = get_actual_variable_range(root,
vardata,
sslot.staop,
&sslot.values[0],
NULL);
else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
have_end = get_actual_variable_range(root,
vardata,
sslot.staop,
NULL,
&sslot.values[probe]);
ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
sslot.stacoll,
sslot.values[probe],
constval));
if (isgt)
ltcmp = !ltcmp;
if (ltcmp)
lobound = probe + 1;
else
hibound = probe;
}
if (lobound <= 0)
{
/*
* Constant is below lower histogram boundary. More
* precisely, we have found that no entry in the histogram
* satisfies the inequality clause (if !isgt) or they all do
* (if isgt). We estimate that that's true of the entire
* table, so set histfrac to 0.0 (which we'll flip to 1.0
* below, if isgt).
*/
histfrac = 0.0;
}
else if (lobound >= sslot.nvalues)
{
/*
* Inverse case: constant is above upper histogram boundary.
*/
histfrac = 1.0;
}
else
{
/* We have values[i-1] <= constant <= values[i]. */
int i = lobound;
double eq_selec = 0;
double val,
high,
low;
double binfrac;
/*
* In the cases where we'll need it below, obtain an estimate
* of the selectivity of "x = constval". We use a calculation
* similar to what var_eq_const() does for a non-MCV constant,
* ie, estimate that all distinct non-MCV values occur equally
* often. But multiplication by "1.0 - sumcommon - nullfrac"
* will be done by our caller, so we shouldn't do that here.
* Therefore we can't try to clamp the estimate by reference
* to the least common MCV; the result would be too small.
*
* Note: since this is effectively assuming that constval
* isn't an MCV, it's logically dubious if constval in fact is
* one. But we have to apply *some* correction for equality,
* and anyway we cannot tell if constval is an MCV, since we
* don't have a suitable equality operator at hand.
*/
if (i == 1 || isgt == iseq)
{
double otherdistinct;
bool isdefault;
AttStatsSlot mcvslot;
/* Get estimated number of distinct values */
otherdistinct = get_variable_numdistinct(vardata,
&isdefault);
/* Subtract off the number of known MCVs */
if (get_attstatsslot(&mcvslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_NUMBERS))
{
otherdistinct -= mcvslot.nnumbers;
free_attstatsslot(&mcvslot);
}
/* If result doesn't seem sane, leave eq_selec at 0 */
if (otherdistinct > 1)
eq_selec = 1.0 / otherdistinct;
}
/*
* 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, sslot.stacoll,
&val,
sslot.values[i - 1], sslot.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) (sslot.nvalues - 1);
/*
* At this point, histfrac is an estimate of the fraction of
* the population represented by the histogram that satisfies
* "x <= constval". Somewhat remarkably, this statement is
* true regardless of which operator we were doing the probes
* with, so long as convert_to_scalar() delivers reasonable
* results. If the probe constant is equal to some histogram
* entry, we would have considered the bin to the left of that
* entry if probing with "<" or ">=", or the bin to the right
* if probing with "<=" or ">"; but binfrac would have come
* out as 1.0 in the first case and 0.0 in the second, leading
* to the same histfrac in either case. For probe constants
* between histogram entries, we find the same bin and get the
* same estimate with any operator.
*
* The fact that the estimate corresponds to "x <= constval"
* and not "x < constval" is because of the way that ANALYZE
* constructs the histogram: each entry is, effectively, the
* rightmost value in its sample bucket. So selectivity
* values that are exact multiples of 1/(histogram_size-1)
* should be understood as estimates including a histogram
* entry plus everything to its left.
*
* However, that breaks down for the first histogram entry,
* which necessarily is the leftmost value in its sample
* bucket. That means the first histogram bin is slightly
* narrower than the rest, by an amount equal to eq_selec.
* Another way to say that is that we want "x <= leftmost" to
* be estimated as eq_selec not zero. So, if we're dealing
* with the first bin (i==1), rescale to make that true while
* adjusting the rest of that bin linearly.
*/
if (i == 1)
histfrac += eq_selec * (1.0 - binfrac);
/*
* "x <= constval" is good if we want an estimate for "<=" or
* ">", but if we are estimating for "<" or ">=", we now need
* to decrease the estimate by eq_selec.
*/
if (isgt == iseq)
histfrac -= eq_selec;
}
/*
* Now the estimate is finished for "<" and "<=" cases. If we are
* estimating for ">" or ">=", flip it.
*/
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 --- unless we
* got actual current endpoint values from the table, in which
* case just do the usual sanity clamp. Somewhat arbitrarily, we
* set the cutoff for other cases at a hundredth of the histogram
* resolution.
*/
if (have_end)
CLAMP_PROBABILITY(hist_selec);
else
{
double cutoff = 0.01 / (double) (sslot.nvalues - 1);
if (hist_selec < cutoff)
hist_selec = cutoff;
else if (hist_selec > 1.0 - cutoff)
hist_selec = 1.0 - cutoff;
}
}
free_attstatsslot(&sslot);
}
return hist_selec;
}
/*
* Common wrapper function for the selectivity estimators that simply
* invoke scalarineqsel().
*/
static Datum
scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
{
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;
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)
{
operator = get_commutator(operator);
if (!operator)
{
/* Use default selectivity (should we raise an error instead?) */
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
isgt = !isgt;
}
/* The rest of the work is done by scalarineqsel(). */
selec = scalarineqsel(root, operator, isgt, iseq,
&vardata, constval, consttype);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* scalarltsel - Selectivity of "<" for scalars.
*/
Datum
scalarltsel(PG_FUNCTION_ARGS)
{
return scalarineqsel_wrapper(fcinfo, false, false);
}
/*
* scalarlesel - Selectivity of "<=" for scalars.
*/
Datum
scalarlesel(PG_FUNCTION_ARGS)
{
return scalarineqsel_wrapper(fcinfo, false, true);
}
/*
* scalargtsel - Selectivity of ">" for scalars.
*/
Datum
scalargtsel(PG_FUNCTION_ARGS)
{
return scalarineqsel_wrapper(fcinfo, true, false);
}
/*
* scalargesel - Selectivity of ">=" for scalars.
*/
Datum
scalargesel(PG_FUNCTION_ARGS)
{
return scalarineqsel_wrapper(fcinfo, true, true);
}
/*
* 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);
Oid collation = PG_GET_COLLATION();
VariableStatData vardata;
Node *other;
bool varonleft;
Datum constval;
Oid consttype;
Oid vartype;
Oid opfamily;
Pattern_Prefix_Status pstatus;
Const *patt;
Const *prefix = NULL;
Selectivity rest_selec = 0;
double nullfrac = 0.0;
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;
}
/*
* 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:
case NAMEOID:
opfamily = TEXT_BTREE_FAM_OID;
break;
case BPCHAROID:
opfamily = BPCHAR_BTREE_FAM_OID;
break;
case BYTEAOID:
opfamily = BYTEA_BTREE_FAM_OID;
break;
default:
ReleaseVariableStats(vardata);
return result;
}
/*
* Grab the nullfrac for use below.
*/
if (HeapTupleIsValid(vardata.statsTuple))
{
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
nullfrac = stats->stanullfrac;
}
/*
* Pull out any fixed prefix implied by the pattern, and estimate the
* fractional selectivity of the remainder of the pattern. Unlike many of
* the other functions in this file, we use the pattern operator's actual
* collation for this step. This is not because we expect the collation
* to make a big difference in the selectivity estimate (it seldom would),
* but because we want to be sure we cache compiled regexps under the
* right cache key, so that they can be re-used at runtime.
*/
patt = (Const *) other;
pstatus = pattern_fixed_prefix(patt, ptype, collation,
&prefix, &rest_selec);
/*
* If necessary, coerce the prefix constant to the right type.
*/
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, false);
}
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 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;
if (pstatus == Pattern_Prefix_Partial)
prefixsel = prefix_selectivity(root, &vardata, vartype,
opfamily, prefix);
else
prefixsel = 1.0;
heursel = prefixsel * rest_selec;
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);
/*
* 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 = selec;
}
/* now adjust if we wanted not-match rather than match */
if (negate)
result = 1.0 - result - nullfrac;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(result);
if (prefix)
{
pfree(DatumGetPointer(prefix->constvalue));
pfree(prefix);
}
ReleaseVariableStats(vardata);
return 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));
}
/*
* prefixsel - selectivity of prefix operator
*/
Datum
prefixsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Prefix, 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));
}
/*
* boolvarsel - Selectivity of Boolean variable.
*
* This can actually be called on any boolean-valued expression. If it
* involves only Vars of the specified relation, and if there are statistics
* about the Var or expression (the latter is possible if it's indexed) then
* we'll produce a real estimate; otherwise it's just a default.
*/
Selectivity
boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
{
VariableStatData vardata;
double selec;
examine_variable(root, arg, varRelid, &vardata);
if (HeapTupleIsValid(vardata.statsTuple))
{
/*
* A boolean variable V is equivalent to the clause V = 't', so we
* compute the selectivity as if that is what we have.
*/
selec = var_eq_const(&vardata, BooleanEqualOperator,
BoolGetDatum(true), false, true, false);
}
else if (is_funcclause(arg))
{
/*
* If we have no stats and it's a function call, estimate 0.3333333.
* This seems a pretty unprincipled choice, but Postgres has been
* using that estimate for function calls since 1992. The hoariness
* of this behavior suggests that we should not be in too much hurry
* to use another value.
*/
selec = 0.3333333;
}
else
{
/* Otherwise, the default estimate is 0.5 */
selec = 0.5;
}
ReleaseVariableStats(vardata);
return selec;
}
/*
* 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;
AttStatsSlot sslot;
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
freq_null = stats->stanullfrac;
if (get_attstatsslot(&sslot, vardata.statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
&& sslot.nnumbers > 0)
{
double freq_true;
double freq_false;
/*
* Get first MCV frequency and derive frequency for true.
*/
if (DatumGetBool(sslot.values[0]))
freq_true = sslot.numbers[0];
else
freq_true = 1.0 - sslot.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(&sslot);
}
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 a 50-50 split of TRUE and FALSE.
*/
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:
case IS_FALSE:
/* Assume we select half of the non-NULL values */
selec = (1.0 - freq_null) / 2.0;
break;
case IS_NOT_TRUE:
case IS_NOT_FALSE:
/* Assume we select NULLs plus half of the non-NULLs */
/* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
selec = (freq_null + 1.0) / 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 if (vardata.var && IsA(vardata.var, Var) &&
((Var *) vardata.var)->varattno < 0)
{
/*
* There are no stats for system columns, but we know they are never
* NULL.
*/
selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
}
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 *acoerce = (ArrayCoerceExpr *) node;
/*
* If the per-element expression is just a RelabelType on top of
* CaseTestExpr, then we know it's a binary-compatible relabeling.
*/
if (IsA(acoerce->elemexpr, RelabelType) &&
IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
node = (Node *) acoerce->arg;
else
break;
}
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;
bool isEquality = false;
bool isInequality = false;
Node *leftop;
Node *rightop;
Oid nominal_element_type;
Oid nominal_element_collation;
TypeCacheEntry *typentry;
RegProcedure oprsel;
FmgrInfo oprselproc;
Selectivity s1;
Selectivity s1disjoint;
/* First, deconstruct the expression */
Assert(list_length(clause->args) == 2);
leftop = (Node *) linitial(clause->args);
rightop = (Node *) lsecond(clause->args);
/* aggressively reduce both sides to constants */
leftop = estimate_expression_value(root, leftop);
rightop = estimate_expression_value(root, rightop);
/* get nominal (after relabeling) element type of rightop */
nominal_element_type = get_base_element_type(exprType(rightop));
if (!OidIsValid(nominal_element_type))
return (Selectivity) 0.5; /* probably shouldn't happen */
/* get nominal collation, too, for generating constants */
nominal_element_collation = exprCollation(rightop);
/* look through any binary-compatible relabeling of rightop */
rightop = strip_array_coercion(rightop);
/*
* Detect whether the operator is the default equality or inequality
* operator of the array element type.
*/
typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
if (OidIsValid(typentry->eq_opr))
{
if (operator == typentry->eq_opr)
isEquality = true;
else if (get_negator(operator) == typentry->eq_opr)
isInequality = true;
}
/*
* If it is equality or inequality, we might be able to estimate this as a
* form of array containment; for instance "const = ANY(column)" can be
* treated as "ARRAY[const] <@ column". scalararraysel_containment tries
* that, and returns the selectivity estimate if successful, or -1 if not.
*/
if ((isEquality || isInequality) && !is_join_clause)
{
s1 = scalararraysel_containment(root, leftop, rightop,
nominal_element_type,
isEquality, useOr, varRelid);
if (s1 >= 0.0)
return s1;
}
/*
* 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);
/*
* In the array-containment check above, we must only believe that an
* operator is equality or inequality if it is the default btree equality
* operator (or its negator) for the element type, since those are the
* operators that array containment will use. But in what follows, we can
* be a little laxer, and also believe that any operators using eqsel() or
* neqsel() as selectivity estimator act like equality or inequality.
*/
if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
isEquality = true;
else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
isInequality = true;
/*
* 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);
/*
* For generic operators, we assume the probability of success is
* independent for each array element. But for "= ANY" or "<> ALL",
* if the array elements are distinct (which'd typically be the case)
* then the probabilities are disjoint, and we should just sum them.
*
* If we were being really tense we would try to confirm that the
* elements are all distinct, but that would be expensive and it
* doesn't seem to be worth the cycles; it would amount to penalizing
* well-written queries in favor of poorly-written ones. However, we
* do protect ourselves a little bit by checking whether the
* disjointness assumption leads to an impossible (out of range)
* probability; if so, we fall back to the normal calculation.
*/
s1 = s1disjoint = (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,
nominal_element_collation,
elmlen,
elem_values[i],
elem_nulls[i],
elmbyval));
if (is_join_clause)
s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
if (useOr)
{
s1 = s1 + s2 - s1 * s2;
if (isEquality)
s1disjoint += s2;
}
else
{
s1 = s1 * s2;
if (isInequality)
s1disjoint += s2 - 1.0;
}
}
/* accept disjoint-probability estimate if in range */
if ((useOr ? isEquality : isInequality) &&
s1disjoint >= 0.0 && s1disjoint <= 1.0)
s1 = s1disjoint;
}
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);
/*
* We use the assumption of disjoint probabilities here too, although
* the odds of equal array elements are rather higher if the elements
* are not all constants (which they won't be, else constant folding
* would have reduced the ArrayExpr to a Const). In this path it's
* critical to have the sanity check on the s1disjoint estimate.
*/
s1 = s1disjoint = (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(FunctionCall5Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
if (useOr)
{
s1 = s1 + s2 - s1 * s2;
if (isEquality)
s1disjoint += s2;
}
else
{
s1 = s1 * s2;
if (isInequality)
s1disjoint += s2 - 1.0;
}
}
/* accept disjoint-probability estimate if in range */
if ((useOr ? isEquality : isInequality) &&
s1disjoint >= 0.0 && s1disjoint <= 1.0)
s1 = s1disjoint;
}
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;
dummyexpr->collation = clause->inputcollid;
args = list_make2(leftop, dummyexpr);
if (is_join_clause)
s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
clause->inputcollid,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
Int16GetDatum(jointype),
PointerGetDatum(sjinfo)));
else
s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
clause->inputcollid,
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). We don't risk an assumption of
* disjoint probabilities here.
*/
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);
Oid inputcollid = linitial_oid(clause->inputcollids);
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,
inputcollid,
jointype,
sjinfo);
}
else
{
/* Estimate selectivity for a restriction clause. */
s1 = restriction_selectivity(root, opno,
opargs,
inputcollid,
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;
double selec_inner;
VariableStatData vardata1;
VariableStatData vardata2;
double nd1;
double nd2;
bool isdefault1;
bool isdefault2;
Oid opfuncoid;
AttStatsSlot sslot1;
AttStatsSlot sslot2;
Form_pg_statistic stats1 = NULL;
Form_pg_statistic stats2 = NULL;
bool have_mcvs1 = false;
bool have_mcvs2 = false;
bool join_is_reversed;
RelOptInfo *inner_rel;
get_join_variables(root, args, sjinfo,
&vardata1, &vardata2, &join_is_reversed);
nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
opfuncoid = get_opcode(operator);
memset(&sslot1, 0, sizeof(sslot1));
memset(&sslot2, 0, sizeof(sslot2));
if (HeapTupleIsValid(vardata1.statsTuple))
{
/* note we allow use of nullfrac regardless of security check */
stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
if (statistic_proc_security_check(&vardata1, opfuncoid))
have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
}
if (HeapTupleIsValid(vardata2.statsTuple))
{
/* note we allow use of nullfrac regardless of security check */
stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
if (statistic_proc_security_check(&vardata2, opfuncoid))
have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
}
/* We need to compute the inner-join selectivity in all cases */
selec_inner = eqjoinsel_inner(opfuncoid,
&vardata1, &vardata2,
nd1, nd2,
isdefault1, isdefault2,
&sslot1, &sslot2,
stats1, stats2,
have_mcvs1, have_mcvs2);
switch (sjinfo->jointype)
{
case JOIN_INNER:
case JOIN_LEFT:
case JOIN_FULL:
selec = selec_inner;
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(opfuncoid,
&vardata1, &vardata2,
nd1, nd2,
isdefault1, isdefault2,
&sslot1, &sslot2,
stats1, stats2,
have_mcvs1, have_mcvs2,
inner_rel);
else
{
Oid commop = get_commutator(operator);
Oid commopfuncoid = OidIsValid(commop) ? get_opcode(commop) : InvalidOid;
selec = eqjoinsel_semi(commopfuncoid,
&vardata2, &vardata1,
nd2, nd1,
isdefault2, isdefault1,
&sslot2, &sslot1,
stats2, stats1,
have_mcvs2, have_mcvs1,
inner_rel);
}
/*
* We should never estimate the output of a semijoin to be more
* rows than we estimate for an inner join with the same input
* rels and join condition; it's obviously impossible for that to
* happen. The former estimate is N1 * Ssemi while the latter is
* N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
* this is worthwhile because of the shakier estimation rules we
* use in eqjoinsel_semi, particularly in cases where it has to
* punt entirely.
*/
selec = Min(selec, inner_rel->rows * selec_inner);
break;
default:
/* other values not expected here */
elog(ERROR, "unrecognized join type: %d",
(int) sjinfo->jointype);
selec = 0; /* keep compiler quiet */
break;
}
free_attstatsslot(&sslot1);
free_attstatsslot(&sslot2);
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 opfuncoid,
VariableStatData *vardata1, VariableStatData *vardata2,
double nd1, double nd2,
bool isdefault1, bool isdefault2,
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
Form_pg_statistic stats1, Form_pg_statistic stats2,
bool have_mcvs1, bool have_mcvs2)
{
double selec;
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(opfuncoid, &eqproc);
hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
hasmatch2 = (bool *) palloc0(sslot2->nvalues * 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 < sslot1->nvalues; i++)
{
int j;
for (j = 0; j < sslot2->nvalues; j++)
{
if (hasmatch2[j])
continue;
if (DatumGetBool(FunctionCall2Coll(&eqproc,
sslot1->stacoll,
sslot1->values[i],
sslot2->values[j])))
{
hasmatch1[i] = hasmatch2[j] = true;
matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
nmatches++;
break;
}
}
}
CLAMP_PROBABILITY(matchprodfreq);
/* Sum up frequencies of matched and unmatched MCVs */
matchfreq1 = unmatchfreq1 = 0.0;
for (i = 0; i < sslot1->nvalues; i++)
{
if (hasmatch1[i])
matchfreq1 += sslot1->numbers[i];
else
unmatchfreq1 += sslot1->numbers[i];
}
CLAMP_PROBABILITY(matchfreq1);
CLAMP_PROBABILITY(unmatchfreq1);
matchfreq2 = unmatchfreq2 = 0.0;
for (i = 0; i < sslot2->nvalues; i++)
{
if (hasmatch2[i])
matchfreq2 += sslot2->numbers[i];
else
unmatchfreq2 += sslot2->numbers[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 > sslot2->nvalues)
totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
if (nd2 > nmatches)
totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
(nd2 - nmatches);
/* Same estimate from the point of view of relation 2. */
totalsel2 = matchprodfreq;
if (nd1 > sslot1->nvalues)
totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
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;
}
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.
* Unlike eqjoinsel_inner, we have to cope with opfuncoid being InvalidOid.
*/
static double
eqjoinsel_semi(Oid opfuncoid,
VariableStatData *vardata1, VariableStatData *vardata2,
double nd1, double nd2,
bool isdefault1, bool isdefault2,
AttStatsSlot *sslot1, AttStatsSlot *sslot2,
Form_pg_statistic stats1, Form_pg_statistic stats2,
bool have_mcvs1, bool have_mcvs2,
RelOptInfo *inner_rel)
{
double selec;
/*
* 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 we clamp, we can treat nd2 as being a non-default estimate; it's not
* great, maybe, but it didn't come out of nowhere either. This is most
* helpful when the inner relation is empty and consequently has no stats.
*/
if (vardata2->rel)
{
if (nd2 >= vardata2->rel->rows)
{
nd2 = vardata2->rel->rows;
isdefault2 = false;
}
}
if (nd2 >= inner_rel->rows)
{
nd2 = inner_rel->rows;
isdefault2 = false;
}
if (have_mcvs1 && have_mcvs2 && OidIsValid(opfuncoid))
{
/*
* 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
* sslot2->nvalues; 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(sslot2->nvalues, nd2);
fmgr_info(opfuncoid, &eqproc);
hasmatch1 = (bool *) palloc0(sslot1->nvalues * 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 < sslot1->nvalues; i++)
{
int j;
for (j = 0; j < clamped_nvalues2; j++)
{
if (hasmatch2[j])
continue;
if (DatumGetBool(FunctionCall2Coll(&eqproc,
sslot1->stacoll,
sslot1->values[i],
sslot2->values[j])))
{
hasmatch1[i] = hasmatch2[j] = true;
nmatches++;
break;
}
}
}
/* Sum up frequencies of matched MCVs */
matchfreq1 = 0.0;
for (i = 0; i < sslot1->nvalues; i++)
{
if (hasmatch1[i])
matchfreq1 += sslot1->numbers[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 (!isdefault1 && !isdefault2)
{
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 (!isdefault1 && !isdefault2)
{
if (nd1 <= nd2 || nd2 < 0)
selec = 1.0 - nullfrac1;
else
selec = (nd2 / nd1) * (1.0 - nullfrac1);
}
else
selec = 0.5 * (1.0 - nullfrac1);
}
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);
float8 result;
if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
{
/*
* For semi-joins, if there is more than one distinct value in the RHS
* relation then every non-null LHS row must find a row to join since
* it can only be equal to one of them. We'll assume that there is
* always more than one distinct RHS value for the sake of stability,
* though in theory we could have special cases for empty RHS
* (selectivity = 0) and single-distinct-value RHS (selectivity =
* fraction of LHS that has the same value as the single RHS value).
*
* For anti-joins, if we use the same assumption that there is more
* than one distinct key in the RHS relation, then every non-null LHS
* row must be suppressed by the anti-join.
*
* So either way, the selectivity estimate should be 1 - nullfrac.
*/
VariableStatData leftvar;
VariableStatData rightvar;
bool reversed;
HeapTuple statsTuple;
double nullfrac;
get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
if (HeapTupleIsValid(statsTuple))
nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
else
nullfrac = 0.0;
ReleaseVariableStats(leftvar);
ReleaseVariableStats(rightvar);
result = 1.0 - nullfrac;
}
else
{
/*
* We want 1 - eqjoinsel() where the equality operator is the one
* associated with this != operator, that is, its negator.
*/
Oid 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 "<" for scalars
*/
Datum
scalarltjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalarlejoinsel - Join selectivity of "<=" for scalars
*/
Datum
scalarlejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalargtjoinsel - Join selectivity of ">" for scalars
*/
Datum
scalargtjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalargejoinsel - Join selectivity of ">=" for scalars
*/
Datum
scalargejoinsel(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));
}
/*
* prefixjoinsel - Join selectivity of prefix operator
*/
Datum
prefixjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Prefix, 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, false,
&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, true, &leftvar,
rightmax, op_righttype);
if (selec != DEFAULT_INEQ_SEL)
*leftend = selec;
/* And similarly for the right variable. */
selec = scalarineqsel(root, revleop, isgt, true, &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, false, &leftvar,
rightmin, op_righttype);
if (selec != DEFAULT_INEQ_SEL)
*leftstart = selec;
/* And similarly for the right variable. */
selec = scalarineqsel(root, revltop, isgt, false, &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;
bool isdefault;
ListCell *lc;
ndistinct = get_variable_numdistinct(vardata, &isdefault);
/* 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
* pgset - NULL, or a List** pointing to a grouping set to filter the
* groupExprs against
*
* 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 a factor based on 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.) The factor
* we multiply by to adjust for the restriction selectivity assumes that
* the restriction clauses are independent of the grouping, which may not
* be a valid 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 **pgset)
{
List *varinfos = NIL;
double srf_multiplier = 1.0;
double numdistinct;
ListCell *l;
int i;
/*
* We don't ever want to return an estimate of zero groups, as that tends
* to lead to division-by-zero and other unpleasantness. The input_rows
* estimate is usually already at least 1, but clamp it just in case it
* isn't.
*/
input_rows = clamp_row_est(input_rows);
/*
* 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 || (pgset && list_length(*pgset) < 1))
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;
i = 0;
foreach(l, groupExprs)
{
Node *groupexpr = (Node *) lfirst(l);
double this_srf_multiplier;
VariableStatData vardata;
List *varshere;
ListCell *l2;
/* is expression in this grouping set? */
if (pgset && !list_member_int(*pgset, i++))
continue;
/*
* Set-returning functions in grouping columns are a bit problematic.
* The code below will effectively ignore their SRF nature and come up
* with a numdistinct estimate as though they were scalar functions.
* We compensate by scaling up the end result by the largest SRF
* rowcount estimate. (This will be an overestimate if the SRF
* produces multiple copies of any output value, but it seems best to
* assume the SRF's outputs are distinct. In any case, it's probably
* pointless to worry too much about this without much better
* estimates for SRF output rowcounts than we have today.)
*/
this_srf_multiplier = expression_returns_set_rows(groupexpr);
if (srf_multiplier < this_srf_multiplier)
srf_multiplier = this_srf_multiplier;
/* 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_AGGREGATES |
PVC_RECURSE_WINDOWFUNCS |
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)
{
/* Apply SRF multiplier as we would do in the long path */
numdistinct *= srf_multiplier;
/* Round off */
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;
}
/*
* 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 = 1;
double relmaxndistinct = reldistinct;
int relvarcount = 0;
List *newvarinfos = NIL;
List *relvarinfos = NIL;
/*
* Split the list of varinfos in two - one for the current rel, one
* for remaining Vars on other rels.
*/
relvarinfos = lcons(varinfo1, relvarinfos);
for_each_cell(l, lnext(list_head(varinfos)))
{
GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
if (varinfo2->rel == varinfo1->rel)
{
/* varinfos on current rel */
relvarinfos = lcons(varinfo2, relvarinfos);
}
else
{
/* not time to process varinfo2 yet */
newvarinfos = lcons(varinfo2, newvarinfos);
}
}
/*
* Get the numdistinct estimate for the Vars of this rel. We
* iteratively search for multivariate n-distinct with maximum number
* of vars; assuming that each var group is independent of the others,
* we multiply them together. Any remaining relvarinfos after no more
* multivariate matches are found are assumed independent too, so
* their individual ndistinct estimates are multiplied also.
*
* While iterating, count how many separate numdistinct values we
* apply. We apply a fudge factor below, but only if we multiplied
* more than one such values.
*/
while (relvarinfos)
{
double mvndistinct;
if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
&mvndistinct))
{
reldistinct *= mvndistinct;
if (relmaxndistinct < mvndistinct)
relmaxndistinct = mvndistinct;
relvarcount++;
}
else
{
foreach(l, relvarinfos)
{
GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
reldistinct *= varinfo2->ndistinct;
if (relmaxndistinct < varinfo2->ndistinct)
relmaxndistinct = varinfo2->ndistinct;
relvarcount++;
}
/* we're done with this relation */
relvarinfos = NIL;
}
}
/*
* Sanity check --- don't divide by zero if empty relation.
*/
Assert(IS_SIMPLE_REL(rel));
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;
/*
* Update the estimate based on the restriction selectivity,
* guarding against division by zero when reldistinct is zero.
* Also skip this if we know that we are returning all rows.
*/
if (reldistinct > 0 && rel->rows < rel->tuples)
{
/*
* Given a table containing N rows with n distinct values in a
* uniform distribution, if we select p rows at random then
* the expected number of distinct values selected is
*
* n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
*
* = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
*
* See "Approximating block accesses in database
* organizations", S. B. Yao, Communications of the ACM,
* Volume 20 Issue 4, April 1977 Pages 260-261.
*
* Alternatively, re-arranging the terms from the factorials,
* this may be written as
*
* n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
*
* This form of the formula is more efficient to compute in
* the common case where p is larger than N/n. Additionally,
* as pointed out by Dell'Era, if i << N for all terms in the
* product, it can be approximated by
*
* n * (1 - ((N-p)/N)^(N/n))
*
* See "Expected distinct values when selecting from a bag
* without replacement", Alberto Dell'Era,
* http://www.adellera.it/investigations/distinct_balls/.
*
* The condition i << N is equivalent to n >> 1, so this is a
* good approximation when the number of distinct values in
* the table is large. It turns out that this formula also
* works well even when n is small.
*/
reldistinct *=
(1 - pow((rel->tuples - rel->rows) / rel->tuples,
rel->tuples / reldistinct));
}
reldistinct = clamp_row_est(reldistinct);
/*
* Update estimate of total distinct groups.
*/
numdistinct *= reldistinct;
}
varinfos = newvarinfos;
} while (varinfos != NIL);
/* Now we can account for the effects of any SRFs */
numdistinct *= srf_multiplier;
/* Round off */
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 bucket statistics when the specified expression is used
* as a hash key for the given number of buckets.
*
* This attempts to determine two values:
*
* 1. The frequency of the most common value of the expression (returns
* zero into *mcv_freq if we can't get that).
*
* 2. The "bucketsize fraction", ie, average number of entries in a bucket
* divided by total tuples in relation.
*
* 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).
*
* The caller should also check that the mcv_freq is not so large that the
* most common value would by itself require an impractically large bucket.
* In a hash join, the executor can split buckets if they get too big, but
* obviously that doesn't help for a bucket that contains many duplicates of
* the same value.
*/
void
estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
Selectivity *mcv_freq,
Selectivity *bucketsize_frac)
{
VariableStatData vardata;
double estfract,
ndistinct,
stanullfrac,
avgfreq;
bool isdefault;
AttStatsSlot sslot;
examine_variable(root, hashkey, 0, &vardata);
/* Look up the frequency of the most common value, if available */
*mcv_freq = 0.0;
if (HeapTupleIsValid(vardata.statsTuple))
{
if (get_attstatsslot(&sslot, vardata.statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_NUMBERS))
{
/*
* The first MCV stat is for the most common value.
*/
if (sslot.nnumbers > 0)
*mcv_freq = sslot.numbers[0];
free_attstatsslot(&sslot);
}
}
/* Get number of distinct values */
ndistinct = get_variable_numdistinct(&vardata, &isdefault);
/*
* If ndistinct isn't real, punt. We normally return 0.1, but if the
* mcv_freq is known to be even higher than that, use it instead.
*/
if (isdefault)
{
*bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
ReleaseVariableStats(vardata);
return;
}
/* Get fraction that are null */
if (HeapTupleIsValid(vardata.statsTuple))
{
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
stanullfrac = stats->stanullfrac;
}
else
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 && vardata.rel->tuples > 0)
{
ndistinct *= vardata.rel->rows / vardata.rel->tuples;
ndistinct = clamp_row_est(ndistinct);
}
/*
* 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;
/*
* Adjust estimated bucketsize upward to account for skewed distribution.
*/
if (avgfreq > 0.0 && *mcv_freq > avgfreq)
estfract *= *mcv_freq / 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;
*bucketsize_frac = (Selectivity) estfract;
ReleaseVariableStats(vardata);
}
/*-------------------------------------------------------------------------
*
* Support routines
*
*-------------------------------------------------------------------------
*/
/*
* Find applicable ndistinct statistics for the given list of VarInfos (which
* must all belong to the given rel), and update *ndistinct to the estimate of
* the MVNDistinctItem that best matches. If a match it found, *varinfos is
* updated to remove the list of matched varinfos.
*
* Varinfos that aren't for simple Vars are ignored.
*
* Return true if we're able to find a match, false otherwise.
*/
static bool
estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
List **varinfos, double *ndistinct)
{
ListCell *lc;
Bitmapset *attnums = NULL;
int nmatches;
Oid statOid = InvalidOid;
MVNDistinct *stats;
Bitmapset *matched = NULL;
/* bail out immediately if the table has no extended statistics */
if (!rel->statlist)
return false;
/* Determine the attnums we're looking for */
foreach(lc, *varinfos)
{
GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
Assert(varinfo->rel == rel);
if (IsA(varinfo->var, Var))
{
attnums = bms_add_member(attnums,
((Var *) varinfo->var)->varattno);
}
}
/* look for the ndistinct statistics matching the most vars */
nmatches = 1; /* we require at least two matches */
foreach(lc, rel->statlist)
{
StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
Bitmapset *shared;
int nshared;
/* skip statistics of other kinds */
if (info->kind != STATS_EXT_NDISTINCT)
continue;
/* compute attnums shared by the vars and the statistics object */
shared = bms_intersect(info->keys, attnums);
nshared = bms_num_members(shared);
/*
* Does this statistics object match more columns than the currently
* best object? If so, use this one instead.
*
* XXX This should break ties using name of the object, or something
* like that, to make the outcome stable.
*/
if (nshared > nmatches)
{
statOid = info->statOid;
nmatches = nshared;
matched = shared;
}
}
/* No match? */
if (statOid == InvalidOid)
return false;
Assert(nmatches > 1 && matched != NULL);
stats = statext_ndistinct_load(statOid);
/*
* If we have a match, search it for the specific item that matches (there
* must be one), and construct the output values.
*/
if (stats)
{
int i;
List *newlist = NIL;
MVNDistinctItem *item = NULL;
/* Find the specific item that exactly matches the combination */
for (i = 0; i < stats->nitems; i++)
{
MVNDistinctItem *tmpitem = &stats->items[i];
if (bms_subset_compare(tmpitem->attrs, matched) == BMS_EQUAL)
{
item = tmpitem;
break;
}
}
/* make sure we found an item */
if (!item)
elog(ERROR, "corrupt MVNDistinct entry");
/* Form the output varinfo list, keeping only unmatched ones */
foreach(lc, *varinfos)
{
GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
AttrNumber attnum;
if (!IsA(varinfo->var, Var))
{
newlist = lappend(newlist, varinfo);
continue;
}
attnum = ((Var *) varinfo->var)->varattno;
if (!bms_is_member(attnum, matched))
newlist = lappend(newlist, varinfo);
}
*varinfos = newlist;
*ndistinct = item->ndistinct;
return true;
}
return false;
}
/*
* 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, Oid collid, double *scaledvalue,
Datum lobound, Datum hibound, Oid boundstypid,
double *scaledlobound, double *scaledhibound)
{
bool failure = false;
/*
* Both the valuetypid and the boundstypid should exactly match the
* declared input type(s) of the operator we are invoked for. However,
* extensions might try to use scalarineqsel as estimator for operators
* with input type(s) we don't handle here; in such cases, we want to
* return false, not fail. In any case, we mustn't assume that valuetypid
* and boundstypid are identical.
*
* 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:
case REGROLEOID:
case REGNAMESPACEOID:
*scaledvalue = convert_numeric_to_scalar(value, valuetypid,
&failure);
*scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
&failure);
*scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
&failure);
return !failure;
/*
* Built-in string types
*/
case CHAROID:
case BPCHAROID:
case VARCHAROID:
case TEXTOID:
case NAMEOID:
{
char *valstr = convert_string_datum(value, valuetypid,
collid, &failure);
char *lostr = convert_string_datum(lobound, boundstypid,
collid, &failure);
char *histr = convert_string_datum(hibound, boundstypid,
collid, &failure);
/*
* Bail out if any of the values is not of string type. We
* might leak converted strings for the other value(s), but
* that's not worth troubling over.
*/
if (failure)
return false;
convert_string_to_scalar(valstr, scaledvalue,
lostr, scaledlobound,
histr, scaledhibound);
pfree(valstr);
pfree(lostr);
pfree(histr);
return true;
}
/*
* Built-in bytea type
*/
case BYTEAOID:
{
/* We only support bytea vs bytea comparison */
if (boundstypid != BYTEAOID)
return false;
convert_bytea_to_scalar(value, scaledvalue,
lobound, scaledlobound,
hibound, scaledhibound);
return true;
}
/*
* Built-in time types
*/
case TIMESTAMPOID:
case TIMESTAMPTZOID:
case DATEOID:
case INTERVALOID:
case TIMEOID:
case TIMETZOID:
*scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
&failure);
*scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
&failure);
*scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
&failure);
return !failure;
/*
* Built-in network types
*/
case INETOID:
case CIDROID:
case MACADDROID:
case MACADDR8OID:
*scaledvalue = convert_network_to_scalar(value, valuetypid,
&failure);
*scaledlobound = convert_network_to_scalar(lobound, boundstypid,
&failure);
*scaledhibound = convert_network_to_scalar(hibound, boundstypid,
&failure);
return !failure;
}
/* Don't know how to convert */
*scaledvalue = *scaledlobound = *scaledhibound = 0;
return false;
}
/*
* Do convert_to_scalar()'s work for any numeric data type.
*
* On failure (e.g., unsupported typid), set *failure to true;
* otherwise, that variable is not changed.
*/
static double
convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
{
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:
case REGROLEOID:
case REGNAMESPACEOID:
/* we can treat OIDs as integers... */
return (double) DatumGetObjectId(value);
}
*failure = true;
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 */
/*
* There seems little point in considering more than a dozen bytes from
* the string. Since base is at least 10, that will give us nominal
* resolution of at least 12 decimal digits, which is surely far more
* precision than this estimation technique has got anyway (especially in
* non-C locales). Also, even with the maximum possible base of 256, this
* ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
* overflow on any known machine.
*/
if (slen > 12)
slen = 12;
/* 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.
*
* On failure (e.g., unsupported typid), set *failure to true;
* otherwise, that variable is not changed. (We'll return NULL on failure.)
*
* 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, Oid collid, bool *failure)
{
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:
*failure = true;
return NULL;
}
if (!lc_collate_is_c(collid))
{
char *xfrmstr;
size_t xfrmlen;
size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
/*
* XXX: We could guess at a suitable output buffer size and only call
* strxfrm twice if our guess is too small.
*
* 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);
/*
* Some systems (e.g., glibc) can return a smaller value from the
* second call than the first; thus the Assert must be <= not ==.
*/
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)
{
bytea *valuep = DatumGetByteaPP(value);
bytea *loboundp = DatumGetByteaPP(lobound);
bytea *hiboundp = DatumGetByteaPP(hibound);
int rangelo,
rangehi,
valuelen = VARSIZE_ANY_EXHDR(valuep),
loboundlen = VARSIZE_ANY_EXHDR(loboundp),
hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
i,
minlen;
unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
/*
* 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.
*
* On failure (e.g., unsupported typid), set *failure to true;
* otherwise, that variable is not changed.
*/
static double
convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
{
switch (typid)
{
case TIMESTAMPOID:
return DatumGetTimestamp(value);
case TIMESTAMPTZOID:
return DatumGetTimestampTz(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.
*/
return interval->time + interval->day * (double) USECS_PER_DAY +
interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
}
case TIMEOID:
return DatumGetTimeADT(value);
case TIMETZOID:
{
TimeTzADT *timetz = DatumGetTimeTzADTP(value);
/* use GMT-equivalent time */
return (double) (timetz->time + (timetz->zone * 1000000.0));
}
}
*failure = true;
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;
}
/* Oops, 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: actual type/typmod of the "var" expression. 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 or a
* single-column DISTINCT clause, implying its values are unique for
* this query. (Caution: this should be trusted for statistical
* purposes only, since we do not check indimmediate nor verify that
* the exact same definition of equality applies.)
* acl_ok: true if current user has permission to read the column(s)
* underlying the pg_statistic entry. This is consulted by
* statistic_proc_security_check().
*
* 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;
/* Set up result fields other than the stats tuple */
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);
/* Try to locate some stats */
examine_simple_variable(root, var, vardata);
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->nkeycolumns == 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 =
SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(index->indexoid),
Int16GetDatum(pos + 1),
BoolGetDatum(false));
vardata->freefunc = ReleaseSysCache;
if (HeapTupleIsValid(vardata->statsTuple))
{
/* Get index's table for permission check */
RangeTblEntry *rte;
rte = planner_rt_fetch(index->rel->relid, root);
Assert(rte->rtekind == RTE_RELATION);
/*
* For simplicity, we insist on the whole
* table being selectable, rather than trying
* to identify which column(s) the index
* depends on.
*/
vardata->acl_ok =
(pg_class_aclcheck(rte->relid, GetUserId(),
ACL_SELECT) == ACLCHECK_OK);
}
else
{
/* suppress leakproofness checks later */
vardata->acl_ok = true;
}
}
if (vardata->statsTuple)
break;
}
indexpr_item = lnext(indexpr_item);
}
}
if (vardata->statsTuple)
break;
}
}
}
/*
* examine_simple_variable
* Handle a simple Var for examine_variable
*
* This is split out as a subroutine so that we can recurse to deal with
* Vars referencing subqueries.
*
* We already filled in all the fields of *vardata except for the stats tuple.
*/
static void
examine_simple_variable(PlannerInfo *root, Var *var,
VariableStatData *vardata)
{
RangeTblEntry *rte = root->simple_rte_array[var->varno];
Assert(IsA(rte, RangeTblEntry));
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->rtekind == RTE_RELATION)
{
/*
* Plain table or parent of an inheritance appendrel, so look up the
* column in pg_statistic
*/
vardata->statsTuple = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(rte->relid),
Int16GetDatum(var->varattno),
BoolGetDatum(rte->inh));
vardata->freefunc = ReleaseSysCache;
if (HeapTupleIsValid(vardata->statsTuple))
{
/* check if user has permission to read this column */
vardata->acl_ok =
(pg_class_aclcheck(rte->relid, GetUserId(),
ACL_SELECT) == ACLCHECK_OK) ||
(pg_attribute_aclcheck(rte->relid, var->varattno, GetUserId(),
ACL_SELECT) == ACLCHECK_OK);
}
else
{
/* suppress any possible leakproofness checks later */
vardata->acl_ok = true;
}
}
else if (rte->rtekind == RTE_SUBQUERY && !rte->inh)
{
/*
* Plain subquery (not one that was converted to an appendrel).
*/
Query *subquery = rte->subquery;
RelOptInfo *rel;
TargetEntry *ste;
/*
* Punt if it's a whole-row var rather than a plain column reference.
*/
if (var->varattno == InvalidAttrNumber)
return;
/*
* Punt if subquery uses set operations or GROUP BY, as these will
* mash underlying columns' stats beyond recognition. (Set ops are
* particularly nasty; if we forged ahead, we would return stats
* relevant to only the leftmost subselect...) DISTINCT is also
* problematic, but we check that later because there is a possibility
* of learning something even with it.
*/
if (subquery->setOperations ||
subquery->groupClause)
return;
/*
* OK, fetch RelOptInfo for subquery. Note that we don't change the
* rel returned in vardata, since caller expects it to be a rel of the
* caller's query level. Because we might already be recursing, we
* can't use that rel pointer either, but have to look up the Var's
* rel afresh.
*/
rel = find_base_rel(root, var->varno);
/* If the subquery hasn't been planned yet, we have to punt */
if (rel->subroot == NULL)
return;
Assert(IsA(rel->subroot, PlannerInfo));
/*
* Switch our attention to the subquery as mangled by the planner. It
* was okay to look at the pre-planning version for the tests above,
* but now we need a Var that will refer to the subroot's live
* RelOptInfos. For instance, if any subquery pullup happened during
* planning, Vars in the targetlist might have gotten replaced, and we
* need to see the replacement expressions.
*/
subquery = rel->subroot->parse;
Assert(IsA(subquery, Query));
/* Get the subquery output expression referenced by the upper Var */
ste = get_tle_by_resno(subquery->targetList, var->varattno);
if (ste == NULL || ste->resjunk)
elog(ERROR, "subquery %s does not have attribute %d",
rte->eref->aliasname, var->varattno);
var = (Var *) ste->expr;
/*
* If subquery uses DISTINCT, we can't make use of any stats for the
* variable ... but, if it's the only DISTINCT column, we are entitled
* to consider it unique. We do the test this way so that it works
* for cases involving DISTINCT ON.
*/
if (subquery->distinctClause)
{
if (list_length(subquery->distinctClause) == 1 &&
targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
vardata->isunique = true;
/* cannot go further */
return;
}
/*
* If the sub-query originated from a view with the security_barrier
* attribute, we must not look at the variable's statistics, though it
* seems all right to notice the existence of a DISTINCT clause. So
* stop here.
*
* This is probably a harsher restriction than necessary; it's
* certainly OK for the selectivity estimator (which is a C function,
* and therefore omnipotent anyway) to look at the statistics. But
* many selectivity estimators will happily *invoke the operator
* function* to try to work out a good estimate - and that's not OK.
* So for now, don't dig down for stats.
*/
if (rte->security_barrier)
return;
/* Can only handle a simple Var of subquery's query level */
if (var && IsA(var, Var) &&
var->varlevelsup == 0)
{
/*
* OK, recurse into the subquery. Note that the original setting
* of vardata->isunique (which will surely be false) is left
* unchanged in this situation. That's what we want, since even
* if the underlying column is unique, the subquery may have
* joined to other tables in a way that creates duplicates.
*/
examine_simple_variable(rel->subroot, var, vardata);
}
}
else
{
/*
* Otherwise, the Var comes from a FUNCTION, VALUES, or CTE RTE. (We
* won't see RTE_JOIN here because join alias Vars have already been
* flattened.) There's not much we can do with function outputs, but
* maybe someday try to be smarter about VALUES and/or CTEs.
*/
}
}
/*
* Check whether it is permitted to call func_oid passing some of the
* pg_statistic data in vardata. We allow this either if the user has SELECT
* privileges on the table or column underlying the pg_statistic data or if
* the function is marked leak-proof.
*/
bool
statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
{
if (vardata->acl_ok)
return true;
if (!OidIsValid(func_oid))
return false;
if (get_func_leakproof(func_oid))
return true;
ereport(DEBUG2,
(errmsg_internal("not using statistics because function \"%s\" is not leak-proof",
get_func_name(func_oid))));
return false;
}
/*
* get_variable_numdistinct
* Estimate the number of distinct values of a variable.
*
* vardata: results of examine_variable
* *isdefault: set to true if the result is a default rather than based on
* anything meaningful.
*
* NB: be careful to produce a positive integral result, since callers may
* compare the result to exact integer counts, or might divide by it.
*/
double
get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
{
double stadistinct;
double stanullfrac = 0.0;
double ntuples;
*isdefault = false;
/*
* 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. Grab stanullfrac too if we can find it
* (otherwise, assume no nulls, for lack of any better idea).
*/
if (HeapTupleIsValid(vardata->statsTuple))
{
/* Use the pg_statistic entry */
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
stadistinct = stats->stadistinct;
stanullfrac = stats->stanullfrac;
}
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 if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
{
/*
* If the Var represents a column of a VALUES RTE, assume it's unique.
* This could of course be very wrong, but it should tend to be true
* in well-written queries. We could consider examining the VALUES'
* contents to get some real statistics; but that only works if the
* entries are all constants, and it would be pretty expensive anyway.
*/
stadistinct = -1.0; /* unique (and all non null) */
}
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 SelfItemPointerAttributeNumber:
stadistinct = -1.0; /* unique (and all non null) */
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 or DISTINCT clause 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. However, we'd better still believe
* the null-fraction statistic.
*/
if (vardata->isunique)
stadistinct = -1.0 * (1.0 - stanullfrac);
/*
* If we had an absolute estimate, use that.
*/
if (stadistinct > 0.0)
return clamp_row_est(stadistinct);
/*
* Otherwise we need to get the relation size; punt if not available.
*/
if (vardata->rel == NULL)
{
*isdefault = true;
return DEFAULT_NUM_DISTINCT;
}
ntuples = vardata->rel->tuples;
if (ntuples <= 0.0)
{
*isdefault = true;
return DEFAULT_NUM_DISTINCT;
}
/*
* If we had a relative estimate, use that.
*/
if (stadistinct < 0.0)
return clamp_row_est(-stadistinct * ntuples);
/*
* With no data, estimate ndistinct = ntuples if the table is small, else
* use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
* that the behavior isn't discontinuous.
*/
if (ntuples < DEFAULT_NUM_DISTINCT)
return clamp_row_est(ntuples);
*isdefault = true;
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;
int16 typLen;
bool typByVal;
Oid opfuncoid;
AttStatsSlot sslot;
int i;
/*
* XXX It's very tempting to try to use the actual column min and max, if
* we can get them relatively-cheaply with an index probe. However, since
* this function is called many times during join planning, that could
* have unpleasant effects on planning speed. Need more investigation
* before enabling this.
*/
#ifdef NOT_USED
if (get_actual_variable_range(root, vardata, sortop, min, max))
return true;
#endif
if (!HeapTupleIsValid(vardata->statsTuple))
{
/* no stats available, so default result */
return false;
}
/*
* If we can't apply the sortop to the stats data, just fail. In
* principle, if there's a histogram and no MCVs, we could return the
* histogram endpoints without ever applying the sortop ... but it's
* probably not worth trying, because whatever the caller wants to do with
* the endpoints would likely fail the security check too.
*/
if (!statistic_proc_security_check(vardata,
(opfuncoid = get_opcode(sortop))))
return false;
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(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, sortop,
ATTSTATSSLOT_VALUES))
{
if (sslot.nvalues > 0)
{
tmin = datumCopy(sslot.values[0], typByVal, typLen);
tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
have_data = true;
}
free_attstatsslot(&sslot);
}
else if (get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
0))
{
free_attstatsslot(&sslot);
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(&sslot, vardata->statsTuple,
STATISTIC_KIND_MCV, InvalidOid,
ATTSTATSSLOT_VALUES))
{
bool tmin_is_mcv = false;
bool tmax_is_mcv = false;
FmgrInfo opproc;
fmgr_info(opfuncoid, &opproc);
for (i = 0; i < sslot.nvalues; i++)
{
if (!have_data)
{
tmin = tmax = sslot.values[i];
tmin_is_mcv = tmax_is_mcv = have_data = true;
continue;
}
if (DatumGetBool(FunctionCall2Coll(&opproc,
sslot.stacoll,
sslot.values[i], tmin)))
{
tmin = sslot.values[i];
tmin_is_mcv = true;
}
if (DatumGetBool(FunctionCall2Coll(&opproc,
sslot.stacoll,
tmax, sslot.values[i])))
{
tmax = sslot.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(&sslot);
}
*min = tmin;
*max = tmax;
return have_data;
}
/*
* get_actual_variable_range
* Attempt to identify the current *actual* minimum and/or maximum
* of the specified variable, by looking for a suitable btree index
* and fetching its low and/or high values.
* If successful, store values in *min and *max, and return true.
* (Either pointer can be NULL if that endpoint isn't needed.)
* If no data available, return false.
*
* sortop is the "<" comparison operator to use.
*/
static bool
get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
Oid sortop,
Datum *min, Datum *max)
{
bool have_data = false;
RelOptInfo *rel = vardata->rel;
RangeTblEntry *rte;
ListCell *lc;
/* No hope if no relation or it doesn't have indexes */
if (rel == NULL || rel->indexlist == NIL)
return false;
/* If it has indexes it must be a plain relation */
rte = root->simple_rte_array[rel->relid];
Assert(rte->rtekind == RTE_RELATION);
/* Search through the indexes to see if any match our problem */
foreach(lc, rel->indexlist)
{
IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
ScanDirection indexscandir;
/* Ignore non-btree indexes */
if (index->relam != BTREE_AM_OID)
continue;
/*
* Ignore partial indexes --- we only want stats that cover the entire
* relation.
*/
if (index->indpred != NIL)
continue;
/*
* The index list might include hypothetical indexes inserted by a
* get_relation_info hook --- don't try to access them.
*/
if (index->hypothetical)
continue;
/*
* The first index column must match the desired variable and sort
* operator --- but we can use a descending-order index.
*/
if (!match_index_to_operand(vardata->var, 0, index))
continue;
switch (get_op_opfamily_strategy(sortop, index->sortopfamily[0]))
{
case BTLessStrategyNumber:
if (index->reverse_sort[0])
indexscandir = BackwardScanDirection;
else
indexscandir = ForwardScanDirection;
break;
case BTGreaterStrategyNumber:
if (index->reverse_sort[0])
indexscandir = ForwardScanDirection;
else
indexscandir = BackwardScanDirection;
break;
default:
/* index doesn't match the sortop */
continue;
}
/*
* Found a suitable index to extract data from. We'll need an EState
* and a bunch of other infrastructure.
*/
{
EState *estate;
ExprContext *econtext;
MemoryContext tmpcontext;
MemoryContext oldcontext;
Relation heapRel;
Relation indexRel;
IndexInfo *indexInfo;
TupleTableSlot *slot;
int16 typLen;
bool typByVal;
ScanKeyData scankeys[1];
IndexScanDesc index_scan;
HeapTuple tup;
Datum values[INDEX_MAX_KEYS];
bool isnull[INDEX_MAX_KEYS];
SnapshotData SnapshotNonVacuumable;
estate = CreateExecutorState();
econtext = GetPerTupleExprContext(estate);
/* Make sure any cruft is generated in the econtext's memory */
tmpcontext = econtext->ecxt_per_tuple_memory;
oldcontext = MemoryContextSwitchTo(tmpcontext);
/*
* Open the table and index so we can read from them. We should
* already have at least AccessShareLock on the table, but not
* necessarily on the index.
*/
heapRel = table_open(rte->relid, NoLock);
indexRel = index_open(index->indexoid, AccessShareLock);
/* extract index key information from the index's pg_index info */
indexInfo = BuildIndexInfo(indexRel);
/* some other stuff */
slot = MakeSingleTupleTableSlot(RelationGetDescr(heapRel),
&TTSOpsHeapTuple);
econtext->ecxt_scantuple = slot;
get_typlenbyval(vardata->atttype, &typLen, &typByVal);
InitNonVacuumableSnapshot(SnapshotNonVacuumable, RecentGlobalXmin);
/* set up an IS NOT NULL scan key so that we ignore nulls */
ScanKeyEntryInitialize(&scankeys[0],
SK_ISNULL | SK_SEARCHNOTNULL,
1, /* index col to scan */
InvalidStrategy, /* no strategy */
InvalidOid, /* no strategy subtype */
InvalidOid, /* no collation */
InvalidOid, /* no reg proc for this */
(Datum) 0); /* constant */
have_data = true;
/* If min is requested ... */
if (min)
{
/*
* In principle, we should scan the index with our current
* active snapshot, which is the best approximation we've got
* to what the query will see when executed. But that won't
* be exact if a new snap is taken before running the query,
* and it can be very expensive if a lot of recently-dead or
* uncommitted rows exist at the beginning or end of the index
* (because we'll laboriously fetch each one and reject it).
* Instead, we use SnapshotNonVacuumable. That will accept
* recently-dead and uncommitted rows as well as normal
* visible rows. On the other hand, it will reject known-dead
* rows, and thus not give a bogus answer when the extreme
* value has been deleted (unless the deletion was quite
* recent); that case motivates not using SnapshotAny here.
*
* A crucial point here is that SnapshotNonVacuumable, with
* RecentGlobalXmin as horizon, yields the inverse of the
* condition that the indexscan will use to decide that index
* entries are killable (see heap_hot_search_buffer()).
* Therefore, if the snapshot rejects a tuple and we have to
* continue scanning past it, we know that the indexscan will
* mark that index entry killed. That means that the next
* get_actual_variable_range() call will not have to visit
* that heap entry. In this way we avoid repetitive work when
* this function is used a lot during planning.
*/
index_scan = index_beginscan(heapRel, indexRel,
&SnapshotNonVacuumable,
1, 0);
index_rescan(index_scan, scankeys, 1, NULL, 0);
/* Fetch first tuple in sortop's direction */
if ((tup = index_getnext(index_scan,
indexscandir)) != NULL)
{
/* Extract the index column values from the heap tuple */
ExecStoreHeapTuple(tup, slot, false);
FormIndexDatum(indexInfo, slot, estate,
values, isnull);
/* Shouldn't have got a null, but be careful */
if (isnull[0])
elog(ERROR, "found unexpected null value in index \"%s\"",
RelationGetRelationName(indexRel));
/* Copy the index column value out to caller's context */
MemoryContextSwitchTo(oldcontext);
*min = datumCopy(values[0], typByVal, typLen);
MemoryContextSwitchTo(tmpcontext);
}
else
have_data = false;
index_endscan(index_scan);
}
/* If max is requested, and we didn't find the index is empty */
if (max && have_data)
{
index_scan = index_beginscan(heapRel, indexRel,
&SnapshotNonVacuumable,
1, 0);
index_rescan(index_scan, scankeys, 1, NULL, 0);
/* Fetch first tuple in reverse direction */
if ((tup = index_getnext(index_scan,
-indexscandir)) != NULL)
{
/* Extract the index column values from the heap tuple */
ExecStoreHeapTuple(tup, slot, false);
FormIndexDatum(indexInfo, slot, estate,
values, isnull);
/* Shouldn't have got a null, but be careful */
if (isnull[0])
elog(ERROR, "found unexpected null value in index \"%s\"",
RelationGetRelationName(indexRel));
/* Copy the index column value out to caller's context */
MemoryContextSwitchTo(oldcontext);
*max = datumCopy(values[0], typByVal, typLen);
MemoryContextSwitchTo(tmpcontext);
}
else
have_data = false;
index_endscan(index_scan);
}
/* Clean everything up */
ExecDropSingleTupleTableSlot(slot);
index_close(indexRel, AccessShareLock);
table_close(heapRel, NoLock);
MemoryContextSwitchTo(oldcontext);
FreeExecutorState(estate);
/* And we're done */
break;
}
}
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.
*
*-------------------------------------------------------------------------
*/
/*
* Check whether char is a letter (and, hence, subject to case-folding)
*
* In multibyte character sets or with ICU, we can't use isalpha, and it does not seem
* worth trying to convert to wchar_t to use iswalpha. Instead, just assume
* any multibyte char is potentially case-varying.
*/
static int
pattern_char_isalpha(char c, bool is_multibyte,
pg_locale_t locale, bool locale_is_c)
{
if (locale_is_c)
return (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z');
else if (is_multibyte && IS_HIGHBIT_SET(c))
return true;
else if (locale && locale->provider == COLLPROVIDER_ICU)
return IS_HIGHBIT_SET(c) ? true : false;
#ifdef HAVE_LOCALE_T
else if (locale && locale->provider == COLLPROVIDER_LIBC)
return isalpha_l((unsigned char) c, locale->info.lt);
#endif
else
return isalpha((unsigned char) c);
}
/*
* 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.
* If rest_selec is not NULL, *rest_selec is set to an estimate of the
* selectivity of the remainder of the pattern (without any fixed prefix).
* The prefix Const has the same type (TEXT or BYTEA) as the input pattern.
*
* 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, Oid collation,
Const **prefix_const, Selectivity *rest_selec)
{
char *match;
char *patt;
int pattlen;
Oid typeid = patt_const->consttype;
int pos,
match_pos;
bool is_multibyte = (pg_database_encoding_max_length() > 1);
pg_locale_t locale = 0;
bool locale_is_c = false;
/* the right-hand const is type text or bytea */
Assert(typeid == BYTEAOID || typeid == TEXTOID);
if (case_insensitive)
{
if (typeid == BYTEAOID)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("case insensitive matching not supported on type bytea")));
/* If case-insensitive, we need locale info */
if (lc_ctype_is_c(collation))
locale_is_c = true;
else if (collation != DEFAULT_COLLATION_OID)
{
if (!OidIsValid(collation))
{
/*
* This typically means that the parser could not resolve a
* conflict of implicit collations, so report it that way.
*/
ereport(ERROR,
(errcode(ERRCODE_INDETERMINATE_COLLATION),
errmsg("could not determine which collation to use for ILIKE"),
errhint("Use the COLLATE clause to set the collation explicitly.")));
}
locale = pg_newlocale_from_collation(collation);
}
}
if (typeid != BYTEAOID)
{
patt = TextDatumGetCString(patt_const->constvalue);
pattlen = strlen(patt);
}
else
{
bytea *bstr = DatumGetByteaPP(patt_const->constvalue);
pattlen = VARSIZE_ANY_EXHDR(bstr);
patt = (char *) palloc(pattlen);
memcpy(patt, VARDATA_ANY(bstr), pattlen);
Assert((Pointer) bstr == DatumGetPointer(patt_const->constvalue));
}
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;
}
/* Stop if case-varying character (it's sort of a wildcard) */
if (case_insensitive &&
pattern_char_isalpha(patt[pos], is_multibyte, locale, locale_is_c))
break;
match[match_pos++] = patt[pos];
}
match[match_pos] = '\0';
if (typeid != BYTEAOID)
*prefix_const = string_to_const(match, typeid);
else
*prefix_const = string_to_bytea_const(match, match_pos);
if (rest_selec != NULL)
*rest_selec = like_selectivity(&patt[pos], pattlen - pos,
case_insensitive);
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, Oid collation,
Const **prefix_const, Selectivity *rest_selec)
{
Oid typeid = patt_const->consttype;
char *prefix;
bool exact;
/*
* 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")));
/* Use the regexp machinery to extract the prefix, if any */
prefix = regexp_fixed_prefix(DatumGetTextPP(patt_const->constvalue),
case_insensitive, collation,
&exact);
if (prefix == NULL)
{
*prefix_const = NULL;
if (rest_selec != NULL)
{
char *patt = TextDatumGetCString(patt_const->constvalue);
*rest_selec = regex_selectivity(patt, strlen(patt),
case_insensitive,
0);
pfree(patt);
}
return Pattern_Prefix_None;
}
*prefix_const = string_to_const(prefix, typeid);
if (rest_selec != NULL)
{
if (exact)
{
/* Exact match, so there's no additional selectivity */
*rest_selec = 1.0;
}
else
{
char *patt = TextDatumGetCString(patt_const->constvalue);
*rest_selec = regex_selectivity(patt, strlen(patt),
case_insensitive,
strlen(prefix));
pfree(patt);
}
}
pfree(prefix);
if (exact)
return Pattern_Prefix_Exact; /* pattern specifies exact match */
else
return Pattern_Prefix_Partial;
}
Pattern_Prefix_Status
pattern_fixed_prefix(Const *patt, Pattern_Type ptype, Oid collation,
Const **prefix, Selectivity *rest_selec)
{
Pattern_Prefix_Status result;
switch (ptype)
{
case Pattern_Type_Like:
result = like_fixed_prefix(patt, false, collation,
prefix, rest_selec);
break;
case Pattern_Type_Like_IC:
result = like_fixed_prefix(patt, true, collation,
prefix, rest_selec);
break;
case Pattern_Type_Regex:
result = regex_fixed_prefix(patt, false, collation,
prefix, rest_selec);
break;
case Pattern_Type_Regex_IC:
result = regex_fixed_prefix(patt, true, collation,
prefix, rest_selec);
break;
case Pattern_Type_Prefix:
/* Prefix type work is trivial. */
result = Pattern_Prefix_Partial;
*rest_selec = 1.0; /* all */
*prefix = makeConst(patt->consttype,
patt->consttypmod,
patt->constcollid,
patt->constlen,
datumCopy(patt->constvalue,
patt->constbyval,
patt->constlen),
patt->constisnull,
patt->constbyval);
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(PlannerInfo *root, VariableStatData *vardata,
Oid vartype, Oid opfamily, Const *prefixcon)
{
Selectivity prefixsel;
Oid cmpopr;
FmgrInfo opproc;
AttStatsSlot sslot;
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(root, vardata,
&opproc, true, 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". We try to generate the string referencing the
* collation of the var's statistics, but if that's not available,
* use DEFAULT_COLLATION_OID.
*-------
*/
if (HeapTupleIsValid(vardata->statsTuple) &&
get_attstatsslot(&sslot, vardata->statsTuple,
STATISTIC_KIND_HISTOGRAM, InvalidOid, 0))
/* sslot.stacoll is set up */ ;
else
sslot.stacoll = DEFAULT_COLLATION_OID;
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, sslot.stacoll);
if (greaterstrcon)
{
Selectivity topsel;
topsel = ineq_histogram_selectivity(root, vardata,
&opproc, false, 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, false);
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,
* so actually we may be looking at just a fragment of the pattern.
*
* 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 char *patt, int pattlen, bool case_insensitive)
{
Selectivity sel = 1.0;
int pos;
/* 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;
return sel;
}
static Selectivity
regex_selectivity_sub(const 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 char *patt, int pattlen, bool case_insensitive,
int fixed_prefix_len)
{
Selectivity sel;
/* 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 there's a fixed prefix, discount its selectivity */
if (fixed_prefix_len > 0)
sel /= pow(FIXED_CHAR_SEL, fixed_prefix_len);
/* Make sure result stays in range */
CLAMP_PROBABILITY(sel);
return sel;
}
/*
* For bytea, the increment function need only increment the current byte
* (there are no multibyte characters to worry about).
*/
static bool
byte_increment(unsigned char *ptr, int len)
{
if (*ptr >= 255)
return false;
(*ptr)++;
return true;
}
/*
* 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, along with the collation to use.
*
* 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 collations
* 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 collation.
*
* 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 collation, and append that to the given
* prefix before trying to find a string that compares as larger.
*
* To search for a greater string, we repeatedly "increment" the rightmost
* character, using an encoding-specific character incrementer function.
* When it's no longer possible to increment the last character, we truncate
* off that character and start incrementing the next-to-rightmost.
* 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.
*
* Note that it's important for the character incrementer not to be too anal
* about producing every possible character code, since in some cases the only
* way to get a larger string is to increment a previous character position.
* So we don't want to spend too much time trying every possible character
* code at the last position. A good rule of thumb is to be sure that we
* don't try more than 256*K values for a K-byte character (and definitely
* not 256^K, which is what an exhaustive search would approach).
*/
Const *
make_greater_string(const Const *str_const, FmgrInfo *ltproc, Oid collation)
{
Oid datatype = str_const->consttype;
char *workstr;
int len;
Datum cmpstr;
char *cmptxt = NULL;
mbcharacter_incrementer charinc;
/*
* 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. Type
* BYTEA sorts bytewise so it never needs a suffix either.
*/
if (datatype == BYTEAOID)
{
bytea *bstr = DatumGetByteaPP(str_const->constvalue);
len = VARSIZE_ANY_EXHDR(bstr);
workstr = (char *) palloc(len);
memcpy(workstr, VARDATA_ANY(bstr), len);
Assert((Pointer) bstr == DatumGetPointer(str_const->constvalue));
cmpstr = str_const->constvalue;
}
else
{
if (datatype == NAMEOID)
workstr = DatumGetCString(DirectFunctionCall1(nameout,
str_const->constvalue));
else
workstr = TextDatumGetCString(str_const->constvalue);
len = strlen(workstr);
if (lc_collate_is_c(collation) || len == 0)
cmpstr = str_const->constvalue;
else
{
/* If first time through, determine the suffix to use */
static char suffixchar = 0;
static Oid suffixcollation = 0;
if (!suffixchar || suffixcollation != collation)
{
char *best;
best = "Z";
if (varstr_cmp(best, 1, "z", 1, collation) < 0)
best = "z";
if (varstr_cmp(best, 1, "y", 1, collation) < 0)
best = "y";
if (varstr_cmp(best, 1, "9", 1, collation) < 0)
best = "9";
suffixchar = *best;
suffixcollation = collation;
}
/* And build the string to compare to */
if (datatype == NAMEOID)
{
cmptxt = palloc(len + 2);
memcpy(cmptxt, workstr, len);
cmptxt[len] = suffixchar;
cmptxt[len + 1] = '\0';
cmpstr = PointerGetDatum(cmptxt);
}
else
{
cmptxt = palloc(VARHDRSZ + len + 1);
SET_VARSIZE(cmptxt, VARHDRSZ + len + 1);
memcpy(VARDATA(cmptxt), workstr, len);
*(VARDATA(cmptxt) + len) = suffixchar;
cmpstr = PointerGetDatum(cmptxt);
}
}
}
/* Select appropriate character-incrementer function */
if (datatype == BYTEAOID)
charinc = byte_increment;
else
charinc = pg_database_encoding_character_incrementer();
/* And search ... */
while (len > 0)
{
int charlen;
unsigned char *lastchar;
/* Identify the last character --- for bytea, just the last byte */
if (datatype == BYTEAOID)
charlen = 1;
else
charlen = len - pg_mbcliplen(workstr, len, len - 1);
lastchar = (unsigned char *) (workstr + len - charlen);
/*
* Try to generate a larger string by incrementing the last character
* (for BYTEA, we treat each byte as a character).
*
* Note: the incrementer function is expected to return true if it's
* generated a valid-per-the-encoding new character, otherwise false.
* The contents of the character on false return are unspecified.
*/
while (charinc(lastchar, charlen))
{
Const *workstr_const;
if (datatype == BYTEAOID)
workstr_const = string_to_bytea_const(workstr, len);
else
workstr_const = string_to_const(workstr, datatype);
if (DatumGetBool(FunctionCall2Coll(ltproc,
collation,
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);
}
/*
* No luck here, so truncate off the last character and try to
* increment the next one.
*/
len -= charlen;
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);
Oid collation;
int constlen;
/*
* We only need to support a few datatypes here, so hard-wire properties
* instead of incurring the expense of catalog lookups.
*/
switch (datatype)
{
case TEXTOID:
case VARCHAROID:
case BPCHAROID:
collation = DEFAULT_COLLATION_OID;
constlen = -1;
break;
case NAMEOID:
collation = C_COLLATION_OID;
constlen = NAMEDATALEN;
break;
case BYTEAOID:
collation = InvalidOid;
constlen = -1;
break;
default:
elog(ERROR, "unexpected datatype in string_to_const: %u",
datatype);
return NULL;
}
return makeConst(datatype, -1, collation, constlen,
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, InvalidOid, -1, conval, false, false);
}
/*-------------------------------------------------------------------------
*
* Index cost estimation functions
*
*-------------------------------------------------------------------------
*/
/* Extract the actual indexquals (as RestrictInfos) from an IndexClause list */
static List *
get_index_quals(List *indexclauses)
{
List *result = NIL;
ListCell *lc;
foreach(lc, indexclauses)
{
IndexClause *iclause = lfirst_node(IndexClause, lc);
if (iclause->indexquals == NIL)
{
/* rinfo->clause is directly usable as an indexqual */
result = lappend(result, iclause->rinfo);
}
else
{
/* report the derived indexquals */
result = list_concat(result, list_copy(iclause->indexquals));
}
}
return result;
}
List *
deconstruct_indexquals(IndexPath *path)
{
List *result = NIL;
IndexOptInfo *index = path->indexinfo;
ListCell *lc;
foreach(lc, path->indexclauses)
{
IndexClause *iclause = lfirst_node(IndexClause, lc);
int indexcol = iclause->indexcol;
IndexQualInfo *qinfo;
if (iclause->indexquals == NIL)
{
/* rinfo->clause is directly usable as an indexqual */
qinfo = deconstruct_indexqual(iclause->rinfo, index, indexcol);
result = lappend(result, qinfo);
}
else
{
/* Process the derived indexquals */
ListCell *lc2;
foreach(lc2, iclause->indexquals)
{
RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
qinfo = deconstruct_indexqual(rinfo, index, indexcol);
result = lappend(result, qinfo);
}
}
}
return result;
}
static IndexQualInfo *
deconstruct_indexqual(RestrictInfo *rinfo, IndexOptInfo *index, int indexcol)
{
{
Expr *clause;
IndexQualInfo *qinfo;
clause = rinfo->clause;
qinfo = (IndexQualInfo *) palloc(sizeof(IndexQualInfo));
qinfo->rinfo = rinfo;
qinfo->indexcol = indexcol;
if (IsA(clause, OpExpr))
{
qinfo->clause_op = ((OpExpr *) clause)->opno;
qinfo->other_operand = get_rightop(clause);
}
else if (IsA(clause, RowCompareExpr))
{
RowCompareExpr *rc = (RowCompareExpr *) clause;
qinfo->clause_op = linitial_oid(rc->opnos);
qinfo->other_operand = (Node *) rc->rargs;
}
else if (IsA(clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
qinfo->clause_op = saop->opno;
qinfo->other_operand = (Node *) lsecond(saop->args);
}
else if (IsA(clause, NullTest))
{
qinfo->clause_op = InvalidOid;
qinfo->other_operand = NULL;
}
else
{
elog(ERROR, "unsupported indexqual type: %d",
(int) nodeTag(clause));
}
return qinfo;
}
}
/*
* Simple function to compute the total eval cost of the "other operands"
* in an IndexQualInfo list. Since we know these will be evaluated just
* once per scan, there's no need to distinguish startup from per-row cost.
*/
static Cost
other_operands_eval_cost(PlannerInfo *root, List *qinfos)
{
Cost qual_arg_cost = 0;
ListCell *lc;
foreach(lc, qinfos)
{
IndexQualInfo *qinfo = (IndexQualInfo *) lfirst(lc);
QualCost index_qual_cost;
cost_qual_eval_node(&index_qual_cost, qinfo->other_operand, root);
qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
}
return qual_arg_cost;
}
/*
* Get other-operand eval cost for an index orderby list.
*
* Index orderby expressions aren't represented as RestrictInfos (since they
* aren't boolean, usually). So we can't apply deconstruct_indexquals to
* them. However, they are much simpler to deal with since they are always
* OpExprs and the index column is always on the left.
*/
static Cost
orderby_operands_eval_cost(PlannerInfo *root, IndexPath *path)
{
Cost qual_arg_cost = 0;
ListCell *lc;
foreach(lc, path->indexorderbys)
{
Expr *clause = (Expr *) lfirst(lc);
Node *other_operand;
QualCost index_qual_cost;
if (IsA(clause, OpExpr))
{
other_operand = get_rightop(clause);
}
else
{
elog(ERROR, "unsupported indexorderby type: %d",
(int) nodeTag(clause));
other_operand = NULL; /* keep compiler quiet */
}
cost_qual_eval_node(&index_qual_cost, other_operand, root);
qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
}
return qual_arg_cost;
}
void
genericcostestimate(PlannerInfo *root,
IndexPath *path,
double loop_count,
List *qinfos,
GenericCosts *costs)
{
IndexOptInfo *index = path->indexinfo;
List *indexQuals = get_index_quals(path->indexclauses);
List *indexOrderBys = path->indexorderbys;
Cost indexStartupCost;
Cost indexTotalCost;
Selectivity indexSelectivity;
double indexCorrelation;
double numIndexPages;
double numIndexTuples;
double spc_random_page_cost;
double num_sa_scans;
double num_outer_scans;
double num_scans;
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.
*/
selectivityQuals = add_predicate_to_quals(index, 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.
*/
numIndexTuples = costs->numIndexTuples;
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 access to upper index levels is often nearly free because
* those tend to stay in cache under load; moreover, the cost involved is
* highly dependent on index type. We therefore ignore such costs here
* and leave it to the caller to add a suitable charge if needed.
*/
if (index->pages > 1 && index->tuples > 1)
numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
else
numIndexPages = 1.0;
/* fetch estimated page cost for tablespace containing index */
get_tablespace_page_costs(index->reltablespace,
&spc_random_page_cost,
NULL);
/*
* 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.
*/
num_outer_scans = loop_count;
num_scans = num_sa_scans * num_outer_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 * spc_random_page_cost)
/ num_outer_scans;
}
else
{
/*
* For a single index scan, we just charge spc_random_page_cost per
* page touched.
*/
indexTotalCost = numIndexPages * spc_random_page_cost;
}
/*
* 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. Similarly add in costs for any index
* ORDER BY expressions.
*
* Note: this neglects the possible costs of rechecking lossy operators.
* Detecting that that might be needed seems more expensive than it's
* worth, though, considering all the other inaccuracies here ...
*/
qual_arg_cost = other_operands_eval_cost(root, qinfos) +
orderby_operands_eval_cost(root, path);
qual_op_cost = cpu_operator_cost *
(list_length(indexQuals) + list_length(indexOrderBys));
indexStartupCost = qual_arg_cost;
indexTotalCost += qual_arg_cost;
indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
/*
* Generic assumption about index correlation: there isn't any.
*/
indexCorrelation = 0.0;
/*
* Return everything to caller.
*/
costs->indexStartupCost = indexStartupCost;
costs->indexTotalCost = indexTotalCost;
costs->indexSelectivity = indexSelectivity;
costs->indexCorrelation = indexCorrelation;
costs->numIndexPages = numIndexPages;
costs->numIndexTuples = numIndexTuples;
costs->spc_random_page_cost = spc_random_page_cost;
costs->num_sa_scans = num_sa_scans;
}
/*
* If the index is partial, add its predicate to the given qual list.
*
* ANDing the index predicate with the explicitly given indexquals produces
* a more accurate idea of the index's 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 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, so the output list will be mixed. This is OK for both
* predicate_implied_by() and clauselist_selectivity(), but might be
* problematic if the result were passed to other things.
*/
static List *
add_predicate_to_quals(IndexOptInfo *index, List *indexQuals)
{
List *predExtraQuals = NIL;
ListCell *lc;
if (index->indpred == NIL)
return indexQuals;
foreach(lc, index->indpred)
{
Node *predQual = (Node *) lfirst(lc);
List *oneQual = list_make1(predQual);
if (!predicate_implied_by(oneQual, indexQuals, false))
predExtraQuals = list_concat(predExtraQuals, oneQual);
}
/* list_concat avoids modifying the passed-in indexQuals list */
return list_concat(predExtraQuals, indexQuals);
}
void
btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
List *qinfos;
GenericCosts costs;
Oid relid;
AttrNumber colnum;
VariableStatData vardata;
double numIndexTuples;
Cost descentCost;
List *indexBoundQuals;
int indexcol;
bool eqQualHere;
bool found_saop;
bool found_is_null_op;
double num_sa_scans;
ListCell *lc;
/* Do preliminary analysis of indexquals */
qinfos = deconstruct_indexquals(path);
/*
* 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_is_null_op = false;
num_sa_scans = 1;
foreach(lc, qinfos)
{
IndexQualInfo *qinfo = (IndexQualInfo *) lfirst(lc);
RestrictInfo *rinfo = qinfo->rinfo;
Expr *clause = rinfo->clause;
Oid clause_op;
int op_strategy;
if (indexcol != qinfo->indexcol)
{
/* Beginning of a new column's quals */
if (!eqQualHere)
break; /* done if no '=' qual for indexcol */
eqQualHere = false;
indexcol++;
if (indexcol != qinfo->indexcol)
break; /* no quals at all for indexcol */
}
if (IsA(clause, ScalarArrayOpExpr))
{
int alength = estimate_array_length(qinfo->other_operand);
found_saop = true;
/* count up number of SA scans induced by indexBoundQuals only */
if (alength > 1)
num_sa_scans *= alength;
}
else if (IsA(clause, NullTest))
{
NullTest *nt = (NullTest *) clause;
if (nt->nulltesttype == IS_NULL)
{
found_is_null_op = true;
/* IS NULL is like = for selectivity determination purposes */
eqQualHere = true;
}
}
/* check for equality operator */
clause_op = qinfo->clause_op;
if (OidIsValid(clause_op))
{
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;
}
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->nkeycolumns - 1 &&
eqQualHere &&
!found_saop &&
!found_is_null_op)
numIndexTuples = 1.0;
else
{
List *selectivityQuals;
Selectivity btreeSelectivity;
/*
* If the index is partial, AND the index predicate with the
* index-bound quals to produce a more accurate idea of the number of
* rows covered by the bound conditions.
*/
selectivityQuals = add_predicate_to_quals(index, indexBoundQuals);
btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
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);
}
/*
* Now do generic index cost estimation.
*/
MemSet(&costs, 0, sizeof(costs));
costs.numIndexTuples = numIndexTuples;
genericcostestimate(root, path, loop_count, qinfos, &costs);
/*
* Add a CPU-cost component to represent the costs of initial btree
* descent. We don't charge any I/O cost for touching upper btree levels,
* since they tend to stay in cache, but we still have to do about log2(N)
* comparisons to descend a btree of N leaf tuples. We charge one
* cpu_operator_cost per comparison.
*
* If there are ScalarArrayOpExprs, charge this once per SA scan. The
* ones after the first one are not startup cost so far as the overall
* plan is concerned, so add them only to "total" cost.
*/
if (index->tuples > 1) /* avoid computing log(0) */
{
descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
}
/*
* Even though we're not charging I/O cost for touching upper btree pages,
* it's still reasonable to charge some CPU cost per page descended
* through. Moreover, if we had no such charge at all, bloated indexes
* would appear to have the same search cost as unbloated ones, at least
* in cases where only a single leaf page is expected to be visited. This
* cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
* touched. The number of such pages is btree tree height plus one (ie,
* we charge for the leaf page too). As above, charge once per SA scan.
*/
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
/*
* 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.)
*/
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 = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(relid),
Int16GetDatum(colnum),
BoolGetDatum(rte->inh));
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 = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(relid),
Int16GetDatum(colnum),
BoolGetDatum(false));
vardata.freefunc = ReleaseSysCache;
}
}
if (HeapTupleIsValid(vardata.statsTuple))
{
Oid sortop;
AttStatsSlot sslot;
sortop = get_opfamily_member(index->opfamily[0],
index->opcintype[0],
index->opcintype[0],
BTLessStrategyNumber);
if (OidIsValid(sortop) &&
get_attstatsslot(&sslot, vardata.statsTuple,
STATISTIC_KIND_CORRELATION, sortop,
ATTSTATSSLOT_NUMBERS))
{
double varCorrelation;
Assert(sslot.nnumbers == 1);
varCorrelation = sslot.numbers[0];
if (index->reverse_sort[0])
varCorrelation = -varCorrelation;
if (index->ncolumns > 1)
costs.indexCorrelation = varCorrelation * 0.75;
else
costs.indexCorrelation = varCorrelation;
free_attstatsslot(&sslot);
}
}
ReleaseVariableStats(vardata);
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
void
hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
List *qinfos;
GenericCosts costs;
/* Do preliminary analysis of indexquals */
qinfos = deconstruct_indexquals(path);
MemSet(&costs, 0, sizeof(costs));
genericcostestimate(root, path, loop_count, qinfos, &costs);
/*
* A hash index has no descent costs as such, since the index AM can go
* directly to the target bucket after computing the hash value. There
* are a couple of other hash-specific costs that we could conceivably add
* here, though:
*
* Ideally we'd charge spc_random_page_cost for each page in the target
* bucket, not just the numIndexPages pages that genericcostestimate
* thought we'd visit. However in most cases we don't know which bucket
* that will be. There's no point in considering the average bucket size
* because the hash AM makes sure that's always one page.
*
* Likewise, we could consider charging some CPU for each index tuple in
* the bucket, if we knew how many there were. But the per-tuple cost is
* just a hash value comparison, not a general datatype-dependent
* comparison, so any such charge ought to be quite a bit less than
* cpu_operator_cost; which makes it probably not worth worrying about.
*
* A bigger issue is that chance hash-value collisions will result in
* wasted probes into the heap. We don't currently attempt to model this
* cost on the grounds that it's rare, but maybe it's not rare enough.
* (Any fix for this ought to consider the generic lossy-operator problem,
* though; it's not entirely hash-specific.)
*/
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
void
gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
List *qinfos;
GenericCosts costs;
Cost descentCost;
/* Do preliminary analysis of indexquals */
qinfos = deconstruct_indexquals(path);
MemSet(&costs, 0, sizeof(costs));
genericcostestimate(root, path, loop_count, qinfos, &costs);
/*
* We model index descent costs similarly to those for btree, but to do
* that we first need an idea of the tree height. We somewhat arbitrarily
* assume that the fanout is 100, meaning the tree height is at most
* log100(index->pages).
*
* Although this computation isn't really expensive enough to require
* caching, we might as well use index->tree_height to cache it.
*/
if (index->tree_height < 0) /* unknown? */
{
if (index->pages > 1) /* avoid computing log(0) */
index->tree_height = (int) (log(index->pages) / log(100.0));
else
index->tree_height = 0;
}
/*
* Add a CPU-cost component to represent the costs of initial descent. We
* just use log(N) here not log2(N) since the branching factor isn't
* necessarily two anyway. As for btree, charge once per SA scan.
*/
if (index->tuples > 1) /* avoid computing log(0) */
{
descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
}
/*
* Likewise add a per-page charge, calculated the same as for btrees.
*/
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
void
spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
List *qinfos;
GenericCosts costs;
Cost descentCost;
/* Do preliminary analysis of indexquals */
qinfos = deconstruct_indexquals(path);
MemSet(&costs, 0, sizeof(costs));
genericcostestimate(root, path, loop_count, qinfos, &costs);
/*
* We model index descent costs similarly to those for btree, but to do
* that we first need an idea of the tree height. We somewhat arbitrarily
* assume that the fanout is 100, meaning the tree height is at most
* log100(index->pages).
*
* Although this computation isn't really expensive enough to require
* caching, we might as well use index->tree_height to cache it.
*/
if (index->tree_height < 0) /* unknown? */
{
if (index->pages > 1) /* avoid computing log(0) */
index->tree_height = (int) (log(index->pages) / log(100.0));
else
index->tree_height = 0;
}
/*
* Add a CPU-cost component to represent the costs of initial descent. We
* just use log(N) here not log2(N) since the branching factor isn't
* necessarily two anyway. As for btree, charge once per SA scan.
*/
if (index->tuples > 1) /* avoid computing log(0) */
{
descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
}
/*
* Likewise add a per-page charge, calculated the same as for btrees.
*/
descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
costs.indexStartupCost += descentCost;
costs.indexTotalCost += costs.num_sa_scans * descentCost;
*indexStartupCost = costs.indexStartupCost;
*indexTotalCost = costs.indexTotalCost;
*indexSelectivity = costs.indexSelectivity;
*indexCorrelation = costs.indexCorrelation;
*indexPages = costs.numIndexPages;
}
/*
* Support routines for gincostestimate
*/
typedef struct
{
bool haveFullScan;
double partialEntries;
double exactEntries;
double searchEntries;
double arrayScans;
} GinQualCounts;
/*
* Estimate the number of index terms that need to be searched for while
* testing the given GIN query, and increment the counts in *counts
* appropriately. If the query is unsatisfiable, return false.
*/
static bool
gincost_pattern(IndexOptInfo *index, int indexcol,
Oid clause_op, Datum query,
GinQualCounts *counts)
{
Oid extractProcOid;
Oid collation;
int strategy_op;
Oid lefttype,
righttype;
int32 nentries = 0;
bool *partial_matches = NULL;
Pointer *extra_data = NULL;
bool *nullFlags = NULL;
int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
int32 i;
Assert(indexcol < index->nkeycolumns);
/*
* Get the operator's strategy number and declared input data types within
* the index opfamily. (We don't need the latter, but we use
* get_op_opfamily_properties because it will throw error if it fails to
* find a matching pg_amop entry.)
*/
get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
&strategy_op, &lefttype, &righttype);
/*
* GIN always uses the "default" support functions, which are those with
* lefttype == righttype == the opclass' opcintype (see
* IndexSupportInitialize in relcache.c).
*/
extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
index->opcintype[indexcol],
index->opcintype[indexcol],
GIN_EXTRACTQUERY_PROC);
if (!OidIsValid(extractProcOid))
{
/* should not happen; throw same error as index_getprocinfo */
elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
GIN_EXTRACTQUERY_PROC, indexcol + 1,
get_rel_name(index->indexoid));
}
/*
* Choose collation to pass to extractProc (should match initGinState).
*/
if (OidIsValid(index->indexcollations[indexcol]))
collation = index->indexcollations[indexcol];
else
collation = DEFAULT_COLLATION_OID;
OidFunctionCall7Coll(extractProcOid,
collation,
query,
PointerGetDatum(&nentries),
UInt16GetDatum(strategy_op),
PointerGetDatum(&partial_matches),
PointerGetDatum(&extra_data),
PointerGetDatum(&nullFlags),
PointerGetDatum(&searchMode));
if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
{
/* No match is possible */
return false;
}
for (i = 0; i < nentries; i++)
{
/*
* For partial match we haven't any information to estimate number of
* matched entries in index, so, we just estimate it as 100
*/
if (partial_matches && partial_matches[i])
counts->partialEntries += 100;
else
counts->exactEntries++;
counts->searchEntries++;
}
if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
{
/* Treat "include empty" like an exact-match item */
counts->exactEntries++;
counts->searchEntries++;
}
else if (searchMode != GIN_SEARCH_MODE_DEFAULT)
{
/* It's GIN_SEARCH_MODE_ALL */
counts->haveFullScan = true;
}
return true;
}
/*
* Estimate the number of index terms that need to be searched for while
* testing the given GIN index clause, and increment the counts in *counts
* appropriately. If the query is unsatisfiable, return false.
*/
static bool
gincost_opexpr(PlannerInfo *root,
IndexOptInfo *index,
IndexQualInfo *qinfo,
GinQualCounts *counts)
{
int indexcol = qinfo->indexcol;
Oid clause_op = qinfo->clause_op;
Node *operand = qinfo->other_operand;
/* aggressively reduce to a constant, and look through relabeling */
operand = estimate_expression_value(root, operand);
if (IsA(operand, RelabelType))
operand = (Node *) ((RelabelType *) operand)->arg;
/*
* It's impossible to call extractQuery method for unknown operand. So
* unless operand is a Const we can't do much; just assume there will be
* one ordinary search entry from the operand at runtime.
*/
if (!IsA(operand, Const))
{
counts->exactEntries++;
counts->searchEntries++;
return true;
}
/* If Const is null, there can be no matches */
if (((Const *) operand)->constisnull)
return false;
/* Otherwise, apply extractQuery and get the actual term counts */
return gincost_pattern(index, indexcol, clause_op,
((Const *) operand)->constvalue,
counts);
}
/*
* Estimate the number of index terms that need to be searched for while
* testing the given GIN index clause, and increment the counts in *counts
* appropriately. If the query is unsatisfiable, return false.
*
* A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
* each of which involves one value from the RHS array, plus all the
* non-array quals (if any). To model this, we average the counts across
* the RHS elements, and add the averages to the counts in *counts (which
* correspond to per-indexscan costs). We also multiply counts->arrayScans
* by N, causing gincostestimate to scale up its estimates accordingly.
*/
static bool
gincost_scalararrayopexpr(PlannerInfo *root,
IndexOptInfo *index,
IndexQualInfo *qinfo,
double numIndexEntries,
GinQualCounts *counts)
{
int indexcol = qinfo->indexcol;
Oid clause_op = qinfo->clause_op;
Node *rightop = qinfo->other_operand;
ArrayType *arrayval;
int16 elmlen;
bool elmbyval;
char elmalign;
int numElems;
Datum *elemValues;
bool *elemNulls;
GinQualCounts arraycounts;
int numPossible = 0;
int i;
Assert(((ScalarArrayOpExpr *) qinfo->rinfo->clause)->useOr);
/* aggressively reduce to a constant, and look through relabeling */
rightop = estimate_expression_value(root, rightop);
if (IsA(rightop, RelabelType))
rightop = (Node *) ((RelabelType *) rightop)->arg;
/*
* It's impossible to call extractQuery method for unknown operand. So
* unless operand is a Const we can't do much; just assume there will be
* one ordinary search entry from each array entry at runtime, and fall
* back on a probably-bad estimate of the number of array entries.
*/
if (!IsA(rightop, Const))
{
counts->exactEntries++;
counts->searchEntries++;
counts->arrayScans *= estimate_array_length(rightop);
return true;
}
/* If Const is null, there can be no matches */
if (((Const *) rightop)->constisnull)
return false;
/* Otherwise, extract the array elements and iterate over them */
arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
&elmlen, &elmbyval, &elmalign);
deconstruct_array(arrayval,
ARR_ELEMTYPE(arrayval),
elmlen, elmbyval, elmalign,
&elemValues, &elemNulls, &numElems);
memset(&arraycounts, 0, sizeof(arraycounts));
for (i = 0; i < numElems; i++)
{
GinQualCounts elemcounts;
/* NULL can't match anything, so ignore, as the executor will */
if (elemNulls[i])
continue;
/* Otherwise, apply extractQuery and get the actual term counts */
memset(&elemcounts, 0, sizeof(elemcounts));
if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
&elemcounts))
{
/* We ignore array elements that are unsatisfiable patterns */
numPossible++;
if (elemcounts.haveFullScan)
{
/*
* Full index scan will be required. We treat this as if
* every key in the index had been listed in the query; is
* that reasonable?
*/
elemcounts.partialEntries = 0;
elemcounts.exactEntries = numIndexEntries;
elemcounts.searchEntries = numIndexEntries;
}
arraycounts.partialEntries += elemcounts.partialEntries;
arraycounts.exactEntries += elemcounts.exactEntries;
arraycounts.searchEntries += elemcounts.searchEntries;
}
}
if (numPossible == 0)
{
/* No satisfiable patterns in the array */
return false;
}
/*
* Now add the averages to the global counts. This will give us an
* estimate of the average number of terms searched for in each indexscan,
* including contributions from both array and non-array quals.
*/
counts->partialEntries += arraycounts.partialEntries / numPossible;
counts->exactEntries += arraycounts.exactEntries / numPossible;
counts->searchEntries += arraycounts.searchEntries / numPossible;
counts->arrayScans *= numPossible;
return true;
}
/*
* GIN has search behavior completely different from other index types
*/
void
gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
List *indexQuals = get_index_quals(path->indexclauses);
List *indexOrderBys = path->indexorderbys;
List *qinfos;
ListCell *l;
List *selectivityQuals;
double numPages = index->pages,
numTuples = index->tuples;
double numEntryPages,
numDataPages,
numPendingPages,
numEntries;
GinQualCounts counts;
bool matchPossible;
double partialScale;
double entryPagesFetched,
dataPagesFetched,
dataPagesFetchedBySel;
double qual_op_cost,
qual_arg_cost,
spc_random_page_cost,
outer_scans;
Relation indexRel;
GinStatsData ginStats;
/* Do preliminary analysis of indexquals */
qinfos = deconstruct_indexquals(path);
/*
* Obtain statistical information from the meta page, if possible. Else
* set ginStats to zeroes, and we'll cope below.
*/
if (!index->hypothetical)
{
indexRel = index_open(index->indexoid, AccessShareLock);
ginGetStats(indexRel, &ginStats);
index_close(indexRel, AccessShareLock);
}
else
{
memset(&ginStats, 0, sizeof(ginStats));
}
/*
* Assuming we got valid (nonzero) stats at all, nPendingPages can be
* trusted, but the other fields are data as of the last VACUUM. We can
* scale them up to account for growth since then, but that method only
* goes so far; in the worst case, the stats might be for a completely
* empty index, and scaling them will produce pretty bogus numbers.
* Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
* it's grown more than that, fall back to estimating things only from the
* assumed-accurate index size. But we'll trust nPendingPages in any case
* so long as it's not clearly insane, ie, more than the index size.
*/
if (ginStats.nPendingPages < numPages)
numPendingPages = ginStats.nPendingPages;
else
numPendingPages = 0;
if (numPages > 0 && ginStats.nTotalPages <= numPages &&
ginStats.nTotalPages > numPages / 4 &&
ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
{
/*
* OK, the stats seem close enough to sane to be trusted. But we
* still need to scale them by the ratio numPages / nTotalPages to
* account for growth since the last VACUUM.
*/
double scale = numPages / ginStats.nTotalPages;
numEntryPages = ceil(ginStats.nEntryPages * scale);
numDataPages = ceil(ginStats.nDataPages * scale);
numEntries = ceil(ginStats.nEntries * scale);
/* ensure we didn't round up too much */
numEntryPages = Min(numEntryPages, numPages - numPendingPages);
numDataPages = Min(numDataPages,
numPages - numPendingPages - numEntryPages);
}
else
{
/*
* We might get here because it's a hypothetical index, or an index
* created pre-9.1 and never vacuumed since upgrading (in which case
* its stats would read as zeroes), or just because it's grown too
* much since the last VACUUM for us to put our faith in scaling.
*
* Invent some plausible internal statistics based on the index page
* count (and clamp that to at least 10 pages, just in case). We
* estimate that 90% of the index is entry pages, and the rest is data
* pages. Estimate 100 entries per entry page; this is rather bogus
* since it'll depend on the size of the keys, but it's more robust
* than trying to predict the number of entries per heap tuple.
*/
numPages = Max(numPages, 10);
numEntryPages = floor((numPages - numPendingPages) * 0.90);
numDataPages = numPages - numPendingPages - numEntryPages;
numEntries = floor(numEntryPages * 100);
}
/* In an empty index, numEntries could be zero. Avoid divide-by-zero */
if (numEntries < 1)
numEntries = 1;
/*
* If the index is partial, AND the index predicate with the index-bound
* quals to produce a more accurate idea of the number of rows covered by
* the bound conditions.
*/
selectivityQuals = add_predicate_to_quals(index, indexQuals);
/* Estimate the fraction of main-table tuples that will be visited */
*indexSelectivity = clauselist_selectivity(root, selectivityQuals,
index->rel->relid,
JOIN_INNER,
NULL);
/* fetch estimated page cost for tablespace containing index */
get_tablespace_page_costs(index->reltablespace,
&spc_random_page_cost,
NULL);
/*
* Generic assumption about index correlation: there isn't any.
*/
*indexCorrelation = 0.0;
/*
* Examine quals to estimate number of search entries & partial matches
*/
memset(&counts, 0, sizeof(counts));
counts.arrayScans = 1;
matchPossible = true;
foreach(l, qinfos)
{
IndexQualInfo *qinfo = (IndexQualInfo *) lfirst(l);
Expr *clause = qinfo->rinfo->clause;
if (IsA(clause, OpExpr))
{
matchPossible = gincost_opexpr(root,
index,
qinfo,
&counts);
if (!matchPossible)
break;
}
else if (IsA(clause, ScalarArrayOpExpr))
{
matchPossible = gincost_scalararrayopexpr(root,
index,
qinfo,
numEntries,
&counts);
if (!matchPossible)
break;
}
else
{
/* shouldn't be anything else for a GIN index */
elog(ERROR, "unsupported GIN indexqual type: %d",
(int) nodeTag(clause));
}
}
/* Fall out if there were any provably-unsatisfiable quals */
if (!matchPossible)
{
*indexStartupCost = 0;
*indexTotalCost = 0;
*indexSelectivity = 0;
return;
}
if (counts.haveFullScan || indexQuals == NIL)
{
/*
* Full index scan will be required. We treat this as if every key in
* the index had been listed in the query; is that reasonable?
*/
counts.partialEntries = 0;
counts.exactEntries = numEntries;
counts.searchEntries = numEntries;
}
/* Will we have more than one iteration of a nestloop scan? */
outer_scans = loop_count;
/*
* Compute cost to begin scan, first of all, pay attention to pending
* list.
*/
entryPagesFetched = numPendingPages;
/*
* Estimate number of entry pages read. We need to do
* counts.searchEntries searches. Use a power function as it should be,
* but tuples on leaf pages usually is much greater. Here we include all
* searches in entry tree, including search of first entry in partial
* match algorithm
*/
entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
/*
* Add an estimate of entry pages read by partial match algorithm. It's a
* scan over leaf pages in entry tree. We haven't any useful stats here,
* so estimate it as proportion. Because counts.partialEntries is really
* pretty bogus (see code above), it's possible that it is more than
* numEntries; clamp the proportion to ensure sanity.
*/
partialScale = counts.partialEntries / numEntries;
partialScale = Min(partialScale, 1.0);
entryPagesFetched += ceil(numEntryPages * partialScale);
/*
* Partial match algorithm reads all data pages before doing actual scan,
* so it's a startup cost. Again, we haven't any useful stats here, so
* estimate it as proportion.
*/
dataPagesFetched = ceil(numDataPages * partialScale);
/*
* Calculate cache effects if more than one scan due to nestloops or array
* quals. The result is pro-rated per nestloop scan, but the array qual
* factor shouldn't be pro-rated (compare genericcostestimate).
*/
if (outer_scans > 1 || counts.arrayScans > 1)
{
entryPagesFetched *= outer_scans * counts.arrayScans;
entryPagesFetched = index_pages_fetched(entryPagesFetched,
(BlockNumber) numEntryPages,
numEntryPages, root);
entryPagesFetched /= outer_scans;
dataPagesFetched *= outer_scans * counts.arrayScans;
dataPagesFetched = index_pages_fetched(dataPagesFetched,
(BlockNumber) numDataPages,
numDataPages, root);
dataPagesFetched /= outer_scans;
}
/*
* Here we use random page cost because logically-close pages could be far
* apart on disk.
*/
*indexStartupCost = (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
/*
* Now compute the number of data pages fetched during the scan.
*
* We assume every entry to have the same number of items, and that there
* is no overlap between them. (XXX: tsvector and array opclasses collect
* statistics on the frequency of individual keys; it would be nice to use
* those here.)
*/
dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
/*
* If there is a lot of overlap among the entries, in particular if one of
* the entries is very frequent, the above calculation can grossly
* under-estimate. As a simple cross-check, calculate a lower bound based
* on the overall selectivity of the quals. At a minimum, we must read
* one item pointer for each matching entry.
*
* The width of each item pointer varies, based on the level of
* compression. We don't have statistics on that, but an average of
* around 3 bytes per item is fairly typical.
*/
dataPagesFetchedBySel = ceil(*indexSelectivity *
(numTuples / (BLCKSZ / 3)));
if (dataPagesFetchedBySel > dataPagesFetched)
dataPagesFetched = dataPagesFetchedBySel;
/* Account for cache effects, the same as above */
if (outer_scans > 1 || counts.arrayScans > 1)
{
dataPagesFetched *= outer_scans * counts.arrayScans;
dataPagesFetched = index_pages_fetched(dataPagesFetched,
(BlockNumber) numDataPages,
numDataPages, root);
dataPagesFetched /= outer_scans;
}
/* And apply random_page_cost as the cost per page */
*indexTotalCost = *indexStartupCost +
dataPagesFetched * spc_random_page_cost;
/*
* Add on index qual eval costs, much as in genericcostestimate
*/
qual_arg_cost = other_operands_eval_cost(root, qinfos) +
orderby_operands_eval_cost(root, path);
qual_op_cost = cpu_operator_cost *
(list_length(indexQuals) + list_length(indexOrderBys));
*indexStartupCost += qual_arg_cost;
*indexTotalCost += qual_arg_cost;
*indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost + qual_op_cost);
*indexPages = dataPagesFetched;
}
/*
* BRIN has search behavior completely different from other index types
*/
void
brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
Cost *indexStartupCost, Cost *indexTotalCost,
Selectivity *indexSelectivity, double *indexCorrelation,
double *indexPages)
{
IndexOptInfo *index = path->indexinfo;
List *indexQuals = get_index_quals(path->indexclauses);
double numPages = index->pages;
RelOptInfo *baserel = index->rel;
RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
List *qinfos;
Cost spc_seq_page_cost;
Cost spc_random_page_cost;
double qual_arg_cost;
double qualSelectivity;
BrinStatsData statsData;
double indexRanges;
double minimalRanges;
double estimatedRanges;
double selec;
Relation indexRel;
ListCell *l;
VariableStatData vardata;
Assert(rte->rtekind == RTE_RELATION);
/* fetch estimated page cost for the tablespace containing the index */
get_tablespace_page_costs(index->reltablespace,
&spc_random_page_cost,
&spc_seq_page_cost);
/*
* Obtain some data from the index itself.
*/
indexRel = index_open(index->indexoid, AccessShareLock);
brinGetStats(indexRel, &statsData);
index_close(indexRel, AccessShareLock);
/*
* Compute index correlation
*
* Because we can use all index quals equally when scanning, we can use
* the largest correlation (in absolute value) among columns used by the
* query. Start at zero, the worst possible case. If we cannot find any
* correlation statistics, we will keep it as 0.
*/
*indexCorrelation = 0;
qinfos = deconstruct_indexquals(path);
foreach(l, qinfos)
{
IndexQualInfo *qinfo = (IndexQualInfo *) lfirst(l);
AttrNumber attnum = index->indexkeys[qinfo->indexcol];
/* attempt to lookup stats in relation for this index column */
if (attnum != 0)
{
/* Simple variable -- look to stats for the underlying table */
if (get_relation_stats_hook &&
(*get_relation_stats_hook) (root, rte, attnum, &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 =
SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(rte->relid),
Int16GetDatum(attnum),
BoolGetDatum(false));
vardata.freefunc = ReleaseSysCache;
}
}
else
{
/*
* Looks like we've found an expression column in the index. Let's
* see if there's any stats for it.
*/
/* get the attnum from the 0-based index. */
attnum = qinfo->indexcol + 1;
if (get_index_stats_hook &&
(*get_index_stats_hook) (root, index->indexoid, attnum, &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 = SearchSysCache3(STATRELATTINH,
ObjectIdGetDatum(index->indexoid),
Int16GetDatum(attnum),
BoolGetDatum(false));
vardata.freefunc = ReleaseSysCache;
}
}
if (HeapTupleIsValid(vardata.statsTuple))
{
AttStatsSlot sslot;
if (get_attstatsslot(&sslot, vardata.statsTuple,
STATISTIC_KIND_CORRELATION, InvalidOid,
ATTSTATSSLOT_NUMBERS))
{
double varCorrelation = 0.0;
if (sslot.nnumbers > 0)
varCorrelation = Abs(sslot.numbers[0]);
if (varCorrelation > *indexCorrelation)
*indexCorrelation = varCorrelation;
free_attstatsslot(&sslot);
}
}
ReleaseVariableStats(vardata);
}
qualSelectivity = clauselist_selectivity(root, indexQuals,
baserel->relid,
JOIN_INNER, NULL);
/* work out the actual number of ranges in the index */
indexRanges = Max(ceil((double) baserel->pages / statsData.pagesPerRange),
1.0);
/*
* Now calculate the minimum possible ranges we could match with if all of
* the rows were in the perfect order in the table's heap.
*/
minimalRanges = ceil(indexRanges * qualSelectivity);
/*
* Now estimate the number of ranges that we'll touch by using the
* indexCorrelation from the stats. Careful not to divide by zero (note
* we're using the absolute value of the correlation).
*/
if (*indexCorrelation < 1.0e-10)
estimatedRanges = indexRanges;
else
estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
/* we expect to visit this portion of the table */
selec = estimatedRanges / indexRanges;
CLAMP_PROBABILITY(selec);
*indexSelectivity = selec;
/*
* Compute the index qual costs, much as in genericcostestimate, to add to
* the index costs.
*/
qual_arg_cost = other_operands_eval_cost(root, qinfos) +
orderby_operands_eval_cost(root, path);
/*
* Compute the startup cost as the cost to read the whole revmap
* sequentially, including the cost to execute the index quals.
*/
*indexStartupCost =
spc_seq_page_cost * statsData.revmapNumPages * loop_count;
*indexStartupCost += qual_arg_cost;
/*
* To read a BRIN index there might be a bit of back and forth over
* regular pages, as revmap might point to them out of sequential order;
* calculate the total cost as reading the whole index in random order.
*/
*indexTotalCost = *indexStartupCost +
spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
/*
* Charge a small amount per range tuple which we expect to match to. This
* is meant to reflect the costs of manipulating the bitmap. The BRIN scan
* will set a bit for each page in the range when we find a matching
* range, so we must multiply the charge by the number of pages in the
* range.
*/
*indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
statsData.pagesPerRange;
*indexPages = index->pages;
}
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