#include "postgres.h"
#include <ctype.h>
#include <math.h>
#include "access/brin.h"
#include "access/brin_page.h"
#include "access/gin.h"
#include "access/table.h"
#include "access/tableam.h"
#include "access/visibilitymap.h"
#include "catalog/pg_collation.h"
#include "catalog/pg_operator.h"
#include "catalog/pg_statistic.h"
#include "catalog/pg_statistic_ext.h"
#include "executor/nodeAgg.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 "parser/parse_clause.h"
#include "parser/parse_relation.h"
#include "parser/parsetree.h"
#include "rewrite/rewriteManip.h"
#include "statistics/statistics.h"
#include "storage/bufmgr.h"
#include "utils/acl.h"
#include "utils/array.h"
#include "utils/builtins.h"
#include "utils/date.h"
#include "utils/datum.h"
#include "utils/fmgroids.h"
#include "utils/index_selfuncs.h"
#include "utils/lsyscache.h"
#include "utils/memutils.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 "lib/simplehash.h"

Include dependency graph for selfuncs.c:

Data Structures
struct	MCVHashEntry

struct	MCVHashContext

struct	GroupVarInfo

struct	GinQualCounts

Macros
#define	DEFAULT_PAGE_CPU_MULTIPLIER 50.0

#define	EQJOINSEL_MCV_HASH_THRESHOLD 200

#define	SH_PREFIX MCVHashTable

#define	SH_ELEMENT_TYPE MCVHashEntry

#define	SH_KEY_TYPE Datum

#define	SH_KEY value

#define	SH_HASH_KEY(tab, key) hash_mcv(tab, key)

#define	SH_EQUAL(tab, key0, key1) mcvs_equal(tab, key0, key1)

#define	SH_SCOPE static inline

#define	SH_STORE_HASH

#define	SH_GET_HASH(tab, ent) (ent)->hash

#define	SH_DEFINE

#define	SH_DECLARE

#define	VISITED_PAGES_LIMIT 100

Typedefs
typedef struct MCVHashEntry	MCVHashEntry

typedef struct MCVHashContext	MCVHashContext

typedef struct MCVHashTable_hash	MCVHashTable_hash

Functions
static double	eqsel_internal (PG_FUNCTION_ARGS, bool negate)

static double	eqjoinsel_inner (FmgrInfo eqproc, Oid collation, Oid hashLeft, Oid hashRight, 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, bool hasmatch1, bool hasmatch2, int p_nmatches)

static double	eqjoinsel_semi (FmgrInfo eqproc, Oid collation, Oid hashLeft, Oid hashRight, bool op_is_reversed, 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, bool hasmatch1, bool hasmatch2, int p_nmatches, RelOptInfo *inner_rel)

static void	eqjoinsel_find_matches (FmgrInfo eqproc, Oid collation, Oid hashLeft, Oid hashRight, bool op_is_reversed, AttStatsSlot sslot1, AttStatsSlot sslot2, int nvalues1, int nvalues2, bool hasmatch1, bool hasmatch2, int p_nmatches, double *p_matchprodfreq)

static uint32	hash_mcv (MCVHashTable_hash *tab, Datum key)

static bool	mcvs_equal (MCVHashTable_hash *tab, Datum key0, Datum key1)

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 Node *	strip_all_phvs_deep (PlannerInfo root, Node node)

static bool	contain_placeholder_walker (Node node, void context)

static Node *	strip_all_phvs_mutator (Node node, void context)

static void	examine_simple_variable (PlannerInfo root, Var var, VariableStatData *vardata)

static void	examine_indexcol_variable (PlannerInfo root, IndexOptInfo index, int indexcol, VariableStatData *vardata)

static bool	get_variable_range (PlannerInfo root, VariableStatData vardata, Oid sortop, Oid collation, Datum min, Datum max)

static void	get_stats_slot_range (AttStatsSlot sslot, Oid opfuncoid, FmgrInfo opproc, Oid collation, int16 typLen, bool typByVal, Datum min, Datum max, bool *p_have_data)

static bool	get_actual_variable_range (PlannerInfo root, VariableStatData vardata, Oid sortop, Oid collation, Datum min, Datum max)

static bool	get_actual_variable_endpoint (Relation heapRel, Relation indexRel, ScanDirection indexscandir, ScanKey scankeys, int16 typLen, bool typByVal, TupleTableSlot tableslot, MemoryContext outercontext, Datum endpointDatum)

static RelOptInfo *	find_join_input_rel (PlannerInfo *root, Relids relids)

static double	btcost_correlation (IndexOptInfo index, VariableStatData vardata)

Datum	eqsel (PG_FUNCTION_ARGS)

double	var_eq_const (VariableStatData *vardata, Oid oproid, Oid collation, Datum constval, bool constisnull, bool varonleft, bool negate)

double	var_eq_non_const (VariableStatData vardata, Oid oproid, Oid collation, Node other, bool varonleft, bool negate)

Datum	neqsel (PG_FUNCTION_ARGS)

static double	scalarineqsel (PlannerInfo root, Oid operator, bool isgt, bool iseq, Oid collation, VariableStatData vardata, Datum constval, Oid consttype)

double	mcv_selectivity (VariableStatData vardata, FmgrInfo opproc, Oid collation, Datum constval, bool varonleft, double *sumcommonp)

double	histogram_selectivity (VariableStatData vardata, FmgrInfo opproc, Oid collation, Datum constval, bool varonleft, int min_hist_size, int n_skip, int *hist_size)

double	generic_restriction_selectivity (PlannerInfo root, Oid oproid, Oid collation, List args, int varRelid, double default_selectivity)

double	ineq_histogram_selectivity (PlannerInfo root, VariableStatData vardata, Oid opoid, FmgrInfo *opproc, bool isgt, bool iseq, Oid collation, Datum constval, Oid consttype)

static Datum	scalarineqsel_wrapper (PG_FUNCTION_ARGS, bool isgt, bool iseq)

Datum	scalarltsel (PG_FUNCTION_ARGS)

Datum	scalarlesel (PG_FUNCTION_ARGS)

Datum	scalargtsel (PG_FUNCTION_ARGS)

Datum	scalargesel (PG_FUNCTION_ARGS)

Selectivity	boolvarsel (PlannerInfo root, Node arg, int varRelid)

Selectivity	booltestsel (PlannerInfo root, BoolTestType booltesttype, Node arg, int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)

Selectivity	nulltestsel (PlannerInfo root, NullTestType nulltesttype, Node arg, int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)

static Node *	strip_array_coercion (Node *node)

Selectivity	scalararraysel (PlannerInfo root, ScalarArrayOpExpr clause, bool is_join_clause, int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)

double	estimate_array_length (PlannerInfo root, Node arrayexpr)

Selectivity	rowcomparesel (PlannerInfo root, RowCompareExpr clause, int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)

Datum	eqjoinsel (PG_FUNCTION_ARGS)

Datum	neqjoinsel (PG_FUNCTION_ARGS)

Datum	scalarltjoinsel (PG_FUNCTION_ARGS)

Datum	scalarlejoinsel (PG_FUNCTION_ARGS)

Datum	scalargtjoinsel (PG_FUNCTION_ARGS)

Datum	scalargejoinsel (PG_FUNCTION_ARGS)

void	mergejoinscansel (PlannerInfo root, Node clause, Oid opfamily, CompareType cmptype, bool nulls_first, Selectivity leftstart, Selectivity leftend, Selectivity rightstart, Selectivity rightend)

Datum	matchingsel (PG_FUNCTION_ARGS)

Datum	matchingjoinsel (PG_FUNCTION_ARGS)

static List *	add_unique_group_var (PlannerInfo root, List varinfos, Node var, VariableStatData vardata)

double	estimate_num_groups (PlannerInfo root, List groupExprs, double input_rows, List *pgset, EstimationInfo estinfo)

List *	estimate_multivariate_bucketsize (PlannerInfo root, RelOptInfo inner, List hashclauses, Selectivity innerbucketsize)

void	estimate_hash_bucket_stats (PlannerInfo root, Node hashkey, double nbuckets, Selectivity mcv_freq, Selectivity bucketsize_frac)

double	estimate_hashagg_tablesize (PlannerInfo root, Path path, const AggClauseCosts *agg_costs, double dNumGroups)

bool	get_restriction_variable (PlannerInfo root, List args, int varRelid, VariableStatData vardata, Node other, bool varonleft)

void	get_join_variables (PlannerInfo root, List args, SpecialJoinInfo sjinfo, VariableStatData vardata1, VariableStatData vardata2, bool join_is_reversed)

static void	ReleaseDummy (HeapTuple tuple)

void	examine_variable (PlannerInfo root, Node node, int varRelid, VariableStatData *vardata)

bool	all_rows_selectable (PlannerInfo root, Index varno, Bitmapset varattnos)

bool	statistic_proc_security_check (VariableStatData *vardata, Oid func_oid)

double	get_variable_numdistinct (VariableStatData vardata, bool isdefault)

List *	get_quals_from_indexclauses (List *indexclauses)

Cost	index_other_operands_eval_cost (PlannerInfo root, List indexquals)

void	genericcostestimate (PlannerInfo root, IndexPath path, double loop_count, GenericCosts *costs)

List *	add_predicate_to_index_quals (IndexOptInfo index, List indexQuals)

void	btcostestimate (PlannerInfo root, IndexPath path, double loop_count, Cost indexStartupCost, Cost indexTotalCost, Selectivity indexSelectivity, double indexCorrelation, double *indexPages)

void	hashcostestimate (PlannerInfo root, IndexPath path, double loop_count, Cost indexStartupCost, Cost indexTotalCost, Selectivity indexSelectivity, double indexCorrelation, double *indexPages)

void	gistcostestimate (PlannerInfo root, IndexPath path, double loop_count, Cost indexStartupCost, Cost indexTotalCost, Selectivity indexSelectivity, double indexCorrelation, double *indexPages)

void	spgcostestimate (PlannerInfo root, IndexPath path, double loop_count, Cost indexStartupCost, Cost indexTotalCost, Selectivity indexSelectivity, double indexCorrelation, double *indexPages)

static bool	gincost_pattern (IndexOptInfo index, int indexcol, Oid clause_op, Datum query, GinQualCounts counts)

static bool	gincost_opexpr (PlannerInfo root, IndexOptInfo index, int indexcol, OpExpr clause, GinQualCounts counts)

static bool	gincost_scalararrayopexpr (PlannerInfo root, IndexOptInfo index, int indexcol, ScalarArrayOpExpr clause, double numIndexEntries, GinQualCounts counts)

void	gincostestimate (PlannerInfo root, IndexPath path, double loop_count, Cost indexStartupCost, Cost indexTotalCost, Selectivity indexSelectivity, double indexCorrelation, double *indexPages)

void	brincostestimate (PlannerInfo root, IndexPath path, double loop_count, Cost indexStartupCost, Cost indexTotalCost, Selectivity indexSelectivity, double indexCorrelation, double *indexPages)

Variables
get_relation_stats_hook_type	get_relation_stats_hook = NULL

get_index_stats_hook_type	get_index_stats_hook = NULL

Macro Definition Documentation

◆ DEFAULT_PAGE_CPU_MULTIPLIER

#define DEFAULT_PAGE_CPU_MULTIPLIER 50.0

Definition at line 144 of file selfuncs.c.

◆ EQJOINSEL_MCV_HASH_THRESHOLD

#define EQJOINSEL_MCV_HASH_THRESHOLD 200

Definition at line 154 of file selfuncs.c.

◆ SH_DECLARE

#define SH_DECLARE

Definition at line 287 of file selfuncs.c.

◆ SH_DEFINE

#define SH_DEFINE

Definition at line 286 of file selfuncs.c.

◆ SH_ELEMENT_TYPE

#define SH_ELEMENT_TYPE MCVHashEntry

Definition at line 278 of file selfuncs.c.

◆ SH_EQUAL

#define SH_EQUAL	(	tab,
		key0,
		key1
	)	mcvs_equal(tab, key0, key1)

Definition at line 282 of file selfuncs.c.

◆ SH_GET_HASH

#define SH_GET_HASH	(	tab,
		ent
	)	(ent)->hash

Definition at line 285 of file selfuncs.c.

◆ SH_HASH_KEY

#define SH_HASH_KEY	(	tab,
		key
	)	hash_mcv(tab, key)

Definition at line 281 of file selfuncs.c.

◆ SH_KEY

#define SH_KEY value

Definition at line 280 of file selfuncs.c.

◆ SH_KEY_TYPE

#define SH_KEY_TYPE Datum

Definition at line 279 of file selfuncs.c.

◆ SH_PREFIX

#define SH_PREFIX MCVHashTable

Definition at line 277 of file selfuncs.c.

◆ SH_SCOPE

#define SH_SCOPE static inline

Definition at line 283 of file selfuncs.c.

◆ SH_STORE_HASH

#define SH_STORE_HASH

Definition at line 284 of file selfuncs.c.

◆ VISITED_PAGES_LIMIT

#define VISITED_PAGES_LIMIT 100

Typedef Documentation

◆ MCVHashContext

typedef struct MCVHashContext MCVHashContext

◆ MCVHashEntry

typedef struct MCVHashEntry MCVHashEntry

◆ MCVHashTable_hash

typedef struct MCVHashTable_hash MCVHashTable_hash

Definition at line 180 of file selfuncs.c.

Function Documentation

◆ add_predicate_to_index_quals()

List * add_predicate_to_index_quals	(	IndexOptInfo *	index,
		List *	indexQuals
	)

Definition at line 7635 of file selfuncs.c.

{
    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);
    }
    return list_concat(predExtraQuals, indexQuals);
}

References fb(), lfirst, list_concat(), list_make1, NIL, and predicate_implied_by().

Referenced by btcostestimate(), genericcostestimate(), and gincostestimate().

◆ add_unique_group_var()

static List * add_unique_group_var	(	PlannerInfo *	root,
		List *	varinfos,
		Node *	var,
		VariableStatData *	vardata
	)

static

Definition at line 3670 of file selfuncs.c.

{
    GroupVarInfo *varinfo;
    double      ndistinct;
    bool        isdefault;
    ListCell   *lc;
 
    ndistinct = get_variable_numdistinct(vardata, &isdefault);
 
    /*
     * The nullingrels bits within the var could cause the same var to be
     * counted multiple times if it's marked with different nullingrels.  They
     * could also prevent us from matching the var to the expressions in
     * extended statistics (see estimate_multivariate_ndistinct).  So strip
     * them out first.
     */
    var = remove_nulling_relids(var, root->outer_join_rels, NULL);
 
    foreach(lc, varinfos)
    {
        varinfo = (GroupVarInfo *) lfirst(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).  We aren't too
         * fussy about the semantics of "equal" here.
         */
        if (vardata->rel != varinfo->rel &&
            exprs_known_equal(root, var, varinfo->var, InvalidOid))
        {
            if (varinfo->ndistinct <= ndistinct)
            {
                /* Keep older item, forget new one */
                return varinfos;
            }
            else
            {
                /* Delete the older item */
                varinfos = foreach_delete_current(varinfos, lc);
            }
        }
    }
 
    varinfo = palloc_object(GroupVarInfo);
 
    varinfo->var = var;
    varinfo->rel = vardata->rel;
    varinfo->ndistinct = ndistinct;
    varinfo->isdefault = isdefault;
    varinfos = lappend(varinfos, varinfo);
    return varinfos;
}

References equal(), exprs_known_equal(), fb(), foreach_delete_current, get_variable_numdistinct(), InvalidOid, lappend(), lfirst, palloc_object, remove_nulling_relids(), and root.

Referenced by estimate_num_groups().

◆ all_rows_selectable()

bool all_rows_selectable	(	PlannerInfo *	root,
		Index	varno,
		Bitmapset *	varattnos
	)

Definition at line 6313 of file selfuncs.c.

{
    RelOptInfo *rel = find_base_rel_noerr(root, varno);
    RangeTblEntry *rte = planner_rt_fetch(varno, root);
    Oid         userid;
    int         varattno;
 
    Assert(rte->rtekind == RTE_RELATION);
 
    /*
     * Determine the user ID to use for privilege checks (either the current
     * user or the view owner, if we're accessing the table via a view).
     *
     * Normally the relation will have an associated RelOptInfo from which we
     * can find the userid, but it might not if it's a RETURNING Var for an
     * INSERT target relation.  In that case use the RTEPermissionInfo
     * associated with the RTE.
     *
     * If we navigate up to a parent relation, we keep using the same userid,
     * since it's the same in all relations of a given inheritance tree.
     */
    if (rel)
        userid = rel->userid;
    else
    {
        RTEPermissionInfo *perminfo;
 
        perminfo = getRTEPermissionInfo(root->parse->rteperminfos, rte);
        userid = perminfo->checkAsUser;
    }
    if (!OidIsValid(userid))
        userid = GetUserId();
 
    /*
     * Permissions and securityQuals must be checked on the table actually
     * mentioned in the query, so if this is an inheritance child, navigate up
     * to the inheritance root parent.  If the user can read the whole table
     * or the required columns there, then they can read from the child table
     * too.  For per-column checks, we must find out which of the root
     * parent's attributes the child relation's attributes correspond to.
     */
    if (root->append_rel_array != NULL)
    {
        AppendRelInfo *appinfo;
 
        appinfo = root->append_rel_array[varno];
 
        /*
         * Partitions are mapped to their immediate parent, not the root
         * parent, so must be ready to walk up multiple AppendRelInfos.  But
         * stop if we hit a parent that is not RTE_RELATION --- that's a
         * flattened UNION ALL subquery, not an inheritance parent.
         */
        while (appinfo &&
               planner_rt_fetch(appinfo->parent_relid,
                                root)->rtekind == RTE_RELATION)
        {
            Bitmapset  *parent_varattnos = NULL;
 
            /*
             * For each child attribute, find the corresponding parent
             * attribute.  In rare cases, the attribute may be local to the
             * child table, in which case, we've got to live with having no
             * access to this column.
             */
            varattno = -1;
            while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
            {
                AttrNumber  attno;
                AttrNumber  parent_attno;
 
                attno = varattno + FirstLowInvalidHeapAttributeNumber;
 
                if (attno == InvalidAttrNumber)
                {
                    /*
                     * Whole-row reference, so must map each column of the
                     * child to the parent table.
                     */
                    for (attno = 1; attno <= appinfo->num_child_cols; attno++)
                    {
                        parent_attno = appinfo->parent_colnos[attno - 1];
                        if (parent_attno == 0)
                            return false;   /* attr is local to child */
                        parent_varattnos =
                            bms_add_member(parent_varattnos,
                                           parent_attno - FirstLowInvalidHeapAttributeNumber);
                    }
                }
                else
                {
                    if (attno < 0)
                    {
                        /* System attnos are the same in all tables */
                        parent_attno = attno;
                    }
                    else
                    {
                        if (attno > appinfo->num_child_cols)
                            return false;   /* safety check */
                        parent_attno = appinfo->parent_colnos[attno - 1];
                        if (parent_attno == 0)
                            return false;   /* attr is local to child */
                    }
                    parent_varattnos =
                        bms_add_member(parent_varattnos,
                                       parent_attno - FirstLowInvalidHeapAttributeNumber);
                }
            }
 
            /* If the parent is itself a child, continue up */
            varno = appinfo->parent_relid;
            varattnos = parent_varattnos;
            appinfo = root->append_rel_array[varno];
        }
 
        /* Perform the access check on this parent rel */
        rte = planner_rt_fetch(varno, root);
        Assert(rte->rtekind == RTE_RELATION);
    }
 
    /*
     * For all rows to be accessible, there must be no securityQuals from
     * security barrier views or RLS policies.
     */
    if (rte->securityQuals != NIL)
        return false;
 
    /*
     * Test for table-level SELECT privilege.
     *
     * If varattnos is non-NULL, this is sufficient to give access to all
     * requested attributes, even for a child table, since we have verified
     * that all required child columns have matching parent columns.
     *
     * If varattnos is NULL (whole-table access requested), this doesn't
     * necessarily guarantee that the user can read all columns of a child
     * table, but we allow it anyway (see comments in examine_variable()) and
     * don't bother checking any column privileges.
     */
    if (pg_class_aclcheck(rte->relid, userid, ACL_SELECT) == ACLCHECK_OK)
        return true;
 
    if (varattnos == NULL)
        return false;           /* whole-table access requested */
 
    /*
     * Don't have table-level SELECT privilege, so check per-column
     * privileges.
     */
    varattno = -1;
    while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
    {
        AttrNumber  attno = varattno + FirstLowInvalidHeapAttributeNumber;
 
        if (attno == InvalidAttrNumber)
        {
            /* Whole-row reference, so must have access to all columns */
            if (pg_attribute_aclcheck_all(rte->relid, userid, ACL_SELECT,
                                          ACLMASK_ALL) != ACLCHECK_OK)
                return false;
        }
        else
        {
            if (pg_attribute_aclcheck(rte->relid, attno, userid,
                                      ACL_SELECT) != ACLCHECK_OK)
                return false;
        }
    }
 
    /* If we reach here, have all required column privileges */
    return true;
}

References ACL_SELECT, ACLCHECK_OK, ACLMASK_ALL, Assert, bms_add_member(), bms_next_member(), fb(), find_base_rel_noerr(), FirstLowInvalidHeapAttributeNumber, getRTEPermissionInfo(), GetUserId(), InvalidAttrNumber, NIL, OidIsValid, pg_attribute_aclcheck(), pg_attribute_aclcheck_all(), pg_class_aclcheck(), planner_rt_fetch, root, RTE_RELATION, and RelOptInfo::userid.

Referenced by examine_simple_variable(), examine_variable(), and statext_is_compatible_clause().

◆ booltestsel()

Selectivity booltestsel	(	PlannerInfo *	root,
		BoolTestType	booltesttype,
		Node *	arg,
		int	varRelid,
		JoinType	jointype,
		SpecialJoinInfo *	sjinfo
	)

Definition at line 1624 of file selfuncs.c.

{
    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;
}

References arg, ATTSTATSSLOT_NUMBERS, ATTSTATSSLOT_VALUES, CLAMP_PROBABILITY, clause_selectivity(), DatumGetBool(), DEFAULT_NOT_UNK_SEL, DEFAULT_UNK_SEL, elog, ERROR, examine_variable(), fb(), free_attstatsslot(), get_attstatsslot(), GETSTRUCT(), HeapTupleIsValid, InvalidOid, IS_FALSE, IS_NOT_FALSE, IS_NOT_TRUE, IS_NOT_UNKNOWN, IS_TRUE, IS_UNKNOWN, AttStatsSlot::numbers, ReleaseVariableStats, and root.

Referenced by clause_selectivity_ext().

◆ boolvarsel()

Selectivity boolvarsel	(	PlannerInfo *	root,
		Node *	arg,
		int	varRelid
	)

Definition at line 1585 of file selfuncs.c.

{
    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, InvalidOid,
                             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;
}

References arg, BoolGetDatum(), examine_variable(), fb(), HeapTupleIsValid, InvalidOid, is_funcclause(), ReleaseVariableStats, root, and var_eq_const().

Referenced by clause_selectivity_ext().

◆ brincostestimate()

void brincostestimate	(	PlannerInfo *	root,
		IndexPath *	path,
		double	loop_count,
		Cost *	indexStartupCost,
		Cost *	indexTotalCost,
		Selectivity *	indexSelectivity,
		double *	indexCorrelation,
		double *	indexPages
	)

Definition at line 9025 of file selfuncs.c.

{
    IndexOptInfo *index = path->indexinfo;
    List       *indexQuals = get_quals_from_indexclauses(path->indexclauses);
    double      numPages = index->pages;
    RelOptInfo *baserel = index->rel;
    RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
    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, if possible.  Otherwise invent
     * some plausible internal statistics based on the relation page count.
     */
    if (!index->hypothetical)
    {
        /*
         * A lock should have already been obtained on the index in plancat.c.
         */
        indexRel = index_open(index->indexoid, NoLock);
        brinGetStats(indexRel, &statsData);
        index_close(indexRel, NoLock);
 
        /* work out the actual number of ranges in the index */
        indexRanges = Max(ceil((double) baserel->pages /
                               statsData.pagesPerRange), 1.0);
    }
    else
    {
        /*
         * Assume default number of pages per range, and estimate the number
         * of ranges based on that.
         */
        indexRanges = Max(ceil((double) baserel->pages /
                               BRIN_DEFAULT_PAGES_PER_RANGE), 1.0);
 
        statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE;
        statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1;
    }
 
    /*
     * 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;
 
    foreach(l, path->indexclauses)
    {
        IndexClause *iclause = lfirst_node(IndexClause, l);
        AttrNumber  attnum = index->indexkeys[iclause->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 = iclause->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 = fabs(sslot.numbers[0]);
 
                if (varCorrelation > *indexCorrelation)
                    *indexCorrelation = varCorrelation;
 
                free_attstatsslot(&sslot);
            }
        }
 
        ReleaseVariableStats(vardata);
    }
 
    qualSelectivity = clauselist_selectivity(root, indexQuals,
                                             baserel->relid,
                                             JOIN_INNER, NULL);
 
    /*
     * 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.  We can disregard indexorderbys, since BRIN doesn't
     * support those.
     */
    qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
 
    /*
     * 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;
}

References Assert, attnum, ATTSTATSSLOT_NUMBERS, BoolGetDatum(), BRIN_DEFAULT_PAGES_PER_RANGE, brinGetStats(), CLAMP_PROBABILITY, clauselist_selectivity(), cpu_operator_cost, elog, ERROR, fb(), free_attstatsslot(), get_attstatsslot(), get_index_stats_hook, get_quals_from_indexclauses(), get_relation_stats_hook, get_tablespace_page_costs(), HeapTupleIsValid, index_close(), index_open(), index_other_operands_eval_cost(), IndexPath::indexclauses, IndexPath::indexinfo, Int16GetDatum(), InvalidOid, JOIN_INNER, lfirst_node, Max, Min, NoLock, ObjectIdGetDatum(), planner_rt_fetch, ReleaseSysCache(), ReleaseVariableStats, REVMAP_PAGE_MAXITEMS, root, RTE_RELATION, and SearchSysCache3().

Referenced by brinhandler().

◆ btcost_correlation()

static double btcost_correlation	(	IndexOptInfo *	index,
		VariableStatData *	vardata
	)

static

Definition at line 7666 of file selfuncs.c.

{
    Oid         sortop;
    AttStatsSlot sslot;
    double      indexCorrelation = 0;
 
    Assert(HeapTupleIsValid(vardata->statsTuple));
 
    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->nkeycolumns > 1)
            indexCorrelation = varCorrelation * 0.75;
        else
            indexCorrelation = varCorrelation;
 
        free_attstatsslot(&sslot);
    }
 
    return indexCorrelation;
}

References Assert, ATTSTATSSLOT_NUMBERS, BTLessStrategyNumber, fb(), free_attstatsslot(), get_attstatsslot(), get_opfamily_member(), HeapTupleIsValid, and OidIsValid.

Referenced by btcostestimate().

◆ btcostestimate()

void btcostestimate	(	PlannerInfo *	root,
		IndexPath *	path,
		double	loop_count,
		Cost *	indexStartupCost,
		Cost *	indexTotalCost,
		Selectivity *	indexSelectivity,
		double *	indexCorrelation,
		double *	indexPages
	)

Definition at line 7703 of file selfuncs.c.

{
    IndexOptInfo *index = path->indexinfo;
    GenericCosts costs = {0};
    VariableStatData vardata = {0};
    double      numIndexTuples;
    Cost        descentCost;
    List       *indexBoundQuals;
    List       *indexSkipQuals;
    int         indexcol;
    bool        eqQualHere;
    bool        found_row_compare;
    bool        found_array;
    bool        found_is_null_op;
    bool        have_correlation = false;
    double      num_sa_scans;
    double      correlation = 0.0;
    ListCell   *lc;
 
    /*
     * 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.  Note that nbtree
     * preprocessing can add skip arrays that act as leading '=' quals in the
     * absence of ordinary input '=' quals, so in practice _most_ input quals
     * are able to act as index bound quals (which we take into account here).
     *
     * For a RowCompareExpr, we consider only the first column, just as
     * rowcomparesel() does.
     *
     * If there's a SAOP or skip array in the quals, we'll actually perform up
     * to N index descents (not just one), but the underlying array key's
     * operator can be considered to act the same as it normally does.
     */
    indexBoundQuals = NIL;
    indexSkipQuals = NIL;
    indexcol = 0;
    eqQualHere = false;
    found_row_compare = false;
    found_array = false;
    found_is_null_op = false;
    num_sa_scans = 1;
    foreach(lc, path->indexclauses)
    {
        IndexClause *iclause = lfirst_node(IndexClause, lc);
        ListCell   *lc2;
 
        if (indexcol < iclause->indexcol)
        {
            double      num_sa_scans_prev_cols = num_sa_scans;
 
            /*
             * Beginning of a new column's quals.
             *
             * Skip scans use skip arrays, which are ScalarArrayOp style
             * arrays that generate their elements procedurally and on demand.
             * Given a multi-column index on "(a, b)", and an SQL WHERE clause
             * "WHERE b = 42", a skip scan will effectively use an indexqual
             * "WHERE a = ANY('{every col a value}') AND b = 42".  (Obviously,
             * the array on "a" must also return "IS NULL" matches, since our
             * WHERE clause used no strict operator on "a").
             *
             * Here we consider how nbtree will backfill skip arrays for any
             * index columns that lacked an '=' qual.  This maintains our
             * num_sa_scans estimate, and determines if this new column (the
             * "iclause->indexcol" column, not the prior "indexcol" column)
             * can have its RestrictInfos/quals added to indexBoundQuals.
             *
             * We'll need to handle columns that have inequality quals, where
             * the skip array generates values from a range constrained by the
             * quals (not every possible value).  We've been maintaining
             * indexSkipQuals to help with this; it will now contain all of
             * the prior column's quals (that is, indexcol's quals) when they
             * might be used for this.
             */
            if (found_row_compare)
            {
                /*
                 * Skip arrays can't be added after a RowCompare input qual
                 * due to limitations in nbtree
                 */
                break;
            }
            if (eqQualHere)
            {
                /*
                 * Don't need to add a skip array for an indexcol that already
                 * has an '=' qual/equality constraint
                 */
                indexcol++;
                indexSkipQuals = NIL;
            }
            eqQualHere = false;
 
            while (indexcol < iclause->indexcol)
            {
                double      ndistinct;
                bool        isdefault = true;
 
                found_array = true;
 
                /*
                 * A skipped attribute's ndistinct forms the basis of our
                 * estimate of the total number of "array elements" used by
                 * its skip array at runtime.  Look that up first.
                 */
                examine_indexcol_variable(root, index, indexcol, &vardata);
                ndistinct = get_variable_numdistinct(&vardata, &isdefault);
 
                if (indexcol == 0)
                {
                    /*
                     * Get an estimate of the leading column's correlation in
                     * passing (avoids rereading variable stats below)
                     */
                    if (HeapTupleIsValid(vardata.statsTuple))
                        correlation = btcost_correlation(index, &vardata);
                    have_correlation = true;
                }
 
                ReleaseVariableStats(vardata);
 
                /*
                 * If ndistinct is a default estimate, conservatively assume
                 * that no skipping will happen at runtime
                 */
                if (isdefault)
                {
                    num_sa_scans = num_sa_scans_prev_cols;
                    break;      /* done building indexBoundQuals */
                }
 
                /*
                 * Apply indexcol's indexSkipQuals selectivity to ndistinct
                 */
                if (indexSkipQuals != NIL)
                {
                    List       *partialSkipQuals;
                    Selectivity ndistinctfrac;
 
                    /*
                     * If the index is partial, AND the index predicate with
                     * the index-bound quals to produce a more accurate idea
                     * of the number of distinct values for prior indexcol
                     */
                    partialSkipQuals = add_predicate_to_index_quals(index,
                                                                    indexSkipQuals);
 
                    ndistinctfrac = clauselist_selectivity(root, partialSkipQuals,
                                                           index->rel->relid,
                                                           JOIN_INNER,
                                                           NULL);
 
                    /*
                     * If ndistinctfrac is selective (on its own), the scan is
                     * unlikely to benefit from repositioning itself using
                     * later quals.  Do not allow iclause->indexcol's quals to
                     * be added to indexBoundQuals (it would increase descent
                     * costs, without lowering numIndexTuples costs by much).
                     */
                    if (ndistinctfrac < DEFAULT_RANGE_INEQ_SEL)
                    {
                        num_sa_scans = num_sa_scans_prev_cols;
                        break;  /* done building indexBoundQuals */
                    }
 
                    /* Adjust ndistinct downward */
                    ndistinct = rint(ndistinct * ndistinctfrac);
                    ndistinct = Max(ndistinct, 1);
                }
 
                /*
                 * When there's no inequality quals, account for the need to
                 * find an initial value by counting -inf/+inf as a value.
                 *
                 * We don't charge anything extra for possible next/prior key
                 * index probes, which are sometimes used to find the next
                 * valid skip array element (ahead of using the located
                 * element value to relocate the scan to the next position
                 * that might contain matching tuples).  It seems hard to do
                 * better here.  Use of the skip support infrastructure often
                 * avoids most next/prior key probes.  But even when it can't,
                 * there's a decent chance that most individual next/prior key
                 * probes will locate a leaf page whose key space overlaps all
                 * of the scan's keys (even the lower-order keys) -- which
                 * also avoids the need for a separate, extra index descent.
                 * Note also that these probes are much cheaper than non-probe
                 * primitive index scans: they're reliably very selective.
                 */
                if (indexSkipQuals == NIL)
                    ndistinct += 1;
 
                /*
                 * Update num_sa_scans estimate by multiplying by ndistinct.
                 *
                 * We make the pessimistic assumption that there is no
                 * naturally occurring cross-column correlation.  This is
                 * often wrong, but it seems best to err on the side of not
                 * expecting skipping to be helpful...
                 */
                num_sa_scans *= ndistinct;
 
                /*
                 * ...but back out of adding this latest group of 1 or more
                 * skip arrays when num_sa_scans exceeds the total number of
                 * index pages (revert to num_sa_scans from before indexcol).
                 * This causes a sharp discontinuity in cost (as a function of
                 * the indexcol's ndistinct), but that is representative of
                 * actual runtime costs.
                 *
                 * Note that skipping is helpful when each primitive index
                 * scan only manages to skip over 1 or 2 irrelevant leaf pages
                 * on average.  Skip arrays bring savings in CPU costs due to
                 * the scan not needing to evaluate indexquals against every
                 * tuple, which can greatly exceed any savings in I/O costs.
                 * This test is a test of whether num_sa_scans implies that
                 * we're past the point where the ability to skip ceases to
                 * lower the scan's costs (even qual evaluation CPU costs).
                 */
                if (index->pages < num_sa_scans)
                {
                    num_sa_scans = num_sa_scans_prev_cols;
                    break;      /* done building indexBoundQuals */
                }
 
                indexcol++;
                indexSkipQuals = NIL;
            }
 
            /*
             * Finished considering the need to add skip arrays to bridge an
             * initial eqQualHere gap between the old and new index columns
             * (or there was no initial eqQualHere gap in the first place).
             *
             * If an initial gap could not be bridged, then new column's quals
             * (i.e. iclause->indexcol's quals) won't go into indexBoundQuals,
             * and so won't affect our final numIndexTuples estimate.
             */
            if (indexcol != iclause->indexcol)
                break;          /* done building indexBoundQuals */
        }
 
        Assert(indexcol == iclause->indexcol);
 
        /* Examine each indexqual associated with this index clause */
        foreach(lc2, iclause->indexquals)
        {
            RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
            Expr       *clause = rinfo->clause;
            Oid         clause_op = InvalidOid;
            int         op_strategy;
 
            if (IsA(clause, OpExpr))
            {
                OpExpr     *op = (OpExpr *) clause;
 
                clause_op = op->opno;
            }
            else if (IsA(clause, RowCompareExpr))
            {
                RowCompareExpr *rc = (RowCompareExpr *) clause;
 
                clause_op = linitial_oid(rc->opnos);
                found_row_compare = true;
            }
            else if (IsA(clause, ScalarArrayOpExpr))
            {
                ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
                Node       *other_operand = (Node *) lsecond(saop->args);
                double      alength = estimate_array_length(root, other_operand);
 
                clause_op = saop->opno;
                found_array = true;
                /* estimate SA descents 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/skip scan purposes */
                    eqQualHere = true;
                }
            }
            else
                elog(ERROR, "unsupported indexqual type: %d",
                     (int) nodeTag(clause));
 
            /* check for equality operator */
            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);
 
            /*
             * We apply inequality selectivities to estimate index descent
             * costs with scans that use skip arrays.  Save this indexcol's
             * RestrictInfos if it looks like they'll be needed for that.
             */
            if (!eqQualHere && !found_row_compare &&
                indexcol < index->nkeycolumns - 1)
                indexSkipQuals = lappend(indexSkipQuals, 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, an array or NullTest
     * always invalidates that theory (even when eqQualHere has been set).
     */
    if (index->unique &&
        indexcol == index->nkeycolumns - 1 &&
        eqQualHere &&
        !found_array &&
        !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_index_quals(index, indexBoundQuals);
 
        btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
                                                  index->rel->relid,
                                                  JOIN_INNER,
                                                  NULL);
        numIndexTuples = btreeSelectivity * index->rel->tuples;
 
        /*
         * btree automatically combines individual array element primitive
         * index scans whenever the tuples covered by the next set of array
         * keys are close to tuples covered by the current set.  That puts a
         * natural ceiling on the worst case number of descents -- there
         * cannot possibly be more than one descent per leaf page scanned.
         *
         * Clamp the number of descents to at most 1/3 the number of index
         * pages.  This avoids implausibly high estimates with low selectivity
         * paths, where scans usually require only one or two descents.  This
         * is most likely to help when there are several SAOP clauses, where
         * naively accepting the total number of distinct combinations of
         * array elements as the number of descents would frequently lead to
         * wild overestimates.
         *
         * We somewhat arbitrarily don't just make the cutoff the total number
         * of leaf pages (we make it 1/3 the total number of pages instead) to
         * give the btree code credit for its ability to continue on the leaf
         * level with low selectivity scans.
         *
         * Note: num_sa_scans includes both ScalarArrayOp array elements and
         * skip array elements whose qual affects our numIndexTuples estimate.
         */
        num_sa_scans = Min(num_sa_scans, ceil(index->pages * 0.3333333));
        num_sa_scans = Max(num_sa_scans, 1);
 
        /*
         * As in genericcostestimate(), we have to adjust for any array quals
         * included in indexBoundQuals, and then round to integer.
         *
         * It is tempting to make genericcostestimate behave as if array
         * clauses work in almost the same way as scalar operators during
         * btree scans, making the top-level scan look like a continuous scan
         * (as opposed to num_sa_scans-many primitive index scans).  After
         * all, btree scans mostly work like that at runtime.  However, such a
         * scheme would badly bias genericcostestimate's simplistic approach
         * to calculating numIndexPages through prorating.
         *
         * Stick with the approach taken by non-native SAOP scans for now.
         * genericcostestimate will use the Mackert-Lohman formula to
         * compensate for repeat page fetches, even though that definitely
         * won't happen during btree scans (not for leaf pages, at least).
         * We're usually very pessimistic about the number of primitive index
         * scans that will be required, but it's not clear how to do better.
         */
        numIndexTuples = rint(numIndexTuples / num_sa_scans);
    }
 
    /*
     * Now do generic index cost estimation.
     *
     * While we expended effort to make realistic estimates of numIndexTuples
     * and num_sa_scans, we are content to count only the btree metapage as
     * non-leaf.  btree fanout is typically high enough that upper pages are
     * few relative to leaf pages, so accounting for them would move the
     * estimates at most a percent or two.  Given the uncertainty in just how
     * many upper pages exist in a particular index, we'll skip trying to
     * handle that.
     */
    costs.numIndexTuples = numIndexTuples;
    costs.num_sa_scans = num_sa_scans;
    costs.numNonLeafPages = 1;
 
    genericcostestimate(root, path, loop_count, &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 SAOP or skip array keys, charge this once per estimated
     * index descent.  The ones after the first one are not startup cost so
     * far as the overall plan goes, so just add them 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 estimated
     * SAOP/skip array descent.
     */
    descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    costs.indexStartupCost += descentCost;
    costs.indexTotalCost += costs.num_sa_scans * descentCost;
 
    if (!have_correlation)
    {
        examine_indexcol_variable(root, index, 0, &vardata);
        if (HeapTupleIsValid(vardata.statsTuple))
            costs.indexCorrelation = btcost_correlation(index, &vardata);
        ReleaseVariableStats(vardata);
    }
    else
    {
        /* btcost_correlation already called earlier on */
        costs.indexCorrelation = correlation;
    }
 
    *indexStartupCost = costs.indexStartupCost;
    *indexTotalCost = costs.indexTotalCost;
    *indexSelectivity = costs.indexSelectivity;
    *indexCorrelation = costs.indexCorrelation;
    *indexPages = costs.numIndexPages;
}

References add_predicate_to_index_quals(), ScalarArrayOpExpr::args, Assert, btcost_correlation(), BTEqualStrategyNumber, RestrictInfo::clause, clauselist_selectivity(), cpu_operator_cost, DEFAULT_PAGE_CPU_MULTIPLIER, DEFAULT_RANGE_INEQ_SEL, elog, ERROR, estimate_array_length(), examine_indexcol_variable(), fb(), genericcostestimate(), get_op_opfamily_strategy(), get_variable_numdistinct(), HeapTupleIsValid, IndexPath::indexclauses, GenericCosts::indexCorrelation, IndexPath::indexinfo, GenericCosts::indexSelectivity, GenericCosts::indexStartupCost, GenericCosts::indexTotalCost, InvalidOid, IS_NULL, IsA, JOIN_INNER, lappend(), lfirst_node, linitial_oid, lsecond, Max, Min, NIL, nodeTag, GenericCosts::num_sa_scans, GenericCosts::numIndexPages, GenericCosts::numIndexTuples, GenericCosts::numNonLeafPages, OidIsValid, OpExpr::opno, ScalarArrayOpExpr::opno, ReleaseVariableStats, and root.

Referenced by bthandler().

◆ contain_placeholder_walker()

static bool contain_placeholder_walker	(	Node *	node,
		void *	context
	)

static

Definition at line 5997 of file selfuncs.c.

{
    if (node == NULL)
        return false;
    if (IsA(node, PlaceHolderVar))
        return true;
 
    return expression_tree_walker(node, contain_placeholder_walker, context);
}

References contain_placeholder_walker(), expression_tree_walker, fb(), and IsA.

Referenced by contain_placeholder_walker(), and strip_all_phvs_deep().

◆ convert_bytea_to_scalar()

static void convert_bytea_to_scalar	(	Datum	value,
		double *	scaledvalue,
		Datum	lobound,
		double *	scaledlobound,
		Datum	hibound,
		double *	scaledhibound
	)

static

Definition at line 5355 of file selfuncs.c.

{
    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);
}

References convert_one_bytea_to_scalar(), DatumGetByteaPP, fb(), i, Min, value, VARDATA_ANY(), and VARSIZE_ANY_EXHDR().

Referenced by convert_to_scalar().

◆ convert_numeric_to_scalar()

static double convert_numeric_to_scalar	(	Datum	value,
		Oid	typid,
		bool *	failure
	)

static

Definition at line 5073 of file selfuncs.c.

{
    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 REGCOLLATIONOID:
        case REGCONFIGOID:
        case REGDICTIONARYOID:
        case REGROLEOID:
        case REGNAMESPACEOID:
        case REGDATABASEOID:
            /* we can treat OIDs as integers... */
            return (double) DatumGetObjectId(value);
    }
 
    *failure = true;
    return 0;
}

References DatumGetBool(), DatumGetFloat4(), DatumGetFloat8(), DatumGetInt16(), DatumGetInt32(), DatumGetInt64(), DatumGetObjectId(), DirectFunctionCall1, fb(), numeric_float8_no_overflow(), and value.

Referenced by convert_to_scalar().

◆ convert_one_bytea_to_scalar()

static double convert_one_bytea_to_scalar	(	unsigned char *	value,
		int	valuelen,
		int	rangelo,
		int	rangehi
	)

static

Definition at line 5403 of file selfuncs.c.

{
    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;
}

References fb(), and value.

Referenced by convert_bytea_to_scalar().

◆ convert_one_string_to_scalar()

static double convert_one_string_to_scalar	(	char *	value,
		int	rangelo,
		int	rangehi
	)

static

Definition at line 5216 of file selfuncs.c.

{
    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;
}

References fb(), and value.

Referenced by convert_string_to_scalar().

◆ convert_string_datum()

static char * convert_string_datum	(	Datum	value,
		Oid	typid,
		Oid	collid,
		bool *	failure
	)

static

Definition at line 5267 of file selfuncs.c.

{
    char       *val;
    pg_locale_t mylocale;
 
    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;
    }
 
    mylocale = pg_newlocale_from_collation(collid);
 
    if (!mylocale->collate_is_c)
    {
        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
         * pg_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.
         *
         * XXX: we do not check pg_strxfrm_enabled(). On some platforms and in
         * some cases, libc strxfrm() may return the wrong results, but that
         * will only lead to an estimation error.
         */
        xfrmlen = pg_strxfrm(NULL, val, 0, mylocale);
#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 = pg_strxfrm(xfrmstr, val, xfrmlen + 1, mylocale);
 
        /*
         * 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;
}

References Assert, collid, DatumGetChar(), DatumGetPointer(), fb(), NameStr, palloc(), pfree(), pg_newlocale_from_collation(), pg_strxfrm(), PG_USED_FOR_ASSERTS_ONLY, pstrdup(), TextDatumGetCString, val, and value.

Referenced by convert_to_scalar().

◆ convert_string_to_scalar()

static void convert_string_to_scalar	(	char *	value,
		double *	scaledvalue,
		char *	lobound,
		double *	scaledlobound,
		char *	hibound,
		double *	scaledhibound
	)

static

Definition at line 5136 of file selfuncs.c.

{
    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);
}

References convert_one_string_to_scalar(), fb(), and value.

Referenced by convert_to_scalar().

◆ convert_timevalue_to_scalar()

static double convert_timevalue_to_scalar	(	Datum	value,
		Oid	typid,
		bool *	failure
	)

static

Definition at line 5446 of file selfuncs.c.

{
    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.
                 *
                 * This also works for infinite intervals, which just have all
                 * fields set to INT_MIN/INT_MAX, and so will produce a result
                 * smaller/larger than any finite interval.
                 */
                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;
}

References date2timestamp_no_overflow(), DatumGetDateADT(), DatumGetIntervalP(), DatumGetTimeADT(), DatumGetTimestamp(), DatumGetTimestampTz(), DatumGetTimeTzADTP(), DAYS_PER_YEAR, fb(), interval::month, MONTHS_PER_YEAR, interval::time, USECS_PER_DAY, and value.

Referenced by convert_to_scalar().

◆ convert_to_scalar()

static bool convert_to_scalar	(	Datum	value,
		Oid	valuetypid,
		Oid	collid,
		double *	scaledvalue,
		Datum	lobound,
		Datum	hibound,
		Oid	boundstypid,
		double *	scaledlobound,
		double *	scaledhibound
	)

static

Definition at line 4925 of file selfuncs.c.

{
    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 REGCOLLATIONOID:
        case REGCONFIGOID:
        case REGDICTIONARYOID:
        case REGROLEOID:
        case REGNAMESPACEOID:
        case REGDATABASEOID:
            *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;
}

References collid, convert_bytea_to_scalar(), convert_network_to_scalar(), convert_numeric_to_scalar(), convert_string_datum(), convert_string_to_scalar(), convert_timevalue_to_scalar(), fb(), pfree(), and value.

Referenced by ineq_histogram_selectivity().

◆ eqjoinsel()

Datum eqjoinsel ( PG_FUNCTION_ARGS )

Definition at line 2385 of file selfuncs.c.

{
    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);
    Oid         collation = PG_GET_COLLATION();
    double      selec;
    double      selec_inner;
    VariableStatData vardata1;
    VariableStatData vardata2;
    double      nd1;
    double      nd2;
    bool        isdefault1;
    bool        isdefault2;
    Oid         opfuncoid;
    FmgrInfo    eqproc;
    Oid         hashLeft = InvalidOid;
    Oid         hashRight = InvalidOid;
    AttStatsSlot sslot1;
    AttStatsSlot sslot2;
    Form_pg_statistic stats1 = NULL;
    Form_pg_statistic stats2 = NULL;
    bool        have_mcvs1 = false;
    bool        have_mcvs2 = false;
    bool       *hasmatch1 = NULL;
    bool       *hasmatch2 = NULL;
    int         nmatches = 0;
    bool        get_mcv_stats;
    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));
 
    /*
     * There is no use in fetching one side's MCVs if we lack MCVs for the
     * other side, so do a quick check to verify that both stats exist.
     */
    get_mcv_stats = (HeapTupleIsValid(vardata1.statsTuple) &&
                     HeapTupleIsValid(vardata2.statsTuple) &&
                     get_attstatsslot(&sslot1, vardata1.statsTuple,
                                      STATISTIC_KIND_MCV, InvalidOid,
                                      0) &&
                     get_attstatsslot(&sslot2, vardata2.statsTuple,
                                      STATISTIC_KIND_MCV, InvalidOid,
                                      0));
 
    if (HeapTupleIsValid(vardata1.statsTuple))
    {
        /* note we allow use of nullfrac regardless of security check */
        stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
        if (get_mcv_stats &&
            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 (get_mcv_stats &&
            statistic_proc_security_check(&vardata2, opfuncoid))
            have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
                                          STATISTIC_KIND_MCV, InvalidOid,
                                          ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
    }
 
    /* Prepare info usable by both eqjoinsel_inner and eqjoinsel_semi */
    if (have_mcvs1 && have_mcvs2)
    {
        fmgr_info(opfuncoid, &eqproc);
        hasmatch1 = (bool *) palloc0(sslot1.nvalues * sizeof(bool));
        hasmatch2 = (bool *) palloc0(sslot2.nvalues * sizeof(bool));
 
        /*
         * If the MCV lists are long enough to justify hashing, try to look up
         * hash functions for the join operator.
         */
        if ((sslot1.nvalues + sslot2.nvalues) >= EQJOINSEL_MCV_HASH_THRESHOLD)
            (void) get_op_hash_functions(operator, &hashLeft, &hashRight);
    }
    else
        memset(&eqproc, 0, sizeof(eqproc)); /* silence uninit-var warnings */
 
    /* We need to compute the inner-join selectivity in all cases */
    selec_inner = eqjoinsel_inner(&eqproc, collation,
                                  hashLeft, hashRight,
                                  &vardata1, &vardata2,
                                  nd1, nd2,
                                  isdefault1, isdefault2,
                                  &sslot1, &sslot2,
                                  stats1, stats2,
                                  have_mcvs1, have_mcvs2,
                                  hasmatch1, hasmatch2,
                                  &nmatches);
 
    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(&eqproc, collation,
                                       hashLeft, hashRight,
                                       false,
                                       &vardata1, &vardata2,
                                       nd1, nd2,
                                       isdefault1, isdefault2,
                                       &sslot1, &sslot2,
                                       stats1, stats2,
                                       have_mcvs1, have_mcvs2,
                                       hasmatch1, hasmatch2,
                                       &nmatches,
                                       inner_rel);
            else
                selec = eqjoinsel_semi(&eqproc, collation,
                                       hashLeft, hashRight,
                                       true,
                                       &vardata2, &vardata1,
                                       nd2, nd1,
                                       isdefault2, isdefault1,
                                       &sslot2, &sslot1,
                                       stats2, stats1,
                                       have_mcvs2, have_mcvs1,
                                       hasmatch2, hasmatch1,
                                       &nmatches,
                                       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);
 
    if (hasmatch1)
        pfree(hasmatch1);
    if (hasmatch2)
        pfree(hasmatch2);
 
    CLAMP_PROBABILITY(selec);
 
    PG_RETURN_FLOAT8((float8) selec);
}

References ATTSTATSSLOT_NUMBERS, ATTSTATSSLOT_VALUES, CLAMP_PROBABILITY, elog, eqjoinsel_inner(), EQJOINSEL_MCV_HASH_THRESHOLD, eqjoinsel_semi(), ERROR, fb(), find_join_input_rel(), fmgr_info(), free_attstatsslot(), get_attstatsslot(), get_join_variables(), get_op_hash_functions(), get_opcode(), get_variable_numdistinct(), GETSTRUCT(), HeapTupleIsValid, InvalidOid, JOIN_ANTI, JOIN_FULL, JOIN_INNER, JOIN_LEFT, JOIN_SEMI, SpecialJoinInfo::jointype, Min, SpecialJoinInfo::min_righthand, palloc0(), pfree(), PG_GET_COLLATION, PG_GETARG_INT16, PG_GETARG_OID, PG_GETARG_POINTER, PG_RETURN_FLOAT8, ReleaseVariableStats, root, and statistic_proc_security_check().

Referenced by neqjoinsel().

◆ eqjoinsel_find_matches()

static void eqjoinsel_find_matches	(	FmgrInfo *	eqproc,
		Oid	collation,
		Oid	hashLeft,
		Oid	hashRight,
		bool	op_is_reversed,
		AttStatsSlot *	sslot1,
		AttStatsSlot *	sslot2,
		int	nvalues1,
		int	nvalues2,
		bool *	hasmatch1,
		bool *	hasmatch2,
		int *	p_nmatches,
		double *	p_matchprodfreq
	)

static

Definition at line 2938 of file selfuncs.c.

{
    LOCAL_FCINFO(fcinfo, 2);
    double      matchprodfreq = 0.0;
    int         nmatches = 0;
 
    /*
     * Save a few cycles by setting up the fcinfo struct just once.  Using
     * FunctionCallInvoke directly also avoids failure if the eqproc returns
     * NULL, though really equality functions should never do that.
     */
    InitFunctionCallInfoData(*fcinfo, eqproc, 2, collation,
                             NULL, NULL);
    fcinfo->args[0].isnull = false;
    fcinfo->args[1].isnull = false;
 
    if (OidIsValid(hashLeft) && OidIsValid(hashRight))
    {
        /* Use a hash table to speed up the matching */
        LOCAL_FCINFO(hash_fcinfo, 1);
        FmgrInfo    hash_proc;
        MCVHashContext hashContext;
        MCVHashTable_hash *hashTable;
        AttStatsSlot *statsProbe;
        AttStatsSlot *statsHash;
        bool       *hasMatchProbe;
        bool       *hasMatchHash;
        int         nvaluesProbe;
        int         nvaluesHash;
 
        /* Make sure we build the hash table on the smaller array. */
        if (sslot1->nvalues >= sslot2->nvalues)
        {
            statsProbe = sslot1;
            statsHash = sslot2;
            hasMatchProbe = hasmatch1;
            hasMatchHash = hasmatch2;
            nvaluesProbe = nvalues1;
            nvaluesHash = nvalues2;
        }
        else
        {
            /* We'll have to reverse the direction of use of the operator. */
            op_is_reversed = !op_is_reversed;
            statsProbe = sslot2;
            statsHash = sslot1;
            hasMatchProbe = hasmatch2;
            hasMatchHash = hasmatch1;
            nvaluesProbe = nvalues2;
            nvaluesHash = nvalues1;
        }
 
        /*
         * Build the hash table on the smaller array, using the appropriate
         * hash function for its data type.
         */
        fmgr_info(op_is_reversed ? hashLeft : hashRight, &hash_proc);
        InitFunctionCallInfoData(*hash_fcinfo, &hash_proc, 1, collation,
                                 NULL, NULL);
        hash_fcinfo->args[0].isnull = false;
 
        hashContext.equal_fcinfo = fcinfo;
        hashContext.hash_fcinfo = hash_fcinfo;
        hashContext.op_is_reversed = op_is_reversed;
        hashContext.insert_mode = true;
        get_typlenbyval(statsHash->valuetype,
                        &hashContext.hash_typlen,
                        &hashContext.hash_typbyval);
 
        hashTable = MCVHashTable_create(CurrentMemoryContext,
                                        nvaluesHash,
                                        &hashContext);
 
        for (int i = 0; i < nvaluesHash; i++)
        {
            bool        found = false;
            MCVHashEntry *entry = MCVHashTable_insert(hashTable,
                                                      statsHash->values[i],
                                                      &found);
 
            /*
             * MCVHashTable_insert will only report "found" if the new value
             * is equal to some previous one per datum_image_eq().  That
             * probably shouldn't happen, since we're not expecting duplicates
             * in the MCV list.  If we do find a dup, just ignore it, leaving
             * the hash entry's index pointing at the first occurrence.  That
             * matches the behavior that the non-hashed code path would have.
             */
            if (likely(!found))
                entry->index = i;
        }
 
        /*
         * Prepare to probe the hash table.  If the probe values are of a
         * different data type, then we need to change hash functions.  (This
         * code relies on the assumption that since we defined SH_STORE_HASH,
         * simplehash.h will never need to compute hash values for existing
         * hash table entries.)
         */
        hashContext.insert_mode = false;
        if (hashLeft != hashRight)
        {
            fmgr_info(op_is_reversed ? hashRight : hashLeft, &hash_proc);
            /* Resetting hash_fcinfo is probably unnecessary, but be safe */
            InitFunctionCallInfoData(*hash_fcinfo, &hash_proc, 1, collation,
                                     NULL, NULL);
            hash_fcinfo->args[0].isnull = false;
        }
 
        /* Look up each probe value in turn. */
        for (int i = 0; i < nvaluesProbe; i++)
        {
            MCVHashEntry *entry = MCVHashTable_lookup(hashTable,
                                                      statsProbe->values[i]);
 
            /* As in the other code path, skip already-matched hash entries */
            if (entry != NULL && !hasMatchHash[entry->index])
            {
                hasMatchHash[entry->index] = hasMatchProbe[i] = true;
                nmatches++;
                matchprodfreq += statsHash->numbers[entry->index] * statsProbe->numbers[i];
            }
        }
 
        MCVHashTable_destroy(hashTable);
    }
    else
    {
        /* We're not to use hashing, so do it the O(N^2) way */
        int         index1,
                    index2;
 
        /* Set up to supply the values in the order the operator expects */
        if (op_is_reversed)
        {
            index1 = 1;
            index2 = 0;
        }
        else
        {
            index1 = 0;
            index2 = 1;
        }
 
        for (int i = 0; i < nvalues1; i++)
        {
            fcinfo->args[index1].value = sslot1->values[i];
 
            for (int j = 0; j < nvalues2; j++)
            {
                Datum       fresult;
 
                if (hasmatch2[j])
                    continue;
                fcinfo->args[index2].value = sslot2->values[j];
                fcinfo->isnull = false;
                fresult = FunctionCallInvoke(fcinfo);
                if (!fcinfo->isnull && DatumGetBool(fresult))
                {
                    hasmatch1[i] = hasmatch2[j] = true;
                    matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
                    nmatches++;
                    break;
                }
            }
        }
    }
 
    *p_nmatches = nmatches;
    *p_matchprodfreq = matchprodfreq;
}

References CurrentMemoryContext, DatumGetBool(), fb(), fmgr_info(), FunctionCallInvoke, get_typlenbyval(), i, MCVHashEntry::index, InitFunctionCallInfoData, j, likely, LOCAL_FCINFO, and OidIsValid.

Referenced by eqjoinsel_inner(), and eqjoinsel_semi().

◆ eqjoinsel_inner()

static double eqjoinsel_inner	(	FmgrInfo *	eqproc,
		Oid	collation,
		Oid	hashLeft,
		Oid	hashRight,
		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,
		bool *	hasmatch1,
		bool *	hasmatch2,
		int *	p_nmatches
	)

static

Definition at line 2588 of file selfuncs.c.

{
    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).
         */
        double      nullfrac1 = stats1->stanullfrac;
        double      nullfrac2 = stats2->stanullfrac;
        double      matchprodfreq,
                    matchfreq1,
                    matchfreq2,
                    unmatchfreq1,
                    unmatchfreq2,
                    otherfreq1,
                    otherfreq2,
                    totalsel1,
                    totalsel2;
        int         i,
                    nmatches;
 
        /* Fill the match arrays */
        eqjoinsel_find_matches(eqproc, collation,
                               hashLeft, hashRight,
                               false,
                               sslot1, sslot2,
                               sslot1->nvalues, sslot2->nvalues,
                               hasmatch1, hasmatch2,
                               p_nmatches, &matchprodfreq);
        nmatches = *p_nmatches;
        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);
 
        /*
         * 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;
}

References CLAMP_PROBABILITY, eqjoinsel_find_matches(), fb(), and i.

Referenced by eqjoinsel().

◆ eqjoinsel_semi()

static double eqjoinsel_semi	(	FmgrInfo *	eqproc,
		Oid	collation,
		Oid	hashLeft,
		Oid	hashRight,
		bool	op_is_reversed,
		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,
		bool *	hasmatch1,
		bool *	hasmatch2,
		int *	p_nmatches,
		RelOptInfo *	inner_rel
	)

static

Definition at line 2747 of file selfuncs.c.

{
    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)
    {
        /*
         * 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.
         */
        double      nullfrac1 = stats1->stanullfrac;
        double      matchprodfreq,
                    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);
 
        /*
         * If we did not set clamped_nvalues2 to less than sslot2->nvalues,
         * then the hasmatch1[] and hasmatch2[] match flags computed by
         * eqjoinsel_inner are still perfectly applicable, so we need not
         * re-do the matching work.  Note that it does not matter if
         * op_is_reversed: we'd get the same answers.
         *
         * If we did clamp, then a different set of sslot2 values is to be
         * compared, so we have to re-do the matching.
         */
        if (clamped_nvalues2 != sslot2->nvalues)
        {
            /* Must re-zero the arrays */
            memset(hasmatch1, 0, sslot1->nvalues * sizeof(bool));
            memset(hasmatch2, 0, clamped_nvalues2 * sizeof(bool));
            /* Re-fill the match arrays */
            eqjoinsel_find_matches(eqproc, collation,
                                   hashLeft, hashRight,
                                   op_is_reversed,
                                   sslot1, sslot2,
                                   sslot1->nvalues, clamped_nvalues2,
                                   hasmatch1, hasmatch2,
                                   p_nmatches, &matchprodfreq);
        }
        nmatches = *p_nmatches;
 
        /* 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);
 
        /*
         * 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;
}

References CLAMP_PROBABILITY, eqjoinsel_find_matches(), fb(), i, and Min.

Referenced by eqjoinsel().

◆ eqsel()

Datum eqsel ( PG_FUNCTION_ARGS )

Definition at line 300 of file selfuncs.c.

{
    PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
}

References eqsel_internal(), and PG_RETURN_FLOAT8.

◆ eqsel_internal()

static double eqsel_internal	(	PG_FUNCTION_ARGS	,
		bool	negate
	)

static

Definition at line 309 of file selfuncs.c.

{
    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;
    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, collation,
                             ((Const *) other)->constvalue,
                             ((Const *) other)->constisnull,
                             varonleft, negate);
    else
        selec = var_eq_non_const(&vardata, operator, collation, other,
                                 varonleft, negate);
 
    ReleaseVariableStats(vardata);
 
    return selec;
}

References DEFAULT_EQ_SEL, fb(), get_negator(), get_restriction_variable(), IsA, OidIsValid, PG_GET_COLLATION, PG_GETARG_INT32, PG_GETARG_OID, PG_GETARG_POINTER, ReleaseVariableStats, root, var_eq_const(), and var_eq_non_const().

Referenced by eqsel(), and neqsel().

◆ estimate_array_length()

double estimate_array_length	(	PlannerInfo *	root,
		Node *	arrayexpr
	)

Definition at line 2240 of file selfuncs.c.

{
    /* 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 if (arrayexpr && root)
    {
        /* See if we can find any statistics about it */
        VariableStatData vardata;
        AttStatsSlot sslot;
        double      nelem = 0;
 
        /*
         * Skip calling examine_variable for Var with varno 0, which has no
         * valid relation entry and would error in find_base_rel.  Such a Var
         * can appear when a nested set operation's output type doesn't match
         * the parent's expected type, because recurse_set_operations builds a
         * projection target list using generate_setop_tlist with varno 0, and
         * if the required type coercion involves an ArrayCoerceExpr, we can
         * be called on that Var.
         */
        if (IsA(arrayexpr, Var) && ((Var *) arrayexpr)->varno == 0)
            return 10;          /* default guess, should match scalararraysel */
 
        examine_variable(root, arrayexpr, 0, &vardata);
        if (HeapTupleIsValid(vardata.statsTuple))
        {
            /*
             * Found stats, so use the average element count, which is stored
             * in the last stanumbers element of the DECHIST statistics.
             * Actually that is the average count of *distinct* elements;
             * perhaps we should scale it up somewhat?
             */
            if (get_attstatsslot(&sslot, vardata.statsTuple,
                                 STATISTIC_KIND_DECHIST, InvalidOid,
                                 ATTSTATSSLOT_NUMBERS))
            {
                if (sslot.nnumbers > 0)
                    nelem = clamp_row_est(sslot.numbers[sslot.nnumbers - 1]);
                free_attstatsslot(&sslot);
            }
        }
        ReleaseVariableStats(vardata);
 
        if (nelem > 0)
            return nelem;
    }
 
    /* Else use a default guess --- this should match scalararraysel */
    return 10;
}

References ARR_DIMS, ARR_NDIM, ArrayGetNItems(), ATTSTATSSLOT_NUMBERS, clamp_row_est(), DatumGetArrayTypeP, examine_variable(), fb(), free_attstatsslot(), get_attstatsslot(), HeapTupleIsValid, InvalidOid, IsA, list_length(), ReleaseVariableStats, root, and strip_array_coercion().

Referenced by array_unnest_support(), btcostestimate(), cost_qual_eval_walker(), cost_tidscan(), genericcostestimate(), and gincost_scalararrayopexpr().

◆ estimate_hash_bucket_stats()

void estimate_hash_bucket_stats	(	PlannerInfo *	root,
		Node *	hashkey,
		double	nbuckets,
		Selectivity *	mcv_freq,
		Selectivity *	bucketsize_frac
	)

Definition at line 4420 of file selfuncs.c.

{
    VariableStatData vardata;
    double      estfract,
                ndistinct;
    bool        isdefault;
    AttStatsSlot sslot;
 
    examine_variable(root, hashkey, 0, &vardata);
 
    /* Initialize *mcv_freq to "unknown" */
    *mcv_freq = 0.0;
 
    /* Look up the frequency of the most common value, if available */
    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);
        }
        else if (get_attstatsslot(&sslot, vardata.statsTuple,
                                  STATISTIC_KIND_HISTOGRAM, InvalidOid,
                                  0))
        {
            /*
             * If there are no recorded MCVs, but we do have a histogram, then
             * assume that ANALYZE determined that the column is unique.
             */
            if (vardata.rel && vardata.rel->tuples > 0)
                *mcv_freq = 1.0 / vardata.rel->tuples;
        }
    }
 
    /* 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;
    }
 
    /*
     * 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;
 
    /*
     * Clamp the bucketsize fraction to be not less than the MCV frequency,
     * since whichever bucket the MCV values end up in will have at least that
     * size.  This has no effect if *mcv_freq is still zero.
     */
    estfract = Max(estfract, *mcv_freq);
 
    *bucketsize_frac = (Selectivity) estfract;
 
    ReleaseVariableStats(vardata);
}

References ATTSTATSSLOT_NUMBERS, clamp_row_est(), examine_variable(), fb(), free_attstatsslot(), get_attstatsslot(), get_variable_numdistinct(), HeapTupleIsValid, InvalidOid, Max, ReleaseVariableStats, and root.

Referenced by final_cost_hashjoin().

◆ estimate_hashagg_tablesize()

double estimate_hashagg_tablesize	(	PlannerInfo *	root,
		Path *	path,
		const AggClauseCosts *	agg_costs,
		double	dNumGroups
	)

Definition at line 4526 of file selfuncs.c.

{
    Size        hashentrysize;
 
    hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
                                        path->pathtarget->width,
                                        agg_costs->transitionSpace);
 
    /*
     * Note that this disregards the effect of fill-factor and growth policy
     * of the hash table.  That's probably ok, given that the default
     * fill-factor is relatively high.  It'd be hard to meaningfully factor in
     * "double-in-size" growth policies here.
     */
    return hashentrysize * dNumGroups;
}

References fb(), hash_agg_entry_size(), list_length(), and root.

Referenced by consider_groupingsets_paths().

◆ estimate_multivariate_bucketsize()

List * estimate_multivariate_bucketsize	(	PlannerInfo *	root,
		RelOptInfo *	inner,
		List *	hashclauses,
		Selectivity *	innerbucketsize
	)

Definition at line 4152 of file selfuncs.c.

{
    List       *clauses;
    List       *otherclauses;
    double      ndistinct;
 
    if (list_length(hashclauses) <= 1)
    {
        /*
         * Nothing to do for a single clause.  Could we employ univariate
         * extended stat here?
         */
        return hashclauses;
    }
 
    /* "clauses" is the list of hashclauses we've not dealt with yet */
    clauses = list_copy(hashclauses);
    /* "otherclauses" holds clauses we are going to return to caller */
    otherclauses = NIL;
    /* current estimate of ndistinct */
    ndistinct = 1.0;
    while (clauses != NIL)
    {
        ListCell   *lc;
        int         relid = -1;
        List       *varinfos = NIL;
        List       *origin_rinfos = NIL;
        double      mvndistinct;
        List       *origin_varinfos;
        int         group_relid = -1;
        RelOptInfo *group_rel = NULL;
        ListCell   *lc1,
                   *lc2;
 
        /*
         * Find clauses, referencing the same single base relation and try to
         * estimate such a group with extended statistics.  Create varinfo for
         * an approved clause, push it to otherclauses, if it can't be
         * estimated here or ignore to process at the next iteration.
         */
        foreach(lc, clauses)
        {
            RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
            Node       *expr;
            Relids      relids;
            GroupVarInfo *varinfo;
 
            /*
             * Find the inner side of the join, which we need to estimate the
             * number of buckets.  Use outer_is_left because the
             * clause_sides_match_join routine has called on hash clauses.
             */
            relids = rinfo->outer_is_left ?
                rinfo->right_relids : rinfo->left_relids;
            expr = rinfo->outer_is_left ?
                get_rightop(rinfo->clause) : get_leftop(rinfo->clause);
 
            if (bms_get_singleton_member(relids, &relid) &&
                root->simple_rel_array[relid]->statlist != NIL)
            {
                bool        is_duplicate = false;
 
                /*
                 * This inner-side expression references only one relation.
                 * Extended statistics on this clause can exist.
                 */
                if (group_relid < 0)
                {
                    RangeTblEntry *rte = root->simple_rte_array[relid];
 
                    if (!rte || (rte->relkind != RELKIND_RELATION &&
                                 rte->relkind != RELKIND_MATVIEW &&
                                 rte->relkind != RELKIND_FOREIGN_TABLE &&
                                 rte->relkind != RELKIND_PARTITIONED_TABLE))
                    {
                        /* Extended statistics can't exist in principle */
                        otherclauses = lappend(otherclauses, rinfo);
                        clauses = foreach_delete_current(clauses, lc);
                        continue;
                    }
 
                    group_relid = relid;
                    group_rel = root->simple_rel_array[relid];
                }
                else if (group_relid != relid)
                {
                    /*
                     * Being in the group forming state we don't need other
                     * clauses.
                     */
                    continue;
                }
 
                /*
                 * We're going to add the new clause to the varinfos list.  We
                 * might re-use add_unique_group_var(), but we don't do so for
                 * two reasons.
                 *
                 * 1) We must keep the origin_rinfos list ordered exactly the
                 * same way as varinfos.
                 *
                 * 2) add_unique_group_var() is designed for
                 * estimate_num_groups(), where a larger number of groups is
                 * worse.   While estimating the number of hash buckets, we
                 * have the opposite: a lesser number of groups is worse.
                 * Therefore, we don't have to remove "known equal" vars: the
                 * removed var may valuably contribute to the multivariate
                 * statistics to grow the number of groups.
                 */
 
                /*
                 * Clear nullingrels to correctly match hash keys.  See
                 * add_unique_group_var()'s comment for details.
                 */
                expr = remove_nulling_relids(expr, root->outer_join_rels, NULL);
 
                /*
                 * Detect and exclude exact duplicates from the list of hash
                 * keys (like add_unique_group_var does).
                 */
                foreach(lc1, varinfos)
                {
                    varinfo = (GroupVarInfo *) lfirst(lc1);
 
                    if (!equal(expr, varinfo->var))
                        continue;
 
                    is_duplicate = true;
                    break;
                }
 
                if (is_duplicate)
                {
                    /*
                     * Skip exact duplicates. Adding them to the otherclauses
                     * list also doesn't make sense.
                     */
                    continue;
                }
 
                /*
                 * Initialize GroupVarInfo.  We only use it to call
                 * estimate_multivariate_ndistinct(), which doesn't care about
                 * ndistinct and isdefault fields.  Thus, skip these fields.
                 */
                varinfo = palloc0_object(GroupVarInfo);
                varinfo->var = expr;
                varinfo->rel = root->simple_rel_array[relid];
                varinfos = lappend(varinfos, varinfo);
 
                /*
                 * Remember the link to RestrictInfo for the case the clause
                 * is failed to be estimated.
                 */
                origin_rinfos = lappend(origin_rinfos, rinfo);
            }
            else
            {
                /* This clause can't be estimated with extended statistics */
                otherclauses = lappend(otherclauses, rinfo);
            }
 
            clauses = foreach_delete_current(clauses, lc);
        }
 
        if (list_length(varinfos) < 2)
        {
            /*
             * Multivariate statistics doesn't apply to single columns except
             * for expressions, but it has not been implemented yet.
             */
            otherclauses = list_concat(otherclauses, origin_rinfos);
            list_free_deep(varinfos);
            list_free(origin_rinfos);
            continue;
        }
 
        Assert(group_rel != NULL);
 
        /* Employ the extended statistics. */
        origin_varinfos = varinfos;
        for (;;)
        {
            bool        estimated = estimate_multivariate_ndistinct(root,
                                                                    group_rel,
                                                                    &varinfos,
                                                                    &mvndistinct);
 
            if (!estimated)
                break;
 
            /*
             * We've got an estimation.  Use ndistinct value in a consistent
             * way - according to the caller's logic (see
             * final_cost_hashjoin).
             */
            if (ndistinct < mvndistinct)
                ndistinct = mvndistinct;
            Assert(ndistinct >= 1.0);
        }
 
        Assert(list_length(origin_varinfos) == list_length(origin_rinfos));
 
        /* Collect unmatched clauses as otherclauses. */
        forboth(lc1, origin_varinfos, lc2, origin_rinfos)
        {
            GroupVarInfo *vinfo = lfirst(lc1);
 
            if (!list_member_ptr(varinfos, vinfo))
                /* Already estimated */
                continue;
 
            /* Can't be estimated here - push to the returning list */
            otherclauses = lappend(otherclauses, lfirst(lc2));
        }
    }
 
    *innerbucketsize = 1.0 / ndistinct;
    return otherclauses;
}

References Assert, bms_get_singleton_member(), RestrictInfo::clause, equal(), estimate_multivariate_ndistinct(), fb(), forboth, foreach_delete_current, get_leftop(), get_rightop(), lappend(), lfirst, lfirst_node, list_concat(), list_copy(), list_free(), list_free_deep(), list_length(), list_member_ptr(), NIL, palloc0_object, remove_nulling_relids(), and root.

Referenced by final_cost_hashjoin().

◆ estimate_multivariate_ndistinct()

static bool estimate_multivariate_ndistinct	(	PlannerInfo *	root,
		RelOptInfo *	rel,
		List **	varinfos,
		double *	ndistinct
	)

static

Definition at line 4567 of file selfuncs.c.

{
    ListCell   *lc;
    int         nmatches_vars;
    int         nmatches_exprs;
    Oid         statOid = InvalidOid;
    MVNDistinct *stats;
    StatisticExtInfo *matched_info = NULL;
    RangeTblEntry *rte = planner_rt_fetch(rel->relid, root);
 
    /* bail out immediately if the table has no extended statistics */
    if (!rel->statlist)
        return false;
 
    /* look for the ndistinct statistics object matching the most vars */
    nmatches_vars = 0;          /* we require at least two matches */
    nmatches_exprs = 0;
    foreach(lc, rel->statlist)
    {
        ListCell   *lc2;
        StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
        int         nshared_vars = 0;
        int         nshared_exprs = 0;
 
        /* skip statistics of other kinds */
        if (info->kind != STATS_EXT_NDISTINCT)
            continue;
 
        /* skip statistics with mismatching stxdinherit value */
        if (info->inherit != rte->inh)
            continue;
 
        /*
         * Determine how many expressions (and variables in non-matched
         * expressions) match. We'll then use these numbers to pick the
         * statistics object that best matches the clauses.
         */
        foreach(lc2, *varinfos)
        {
            ListCell   *lc3;
            GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
            AttrNumber  attnum;
 
            Assert(varinfo->rel == rel);
 
            /* simple Var, search in statistics keys directly */
            if (IsA(varinfo->var, Var))
            {
                attnum = ((Var *) varinfo->var)->varattno;
 
                /*
                 * Ignore system attributes - we don't support statistics on
                 * them, so can't match them (and it'd fail as the values are
                 * negative).
                 */
                if (!AttrNumberIsForUserDefinedAttr(attnum))
                    continue;
 
                if (bms_is_member(attnum, info->keys))
                    nshared_vars++;
 
                continue;
            }
 
            /* expression - see if it's in the statistics object */
            foreach(lc3, info->exprs)
            {
                Node       *expr = (Node *) lfirst(lc3);
 
                if (equal(varinfo->var, expr))
                {
                    nshared_exprs++;
                    break;
                }
            }
        }
 
        /*
         * The ndistinct extended statistics contain estimates for a minimum
         * of pairs of columns which the statistics are defined on and
         * certainly not single columns.  Here we skip unless we managed to
         * match to at least two columns.
         */
        if (nshared_vars + nshared_exprs < 2)
            continue;
 
        /*
         * Check if these statistics are a better match than the previous best
         * match and if so, take note of the StatisticExtInfo.
         *
         * The statslist is sorted by statOid, so the StatisticExtInfo we
         * select as the best match is deterministic even when multiple sets
         * of statistics match equally as well.
         */
        if ((nshared_exprs > nmatches_exprs) ||
            (((nshared_exprs == nmatches_exprs)) && (nshared_vars > nmatches_vars)))
        {
            statOid = info->statOid;
            nmatches_vars = nshared_vars;
            nmatches_exprs = nshared_exprs;
            matched_info = info;
        }
    }
 
    /* No match? */
    if (statOid == InvalidOid)
        return false;
 
    Assert(nmatches_vars + nmatches_exprs > 1);
 
    stats = statext_ndistinct_load(statOid, rte->inh);
 
    /*
     * 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;
        ListCell   *lc2;
        Bitmapset  *matched = NULL;
        AttrNumber  attnum_offset;
 
        /*
         * How much we need to offset the attnums? If there are no
         * expressions, no offset is needed. Otherwise offset enough to move
         * the lowest one (which is equal to number of expressions) to 1.
         */
        if (matched_info->exprs)
            attnum_offset = (list_length(matched_info->exprs) + 1);
        else
            attnum_offset = 0;
 
        /* see what actually matched */
        foreach(lc2, *varinfos)
        {
            ListCell   *lc3;
            int         idx;
            bool        found = false;
 
            GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
 
            /*
             * Process a simple Var expression, by matching it to keys
             * directly. If there's a matching expression, we'll try matching
             * it later.
             */
            if (IsA(varinfo->var, Var))
            {
                AttrNumber  attnum = ((Var *) varinfo->var)->varattno;
 
                /*
                 * Ignore expressions on system attributes. Can't rely on the
                 * bms check for negative values.
                 */
                if (!AttrNumberIsForUserDefinedAttr(attnum))
                    continue;
 
                /* Is the variable covered by the statistics object? */
                if (!bms_is_member(attnum, matched_info->keys))
                    continue;
 
                attnum = attnum + attnum_offset;
 
                /* ensure sufficient offset */
                Assert(AttrNumberIsForUserDefinedAttr(attnum));
 
                matched = bms_add_member(matched, attnum);
 
                found = true;
            }
 
            /*
             * XXX Maybe we should allow searching the expressions even if we
             * found an attribute matching the expression? That would handle
             * trivial expressions like "(a)" but it seems fairly useless.
             */
            if (found)
                continue;
 
            /* expression - see if it's in the statistics object */
            idx = 0;
            foreach(lc3, matched_info->exprs)
            {
                Node       *expr = (Node *) lfirst(lc3);
 
                if (equal(varinfo->var, expr))
                {
                    AttrNumber  attnum = -(idx + 1);
 
                    attnum = attnum + attnum_offset;
 
                    /* ensure sufficient offset */
                    Assert(AttrNumberIsForUserDefinedAttr(attnum));
 
                    matched = bms_add_member(matched, attnum);
 
                    /* there should be just one matching expression */
                    break;
                }
 
                idx++;
            }
        }
 
        /* Find the specific item that exactly matches the combination */
        for (i = 0; i < stats->nitems; i++)
        {
            int         j;
            MVNDistinctItem *tmpitem = &stats->items[i];
 
            if (tmpitem->nattributes != bms_num_members(matched))
                continue;
 
            /* assume it's the right item */
            item = tmpitem;
 
            /* check that all item attributes/expressions fit the match */
            for (j = 0; j < tmpitem->nattributes; j++)
            {
                AttrNumber  attnum = tmpitem->attributes[j];
 
                /*
                 * Thanks to how we constructed the matched bitmap above, we
                 * can just offset all attnums the same way.
                 */
                attnum = attnum + attnum_offset;
 
                if (!bms_is_member(attnum, matched))
                {
                    /* nah, it's not this item */
                    item = NULL;
                    break;
                }
            }
 
            /*
             * If the item has all the matched attributes, we know it's the
             * right one - there can't be a better one. matching more.
             */
            if (item)
                break;
        }
 
        /*
         * Make sure we found an item. There has to be one, because ndistinct
         * statistics includes all combinations of attributes.
         */
        if (!item)
            elog(ERROR, "corrupt MVNDistinct entry");
 
        /* Form the output varinfo list, keeping only unmatched ones */
        foreach(lc, *varinfos)
        {
            GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
            ListCell   *lc3;
            bool        found = false;
 
            /*
             * Let's look at plain variables first, because it's the most
             * common case and the check is quite cheap. We can simply get the
             * attnum and check (with an offset) matched bitmap.
             */
            if (IsA(varinfo->var, Var))
            {
                AttrNumber  attnum = ((Var *) varinfo->var)->varattno;
 
                /*
                 * If it's a system attribute, we're done. We don't support
                 * extended statistics on system attributes, so it's clearly
                 * not matched. Just keep the expression and continue.
                 */
                if (!AttrNumberIsForUserDefinedAttr(attnum))
                {
                    newlist = lappend(newlist, varinfo);
                    continue;
                }
 
                /* apply the same offset as above */
                attnum += attnum_offset;
 
                /* if it's not matched, keep the varinfo */
                if (!bms_is_member(attnum, matched))
                    newlist = lappend(newlist, varinfo);
 
                /* The rest of the loop deals with complex expressions. */
                continue;
            }
 
            /*
             * Process complex expressions, not just simple Vars.
             *
             * First, we search for an exact match of an expression. If we
             * find one, we can just discard the whole GroupVarInfo, with all
             * the variables we extracted from it.
             *
             * Otherwise we inspect the individual vars, and try matching it
             * to variables in the item.
             */
            foreach(lc3, matched_info->exprs)
            {
                Node       *expr = (Node *) lfirst(lc3);
 
                if (equal(varinfo->var, expr))
                {
                    found = true;
                    break;
                }
            }
 
            /* found exact match, skip */
            if (found)
                continue;
 
            newlist = lappend(newlist, varinfo);
        }
 
        *varinfos = newlist;
        *ndistinct = item->ndistinct;
        return true;
    }
 
    return false;
}

References Assert, attnum, MVNDistinctItem::attributes, AttrNumberIsForUserDefinedAttr, bms_add_member(), bms_is_member(), bms_num_members(), elog, equal(), ERROR, StatisticExtInfo::exprs, fb(), i, idx(), StatisticExtInfo::inherit, InvalidOid, IsA, MVNDistinct::items, j, StatisticExtInfo::keys, StatisticExtInfo::kind, lappend(), lfirst, list_length(), MVNDistinctItem::ndistinct, NIL, MVNDistinct::nitems, planner_rt_fetch, RelOptInfo::relid, root, statext_ndistinct_load(), RelOptInfo::statlist, and StatisticExtInfo::statOid.

Referenced by estimate_multivariate_bucketsize(), and estimate_num_groups().

◆ estimate_num_groups()

double estimate_num_groups	(	PlannerInfo *	root,
		List *	groupExprs,
		double	input_rows,
		List **	pgset,
		EstimationInfo *	estinfo
	)

Definition at line 3800 of file selfuncs.c.

{
    List       *varinfos = NIL;
    double      srf_multiplier = 1.0;
    double      numdistinct;
    ListCell   *l;
    int         i;
 
    /* Zero the estinfo output parameter, if non-NULL */
    if (estinfo != NULL)
        memset(estinfo, 0, sizeof(EstimationInfo));
 
    /*
     * 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 && *pgset == NIL))
        return 1.0;
 
    /*
     * Count groups derived from boolean grouping expressions.  For other
     * expressions, find the unique Vars used, treating an expression as a Var
     * if we can find stats for it.  For each one, record the statistical
     * estimate of number of distinct values (total in its table, without
     * regard for filtering).
     */
    numdistinct = 1.0;
 
    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(root, 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.
         *
         * XXX This has the consequence that if there's a statistics object on
         * the expression, we don't split it into individual Vars. This
         * affects our selection of statistics in
         * estimate_multivariate_ndistinct, because it's probably better to
         * use more accurate estimate for each expression and treat them as
         * independent, than to combine estimates for the extracted variables
         * when we don't know how that relates to the expressions.
         */
        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 = lappend(relvarinfos, varinfo1);
        for_each_from(l, varinfos, 1)
        {
            GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
 
            if (varinfo2->rel == varinfo1->rel)
            {
                /* varinfos on current rel */
                relvarinfos = lappend(relvarinfos, varinfo2);
            }
            else
            {
                /* not time to process varinfo2 yet */
                newvarinfos = lappend(newvarinfos, varinfo2);
            }
        }
 
        /*
         * 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++;
 
                    /*
                     * When varinfo2's isdefault is set then we'd better set
                     * the SELFLAG_USED_DEFAULT bit in the EstimationInfo.
                     */
                    if (estinfo != NULL && varinfo2->isdefault)
                        estinfo->flags |= SELFLAG_USED_DEFAULT;
                }
 
                /* 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;
}

References add_unique_group_var(), Assert, clamp_row_est(), contain_volatile_functions(), estimate_multivariate_ndistinct(), examine_variable(), expression_returns_set_rows(), exprType(), fb(), for_each_from, HeapTupleIsValid, i, IS_SIMPLE_REL, lappend(), lfirst, linitial, list_member_int(), GroupVarInfo::ndistinct, NIL, pull_var_clause(), PVC_RECURSE_AGGREGATES, PVC_RECURSE_PLACEHOLDERS, PVC_RECURSE_WINDOWFUNCS, ReleaseVariableStats, root, RelOptInfo::rows, SELFLAG_USED_DEFAULT, and RelOptInfo::tuples.

Referenced by adjust_rowcount_for_semijoins(), build_setop_child_paths(), cost_incremental_sort(), cost_memoize_rescan(), create_final_distinct_paths(), create_final_unique_paths(), create_partial_distinct_paths(), create_partial_unique_paths(), create_rel_agg_info(), estimate_path_cost_size(), generate_grouped_paths(), generate_union_paths(), get_number_of_groups(), and get_windowclause_startup_tuples().

◆ examine_indexcol_variable()

static void examine_indexcol_variable	(	PlannerInfo *	root,
		IndexOptInfo *	index,
		int	indexcol,
		VariableStatData *	vardata
	)

static

Definition at line 6508 of file selfuncs.c.

{
    AttrNumber  colnum;
    Oid         relid;
 
    if (index->indexkeys[indexcol] != 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[indexcol];
        vardata->rel = index->rel;
 
        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 = indexcol + 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;
        }
    }
}

References Assert, BoolGetDatum(), elog, ERROR, fb(), get_index_stats_hook, get_relation_stats_hook, HeapTupleIsValid, Int16GetDatum(), InvalidOid, ObjectIdGetDatum(), planner_rt_fetch, ReleaseSysCache(), root, RTE_RELATION, and SearchSysCache3().

Referenced by btcostestimate().

◆ examine_simple_variable()

static void examine_simple_variable	(	PlannerInfo *	root,
		Var *	var,
		VariableStatData *	vardata
	)

static

Definition at line 6037 of file selfuncs.c.

{
    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))
        {
            /*
             * Test if user has permission to read all rows from this column.
             *
             * This requires that the user has the appropriate SELECT
             * privileges and that there are no securityQuals from security
             * barrier views or RLS policies.  If that's not the case, then we
             * only permit leakproof functions to be passed pg_statistic data
             * in vardata, otherwise the functions might reveal data that the
             * user doesn't have permission to see --- see
             * statistic_proc_security_check().
             */
            vardata->acl_ok =
                all_rows_selectable(root, var->varno,
                                    bms_make_singleton(var->varattno - FirstLowInvalidHeapAttributeNumber));
        }
        else
        {
            /* suppress any possible leakproofness checks later */
            vardata->acl_ok = true;
        }
    }
    else if ((rte->rtekind == RTE_SUBQUERY && !rte->inh) ||
             (rte->rtekind == RTE_CTE && !rte->self_reference))
    {
        /*
         * Plain subquery (not one that was converted to an appendrel) or
         * non-recursive CTE.  In either case, we can try to find out what the
         * Var refers to within the subquery.  We skip this for appendrel and
         * recursive-CTE cases because any column stats we did find would
         * likely not be very relevant.
         */
        PlannerInfo *subroot;
        Query      *subquery;
        List       *subtlist;
        TargetEntry *ste;
 
        /*
         * Punt if it's a whole-row var rather than a plain column reference.
         */
        if (var->varattno == InvalidAttrNumber)
            return;
 
        /*
         * Otherwise, find the subquery's planner subroot.
         */
        if (rte->rtekind == RTE_SUBQUERY)
        {
            RelOptInfo *rel;
 
            /*
             * 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);
 
            subroot = rel->subroot;
        }
        else
        {
            /* CTE case is more difficult */
            PlannerInfo *cteroot;
            Index       levelsup;
            int         ndx;
            int         plan_id;
            ListCell   *lc;
 
            /*
             * Find the referenced CTE, and locate the subroot previously made
             * for it.
             */
            levelsup = rte->ctelevelsup;
            cteroot = root;
            while (levelsup-- > 0)
            {
                cteroot = cteroot->parent_root;
                if (!cteroot)   /* shouldn't happen */
                    elog(ERROR, "bad levelsup for CTE \"%s\"", rte->ctename);
            }
 
            /*
             * Note: cte_plan_ids can be shorter than cteList, if we are still
             * working on planning the CTEs (ie, this is a side-reference from
             * another CTE).  So we mustn't use forboth here.
             */
            ndx = 0;
            foreach(lc, cteroot->parse->cteList)
            {
                CommonTableExpr *cte = (CommonTableExpr *) lfirst(lc);
 
                if (strcmp(cte->ctename, rte->ctename) == 0)
                    break;
                ndx++;
            }
            if (lc == NULL)     /* shouldn't happen */
                elog(ERROR, "could not find CTE \"%s\"", rte->ctename);
            if (ndx >= list_length(cteroot->cte_plan_ids))
                elog(ERROR, "could not find plan for CTE \"%s\"", rte->ctename);
            plan_id = list_nth_int(cteroot->cte_plan_ids, ndx);
            if (plan_id <= 0)
                elog(ERROR, "no plan was made for CTE \"%s\"", rte->ctename);
            subroot = list_nth(root->glob->subroots, plan_id - 1);
        }
 
        /* If the subquery hasn't been planned yet, we have to punt */
        if (subroot == NULL)
            return;
        Assert(IsA(subroot, PlannerInfo));
 
        /*
         * We must use the subquery parsetree as mangled by the planner, not
         * the raw version from the RTE, because 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 = subroot->parse;
        Assert(IsA(subquery, Query));
 
        /*
         * Punt if subquery uses set operations or grouping sets, 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->groupingSets)
            return;
 
        /* Get the subquery output expression referenced by the upper Var */
        if (subquery->returningList)
            subtlist = subquery->returningList;
        else
            subtlist = subquery->targetList;
        ste = get_tle_by_resno(subtlist, 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;
        }
 
        /* The same idea as with DISTINCT clause works for a GROUP-BY too */
        if (subquery->groupClause)
        {
            if (list_length(subquery->groupClause) == 1 &&
                targetIsInSortList(ste, InvalidOid, subquery->groupClause))
                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(subroot, var, vardata);
        }
    }
    else
    {
        /*
         * Otherwise, the Var comes from a FUNCTION or VALUES 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.
         */
    }
}

References all_rows_selectable(), Assert, bms_make_singleton(), BoolGetDatum(), CommonTableExpr::ctename, Query::distinctClause, elog, ERROR, examine_simple_variable(), fb(), find_base_rel(), FirstLowInvalidHeapAttributeNumber, get_relation_stats_hook, get_tle_by_resno(), Query::groupClause, Query::groupingSets, HeapTupleIsValid, Int16GetDatum(), InvalidAttrNumber, InvalidOid, IsA, lfirst, list_length(), list_nth(), list_nth_int(), ObjectIdGetDatum(), PlannerInfo::parse, ReleaseSysCache(), Query::returningList, root, RTE_CTE, RTE_RELATION, RTE_SUBQUERY, SearchSysCache3(), Query::setOperations, RelOptInfo::subroot, targetIsInSortList(), Query::targetList, Var::varattno, Var::varlevelsup, and Var::varno.

Referenced by examine_simple_variable(), and examine_variable().

◆ examine_variable()

void examine_variable	(	PlannerInfo *	root,
		Node *	node,
		int	varRelid,
		VariableStatData *	vardata
	)

Definition at line 5641 of file selfuncs.c.

{
    Node       *basenode;
    Relids      varnos;
    Relids      basevarnos;
    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);
 
    /*
     * PlaceHolderVars are transparent for the purpose of statistics lookup;
     * they do not alter the value distribution of the underlying expression.
     * However, they can obscure the structure, preventing us from recognizing
     * matches to base columns, index expressions, or extended statistics.  So
     * strip them out first.
     */
    basenode = strip_all_phvs_deep(root, node);
 
    /*
     * Look inside any binary-compatible relabeling.  We need to handle nested
     * RelabelType nodes here, because the prior stripping of PlaceHolderVars
     * may have brought separate RelabelTypes into adjacency.
     */
    while (IsA(basenode, RelabelType))
        basenode = (Node *) ((RelabelType *) basenode)->arg;
 
    /* 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 phvs or 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(root, basenode);
    basevarnos = bms_difference(varnos, root->outer_join_rels);
 
    onerel = NULL;
 
    if (bms_is_empty(basevarnos))
    {
        /* No Vars at all ... must be pseudo-constant clause */
    }
    else
    {
        int         relid;
 
        /* Check if the expression is in vars of a single base relation */
        if (bms_get_singleton_member(basevarnos, &relid))
        {
            if (varRelid == 0 || varRelid == relid)
            {
                onerel = find_base_rel(root, relid);
                vardata->rel = onerel;
                node = basenode;    /* strip any phvs or relabeling */
            }
            /* else treat it as a constant */
        }
        else
        {
            /* varnos has multiple relids */
            if (varRelid == 0)
            {
                /* treat it as a variable of a join relation */
                vardata->rel = find_join_rel(root, varnos);
                node = basenode;    /* strip any phvs or 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 phvs or relabeling */
                /* note: no point in expressional-index search here */
            }
            /* else treat it as a constant */
        }
    }
 
    bms_free(basevarnos);
 
    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.
         *
         * Note that we consider all index columns including INCLUDE columns,
         * since there could be stats for such columns.  But the test for
         * uniqueness needs to be warier.
         *
         * 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;
        ListCell   *slist;
 
        /*
         * The nullingrels bits within the expression could prevent us from
         * matching it to expressional index columns or to the expressions in
         * extended statistics.  So strip them out first.
         */
        if (bms_overlap(varnos, root->outer_join_rels))
            node = remove_nulling_relids(node, root->outer_join_rels, NULL);
 
        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 &&
                            pos == 0 &&
                            (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))
                            {
                                /*
                                 * Test if user has permission to access all
                                 * rows from the index's table.
                                 *
                                 * For simplicity, we insist on the whole
                                 * table being selectable, rather than trying
                                 * to identify which column(s) the index
                                 * depends on.
                                 *
                                 * Note that for an inheritance child,
                                 * permissions are checked on the inheritance
                                 * root parent, and whole-table select
                                 * privilege on the parent doesn't quite
                                 * guarantee that the user could read all
                                 * columns of the child.  But in practice it's
                                 * unlikely that any interesting security
                                 * violation could result from allowing access
                                 * to the expression index's stats, so we
                                 * allow it anyway.  See similar code in
                                 * examine_simple_variable() for additional
                                 * comments.
                                 */
                                vardata->acl_ok =
                                    all_rows_selectable(root,
                                                        index->rel->relid,
                                                        NULL);
                            }
                            else
                            {
                                /* suppress leakproofness checks later */
                                vardata->acl_ok = true;
                            }
                        }
                        if (vardata->statsTuple)
                            break;
                    }
                    indexpr_item = lnext(index->indexprs, indexpr_item);
                }
            }
            if (vardata->statsTuple)
                break;
        }
 
        /*
         * Search extended statistics for one with a matching expression.
         * There might be multiple ones, so just grab the first one. In the
         * future, we might consider the statistics target (and pick the most
         * accurate statistics) and maybe some other parameters.
         */
        foreach(slist, onerel->statlist)
        {
            StatisticExtInfo *info = (StatisticExtInfo *) lfirst(slist);
            RangeTblEntry *rte = planner_rt_fetch(onerel->relid, root);
            ListCell   *expr_item;
            int         pos;
 
            /*
             * Stop once we've found statistics for the expression (either
             * from extended stats, or for an index in the preceding loop).
             */
            if (vardata->statsTuple)
                break;
 
            /* skip stats without per-expression stats */
            if (info->kind != STATS_EXT_EXPRESSIONS)
                continue;
 
            /* skip stats with mismatching stxdinherit value */
            if (info->inherit != rte->inh)
                continue;
 
            pos = 0;
            foreach(expr_item, info->exprs)
            {
                Node       *expr = (Node *) lfirst(expr_item);
 
                Assert(expr);
 
                /* strip RelabelType before comparing it */
                if (expr && IsA(expr, RelabelType))
                    expr = (Node *) ((RelabelType *) expr)->arg;
 
                /* found a match, see if we can extract pg_statistic row */
                if (equal(node, expr))
                {
                    /*
                     * XXX Not sure if we should cache the tuple somewhere.
                     * Now we just create a new copy every time.
                     */
                    vardata->statsTuple =
                        statext_expressions_load(info->statOid, rte->inh, pos);
 
                    /* Nothing to release if no data found */
                    if (vardata->statsTuple != NULL)
                    {
                        vardata->freefunc = ReleaseDummy;
                    }
 
                    /*
                     * Test if user has permission to access all rows from the
                     * table.
                     *
                     * For simplicity, we insist on the whole table being
                     * selectable, rather than trying to identify which
                     * column(s) the statistics object depends on.
                     *
                     * Note that for an inheritance child, permissions are
                     * checked on the inheritance root parent, and whole-table
                     * select privilege on the parent doesn't quite guarantee
                     * that the user could read all columns of the child.  But
                     * in practice it's unlikely that any interesting security
                     * violation could result from allowing access to the
                     * expression stats, so we allow it anyway.  See similar
                     * code in examine_simple_variable() for additional
                     * comments.
                     */
                    vardata->acl_ok = all_rows_selectable(root,
                                                          onerel->relid,
                                                          NULL);
 
                    break;
                }
 
                pos++;
            }
        }
    }
 
    bms_free(varnos);
}

References all_rows_selectable(), arg, Assert, bms_difference(), bms_free(), bms_get_singleton_member(), bms_is_empty, bms_is_member(), bms_overlap(), BoolGetDatum(), elog, equal(), ERROR, examine_simple_variable(), StatisticExtInfo::exprs, exprType(), exprTypmod(), fb(), find_base_rel(), find_join_rel(), get_index_stats_hook, has_unique_index(), HeapTupleIsValid, StatisticExtInfo::inherit, Int16GetDatum(), IsA, StatisticExtInfo::kind, lfirst, list_head(), lnext(), MemSet, NIL, ObjectIdGetDatum(), planner_rt_fetch, pull_varnos(), ReleaseDummy(), ReleaseSysCache(), remove_nulling_relids(), root, SearchSysCache3(), statext_expressions_load(), StatisticExtInfo::statOid, strip_all_phvs_deep(), Var::varattno, and Var::varno.

Referenced by booltestsel(), boolvarsel(), estimate_array_length(), estimate_hash_bucket_stats(), estimate_num_groups(), get_join_variables(), get_restriction_variable(), mergejoinscansel(), nulltestsel(), and scalararraysel_containment().

◆ find_join_input_rel()

static RelOptInfo * find_join_input_rel	(	PlannerInfo *	root,
		Relids	relids
	)

static

Definition at line 7289 of file selfuncs.c.

{
    RelOptInfo *rel = NULL;
 
    if (!bms_is_empty(relids))
    {
        int         relid;
 
        if (bms_get_singleton_member(relids, &relid))
            rel = find_base_rel(root, relid);
        else
            rel = find_join_rel(root, relids);
    }
 
    if (rel == NULL)
        elog(ERROR, "could not find RelOptInfo for given relids");
 
    return rel;
}

References bms_get_singleton_member(), bms_is_empty, elog, ERROR, fb(), find_base_rel(), find_join_rel(), and root.

Referenced by eqjoinsel().

◆ generic_restriction_selectivity()

double generic_restriction_selectivity	(	PlannerInfo *	root,
		Oid	oproid,
		Oid	collation,
		List *	args,
		int	varRelid,
		double	default_selectivity
	)

Definition at line 987 of file selfuncs.c.

{
    double      selec;
    VariableStatData vardata;
    Node       *other;
    bool        varonleft;
 
    /*
     * If expression is not variable OP something or something OP variable,
     * then punt and return the default estimate.
     */
    if (!get_restriction_variable(root, args, varRelid,
                                  &vardata, &other, &varonleft))
        return default_selectivity;
 
    /*
     * If the something is a NULL constant, assume operator is strict and
     * return zero, ie, operator will never return TRUE.
     */
    if (IsA(other, Const) &&
        ((Const *) other)->constisnull)
    {
        ReleaseVariableStats(vardata);
        return 0.0;
    }
 
    if (IsA(other, Const))
    {
        /* Variable is being compared to a known non-null constant */
        Datum       constval = ((Const *) other)->constvalue;
        FmgrInfo    opproc;
        double      mcvsum;
        double      mcvsel;
        double      nullfrac;
        int         hist_size;
 
        fmgr_info(get_opcode(oproid), &opproc);
 
        /*
         * Calculate the selectivity for the column's most common values.
         */
        mcvsel = mcv_selectivity(&vardata, &opproc, collation,
                                 constval, varonleft,
                                 &mcvsum);
 
        /*
         * If the histogram is large enough, see what fraction of it matches
         * the query, and assume that's representative of the non-MCV
         * population.  Otherwise use the default selectivity for the non-MCV
         * population.
         */
        selec = histogram_selectivity(&vardata, &opproc, collation,
                                      constval, varonleft,
                                      10, 1, &hist_size);
        if (selec < 0)
        {
            /* Nope, fall back on default */
            selec = default_selectivity;
        }
        else if (hist_size < 100)
        {
            /*
             * For histogram sizes from 10 to 100, we combine the histogram
             * and default selectivities, putting increasingly more trust in
             * the histogram for larger sizes.
             */
            double      hist_weight = hist_size / 100.0;
 
            selec = selec * hist_weight +
                default_selectivity * (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;
 
        /* Don't forget to account for nulls. */
        if (HeapTupleIsValid(vardata.statsTuple))
            nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
        else
            nullfrac = 0.0;
 
        /*
         * Now merge the results from the MCV and histogram calculations,
         * realizing that the histogram covers only the non-null values that
         * are not listed in MCV.
         */
        selec *= 1.0 - nullfrac - mcvsum;
        selec += mcvsel;
    }
    else
    {
        /* Comparison value is not constant, so we can't do anything */
        selec = default_selectivity;
    }
 
    ReleaseVariableStats(vardata);
 
    /* result should be in range, but make sure... */
    CLAMP_PROBABILITY(selec);
 
    return selec;
}

References CLAMP_PROBABILITY, fb(), fmgr_info(), get_opcode(), get_restriction_variable(), GETSTRUCT(), HeapTupleIsValid, histogram_selectivity(), IsA, mcv_selectivity(), ReleaseVariableStats, and root.

Referenced by ltreeparentsel(), and matchingsel().

◆ genericcostestimate()

void genericcostestimate	(	PlannerInfo *	root,
		IndexPath *	path,
		double	loop_count,
		GenericCosts *	costs
	)

Definition at line 7410 of file selfuncs.c.

{
    IndexOptInfo *index = path->indexinfo;
    List       *indexQuals = get_quals_from_indexclauses(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_index_quals(index, indexQuals);
 
    /*
     * If caller didn't give us an estimate for ScalarArrayOpExpr index scans,
     * just assume that the number of index descents is the number of distinct
     * combinations of array elements from all of the scan's SAOP clauses.
     */
    num_sa_scans = costs->num_sa_scans;
    if (num_sa_scans < 1)
    {
        num_sa_scans = 1;
        foreach(l, indexQuals)
        {
            RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
 
            if (IsA(rinfo->clause, ScalarArrayOpExpr))
            {
                ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
                double      alength = estimate_array_length(root, 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 leaf pages.  We disregard any overhead such as index
     * metapages 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 > costs->numNonLeafPages && index->tuples > 1)
        numIndexPages =
            ceil(numIndexTuples * (index->pages - costs->numNonLeafPages)
                 / 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 = index_other_operands_eval_cost(root, indexQuals) +
        index_other_operands_eval_cost(root, indexOrderBys);
    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;
}

References add_predicate_to_index_quals(), ScalarArrayOpExpr::args, RestrictInfo::clause, clauselist_selectivity(), cpu_index_tuple_cost, cpu_operator_cost, estimate_array_length(), fb(), get_quals_from_indexclauses(), get_tablespace_page_costs(), index_other_operands_eval_cost(), index_pages_fetched(), IndexPath::indexclauses, GenericCosts::indexCorrelation, IndexPath::indexinfo, IndexPath::indexorderbys, GenericCosts::indexSelectivity, GenericCosts::indexStartupCost, GenericCosts::indexTotalCost, IsA, JOIN_INNER, lfirst, list_length(), lsecond, GenericCosts::num_sa_scans, GenericCosts::numIndexPages, GenericCosts::numIndexTuples, GenericCosts::numNonLeafPages, root, and GenericCosts::spc_random_page_cost.

Referenced by blcostestimate(), btcostestimate(), gistcostestimate(), hashcostestimate(), and spgcostestimate().

◆ get_actual_variable_endpoint()

static bool get_actual_variable_endpoint	(	Relation	heapRel,
		Relation	indexRel,
		ScanDirection	indexscandir,
		ScanKey	scankeys,
		int16	typLen,
		bool	typByVal,
		TupleTableSlot *	tableslot,
		MemoryContext	outercontext,
		Datum *	endpointDatum
	)

static

Definition at line 7123 of file selfuncs.c.

{
    bool        have_data = false;
    SnapshotData SnapshotNonVacuumable;
    IndexScanDesc index_scan;
    Buffer      vmbuffer = InvalidBuffer;
    BlockNumber last_heap_block = InvalidBlockNumber;
    int         n_visited_heap_pages = 0;
    ItemPointer tid;
    Datum       values[INDEX_MAX_KEYS];
    bool        isnull[INDEX_MAX_KEYS];
    MemoryContext oldcontext;
 
    /*
     * We use the index-only-scan machinery for this.  With mostly-static
     * tables that's a win because it avoids a heap visit.  It's also a win
     * for dynamic data, but the reason is less obvious; read on for details.
     *
     * 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
     * GlobalVisTestFor(heapRel) 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 (or more precisely, all tuples of a HOT chain) 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_endpoint() call will not have to re-consider that
     * index entry.  In this way we avoid repetitive work when this function
     * is used a lot during planning.
     *
     * But using SnapshotNonVacuumable creates a hazard of its own.  In a
     * recently-created index, some index entries may point at "broken" HOT
     * chains in which not all the tuple versions contain data matching the
     * index entry.  The live tuple version(s) certainly do match the index,
     * but SnapshotNonVacuumable can accept recently-dead tuple versions that
     * don't match.  Hence, if we took data from the selected heap tuple, we
     * might get a bogus answer that's not close to the index extremal value,
     * or could even be NULL.  We avoid this hazard because we take the data
     * from the index entry not the heap.
     *
     * Despite all this care, there are situations where we might find many
     * non-visible tuples near the end of the index.  We don't want to expend
     * a huge amount of time here, so we give up once we've read too many heap
     * pages.  When we fail for that reason, the caller will end up using
     * whatever extremal value is recorded in pg_statistic.
     */
    InitNonVacuumableSnapshot(SnapshotNonVacuumable,
                              GlobalVisTestFor(heapRel));
 
    index_scan = index_beginscan(heapRel, indexRel,
                                 &SnapshotNonVacuumable, NULL,
                                 1, 0,
                                 SO_NONE);
    /* Set it up for index-only scan */
    index_scan->xs_want_itup = true;
    index_rescan(index_scan, scankeys, 1, NULL, 0);
 
    /* Fetch first/next tuple in specified direction */
    while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
    {
        BlockNumber block = ItemPointerGetBlockNumber(tid);
 
        if (!VM_ALL_VISIBLE(heapRel,
                            block,
                            &vmbuffer))
        {
            /* Rats, we have to visit the heap to check visibility */
            if (!index_fetch_heap(index_scan, tableslot))
            {
                /*
                 * No visible tuple for this index entry, so we need to
                 * advance to the next entry.  Before doing so, count heap
                 * page fetches and give up if we've done too many.
                 *
                 * We don't charge a page fetch if this is the same heap page
                 * as the previous tuple.  This is on the conservative side,
                 * since other recently-accessed pages are probably still in
                 * buffers too; but it's good enough for this heuristic.
                 */
#define VISITED_PAGES_LIMIT 100
 
                if (block != last_heap_block)
                {
                    last_heap_block = block;
                    n_visited_heap_pages++;
                    if (n_visited_heap_pages > VISITED_PAGES_LIMIT)
                        break;
                }
 
                continue;       /* no visible tuple, try next index entry */
            }
 
            /* We don't actually need the heap tuple for anything */
            ExecClearTuple(tableslot);
 
            /*
             * We don't care whether there's more than one visible tuple in
             * the HOT chain; if any are visible, that's good enough.
             */
        }
 
        /*
         * We expect that the index will return data in IndexTuple not
         * HeapTuple format.
         */
        if (!index_scan->xs_itup)
            elog(ERROR, "no data returned for index-only scan");
 
        /*
         * We do not yet support recheck here.
         */
        if (index_scan->xs_recheck)
            break;
 
        /* OK to deconstruct the index tuple */
        index_deform_tuple(index_scan->xs_itup,
                           index_scan->xs_itupdesc,
                           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 */
        oldcontext = MemoryContextSwitchTo(outercontext);
        *endpointDatum = datumCopy(values[0], typByVal, typLen);
        MemoryContextSwitchTo(oldcontext);
        have_data = true;
        break;
    }
 
    if (vmbuffer != InvalidBuffer)
        ReleaseBuffer(vmbuffer);
    index_endscan(index_scan);
 
    return have_data;
}

References datumCopy(), elog, ERROR, ExecClearTuple(), fb(), GlobalVisTestFor(), index_beginscan(), index_deform_tuple(), index_endscan(), index_fetch_heap(), index_getnext_tid(), INDEX_MAX_KEYS, index_rescan(), InitNonVacuumableSnapshot, InvalidBlockNumber, InvalidBuffer, ItemPointerGetBlockNumber(), MemoryContextSwitchTo(), RelationGetRelationName, ReleaseBuffer(), SO_NONE, values, VISITED_PAGES_LIMIT, and VM_ALL_VISIBLE.

Referenced by get_actual_variable_range().

◆ get_actual_variable_range()

static bool get_actual_variable_range	(	PlannerInfo *	root,
		VariableStatData *	vardata,
		Oid	sortop,
		Oid	collation,
		Datum *	min,
		Datum *	max
	)

static

Definition at line 6934 of file selfuncs.c.

{
    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);
 
    /* ignore partitioned tables.  Any indexes here are not real indexes */
    if (rte->relkind == RELKIND_PARTITIONED_TABLE)
        return false;
 
    /* Search through the indexes to see if any match our problem */
    foreach(lc, rel->indexlist)
    {
        IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
        ScanDirection indexscandir;
        StrategyNumber strategy;
 
        /* Ignore non-ordering indexes */
        if (index->sortopfamily == NULL)
            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;
 
        /*
         * get_actual_variable_endpoint uses the index-only-scan machinery, so
         * ignore indexes that can't use it on their first column.
         */
        if (!index->canreturn[0])
            continue;
 
        /*
         * The first index column must match the desired variable, sortop, and
         * collation --- but we can use a descending-order index.
         */
        if (collation != index->indexcollations[0])
            continue;           /* test first 'cause it's cheapest */
        if (!match_index_to_operand(vardata->var, 0, index))
            continue;
        strategy = get_op_opfamily_strategy(sortop, index->sortopfamily[0]);
        switch (IndexAmTranslateStrategy(strategy, index->relam, index->sortopfamily[0], true))
        {
            case COMPARE_LT:
                if (index->reverse_sort[0])
                    indexscandir = BackwardScanDirection;
                else
                    indexscandir = ForwardScanDirection;
                break;
            case COMPARE_GT:
                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.  Set up some data that
         * can be used by both invocations of get_actual_variable_endpoint.
         */
        {
            MemoryContext tmpcontext;
            MemoryContext oldcontext;
            Relation    heapRel;
            Relation    indexRel;
            TupleTableSlot *slot;
            int16       typLen;
            bool        typByVal;
            ScanKeyData scankeys[1];
 
            /* Make sure any cruft gets recycled when we're done */
            tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
                                               "get_actual_variable_range workspace",
                                               ALLOCSET_DEFAULT_SIZES);
            oldcontext = MemoryContextSwitchTo(tmpcontext);
 
            /*
             * Open the table and index so we can read from them.  We should
             * already have some type of lock on each.
             */
            heapRel = table_open(rte->relid, NoLock);
            indexRel = index_open(index->indexoid, NoLock);
 
            /* build some stuff needed for indexscan execution */
            slot = table_slot_create(heapRel, NULL);
            get_typlenbyval(vardata->atttype, &typLen, &typByVal);
 
            /* 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 */
 
            /* If min is requested ... */
            if (min)
            {
                have_data = get_actual_variable_endpoint(heapRel,
                                                         indexRel,
                                                         indexscandir,
                                                         scankeys,
                                                         typLen,
                                                         typByVal,
                                                         slot,
                                                         oldcontext,
                                                         min);
            }
            else
            {
                /* If min not requested, still want to fetch max */
                have_data = true;
            }
 
            /* If max is requested, and we didn't already fail ... */
            if (max && have_data)
            {
                /* scan in the opposite direction; all else is the same */
                have_data = get_actual_variable_endpoint(heapRel,
                                                         indexRel,
                                                         -indexscandir,
                                                         scankeys,
                                                         typLen,
                                                         typByVal,
                                                         slot,
                                                         oldcontext,
                                                         max);
            }
 
            /* Clean everything up */
            ExecDropSingleTupleTableSlot(slot);
 
            index_close(indexRel, NoLock);
            table_close(heapRel, NoLock);
 
            MemoryContextSwitchTo(oldcontext);
            MemoryContextDelete(tmpcontext);
 
            /* And we're done */
            break;
        }
    }
 
    return have_data;
}

References ALLOCSET_DEFAULT_SIZES, AllocSetContextCreate, Assert, BackwardScanDirection, COMPARE_GT, COMPARE_LT, CurrentMemoryContext, ExecDropSingleTupleTableSlot(), fb(), ForwardScanDirection, get_actual_variable_endpoint(), get_op_opfamily_strategy(), get_typlenbyval(), index_close(), index_open(), IndexAmTranslateStrategy(), RelOptInfo::indexlist, InvalidOid, InvalidStrategy, lfirst, match_index_to_operand(), MemoryContextDelete(), MemoryContextSwitchTo(), NIL, NoLock, RelOptInfo::relid, root, RTE_RELATION, ScanKeyEntryInitialize(), SK_ISNULL, SK_SEARCHNOTNULL, table_close(), table_open(), and table_slot_create().

Referenced by get_variable_range(), and ineq_histogram_selectivity().

◆ get_join_variables()

void get_join_variables	(	PlannerInfo *	root,
		List *	args,
		SpecialJoinInfo *	sjinfo,
		VariableStatData *	vardata1,
		VariableStatData *	vardata2,
		bool *	join_is_reversed
	)

Definition at line 5572 of file selfuncs.c.

{
    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;
}

References bms_is_subset(), elog, ERROR, examine_variable(), fb(), linitial, list_length(), lsecond, root, SpecialJoinInfo::syn_lefthand, and SpecialJoinInfo::syn_righthand.

Referenced by eqjoinsel(), neqjoinsel(), and networkjoinsel().

◆ get_quals_from_indexclauses()

List * get_quals_from_indexclauses ( List * indexclauses )

Definition at line 7321 of file selfuncs.c.

{
    List       *result = NIL;
    ListCell   *lc;
 
    foreach(lc, indexclauses)
    {
        IndexClause *iclause = lfirst_node(IndexClause, lc);
        ListCell   *lc2;
 
        foreach(lc2, iclause->indexquals)
        {
            RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
 
            result = lappend(result, rinfo);
        }
    }
    return result;
}

References fb(), lappend(), lfirst_node, NIL, and result.

Referenced by brincostestimate(), genericcostestimate(), and gincostestimate().

◆ get_restriction_variable()

bool get_restriction_variable	(	PlannerInfo *	root,
		List *	args,
		int	varRelid,
		VariableStatData *	vardata,
		Node **	other,
		bool *	varonleft
	)

Definition at line 5512 of file selfuncs.c.

{
    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;
}

References estimate_expression_value(), examine_variable(), fb(), linitial, list_length(), lsecond, ReleaseVariableStats, and root.

Referenced by _int_matchsel(), arraycontsel(), eqsel_internal(), generic_restriction_selectivity(), multirangesel(), networksel(), patternsel_common(), rangesel(), scalarineqsel_wrapper(), and tsmatchsel().

◆ get_stats_slot_range()

static void get_stats_slot_range	(	AttStatsSlot *	sslot,
		Oid	opfuncoid,
		FmgrInfo *	opproc,
		Oid	collation,
		int16	typLen,
		bool	typByVal,
		Datum *	min,
		Datum *	max,
		bool *	p_have_data
	)

static

Definition at line 6871 of file selfuncs.c.

{
    Datum       tmin = *min;
    Datum       tmax = *max;
    bool        have_data = *p_have_data;
    bool        found_tmin = false;
    bool        found_tmax = false;
 
    /* Look up the comparison function, if we didn't already do so */
    if (opproc->fn_oid != opfuncoid)
        fmgr_info(opfuncoid, opproc);
 
    /* Scan all the slot's values */
    for (int i = 0; i < sslot->nvalues; i++)
    {
        if (!have_data)
        {
            tmin = tmax = sslot->values[i];
            found_tmin = found_tmax = true;
            *p_have_data = have_data = true;
            continue;
        }
        if (DatumGetBool(FunctionCall2Coll(opproc,
                                           collation,
                                           sslot->values[i], tmin)))
        {
            tmin = sslot->values[i];
            found_tmin = true;
        }
        if (DatumGetBool(FunctionCall2Coll(opproc,
                                           collation,
                                           tmax, sslot->values[i])))
        {
            tmax = sslot->values[i];
            found_tmax = true;
        }
    }
 
    /*
     * Copy the slot's values, if we found new extreme values.
     */
    if (found_tmin)
        *min = datumCopy(tmin, typByVal, typLen);
    if (found_tmax)
        *max = datumCopy(tmax, typByVal, typLen);
}

References datumCopy(), DatumGetBool(), fb(), fmgr_info(), FunctionCall2Coll(), and i.

Referenced by get_variable_range().

◆ get_variable_numdistinct()

double get_variable_numdistinct	(	VariableStatData *	vardata,
		bool *	isdefault
	)

Definition at line 6611 of file selfuncs.c.

{
    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, DISTINCT or GROUP-BY 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;
}

References clamp_row_est(), DEFAULT_NUM_DISTINCT, fb(), GETSTRUCT(), HeapTupleIsValid, IsA, RTE_VALUES, SelfItemPointerAttributeNumber, and TableOidAttributeNumber.

Referenced by add_unique_group_var(), btcostestimate(), eqjoinsel(), estimate_hash_bucket_stats(), ineq_histogram_selectivity(), var_eq_const(), and var_eq_non_const().

◆ get_variable_range()

static bool get_variable_range	(	PlannerInfo *	root,
		VariableStatData *	vardata,
		Oid	sortop,
		Oid	collation,
		Datum *	min,
		Datum *	max
	)

static

Definition at line 6744 of file selfuncs.c.

{
    Datum       tmin = 0;
    Datum       tmax = 0;
    bool        have_data = false;
    int16       typLen;
    bool        typByVal;
    Oid         opfuncoid;
    FmgrInfo    opproc;
    AttStatsSlot sslot;
 
    /*
     * 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, collation, 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;
 
    opproc.fn_oid = InvalidOid; /* mark this as not looked up yet */
 
    get_typlenbyval(vardata->atttype, &typLen, &typByVal);
 
    /*
     * If there is a histogram with the ordering we want, grab the first and
     * last values.
     */
    if (get_attstatsslot(&sslot, vardata->statsTuple,
                         STATISTIC_KIND_HISTOGRAM, sortop,
                         ATTSTATSSLOT_VALUES))
    {
        if (sslot.stacoll == collation && 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);
    }
 
    /*
     * Otherwise, if there is a histogram with some other ordering, scan it
     * and get the min and max values according to the ordering we want.  This
     * of course may not find values that are really extremal according to our
     * ordering, but it beats ignoring available data.
     */
    if (!have_data &&
        get_attstatsslot(&sslot, vardata->statsTuple,
                         STATISTIC_KIND_HISTOGRAM, InvalidOid,
                         ATTSTATSSLOT_VALUES))
    {
        get_stats_slot_range(&sslot, opfuncoid, &opproc,
                             collation, typLen, typByVal,
                             &tmin, &tmax, &have_data);
        free_attstatsslot(&sslot);
    }
 
    /*
     * 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, if we *only* have MCVs and no histogram, we should
     * be pretty wary of deciding that that is a full representation of the
     * data.  Proceed only if the MCVs represent the whole table (to within
     * roundoff error).
     */
    if (get_attstatsslot(&sslot, vardata->statsTuple,
                         STATISTIC_KIND_MCV, InvalidOid,
                         have_data ? ATTSTATSSLOT_VALUES :
                         (ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)))
    {
        bool        use_mcvs = have_data;
 
        if (!have_data)
        {
            double      sumcommon = 0.0;
            double      nullfrac;
            int         i;
 
            for (i = 0; i < sslot.nnumbers; i++)
                sumcommon += sslot.numbers[i];
            nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata->statsTuple))->stanullfrac;
            if (sumcommon + nullfrac > 0.99999)
                use_mcvs = true;
        }
 
        if (use_mcvs)
            get_stats_slot_range(&sslot, opfuncoid, &opproc,
                                 collation, typLen, typByVal,
                                 &tmin, &tmax, &have_data);
        free_attstatsslot(&sslot);
    }
 
    *min = tmin;
    *max = tmax;
    return have_data;
}

References ATTSTATSSLOT_NUMBERS, ATTSTATSSLOT_VALUES, datumCopy(), fb(), free_attstatsslot(), get_actual_variable_range(), get_attstatsslot(), get_opcode(), get_stats_slot_range(), get_typlenbyval(), GETSTRUCT(), HeapTupleIsValid, i, InvalidOid, root, and statistic_proc_security_check().

Referenced by mergejoinscansel().

◆ gincost_opexpr()

static bool gincost_opexpr	(	PlannerInfo *	root,
		IndexOptInfo *	index,
		int	indexcol,
		OpExpr *	clause,
		GinQualCounts *	counts
	)

static

Definition at line 8469 of file selfuncs.c.

{
    Oid         clause_op = clause->opno;
    Node       *operand = (Node *) lsecond(clause->args);
 
    /* 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);
}

References arg, OpExpr::args, estimate_expression_value(), GinQualCounts::exactEntries, fb(), gincost_pattern(), IsA, lsecond, OpExpr::opno, root, and GinQualCounts::searchEntries.

Referenced by gincostestimate().

◆ gincost_pattern()

static bool gincost_pattern	(	IndexOptInfo *	index,
		int	indexcol,
		Oid	clause_op,
		Datum	query,
		GinQualCounts *	counts
	)

static

Definition at line 8355 of file selfuncs.c.

{
    FmgrInfo    flinfo;
    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;
 
    fmgr_info(extractProcOid, &flinfo);
 
    set_fn_opclass_options(&flinfo, index->opclassoptions[indexcol]);
 
    FunctionCall7Coll(&flinfo,
                      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_DEFAULT)
    {
        counts->attHasNormalScan[indexcol] = true;
    }
    else if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
    {
        /* Treat "include empty" like an exact-match item */
        counts->attHasNormalScan[indexcol] = true;
        counts->exactEntries++;
        counts->searchEntries++;
    }
    else
    {
        /* It's GIN_SEARCH_MODE_ALL */
        counts->attHasFullScan[indexcol] = true;
    }
 
    return true;
}

References Assert, GinQualCounts::attHasFullScan, GinQualCounts::attHasNormalScan, elog, ERROR, GinQualCounts::exactEntries, fb(), fmgr_info(), FunctionCall7Coll(), get_op_opfamily_properties(), get_opfamily_proc(), get_rel_name(), GIN_EXTRACTQUERY_PROC, GIN_SEARCH_MODE_DEFAULT, GIN_SEARCH_MODE_INCLUDE_EMPTY, i, OidIsValid, GinQualCounts::partialEntries, PointerGetDatum, GinQualCounts::searchEntries, set_fn_opclass_options(), and UInt16GetDatum().

Referenced by gincost_opexpr(), and gincost_scalararrayopexpr().

◆ gincost_scalararrayopexpr()

static bool gincost_scalararrayopexpr	(	PlannerInfo *	root,
		IndexOptInfo *	index,
		int	indexcol,
		ScalarArrayOpExpr *	clause,
		double	numIndexEntries,
		GinQualCounts *	counts
	)

static

Definition at line 8519 of file selfuncs.c.

{
    Oid         clause_op = clause->opno;
    Node       *rightop = (Node *) lsecond(clause->args);
    ArrayType  *arrayval;
    int16       elmlen;
    bool        elmbyval;
    char        elmalign;
    int         numElems;
    Datum      *elemValues;
    bool       *elemNulls;
    GinQualCounts arraycounts;
    int         numPossible = 0;
    int         i;
 
    Assert(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(root, 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.attHasFullScan[indexcol] &&
                !elemcounts.attHasNormalScan[indexcol])
            {
                /*
                 * 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;
}

References arg, ScalarArrayOpExpr::args, ARR_ELEMTYPE, GinQualCounts::arrayScans, Assert, DatumGetArrayTypeP, deconstruct_array(), estimate_array_length(), estimate_expression_value(), GinQualCounts::exactEntries, fb(), get_typlenbyvalalign(), gincost_pattern(), i, IsA, lsecond, ScalarArrayOpExpr::opno, GinQualCounts::partialEntries, root, GinQualCounts::searchEntries, and ScalarArrayOpExpr::useOr.

Referenced by gincostestimate().

◆ gincostestimate()

void gincostestimate	(	PlannerInfo *	root,
		IndexPath *	path,
		double	loop_count,
		Cost *	indexStartupCost,
		Cost *	indexTotalCost,
		Selectivity *	indexSelectivity,
		double *	indexCorrelation,
		double *	indexPages
	)

Definition at line 8635 of file selfuncs.c.

{
    IndexOptInfo *index = path->indexinfo;
    List       *indexQuals = get_quals_from_indexclauses(path->indexclauses);
    List       *selectivityQuals;
    double      numPages = index->pages,
                numTuples = index->tuples;
    double      numEntryPages,
                numDataPages,
                numPendingPages,
                numEntries;
    GinQualCounts counts;
    bool        matchPossible;
    bool        fullIndexScan;
    double      partialScale;
    double      entryPagesFetched,
                dataPagesFetched,
                dataPagesFetchedBySel;
    double      qual_op_cost,
                qual_arg_cost,
                spc_random_page_cost,
                outer_scans;
    Cost        descentCost;
    Relation    indexRel;
    GinStatsData ginStats;
    ListCell   *lc;
    int         i;
 
    /*
     * Obtain statistical information from the meta page, if possible.  Else
     * set ginStats to zeroes, and we'll cope below.
     */
    if (!index->hypothetical)
    {
        /* Lock should have already been obtained in plancat.c */
        indexRel = index_open(index->indexoid, NoLock);
        ginGetStats(indexRel, &ginStats);
        index_close(indexRel, NoLock);
    }
    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_index_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(lc, path->indexclauses)
    {
        IndexClause *iclause = lfirst_node(IndexClause, lc);
        ListCell   *lc2;
 
        foreach(lc2, iclause->indexquals)
        {
            RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
            Expr       *clause = rinfo->clause;
 
            if (IsA(clause, OpExpr))
            {
                matchPossible = gincost_opexpr(root,
                                               index,
                                               iclause->indexcol,
                                               (OpExpr *) clause,
                                               &counts);
                if (!matchPossible)
                    break;
            }
            else if (IsA(clause, ScalarArrayOpExpr))
            {
                matchPossible = gincost_scalararrayopexpr(root,
                                                          index,
                                                          iclause->indexcol,
                                                          (ScalarArrayOpExpr *) clause,
                                                          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 attribute has a full scan and at the same time doesn't have normal
     * scan, then we'll have to scan all non-null entries of that attribute.
     * Currently, we don't have per-attribute statistics for GIN.  Thus, we
     * must assume the whole GIN index has to be scanned in this case.
     */
    fullIndexScan = false;
    for (i = 0; i < index->nkeycolumns; i++)
    {
        if (counts.attHasFullScan[i] && !counts.attHasNormalScan[i])
        {
            fullIndexScan = true;
            break;
        }
    }
 
    if (fullIndexScan || 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);
 
    *indexStartupCost = 0;
    *indexTotalCost = 0;
 
    /*
     * Add a CPU-cost component to represent the costs of initial entry 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 (numEntries > 1)         /* avoid computing log(0) */
    {
        descentCost = ceil(log(numEntries) / log(2.0)) * cpu_operator_cost;
        *indexStartupCost += descentCost * counts.searchEntries;
        *indexTotalCost += counts.arrayScans * descentCost * counts.searchEntries;
    }
 
    /*
     * Add a cpu cost per entry-page fetched. This is not amortized over a
     * loop.
     */
    *indexStartupCost += entryPagesFetched * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    *indexTotalCost += entryPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
 
    /*
     * Add a cpu cost per data-page fetched. This is also not amortized over a
     * loop. Since those are the data pages from the partial match algorithm,
     * charge them as startup cost.
     */
    *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * dataPagesFetched;
 
    /*
     * Since we add the startup cost to the total cost later on, remove the
     * initial arrayscan from the total.
     */
    *indexTotalCost += dataPagesFetched * (counts.arrayScans - 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
 
    /*
     * 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;
 
    /* Add one page cpu-cost to the startup cost */
    *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * counts.searchEntries;
 
    /*
     * Add once again a CPU-cost for those data pages, before amortizing for
     * cache.
     */
    *indexTotalCost += dataPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
 
    /* 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. We charge
     * cpu but we can disregard indexorderbys, since GIN doesn't support
     * those.
     */
    qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
    qual_op_cost = cpu_operator_cost * list_length(indexQuals);
 
    *indexStartupCost += qual_arg_cost;
    *indexTotalCost += qual_arg_cost;
 
    /*
     * Add a cpu cost per search entry, corresponding to the actual visited
     * entries.
     */
    *indexTotalCost += (counts.searchEntries * counts.arrayScans) * (qual_op_cost);
    /* Now add a cpu cost per tuple in the posting lists / trees */
    *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost);
    *indexPages = dataPagesFetched;
}

References add_predicate_to_index_quals(), GinQualCounts::arrayScans, GinQualCounts::attHasFullScan, GinQualCounts::attHasNormalScan, RestrictInfo::clause, clauselist_selectivity(), cpu_index_tuple_cost, cpu_operator_cost, DEFAULT_PAGE_CPU_MULTIPLIER, elog, ERROR, GinQualCounts::exactEntries, fb(), get_quals_from_indexclauses(), get_tablespace_page_costs(), gincost_opexpr(), gincost_scalararrayopexpr(), ginGetStats(), i, index_close(), index_open(), index_other_operands_eval_cost(), index_pages_fetched(), IndexPath::indexclauses, IndexPath::indexinfo, IsA, JOIN_INNER, lfirst_node, list_length(), Max, Min, NIL, nodeTag, NoLock, GinQualCounts::partialEntries, root, scale, and GinQualCounts::searchEntries.

Referenced by ginhandler().

◆ gistcostestimate()

void gistcostestimate	(	PlannerInfo *	root,
		IndexPath *	path,
		double	loop_count,
		Cost *	indexStartupCost,
		Cost *	indexTotalCost,
		Selectivity *	indexSelectivity,
		double *	indexCorrelation,
		double *	indexPages
	)

Definition at line 8220 of file selfuncs.c.

{
    IndexOptInfo *index = path->indexinfo;
    GenericCosts costs = {0};
    Cost        descentCost;
 
    /* GiST has no metapage, so we treat all pages as leaf pages */
 
    genericcostestimate(root, path, loop_count, &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) * DEFAULT_PAGE_CPU_MULTIPLIER * 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;
}

References cpu_operator_cost, DEFAULT_PAGE_CPU_MULTIPLIER, fb(), genericcostestimate(), GenericCosts::indexCorrelation, IndexPath::indexinfo, GenericCosts::indexSelectivity, GenericCosts::indexStartupCost, GenericCosts::indexTotalCost, GenericCosts::num_sa_scans, GenericCosts::numIndexPages, and root.

Referenced by gisthandler().

◆ hash_mcv()

static uint32 hash_mcv	(	MCVHashTable_hash *	tab,
		Datum	key
	)

static

Definition at line 3120 of file selfuncs.c.

{
    MCVHashContext *context = (MCVHashContext *) tab->private_data;
    FunctionCallInfo fcinfo = context->hash_fcinfo;
    Datum       fresult;
 
    fcinfo->args[0].value = key;
    fcinfo->isnull = false;
    fresult = FunctionCallInvoke(fcinfo);
    Assert(!fcinfo->isnull);
    return DatumGetUInt32(fresult);
}

References FunctionCallInfoBaseData::args, Assert, DatumGetUInt32(), fb(), FunctionCallInvoke, MCVHashContext::hash_fcinfo, FunctionCallInfoBaseData::isnull, and NullableDatum::value.

◆ hashcostestimate()

void hashcostestimate	(	PlannerInfo *	root,
		IndexPath *	path,
		double	loop_count,
		Cost *	indexStartupCost,
		Cost *	indexTotalCost,
		Selectivity *	indexSelectivity,
		double *	indexCorrelation,
		double *	indexPages
	)

Definition at line 8175 of file selfuncs.c.

{
    GenericCosts costs = {0};
 
    /* As in btcostestimate, count only the metapage as non-leaf */
    costs.numNonLeafPages = 1;
 
    genericcostestimate(root, path, loop_count, &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;
}

References fb(), genericcostestimate(), GenericCosts::indexCorrelation, GenericCosts::indexSelectivity, GenericCosts::indexStartupCost, GenericCosts::indexTotalCost, GenericCosts::numIndexPages, GenericCosts::numNonLeafPages, and root.

Referenced by hashhandler().

◆ histogram_selectivity()

double histogram_selectivity	(	VariableStatData *	vardata,
		FmgrInfo *	opproc,
		Oid	collation,
		Datum	constval,
		bool	varonleft,
		int	min_hist_size,
		int	n_skip,
		int *	hist_size
	)

Definition at line 896 of file selfuncs.c.

{
    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)
        {
            LOCAL_FCINFO(fcinfo, 2);
            int         nmatch = 0;
            int         i;
 
            /*
             * We invoke the opproc "by hand" so that we won't fail on NULL
             * results.  Such cases won't arise for normal comparison
             * functions, but generic_restriction_selectivity could perhaps be
             * used with operators that can return NULL.  A small side benefit
             * is to not need to re-initialize the fcinfo struct from scratch
             * each time.
             */
            InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
                                     NULL, NULL);
            fcinfo->args[0].isnull = false;
            fcinfo->args[1].isnull = false;
            /* be careful to apply operator right way 'round */
            if (varonleft)
                fcinfo->args[1].value = constval;
            else
                fcinfo->args[0].value = constval;
 
            for (i = n_skip; i < sslot.nvalues - n_skip; i++)
            {
                Datum       fresult;
 
                if (varonleft)
                    fcinfo->args[0].value = sslot.values[i];
                else
                    fcinfo->args[1].value = sslot.values[i];
                fcinfo->isnull = false;
                fresult = FunctionCallInvoke(fcinfo);
                if (!fcinfo->isnull && DatumGetBool(fresult))
                    nmatch++;
            }
            result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
        }
        else
            result = -1;
        free_attstatsslot(&sslot);
    }
    else
    {
        *hist_size = 0;
        result = -1;
    }
 
    return result;
}

References Assert, ATTSTATSSLOT_VALUES, DatumGetBool(), fb(), free_attstatsslot(), FunctionCallInvoke, get_attstatsslot(), HeapTupleIsValid, i, InitFunctionCallInfoData, InvalidOid, LOCAL_FCINFO, result, and statistic_proc_security_check().

Referenced by generic_restriction_selectivity(), and patternsel_common().

◆ index_other_operands_eval_cost()

Cost index_other_operands_eval_cost	(	PlannerInfo *	root,
		List *	indexquals
	)

Definition at line 7351 of file selfuncs.c.

{
    Cost        qual_arg_cost = 0;
    ListCell   *lc;
 
    foreach(lc, indexquals)
    {
        Expr       *clause = (Expr *) lfirst(lc);
        Node       *other_operand;
        QualCost    index_qual_cost;
 
        /*
         * Index quals will have RestrictInfos, indexorderbys won't.  Look
         * through RestrictInfo if present.
         */
        if (IsA(clause, RestrictInfo))
            clause = ((RestrictInfo *) clause)->clause;
 
        if (IsA(clause, OpExpr))
        {
            OpExpr     *op = (OpExpr *) clause;
 
            other_operand = (Node *) lsecond(op->args);
        }
        else if (IsA(clause, RowCompareExpr))
        {
            RowCompareExpr *rc = (RowCompareExpr *) clause;
 
            other_operand = (Node *) rc->rargs;
        }
        else if (IsA(clause, ScalarArrayOpExpr))
        {
            ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
 
            other_operand = (Node *) lsecond(saop->args);
        }
        else if (IsA(clause, NullTest))
        {
            other_operand = NULL;
        }
        else
        {
            elog(ERROR, "unsupported indexqual 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;
}

References OpExpr::args, ScalarArrayOpExpr::args, cost_qual_eval_node(), elog, ERROR, fb(), IsA, lfirst, lsecond, nodeTag, RowCompareExpr::rargs, and root.

Referenced by brincostestimate(), genericcostestimate(), and gincostestimate().

◆ ineq_histogram_selectivity()

double ineq_histogram_selectivity	(	PlannerInfo *	root,
		VariableStatData *	vardata,
		Oid	opoid,
		FmgrInfo *	opproc,
		bool	isgt,
		bool	iseq,
		Oid	collation,
		Datum	constval,
		Oid	consttype
	)

Definition at line 1114 of file selfuncs.c.

{
    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.  Right now, we know there is only one, so just grab it and
     * see if it matches the query.
     *
     * Note that we can't use opoid as search argument; the staop appearing in
     * pg_statistic will be for the relevant '<' operator, but what we have
     * might be some other inequality operator such as '>='.  (Even if opoid
     * is a '<' operator, it could be cross-type.)  Hence we must use
     * comparison_ops_are_compatible() to see if the operators match.
     */
    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 &&
            sslot.stacoll == collation &&
            comparison_ops_are_compatible(sslot.staop, opoid))
        {
            /*
             * 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).
             *
             * 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,
                                                     collation,
                                                     &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,
                                                         collation,
                                                         &sslot.values[0],
                                                         NULL);
                else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
                    have_end = get_actual_variable_range(root,
                                                         vardata,
                                                         sslot.staop,
                                                         collation,
                                                         NULL,
                                                         &sslot.values[probe]);
 
                ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
                                                       collation,
                                                       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, collation,
                                      &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;
            }
        }
        else if (sslot.nvalues > 1)
        {
            /*
             * If we get here, we have a histogram but it's not sorted the way
             * we want.  Do a brute-force search to see how many of the
             * entries satisfy the comparison condition, and take that
             * fraction as our estimate.  (This is identical to the inner loop
             * of histogram_selectivity; maybe share code?)
             */
            LOCAL_FCINFO(fcinfo, 2);
            int         nmatch = 0;
 
            InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
                                     NULL, NULL);
            fcinfo->args[0].isnull = false;
            fcinfo->args[1].isnull = false;
            fcinfo->args[1].value = constval;
            for (int i = 0; i < sslot.nvalues; i++)
            {
                Datum       fresult;
 
                fcinfo->args[0].value = sslot.values[i];
                fcinfo->isnull = false;
                fresult = FunctionCallInvoke(fcinfo);
                if (!fcinfo->isnull && DatumGetBool(fresult))
                    nmatch++;
            }
            hist_selec = ((double) nmatch) / ((double) sslot.nvalues);
 
            /*
             * As above, clamp to a hundredth of the histogram resolution.
             * This case is surely even less trustworthy than the normal one,
             * so we shouldn't believe exact 0 or 1 selectivity.  (Maybe the
             * clamp should be more restrictive in this case?)
             */
            {
                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;
}

References ATTSTATSSLOT_NUMBERS, ATTSTATSSLOT_VALUES, CLAMP_PROBABILITY, comparison_ops_are_compatible(), convert_to_scalar(), DatumGetBool(), fb(), free_attstatsslot(), FunctionCall2Coll(), FunctionCallInvoke, get_actual_variable_range(), get_attstatsslot(), get_variable_numdistinct(), HeapTupleIsValid, i, InitFunctionCallInfoData, InvalidOid, LOCAL_FCINFO, AttStatsSlot::nvalues, root, statistic_proc_security_check(), and val.

Referenced by prefix_selectivity(), and scalarineqsel().

◆ matchingjoinsel()

Datum matchingjoinsel ( PG_FUNCTION_ARGS )

Definition at line 3649 of file selfuncs.c.

{
    /* Just punt, for the moment. */
    PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
}

References DEFAULT_MATCHING_SEL, and PG_RETURN_FLOAT8.

◆ matchingsel()

Datum matchingsel ( PG_FUNCTION_ARGS )

Definition at line 3631 of file selfuncs.c.

{
    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();
    double      selec;
 
    /* Use generic restriction selectivity logic. */
    selec = generic_restriction_selectivity(root, operator, collation,
                                            args, varRelid,
                                            DEFAULT_MATCHING_SEL);
 
    PG_RETURN_FLOAT8((float8) selec);
}

References DEFAULT_MATCHING_SEL, fb(), generic_restriction_selectivity(), PG_GET_COLLATION, PG_GETARG_INT32, PG_GETARG_OID, PG_GETARG_POINTER, PG_RETURN_FLOAT8, and root.

◆ mcv_selectivity()

double mcv_selectivity	(	VariableStatData *	vardata,
		FmgrInfo *	opproc,
		Oid	collation,
		Datum	constval,
		bool	varonleft,
		double *	sumcommonp
	)

Definition at line 805 of file selfuncs.c.

{
    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))
    {
        LOCAL_FCINFO(fcinfo, 2);
 
        /*
         * We invoke the opproc "by hand" so that we won't fail on NULL
         * results.  Such cases won't arise for normal comparison functions,
         * but generic_restriction_selectivity could perhaps be used with
         * operators that can return NULL.  A small side benefit is to not
         * need to re-initialize the fcinfo struct from scratch each time.
         */
        InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
                                 NULL, NULL);
        fcinfo->args[0].isnull = false;
        fcinfo->args[1].isnull = false;
        /* be careful to apply operator right way 'round */
        if (varonleft)
            fcinfo->args[1].value = constval;
        else
            fcinfo->args[0].value = constval;
 
        for (i = 0; i < sslot.nvalues; i++)
        {
            Datum       fresult;
 
            if (varonleft)
                fcinfo->args[0].value = sslot.values[i];
            else
                fcinfo->args[1].value = sslot.values[i];
            fcinfo->isnull = false;
            fresult = FunctionCallInvoke(fcinfo);
            if (!fcinfo->isnull && DatumGetBool(fresult))
                mcv_selec += sslot.numbers[i];
            sumcommon += sslot.numbers[i];
        }
        free_attstatsslot(&sslot);
    }
 
    *sumcommonp = sumcommon;
    return mcv_selec;
}

References ATTSTATSSLOT_NUMBERS, ATTSTATSSLOT_VALUES, DatumGetBool(), fb(), free_attstatsslot(), FunctionCallInvoke, get_attstatsslot(), HeapTupleIsValid, i, InitFunctionCallInfoData, InvalidOid, LOCAL_FCINFO, and statistic_proc_security_check().

Referenced by generic_restriction_selectivity(), networksel(), patternsel_common(), and scalarineqsel().

◆ mcvs_equal()

static bool mcvs_equal	(	MCVHashTable_hash *	tab,
		Datum	key0,
		Datum	key1
	)

static

Definition at line 3134 of file selfuncs.c.

{
    MCVHashContext *context = (MCVHashContext *) tab->private_data;
 
    if (context->insert_mode)
    {
        /*
         * During the insertion step, any comparisons will be between two
         * Datums of the hash table's data type, so if the given operator is
         * cross-type it will be the wrong thing to use.  Fortunately, we can
         * use datum_image_eq instead.  The MCV values should all be distinct
         * anyway, so it's mostly pro-forma to compare them at all.
         */
        return datum_image_eq(key0, key1,
                              context->hash_typbyval, context->hash_typlen);
    }
    else
    {
        FunctionCallInfo fcinfo = context->equal_fcinfo;
        Datum       fresult;
 
        /*
         * Apply the operator the correct way around.  Although simplehash.h
         * doesn't document this explicitly, during lookups key0 is from the
         * hash table while key1 is the probe value, so we should compare them
         * in that order only if op_is_reversed.
         */
        if (context->op_is_reversed)
        {
            fcinfo->args[0].value = key0;
            fcinfo->args[1].value = key1;
        }
        else
        {
            fcinfo->args[0].value = key1;
            fcinfo->args[1].value = key0;
        }
        fcinfo->isnull = false;
        fresult = FunctionCallInvoke(fcinfo);
        return (!fcinfo->isnull && DatumGetBool(fresult));
    }
}

References FunctionCallInfoBaseData::args, datum_image_eq(), DatumGetBool(), MCVHashContext::equal_fcinfo, fb(), FunctionCallInvoke, MCVHashContext::hash_typbyval, MCVHashContext::hash_typlen, MCVHashContext::insert_mode, FunctionCallInfoBaseData::isnull, MCVHashContext::op_is_reversed, and NullableDatum::value.

◆ mergejoinscansel()

void mergejoinscansel	(	PlannerInfo *	root,
		Node *	clause,
		Oid	opfamily,
		CompareType	cmptype,
		bool	nulls_first,
		Selectivity *	leftstart,
		Selectivity *	leftend,
		Selectivity *	rightstart,
		Selectivity *	rightend
	)

Definition at line 3314 of file selfuncs.c.

{
    Node       *left,
               *right;
    VariableStatData leftvar,
                rightvar;
    Oid         opmethod;
    int         op_strategy;
    Oid         op_lefttype;
    Oid         op_righttype;
    Oid         opno,
                collation,
                lsortop,
                rsortop,
                lstatop,
                rstatop,
                ltop,
                leop,
                revltop,
                revleop;
    StrategyNumber ltstrat,
                lestrat,
                gtstrat,
                gestrat;
    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;
    collation = ((OpExpr *) clause)->inputcollid;
    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);
 
    opmethod = get_opfamily_method(opfamily);
 
    /* Extract the operator's declared left/right datatypes */
    get_op_opfamily_properties(opno, opfamily, false,
                               &op_strategy,
                               &op_lefttype,
                               &op_righttype);
    Assert(IndexAmTranslateStrategy(op_strategy, opmethod, opfamily, true) == COMPARE_EQ);
 
    /*
     * 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 (cmptype)
    {
        case COMPARE_LT:
            isgt = false;
            ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
            lestrat = IndexAmTranslateCompareType(COMPARE_LE, opmethod, opfamily, true);
            if (op_lefttype == op_righttype)
            {
                /* easy case */
                ltop = get_opfamily_member(opfamily,
                                           op_lefttype, op_righttype,
                                           ltstrat);
                leop = get_opfamily_member(opfamily,
                                           op_lefttype, op_righttype,
                                           lestrat);
                lsortop = ltop;
                rsortop = ltop;
                lstatop = lsortop;
                rstatop = rsortop;
                revltop = ltop;
                revleop = leop;
            }
            else
            {
                ltop = get_opfamily_member(opfamily,
                                           op_lefttype, op_righttype,
                                           ltstrat);
                leop = get_opfamily_member(opfamily,
                                           op_lefttype, op_righttype,
                                           lestrat);
                lsortop = get_opfamily_member(opfamily,
                                              op_lefttype, op_lefttype,
                                              ltstrat);
                rsortop = get_opfamily_member(opfamily,
                                              op_righttype, op_righttype,
                                              ltstrat);
                lstatop = lsortop;
                rstatop = rsortop;
                revltop = get_opfamily_member(opfamily,
                                              op_righttype, op_lefttype,
                                              ltstrat);
                revleop = get_opfamily_member(opfamily,
                                              op_righttype, op_lefttype,
                                              lestrat);
            }
            break;
        case COMPARE_GT:
            /* descending-order case */
            isgt = true;
            ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
            gtstrat = IndexAmTranslateCompareType(COMPARE_GT, opmethod, opfamily, true);
            gestrat = IndexAmTranslateCompareType(COMPARE_GE, opmethod, opfamily, true);
            if (op_lefttype == op_righttype)
            {
                /* easy case */
                ltop = get_opfamily_member(opfamily,
                                           op_lefttype, op_righttype,
                                           gtstrat);
                leop = get_opfamily_member(opfamily,
                                           op_lefttype, op_righttype,
                                           gestrat);
                lsortop = ltop;
                rsortop = ltop;
                lstatop = get_opfamily_member(opfamily,
                                              op_lefttype, op_lefttype,
                                              ltstrat);
                rstatop = lstatop;
                revltop = ltop;
                revleop = leop;
            }
            else
            {
                ltop = get_opfamily_member(opfamily,
                                           op_lefttype, op_righttype,
                                           gtstrat);
                leop = get_opfamily_member(opfamily,
                                           op_lefttype, op_righttype,
                                           gestrat);
                lsortop = get_opfamily_member(opfamily,
                                              op_lefttype, op_lefttype,
                                              gtstrat);
                rsortop = get_opfamily_member(opfamily,
                                              op_righttype, op_righttype,
                                              gtstrat);
                lstatop = get_opfamily_member(opfamily,
                                              op_lefttype, op_lefttype,
                                              ltstrat);
                rstatop = get_opfamily_member(opfamily,
                                              op_righttype, op_righttype,
                                              ltstrat);
                revltop = get_opfamily_member(opfamily,
                                              op_righttype, op_lefttype,
                                              gtstrat);
                revleop = get_opfamily_member(opfamily,
                                              op_righttype, op_lefttype,
                                              gestrat);
            }
            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, collation,
                                &leftmin, &leftmax))
            goto fail;          /* no range available from stats */
        if (!get_variable_range(root, &rightvar, rstatop, collation,
                                &rightmin, &rightmax))
            goto fail;          /* no range available from stats */
    }
    else
    {
        /* need to swap the max and min */
        if (!get_variable_range(root, &leftvar, lstatop, collation,
                                &leftmax, &leftmin))
            goto fail;          /* no range available from stats */
        if (!get_variable_range(root, &rightvar, rstatop, collation,
                                &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, collation, &leftvar,
                          rightmax, op_righttype);
    if (selec != DEFAULT_INEQ_SEL)
        *leftend = selec;
 
    /* And similarly for the right variable. */
    selec = scalarineqsel(root, revleop, isgt, true, collation, &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, collation, &leftvar,
                          rightmin, op_righttype);
    if (selec != DEFAULT_INEQ_SEL)
        *leftstart = selec;
 
    /* And similarly for the right variable. */
    selec = scalarineqsel(root, revltop, isgt, false, collation, &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);
}

References Assert, CLAMP_PROBABILITY, COMPARE_EQ, COMPARE_GE, COMPARE_GT, COMPARE_LE, COMPARE_LT, DEFAULT_INEQ_SEL, examine_variable(), fb(), get_leftop(), get_op_opfamily_properties(), get_opfamily_member(), get_opfamily_method(), get_rightop(), get_variable_range(), GETSTRUCT(), HeapTupleIsValid, IndexAmTranslateCompareType(), IndexAmTranslateStrategy(), is_opclause(), OidIsValid, ReleaseVariableStats, root, and scalarineqsel().

Referenced by cached_scansel().

◆ neqjoinsel()

Datum neqjoinsel ( PG_FUNCTION_ARGS )

Definition at line 3181 of file selfuncs.c.

{
    PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
    Oid         operator = PG_GETARG_OID(1);
    List       *args = (List *) PG_GETARG_POINTER(2);
    JoinType    jointype = (JoinType) PG_GETARG_INT16(3);
    SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
    Oid         collation = PG_GET_COLLATION();
    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(DirectFunctionCall5Coll(eqjoinsel,
                                                       collation,
                                                       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);
}

References DatumGetFloat8(), DEFAULT_EQ_SEL, DirectFunctionCall5Coll(), eqjoinsel(), fb(), get_join_variables(), get_negator(), GETSTRUCT(), HeapTupleIsValid, Int16GetDatum(), JOIN_ANTI, JOIN_SEMI, ObjectIdGetDatum(), PG_GET_COLLATION, PG_GETARG_INT16, PG_GETARG_OID, PG_GETARG_POINTER, PG_RETURN_FLOAT8, PointerGetDatum, ReleaseVariableStats, result, and root.

◆ neqsel()

Datum neqsel ( PG_FUNCTION_ARGS )

Definition at line 630 of file selfuncs.c.

{
    PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
}

References eqsel_internal(), and PG_RETURN_FLOAT8.

◆ nulltestsel()

Selectivity nulltestsel	(	PlannerInfo *	root,
		NullTestType	nulltesttype,
		Node *	arg,
		int	varRelid,
		JoinType	jointype,
		SpecialJoinInfo *	sjinfo
	)

Definition at line 1782 of file selfuncs.c.

{
    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;
}

References arg, CLAMP_PROBABILITY, DEFAULT_NOT_UNK_SEL, DEFAULT_UNK_SEL, elog, ERROR, examine_variable(), fb(), GETSTRUCT(), HeapTupleIsValid, IS_NOT_NULL, IS_NULL, IsA, ReleaseVariableStats, and root.

Referenced by clause_selectivity_ext(), and clauselist_selectivity_ext().

◆ ReleaseDummy()

static void ReleaseDummy ( HeapTuple tuple )

static

Definition at line 5600 of file selfuncs.c.

{
    pfree(tuple);
}

References pfree().

Referenced by examine_variable().

◆ rowcomparesel()

Selectivity rowcomparesel	(	PlannerInfo *	root,
		RowCompareExpr *	clause,
		int	varRelid,
		JoinType	jointype,
		SpecialJoinInfo *	sjinfo
	)

Definition at line 2318 of file selfuncs.c.

{
    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 base relation used.
         */
        is_join_clause = (NumRelids(root, (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;
}

References fb(), join_selectivity(), RowCompareExpr::largs, linitial, linitial_oid, list_make2, NumRelids(), RowCompareExpr::rargs, restriction_selectivity(), root, and s1.

Referenced by clause_selectivity_ext().

◆ scalararraysel()

Selectivity scalararraysel	(	PlannerInfo *	root,
		ScalarArrayOpExpr *	clause,
		bool	is_join_clause,
		int	varRelid,
		JoinType	jointype,
		SpecialJoinInfo *	sjinfo
	)

Definition at line 1900 of file selfuncs.c.

{
    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);
 
        /*
         * When the array contains a NULL constant, same as var_eq_const, we
         * assume the operator is strict and nothing will match, thus return
         * 0.0.
         */
        if (!useOr && array_contains_nulls(arrayval))
            return (Selectivity) 0.0;
 
        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;
 
            /*
             * When the array contains a NULL constant, same as var_eq_const,
             * we assume the operator is strict and nothing will match, thus
             * return 0.0.
             */
            if (!useOr && IsA(elem, Const) && ((Const *) elem)->constisnull)
                return (Selectivity) 0.0;
 
            /*
             * 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;
}

References ScalarArrayOpExpr::args, ARR_ELEMTYPE, array_contains_nulls(), Assert, CLAMP_PROBABILITY, DatumGetArrayTypeP, DatumGetFloat8(), deconstruct_array(), TypeCacheEntry::eq_opr, estimate_expression_value(), exprCollation(), exprType(), fb(), fmgr_info(), FunctionCall4Coll(), FunctionCall5Coll(), get_base_element_type(), get_negator(), get_oprjoin(), get_oprrest(), get_typlenbyval(), get_typlenbyvalalign(), i, Int16GetDatum(), Int32GetDatum(), IsA, lfirst, linitial, list_length(), list_make2, lookup_type_cache(), lsecond, makeConst(), makeNode, ObjectIdGetDatum(), OidIsValid, ScalarArrayOpExpr::opno, PointerGetDatum, root, s1, s2, scalararraysel_containment(), strip_array_coercion(), TYPECACHE_EQ_OPR, and ScalarArrayOpExpr::useOr.

Referenced by clause_selectivity_ext().

◆ scalargejoinsel()

Datum scalargejoinsel ( PG_FUNCTION_ARGS )

Definition at line 3286 of file selfuncs.c.

{
    PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}

References DEFAULT_INEQ_SEL, and PG_RETURN_FLOAT8.

◆ scalargesel()

Datum scalargesel ( PG_FUNCTION_ARGS )

Definition at line 1571 of file selfuncs.c.

{
    return scalarineqsel_wrapper(fcinfo, true, true);
}

References scalarineqsel_wrapper().

◆ scalargtjoinsel()

Datum scalargtjoinsel ( PG_FUNCTION_ARGS )

Definition at line 3277 of file selfuncs.c.

{
    PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}

References DEFAULT_INEQ_SEL, and PG_RETURN_FLOAT8.

◆ scalargtsel()

Datum scalargtsel ( PG_FUNCTION_ARGS )

Definition at line 1562 of file selfuncs.c.

{
    return scalarineqsel_wrapper(fcinfo, true, false);
}

References scalarineqsel_wrapper().

◆ scalarineqsel()

static double scalarineqsel	(	PlannerInfo *	root,
		Oid	operator,
		bool	isgt,
		bool	iseq,
		Oid	collation,
		VariableStatData *	vardata,
		Datum	constval,
		Oid	consttype
	)

static

Definition at line 653 of file selfuncs.c.

{
    Form_pg_statistic stats;
    FmgrInfo    opproc;
    double      mcv_selec,
                hist_selec,
                sumcommon;
    double      selec;
 
    if (!HeapTupleIsValid(vardata->statsTuple))
    {
        /*
         * No stats are available.  Typically this means we have to fall back
         * on the default estimate; but if the variable is CTID then we can
         * make an estimate based on comparing the constant to the table size.
         */
        if (vardata->var && IsA(vardata->var, Var) &&
            ((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
        {
            ItemPointer itemptr;
            double      block;
            double      density;
 
            /*
             * If the relation's empty, we're going to include all of it.
             * (This is mostly to avoid divide-by-zero below.)
             */
            if (vardata->rel->pages == 0)
                return 1.0;
 
            itemptr = (ItemPointer) DatumGetPointer(constval);
            block = ItemPointerGetBlockNumberNoCheck(itemptr);
 
            /*
             * Determine the average number of tuples per page (density).
             *
             * Since the last page will, on average, be only half full, we can
             * estimate it to have half as many tuples as earlier pages.  So
             * give it half the weight of a regular page.
             */
            density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
 
            /* If target is the last page, use half the density. */
            if (block >= vardata->rel->pages - 1)
                density *= 0.5;
 
            /*
             * Using the average tuples per page, calculate how far into the
             * page the itemptr is likely to be and adjust block accordingly,
             * by adding that fraction of a whole block (but never more than a
             * whole block, no matter how high the itemptr's offset is).  Here
             * we are ignoring the possibility of dead-tuple line pointers,
             * which is fairly bogus, but we lack the info to do better.
             */
            if (density > 0.0)
            {
                OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
 
                block += Min(offset / density, 1.0);
            }
 
            /*
             * Convert relative block number to selectivity.  Again, the last
             * page has only half weight.
             */
            selec = block / (vardata->rel->pages - 0.5);
 
            /*
             * The calculation so far gave us a selectivity for the "<=" case.
             * We'll have one fewer tuple for "<" and one additional tuple for
             * ">=", the latter of which we'll reverse the selectivity for
             * below, so we can simply subtract one tuple for both cases.  The
             * cases that need this adjustment can be identified by iseq being
             * equal to isgt.
             */
            if (iseq == isgt && vardata->rel->tuples >= 1.0)
                selec -= (1.0 / vardata->rel->tuples);
 
            /* Finally, reverse the selectivity for the ">", ">=" cases. */
            if (isgt)
                selec = 1.0 - selec;
 
            CLAMP_PROBABILITY(selec);
            return selec;
        }
 
        /* 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, collation, 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,
                                            operator, &opproc, isgt, iseq,
                                            collation,
                                            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;
}

References CLAMP_PROBABILITY, DatumGetPointer(), DEFAULT_INEQ_SEL, fb(), fmgr_info(), get_opcode(), GETSTRUCT(), HeapTupleIsValid, ineq_histogram_selectivity(), IsA, ItemPointerGetBlockNumberNoCheck(), ItemPointerGetOffsetNumberNoCheck(), mcv_selectivity(), Min, root, and SelfItemPointerAttributeNumber.

Referenced by mergejoinscansel(), and scalarineqsel_wrapper().

◆ scalarineqsel_wrapper()

static Datum scalarineqsel_wrapper	(	PG_FUNCTION_ARGS	,
		bool	isgt,
		bool	iseq
	)

static

Definition at line 1473 of file selfuncs.c.

{
    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;
    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, collation,
                          &vardata, constval, consttype);
 
    ReleaseVariableStats(vardata);
 
    PG_RETURN_FLOAT8((float8) selec);
}

References DEFAULT_INEQ_SEL, fb(), get_commutator(), get_restriction_variable(), IsA, PG_GET_COLLATION, PG_GETARG_INT32, PG_GETARG_OID, PG_GETARG_POINTER, PG_RETURN_FLOAT8, ReleaseVariableStats, root, and scalarineqsel().

Referenced by scalargesel(), scalargtsel(), scalarlesel(), and scalarltsel().

◆ scalarlejoinsel()

Datum scalarlejoinsel ( PG_FUNCTION_ARGS )

Definition at line 3268 of file selfuncs.c.

{
    PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}

References DEFAULT_INEQ_SEL, and PG_RETURN_FLOAT8.

◆ scalarlesel()

Datum scalarlesel ( PG_FUNCTION_ARGS )

Definition at line 1553 of file selfuncs.c.

{
    return scalarineqsel_wrapper(fcinfo, false, true);
}

References scalarineqsel_wrapper().

◆ scalarltjoinsel()

Datum scalarltjoinsel ( PG_FUNCTION_ARGS )

Definition at line 3259 of file selfuncs.c.

{
    PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}

References DEFAULT_INEQ_SEL, and PG_RETURN_FLOAT8.

◆ scalarltsel()

Datum scalarltsel ( PG_FUNCTION_ARGS )

Definition at line 1544 of file selfuncs.c.

{
    return scalarineqsel_wrapper(fcinfo, false, false);
}

References scalarineqsel_wrapper().

◆ spgcostestimate()

void spgcostestimate	(	PlannerInfo *	root,
		IndexPath *	path,
		double	loop_count,
		Cost *	indexStartupCost,
		Cost *	indexTotalCost,
		Selectivity *	indexSelectivity,
		double *	indexCorrelation,
		double *	indexPages
	)

Definition at line 8277 of file selfuncs.c.

{
    IndexOptInfo *index = path->indexinfo;
    GenericCosts costs = {0};
    Cost        descentCost;
 
    /* As in btcostestimate, count only the metapage as non-leaf */
    costs.numNonLeafPages = 1;
 
    genericcostestimate(root, path, loop_count, &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) * DEFAULT_PAGE_CPU_MULTIPLIER * 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;
}

References cpu_operator_cost, DEFAULT_PAGE_CPU_MULTIPLIER, fb(), genericcostestimate(), GenericCosts::indexCorrelation, IndexPath::indexinfo, GenericCosts::indexSelectivity, GenericCosts::indexStartupCost, GenericCosts::indexTotalCost, GenericCosts::num_sa_scans, GenericCosts::numIndexPages, GenericCosts::numNonLeafPages, and root.

Referenced by spghandler().

◆ statistic_proc_security_check()

bool statistic_proc_security_check	(	VariableStatData *	vardata,
		Oid	func_oid
	)

Definition at line 6582 of file selfuncs.c.

{
    if (vardata->acl_ok)
        return true;            /* have SELECT privs and no securityQuals */
 
    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 leakproof",
                             get_func_name(func_oid))));
    return false;
}

References DEBUG2, ereport, errmsg_internal(), fb(), get_func_leakproof(), get_func_name(), and OidIsValid.

Referenced by calc_arraycontsel(), calc_hist_selectivity(), calc_hist_selectivity(), eqjoinsel(), get_variable_range(), histogram_selectivity(), ineq_histogram_selectivity(), mcv_selectivity(), scalararraysel_containment(), and var_eq_const().

◆ strip_all_phvs_deep()

static Node * strip_all_phvs_deep	(	PlannerInfo *	root,
		Node *	node
	)

static

Definition at line 5980 of file selfuncs.c.

{
    /* If there are no PHVs anywhere, we needn't work hard */
    if (root->glob->lastPHId == 0)
        return node;
 
    if (!contain_placeholder_walker(node, NULL))
        return node;
    return strip_all_phvs_mutator(node, NULL);
}

References contain_placeholder_walker(), fb(), root, and strip_all_phvs_mutator().

Referenced by examine_variable().

◆ strip_all_phvs_mutator()

static Node * strip_all_phvs_mutator	(	Node *	node,
		void *	context
	)

static

Definition at line 6012 of file selfuncs.c.

{
    if (node == NULL)
        return NULL;
    if (IsA(node, PlaceHolderVar))
    {
        /* Strip it and recurse into its contained expression */
        PlaceHolderVar *phv = (PlaceHolderVar *) node;
 
        return strip_all_phvs_mutator((Node *) phv->phexpr, context);
    }
 
    return expression_tree_mutator(node, strip_all_phvs_mutator, context);
}

References expression_tree_mutator, fb(), IsA, and strip_all_phvs_mutator().

Referenced by strip_all_phvs_deep(), and strip_all_phvs_mutator().

◆ strip_array_coercion()

static Node * strip_array_coercion ( Node * node )

static

Definition at line 1867 of file selfuncs.c.

{
    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;
}

References arg, fb(), and IsA.

Referenced by estimate_array_length(), and scalararraysel().

◆ var_eq_const()

double var_eq_const	(	VariableStatData *	vardata,
		Oid	oproid,
		Oid	collation,
		Datum	constval,
		bool	constisnull,
		bool	varonleft,
		bool	negate
	)

Definition at line 368 of file selfuncs.c.

{
    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, DISTINCT or GROUP-BY 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(oproid))))
    {
        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))
        {
            LOCAL_FCINFO(fcinfo, 2);
            FmgrInfo    eqproc;
 
            fmgr_info(opfuncoid, &eqproc);
 
            /*
             * Save a few cycles by setting up the fcinfo struct just once.
             * Using FunctionCallInvoke directly also avoids failure if the
             * eqproc returns NULL, though really equality functions should
             * never do that.
             */
            InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
                                     NULL, NULL);
            fcinfo->args[0].isnull = false;
            fcinfo->args[1].isnull = false;
            /* be careful to apply operator right way 'round */
            if (varonleft)
                fcinfo->args[1].value = constval;
            else
                fcinfo->args[0].value = constval;
 
            for (i = 0; i < sslot.nvalues; i++)
            {
                Datum       fresult;
 
                if (varonleft)
                    fcinfo->args[0].value = sslot.values[i];
                else
                    fcinfo->args[1].value = sslot.values[i];
                fcinfo->isnull = false;
                fresult = FunctionCallInvoke(fcinfo);
                if (!fcinfo->isnull && DatumGetBool(fresult))
                {
                    match = true;
                    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;
}

References ATTSTATSSLOT_NUMBERS, ATTSTATSSLOT_VALUES, CLAMP_PROBABILITY, DatumGetBool(), fb(), fmgr_info(), free_attstatsslot(), FunctionCallInvoke, get_attstatsslot(), get_opcode(), get_variable_numdistinct(), GETSTRUCT(), HeapTupleIsValid, i, InitFunctionCallInfoData, InvalidOid, LOCAL_FCINFO, and statistic_proc_security_check().

Referenced by boolvarsel(), eqsel_internal(), patternsel_common(), and prefix_selectivity().

◆ var_eq_non_const()

double var_eq_non_const	(	VariableStatData *	vardata,
		Oid	oproid,
		Oid	collation,
		Node *	other,
		bool	varonleft,
		bool	negate
	)

Definition at line 539 of file selfuncs.c.

{
    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, DISTINCT or GROUP-BY 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;
}

References ATTSTATSSLOT_NUMBERS, CLAMP_PROBABILITY, fb(), free_attstatsslot(), get_attstatsslot(), get_variable_numdistinct(), GETSTRUCT(), HeapTupleIsValid, and InvalidOid.

Referenced by eqsel_internal().

Variable Documentation

◆ get_index_stats_hook

get_index_stats_hook_type get_index_stats_hook = NULL

Definition at line 184 of file selfuncs.c.

Referenced by brincostestimate(), examine_indexcol_variable(), and examine_variable().

◆ get_relation_stats_hook

get_relation_stats_hook_type get_relation_stats_hook = NULL

Definition at line 183 of file selfuncs.c.

Referenced by brincostestimate(), examine_indexcol_variable(), and examine_simple_variable().

Data Structures

Macros

Typedefs

Functions

Variables

Macro Definition Documentation

◆ DEFAULT_PAGE_CPU_MULTIPLIER

◆ EQJOINSEL_MCV_HASH_THRESHOLD

◆ SH_DECLARE

◆ SH_DEFINE

◆ SH_ELEMENT_TYPE

◆ SH_EQUAL

◆ SH_GET_HASH

◆ SH_HASH_KEY

◆ SH_KEY

◆ SH_KEY_TYPE

◆ SH_PREFIX

◆ SH_SCOPE

◆ SH_STORE_HASH

◆ VISITED_PAGES_LIMIT

Typedef Documentation

◆ MCVHashContext

◆ MCVHashEntry

◆ MCVHashTable_hash

Function Documentation

◆ add_predicate_to_index_quals()

◆ add_unique_group_var()

◆ all_rows_selectable()

◆ booltestsel()

◆ boolvarsel()

◆ brincostestimate()

◆ btcost_correlation()

◆ btcostestimate()

◆ contain_placeholder_walker()

◆ convert_bytea_to_scalar()

◆ convert_numeric_to_scalar()

◆ convert_one_bytea_to_scalar()

◆ convert_one_string_to_scalar()

◆ convert_string_datum()

◆ convert_string_to_scalar()

◆ convert_timevalue_to_scalar()

◆ convert_to_scalar()

◆ eqjoinsel()

◆ eqjoinsel_find_matches()

◆ eqjoinsel_inner()

◆ eqjoinsel_semi()

◆ eqsel()

◆ eqsel_internal()

◆ estimate_array_length()

◆ estimate_hash_bucket_stats()

◆ estimate_hashagg_tablesize()

◆ estimate_multivariate_bucketsize()

◆ estimate_multivariate_ndistinct()

◆ estimate_num_groups()

◆ examine_indexcol_variable()

◆ examine_simple_variable()

◆ examine_variable()

◆ find_join_input_rel()

◆ generic_restriction_selectivity()

◆ genericcostestimate()

◆ get_actual_variable_endpoint()

◆ get_actual_variable_range()

◆ get_join_variables()

◆ get_quals_from_indexclauses()

◆ get_restriction_variable()

◆ get_stats_slot_range()

◆ get_variable_numdistinct()

◆ get_variable_range()

◆ gincost_opexpr()

◆ gincost_pattern()

◆ gincost_scalararrayopexpr()

◆ gincostestimate()

◆ gistcostestimate()

◆ hash_mcv()

◆ hashcostestimate()

◆ histogram_selectivity()

◆ index_other_operands_eval_cost()

◆ ineq_histogram_selectivity()

◆ matchingjoinsel()

◆ matchingsel()