LCOV - code coverage report
Current view: top level - src/backend/utils/adt - selfuncs.c (source / functions) Hit Total Coverage
Test: PostgreSQL 17devel Lines: 2052 2362 86.9 %
Date: 2024-03-29 15:11:20 Functions: 68 71 95.8 %
Legend: Lines: hit not hit

          Line data    Source code
       1             : /*-------------------------------------------------------------------------
       2             :  *
       3             :  * selfuncs.c
       4             :  *    Selectivity functions and index cost estimation functions for
       5             :  *    standard operators and index access methods.
       6             :  *
       7             :  *    Selectivity routines are registered in the pg_operator catalog
       8             :  *    in the "oprrest" and "oprjoin" attributes.
       9             :  *
      10             :  *    Index cost functions are located via the index AM's API struct,
      11             :  *    which is obtained from the handler function registered in pg_am.
      12             :  *
      13             :  * Portions Copyright (c) 1996-2024, PostgreSQL Global Development Group
      14             :  * Portions Copyright (c) 1994, Regents of the University of California
      15             :  *
      16             :  *
      17             :  * IDENTIFICATION
      18             :  *    src/backend/utils/adt/selfuncs.c
      19             :  *
      20             :  *-------------------------------------------------------------------------
      21             :  */
      22             : 
      23             : /*----------
      24             :  * Operator selectivity estimation functions are called to estimate the
      25             :  * selectivity of WHERE clauses whose top-level operator is their operator.
      26             :  * We divide the problem into two cases:
      27             :  *      Restriction clause estimation: the clause involves vars of just
      28             :  *          one relation.
      29             :  *      Join clause estimation: the clause involves vars of multiple rels.
      30             :  * Join selectivity estimation is far more difficult and usually less accurate
      31             :  * than restriction estimation.
      32             :  *
      33             :  * When dealing with the inner scan of a nestloop join, we consider the
      34             :  * join's joinclauses as restriction clauses for the inner relation, and
      35             :  * treat vars of the outer relation as parameters (a/k/a constants of unknown
      36             :  * values).  So, restriction estimators need to be able to accept an argument
      37             :  * telling which relation is to be treated as the variable.
      38             :  *
      39             :  * The call convention for a restriction estimator (oprrest function) is
      40             :  *
      41             :  *      Selectivity oprrest (PlannerInfo *root,
      42             :  *                           Oid operator,
      43             :  *                           List *args,
      44             :  *                           int varRelid);
      45             :  *
      46             :  * root: general information about the query (rtable and RelOptInfo lists
      47             :  * are particularly important for the estimator).
      48             :  * operator: OID of the specific operator in question.
      49             :  * args: argument list from the operator clause.
      50             :  * varRelid: if not zero, the relid (rtable index) of the relation to
      51             :  * be treated as the variable relation.  May be zero if the args list
      52             :  * is known to contain vars of only one relation.
      53             :  *
      54             :  * This is represented at the SQL level (in pg_proc) as
      55             :  *
      56             :  *      float8 oprrest (internal, oid, internal, int4);
      57             :  *
      58             :  * The result is a selectivity, that is, a fraction (0 to 1) of the rows
      59             :  * of the relation that are expected to produce a TRUE result for the
      60             :  * given operator.
      61             :  *
      62             :  * The call convention for a join estimator (oprjoin function) is similar
      63             :  * except that varRelid is not needed, and instead join information is
      64             :  * supplied:
      65             :  *
      66             :  *      Selectivity oprjoin (PlannerInfo *root,
      67             :  *                           Oid operator,
      68             :  *                           List *args,
      69             :  *                           JoinType jointype,
      70             :  *                           SpecialJoinInfo *sjinfo);
      71             :  *
      72             :  *      float8 oprjoin (internal, oid, internal, int2, internal);
      73             :  *
      74             :  * (Before Postgres 8.4, join estimators had only the first four of these
      75             :  * parameters.  That signature is still allowed, but deprecated.)  The
      76             :  * relationship between jointype and sjinfo is explained in the comments for
      77             :  * clause_selectivity() --- the short version is that jointype is usually
      78             :  * best ignored in favor of examining sjinfo.
      79             :  *
      80             :  * Join selectivity for regular inner and outer joins is defined as the
      81             :  * fraction (0 to 1) of the cross product of the relations that is expected
      82             :  * to produce a TRUE result for the given operator.  For both semi and anti
      83             :  * joins, however, the selectivity is defined as the fraction of the left-hand
      84             :  * side relation's rows that are expected to have a match (ie, at least one
      85             :  * row with a TRUE result) in the right-hand side.
      86             :  *
      87             :  * For both oprrest and oprjoin functions, the operator's input collation OID
      88             :  * (if any) is passed using the standard fmgr mechanism, so that the estimator
      89             :  * function can fetch it with PG_GET_COLLATION().  Note, however, that all
      90             :  * statistics in pg_statistic are currently built using the relevant column's
      91             :  * collation.
      92             :  *----------
      93             :  */
      94             : 
      95             : #include "postgres.h"
      96             : 
      97             : #include <ctype.h>
      98             : #include <math.h>
      99             : 
     100             : #include "access/brin.h"
     101             : #include "access/brin_page.h"
     102             : #include "access/gin.h"
     103             : #include "access/table.h"
     104             : #include "access/tableam.h"
     105             : #include "access/visibilitymap.h"
     106             : #include "catalog/pg_am.h"
     107             : #include "catalog/pg_collation.h"
     108             : #include "catalog/pg_operator.h"
     109             : #include "catalog/pg_statistic.h"
     110             : #include "catalog/pg_statistic_ext.h"
     111             : #include "executor/nodeAgg.h"
     112             : #include "miscadmin.h"
     113             : #include "nodes/makefuncs.h"
     114             : #include "nodes/nodeFuncs.h"
     115             : #include "optimizer/clauses.h"
     116             : #include "optimizer/cost.h"
     117             : #include "optimizer/optimizer.h"
     118             : #include "optimizer/pathnode.h"
     119             : #include "optimizer/paths.h"
     120             : #include "optimizer/plancat.h"
     121             : #include "parser/parse_clause.h"
     122             : #include "parser/parse_relation.h"
     123             : #include "parser/parsetree.h"
     124             : #include "statistics/statistics.h"
     125             : #include "storage/bufmgr.h"
     126             : #include "utils/acl.h"
     127             : #include "utils/array.h"
     128             : #include "utils/builtins.h"
     129             : #include "utils/date.h"
     130             : #include "utils/datum.h"
     131             : #include "utils/fmgroids.h"
     132             : #include "utils/index_selfuncs.h"
     133             : #include "utils/lsyscache.h"
     134             : #include "utils/memutils.h"
     135             : #include "utils/pg_locale.h"
     136             : #include "utils/rel.h"
     137             : #include "utils/selfuncs.h"
     138             : #include "utils/snapmgr.h"
     139             : #include "utils/spccache.h"
     140             : #include "utils/syscache.h"
     141             : #include "utils/timestamp.h"
     142             : #include "utils/typcache.h"
     143             : 
     144             : #define DEFAULT_PAGE_CPU_MULTIPLIER 50.0
     145             : 
     146             : /* Hooks for plugins to get control when we ask for stats */
     147             : get_relation_stats_hook_type get_relation_stats_hook = NULL;
     148             : get_index_stats_hook_type get_index_stats_hook = NULL;
     149             : 
     150             : static double eqsel_internal(PG_FUNCTION_ARGS, bool negate);
     151             : static double eqjoinsel_inner(Oid opfuncoid, Oid collation,
     152             :                               VariableStatData *vardata1, VariableStatData *vardata2,
     153             :                               double nd1, double nd2,
     154             :                               bool isdefault1, bool isdefault2,
     155             :                               AttStatsSlot *sslot1, AttStatsSlot *sslot2,
     156             :                               Form_pg_statistic stats1, Form_pg_statistic stats2,
     157             :                               bool have_mcvs1, bool have_mcvs2);
     158             : static double eqjoinsel_semi(Oid opfuncoid, Oid collation,
     159             :                              VariableStatData *vardata1, VariableStatData *vardata2,
     160             :                              double nd1, double nd2,
     161             :                              bool isdefault1, bool isdefault2,
     162             :                              AttStatsSlot *sslot1, AttStatsSlot *sslot2,
     163             :                              Form_pg_statistic stats1, Form_pg_statistic stats2,
     164             :                              bool have_mcvs1, bool have_mcvs2,
     165             :                              RelOptInfo *inner_rel);
     166             : static bool estimate_multivariate_ndistinct(PlannerInfo *root,
     167             :                                             RelOptInfo *rel, List **varinfos, double *ndistinct);
     168             : static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
     169             :                               double *scaledvalue,
     170             :                               Datum lobound, Datum hibound, Oid boundstypid,
     171             :                               double *scaledlobound, double *scaledhibound);
     172             : static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
     173             : static void convert_string_to_scalar(char *value,
     174             :                                      double *scaledvalue,
     175             :                                      char *lobound,
     176             :                                      double *scaledlobound,
     177             :                                      char *hibound,
     178             :                                      double *scaledhibound);
     179             : static void convert_bytea_to_scalar(Datum value,
     180             :                                     double *scaledvalue,
     181             :                                     Datum lobound,
     182             :                                     double *scaledlobound,
     183             :                                     Datum hibound,
     184             :                                     double *scaledhibound);
     185             : static double convert_one_string_to_scalar(char *value,
     186             :                                            int rangelo, int rangehi);
     187             : static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
     188             :                                           int rangelo, int rangehi);
     189             : static char *convert_string_datum(Datum value, Oid typid, Oid collid,
     190             :                                   bool *failure);
     191             : static double convert_timevalue_to_scalar(Datum value, Oid typid,
     192             :                                           bool *failure);
     193             : static void examine_simple_variable(PlannerInfo *root, Var *var,
     194             :                                     VariableStatData *vardata);
     195             : static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
     196             :                                Oid sortop, Oid collation,
     197             :                                Datum *min, Datum *max);
     198             : static void get_stats_slot_range(AttStatsSlot *sslot,
     199             :                                  Oid opfuncoid, FmgrInfo *opproc,
     200             :                                  Oid collation, int16 typLen, bool typByVal,
     201             :                                  Datum *min, Datum *max, bool *p_have_data);
     202             : static bool get_actual_variable_range(PlannerInfo *root,
     203             :                                       VariableStatData *vardata,
     204             :                                       Oid sortop, Oid collation,
     205             :                                       Datum *min, Datum *max);
     206             : static bool get_actual_variable_endpoint(Relation heapRel,
     207             :                                          Relation indexRel,
     208             :                                          ScanDirection indexscandir,
     209             :                                          ScanKey scankeys,
     210             :                                          int16 typLen,
     211             :                                          bool typByVal,
     212             :                                          TupleTableSlot *tableslot,
     213             :                                          MemoryContext outercontext,
     214             :                                          Datum *endpointDatum);
     215             : static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
     216             : 
     217             : 
     218             : /*
     219             :  *      eqsel           - Selectivity of "=" for any data types.
     220             :  *
     221             :  * Note: this routine is also used to estimate selectivity for some
     222             :  * operators that are not "=" but have comparable selectivity behavior,
     223             :  * such as "~=" (geometric approximate-match).  Even for "=", we must
     224             :  * keep in mind that the left and right datatypes may differ.
     225             :  */
     226             : Datum
     227      521522 : eqsel(PG_FUNCTION_ARGS)
     228             : {
     229      521522 :     PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
     230             : }
     231             : 
     232             : /*
     233             :  * Common code for eqsel() and neqsel()
     234             :  */
     235             : static double
     236      562470 : eqsel_internal(PG_FUNCTION_ARGS, bool negate)
     237             : {
     238      562470 :     PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
     239      562470 :     Oid         operator = PG_GETARG_OID(1);
     240      562470 :     List       *args = (List *) PG_GETARG_POINTER(2);
     241      562470 :     int         varRelid = PG_GETARG_INT32(3);
     242      562470 :     Oid         collation = PG_GET_COLLATION();
     243             :     VariableStatData vardata;
     244             :     Node       *other;
     245             :     bool        varonleft;
     246             :     double      selec;
     247             : 
     248             :     /*
     249             :      * When asked about <>, we do the estimation using the corresponding =
     250             :      * operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
     251             :      */
     252      562470 :     if (negate)
     253             :     {
     254       40948 :         operator = get_negator(operator);
     255       40948 :         if (!OidIsValid(operator))
     256             :         {
     257             :             /* Use default selectivity (should we raise an error instead?) */
     258           0 :             return 1.0 - DEFAULT_EQ_SEL;
     259             :         }
     260             :     }
     261             : 
     262             :     /*
     263             :      * If expression is not variable = something or something = variable, then
     264             :      * punt and return a default estimate.
     265             :      */
     266      562470 :     if (!get_restriction_variable(root, args, varRelid,
     267             :                                   &vardata, &other, &varonleft))
     268        3686 :         return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
     269             : 
     270             :     /*
     271             :      * We can do a lot better if the something is a constant.  (Note: the
     272             :      * Const might result from estimation rather than being a simple constant
     273             :      * in the query.)
     274             :      */
     275      558784 :     if (IsA(other, Const))
     276      246424 :         selec = var_eq_const(&vardata, operator, collation,
     277      246424 :                              ((Const *) other)->constvalue,
     278      246424 :                              ((Const *) other)->constisnull,
     279             :                              varonleft, negate);
     280             :     else
     281      312360 :         selec = var_eq_non_const(&vardata, operator, collation, other,
     282             :                                  varonleft, negate);
     283             : 
     284      558784 :     ReleaseVariableStats(vardata);
     285             : 
     286      558784 :     return selec;
     287             : }
     288             : 
     289             : /*
     290             :  * var_eq_const --- eqsel for var = const case
     291             :  *
     292             :  * This is exported so that some other estimation functions can use it.
     293             :  */
     294             : double
     295      290430 : var_eq_const(VariableStatData *vardata, Oid oproid, Oid collation,
     296             :              Datum constval, bool constisnull,
     297             :              bool varonleft, bool negate)
     298             : {
     299             :     double      selec;
     300      290430 :     double      nullfrac = 0.0;
     301             :     bool        isdefault;
     302             :     Oid         opfuncoid;
     303             : 
     304             :     /*
     305             :      * If the constant is NULL, assume operator is strict and return zero, ie,
     306             :      * operator will never return TRUE.  (It's zero even for a negator op.)
     307             :      */
     308      290430 :     if (constisnull)
     309         320 :         return 0.0;
     310             : 
     311             :     /*
     312             :      * Grab the nullfrac for use below.  Note we allow use of nullfrac
     313             :      * regardless of security check.
     314             :      */
     315      290110 :     if (HeapTupleIsValid(vardata->statsTuple))
     316             :     {
     317             :         Form_pg_statistic stats;
     318             : 
     319      213580 :         stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
     320      213580 :         nullfrac = stats->stanullfrac;
     321             :     }
     322             : 
     323             :     /*
     324             :      * If we matched the var to a unique index or DISTINCT clause, assume
     325             :      * there is exactly one match regardless of anything else.  (This is
     326             :      * slightly bogus, since the index or clause's equality operator might be
     327             :      * different from ours, but it's much more likely to be right than
     328             :      * ignoring the information.)
     329             :      */
     330      290110 :     if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
     331             :     {
     332       76668 :         selec = 1.0 / vardata->rel->tuples;
     333             :     }
     334      367558 :     else if (HeapTupleIsValid(vardata->statsTuple) &&
     335      154116 :              statistic_proc_security_check(vardata,
     336      154116 :                                            (opfuncoid = get_opcode(oproid))))
     337      154116 :     {
     338             :         AttStatsSlot sslot;
     339      154116 :         bool        match = false;
     340             :         int         i;
     341             : 
     342             :         /*
     343             :          * Is the constant "=" to any of the column's most common values?
     344             :          * (Although the given operator may not really be "=", we will assume
     345             :          * that seeing whether it returns TRUE is an appropriate test.  If you
     346             :          * don't like this, maybe you shouldn't be using eqsel for your
     347             :          * operator...)
     348             :          */
     349      154116 :         if (get_attstatsslot(&sslot, vardata->statsTuple,
     350             :                              STATISTIC_KIND_MCV, InvalidOid,
     351             :                              ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
     352             :         {
     353      137930 :             LOCAL_FCINFO(fcinfo, 2);
     354             :             FmgrInfo    eqproc;
     355             : 
     356      137930 :             fmgr_info(opfuncoid, &eqproc);
     357             : 
     358             :             /*
     359             :              * Save a few cycles by setting up the fcinfo struct just once.
     360             :              * Using FunctionCallInvoke directly also avoids failure if the
     361             :              * eqproc returns NULL, though really equality functions should
     362             :              * never do that.
     363             :              */
     364      137930 :             InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
     365             :                                      NULL, NULL);
     366      137930 :             fcinfo->args[0].isnull = false;
     367      137930 :             fcinfo->args[1].isnull = false;
     368             :             /* be careful to apply operator right way 'round */
     369      137930 :             if (varonleft)
     370      137898 :                 fcinfo->args[1].value = constval;
     371             :             else
     372          32 :                 fcinfo->args[0].value = constval;
     373             : 
     374     1935032 :             for (i = 0; i < sslot.nvalues; i++)
     375             :             {
     376             :                 Datum       fresult;
     377             : 
     378     1865910 :                 if (varonleft)
     379     1865854 :                     fcinfo->args[0].value = sslot.values[i];
     380             :                 else
     381          56 :                     fcinfo->args[1].value = sslot.values[i];
     382     1865910 :                 fcinfo->isnull = false;
     383     1865910 :                 fresult = FunctionCallInvoke(fcinfo);
     384     1865910 :                 if (!fcinfo->isnull && DatumGetBool(fresult))
     385             :                 {
     386       68808 :                     match = true;
     387       68808 :                     break;
     388             :                 }
     389             :             }
     390             :         }
     391             :         else
     392             :         {
     393             :             /* no most-common-value info available */
     394       16186 :             i = 0;              /* keep compiler quiet */
     395             :         }
     396             : 
     397      154116 :         if (match)
     398             :         {
     399             :             /*
     400             :              * Constant is "=" to this common value.  We know selectivity
     401             :              * exactly (or as exactly as ANALYZE could calculate it, anyway).
     402             :              */
     403       68808 :             selec = sslot.numbers[i];
     404             :         }
     405             :         else
     406             :         {
     407             :             /*
     408             :              * Comparison is against a constant that is neither NULL nor any
     409             :              * of the common values.  Its selectivity cannot be more than
     410             :              * this:
     411             :              */
     412       85308 :             double      sumcommon = 0.0;
     413             :             double      otherdistinct;
     414             : 
     415     1731824 :             for (i = 0; i < sslot.nnumbers; i++)
     416     1646516 :                 sumcommon += sslot.numbers[i];
     417       85308 :             selec = 1.0 - sumcommon - nullfrac;
     418       85308 :             CLAMP_PROBABILITY(selec);
     419             : 
     420             :             /*
     421             :              * and in fact it's probably a good deal less. We approximate that
     422             :              * all the not-common values share this remaining fraction
     423             :              * equally, so we divide by the number of other distinct values.
     424             :              */
     425       85308 :             otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
     426       85308 :                 sslot.nnumbers;
     427       85308 :             if (otherdistinct > 1)
     428       37720 :                 selec /= otherdistinct;
     429             : 
     430             :             /*
     431             :              * Another cross-check: selectivity shouldn't be estimated as more
     432             :              * than the least common "most common value".
     433             :              */
     434       85308 :             if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
     435           0 :                 selec = sslot.numbers[sslot.nnumbers - 1];
     436             :         }
     437             : 
     438      154116 :         free_attstatsslot(&sslot);
     439             :     }
     440             :     else
     441             :     {
     442             :         /*
     443             :          * No ANALYZE stats available, so make a guess using estimated number
     444             :          * of distinct values and assuming they are equally common. (The guess
     445             :          * is unlikely to be very good, but we do know a few special cases.)
     446             :          */
     447       59326 :         selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
     448             :     }
     449             : 
     450             :     /* now adjust if we wanted <> rather than = */
     451      290110 :     if (negate)
     452       33508 :         selec = 1.0 - selec - nullfrac;
     453             : 
     454             :     /* result should be in range, but make sure... */
     455      290110 :     CLAMP_PROBABILITY(selec);
     456             : 
     457      290110 :     return selec;
     458             : }
     459             : 
     460             : /*
     461             :  * var_eq_non_const --- eqsel for var = something-other-than-const case
     462             :  *
     463             :  * This is exported so that some other estimation functions can use it.
     464             :  */
     465             : double
     466      312360 : var_eq_non_const(VariableStatData *vardata, Oid oproid, Oid collation,
     467             :                  Node *other,
     468             :                  bool varonleft, bool negate)
     469             : {
     470             :     double      selec;
     471      312360 :     double      nullfrac = 0.0;
     472             :     bool        isdefault;
     473             : 
     474             :     /*
     475             :      * Grab the nullfrac for use below.
     476             :      */
     477      312360 :     if (HeapTupleIsValid(vardata->statsTuple))
     478             :     {
     479             :         Form_pg_statistic stats;
     480             : 
     481      220998 :         stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
     482      220998 :         nullfrac = stats->stanullfrac;
     483             :     }
     484             : 
     485             :     /*
     486             :      * If we matched the var to a unique index or DISTINCT clause, assume
     487             :      * there is exactly one match regardless of anything else.  (This is
     488             :      * slightly bogus, since the index or clause's equality operator might be
     489             :      * different from ours, but it's much more likely to be right than
     490             :      * ignoring the information.)
     491             :      */
     492      312360 :     if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
     493             :     {
     494      124770 :         selec = 1.0 / vardata->rel->tuples;
     495             :     }
     496      187590 :     else if (HeapTupleIsValid(vardata->statsTuple))
     497             :     {
     498             :         double      ndistinct;
     499             :         AttStatsSlot sslot;
     500             : 
     501             :         /*
     502             :          * Search is for a value that we do not know a priori, but we will
     503             :          * assume it is not NULL.  Estimate the selectivity as non-null
     504             :          * fraction divided by number of distinct values, so that we get a
     505             :          * result averaged over all possible values whether common or
     506             :          * uncommon.  (Essentially, we are assuming that the not-yet-known
     507             :          * comparison value is equally likely to be any of the possible
     508             :          * values, regardless of their frequency in the table.  Is that a good
     509             :          * idea?)
     510             :          */
     511      110116 :         selec = 1.0 - nullfrac;
     512      110116 :         ndistinct = get_variable_numdistinct(vardata, &isdefault);
     513      110116 :         if (ndistinct > 1)
     514      106018 :             selec /= ndistinct;
     515             : 
     516             :         /*
     517             :          * Cross-check: selectivity should never be estimated as more than the
     518             :          * most common value's.
     519             :          */
     520      110116 :         if (get_attstatsslot(&sslot, vardata->statsTuple,
     521             :                              STATISTIC_KIND_MCV, InvalidOid,
     522             :                              ATTSTATSSLOT_NUMBERS))
     523             :         {
     524       91958 :             if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
     525         588 :                 selec = sslot.numbers[0];
     526       91958 :             free_attstatsslot(&sslot);
     527             :         }
     528             :     }
     529             :     else
     530             :     {
     531             :         /*
     532             :          * No ANALYZE stats available, so make a guess using estimated number
     533             :          * of distinct values and assuming they are equally common. (The guess
     534             :          * is unlikely to be very good, but we do know a few special cases.)
     535             :          */
     536       77474 :         selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
     537             :     }
     538             : 
     539             :     /* now adjust if we wanted <> rather than = */
     540      312360 :     if (negate)
     541        5590 :         selec = 1.0 - selec - nullfrac;
     542             : 
     543             :     /* result should be in range, but make sure... */
     544      312360 :     CLAMP_PROBABILITY(selec);
     545             : 
     546      312360 :     return selec;
     547             : }
     548             : 
     549             : /*
     550             :  *      neqsel          - Selectivity of "!=" for any data types.
     551             :  *
     552             :  * This routine is also used for some operators that are not "!="
     553             :  * but have comparable selectivity behavior.  See above comments
     554             :  * for eqsel().
     555             :  */
     556             : Datum
     557       40948 : neqsel(PG_FUNCTION_ARGS)
     558             : {
     559       40948 :     PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
     560             : }
     561             : 
     562             : /*
     563             :  *  scalarineqsel       - Selectivity of "<", "<=", ">", ">=" for scalars.
     564             :  *
     565             :  * This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
     566             :  * The isgt and iseq flags distinguish which of the four cases apply.
     567             :  *
     568             :  * The caller has commuted the clause, if necessary, so that we can treat
     569             :  * the variable as being on the left.  The caller must also make sure that
     570             :  * the other side of the clause is a non-null Const, and dissect that into
     571             :  * a value and datatype.  (This definition simplifies some callers that
     572             :  * want to estimate against a computed value instead of a Const node.)
     573             :  *
     574             :  * This routine works for any datatype (or pair of datatypes) known to
     575             :  * convert_to_scalar().  If it is applied to some other datatype,
     576             :  * it will return an approximate estimate based on assuming that the constant
     577             :  * value falls in the middle of the bin identified by binary search.
     578             :  */
     579             : static double
     580      266244 : scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
     581             :               Oid collation,
     582             :               VariableStatData *vardata, Datum constval, Oid consttype)
     583             : {
     584             :     Form_pg_statistic stats;
     585             :     FmgrInfo    opproc;
     586             :     double      mcv_selec,
     587             :                 hist_selec,
     588             :                 sumcommon;
     589             :     double      selec;
     590             : 
     591      266244 :     if (!HeapTupleIsValid(vardata->statsTuple))
     592             :     {
     593             :         /*
     594             :          * No stats are available.  Typically this means we have to fall back
     595             :          * on the default estimate; but if the variable is CTID then we can
     596             :          * make an estimate based on comparing the constant to the table size.
     597             :          */
     598       19230 :         if (vardata->var && IsA(vardata->var, Var) &&
     599       14986 :             ((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
     600             :         {
     601             :             ItemPointer itemptr;
     602             :             double      block;
     603             :             double      density;
     604             : 
     605             :             /*
     606             :              * If the relation's empty, we're going to include all of it.
     607             :              * (This is mostly to avoid divide-by-zero below.)
     608             :              */
     609         214 :             if (vardata->rel->pages == 0)
     610           0 :                 return 1.0;
     611             : 
     612         214 :             itemptr = (ItemPointer) DatumGetPointer(constval);
     613         214 :             block = ItemPointerGetBlockNumberNoCheck(itemptr);
     614             : 
     615             :             /*
     616             :              * Determine the average number of tuples per page (density).
     617             :              *
     618             :              * Since the last page will, on average, be only half full, we can
     619             :              * estimate it to have half as many tuples as earlier pages.  So
     620             :              * give it half the weight of a regular page.
     621             :              */
     622         214 :             density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
     623             : 
     624             :             /* If target is the last page, use half the density. */
     625         214 :             if (block >= vardata->rel->pages - 1)
     626          30 :                 density *= 0.5;
     627             : 
     628             :             /*
     629             :              * Using the average tuples per page, calculate how far into the
     630             :              * page the itemptr is likely to be and adjust block accordingly,
     631             :              * by adding that fraction of a whole block (but never more than a
     632             :              * whole block, no matter how high the itemptr's offset is).  Here
     633             :              * we are ignoring the possibility of dead-tuple line pointers,
     634             :              * which is fairly bogus, but we lack the info to do better.
     635             :              */
     636         214 :             if (density > 0.0)
     637             :             {
     638         214 :                 OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
     639             : 
     640         214 :                 block += Min(offset / density, 1.0);
     641             :             }
     642             : 
     643             :             /*
     644             :              * Convert relative block number to selectivity.  Again, the last
     645             :              * page has only half weight.
     646             :              */
     647         214 :             selec = block / (vardata->rel->pages - 0.5);
     648             : 
     649             :             /*
     650             :              * The calculation so far gave us a selectivity for the "<=" case.
     651             :              * We'll have one fewer tuple for "<" and one additional tuple for
     652             :              * ">=", the latter of which we'll reverse the selectivity for
     653             :              * below, so we can simply subtract one tuple for both cases.  The
     654             :              * cases that need this adjustment can be identified by iseq being
     655             :              * equal to isgt.
     656             :              */
     657         214 :             if (iseq == isgt && vardata->rel->tuples >= 1.0)
     658         102 :                 selec -= (1.0 / vardata->rel->tuples);
     659             : 
     660             :             /* Finally, reverse the selectivity for the ">", ">=" cases. */
     661         214 :             if (isgt)
     662         100 :                 selec = 1.0 - selec;
     663             : 
     664         214 :             CLAMP_PROBABILITY(selec);
     665         214 :             return selec;
     666             :         }
     667             : 
     668             :         /* no stats available, so default result */
     669       19016 :         return DEFAULT_INEQ_SEL;
     670             :     }
     671      247014 :     stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
     672             : 
     673      247014 :     fmgr_info(get_opcode(operator), &opproc);
     674             : 
     675             :     /*
     676             :      * If we have most-common-values info, add up the fractions of the MCV
     677             :      * entries that satisfy MCV OP CONST.  These fractions contribute directly
     678             :      * to the result selectivity.  Also add up the total fraction represented
     679             :      * by MCV entries.
     680             :      */
     681      247014 :     mcv_selec = mcv_selectivity(vardata, &opproc, collation, constval, true,
     682             :                                 &sumcommon);
     683             : 
     684             :     /*
     685             :      * If there is a histogram, determine which bin the constant falls in, and
     686             :      * compute the resulting contribution to selectivity.
     687             :      */
     688      247014 :     hist_selec = ineq_histogram_selectivity(root, vardata,
     689             :                                             operator, &opproc, isgt, iseq,
     690             :                                             collation,
     691             :                                             constval, consttype);
     692             : 
     693             :     /*
     694             :      * Now merge the results from the MCV and histogram calculations,
     695             :      * realizing that the histogram covers only the non-null values that are
     696             :      * not listed in MCV.
     697             :      */
     698      247014 :     selec = 1.0 - stats->stanullfrac - sumcommon;
     699             : 
     700      247014 :     if (hist_selec >= 0.0)
     701      193134 :         selec *= hist_selec;
     702             :     else
     703             :     {
     704             :         /*
     705             :          * If no histogram but there are values not accounted for by MCV,
     706             :          * arbitrarily assume half of them will match.
     707             :          */
     708       53880 :         selec *= 0.5;
     709             :     }
     710             : 
     711      247014 :     selec += mcv_selec;
     712             : 
     713             :     /* result should be in range, but make sure... */
     714      247014 :     CLAMP_PROBABILITY(selec);
     715             : 
     716      247014 :     return selec;
     717             : }
     718             : 
     719             : /*
     720             :  *  mcv_selectivity         - Examine the MCV list for selectivity estimates
     721             :  *
     722             :  * Determine the fraction of the variable's MCV population that satisfies
     723             :  * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.  Also
     724             :  * compute the fraction of the total column population represented by the MCV
     725             :  * list.  This code will work for any boolean-returning predicate operator.
     726             :  *
     727             :  * The function result is the MCV selectivity, and the fraction of the
     728             :  * total population is returned into *sumcommonp.  Zeroes are returned
     729             :  * if there is no MCV list.
     730             :  */
     731             : double
     732      252014 : mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc, Oid collation,
     733             :                 Datum constval, bool varonleft,
     734             :                 double *sumcommonp)
     735             : {
     736             :     double      mcv_selec,
     737             :                 sumcommon;
     738             :     AttStatsSlot sslot;
     739             :     int         i;
     740             : 
     741      252014 :     mcv_selec = 0.0;
     742      252014 :     sumcommon = 0.0;
     743             : 
     744      501760 :     if (HeapTupleIsValid(vardata->statsTuple) &&
     745      499450 :         statistic_proc_security_check(vardata, opproc->fn_oid) &&
     746      249704 :         get_attstatsslot(&sslot, vardata->statsTuple,
     747             :                          STATISTIC_KIND_MCV, InvalidOid,
     748             :                          ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
     749             :     {
     750      112198 :         LOCAL_FCINFO(fcinfo, 2);
     751             : 
     752             :         /*
     753             :          * We invoke the opproc "by hand" so that we won't fail on NULL
     754             :          * results.  Such cases won't arise for normal comparison functions,
     755             :          * but generic_restriction_selectivity could perhaps be used with
     756             :          * operators that can return NULL.  A small side benefit is to not
     757             :          * need to re-initialize the fcinfo struct from scratch each time.
     758             :          */
     759      112198 :         InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
     760             :                                  NULL, NULL);
     761      112198 :         fcinfo->args[0].isnull = false;
     762      112198 :         fcinfo->args[1].isnull = false;
     763             :         /* be careful to apply operator right way 'round */
     764      112198 :         if (varonleft)
     765      112198 :             fcinfo->args[1].value = constval;
     766             :         else
     767           0 :             fcinfo->args[0].value = constval;
     768             : 
     769     3044232 :         for (i = 0; i < sslot.nvalues; i++)
     770             :         {
     771             :             Datum       fresult;
     772             : 
     773     2932034 :             if (varonleft)
     774     2932034 :                 fcinfo->args[0].value = sslot.values[i];
     775             :             else
     776           0 :                 fcinfo->args[1].value = sslot.values[i];
     777     2932034 :             fcinfo->isnull = false;
     778     2932034 :             fresult = FunctionCallInvoke(fcinfo);
     779     2932034 :             if (!fcinfo->isnull && DatumGetBool(fresult))
     780     1215120 :                 mcv_selec += sslot.numbers[i];
     781     2932034 :             sumcommon += sslot.numbers[i];
     782             :         }
     783      112198 :         free_attstatsslot(&sslot);
     784             :     }
     785             : 
     786      252014 :     *sumcommonp = sumcommon;
     787      252014 :     return mcv_selec;
     788             : }
     789             : 
     790             : /*
     791             :  *  histogram_selectivity   - Examine the histogram for selectivity estimates
     792             :  *
     793             :  * Determine the fraction of the variable's histogram entries that satisfy
     794             :  * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
     795             :  *
     796             :  * This code will work for any boolean-returning predicate operator, whether
     797             :  * or not it has anything to do with the histogram sort operator.  We are
     798             :  * essentially using the histogram just as a representative sample.  However,
     799             :  * small histograms are unlikely to be all that representative, so the caller
     800             :  * should be prepared to fall back on some other estimation approach when the
     801             :  * histogram is missing or very small.  It may also be prudent to combine this
     802             :  * approach with another one when the histogram is small.
     803             :  *
     804             :  * If the actual histogram size is not at least min_hist_size, we won't bother
     805             :  * to do the calculation at all.  Also, if the n_skip parameter is > 0, we
     806             :  * ignore the first and last n_skip histogram elements, on the grounds that
     807             :  * they are outliers and hence not very representative.  Typical values for
     808             :  * these parameters are 10 and 1.
     809             :  *
     810             :  * The function result is the selectivity, or -1 if there is no histogram
     811             :  * or it's smaller than min_hist_size.
     812             :  *
     813             :  * The output parameter *hist_size receives the actual histogram size,
     814             :  * or zero if no histogram.  Callers may use this number to decide how
     815             :  * much faith to put in the function result.
     816             :  *
     817             :  * Note that the result disregards both the most-common-values (if any) and
     818             :  * null entries.  The caller is expected to combine this result with
     819             :  * statistics for those portions of the column population.  It may also be
     820             :  * prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
     821             :  */
     822             : double
     823        5000 : histogram_selectivity(VariableStatData *vardata,
     824             :                       FmgrInfo *opproc, Oid collation,
     825             :                       Datum constval, bool varonleft,
     826             :                       int min_hist_size, int n_skip,
     827             :                       int *hist_size)
     828             : {
     829             :     double      result;
     830             :     AttStatsSlot sslot;
     831             : 
     832             :     /* check sanity of parameters */
     833             :     Assert(n_skip >= 0);
     834             :     Assert(min_hist_size > 2 * n_skip);
     835             : 
     836        7732 :     if (HeapTupleIsValid(vardata->statsTuple) &&
     837        5458 :         statistic_proc_security_check(vardata, opproc->fn_oid) &&
     838        2726 :         get_attstatsslot(&sslot, vardata->statsTuple,
     839             :                          STATISTIC_KIND_HISTOGRAM, InvalidOid,
     840             :                          ATTSTATSSLOT_VALUES))
     841             :     {
     842        2632 :         *hist_size = sslot.nvalues;
     843        2632 :         if (sslot.nvalues >= min_hist_size)
     844             :         {
     845        1550 :             LOCAL_FCINFO(fcinfo, 2);
     846        1550 :             int         nmatch = 0;
     847             :             int         i;
     848             : 
     849             :             /*
     850             :              * We invoke the opproc "by hand" so that we won't fail on NULL
     851             :              * results.  Such cases won't arise for normal comparison
     852             :              * functions, but generic_restriction_selectivity could perhaps be
     853             :              * used with operators that can return NULL.  A small side benefit
     854             :              * is to not need to re-initialize the fcinfo struct from scratch
     855             :              * each time.
     856             :              */
     857        1550 :             InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
     858             :                                      NULL, NULL);
     859        1550 :             fcinfo->args[0].isnull = false;
     860        1550 :             fcinfo->args[1].isnull = false;
     861             :             /* be careful to apply operator right way 'round */
     862        1550 :             if (varonleft)
     863        1550 :                 fcinfo->args[1].value = constval;
     864             :             else
     865           0 :                 fcinfo->args[0].value = constval;
     866             : 
     867      134080 :             for (i = n_skip; i < sslot.nvalues - n_skip; i++)
     868             :             {
     869             :                 Datum       fresult;
     870             : 
     871      132530 :                 if (varonleft)
     872      132530 :                     fcinfo->args[0].value = sslot.values[i];
     873             :                 else
     874           0 :                     fcinfo->args[1].value = sslot.values[i];
     875      132530 :                 fcinfo->isnull = false;
     876      132530 :                 fresult = FunctionCallInvoke(fcinfo);
     877      132530 :                 if (!fcinfo->isnull && DatumGetBool(fresult))
     878        5424 :                     nmatch++;
     879             :             }
     880        1550 :             result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
     881             :         }
     882             :         else
     883        1082 :             result = -1;
     884        2632 :         free_attstatsslot(&sslot);
     885             :     }
     886             :     else
     887             :     {
     888        2368 :         *hist_size = 0;
     889        2368 :         result = -1;
     890             :     }
     891             : 
     892        5000 :     return result;
     893             : }
     894             : 
     895             : /*
     896             :  *  generic_restriction_selectivity     - Selectivity for almost anything
     897             :  *
     898             :  * This function estimates selectivity for operators that we don't have any
     899             :  * special knowledge about, but are on data types that we collect standard
     900             :  * MCV and/or histogram statistics for.  (Additional assumptions are that
     901             :  * the operator is strict and immutable, or at least stable.)
     902             :  *
     903             :  * If we have "VAR OP CONST" or "CONST OP VAR", selectivity is estimated by
     904             :  * applying the operator to each element of the column's MCV and/or histogram
     905             :  * stats, and merging the results using the assumption that the histogram is
     906             :  * a reasonable random sample of the column's non-MCV population.  Note that
     907             :  * if the operator's semantics are related to the histogram ordering, this
     908             :  * might not be such a great assumption; other functions such as
     909             :  * scalarineqsel() are probably a better match in such cases.
     910             :  *
     911             :  * Otherwise, fall back to the default selectivity provided by the caller.
     912             :  */
     913             : double
     914        1106 : generic_restriction_selectivity(PlannerInfo *root, Oid oproid, Oid collation,
     915             :                                 List *args, int varRelid,
     916             :                                 double default_selectivity)
     917             : {
     918             :     double      selec;
     919             :     VariableStatData vardata;
     920             :     Node       *other;
     921             :     bool        varonleft;
     922             : 
     923             :     /*
     924             :      * If expression is not variable OP something or something OP variable,
     925             :      * then punt and return the default estimate.
     926             :      */
     927        1106 :     if (!get_restriction_variable(root, args, varRelid,
     928             :                                   &vardata, &other, &varonleft))
     929           0 :         return default_selectivity;
     930             : 
     931             :     /*
     932             :      * If the something is a NULL constant, assume operator is strict and
     933             :      * return zero, ie, operator will never return TRUE.
     934             :      */
     935        1106 :     if (IsA(other, Const) &&
     936        1106 :         ((Const *) other)->constisnull)
     937             :     {
     938           0 :         ReleaseVariableStats(vardata);
     939           0 :         return 0.0;
     940             :     }
     941             : 
     942        1106 :     if (IsA(other, Const))
     943             :     {
     944             :         /* Variable is being compared to a known non-null constant */
     945        1106 :         Datum       constval = ((Const *) other)->constvalue;
     946             :         FmgrInfo    opproc;
     947             :         double      mcvsum;
     948             :         double      mcvsel;
     949             :         double      nullfrac;
     950             :         int         hist_size;
     951             : 
     952        1106 :         fmgr_info(get_opcode(oproid), &opproc);
     953             : 
     954             :         /*
     955             :          * Calculate the selectivity for the column's most common values.
     956             :          */
     957        1106 :         mcvsel = mcv_selectivity(&vardata, &opproc, collation,
     958             :                                  constval, varonleft,
     959             :                                  &mcvsum);
     960             : 
     961             :         /*
     962             :          * If the histogram is large enough, see what fraction of it matches
     963             :          * the query, and assume that's representative of the non-MCV
     964             :          * population.  Otherwise use the default selectivity for the non-MCV
     965             :          * population.
     966             :          */
     967        1106 :         selec = histogram_selectivity(&vardata, &opproc, collation,
     968             :                                       constval, varonleft,
     969             :                                       10, 1, &hist_size);
     970        1106 :         if (selec < 0)
     971             :         {
     972             :             /* Nope, fall back on default */
     973        1106 :             selec = default_selectivity;
     974             :         }
     975           0 :         else if (hist_size < 100)
     976             :         {
     977             :             /*
     978             :              * For histogram sizes from 10 to 100, we combine the histogram
     979             :              * and default selectivities, putting increasingly more trust in
     980             :              * the histogram for larger sizes.
     981             :              */
     982           0 :             double      hist_weight = hist_size / 100.0;
     983             : 
     984           0 :             selec = selec * hist_weight +
     985           0 :                 default_selectivity * (1.0 - hist_weight);
     986             :         }
     987             : 
     988             :         /* In any case, don't believe extremely small or large estimates. */
     989        1106 :         if (selec < 0.0001)
     990           0 :             selec = 0.0001;
     991        1106 :         else if (selec > 0.9999)
     992           0 :             selec = 0.9999;
     993             : 
     994             :         /* Don't forget to account for nulls. */
     995        1106 :         if (HeapTupleIsValid(vardata.statsTuple))
     996          84 :             nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
     997             :         else
     998        1022 :             nullfrac = 0.0;
     999             : 
    1000             :         /*
    1001             :          * Now merge the results from the MCV and histogram calculations,
    1002             :          * realizing that the histogram covers only the non-null values that
    1003             :          * are not listed in MCV.
    1004             :          */
    1005        1106 :         selec *= 1.0 - nullfrac - mcvsum;
    1006        1106 :         selec += mcvsel;
    1007             :     }
    1008             :     else
    1009             :     {
    1010             :         /* Comparison value is not constant, so we can't do anything */
    1011           0 :         selec = default_selectivity;
    1012             :     }
    1013             : 
    1014        1106 :     ReleaseVariableStats(vardata);
    1015             : 
    1016             :     /* result should be in range, but make sure... */
    1017        1106 :     CLAMP_PROBABILITY(selec);
    1018             : 
    1019        1106 :     return selec;
    1020             : }
    1021             : 
    1022             : /*
    1023             :  *  ineq_histogram_selectivity  - Examine the histogram for scalarineqsel
    1024             :  *
    1025             :  * Determine the fraction of the variable's histogram population that
    1026             :  * satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
    1027             :  * The isgt and iseq flags distinguish which of the four cases apply.
    1028             :  *
    1029             :  * While opproc could be looked up from the operator OID, common callers
    1030             :  * also need to call it separately, so we make the caller pass both.
    1031             :  *
    1032             :  * Returns -1 if there is no histogram (valid results will always be >= 0).
    1033             :  *
    1034             :  * Note that the result disregards both the most-common-values (if any) and
    1035             :  * null entries.  The caller is expected to combine this result with
    1036             :  * statistics for those portions of the column population.
    1037             :  *
    1038             :  * This is exported so that some other estimation functions can use it.
    1039             :  */
    1040             : double
    1041      250182 : ineq_histogram_selectivity(PlannerInfo *root,
    1042             :                            VariableStatData *vardata,
    1043             :                            Oid opoid, FmgrInfo *opproc, bool isgt, bool iseq,
    1044             :                            Oid collation,
    1045             :                            Datum constval, Oid consttype)
    1046             : {
    1047             :     double      hist_selec;
    1048             :     AttStatsSlot sslot;
    1049             : 
    1050      250182 :     hist_selec = -1.0;
    1051             : 
    1052             :     /*
    1053             :      * Someday, ANALYZE might store more than one histogram per rel/att,
    1054             :      * corresponding to more than one possible sort ordering defined for the
    1055             :      * column type.  Right now, we know there is only one, so just grab it and
    1056             :      * see if it matches the query.
    1057             :      *
    1058             :      * Note that we can't use opoid as search argument; the staop appearing in
    1059             :      * pg_statistic will be for the relevant '<' operator, but what we have
    1060             :      * might be some other inequality operator such as '>='.  (Even if opoid
    1061             :      * is a '<' operator, it could be cross-type.)  Hence we must use
    1062             :      * comparison_ops_are_compatible() to see if the operators match.
    1063             :      */
    1064      499802 :     if (HeapTupleIsValid(vardata->statsTuple) &&
    1065      499204 :         statistic_proc_security_check(vardata, opproc->fn_oid) &&
    1066      249584 :         get_attstatsslot(&sslot, vardata->statsTuple,
    1067             :                          STATISTIC_KIND_HISTOGRAM, InvalidOid,
    1068             :                          ATTSTATSSLOT_VALUES))
    1069             :     {
    1070      195738 :         if (sslot.nvalues > 1 &&
    1071      391400 :             sslot.stacoll == collation &&
    1072      195662 :             comparison_ops_are_compatible(sslot.staop, opoid))
    1073      195554 :         {
    1074             :             /*
    1075             :              * Use binary search to find the desired location, namely the
    1076             :              * right end of the histogram bin containing the comparison value,
    1077             :              * which is the leftmost entry for which the comparison operator
    1078             :              * succeeds (if isgt) or fails (if !isgt).
    1079             :              *
    1080             :              * In this loop, we pay no attention to whether the operator iseq
    1081             :              * or not; that detail will be mopped up below.  (We cannot tell,
    1082             :              * anyway, whether the operator thinks the values are equal.)
    1083             :              *
    1084             :              * If the binary search accesses the first or last histogram
    1085             :              * entry, we try to replace that endpoint with the true column min
    1086             :              * or max as found by get_actual_variable_range().  This
    1087             :              * ameliorates misestimates when the min or max is moving as a
    1088             :              * result of changes since the last ANALYZE.  Note that this could
    1089             :              * result in effectively including MCVs into the histogram that
    1090             :              * weren't there before, but we don't try to correct for that.
    1091             :              */
    1092             :             double      histfrac;
    1093      195554 :             int         lobound = 0;    /* first possible slot to search */
    1094      195554 :             int         hibound = sslot.nvalues;    /* last+1 slot to search */
    1095      195554 :             bool        have_end = false;
    1096             : 
    1097             :             /*
    1098             :              * If there are only two histogram entries, we'll want up-to-date
    1099             :              * values for both.  (If there are more than two, we need at most
    1100             :              * one of them to be updated, so we deal with that within the
    1101             :              * loop.)
    1102             :              */
    1103      195554 :             if (sslot.nvalues == 2)
    1104        1754 :                 have_end = get_actual_variable_range(root,
    1105             :                                                      vardata,
    1106             :                                                      sslot.staop,
    1107             :                                                      collation,
    1108             :                                                      &sslot.values[0],
    1109        1754 :                                                      &sslot.values[1]);
    1110             : 
    1111     1294784 :             while (lobound < hibound)
    1112             :             {
    1113     1099230 :                 int         probe = (lobound + hibound) / 2;
    1114             :                 bool        ltcmp;
    1115             : 
    1116             :                 /*
    1117             :                  * If we find ourselves about to compare to the first or last
    1118             :                  * histogram entry, first try to replace it with the actual
    1119             :                  * current min or max (unless we already did so above).
    1120             :                  */
    1121     1099230 :                 if (probe == 0 && sslot.nvalues > 2)
    1122       96308 :                     have_end = get_actual_variable_range(root,
    1123             :                                                          vardata,
    1124             :                                                          sslot.staop,
    1125             :                                                          collation,
    1126             :                                                          &sslot.values[0],
    1127             :                                                          NULL);
    1128     1002922 :                 else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
    1129       65638 :                     have_end = get_actual_variable_range(root,
    1130             :                                                          vardata,
    1131             :                                                          sslot.staop,
    1132             :                                                          collation,
    1133             :                                                          NULL,
    1134       65638 :                                                          &sslot.values[probe]);
    1135             : 
    1136     1099230 :                 ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
    1137             :                                                        collation,
    1138     1099230 :                                                        sslot.values[probe],
    1139             :                                                        constval));
    1140     1099230 :                 if (isgt)
    1141       55618 :                     ltcmp = !ltcmp;
    1142     1099230 :                 if (ltcmp)
    1143      413704 :                     lobound = probe + 1;
    1144             :                 else
    1145      685526 :                     hibound = probe;
    1146             :             }
    1147             : 
    1148      195554 :             if (lobound <= 0)
    1149             :             {
    1150             :                 /*
    1151             :                  * Constant is below lower histogram boundary.  More
    1152             :                  * precisely, we have found that no entry in the histogram
    1153             :                  * satisfies the inequality clause (if !isgt) or they all do
    1154             :                  * (if isgt).  We estimate that that's true of the entire
    1155             :                  * table, so set histfrac to 0.0 (which we'll flip to 1.0
    1156             :                  * below, if isgt).
    1157             :                  */
    1158       86258 :                 histfrac = 0.0;
    1159             :             }
    1160      109296 :             else if (lobound >= sslot.nvalues)
    1161             :             {
    1162             :                 /*
    1163             :                  * Inverse case: constant is above upper histogram boundary.
    1164             :                  */
    1165       32244 :                 histfrac = 1.0;
    1166             :             }
    1167             :             else
    1168             :             {
    1169             :                 /* We have values[i-1] <= constant <= values[i]. */
    1170       77052 :                 int         i = lobound;
    1171       77052 :                 double      eq_selec = 0;
    1172             :                 double      val,
    1173             :                             high,
    1174             :                             low;
    1175             :                 double      binfrac;
    1176             : 
    1177             :                 /*
    1178             :                  * In the cases where we'll need it below, obtain an estimate
    1179             :                  * of the selectivity of "x = constval".  We use a calculation
    1180             :                  * similar to what var_eq_const() does for a non-MCV constant,
    1181             :                  * ie, estimate that all distinct non-MCV values occur equally
    1182             :                  * often.  But multiplication by "1.0 - sumcommon - nullfrac"
    1183             :                  * will be done by our caller, so we shouldn't do that here.
    1184             :                  * Therefore we can't try to clamp the estimate by reference
    1185             :                  * to the least common MCV; the result would be too small.
    1186             :                  *
    1187             :                  * Note: since this is effectively assuming that constval
    1188             :                  * isn't an MCV, it's logically dubious if constval in fact is
    1189             :                  * one.  But we have to apply *some* correction for equality,
    1190             :                  * and anyway we cannot tell if constval is an MCV, since we
    1191             :                  * don't have a suitable equality operator at hand.
    1192             :                  */
    1193       77052 :                 if (i == 1 || isgt == iseq)
    1194             :                 {
    1195             :                     double      otherdistinct;
    1196             :                     bool        isdefault;
    1197             :                     AttStatsSlot mcvslot;
    1198             : 
    1199             :                     /* Get estimated number of distinct values */
    1200       25158 :                     otherdistinct = get_variable_numdistinct(vardata,
    1201             :                                                              &isdefault);
    1202             : 
    1203             :                     /* Subtract off the number of known MCVs */
    1204       25158 :                     if (get_attstatsslot(&mcvslot, vardata->statsTuple,
    1205             :                                          STATISTIC_KIND_MCV, InvalidOid,
    1206             :                                          ATTSTATSSLOT_NUMBERS))
    1207             :                     {
    1208        3496 :                         otherdistinct -= mcvslot.nnumbers;
    1209        3496 :                         free_attstatsslot(&mcvslot);
    1210             :                     }
    1211             : 
    1212             :                     /* If result doesn't seem sane, leave eq_selec at 0 */
    1213       25158 :                     if (otherdistinct > 1)
    1214       25116 :                         eq_selec = 1.0 / otherdistinct;
    1215             :                 }
    1216             : 
    1217             :                 /*
    1218             :                  * Convert the constant and the two nearest bin boundary
    1219             :                  * values to a uniform comparison scale, and do a linear
    1220             :                  * interpolation within this bin.
    1221             :                  */
    1222       77052 :                 if (convert_to_scalar(constval, consttype, collation,
    1223             :                                       &val,
    1224       77052 :                                       sslot.values[i - 1], sslot.values[i],
    1225             :                                       vardata->vartype,
    1226             :                                       &low, &high))
    1227             :                 {
    1228       77052 :                     if (high <= low)
    1229             :                     {
    1230             :                         /* cope if bin boundaries appear identical */
    1231           0 :                         binfrac = 0.5;
    1232             :                     }
    1233       77052 :                     else if (val <= low)
    1234       16312 :                         binfrac = 0.0;
    1235       60740 :                     else if (val >= high)
    1236        3572 :                         binfrac = 1.0;
    1237             :                     else
    1238             :                     {
    1239       57168 :                         binfrac = (val - low) / (high - low);
    1240             : 
    1241             :                         /*
    1242             :                          * Watch out for the possibility that we got a NaN or
    1243             :                          * Infinity from the division.  This can happen
    1244             :                          * despite the previous checks, if for example "low"
    1245             :                          * is -Infinity.
    1246             :                          */
    1247       57168 :                         if (isnan(binfrac) ||
    1248       57168 :                             binfrac < 0.0 || binfrac > 1.0)
    1249           0 :                             binfrac = 0.5;
    1250             :                     }
    1251             :                 }
    1252             :                 else
    1253             :                 {
    1254             :                     /*
    1255             :                      * Ideally we'd produce an error here, on the grounds that
    1256             :                      * the given operator shouldn't have scalarXXsel
    1257             :                      * registered as its selectivity func unless we can deal
    1258             :                      * with its operand types.  But currently, all manner of
    1259             :                      * stuff is invoking scalarXXsel, so give a default
    1260             :                      * estimate until that can be fixed.
    1261             :                      */
    1262           0 :                     binfrac = 0.5;
    1263             :                 }
    1264             : 
    1265             :                 /*
    1266             :                  * Now, compute the overall selectivity across the values
    1267             :                  * represented by the histogram.  We have i-1 full bins and
    1268             :                  * binfrac partial bin below the constant.
    1269             :                  */
    1270       77052 :                 histfrac = (double) (i - 1) + binfrac;
    1271       77052 :                 histfrac /= (double) (sslot.nvalues - 1);
    1272             : 
    1273             :                 /*
    1274             :                  * At this point, histfrac is an estimate of the fraction of
    1275             :                  * the population represented by the histogram that satisfies
    1276             :                  * "x <= constval".  Somewhat remarkably, this statement is
    1277             :                  * true regardless of which operator we were doing the probes
    1278             :                  * with, so long as convert_to_scalar() delivers reasonable
    1279             :                  * results.  If the probe constant is equal to some histogram
    1280             :                  * entry, we would have considered the bin to the left of that
    1281             :                  * entry if probing with "<" or ">=", or the bin to the right
    1282             :                  * if probing with "<=" or ">"; but binfrac would have come
    1283             :                  * out as 1.0 in the first case and 0.0 in the second, leading
    1284             :                  * to the same histfrac in either case.  For probe constants
    1285             :                  * between histogram entries, we find the same bin and get the
    1286             :                  * same estimate with any operator.
    1287             :                  *
    1288             :                  * The fact that the estimate corresponds to "x <= constval"
    1289             :                  * and not "x < constval" is because of the way that ANALYZE
    1290             :                  * constructs the histogram: each entry is, effectively, the
    1291             :                  * rightmost value in its sample bucket.  So selectivity
    1292             :                  * values that are exact multiples of 1/(histogram_size-1)
    1293             :                  * should be understood as estimates including a histogram
    1294             :                  * entry plus everything to its left.
    1295             :                  *
    1296             :                  * However, that breaks down for the first histogram entry,
    1297             :                  * which necessarily is the leftmost value in its sample
    1298             :                  * bucket.  That means the first histogram bin is slightly
    1299             :                  * narrower than the rest, by an amount equal to eq_selec.
    1300             :                  * Another way to say that is that we want "x <= leftmost" to
    1301             :                  * be estimated as eq_selec not zero.  So, if we're dealing
    1302             :                  * with the first bin (i==1), rescale to make that true while
    1303             :                  * adjusting the rest of that bin linearly.
    1304             :                  */
    1305       77052 :                 if (i == 1)
    1306       11332 :                     histfrac += eq_selec * (1.0 - binfrac);
    1307             : 
    1308             :                 /*
    1309             :                  * "x <= constval" is good if we want an estimate for "<=" or
    1310             :                  * ">", but if we are estimating for "<" or ">=", we now need
    1311             :                  * to decrease the estimate by eq_selec.
    1312             :                  */
    1313       77052 :                 if (isgt == iseq)
    1314       20424 :                     histfrac -= eq_selec;
    1315             :             }
    1316             : 
    1317             :             /*
    1318             :              * Now the estimate is finished for "<" and "<=" cases.  If we are
    1319             :              * estimating for ">" or ">=", flip it.
    1320             :              */
    1321      195554 :             hist_selec = isgt ? (1.0 - histfrac) : histfrac;
    1322             : 
    1323             :             /*
    1324             :              * The histogram boundaries are only approximate to begin with,
    1325             :              * and may well be out of date anyway.  Therefore, don't believe
    1326             :              * extremely small or large selectivity estimates --- unless we
    1327             :              * got actual current endpoint values from the table, in which
    1328             :              * case just do the usual sanity clamp.  Somewhat arbitrarily, we
    1329             :              * set the cutoff for other cases at a hundredth of the histogram
    1330             :              * resolution.
    1331             :              */
    1332      195554 :             if (have_end)
    1333      102342 :                 CLAMP_PROBABILITY(hist_selec);
    1334             :             else
    1335             :             {
    1336       93212 :                 double      cutoff = 0.01 / (double) (sslot.nvalues - 1);
    1337             : 
    1338       93212 :                 if (hist_selec < cutoff)
    1339       31590 :                     hist_selec = cutoff;
    1340       61622 :                 else if (hist_selec > 1.0 - cutoff)
    1341       24974 :                     hist_selec = 1.0 - cutoff;
    1342             :             }
    1343             :         }
    1344         184 :         else if (sslot.nvalues > 1)
    1345             :         {
    1346             :             /*
    1347             :              * If we get here, we have a histogram but it's not sorted the way
    1348             :              * we want.  Do a brute-force search to see how many of the
    1349             :              * entries satisfy the comparison condition, and take that
    1350             :              * fraction as our estimate.  (This is identical to the inner loop
    1351             :              * of histogram_selectivity; maybe share code?)
    1352             :              */
    1353         184 :             LOCAL_FCINFO(fcinfo, 2);
    1354         184 :             int         nmatch = 0;
    1355             : 
    1356         184 :             InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
    1357             :                                      NULL, NULL);
    1358         184 :             fcinfo->args[0].isnull = false;
    1359         184 :             fcinfo->args[1].isnull = false;
    1360         184 :             fcinfo->args[1].value = constval;
    1361      962084 :             for (int i = 0; i < sslot.nvalues; i++)
    1362             :             {
    1363             :                 Datum       fresult;
    1364             : 
    1365      961900 :                 fcinfo->args[0].value = sslot.values[i];
    1366      961900 :                 fcinfo->isnull = false;
    1367      961900 :                 fresult = FunctionCallInvoke(fcinfo);
    1368      961900 :                 if (!fcinfo->isnull && DatumGetBool(fresult))
    1369        1924 :                     nmatch++;
    1370             :             }
    1371         184 :             hist_selec = ((double) nmatch) / ((double) sslot.nvalues);
    1372             : 
    1373             :             /*
    1374             :              * As above, clamp to a hundredth of the histogram resolution.
    1375             :              * This case is surely even less trustworthy than the normal one,
    1376             :              * so we shouldn't believe exact 0 or 1 selectivity.  (Maybe the
    1377             :              * clamp should be more restrictive in this case?)
    1378             :              */
    1379             :             {
    1380         184 :                 double      cutoff = 0.01 / (double) (sslot.nvalues - 1);
    1381             : 
    1382         184 :                 if (hist_selec < cutoff)
    1383          12 :                     hist_selec = cutoff;
    1384         172 :                 else if (hist_selec > 1.0 - cutoff)
    1385          12 :                     hist_selec = 1.0 - cutoff;
    1386             :             }
    1387             :         }
    1388             : 
    1389      195738 :         free_attstatsslot(&sslot);
    1390             :     }
    1391             : 
    1392      250182 :     return hist_selec;
    1393             : }
    1394             : 
    1395             : /*
    1396             :  * Common wrapper function for the selectivity estimators that simply
    1397             :  * invoke scalarineqsel().
    1398             :  */
    1399             : static Datum
    1400       38992 : scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
    1401             : {
    1402       38992 :     PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
    1403       38992 :     Oid         operator = PG_GETARG_OID(1);
    1404       38992 :     List       *args = (List *) PG_GETARG_POINTER(2);
    1405       38992 :     int         varRelid = PG_GETARG_INT32(3);
    1406       38992 :     Oid         collation = PG_GET_COLLATION();
    1407             :     VariableStatData vardata;
    1408             :     Node       *other;
    1409             :     bool        varonleft;
    1410             :     Datum       constval;
    1411             :     Oid         consttype;
    1412             :     double      selec;
    1413             : 
    1414             :     /*
    1415             :      * If expression is not variable op something or something op variable,
    1416             :      * then punt and return a default estimate.
    1417             :      */
    1418       38992 :     if (!get_restriction_variable(root, args, varRelid,
    1419             :                                   &vardata, &other, &varonleft))
    1420         890 :         PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
    1421             : 
    1422             :     /*
    1423             :      * Can't do anything useful if the something is not a constant, either.
    1424             :      */
    1425       38102 :     if (!IsA(other, Const))
    1426             :     {
    1427        2636 :         ReleaseVariableStats(vardata);
    1428        2636 :         PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
    1429             :     }
    1430             : 
    1431             :     /*
    1432             :      * If the constant is NULL, assume operator is strict and return zero, ie,
    1433             :      * operator will never return TRUE.
    1434             :      */
    1435       35466 :     if (((Const *) other)->constisnull)
    1436             :     {
    1437          54 :         ReleaseVariableStats(vardata);
    1438          54 :         PG_RETURN_FLOAT8(0.0);
    1439             :     }
    1440       35412 :     constval = ((Const *) other)->constvalue;
    1441       35412 :     consttype = ((Const *) other)->consttype;
    1442             : 
    1443             :     /*
    1444             :      * Force the var to be on the left to simplify logic in scalarineqsel.
    1445             :      */
    1446       35412 :     if (!varonleft)
    1447             :     {
    1448         336 :         operator = get_commutator(operator);
    1449         336 :         if (!operator)
    1450             :         {
    1451             :             /* Use default selectivity (should we raise an error instead?) */
    1452           0 :             ReleaseVariableStats(vardata);
    1453           0 :             PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
    1454             :         }
    1455         336 :         isgt = !isgt;
    1456             :     }
    1457             : 
    1458             :     /* The rest of the work is done by scalarineqsel(). */
    1459       35412 :     selec = scalarineqsel(root, operator, isgt, iseq, collation,
    1460             :                           &vardata, constval, consttype);
    1461             : 
    1462       35412 :     ReleaseVariableStats(vardata);
    1463             : 
    1464       35412 :     PG_RETURN_FLOAT8((float8) selec);
    1465             : }
    1466             : 
    1467             : /*
    1468             :  *      scalarltsel     - Selectivity of "<" for scalars.
    1469             :  */
    1470             : Datum
    1471       13536 : scalarltsel(PG_FUNCTION_ARGS)
    1472             : {
    1473       13536 :     return scalarineqsel_wrapper(fcinfo, false, false);
    1474             : }
    1475             : 
    1476             : /*
    1477             :  *      scalarlesel     - Selectivity of "<=" for scalars.
    1478             :  */
    1479             : Datum
    1480        4290 : scalarlesel(PG_FUNCTION_ARGS)
    1481             : {
    1482        4290 :     return scalarineqsel_wrapper(fcinfo, false, true);
    1483             : }
    1484             : 
    1485             : /*
    1486             :  *      scalargtsel     - Selectivity of ">" for scalars.
    1487             :  */
    1488             : Datum
    1489       13860 : scalargtsel(PG_FUNCTION_ARGS)
    1490             : {
    1491       13860 :     return scalarineqsel_wrapper(fcinfo, true, false);
    1492             : }
    1493             : 
    1494             : /*
    1495             :  *      scalargesel     - Selectivity of ">=" for scalars.
    1496             :  */
    1497             : Datum
    1498        7306 : scalargesel(PG_FUNCTION_ARGS)
    1499             : {
    1500        7306 :     return scalarineqsel_wrapper(fcinfo, true, true);
    1501             : }
    1502             : 
    1503             : /*
    1504             :  *      boolvarsel      - Selectivity of Boolean variable.
    1505             :  *
    1506             :  * This can actually be called on any boolean-valued expression.  If it
    1507             :  * involves only Vars of the specified relation, and if there are statistics
    1508             :  * about the Var or expression (the latter is possible if it's indexed) then
    1509             :  * we'll produce a real estimate; otherwise it's just a default.
    1510             :  */
    1511             : Selectivity
    1512       43712 : boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
    1513             : {
    1514             :     VariableStatData vardata;
    1515             :     double      selec;
    1516             : 
    1517       43712 :     examine_variable(root, arg, varRelid, &vardata);
    1518       43712 :     if (HeapTupleIsValid(vardata.statsTuple))
    1519             :     {
    1520             :         /*
    1521             :          * A boolean variable V is equivalent to the clause V = 't', so we
    1522             :          * compute the selectivity as if that is what we have.
    1523             :          */
    1524       36662 :         selec = var_eq_const(&vardata, BooleanEqualOperator, InvalidOid,
    1525             :                              BoolGetDatum(true), false, true, false);
    1526             :     }
    1527             :     else
    1528             :     {
    1529             :         /* Otherwise, the default estimate is 0.5 */
    1530        7050 :         selec = 0.5;
    1531             :     }
    1532       43712 :     ReleaseVariableStats(vardata);
    1533       43712 :     return selec;
    1534             : }
    1535             : 
    1536             : /*
    1537             :  *      booltestsel     - Selectivity of BooleanTest Node.
    1538             :  */
    1539             : Selectivity
    1540         850 : booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
    1541             :             int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
    1542             : {
    1543             :     VariableStatData vardata;
    1544             :     double      selec;
    1545             : 
    1546         850 :     examine_variable(root, arg, varRelid, &vardata);
    1547             : 
    1548         850 :     if (HeapTupleIsValid(vardata.statsTuple))
    1549             :     {
    1550             :         Form_pg_statistic stats;
    1551             :         double      freq_null;
    1552             :         AttStatsSlot sslot;
    1553             : 
    1554           0 :         stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
    1555           0 :         freq_null = stats->stanullfrac;
    1556             : 
    1557           0 :         if (get_attstatsslot(&sslot, vardata.statsTuple,
    1558             :                              STATISTIC_KIND_MCV, InvalidOid,
    1559             :                              ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
    1560           0 :             && sslot.nnumbers > 0)
    1561           0 :         {
    1562             :             double      freq_true;
    1563             :             double      freq_false;
    1564             : 
    1565             :             /*
    1566             :              * Get first MCV frequency and derive frequency for true.
    1567             :              */
    1568           0 :             if (DatumGetBool(sslot.values[0]))
    1569           0 :                 freq_true = sslot.numbers[0];
    1570             :             else
    1571           0 :                 freq_true = 1.0 - sslot.numbers[0] - freq_null;
    1572             : 
    1573             :             /*
    1574             :              * Next derive frequency for false. Then use these as appropriate
    1575             :              * to derive frequency for each case.
    1576             :              */
    1577           0 :             freq_false = 1.0 - freq_true - freq_null;
    1578             : 
    1579           0 :             switch (booltesttype)
    1580             :             {
    1581           0 :                 case IS_UNKNOWN:
    1582             :                     /* select only NULL values */
    1583           0 :                     selec = freq_null;
    1584           0 :                     break;
    1585           0 :                 case IS_NOT_UNKNOWN:
    1586             :                     /* select non-NULL values */
    1587           0 :                     selec = 1.0 - freq_null;
    1588           0 :                     break;
    1589           0 :                 case IS_TRUE:
    1590             :                     /* select only TRUE values */
    1591           0 :                     selec = freq_true;
    1592           0 :                     break;
    1593           0 :                 case IS_NOT_TRUE:
    1594             :                     /* select non-TRUE values */
    1595           0 :                     selec = 1.0 - freq_true;
    1596           0 :                     break;
    1597           0 :                 case IS_FALSE:
    1598             :                     /* select only FALSE values */
    1599           0 :                     selec = freq_false;
    1600           0 :                     break;
    1601           0 :                 case IS_NOT_FALSE:
    1602             :                     /* select non-FALSE values */
    1603           0 :                     selec = 1.0 - freq_false;
    1604           0 :                     break;
    1605           0 :                 default:
    1606           0 :                     elog(ERROR, "unrecognized booltesttype: %d",
    1607             :                          (int) booltesttype);
    1608             :                     selec = 0.0;    /* Keep compiler quiet */
    1609             :                     break;
    1610             :             }
    1611             : 
    1612           0 :             free_attstatsslot(&sslot);
    1613             :         }
    1614             :         else
    1615             :         {
    1616             :             /*
    1617             :              * No most-common-value info available. Still have null fraction
    1618             :              * information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
    1619             :              * for null fraction and assume a 50-50 split of TRUE and FALSE.
    1620             :              */
    1621           0 :             switch (booltesttype)
    1622             :             {
    1623           0 :                 case IS_UNKNOWN:
    1624             :                     /* select only NULL values */
    1625           0 :                     selec = freq_null;
    1626           0 :                     break;
    1627           0 :                 case IS_NOT_UNKNOWN:
    1628             :                     /* select non-NULL values */
    1629           0 :                     selec = 1.0 - freq_null;
    1630           0 :                     break;
    1631           0 :                 case IS_TRUE:
    1632             :                 case IS_FALSE:
    1633             :                     /* Assume we select half of the non-NULL values */
    1634           0 :                     selec = (1.0 - freq_null) / 2.0;
    1635           0 :                     break;
    1636           0 :                 case IS_NOT_TRUE:
    1637             :                 case IS_NOT_FALSE:
    1638             :                     /* Assume we select NULLs plus half of the non-NULLs */
    1639             :                     /* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
    1640           0 :                     selec = (freq_null + 1.0) / 2.0;
    1641           0 :                     break;
    1642           0 :                 default:
    1643           0 :                     elog(ERROR, "unrecognized booltesttype: %d",
    1644             :                          (int) booltesttype);
    1645             :                     selec = 0.0;    /* Keep compiler quiet */
    1646             :                     break;
    1647             :             }
    1648             :         }
    1649             :     }
    1650             :     else
    1651             :     {
    1652             :         /*
    1653             :          * If we can't get variable statistics for the argument, perhaps
    1654             :          * clause_selectivity can do something with it.  We ignore the
    1655             :          * possibility of a NULL value when using clause_selectivity, and just
    1656             :          * assume the value is either TRUE or FALSE.
    1657             :          */
    1658         850 :         switch (booltesttype)
    1659             :         {
    1660          48 :             case IS_UNKNOWN:
    1661          48 :                 selec = DEFAULT_UNK_SEL;
    1662          48 :                 break;
    1663         108 :             case IS_NOT_UNKNOWN:
    1664         108 :                 selec = DEFAULT_NOT_UNK_SEL;
    1665         108 :                 break;
    1666         252 :             case IS_TRUE:
    1667             :             case IS_NOT_FALSE:
    1668         252 :                 selec = (double) clause_selectivity(root, arg,
    1669             :                                                     varRelid,
    1670             :                                                     jointype, sjinfo);
    1671         252 :                 break;
    1672         442 :             case IS_FALSE:
    1673             :             case IS_NOT_TRUE:
    1674         442 :                 selec = 1.0 - (double) clause_selectivity(root, arg,
    1675             :                                                           varRelid,
    1676             :                                                           jointype, sjinfo);
    1677         442 :                 break;
    1678           0 :             default:
    1679           0 :                 elog(ERROR, "unrecognized booltesttype: %d",
    1680             :                      (int) booltesttype);
    1681             :                 selec = 0.0;    /* Keep compiler quiet */
    1682             :                 break;
    1683             :         }
    1684             :     }
    1685             : 
    1686         850 :     ReleaseVariableStats(vardata);
    1687             : 
    1688             :     /* result should be in range, but make sure... */
    1689         850 :     CLAMP_PROBABILITY(selec);
    1690             : 
    1691         850 :     return (Selectivity) selec;
    1692             : }
    1693             : 
    1694             : /*
    1695             :  *      nulltestsel     - Selectivity of NullTest Node.
    1696             :  */
    1697             : Selectivity
    1698       16366 : nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
    1699             :             int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
    1700             : {
    1701             :     VariableStatData vardata;
    1702             :     double      selec;
    1703             : 
    1704       16366 :     examine_variable(root, arg, varRelid, &vardata);
    1705             : 
    1706       16366 :     if (HeapTupleIsValid(vardata.statsTuple))
    1707             :     {
    1708             :         Form_pg_statistic stats;
    1709             :         double      freq_null;
    1710             : 
    1711        9510 :         stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
    1712        9510 :         freq_null = stats->stanullfrac;
    1713             : 
    1714        9510 :         switch (nulltesttype)
    1715             :         {
    1716        6682 :             case IS_NULL:
    1717             : 
    1718             :                 /*
    1719             :                  * Use freq_null directly.
    1720             :                  */
    1721        6682 :                 selec = freq_null;
    1722        6682 :                 break;
    1723        2828 :             case IS_NOT_NULL:
    1724             : 
    1725             :                 /*
    1726             :                  * Select not unknown (not null) values. Calculate from
    1727             :                  * freq_null.
    1728             :                  */
    1729        2828 :                 selec = 1.0 - freq_null;
    1730        2828 :                 break;
    1731           0 :             default:
    1732           0 :                 elog(ERROR, "unrecognized nulltesttype: %d",
    1733             :                      (int) nulltesttype);
    1734             :                 return (Selectivity) 0; /* keep compiler quiet */
    1735             :         }
    1736             :     }
    1737        6856 :     else if (vardata.var && IsA(vardata.var, Var) &&
    1738        6190 :              ((Var *) vardata.var)->varattno < 0)
    1739             :     {
    1740             :         /*
    1741             :          * There are no stats for system columns, but we know they are never
    1742             :          * NULL.
    1743             :          */
    1744          60 :         selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
    1745             :     }
    1746             :     else
    1747             :     {
    1748             :         /*
    1749             :          * No ANALYZE stats available, so make a guess
    1750             :          */
    1751        6796 :         switch (nulltesttype)
    1752             :         {
    1753        1992 :             case IS_NULL:
    1754        1992 :                 selec = DEFAULT_UNK_SEL;
    1755        1992 :                 break;
    1756        4804 :             case IS_NOT_NULL:
    1757        4804 :                 selec = DEFAULT_NOT_UNK_SEL;
    1758        4804 :                 break;
    1759           0 :             default:
    1760           0 :                 elog(ERROR, "unrecognized nulltesttype: %d",
    1761             :                      (int) nulltesttype);
    1762             :                 return (Selectivity) 0; /* keep compiler quiet */
    1763             :         }
    1764             :     }
    1765             : 
    1766       16366 :     ReleaseVariableStats(vardata);
    1767             : 
    1768             :     /* result should be in range, but make sure... */
    1769       16366 :     CLAMP_PROBABILITY(selec);
    1770             : 
    1771       16366 :     return (Selectivity) selec;
    1772             : }
    1773             : 
    1774             : /*
    1775             :  * strip_array_coercion - strip binary-compatible relabeling from an array expr
    1776             :  *
    1777             :  * For array values, the parser normally generates ArrayCoerceExpr conversions,
    1778             :  * but it seems possible that RelabelType might show up.  Also, the planner
    1779             :  * is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
    1780             :  * so we need to be ready to deal with more than one level.
    1781             :  */
    1782             : static Node *
    1783      108626 : strip_array_coercion(Node *node)
    1784             : {
    1785             :     for (;;)
    1786             :     {
    1787      108626 :         if (node && IsA(node, ArrayCoerceExpr))
    1788          36 :         {
    1789        2702 :             ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
    1790             : 
    1791             :             /*
    1792             :              * If the per-element expression is just a RelabelType on top of
    1793             :              * CaseTestExpr, then we know it's a binary-compatible relabeling.
    1794             :              */
    1795        2702 :             if (IsA(acoerce->elemexpr, RelabelType) &&
    1796          36 :                 IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
    1797          36 :                 node = (Node *) acoerce->arg;
    1798             :             else
    1799             :                 break;
    1800             :         }
    1801      105924 :         else if (node && IsA(node, RelabelType))
    1802             :         {
    1803             :             /* We don't really expect this case, but may as well cope */
    1804           0 :             node = (Node *) ((RelabelType *) node)->arg;
    1805             :         }
    1806             :         else
    1807             :             break;
    1808             :     }
    1809      108590 :     return node;
    1810             : }
    1811             : 
    1812             : /*
    1813             :  *      scalararraysel      - Selectivity of ScalarArrayOpExpr Node.
    1814             :  */
    1815             : Selectivity
    1816       16032 : scalararraysel(PlannerInfo *root,
    1817             :                ScalarArrayOpExpr *clause,
    1818             :                bool is_join_clause,
    1819             :                int varRelid,
    1820             :                JoinType jointype,
    1821             :                SpecialJoinInfo *sjinfo)
    1822             : {
    1823       16032 :     Oid         operator = clause->opno;
    1824       16032 :     bool        useOr = clause->useOr;
    1825       16032 :     bool        isEquality = false;
    1826       16032 :     bool        isInequality = false;
    1827             :     Node       *leftop;
    1828             :     Node       *rightop;
    1829             :     Oid         nominal_element_type;
    1830             :     Oid         nominal_element_collation;
    1831             :     TypeCacheEntry *typentry;
    1832             :     RegProcedure oprsel;
    1833             :     FmgrInfo    oprselproc;
    1834             :     Selectivity s1;
    1835             :     Selectivity s1disjoint;
    1836             : 
    1837             :     /* First, deconstruct the expression */
    1838             :     Assert(list_length(clause->args) == 2);
    1839       16032 :     leftop = (Node *) linitial(clause->args);
    1840       16032 :     rightop = (Node *) lsecond(clause->args);
    1841             : 
    1842             :     /* aggressively reduce both sides to constants */
    1843       16032 :     leftop = estimate_expression_value(root, leftop);
    1844       16032 :     rightop = estimate_expression_value(root, rightop);
    1845             : 
    1846             :     /* get nominal (after relabeling) element type of rightop */
    1847       16032 :     nominal_element_type = get_base_element_type(exprType(rightop));
    1848       16032 :     if (!OidIsValid(nominal_element_type))
    1849           0 :         return (Selectivity) 0.5;   /* probably shouldn't happen */
    1850             :     /* get nominal collation, too, for generating constants */
    1851       16032 :     nominal_element_collation = exprCollation(rightop);
    1852             : 
    1853             :     /* look through any binary-compatible relabeling of rightop */
    1854       16032 :     rightop = strip_array_coercion(rightop);
    1855             : 
    1856             :     /*
    1857             :      * Detect whether the operator is the default equality or inequality
    1858             :      * operator of the array element type.
    1859             :      */
    1860       16032 :     typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
    1861       16032 :     if (OidIsValid(typentry->eq_opr))
    1862             :     {
    1863       16032 :         if (operator == typentry->eq_opr)
    1864       13620 :             isEquality = true;
    1865        2412 :         else if (get_negator(operator) == typentry->eq_opr)
    1866        1932 :             isInequality = true;
    1867             :     }
    1868             : 
    1869             :     /*
    1870             :      * If it is equality or inequality, we might be able to estimate this as a
    1871             :      * form of array containment; for instance "const = ANY(column)" can be
    1872             :      * treated as "ARRAY[const] <@ column".  scalararraysel_containment tries
    1873             :      * that, and returns the selectivity estimate if successful, or -1 if not.
    1874             :      */
    1875       16032 :     if ((isEquality || isInequality) && !is_join_clause)
    1876             :     {
    1877       15552 :         s1 = scalararraysel_containment(root, leftop, rightop,
    1878             :                                         nominal_element_type,
    1879             :                                         isEquality, useOr, varRelid);
    1880       15552 :         if (s1 >= 0.0)
    1881         118 :             return s1;
    1882             :     }
    1883             : 
    1884             :     /*
    1885             :      * Look up the underlying operator's selectivity estimator. Punt if it
    1886             :      * hasn't got one.
    1887             :      */
    1888       15914 :     if (is_join_clause)
    1889           0 :         oprsel = get_oprjoin(operator);
    1890             :     else
    1891       15914 :         oprsel = get_oprrest(operator);
    1892       15914 :     if (!oprsel)
    1893           0 :         return (Selectivity) 0.5;
    1894       15914 :     fmgr_info(oprsel, &oprselproc);
    1895             : 
    1896             :     /*
    1897             :      * In the array-containment check above, we must only believe that an
    1898             :      * operator is equality or inequality if it is the default btree equality
    1899             :      * operator (or its negator) for the element type, since those are the
    1900             :      * operators that array containment will use.  But in what follows, we can
    1901             :      * be a little laxer, and also believe that any operators using eqsel() or
    1902             :      * neqsel() as selectivity estimator act like equality or inequality.
    1903             :      */
    1904       15914 :     if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
    1905       13666 :         isEquality = true;
    1906        2248 :     else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
    1907        1822 :         isInequality = true;
    1908             : 
    1909             :     /*
    1910             :      * We consider three cases:
    1911             :      *
    1912             :      * 1. rightop is an Array constant: deconstruct the array, apply the
    1913             :      * operator's selectivity function for each array element, and merge the
    1914             :      * results in the same way that clausesel.c does for AND/OR combinations.
    1915             :      *
    1916             :      * 2. rightop is an ARRAY[] construct: apply the operator's selectivity
    1917             :      * function for each element of the ARRAY[] construct, and merge.
    1918             :      *
    1919             :      * 3. otherwise, make a guess ...
    1920             :      */
    1921       15914 :     if (rightop && IsA(rightop, Const))
    1922       12452 :     {
    1923       12470 :         Datum       arraydatum = ((Const *) rightop)->constvalue;
    1924       12470 :         bool        arrayisnull = ((Const *) rightop)->constisnull;
    1925             :         ArrayType  *arrayval;
    1926             :         int16       elmlen;
    1927             :         bool        elmbyval;
    1928             :         char        elmalign;
    1929             :         int         num_elems;
    1930             :         Datum      *elem_values;
    1931             :         bool       *elem_nulls;
    1932             :         int         i;
    1933             : 
    1934       12470 :         if (arrayisnull)        /* qual can't succeed if null array */
    1935          18 :             return (Selectivity) 0.0;
    1936       12452 :         arrayval = DatumGetArrayTypeP(arraydatum);
    1937       12452 :         get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
    1938             :                              &elmlen, &elmbyval, &elmalign);
    1939       12452 :         deconstruct_array(arrayval,
    1940             :                           ARR_ELEMTYPE(arrayval),
    1941             :                           elmlen, elmbyval, elmalign,
    1942             :                           &elem_values, &elem_nulls, &num_elems);
    1943             : 
    1944             :         /*
    1945             :          * For generic operators, we assume the probability of success is
    1946             :          * independent for each array element.  But for "= ANY" or "<> ALL",
    1947             :          * if the array elements are distinct (which'd typically be the case)
    1948             :          * then the probabilities are disjoint, and we should just sum them.
    1949             :          *
    1950             :          * If we were being really tense we would try to confirm that the
    1951             :          * elements are all distinct, but that would be expensive and it
    1952             :          * doesn't seem to be worth the cycles; it would amount to penalizing
    1953             :          * well-written queries in favor of poorly-written ones.  However, we
    1954             :          * do protect ourselves a little bit by checking whether the
    1955             :          * disjointness assumption leads to an impossible (out of range)
    1956             :          * probability; if so, we fall back to the normal calculation.
    1957             :          */
    1958       12452 :         s1 = s1disjoint = (useOr ? 0.0 : 1.0);
    1959             : 
    1960       50820 :         for (i = 0; i < num_elems; i++)
    1961             :         {
    1962             :             List       *args;
    1963             :             Selectivity s2;
    1964             : 
    1965       38368 :             args = list_make2(leftop,
    1966             :                               makeConst(nominal_element_type,
    1967             :                                         -1,
    1968             :                                         nominal_element_collation,
    1969             :                                         elmlen,
    1970             :                                         elem_values[i],
    1971             :                                         elem_nulls[i],
    1972             :                                         elmbyval));
    1973       38368 :             if (is_join_clause)
    1974           0 :                 s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
    1975             :                                                       clause->inputcollid,
    1976             :                                                       PointerGetDatum(root),
    1977             :                                                       ObjectIdGetDatum(operator),
    1978             :                                                       PointerGetDatum(args),
    1979             :                                                       Int16GetDatum(jointype),
    1980             :                                                       PointerGetDatum(sjinfo)));
    1981             :             else
    1982       38368 :                 s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
    1983             :                                                       clause->inputcollid,
    1984             :                                                       PointerGetDatum(root),
    1985             :                                                       ObjectIdGetDatum(operator),
    1986             :                                                       PointerGetDatum(args),
    1987             :                                                       Int32GetDatum(varRelid)));
    1988             : 
    1989       38368 :             if (useOr)
    1990             :             {
    1991       32476 :                 s1 = s1 + s2 - s1 * s2;
    1992       32476 :                 if (isEquality)
    1993       31612 :                     s1disjoint += s2;
    1994             :             }
    1995             :             else
    1996             :             {
    1997        5892 :                 s1 = s1 * s2;
    1998        5892 :                 if (isInequality)
    1999        5580 :                     s1disjoint += s2 - 1.0;
    2000             :             }
    2001             :         }
    2002             : 
    2003             :         /* accept disjoint-probability estimate if in range */
    2004       12452 :         if ((useOr ? isEquality : isInequality) &&
    2005       11876 :             s1disjoint >= 0.0 && s1disjoint <= 1.0)
    2006       11846 :             s1 = s1disjoint;
    2007             :     }
    2008        3444 :     else if (rightop && IsA(rightop, ArrayExpr) &&
    2009         122 :              !((ArrayExpr *) rightop)->multidims)
    2010         122 :     {
    2011         122 :         ArrayExpr  *arrayexpr = (ArrayExpr *) rightop;
    2012             :         int16       elmlen;
    2013             :         bool        elmbyval;
    2014             :         ListCell   *l;
    2015             : 
    2016         122 :         get_typlenbyval(arrayexpr->element_typeid,
    2017             :                         &elmlen, &elmbyval);
    2018             : 
    2019             :         /*
    2020             :          * We use the assumption of disjoint probabilities here too, although
    2021             :          * the odds of equal array elements are rather higher if the elements
    2022             :          * are not all constants (which they won't be, else constant folding
    2023             :          * would have reduced the ArrayExpr to a Const).  In this path it's
    2024             :          * critical to have the sanity check on the s1disjoint estimate.
    2025             :          */
    2026         122 :         s1 = s1disjoint = (useOr ? 0.0 : 1.0);
    2027             : 
    2028         440 :         foreach(l, arrayexpr->elements)
    2029             :         {
    2030         318 :             Node       *elem = (Node *) lfirst(l);
    2031             :             List       *args;
    2032             :             Selectivity s2;
    2033             : 
    2034             :             /*
    2035             :              * Theoretically, if elem isn't of nominal_element_type we should
    2036             :              * insert a RelabelType, but it seems unlikely that any operator
    2037             :              * estimation function would really care ...
    2038             :              */
    2039         318 :             args = list_make2(leftop, elem);
    2040         318 :             if (is_join_clause)
    2041           0 :                 s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
    2042             :                                                       clause->inputcollid,
    2043             :                                                       PointerGetDatum(root),
    2044             :                                                       ObjectIdGetDatum(operator),
    2045             :                                                       PointerGetDatum(args),
    2046             :                                                       Int16GetDatum(jointype),
    2047             :                                                       PointerGetDatum(sjinfo)));
    2048             :             else
    2049         318 :                 s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
    2050             :                                                       clause->inputcollid,
    2051             :                                                       PointerGetDatum(root),
    2052             :                                                       ObjectIdGetDatum(operator),
    2053             :                                                       PointerGetDatum(args),
    2054             :                                                       Int32GetDatum(varRelid)));
    2055             : 
    2056         318 :             if (useOr)
    2057             :             {
    2058         318 :                 s1 = s1 + s2 - s1 * s2;
    2059         318 :                 if (isEquality)
    2060         318 :                     s1disjoint += s2;
    2061             :             }
    2062             :             else
    2063             :             {
    2064           0 :                 s1 = s1 * s2;
    2065           0 :                 if (isInequality)
    2066           0 :                     s1disjoint += s2 - 1.0;
    2067             :             }
    2068             :         }
    2069             : 
    2070             :         /* accept disjoint-probability estimate if in range */
    2071         122 :         if ((useOr ? isEquality : isInequality) &&
    2072         122 :             s1disjoint >= 0.0 && s1disjoint <= 1.0)
    2073         122 :             s1 = s1disjoint;
    2074             :     }
    2075             :     else
    2076             :     {
    2077             :         CaseTestExpr *dummyexpr;
    2078             :         List       *args;
    2079             :         Selectivity s2;
    2080             :         int         i;
    2081             : 
    2082             :         /*
    2083             :          * We need a dummy rightop to pass to the operator selectivity
    2084             :          * routine.  It can be pretty much anything that doesn't look like a
    2085             :          * constant; CaseTestExpr is a convenient choice.
    2086             :          */
    2087        3322 :         dummyexpr = makeNode(CaseTestExpr);
    2088        3322 :         dummyexpr->typeId = nominal_element_type;
    2089        3322 :         dummyexpr->typeMod = -1;
    2090        3322 :         dummyexpr->collation = clause->inputcollid;
    2091        3322 :         args = list_make2(leftop, dummyexpr);
    2092        3322 :         if (is_join_clause)
    2093           0 :             s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
    2094             :                                                   clause->inputcollid,
    2095             :                                                   PointerGetDatum(root),
    2096             :                                                   ObjectIdGetDatum(operator),
    2097             :                                                   PointerGetDatum(args),
    2098             :                                                   Int16GetDatum(jointype),
    2099             :                                                   PointerGetDatum(sjinfo)));
    2100             :         else
    2101        3322 :             s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
    2102             :                                                   clause->inputcollid,
    2103             :                                                   PointerGetDatum(root),
    2104             :                                                   ObjectIdGetDatum(operator),
    2105             :                                                   PointerGetDatum(args),
    2106             :                                                   Int32GetDatum(varRelid)));
    2107        3322 :         s1 = useOr ? 0.0 : 1.0;
    2108             : 
    2109             :         /*
    2110             :          * Arbitrarily assume 10 elements in the eventual array value (see
    2111             :          * also estimate_array_length).  We don't risk an assumption of
    2112             :          * disjoint probabilities here.
    2113             :          */
    2114       36542 :         for (i = 0; i < 10; i++)
    2115             :         {
    2116       33220 :             if (useOr)
    2117       33220 :                 s1 = s1 + s2 - s1 * s2;
    2118             :             else
    2119           0 :                 s1 = s1 * s2;
    2120             :         }
    2121             :     }
    2122             : 
    2123             :     /* result should be in range, but make sure... */
    2124       15896 :     CLAMP_PROBABILITY(s1);
    2125             : 
    2126       15896 :     return s1;
    2127             : }
    2128             : 
    2129             : /*
    2130             :  * Estimate number of elements in the array yielded by an expression.
    2131             :  *
    2132             :  * Note: the result is integral, but we use "double" to avoid overflow
    2133             :  * concerns.  Most callers will use it in double-type expressions anyway.
    2134             :  */
    2135             : double
    2136       92558 : estimate_array_length(PlannerInfo *root, Node *arrayexpr)
    2137             : {
    2138             :     /* look through any binary-compatible relabeling of arrayexpr */
    2139       92558 :     arrayexpr = strip_array_coercion(arrayexpr);
    2140             : 
    2141       92558 :     if (arrayexpr && IsA(arrayexpr, Const))
    2142             :     {
    2143       39734 :         Datum       arraydatum = ((Const *) arrayexpr)->constvalue;
    2144       39734 :         bool        arrayisnull = ((Const *) arrayexpr)->constisnull;
    2145             :         ArrayType  *arrayval;
    2146             : 
    2147       39734 :         if (arrayisnull)
    2148          36 :             return 0;
    2149       39698 :         arrayval = DatumGetArrayTypeP(arraydatum);
    2150       39698 :         return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
    2151             :     }
    2152       52824 :     else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
    2153         490 :              !((ArrayExpr *) arrayexpr)->multidims)
    2154             :     {
    2155         490 :         return list_length(((ArrayExpr *) arrayexpr)->elements);
    2156             :     }
    2157       52334 :     else if (arrayexpr)
    2158             :     {
    2159             :         /* See if we can find any statistics about it */
    2160             :         VariableStatData vardata;
    2161             :         AttStatsSlot sslot;
    2162       52334 :         double      nelem = 0;
    2163             : 
    2164       52334 :         examine_variable(root, arrayexpr, 0, &vardata);
    2165       52334 :         if (HeapTupleIsValid(vardata.statsTuple))
    2166             :         {
    2167             :             /*
    2168             :              * Found stats, so use the average element count, which is stored
    2169             :              * in the last stanumbers element of the DECHIST statistics.
    2170             :              * Actually that is the average count of *distinct* elements;
    2171             :              * perhaps we should scale it up somewhat?
    2172             :              */
    2173        2988 :             if (get_attstatsslot(&sslot, vardata.statsTuple,
    2174             :                                  STATISTIC_KIND_DECHIST, InvalidOid,
    2175             :                                  ATTSTATSSLOT_NUMBERS))
    2176             :             {
    2177        2874 :                 if (sslot.nnumbers > 0)
    2178        2874 :                     nelem = clamp_row_est(sslot.numbers[sslot.nnumbers - 1]);
    2179        2874 :                 free_attstatsslot(&sslot);
    2180             :             }
    2181             :         }
    2182       52334 :         ReleaseVariableStats(vardata);
    2183             : 
    2184       52334 :         if (nelem > 0)
    2185        2874 :             return nelem;
    2186             :     }
    2187             : 
    2188             :     /* Else use a default guess --- this should match scalararraysel */
    2189       49460 :     return 10;
    2190             : }
    2191             : 
    2192             : /*
    2193             :  *      rowcomparesel       - Selectivity of RowCompareExpr Node.
    2194             :  *
    2195             :  * We estimate RowCompare selectivity by considering just the first (high
    2196             :  * order) columns, which makes it equivalent to an ordinary OpExpr.  While
    2197             :  * this estimate could be refined by considering additional columns, it
    2198             :  * seems unlikely that we could do a lot better without multi-column
    2199             :  * statistics.
    2200             :  */
    2201             : Selectivity
    2202         156 : rowcomparesel(PlannerInfo *root,
    2203             :               RowCompareExpr *clause,
    2204             :               int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
    2205             : {
    2206             :     Selectivity s1;
    2207         156 :     Oid         opno = linitial_oid(clause->opnos);
    2208         156 :     Oid         inputcollid = linitial_oid(clause->inputcollids);
    2209             :     List       *opargs;
    2210             :     bool        is_join_clause;
    2211             : 
    2212             :     /* Build equivalent arg list for single operator */
    2213         156 :     opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
    2214             : 
    2215             :     /*
    2216             :      * Decide if it's a join clause.  This should match clausesel.c's
    2217             :      * treat_as_join_clause(), except that we intentionally consider only the
    2218             :      * leading columns and not the rest of the clause.
    2219             :      */
    2220         156 :     if (varRelid != 0)
    2221             :     {
    2222             :         /*
    2223             :          * Caller is forcing restriction mode (eg, because we are examining an
    2224             :          * inner indexscan qual).
    2225             :          */
    2226          54 :         is_join_clause = false;
    2227             :     }
    2228         102 :     else if (sjinfo == NULL)
    2229             :     {
    2230             :         /*
    2231             :          * It must be a restriction clause, since it's being evaluated at a
    2232             :          * scan node.
    2233             :          */
    2234          90 :         is_join_clause = false;
    2235             :     }
    2236             :     else
    2237             :     {
    2238             :         /*
    2239             :          * Otherwise, it's a join if there's more than one base relation used.
    2240             :          */
    2241          12 :         is_join_clause = (NumRelids(root, (Node *) opargs) > 1);
    2242             :     }
    2243             : 
    2244         156 :     if (is_join_clause)
    2245             :     {
    2246             :         /* Estimate selectivity for a join clause. */
    2247          12 :         s1 = join_selectivity(root, opno,
    2248             :                               opargs,
    2249             :                               inputcollid,
    2250             :                               jointype,
    2251             :                               sjinfo);
    2252             :     }
    2253             :     else
    2254             :     {
    2255             :         /* Estimate selectivity for a restriction clause. */
    2256         144 :         s1 = restriction_selectivity(root, opno,
    2257             :                                      opargs,
    2258             :                                      inputcollid,
    2259             :                                      varRelid);
    2260             :     }
    2261             : 
    2262         156 :     return s1;
    2263             : }
    2264             : 
    2265             : /*
    2266             :  *      eqjoinsel       - Join selectivity of "="
    2267             :  */
    2268             : Datum
    2269      189276 : eqjoinsel(PG_FUNCTION_ARGS)
    2270             : {
    2271      189276 :     PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
    2272      189276 :     Oid         operator = PG_GETARG_OID(1);
    2273      189276 :     List       *args = (List *) PG_GETARG_POINTER(2);
    2274             : 
    2275             : #ifdef NOT_USED
    2276             :     JoinType    jointype = (JoinType) PG_GETARG_INT16(3);
    2277             : #endif
    2278      189276 :     SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
    2279      189276 :     Oid         collation = PG_GET_COLLATION();
    2280             :     double      selec;
    2281             :     double      selec_inner;
    2282             :     VariableStatData vardata1;
    2283             :     VariableStatData vardata2;
    2284             :     double      nd1;
    2285             :     double      nd2;
    2286             :     bool        isdefault1;
    2287             :     bool        isdefault2;
    2288             :     Oid         opfuncoid;
    2289             :     AttStatsSlot sslot1;
    2290             :     AttStatsSlot sslot2;
    2291      189276 :     Form_pg_statistic stats1 = NULL;
    2292      189276 :     Form_pg_statistic stats2 = NULL;
    2293      189276 :     bool        have_mcvs1 = false;
    2294      189276 :     bool        have_mcvs2 = false;
    2295             :     bool        get_mcv_stats;
    2296             :     bool        join_is_reversed;
    2297             :     RelOptInfo *inner_rel;
    2298             : 
    2299      189276 :     get_join_variables(root, args, sjinfo,
    2300             :                        &vardata1, &vardata2, &join_is_reversed);
    2301             : 
    2302      189276 :     nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
    2303      189276 :     nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
    2304             : 
    2305      189276 :     opfuncoid = get_opcode(operator);
    2306             : 
    2307      189276 :     memset(&sslot1, 0, sizeof(sslot1));
    2308      189276 :     memset(&sslot2, 0, sizeof(sslot2));
    2309             : 
    2310             :     /*
    2311             :      * There is no use in fetching one side's MCVs if we lack MCVs for the
    2312             :      * other side, so do a quick check to verify that both stats exist.
    2313             :      */
    2314      531364 :     get_mcv_stats = (HeapTupleIsValid(vardata1.statsTuple) &&
    2315      271040 :                      HeapTupleIsValid(vardata2.statsTuple) &&
    2316      118228 :                      get_attstatsslot(&sslot1, vardata1.statsTuple,
    2317             :                                       STATISTIC_KIND_MCV, InvalidOid,
    2318      342088 :                                       0) &&
    2319       54862 :                      get_attstatsslot(&sslot2, vardata2.statsTuple,
    2320             :                                       STATISTIC_KIND_MCV, InvalidOid,
    2321             :                                       0));
    2322             : 
    2323      189276 :     if (HeapTupleIsValid(vardata1.statsTuple))
    2324             :     {
    2325             :         /* note we allow use of nullfrac regardless of security check */
    2326      152812 :         stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
    2327      169450 :         if (get_mcv_stats &&
    2328       16638 :             statistic_proc_security_check(&vardata1, opfuncoid))
    2329       16638 :             have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
    2330             :                                           STATISTIC_KIND_MCV, InvalidOid,
    2331             :                                           ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
    2332             :     }
    2333             : 
    2334      189276 :     if (HeapTupleIsValid(vardata2.statsTuple))
    2335             :     {
    2336             :         /* note we allow use of nullfrac regardless of security check */
    2337      127004 :         stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
    2338      143642 :         if (get_mcv_stats &&
    2339       16638 :             statistic_proc_security_check(&vardata2, opfuncoid))
    2340       16638 :             have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
    2341             :                                           STATISTIC_KIND_MCV, InvalidOid,
    2342             :                                           ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
    2343             :     }
    2344             : 
    2345             :     /* We need to compute the inner-join selectivity in all cases */
    2346      189276 :     selec_inner = eqjoinsel_inner(opfuncoid, collation,
    2347             :                                   &vardata1, &vardata2,
    2348             :                                   nd1, nd2,
    2349             :                                   isdefault1, isdefault2,
    2350             :                                   &sslot1, &sslot2,
    2351             :                                   stats1, stats2,
    2352             :                                   have_mcvs1, have_mcvs2);
    2353             : 
    2354      189276 :     switch (sjinfo->jointype)
    2355             :     {
    2356      182236 :         case JOIN_INNER:
    2357             :         case JOIN_LEFT:
    2358             :         case JOIN_FULL:
    2359      182236 :             selec = selec_inner;
    2360      182236 :             break;
    2361        7040 :         case JOIN_SEMI:
    2362             :         case JOIN_ANTI:
    2363             : 
    2364             :             /*
    2365             :              * Look up the join's inner relation.  min_righthand is sufficient
    2366             :              * information because neither SEMI nor ANTI joins permit any
    2367             :              * reassociation into or out of their RHS, so the righthand will
    2368             :              * always be exactly that set of rels.
    2369             :              */
    2370        7040 :             inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
    2371             : 
    2372        7040 :             if (!join_is_reversed)
    2373        3428 :                 selec = eqjoinsel_semi(opfuncoid, collation,
    2374             :                                        &vardata1, &vardata2,
    2375             :                                        nd1, nd2,
    2376             :                                        isdefault1, isdefault2,
    2377             :                                        &sslot1, &sslot2,
    2378             :                                        stats1, stats2,
    2379             :                                        have_mcvs1, have_mcvs2,
    2380             :                                        inner_rel);
    2381             :             else
    2382             :             {
    2383        3612 :                 Oid         commop = get_commutator(operator);
    2384        3612 :                 Oid         commopfuncoid = OidIsValid(commop) ? get_opcode(commop) : InvalidOid;
    2385             : 
    2386        3612 :                 selec = eqjoinsel_semi(commopfuncoid, collation,
    2387             :                                        &vardata2, &vardata1,
    2388             :                                        nd2, nd1,
    2389             :                                        isdefault2, isdefault1,
    2390             :                                        &sslot2, &sslot1,
    2391             :                                        stats2, stats1,
    2392             :                                        have_mcvs2, have_mcvs1,
    2393             :                                        inner_rel);
    2394             :             }
    2395             : 
    2396             :             /*
    2397             :              * We should never estimate the output of a semijoin to be more
    2398             :              * rows than we estimate for an inner join with the same input
    2399             :              * rels and join condition; it's obviously impossible for that to
    2400             :              * happen.  The former estimate is N1 * Ssemi while the latter is
    2401             :              * N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner.  Doing
    2402             :              * this is worthwhile because of the shakier estimation rules we
    2403             :              * use in eqjoinsel_semi, particularly in cases where it has to
    2404             :              * punt entirely.
    2405             :              */
    2406        7040 :             selec = Min(selec, inner_rel->rows * selec_inner);
    2407        7040 :             break;
    2408           0 :         default:
    2409             :             /* other values not expected here */
    2410           0 :             elog(ERROR, "unrecognized join type: %d",
    2411             :                  (int) sjinfo->jointype);
    2412             :             selec = 0;          /* keep compiler quiet */
    2413             :             break;
    2414             :     }
    2415             : 
    2416      189276 :     free_attstatsslot(&sslot1);
    2417      189276 :     free_attstatsslot(&sslot2);
    2418             : 
    2419      189276 :     ReleaseVariableStats(vardata1);
    2420      189276 :     ReleaseVariableStats(vardata2);
    2421             : 
    2422      189276 :     CLAMP_PROBABILITY(selec);
    2423             : 
    2424      189276 :     PG_RETURN_FLOAT8((float8) selec);
    2425             : }
    2426             : 
    2427             : /*
    2428             :  * eqjoinsel_inner --- eqjoinsel for normal inner join
    2429             :  *
    2430             :  * We also use this for LEFT/FULL outer joins; it's not presently clear
    2431             :  * that it's worth trying to distinguish them here.
    2432             :  */
    2433             : static double
    2434      189276 : eqjoinsel_inner(Oid opfuncoid, Oid collation,
    2435             :                 VariableStatData *vardata1, VariableStatData *vardata2,
    2436             :                 double nd1, double nd2,
    2437             :                 bool isdefault1, bool isdefault2,
    2438             :                 AttStatsSlot *sslot1, AttStatsSlot *sslot2,
    2439             :                 Form_pg_statistic stats1, Form_pg_statistic stats2,
    2440             :                 bool have_mcvs1, bool have_mcvs2)
    2441             : {
    2442             :     double      selec;
    2443             : 
    2444      189276 :     if (have_mcvs1 && have_mcvs2)
    2445       16638 :     {
    2446             :         /*
    2447             :          * We have most-common-value lists for both relations.  Run through
    2448             :          * the lists to see which MCVs actually join to each other with the
    2449             :          * given operator.  This allows us to determine the exact join
    2450             :          * selectivity for the portion of the relations represented by the MCV
    2451             :          * lists.  We still have to estimate for the remaining population, but
    2452             :          * in a skewed distribution this gives us a big leg up in accuracy.
    2453             :          * For motivation see the analysis in Y. Ioannidis and S.
    2454             :          * Christodoulakis, "On the propagation of errors in the size of join
    2455             :          * results", Technical Report 1018, Computer Science Dept., University
    2456             :          * of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
    2457             :          */
    2458       16638 :         LOCAL_FCINFO(fcinfo, 2);
    2459             :         FmgrInfo    eqproc;
    2460             :         bool       *hasmatch1;
    2461             :         bool       *hasmatch2;
    2462       16638 :         double      nullfrac1 = stats1->stanullfrac;
    2463       16638 :         double      nullfrac2 = stats2->stanullfrac;
    2464             :         double      matchprodfreq,
    2465             :                     matchfreq1,
    2466             :                     matchfreq2,
    2467             :                     unmatchfreq1,
    2468             :                     unmatchfreq2,
    2469             :                     otherfreq1,
    2470             :                     otherfreq2,
    2471             :                     totalsel1,
    2472             :                     totalsel2;
    2473             :         int         i,
    2474             :                     nmatches;
    2475             : 
    2476       16638 :         fmgr_info(opfuncoid, &eqproc);
    2477             : 
    2478             :         /*
    2479             :          * Save a few cycles by setting up the fcinfo struct just once. Using
    2480             :          * FunctionCallInvoke directly also avoids failure if the eqproc
    2481             :          * returns NULL, though really equality functions should never do
    2482             :          * that.
    2483             :          */
    2484       16638 :         InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
    2485             :                                  NULL, NULL);
    2486       16638 :         fcinfo->args[0].isnull = false;
    2487       16638 :         fcinfo->args[1].isnull = false;
    2488             : 
    2489       16638 :         hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
    2490       16638 :         hasmatch2 = (bool *) palloc0(sslot2->nvalues * sizeof(bool));
    2491             : 
    2492             :         /*
    2493             :          * Note we assume that each MCV will match at most one member of the
    2494             :          * other MCV list.  If the operator isn't really equality, there could
    2495             :          * be multiple matches --- but we don't look for them, both for speed
    2496             :          * and because the math wouldn't add up...
    2497             :          */
    2498       16638 :         matchprodfreq = 0.0;
    2499       16638 :         nmatches = 0;
    2500      522708 :         for (i = 0; i < sslot1->nvalues; i++)
    2501             :         {
    2502             :             int         j;
    2503             : 
    2504      506070 :             fcinfo->args[0].value = sslot1->values[i];
    2505             : 
    2506    18984574 :             for (j = 0; j < sslot2->nvalues; j++)
    2507             :             {
    2508             :                 Datum       fresult;
    2509             : 
    2510    18643528 :                 if (hasmatch2[j])
    2511     5172842 :                     continue;
    2512    13470686 :                 fcinfo->args[1].value = sslot2->values[j];
    2513    13470686 :                 fcinfo->isnull = false;
    2514    13470686 :                 fresult = FunctionCallInvoke(fcinfo);
    2515    13470686 :                 if (!fcinfo->isnull && DatumGetBool(fresult))
    2516             :                 {
    2517      165024 :                     hasmatch1[i] = hasmatch2[j] = true;
    2518      165024 :                     matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
    2519      165024 :                     nmatches++;
    2520      165024 :                     break;
    2521             :                 }
    2522             :             }
    2523             :         }
    2524       16638 :         CLAMP_PROBABILITY(matchprodfreq);
    2525             :         /* Sum up frequencies of matched and unmatched MCVs */
    2526       16638 :         matchfreq1 = unmatchfreq1 = 0.0;
    2527      522708 :         for (i = 0; i < sslot1->nvalues; i++)
    2528             :         {
    2529      506070 :             if (hasmatch1[i])
    2530      165024 :                 matchfreq1 += sslot1->numbers[i];
    2531             :             else
    2532      341046 :                 unmatchfreq1 += sslot1->numbers[i];
    2533             :         }
    2534       16638 :         CLAMP_PROBABILITY(matchfreq1);
    2535       16638 :         CLAMP_PROBABILITY(unmatchfreq1);
    2536       16638 :         matchfreq2 = unmatchfreq2 = 0.0;
    2537      544136 :         for (i = 0; i < sslot2->nvalues; i++)
    2538             :         {
    2539      527498 :             if (hasmatch2[i])
    2540      165024 :                 matchfreq2 += sslot2->numbers[i];
    2541             :             else
    2542      362474 :                 unmatchfreq2 += sslot2->numbers[i];
    2543             :         }
    2544       16638 :         CLAMP_PROBABILITY(matchfreq2);
    2545       16638 :         CLAMP_PROBABILITY(unmatchfreq2);
    2546       16638 :         pfree(hasmatch1);
    2547       16638 :         pfree(hasmatch2);
    2548             : 
    2549             :         /*
    2550             :          * Compute total frequency of non-null values that are not in the MCV
    2551             :          * lists.
    2552             :          */
    2553       16638 :         otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
    2554       16638 :         otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
    2555       16638 :         CLAMP_PROBABILITY(otherfreq1);
    2556       16638 :         CLAMP_PROBABILITY(otherfreq2);
    2557             : 
    2558             :         /*
    2559             :          * We can estimate the total selectivity from the point of view of
    2560             :          * relation 1 as: the known selectivity for matched MCVs, plus
    2561             :          * unmatched MCVs that are assumed to match against random members of
    2562             :          * relation 2's non-MCV population, plus non-MCV values that are
    2563             :          * assumed to match against random members of relation 2's unmatched
    2564             :          * MCVs plus non-MCV values.
    2565             :          */
    2566       16638 :         totalsel1 = matchprodfreq;
    2567       16638 :         if (nd2 > sslot2->nvalues)
    2568       10388 :             totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
    2569       16638 :         if (nd2 > nmatches)
    2570       13898 :             totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
    2571       13898 :                 (nd2 - nmatches);
    2572             :         /* Same estimate from the point of view of relation 2. */
    2573       16638 :         totalsel2 = matchprodfreq;
    2574       16638 :         if (nd1 > sslot1->nvalues)
    2575       10474 :             totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
    2576       16638 :         if (nd1 > nmatches)
    2577       12874 :             totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
    2578       12874 :                 (nd1 - nmatches);
    2579             : 
    2580             :         /*
    2581             :          * Use the smaller of the two estimates.  This can be justified in
    2582             :          * essentially the same terms as given below for the no-stats case: to
    2583             :          * a first approximation, we are estimating from the point of view of
    2584             :          * the relation with smaller nd.
    2585             :          */
    2586       16638 :         selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
    2587             :     }
    2588             :     else
    2589             :     {
    2590             :         /*
    2591             :          * We do not have MCV lists for both sides.  Estimate the join
    2592             :          * selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
    2593             :          * is plausible if we assume that the join operator is strict and the
    2594             :          * non-null values are about equally distributed: a given non-null
    2595             :          * tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
    2596             :          * of rel2, so total join rows are at most
    2597             :          * N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
    2598             :          * not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
    2599             :          * is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
    2600             :          * with MIN() is an upper bound.  Using the MIN() means we estimate
    2601             :          * from the point of view of the relation with smaller nd (since the
    2602             :          * larger nd is determining the MIN).  It is reasonable to assume that
    2603             :          * most tuples in this rel will have join partners, so the bound is
    2604             :          * probably reasonably tight and should be taken as-is.
    2605             :          *
    2606             :          * XXX Can we be smarter if we have an MCV list for just one side? It
    2607             :          * seems that if we assume equal distribution for the other side, we
    2608             :          * end up with the same answer anyway.
    2609             :          */
    2610      172638 :         double      nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
    2611      172638 :         double      nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
    2612             : 
    2613      172638 :         selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
    2614      172638 :         if (nd1 > nd2)
    2615       78620 :             selec /= nd1;
    2616             :         else
    2617       94018 :             selec /= nd2;
    2618             :     }
    2619             : 
    2620      189276 :     return selec;
    2621             : }
    2622             : 
    2623             : /*
    2624             :  * eqjoinsel_semi --- eqjoinsel for semi join
    2625             :  *
    2626             :  * (Also used for anti join, which we are supposed to estimate the same way.)
    2627             :  * Caller has ensured that vardata1 is the LHS variable.
    2628             :  * Unlike eqjoinsel_inner, we have to cope with opfuncoid being InvalidOid.
    2629             :  */
    2630             : static double
    2631        7040 : eqjoinsel_semi(Oid opfuncoid, Oid collation,
    2632             :                VariableStatData *vardata1, VariableStatData *vardata2,
    2633             :                double nd1, double nd2,
    2634             :                bool isdefault1, bool isdefault2,
    2635             :                AttStatsSlot *sslot1, AttStatsSlot *sslot2,
    2636             :                Form_pg_statistic stats1, Form_pg_statistic stats2,
    2637             :                bool have_mcvs1, bool have_mcvs2,
    2638             :                RelOptInfo *inner_rel)
    2639             : {
    2640             :     double      selec;
    2641             : 
    2642             :     /*
    2643             :      * We clamp nd2 to be not more than what we estimate the inner relation's
    2644             :      * size to be.  This is intuitively somewhat reasonable since obviously
    2645             :      * there can't be more than that many distinct values coming from the
    2646             :      * inner rel.  The reason for the asymmetry (ie, that we don't clamp nd1
    2647             :      * likewise) is that this is the only pathway by which restriction clauses
    2648             :      * applied to the inner rel will affect the join result size estimate,
    2649             :      * since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
    2650             :      * only the outer rel's size.  If we clamped nd1 we'd be double-counting
    2651             :      * the selectivity of outer-rel restrictions.
    2652             :      *
    2653             :      * We can apply this clamping both with respect to the base relation from
    2654             :      * which the join variable comes (if there is just one), and to the
    2655             :      * immediate inner input relation of the current join.
    2656             :      *
    2657             :      * If we clamp, we can treat nd2 as being a non-default estimate; it's not
    2658             :      * great, maybe, but it didn't come out of nowhere either.  This is most
    2659             :      * helpful when the inner relation is empty and consequently has no stats.
    2660             :      */
    2661        7040 :     if (vardata2->rel)
    2662             :     {
    2663        7034 :         if (nd2 >= vardata2->rel->rows)
    2664             :         {
    2665        5398 :             nd2 = vardata2->rel->rows;
    2666        5398 :             isdefault2 = false;
    2667             :         }
    2668             :     }
    2669        7040 :     if (nd2 >= inner_rel->rows)
    2670             :     {
    2671        5338 :         nd2 = inner_rel->rows;
    2672        5338 :         isdefault2 = false;
    2673             :     }
    2674             : 
    2675        7040 :     if (have_mcvs1 && have_mcvs2 && OidIsValid(opfuncoid))
    2676         510 :     {
    2677             :         /*
    2678             :          * We have most-common-value lists for both relations.  Run through
    2679             :          * the lists to see which MCVs actually join to each other with the
    2680             :          * given operator.  This allows us to determine the exact join
    2681             :          * selectivity for the portion of the relations represented by the MCV
    2682             :          * lists.  We still have to estimate for the remaining population, but
    2683             :          * in a skewed distribution this gives us a big leg up in accuracy.
    2684             :          */
    2685         510 :         LOCAL_FCINFO(fcinfo, 2);
    2686             :         FmgrInfo    eqproc;
    2687             :         bool       *hasmatch1;
    2688             :         bool       *hasmatch2;
    2689         510 :         double      nullfrac1 = stats1->stanullfrac;
    2690             :         double      matchfreq1,
    2691             :                     uncertainfrac,
    2692             :                     uncertain;
    2693             :         int         i,
    2694             :                     nmatches,
    2695             :                     clamped_nvalues2;
    2696             : 
    2697             :         /*
    2698             :          * The clamping above could have resulted in nd2 being less than
    2699             :          * sslot2->nvalues; in which case, we assume that precisely the nd2
    2700             :          * most common values in the relation will appear in the join input,
    2701             :          * and so compare to only the first nd2 members of the MCV list.  Of
    2702             :          * course this is frequently wrong, but it's the best bet we can make.
    2703             :          */
    2704         510 :         clamped_nvalues2 = Min(sslot2->nvalues, nd2);
    2705             : 
    2706         510 :         fmgr_info(opfuncoid, &eqproc);
    2707             : 
    2708             :         /*
    2709             :          * Save a few cycles by setting up the fcinfo struct just once. Using
    2710             :          * FunctionCallInvoke directly also avoids failure if the eqproc
    2711             :          * returns NULL, though really equality functions should never do
    2712             :          * that.
    2713             :          */
    2714         510 :         InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
    2715             :                                  NULL, NULL);
    2716         510 :         fcinfo->args[0].isnull = false;
    2717         510 :         fcinfo->args[1].isnull = false;
    2718             : 
    2719         510 :         hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
    2720         510 :         hasmatch2 = (bool *) palloc0(clamped_nvalues2 * sizeof(bool));
    2721             : 
    2722             :         /*
    2723             :          * Note we assume that each MCV will match at most one member of the
    2724             :          * other MCV list.  If the operator isn't really equality, there could
    2725             :          * be multiple matches --- but we don't look for them, both for speed
    2726             :          * and because the math wouldn't add up...
    2727             :          */
    2728         510 :         nmatches = 0;
    2729       11724 :         for (i = 0; i < sslot1->nvalues; i++)
    2730             :         {
    2731             :             int         j;
    2732             : 
    2733       11214 :             fcinfo->args[0].value = sslot1->values[i];
    2734             : 
    2735      424050 :             for (j = 0; j < clamped_nvalues2; j++)
    2736             :             {
    2737             :                 Datum       fresult;
    2738             : 
    2739      423042 :                 if (hasmatch2[j])
    2740      374016 :                     continue;
    2741       49026 :                 fcinfo->args[1].value = sslot2->values[j];
    2742       49026 :                 fcinfo->isnull = false;
    2743       49026 :                 fresult = FunctionCallInvoke(fcinfo);
    2744       49026 :                 if (!fcinfo->isnull && DatumGetBool(fresult))
    2745             :                 {
    2746       10206 :                     hasmatch1[i] = hasmatch2[j] = true;
    2747       10206 :                     nmatches++;
    2748       10206 :                     break;
    2749             :                 }
    2750             :             }
    2751             :         }
    2752             :         /* Sum up frequencies of matched MCVs */
    2753         510 :         matchfreq1 = 0.0;
    2754       11724 :         for (i = 0; i < sslot1->nvalues; i++)
    2755             :         {
    2756       11214 :             if (hasmatch1[i])
    2757       10206 :                 matchfreq1 += sslot1->numbers[i];
    2758             :         }
    2759         510 :         CLAMP_PROBABILITY(matchfreq1);
    2760         510 :         pfree(hasmatch1);
    2761         510 :         pfree(hasmatch2);
    2762             : 
    2763             :         /*
    2764             :          * Now we need to estimate the fraction of relation 1 that has at
    2765             :          * least one join partner.  We know for certain that the matched MCVs
    2766             :          * do, so that gives us a lower bound, but we're really in the dark
    2767             :          * about everything else.  Our crude approach is: if nd1 <= nd2 then
    2768             :          * assume all non-null rel1 rows have join partners, else assume for
    2769             :          * the uncertain rows that a fraction nd2/nd1 have join partners. We
    2770             :          * can discount the known-matched MCVs from the distinct-values counts
    2771             :          * before doing the division.
    2772             :          *
    2773             :          * Crude as the above is, it's completely useless if we don't have
    2774             :          * reliable ndistinct values for both sides.  Hence, if either nd1 or
    2775             :          * nd2 is default, punt and assume half of the uncertain rows have
    2776             :          * join partners.
    2777             :          */
    2778         510 :         if (!isdefault1 && !isdefault2)
    2779             :         {
    2780         510 :             nd1 -= nmatches;
    2781         510 :             nd2 -= nmatches;
    2782         510 :             if (nd1 <= nd2 || nd2 < 0)
    2783         480 :                 uncertainfrac = 1.0;
    2784             :             else
    2785          30 :                 uncertainfrac = nd2 / nd1;
    2786             :         }
    2787             :         else
    2788           0 :             uncertainfrac = 0.5;
    2789         510 :         uncertain = 1.0 - matchfreq1 - nullfrac1;
    2790         510 :         CLAMP_PROBABILITY(uncertain);
    2791         510 :         selec = matchfreq1 + uncertainfrac * uncertain;
    2792             :     }
    2793             :     else
    2794             :     {
    2795             :         /*
    2796             :          * Without MCV lists for both sides, we can only use the heuristic
    2797             :          * about nd1 vs nd2.
    2798             :          */
    2799        6530 :         double      nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
    2800             : 
    2801        6530 :         if (!isdefault1 && !isdefault2)
    2802             :         {
    2803        4616 :             if (nd1 <= nd2 || nd2 < 0)
    2804        2500 :                 selec = 1.0 - nullfrac1;
    2805             :             else
    2806        2116 :                 selec = (nd2 / nd1) * (1.0 - nullfrac1);
    2807             :         }
    2808             :         else
    2809        1914 :             selec = 0.5 * (1.0 - nullfrac1);
    2810             :     }
    2811             : 
    2812        7040 :     return selec;
    2813             : }
    2814             : 
    2815             : /*
    2816             :  *      neqjoinsel      - Join selectivity of "!="
    2817             :  */
    2818             : Datum
    2819        3292 : neqjoinsel(PG_FUNCTION_ARGS)
    2820             : {
    2821        3292 :     PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
    2822        3292 :     Oid         operator = PG_GETARG_OID(1);
    2823        3292 :     List       *args = (List *) PG_GETARG_POINTER(2);
    2824        3292 :     JoinType    jointype = (JoinType) PG_GETARG_INT16(3);
    2825        3292 :     SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
    2826        3292 :     Oid         collation = PG_GET_COLLATION();
    2827             :     float8      result;
    2828             : 
    2829        3292 :     if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
    2830        1090 :     {
    2831             :         /*
    2832             :          * For semi-joins, if there is more than one distinct value in the RHS
    2833             :          * relation then every non-null LHS row must find a row to join since
    2834             :          * it can only be equal to one of them.  We'll assume that there is
    2835             :          * always more than one distinct RHS value for the sake of stability,
    2836             :          * though in theory we could have special cases for empty RHS
    2837             :          * (selectivity = 0) and single-distinct-value RHS (selectivity =
    2838             :          * fraction of LHS that has the same value as the single RHS value).
    2839             :          *
    2840             :          * For anti-joins, if we use the same assumption that there is more
    2841             :          * than one distinct key in the RHS relation, then every non-null LHS
    2842             :          * row must be suppressed by the anti-join.
    2843             :          *
    2844             :          * So either way, the selectivity estimate should be 1 - nullfrac.
    2845             :          */
    2846             :         VariableStatData leftvar;
    2847             :         VariableStatData rightvar;
    2848             :         bool        reversed;
    2849             :         HeapTuple   statsTuple;
    2850             :         double      nullfrac;
    2851             : 
    2852        1090 :         get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
    2853        1090 :         statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
    2854        1090 :         if (HeapTupleIsValid(statsTuple))
    2855         896 :             nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
    2856             :         else
    2857         194 :             nullfrac = 0.0;
    2858        1090 :         ReleaseVariableStats(leftvar);
    2859        1090 :         ReleaseVariableStats(rightvar);
    2860             : 
    2861        1090 :         result = 1.0 - nullfrac;
    2862             :     }
    2863             :     else
    2864             :     {
    2865             :         /*
    2866             :          * We want 1 - eqjoinsel() where the equality operator is the one
    2867             :          * associated with this != operator, that is, its negator.
    2868             :          */
    2869        2202 :         Oid         eqop = get_negator(operator);
    2870             : 
    2871        2202 :         if (eqop)
    2872             :         {
    2873             :             result =
    2874        2202 :                 DatumGetFloat8(DirectFunctionCall5Coll(eqjoinsel,
    2875             :                                                        collation,
    2876             :                                                        PointerGetDatum(root),
    2877             :                                                        ObjectIdGetDatum(eqop),
    2878             :                                                        PointerGetDatum(args),
    2879             :                                                        Int16GetDatum(jointype),
    2880             :                                                        PointerGetDatum(sjinfo)));
    2881             :         }
    2882             :         else
    2883             :         {
    2884             :             /* Use default selectivity (should we raise an error instead?) */
    2885           0 :             result = DEFAULT_EQ_SEL;
    2886             :         }
    2887        2202 :         result = 1.0 - result;
    2888             :     }
    2889             : 
    2890        3292 :     PG_RETURN_FLOAT8(result);
    2891             : }
    2892             : 
    2893             : /*
    2894             :  *      scalarltjoinsel - Join selectivity of "<" for scalars
    2895             :  */
    2896             : Datum
    2897         318 : scalarltjoinsel(PG_FUNCTION_ARGS)
    2898             : {
    2899         318 :     PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
    2900             : }
    2901             : 
    2902             : /*
    2903             :  *      scalarlejoinsel - Join selectivity of "<=" for scalars
    2904             :  */
    2905             : Datum
    2906         276 : scalarlejoinsel(PG_FUNCTION_ARGS)
    2907             : {
    2908         276 :     PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
    2909             : }
    2910             : 
    2911             : /*
    2912             :  *      scalargtjoinsel - Join selectivity of ">" for scalars
    2913             :  */
    2914             : Datum
    2915         228 : scalargtjoinsel(PG_FUNCTION_ARGS)
    2916             : {
    2917         228 :     PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
    2918             : }
    2919             : 
    2920             : /*
    2921             :  *      scalargejoinsel - Join selectivity of ">=" for scalars
    2922             :  */
    2923             : Datum
    2924         184 : scalargejoinsel(PG_FUNCTION_ARGS)
    2925             : {
    2926         184 :     PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
    2927             : }
    2928             : 
    2929             : 
    2930             : /*
    2931             :  * mergejoinscansel         - Scan selectivity of merge join.
    2932             :  *
    2933             :  * A merge join will stop as soon as it exhausts either input stream.
    2934             :  * Therefore, if we can estimate the ranges of both input variables,
    2935             :  * we can estimate how much of the input will actually be read.  This
    2936             :  * can have a considerable impact on the cost when using indexscans.
    2937             :  *
    2938             :  * Also, we can estimate how much of each input has to be read before the
    2939             :  * first join pair is found, which will affect the join's startup time.
    2940             :  *
    2941             :  * clause should be a clause already known to be mergejoinable.  opfamily,
    2942             :  * strategy, and nulls_first specify the sort ordering being used.
    2943             :  *
    2944             :  * The outputs are:
    2945             :  *      *leftstart is set to the fraction of the left-hand variable expected
    2946             :  *       to be scanned before the first join pair is found (0 to 1).
    2947             :  *      *leftend is set to the fraction of the left-hand variable expected
    2948             :  *       to be scanned before the join terminates (0 to 1).
    2949             :  *      *rightstart, *rightend similarly for the right-hand variable.
    2950             :  */
    2951             : void
    2952       95742 : mergejoinscansel(PlannerInfo *root, Node *clause,
    2953             :                  Oid opfamily, int strategy, bool nulls_first,
    2954             :                  Selectivity *leftstart, Selectivity *leftend,
    2955             :                  Selectivity *rightstart, Selectivity *rightend)
    2956             : {
    2957             :     Node       *left,
    2958             :                *right;
    2959             :     VariableStatData leftvar,
    2960             :                 rightvar;
    2961             :     int         op_strategy;
    2962             :     Oid         op_lefttype;
    2963             :     Oid         op_righttype;
    2964             :     Oid         opno,
    2965             :                 collation,
    2966             :                 lsortop,
    2967             :                 rsortop,
    2968             :                 lstatop,
    2969             :                 rstatop,
    2970             :                 ltop,
    2971             :                 leop,
    2972             :                 revltop,
    2973             :                 revleop;
    2974             :     bool        isgt;
    2975             :     Datum       leftmin,
    2976             :                 leftmax,
    2977             :                 rightmin,
    2978             :                 rightmax;
    2979             :     double      selec;
    2980             : 
    2981             :     /* Set default results if we can't figure anything out. */
    2982             :     /* XXX should default "start" fraction be a bit more than 0? */
    2983       95742 :     *leftstart = *rightstart = 0.0;
    2984       95742 :     *leftend = *rightend = 1.0;
    2985             : 
    2986             :     /* Deconstruct the merge clause */
    2987       95742 :     if (!is_opclause(clause))
    2988           0 :         return;                 /* shouldn't happen */
    2989       95742 :     opno = ((OpExpr *) clause)->opno;
    2990       95742 :     collation = ((OpExpr *) clause)->inputcollid;
    2991       95742 :     left = get_leftop((Expr *) clause);
    2992       95742 :     right = get_rightop((Expr *) clause);
    2993       95742 :     if (!right)
    2994           0 :         return;                 /* shouldn't happen */
    2995             : 
    2996             :     /* Look for stats for the inputs */
    2997       95742 :     examine_variable(root, left, 0, &leftvar);
    2998       95742 :     examine_variable(root, right, 0, &rightvar);
    2999             : 
    3000             :     /* Extract the operator's declared left/right datatypes */
    3001       95742 :     get_op_opfamily_properties(opno, opfamily, false,
    3002             :                                &op_strategy,
    3003             :                                &op_lefttype,
    3004             :                                &op_righttype);
    3005             :     Assert(op_strategy == BTEqualStrategyNumber);
    3006             : 
    3007             :     /*
    3008             :      * Look up the various operators we need.  If we don't find them all, it
    3009             :      * probably means the opfamily is broken, but we just fail silently.
    3010             :      *
    3011             :      * Note: we expect that pg_statistic histograms will be sorted by the '<'
    3012             :      * operator, regardless of which sort direction we are considering.
    3013             :      */
    3014       95742 :     switch (strategy)
    3015             :     {
    3016       95688 :         case BTLessStrategyNumber:
    3017       95688 :             isgt = false;
    3018       95688 :             if (op_lefttype == op_righttype)
    3019             :             {
    3020             :                 /* easy case */
    3021       94438 :                 ltop = get_opfamily_member(opfamily,
    3022             :                                            op_lefttype, op_righttype,
    3023             :                                            BTLessStrategyNumber);
    3024       94438 :                 leop = get_opfamily_member(opfamily,
    3025             :                                            op_lefttype, op_righttype,
    3026             :                                            BTLessEqualStrategyNumber);
    3027       94438 :                 lsortop = ltop;
    3028       94438 :                 rsortop = ltop;
    3029       94438 :                 lstatop = lsortop;
    3030       94438 :                 rstatop = rsortop;
    3031       94438 :                 revltop = ltop;
    3032       94438 :                 revleop = leop;
    3033             :             }
    3034             :             else
    3035             :             {
    3036        1250 :                 ltop = get_opfamily_member(opfamily,
    3037             :                                            op_lefttype, op_righttype,
    3038             :                                            BTLessStrategyNumber);
    3039        1250 :                 leop = get_opfamily_member(opfamily,
    3040             :                                            op_lefttype, op_righttype,
    3041             :                                            BTLessEqualStrategyNumber);
    3042        1250 :                 lsortop = get_opfamily_member(opfamily,
    3043             :                                               op_lefttype, op_lefttype,
    3044             :                                               BTLessStrategyNumber);
    3045        1250 :                 rsortop = get_opfamily_member(opfamily,
    3046             :                                               op_righttype, op_righttype,
    3047             :                                               BTLessStrategyNumber);
    3048        1250 :                 lstatop = lsortop;
    3049        1250 :                 rstatop = rsortop;
    3050        1250 :                 revltop = get_opfamily_member(opfamily,
    3051             :                                               op_righttype, op_lefttype,
    3052             :                                               BTLessStrategyNumber);
    3053        1250 :                 revleop = get_opfamily_member(opfamily,
    3054             :                                               op_righttype, op_lefttype,
    3055             :                                               BTLessEqualStrategyNumber);
    3056             :             }
    3057       95688 :             break;
    3058          54 :         case BTGreaterStrategyNumber:
    3059             :             /* descending-order case */
    3060          54 :             isgt = true;
    3061          54 :             if (op_lefttype == op_righttype)
    3062             :             {
    3063             :                 /* easy case */
    3064          54 :                 ltop = get_opfamily_member(opfamily,
    3065             :                                            op_lefttype, op_righttype,
    3066             :                                            BTGreaterStrategyNumber);
    3067          54 :                 leop = get_opfamily_member(opfamily,
    3068             :                                            op_lefttype, op_righttype,
    3069             :                                            BTGreaterEqualStrategyNumber);
    3070          54 :                 lsortop = ltop;
    3071          54 :                 rsortop = ltop;
    3072          54 :                 lstatop = get_opfamily_member(opfamily,
    3073             :                                               op_lefttype, op_lefttype,
    3074             :                                               BTLessStrategyNumber);
    3075          54 :                 rstatop = lstatop;
    3076          54 :                 revltop = ltop;
    3077          54 :                 revleop = leop;
    3078             :             }
    3079             :             else
    3080             :             {
    3081           0 :                 ltop = get_opfamily_member(opfamily,
    3082             :                                            op_lefttype, op_righttype,
    3083             :                                            BTGreaterStrategyNumber);
    3084           0 :                 leop = get_opfamily_member(opfamily,
    3085             :                                            op_lefttype, op_righttype,
    3086             :                                            BTGreaterEqualStrategyNumber);
    3087           0 :                 lsortop = get_opfamily_member(opfamily,
    3088             :                                               op_lefttype, op_lefttype,
    3089             :                                               BTGreaterStrategyNumber);
    3090           0 :                 rsortop = get_opfamily_member(opfamily,
    3091             :                                               op_righttype, op_righttype,
    3092             :                                               BTGreaterStrategyNumber);
    3093           0 :                 lstatop = get_opfamily_member(opfamily,
    3094             :                                               op_lefttype, op_lefttype,
    3095             :                                               BTLessStrategyNumber);
    3096           0 :                 rstatop = get_opfamily_member(opfamily,
    3097             :                                               op_righttype, op_righttype,
    3098             :                                               BTLessStrategyNumber);
    3099           0 :                 revltop = get_opfamily_member(opfamily,
    3100             :                                               op_righttype, op_lefttype,
    3101             :                                               BTGreaterStrategyNumber);
    3102           0 :                 revleop = get_opfamily_member(opfamily,
    3103             :                                               op_righttype, op_lefttype,
    3104             :                                               BTGreaterEqualStrategyNumber);
    3105             :             }
    3106          54 :             break;
    3107           0 :         default:
    3108           0 :             goto fail;          /* shouldn't get here */
    3109             :     }
    3110             : 
    3111       95742 :     if (!OidIsValid(lsortop) ||
    3112       95742 :         !OidIsValid(rsortop) ||
    3113       95742 :         !OidIsValid(lstatop) ||
    3114       95742 :         !OidIsValid(rstatop) ||
    3115       95730 :         !OidIsValid(ltop) ||
    3116       95730 :         !OidIsValid(leop) ||
    3117       95730 :         !OidIsValid(revltop) ||
    3118             :         !OidIsValid(revleop))
    3119          12 :         goto fail;              /* insufficient info in catalogs */
    3120             : 
    3121             :     /* Try to get ranges of both inputs */
    3122       95730 :     if (!isgt)
    3123             :     {
    3124       95676 :         if (!get_variable_range(root, &leftvar, lstatop, collation,
    3125             :                                 &leftmin, &leftmax))
    3126       19382 :             goto fail;          /* no range available from stats */
    3127       76294 :         if (!get_variable_range(root, &rightvar, rstatop, collation,
    3128             :                                 &rightmin, &rightmax))
    3129       18610 :             goto fail;          /* no range available from stats */
    3130             :     }
    3131             :     else
    3132             :     {
    3133             :         /* need to swap the max and min */
    3134          54 :         if (!get_variable_range(root, &leftvar, lstatop, collation,
    3135             :                                 &leftmax, &leftmin))
    3136          30 :             goto fail;          /* no range available from stats */
    3137          24 :         if (!get_variable_range(root, &rightvar, rstatop, collation,
    3138             :                                 &rightmax, &rightmin))
    3139           0 :             goto fail;          /* no range available from stats */
    3140             :     }
    3141             : 
    3142             :     /*
    3143             :      * Now, the fraction of the left variable that will be scanned is the
    3144             :      * fraction that's <= the right-side maximum value.  But only believe
    3145             :      * non-default estimates, else stick with our 1.0.
    3146             :      */
    3147       57708 :     selec = scalarineqsel(root, leop, isgt, true, collation, &leftvar,
    3148             :                           rightmax, op_righttype);
    3149       57708 :     if (selec != DEFAULT_INEQ_SEL)
    3150       57702 :         *leftend = selec;
    3151             : 
    3152             :     /* And similarly for the right variable. */
    3153       57708 :     selec = scalarineqsel(root, revleop, isgt, true, collation, &rightvar,
    3154             :                           leftmax, op_lefttype);
    3155       57708 :     if (selec != DEFAULT_INEQ_SEL)
    3156       57708 :         *rightend = selec;
    3157             : 
    3158             :     /*
    3159             :      * Only one of the two "end" fractions can really be less than 1.0;
    3160             :      * believe the smaller estimate and reset the other one to exactly 1.0. If
    3161             :      * we get exactly equal estimates (as can easily happen with self-joins),
    3162             :      * believe neither.
    3163             :      */
    3164       57708 :     if (*leftend > *rightend)
    3165       29028 :         *leftend = 1.0;
    3166       28680 :     else if (*leftend < *rightend)
    3167       22870 :         *rightend = 1.0;
    3168             :     else
    3169        5810 :         *leftend = *rightend = 1.0;
    3170             : 
    3171             :     /*
    3172             :      * Also, the fraction of the left variable that will be scanned before the
    3173             :      * first join pair is found is the fraction that's < the right-side
    3174             :      * minimum value.  But only believe non-default estimates, else stick with
    3175             :      * our own default.
    3176             :      */
    3177       57708 :     selec = scalarineqsel(root, ltop, isgt, false, collation, &leftvar,
    3178             :                           rightmin, op_righttype);
    3179       57708 :     if (selec != DEFAULT_INEQ_SEL)
    3180       57708 :         *leftstart = selec;
    3181             : 
    3182             :     /* And similarly for the right variable. */
    3183       57708 :     selec = scalarineqsel(root, revltop, isgt, false, collation, &rightvar,
    3184             :                           leftmin, op_lefttype);
    3185       57708 :     if (selec != DEFAULT_INEQ_SEL)
    3186       57708 :         *rightstart = selec;
    3187             : 
    3188             :     /*
    3189             :      * Only one of the two "start" fractions can really be more than zero;
    3190             :      * believe the larger estimate and reset the other one to exactly 0.0. If
    3191             :      * we get exactly equal estimates (as can easily happen with self-joins),
    3192             :      * believe neither.
    3193             :      */
    3194       57708 :     if (*leftstart < *rightstart)
    3195       16306 :         *leftstart = 0.0;
    3196       41402 :     else if (*leftstart > *rightstart)
    3197       25684 :         *rightstart = 0.0;
    3198             :     else
    3199       15718 :         *leftstart = *rightstart = 0.0;
    3200             : 
    3201             :     /*
    3202             :      * If the sort order is nulls-first, we're going to have to skip over any
    3203             :      * nulls too.  These would not have been counted by scalarineqsel, and we
    3204             :      * can safely add in this fraction regardless of whether we believe
    3205             :      * scalarineqsel's results or not.  But be sure to clamp the sum to 1.0!
    3206             :      */
    3207       57708 :     if (nulls_first)
    3208             :     {
    3209             :         Form_pg_statistic stats;
    3210             : 
    3211          24 :         if (HeapTupleIsValid(leftvar.statsTuple))
    3212             :         {
    3213          24 :             stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
    3214          24 :             *leftstart += stats->stanullfrac;
    3215          24 :             CLAMP_PROBABILITY(*leftstart);
    3216          24 :             *leftend += stats->stanullfrac;
    3217          24 :             CLAMP_PROBABILITY(*leftend);
    3218             :         }
    3219          24 :         if (HeapTupleIsValid(rightvar.statsTuple))
    3220             :         {
    3221          24 :             stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple);
    3222          24 :             *rightstart += stats->stanullfrac;
    3223          24 :             CLAMP_PROBABILITY(*rightstart);
    3224          24 :             *rightend += stats->stanullfrac;
    3225          24 :             CLAMP_PROBABILITY(*rightend);
    3226             :         }
    3227             :     }
    3228             : 
    3229             :     /* Disbelieve start >= end, just in case that can happen */
    3230       57708 :     if (*leftstart >= *leftend)
    3231             :     {
    3232         194 :         *leftstart = 0.0;
    3233         194 :         *leftend = 1.0;
    3234             :     }
    3235       57708 :     if (*rightstart >= *rightend)
    3236             :     {
    3237         914 :         *rightstart = 0.0;
    3238         914 :         *rightend = 1.0;
    3239             :     }
    3240             : 
    3241       56794 : fail:
    3242       95742 :     ReleaseVariableStats(leftvar);
    3243       95742 :     ReleaseVariableStats(rightvar);
    3244             : }
    3245             : 
    3246             : 
    3247             : /*
    3248             :  *  matchingsel -- generic matching-operator selectivity support
    3249             :  *
    3250             :  * Use these for any operators that (a) are on data types for which we collect
    3251             :  * standard statistics, and (b) have behavior for which the default estimate
    3252             :  * (twice DEFAULT_EQ_SEL) is sane.  Typically that is good for match-like
    3253             :  * operators.
    3254             :  */
    3255             : 
    3256             : Datum
    3257        1106 : matchingsel(PG_FUNCTION_ARGS)
    3258             : {
    3259        1106 :     PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
    3260        1106 :     Oid         operator = PG_GETARG_OID(1);
    3261        1106 :     List       *args = (List *) PG_GETARG_POINTER(2);
    3262        1106 :     int         varRelid = PG_GETARG_INT32(3);
    3263        1106 :     Oid         collation = PG_GET_COLLATION();
    3264             :     double      selec;
    3265             : 
    3266             :     /* Use generic restriction selectivity logic. */
    3267        1106 :     selec = generic_restriction_selectivity(root, operator, collation,
    3268             :                                             args, varRelid,
    3269             :                                             DEFAULT_MATCHING_SEL);
    3270             : 
    3271        1106 :     PG_RETURN_FLOAT8((float8) selec);
    3272             : }
    3273             : 
    3274             : Datum
    3275           6 : matchingjoinsel(PG_FUNCTION_ARGS)
    3276             : {
    3277             :     /* Just punt, for the moment. */
    3278           6 :     PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
    3279             : }
    3280             : 
    3281             : 
    3282             : /*
    3283             :  * Helper routine for estimate_num_groups: add an item to a list of
    3284             :  * GroupVarInfos, but only if it's not known equal to any of the existing
    3285             :  * entries.
    3286             :  */
    3287             : typedef struct
    3288             : {
    3289             :     Node       *var;            /* might be an expression, not just a Var */
    3290             :     RelOptInfo *rel;            /* relation it belongs to */
    3291             :     double      ndistinct;      /* # distinct values */
    3292             :     bool        isdefault;      /* true if DEFAULT_NUM_DISTINCT was used */
    3293             : } GroupVarInfo;
    3294             : 
    3295             : static List *
    3296      286028 : add_unique_group_var(PlannerInfo *root, List *varinfos,
    3297             :                      Node *var, VariableStatData *vardata)
    3298             : {
    3299             :     GroupVarInfo *varinfo;
    3300             :     double      ndistinct;
    3301             :     bool        isdefault;
    3302             :     ListCell   *lc;
    3303             : 
    3304      286028 :     ndistinct = get_variable_numdistinct(vardata, &isdefault);
    3305             : 
    3306      329128 :     foreach(lc, varinfos)
    3307             :     {
    3308       43852 :         varinfo = (GroupVarInfo *) lfirst(lc);
    3309             : 
    3310             :         /* Drop exact duplicates */
    3311       43852 :         if (equal(var, varinfo->var))
    3312         752 :             return varinfos;
    3313             : 
    3314             :         /*
    3315             :          * Drop known-equal vars, but only if they belong to different
    3316             :          * relations (see comments for estimate_num_groups)
    3317             :          */
    3318       46430 :         if (vardata->rel != varinfo->rel &&
    3319        3210 :             exprs_known_equal(root, var, varinfo->var))
    3320             :         {
    3321         120 :             if (varinfo->ndistinct <= ndistinct)
    3322             :             {
    3323             :                 /* Keep older item, forget new one */
    3324         120 :                 return varinfos;
    3325             :             }
    3326             :             else
    3327             :             {
    3328             :                 /* Delete the older item */
    3329           0 :                 varinfos = foreach_delete_current(varinfos, lc);
    3330             :             }
    3331             :         }
    3332             :     }
    3333             : 
    3334      285276 :     varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
    3335             : 
    3336      285276 :     varinfo->var = var;
    3337      285276 :     varinfo->rel = vardata->rel;
    3338      285276 :     varinfo->ndistinct = ndistinct;
    3339      285276 :     varinfo->isdefault = isdefault;
    3340      285276 :     varinfos = lappend(varinfos, varinfo);
    3341      285276 :     return varinfos;
    3342             : }
    3343             : 
    3344             : /*
    3345             :  * estimate_num_groups      - Estimate number of groups in a grouped query
    3346             :  *
    3347             :  * Given a query having a GROUP BY clause, estimate how many groups there
    3348             :  * will be --- ie, the number of distinct combinations of the GROUP BY
    3349             :  * expressions.
    3350             :  *
    3351             :  * This routine is also used to estimate the number of rows emitted by
    3352             :  * a DISTINCT filtering step; that is an isomorphic problem.  (Note:
    3353             :  * actually, we only use it for DISTINCT when there's no grouping or
    3354             :  * aggregation ahead of the DISTINCT.)
    3355             :  *
    3356             :  * Inputs:
    3357             :  *  root - the query
    3358             :  *  groupExprs - list of expressions being grouped by
    3359             :  *  input_rows - number of rows estimated to arrive at the group/unique
    3360             :  *      filter step
    3361             :  *  pgset - NULL, or a List** pointing to a grouping set to filter the
    3362             :  *      groupExprs against
    3363             :  *
    3364             :  * Outputs:
    3365             :  *  estinfo - When passed as non-NULL, the function will set bits in the
    3366             :  *      "flags" field in order to provide callers with additional information
    3367             :  *      about the estimation.  Currently, we only set the SELFLAG_USED_DEFAULT
    3368             :  *      bit if we used any default values in the estimation.
    3369             :  *
    3370             :  * Given the lack of any cross-correlation statistics in the system, it's
    3371             :  * impossible to do anything really trustworthy with GROUP BY conditions
    3372             :  * involving multiple Vars.  We should however avoid assuming the worst
    3373             :  * case (all possible cross-product terms actually appear as groups) since
    3374             :  * very often the grouped-by Vars are highly correlated.  Our current approach
    3375             :  * is as follows:
    3376             :  *  1.  Expressions yielding boolean are assumed to contribute two groups,
    3377             :  *      independently of their content, and are ignored in the subsequent
    3378             :  *      steps.  This is mainly because tests like "col IS NULL" break the
    3379             :  *      heuristic used in step 2 especially badly.
    3380             :  *  2.  Reduce the given expressions to a list of unique Vars used.  For
    3381             :  *      example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
    3382             :  *      It is clearly correct not to count the same Var more than once.
    3383             :  *      It is also reasonable to treat f(x) the same as x: f() cannot
    3384             :  *      increase the number of distinct values (unless it is volatile,
    3385             :  *      which we consider unlikely for grouping), but it probably won't
    3386             :  *      reduce the number of distinct values much either.
    3387             :  *      As a special case, if a GROUP BY expression can be matched to an
    3388             :  *      expressional index for which we have statistics, then we treat the
    3389             :  *      whole expression as though it were just a Var.
    3390             :  *  3.  If the list contains Vars of different relations that are known equal
    3391             :  *      due to equivalence classes, then drop all but one of the Vars from each
    3392             :  *      known-equal set, keeping the one with smallest estimated # of values
    3393             :  *      (since the extra values of the others can't appear in joined rows).
    3394             :  *      Note the reason we only consider Vars of different relations is that
    3395             :  *      if we considered ones of the same rel, we'd be double-counting the
    3396             :  *      restriction selectivity of the equality in the next step.
    3397             :  *  4.  For Vars within a single source rel, we multiply together the numbers
    3398             :  *      of values, clamp to the number of rows in the rel (divided by 10 if
    3399             :  *      more than one Var), and then multiply by a factor based on the
    3400             :  *      selectivity of the restriction clauses for that rel.  When there's
    3401             :  *      more than one Var, the initial product is probably too high (it's the
    3402             :  *      worst case) but clamping to a fraction of the rel's rows seems to be a
    3403             :  *      helpful heuristic for not letting the estimate get out of hand.  (The
    3404             :  *      factor of 10 is derived from pre-Postgres-7.4 practice.)  The factor
    3405             :  *      we multiply by to adjust for the restriction selectivity assumes that
    3406             :  *      the restriction clauses are independent of the grouping, which may not
    3407             :  *      be a valid assumption, but it's hard to do better.
    3408             :  *  5.  If there are Vars from multiple rels, we repeat step 4 for each such
    3409             :  *      rel, and multiply the results together.
    3410             :  * Note that rels not containing grouped Vars are ignored completely, as are
    3411             :  * join clauses.  Such rels cannot increase the number of groups, and we
    3412             :  * assume such clauses do not reduce the number either (somewhat bogus,
    3413             :  * but we don't have the info to do better).
    3414             :  */
    3415             : double
    3416      252334 : estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
    3417             :                     List **pgset, EstimationInfo *estinfo)
    3418             : {
    3419      252334 :     List       *varinfos = NIL;
    3420      252334 :     double      srf_multiplier = 1.0;
    3421             :     double      numdistinct;
    3422             :     ListCell   *l;
    3423             :     int         i;
    3424             : 
    3425             :     /* Zero the estinfo output parameter, if non-NULL */
    3426      252334 :     if (estinfo != NULL)
    3427      225630 :         memset(estinfo, 0, sizeof(EstimationInfo));
    3428             : 
    3429             :     /*
    3430             :      * We don't ever want to return an estimate of zero groups, as that tends
    3431             :      * to lead to division-by-zero and other unpleasantness.  The input_rows
    3432             :      * estimate is usually already at least 1, but clamp it just in case it
    3433             :      * isn't.
    3434             :      */
    3435      252334 :     input_rows = clamp_row_est(input_rows);
    3436             : 
    3437             :     /*
    3438             :      * If no grouping columns, there's exactly one group.  (This can't happen
    3439             :      * for normal cases with GROUP BY or DISTINCT, but it is possible for
    3440             :      * corner cases with set operations.)
    3441             :      */
    3442      252334 :     if (groupExprs == NIL || (pgset && *pgset == NIL))
    3443         956 :         return 1.0;
    3444             : 
    3445             :     /*
    3446             :      * Count groups derived from boolean grouping expressions.  For other
    3447             :      * expressions, find the unique Vars used, treating an expression as a Var
    3448             :      * if we can find stats for it.  For each one, record the statistical
    3449             :      * estimate of number of distinct values (total in its table, without
    3450             :      * regard for filtering).
    3451             :      */
    3452      251378 :     numdistinct = 1.0;
    3453             : 
    3454      251378 :     i = 0;
    3455      535660 :     foreach(l, groupExprs)
    3456             :     {
    3457      284330 :         Node       *groupexpr = (Node *) lfirst(l);
    3458             :         double      this_srf_multiplier;
    3459             :         VariableStatData vardata;
    3460             :         List       *varshere;
    3461             :         ListCell   *l2;
    3462             : 
    3463             :         /* is expression in this grouping set? */
    3464      284330 :         if (pgset && !list_member_int(*pgset, i++))
    3465      238492 :             continue;
    3466             : 
    3467             :         /*
    3468             :          * Set-returning functions in grouping columns are a bit problematic.
    3469             :          * The code below will effectively ignore their SRF nature and come up
    3470             :          * with a numdistinct estimate as though they were scalar functions.
    3471             :          * We compensate by scaling up the end result by the largest SRF
    3472             :          * rowcount estimate.  (This will be an overestimate if the SRF
    3473             :          * produces multiple copies of any output value, but it seems best to
    3474             :          * assume the SRF's outputs are distinct.  In any case, it's probably
    3475             :          * pointless to worry too much about this without much better
    3476             :          * estimates for SRF output rowcounts than we have today.)
    3477             :          */
    3478      283602 :         this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
    3479      283602 :         if (srf_multiplier < this_srf_multiplier)
    3480         132 :             srf_multiplier = this_srf_multiplier;
    3481             : 
    3482             :         /* Short-circuit for expressions returning boolean */
    3483      283602 :         if (exprType(groupexpr) == BOOLOID)
    3484             :         {
    3485          50 :             numdistinct *= 2.0;
    3486          50 :             continue;
    3487             :         }
    3488             : 
    3489             :         /*
    3490             :          * If examine_variable is able to deduce anything about the GROUP BY
    3491             :          * expression, treat it as a single variable even if it's really more
    3492             :          * complicated.
    3493             :          *
    3494             :          * XXX This has the consequence that if there's a statistics object on
    3495             :          * the expression, we don't split it into individual Vars. This
    3496             :          * affects our selection of statistics in
    3497             :          * estimate_multivariate_ndistinct, because it's probably better to
    3498             :          * use more accurate estimate for each expression and treat them as
    3499             :          * independent, than to combine estimates for the extracted variables
    3500             :          * when we don't know how that relates to the expressions.
    3501             :          */
    3502      283552 :         examine_variable(root, groupexpr, 0, &vardata);
    3503      283552 :         if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
    3504             :         {
    3505      237198 :             varinfos = add_unique_group_var(root, varinfos,
    3506             :                                             groupexpr, &vardata);
    3507      237198 :             ReleaseVariableStats(vardata);
    3508      237198 :             continue;
    3509             :         }
    3510       46354 :         ReleaseVariableStats(vardata);
    3511             : 
    3512             :         /*
    3513             :          * Else pull out the component Vars.  Handle PlaceHolderVars by
    3514             :          * recursing into their arguments (effectively assuming that the
    3515             :          * PlaceHolderVar doesn't change the number of groups, which boils
    3516             :          * down to ignoring the possible addition of nulls to the result set).
    3517             :          */
    3518       46354 :         varshere = pull_var_clause(groupexpr,
    3519             :                                    PVC_RECURSE_AGGREGATES |
    3520             :                                    PVC_RECURSE_WINDOWFUNCS |
    3521             :                                    PVC_RECURSE_PLACEHOLDERS);
    3522             : 
    3523             :         /*
    3524             :          * If we find any variable-free GROUP BY item, then either it is a
    3525             :          * constant (and we can ignore it) or it contains a volatile function;
    3526             :          * in the latter case we punt and assume that each input row will
    3527             :          * yield a distinct group.
    3528             :          */
    3529       46354 :         if (varshere == NIL)
    3530             :         {
    3531         564 :             if (contain_volatile_functions(groupexpr))
    3532          48 :                 return input_rows;
    3533         516 :             continue;
    3534             :         }
    3535             : 
    3536             :         /*
    3537             :          * Else add variables to varinfos list
    3538             :          */
    3539       94620 :         foreach(l2, varshere)
    3540             :         {
    3541       48830 :             Node       *var = (Node *) lfirst(l2);
    3542             : 
    3543       48830 :             examine_variable(root, var, 0, &vardata);
    3544       48830 :             varinfos = add_unique_group_var(root, varinfos, var, &vardata);
    3545       48830 :             ReleaseVariableStats(vardata);
    3546             :         }
    3547             :     }
    3548             : 
    3549             :     /*
    3550             :      * If now no Vars, we must have an all-constant or all-boolean GROUP BY
    3551             :      * list.
    3552             :      */
    3553      251330 :     if (varinfos == NIL)
    3554             :     {
    3555             :         /* Apply SRF multiplier as we would do in the long path */
    3556         292 :         numdistinct *= srf_multiplier;
    3557             :         /* Round off */
    3558         292 :         numdistinct = ceil(numdistinct);
    3559             :         /* Guard against out-of-range answers */
    3560         292 :         if (numdistinct > input_rows)
    3561          14 :             numdistinct = input_rows;
    3562         292 :         if (numdistinct < 1.0)
    3563           0 :             numdistinct = 1.0;
    3564         292 :         return numdistinct;
    3565             :     }
    3566             : 
    3567             :     /*
    3568             :      * Group Vars by relation and estimate total numdistinct.
    3569             :      *
    3570             :      * For each iteration of the outer loop, we process the frontmost Var in
    3571             :      * varinfos, plus all other Vars in the same relation.  We remove these
    3572             :      * Vars from the newvarinfos list for the next iteration. This is the
    3573             :      * easiest way to group Vars of same rel together.
    3574             :      */
    3575             :     do
    3576             :     {
    3577      252766 :         GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
    3578      252766 :         RelOptInfo *rel = varinfo1->rel;
    3579      252766 :         double      reldistinct = 1;
    3580      252766 :         double      relmaxndistinct = reldistinct;
    3581      252766 :         int         relvarcount = 0;
    3582      252766 :         List       *newvarinfos = NIL;
    3583      252766 :         List       *relvarinfos = NIL;
    3584             : 
    3585             :         /*
    3586             :          * Split the list of varinfos in two - one for the current rel, one
    3587             :          * for remaining Vars on other rels.
    3588             :          */
    3589      252766 :         relvarinfos = lappend(relvarinfos, varinfo1);
    3590      287652 :         for_each_from(l, varinfos, 1)
    3591             :         {
    3592       34886 :             GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
    3593             : 
    3594       34886 :             if (varinfo2->rel == varinfo1->rel)
    3595             :             {
    3596             :                 /* varinfos on current rel */
    3597       32510 :                 relvarinfos = lappend(relvarinfos, varinfo2);
    3598             :             }
    3599             :             else
    3600             :             {
    3601             :                 /* not time to process varinfo2 yet */
    3602        2376 :                 newvarinfos = lappend(newvarinfos, varinfo2);
    3603             :             }
    3604             :         }
    3605             : 
    3606             :         /*
    3607             :          * Get the numdistinct estimate for the Vars of this rel.  We
    3608             :          * iteratively search for multivariate n-distinct with maximum number
    3609             :          * of vars; assuming that each var group is independent of the others,
    3610             :          * we multiply them together.  Any remaining relvarinfos after no more
    3611             :          * multivariate matches are found are assumed independent too, so
    3612             :          * their individual ndistinct estimates are multiplied also.
    3613             :          *
    3614             :          * While iterating, count how many separate numdistinct values we
    3615             :          * apply.  We apply a fudge factor below, but only if we multiplied
    3616             :          * more than one such values.
    3617             :          */
    3618      505658 :         while (relvarinfos)
    3619             :         {
    3620             :             double      mvndistinct;
    3621             : 
    3622      252892 :             if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
    3623             :                                                 &mvndistinct))
    3624             :             {
    3625         402 :                 reldistinct *= mvndistinct;
    3626         402 :                 if (relmaxndistinct < mvndistinct)
    3627         390 :                     relmaxndistinct = mvndistinct;
    3628         402 :                 relvarcount++;
    3629             :             }
    3630             :             else
    3631             :             {
    3632      536914 :                 foreach(l, relvarinfos)
    3633             :                 {
    3634      284424 :                     GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
    3635             : 
    3636      284424 :                     reldistinct *= varinfo2->ndistinct;
    3637      284424 :                     if (relmaxndistinct < varinfo2->ndistinct)
    3638      257260 :                         relmaxndistinct = varinfo2->ndistinct;
    3639      284424 :                     relvarcount++;
    3640             : 
    3641             :                     /*
    3642             :                      * When varinfo2's isdefault is set then we'd better set
    3643             :                      * the SELFLAG_USED_DEFAULT bit in the EstimationInfo.
    3644             :                      */
    3645      284424 :                     if (estinfo != NULL && varinfo2->isdefault)
    3646       12170 :                         estinfo->flags |= SELFLAG_USED_DEFAULT;
    3647             :                 }
    3648             : 
    3649             :                 /* we're done with this relation */
    3650      252490 :                 relvarinfos = NIL;
    3651             :             }
    3652             :         }
    3653             : 
    3654             :         /*
    3655             :          * Sanity check --- don't divide by zero if empty relation.
    3656             :          */
    3657             :         Assert(IS_SIMPLE_REL(rel));
    3658      252766 :         if (rel->tuples > 0)
    3659             :         {
    3660             :             /*
    3661             :              * Clamp to size of rel, or size of rel / 10 if multiple Vars. The
    3662             :              * fudge factor is because the Vars are probably correlated but we
    3663             :              * don't know by how much.  We should never clamp to less than the
    3664             :              * largest ndistinct value for any of the Vars, though, since
    3665             :              * there will surely be at least that many groups.
    3666             :              */
    3667      251818 :             double      clamp = rel->tuples;
    3668             : 
    3669      251818 :             if (relvarcount > 1)
    3670             :             {
    3671       29600 :                 clamp *= 0.1;
    3672       29600 :                 if (clamp < relmaxndistinct)
    3673             :                 {
    3674       28126 :                     clamp = relmaxndistinct;
    3675             :                     /* for sanity in case some ndistinct is too large: */
    3676       28126 :                     if (clamp > rel->tuples)
    3677          78 :                         clamp = rel->tuples;
    3678             :                 }
    3679             :             }
    3680      251818 :             if (reldistinct > clamp)
    3681       27840 :                 reldistinct = clamp;
    3682             : 
    3683             :             /*
    3684             :              * Update the estimate based on the restriction selectivity,
    3685             :              * guarding against division by zero when reldistinct is zero.
    3686             :              * Also skip this if we know that we are returning all rows.
    3687             :              */
    3688      251818 :             if (reldistinct > 0 && rel->rows < rel->tuples)
    3689             :             {
    3690             :                 /*
    3691             :                  * Given a table containing N rows with n distinct values in a
    3692             :                  * uniform distribution, if we select p rows at random then
    3693             :                  * the expected number of distinct values selected is
    3694             :                  *
    3695             :                  * n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
    3696             :                  *
    3697             :                  * = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
    3698             :                  *
    3699             :                  * See "Approximating block accesses in database
    3700             :                  * organizations", S. B. Yao, Communications of the ACM,
    3701             :                  * Volume 20 Issue 4, April 1977 Pages 260-261.
    3702             :                  *
    3703             :                  * Alternatively, re-arranging the terms from the factorials,
    3704             :                  * this may be written as
    3705             :                  *
    3706             :                  * n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
    3707             :                  *
    3708             :                  * This form of the formula is more efficient to compute in
    3709             :                  * the common case where p is larger than N/n.  Additionally,
    3710             :                  * as pointed out by Dell'Era, if i << N for all terms in the
    3711             :                  * product, it can be approximated by
    3712             :                  *
    3713             :                  * n * (1 - ((N-p)/N)^(N/n))
    3714             :                  *
    3715             :                  * See "Expected distinct values when selecting from a bag
    3716             :                  * without replacement", Alberto Dell'Era,
    3717             :                  * http://www.adellera.it/investigations/distinct_balls/.
    3718             :                  *
    3719             :                  * The condition i << N is equivalent to n >> 1, so this is a
    3720             :                  * good approximation when the number of distinct values in
    3721             :                  * the table is large.  It turns out that this formula also
    3722             :                  * works well even when n is small.
    3723             :                  */
    3724       70530 :                 reldistinct *=
    3725       70530 :                     (1 - pow((rel->tuples - rel->rows) / rel->tuples,
    3726       70530 :                              rel->tuples / reldistinct));
    3727             :             }
    3728      251818 :             reldistinct = clamp_row_est(reldistinct);
    3729             : 
    3730             :             /*
    3731             :              * Update estimate of total distinct groups.
    3732             :              */
    3733      251818 :             numdistinct *= reldistinct;
    3734             :         }
    3735             : 
    3736      252766 :         varinfos = newvarinfos;
    3737      252766 :     } while (varinfos != NIL);
    3738             : 
    3739             :     /* Now we can account for the effects of any SRFs */
    3740      251038 :     numdistinct *= srf_multiplier;
    3741             : 
    3742             :     /* Round off */
    3743      251038 :     numdistinct = ceil(numdistinct);
    3744             : 
    3745             :     /* Guard against out-of-range answers */
    3746      251038 :     if (numdistinct > input_rows)
    3747       56172 :         numdistinct = input_rows;
    3748      251038 :     if (numdistinct < 1.0)
    3749           0 :         numdistinct = 1.0;
    3750             : 
    3751      251038 :     return numdistinct;
    3752             : }
    3753             : 
    3754             : /*
    3755             :  * Estimate hash bucket statistics when the specified expression is used
    3756             :  * as a hash key for the given number of buckets.
    3757             :  *
    3758             :  * This attempts to determine two values:
    3759             :  *
    3760             :  * 1. The frequency of the most common value of the expression (returns
    3761             :  * zero into *mcv_freq if we can't get that).
    3762             :  *
    3763             :  * 2. The "bucketsize fraction", ie, average number of entries in a bucket
    3764             :  * divided by total tuples in relation.
    3765             :  *
    3766             :  * XXX This is really pretty bogus since we're effectively assuming that the
    3767             :  * distribution of hash keys will be the same after applying restriction
    3768             :  * clauses as it was in the underlying relation.  However, we are not nearly
    3769             :  * smart enough to figure out how the restrict clauses might change the
    3770             :  * distribution, so this will have to do for now.
    3771             :  *
    3772             :  * We are passed the number of buckets the executor will use for the given
    3773             :  * input relation.  If the data were perfectly distributed, with the same
    3774             :  * number of tuples going into each available bucket, then the bucketsize
    3775             :  * fraction would be 1/nbuckets.  But this happy state of affairs will occur
    3776             :  * only if (a) there are at least nbuckets distinct data values, and (b)
    3777             :  * we have a not-too-skewed data distribution.  Otherwise the buckets will
    3778             :  * be nonuniformly occupied.  If the other relation in the join has a key
    3779             :  * distribution similar to this one's, then the most-loaded buckets are
    3780             :  * exactly those that will be probed most often.  Therefore, the "average"
    3781             :  * bucket size for costing purposes should really be taken as something close
    3782             :  * to the "worst case" bucket size.  We try to estimate this by adjusting the
    3783             :  * fraction if there are too few distinct data values, and then scaling up
    3784             :  * by the ratio of the most common value's frequency to the average frequency.
    3785             :  *
    3786             :  * If no statistics are available, use a default estimate of 0.1.  This will
    3787             :  * discourage use of a hash rather strongly if the inner relation is large,
    3788             :  * which is what we want.  We do not want to hash unless we know that the
    3789             :  * inner rel is well-dispersed (or the alternatives seem much worse).
    3790             :  *
    3791             :  * The caller should also check that the mcv_freq is not so large that the
    3792             :  * most common value would by itself require an impractically large bucket.
    3793             :  * In a hash join, the executor can split buckets if they get too big, but
    3794             :  * obviously that doesn't help for a bucket that contains many duplicates of
    3795             :  * the same value.
    3796             :  */
    3797             : void
    3798      129264 : estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
    3799             :                            Selectivity *mcv_freq,
    3800             :                            Selectivity *bucketsize_frac)
    3801             : {
    3802             :     VariableStatData vardata;
    3803             :     double      estfract,
    3804             :                 ndistinct,
    3805             :                 stanullfrac,
    3806             :                 avgfreq;
    3807             :     bool        isdefault;
    3808             :     AttStatsSlot sslot;
    3809             : 
    3810      129264 :     examine_variable(root, hashkey, 0, &vardata);
    3811             : 
    3812             :     /* Look up the frequency of the most common value, if available */
    3813      129264 :     *mcv_freq = 0.0;
    3814             : 
    3815      129264 :     if (HeapTupleIsValid(vardata.statsTuple))
    3816             :     {
    3817       88950 :         if (get_attstatsslot(&sslot, vardata.statsTuple,
    3818             :                              STATISTIC_KIND_MCV, InvalidOid,
    3819             :                              ATTSTATSSLOT_NUMBERS))
    3820             :         {
    3821             :             /*
    3822             :              * The first MCV stat is for the most common value.
    3823             :              */
    3824       40150 :             if (sslot.nnumbers > 0)
    3825       40150 :                 *mcv_freq = sslot.numbers[0];
    3826       40150 :             free_attstatsslot(&sslot);
    3827             :         }
    3828             :     }
    3829             : 
    3830             :     /* Get number of distinct values */
    3831      129264 :     ndistinct = get_variable_numdistinct(&vardata, &isdefault);
    3832             : 
    3833             :     /*
    3834             :      * If ndistinct isn't real, punt.  We normally return 0.1, but if the
    3835             :      * mcv_freq is known to be even higher than that, use it instead.
    3836             :      */
    3837      129264 :     if (isdefault)
    3838             :     {
    3839       17452 :         *bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
    3840       17452 :         ReleaseVariableStats(vardata);
    3841       17452 :         return;
    3842             :     }
    3843             : 
    3844             :     /* Get fraction that are null */
    3845      111812 :     if (HeapTupleIsValid(vardata.statsTuple))
    3846             :     {
    3847             :         Form_pg_statistic stats;
    3848             : 
    3849       88932 :         stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
    3850       88932 :         stanullfrac = stats->stanullfrac;
    3851             :     }
    3852             :     else
    3853       22880 :         stanullfrac = 0.0;
    3854             : 
    3855             :     /* Compute avg freq of all distinct data values in raw relation */
    3856      111812 :     avgfreq = (1.0 - stanullfrac) / ndistinct;
    3857             : 
    3858             :     /*
    3859             :      * Adjust ndistinct to account for restriction clauses.  Observe we are
    3860             :      * assuming that the data distribution is affected uniformly by the
    3861             :      * restriction clauses!
    3862             :      *
    3863             :      * XXX Possibly better way, but much more expensive: multiply by
    3864             :      * selectivity of rel's restriction clauses that mention the target Var.
    3865             :      */
    3866      111812 :     if (vardata.rel && vardata.rel->tuples > 0)
    3867             :     {
    3868      111798 :         ndistinct *= vardata.rel->rows / vardata.rel->tuples;
    3869      111798 :         ndistinct = clamp_row_est(ndistinct);
    3870             :     }
    3871             : 
    3872             :     /*
    3873             :      * Initial estimate of bucketsize fraction is 1/nbuckets as long as the
    3874             :      * number of buckets is less than the expected number of distinct values;
    3875             :      * otherwise it is 1/ndistinct.
    3876             :      */
    3877      111812 :     if (ndistinct > nbuckets)
    3878          96 :         estfract = 1.0 / nbuckets;
    3879             :     else
    3880      111716 :         estfract = 1.0 / ndistinct;
    3881             : 
    3882             :     /*
    3883             :      * Adjust estimated bucketsize upward to account for skewed distribution.
    3884             :      */
    3885      111812 :     if (avgfreq > 0.0 && *mcv_freq > avgfreq)
    3886       35508 :         estfract *= *mcv_freq / avgfreq;
    3887             : 
    3888             :     /*
    3889             :      * Clamp bucketsize to sane range (the above adjustment could easily
    3890             :      * produce an out-of-range result).  We set the lower bound a little above
    3891             :      * zero, since zero isn't a very sane result.
    3892             :      */
    3893      111812 :     if (estfract < 1.0e-6)
    3894           0 :         estfract = 1.0e-6;
    3895      111812 :     else if (estfract > 1.0)
    3896       24868 :         estfract = 1.0;
    3897             : 
    3898      111812 :     *bucketsize_frac = (Selectivity) estfract;
    3899             : 
    3900      111812 :     ReleaseVariableStats(vardata);
    3901             : }
    3902             : 
    3903             : /*
    3904             :  * estimate_hashagg_tablesize
    3905             :  *    estimate the number of bytes that a hash aggregate hashtable will
    3906             :  *    require based on the agg_costs, path width and number of groups.
    3907             :  *
    3908             :  * We return the result as "double" to forestall any possible overflow
    3909             :  * problem in the multiplication by dNumGroups.
    3910             :  *
    3911             :  * XXX this may be over-estimating the size now that hashagg knows to omit
    3912             :  * unneeded columns from the hashtable.  Also for mixed-mode grouping sets,
    3913             :  * grouping columns not in the hashed set are counted here even though hashagg
    3914             :  * won't store them.  Is this a problem?
    3915             :  */
    3916             : double
    3917        1978 : estimate_hashagg_tablesize(PlannerInfo *root, Path *path,
    3918             :                            const AggClauseCosts *agg_costs, double dNumGroups)
    3919             : {
    3920             :     Size        hashentrysize;
    3921             : 
    3922        1978 :     hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
    3923        1978 :                                         path->pathtarget->width,
    3924             :                                         agg_costs->transitionSpace);
    3925             : 
    3926             :     /*
    3927             :      * Note that this disregards the effect of fill-factor and growth policy
    3928             :      * of the hash table.  That's probably ok, given that the default
    3929             :      * fill-factor is relatively high.  It'd be hard to meaningfully factor in
    3930             :      * "double-in-size" growth policies here.
    3931             :      */
    3932        1978 :     return hashentrysize * dNumGroups;
    3933             : }
    3934             : 
    3935             : 
    3936             : /*-------------------------------------------------------------------------
    3937             :  *
    3938             :  * Support routines
    3939             :  *
    3940             :  *-------------------------------------------------------------------------
    3941             :  */
    3942             : 
    3943             : /*
    3944             :  * Find applicable ndistinct statistics for the given list of VarInfos (which
    3945             :  * must all belong to the given rel), and update *ndistinct to the estimate of
    3946             :  * the MVNDistinctItem that best matches.  If a match it found, *varinfos is
    3947             :  * updated to remove the list of matched varinfos.
    3948             :  *
    3949             :  * Varinfos that aren't for simple Vars are ignored.
    3950             :  *
    3951             :  * Return true if we're able to find a match, false otherwise.
    3952             :  */
    3953             : static bool
    3954      252892 : estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
    3955             :                                 List **varinfos, double *ndistinct)
    3956             : {
    3957             :     ListCell   *lc;
    3958             :     int         nmatches_vars;
    3959             :     int         nmatches_exprs;
    3960      252892 :     Oid         statOid = InvalidOid;
    3961             :     MVNDistinct *stats;
    3962      252892 :     StatisticExtInfo *matched_info = NULL;
    3963      252892 :     RangeTblEntry *rte = planner_rt_fetch(rel->relid, root);
    3964             : 
    3965             :     /* bail out immediately if the table has no extended statistics */
    3966      252892 :     if (!rel->statlist)
    3967      252370 :         return false;
    3968             : 
    3969             :     /* look for the ndistinct statistics object matching the most vars */
    3970         522 :     nmatches_vars = 0;          /* we require at least two matches */
    3971         522 :     nmatches_exprs = 0;
    3972        2112 :     foreach(lc, rel->statlist)
    3973             :     {
    3974             :         ListCell   *lc2;
    3975        1590 :         StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
    3976        1590 :         int         nshared_vars = 0;
    3977        1590 :         int         nshared_exprs = 0;
    3978             : 
    3979             :         /* skip statistics of other kinds */
    3980        1590 :         if (info->kind != STATS_EXT_NDISTINCT)
    3981         750 :             continue;
    3982             : 
    3983             :         /* skip statistics with mismatching stxdinherit value */
    3984         840 :         if (info->inherit != rte->inh)
    3985          24 :             continue;
    3986             : 
    3987             :         /*
    3988             :          * Determine how many expressions (and variables in non-matched
    3989             :          * expressions) match. We'll then use these numbers to pick the
    3990             :          * statistics object that best matches the clauses.
    3991             :          */
    3992        2616 :         foreach(lc2, *varinfos)
    3993             :         {
    3994             :             ListCell   *lc3;
    3995        1800 :             GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
    3996             :             AttrNumber  attnum;
    3997             : 
    3998             :             Assert(varinfo->rel == rel);
    3999             : 
    4000             :             /* simple Var, search in statistics keys directly */
    4001        1800 :             if (IsA(varinfo->var, Var))
    4002             :             {
    4003        1434 :                 attnum = ((Var *) varinfo->var)->varattno;
    4004             : 
    4005             :                 /*
    4006             :                  * Ignore system attributes - we don't support statistics on
    4007             :                  * them, so can't match them (and it'd fail as the values are
    4008             :                  * negative).
    4009             :                  */
    4010        1434 :                 if (!AttrNumberIsForUserDefinedAttr(attnum))
    4011          12 :                     continue;
    4012             : 
    4013        1422 :                 if (bms_is_member(attnum, info->keys))
    4014         816 :                     nshared_vars++;
    4015             : 
    4016        1422 :                 continue;
    4017             :             }
    4018             : 
    4019             :             /* expression - see if it's in the statistics object */
    4020         660 :             foreach(lc3, info->exprs)
    4021             :             {
    4022         528 :                 Node       *expr = (Node *) lfirst(lc3);
    4023             : 
    4024         528 :                 if (equal(varinfo->var, expr))
    4025             :                 {
    4026         234 :                     nshared_exprs++;
    4027         234 :                     break;
    4028             :                 }
    4029             :             }
    4030             :         }
    4031             : 
    4032         816 :         if (nshared_vars + nshared_exprs < 2)
    4033         378 :             continue;
    4034             : 
    4035             :         /*
    4036             :          * Does this statistics object match more columns than the currently
    4037             :          * best object?  If so, use this one instead.
    4038             :          *
    4039             :          * XXX This should break ties using name of the object, or something
    4040             :          * like that, to make the outcome stable.
    4041             :          */
    4042         438 :         if ((nshared_exprs > nmatches_exprs) ||
    4043         330 :             (((nshared_exprs == nmatches_exprs)) && (nshared_vars > nmatches_vars)))
    4044             :         {
    4045         414 :             statOid = info->statOid;
    4046         414 :             nmatches_vars = nshared_vars;
    4047         414 :             nmatches_exprs = nshared_exprs;
    4048         414 :             matched_info = info;
    4049             :         }
    4050             :     }
    4051             : 
    4052             :     /* No match? */
    4053         522 :     if (statOid == InvalidOid)
    4054         120 :         return false;
    4055             : 
    4056             :     Assert(nmatches_vars + nmatches_exprs > 1);
    4057             : 
    4058         402 :     stats = statext_ndistinct_load(statOid, rte->inh);
    4059             : 
    4060             :     /*
    4061             :      * If we have a match, search it for the specific item that matches (there
    4062             :      * must be one), and construct the output values.
    4063             :      */
    4064         402 :     if (stats)
    4065             :     {
    4066             :         int         i;
    4067         402 :         List       *newlist = NIL;
    4068         402 :         MVNDistinctItem *item = NULL;
    4069             :         ListCell   *lc2;
    4070         402 :         Bitmapset  *matched = NULL;
    4071             :         AttrNumber  attnum_offset;
    4072             : 
    4073             :         /*
    4074             :          * How much we need to offset the attnums? If there are no
    4075             :          * expressions, no offset is needed. Otherwise offset enough to move
    4076             :          * the lowest one (which is equal to number of expressions) to 1.
    4077             :          */
    4078         402 :         if (matched_info->exprs)
    4079         144 :             attnum_offset = (list_length(matched_info->exprs) + 1);
    4080             :         else
    4081         258 :             attnum_offset = 0;
    4082             : 
    4083             :         /* see what actually matched */
    4084        1410 :         foreach(lc2, *varinfos)
    4085             :         {
    4086             :             ListCell   *lc3;
    4087             :             int         idx;
    4088        1008 :             bool        found = false;
    4089             : 
    4090        1008 :             GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
    4091             : 
    4092             :             /*
    4093             :              * Process a simple Var expression, by matching it to keys
    4094             :              * directly. If there's a matching expression, we'll try matching
    4095             :              * it later.
    4096             :              */
    4097        1008 :             if (IsA(varinfo->var, Var))
    4098             :             {
    4099         822 :                 AttrNumber  attnum = ((Var *) varinfo->var)->varattno;
    4100             : 
    4101             :                 /*
    4102             :                  * Ignore expressions on system attributes. Can't rely on the
    4103             :                  * bms check for negative values.
    4104             :                  */
    4105         822 :                 if (!AttrNumberIsForUserDefinedAttr(attnum))
    4106           6 :                     continue;
    4107             : 
    4108             :                 /* Is the variable covered by the statistics object? */
    4109         816 :                 if (!bms_is_member(attnum, matched_info->keys))
    4110         120 :                     continue;
    4111             : 
    4112         696 :                 attnum = attnum + attnum_offset;
    4113             : 
    4114             :                 /* ensure sufficient offset */
    4115             :                 Assert(AttrNumberIsForUserDefinedAttr(attnum));
    4116             : 
    4117         696 :                 matched = bms_add_member(matched, attnum);
    4118             : 
    4119         696 :                 found = true;
    4120             :             }
    4121             : 
    4122             :             /*
    4123             :              * XXX Maybe we should allow searching the expressions even if we
    4124             :              * found an attribute matching the expression? That would handle
    4125             :              * trivial expressions like "(a)" but it seems fairly useless.
    4126             :              */
    4127         882 :             if (found)
    4128         696 :                 continue;
    4129             : 
    4130             :             /* expression - see if it's in the statistics object */
    4131         186 :             idx = 0;
    4132         306 :             foreach(lc3, matched_info->exprs)
    4133             :             {
    4134         276 :                 Node       *expr = (Node *) lfirst(lc3);
    4135             : 
    4136         276 :                 if (equal(varinfo->var, expr))
    4137             :                 {
    4138         156 :                     AttrNumber  attnum = -(idx + 1);
    4139             : 
    4140         156 :                     attnum = attnum + attnum_offset;
    4141             : 
    4142             :                     /* ensure sufficient offset */
    4143             :                     Assert(AttrNumberIsForUserDefinedAttr(attnum));
    4144             : 
    4145         156 :                     matched = bms_add_member(matched, attnum);
    4146             : 
    4147             :                     /* there should be just one matching expression */
    4148         156 :                     break;
    4149             :                 }
    4150             : 
    4151         120 :                 idx++;
    4152             :             }
    4153             :         }
    4154             : 
    4155             :         /* Find the specific item that exactly matches the combination */
    4156         822 :         for (i = 0; i < stats->nitems; i++)
    4157             :         {
    4158             :             int         j;
    4159         822 :             MVNDistinctItem *tmpitem = &stats->items[i];
    4160             : 
    4161         822 :             if (tmpitem->nattributes != bms_num_members(matched))
    4162         144 :                 continue;
    4163             : 
    4164             :             /* assume it's the right item */
    4165         678 :             item = tmpitem;
    4166             : 
    4167             :             /* check that all item attributes/expressions fit the match */
    4168        1614 :             for (j = 0; j < tmpitem->nattributes; j++)
    4169             :             {
    4170        1212 :                 AttrNumber  attnum = tmpitem->attributes[j];
    4171             : 
    4172             :                 /*
    4173             :                  * Thanks to how we constructed the matched bitmap above, we
    4174             :                  * can just offset all attnums the same way.
    4175             :                  */
    4176        1212 :                 attnum = attnum + attnum_offset;
    4177             : 
    4178        1212 :                 if (!bms_is_member(attnum, matched))
    4179             :                 {
    4180             :                     /* nah, it's not this item */
    4181         276 :                     item = NULL;
    4182         276 :                     break;
    4183             :                 }
    4184             :             }
    4185             : 
    4186             :             /*
    4187             :              * If the item has all the matched attributes, we know it's the
    4188             :              * right one - there can't be a better one. matching more.
    4189             :              */
    4190         678 :             if (item)
    4191         402 :                 break;
    4192             :         }
    4193             : 
    4194             :         /*
    4195             :          * Make sure we found an item. There has to be one, because ndistinct
    4196             :          * statistics includes all combinations of attributes.
    4197             :          */
    4198         402 :         if (!item)
    4199           0 :             elog(ERROR, "corrupt MVNDistinct entry");
    4200             : 
    4201             :         /* Form the output varinfo list, keeping only unmatched ones */
    4202        1410 :         foreach(lc, *varinfos)
    4203             :         {
    4204        1008 :             GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
    4205             :             ListCell   *lc3;
    4206        1008 :             bool        found = false;
    4207             : 
    4208             :             /*
    4209             :              * Let's look at plain variables first, because it's the most
    4210             :              * common case and the check is quite cheap. We can simply get the
    4211             :              * attnum and check (with an offset) matched bitmap.
    4212             :              */
    4213        1008 :             if (IsA(varinfo->var, Var))
    4214             :             {
    4215         822 :                 AttrNumber  attnum = ((Var *) varinfo->var)->varattno;
    4216             : 
    4217             :                 /*
    4218             :                  * If it's a system attribute, we're done. We don't support
    4219             :                  * extended statistics on system attributes, so it's clearly
    4220             :                  * not matched. Just keep the expression and continue.
    4221             :                  */
    4222         822 :                 if (!AttrNumberIsForUserDefinedAttr(attnum))
    4223             :                 {
    4224           6 :                     newlist = lappend(newlist, varinfo);
    4225           6 :                     continue;
    4226             :                 }
    4227             : 
    4228             :                 /* apply the same offset as above */
    4229         816 :                 attnum += attnum_offset;
    4230             : 
    4231             :                 /* if it's not matched, keep the varinfo */
    4232         816 :                 if (!bms_is_member(attnum, matched))
    4233         120 :                     newlist = lappend(newlist, varinfo);
    4234             : 
    4235             :                 /* The rest of the loop deals with complex expressions. */
    4236         816 :                 continue;
    4237             :             }
    4238             : 
    4239             :             /*
    4240             :              * Process complex expressions, not just simple Vars.
    4241             :              *
    4242             :              * First, we search for an exact match of an expression. If we
    4243             :              * find one, we can just discard the whole GroupVarInfo, with all
    4244             :              * the variables we extracted from it.
    4245             :              *
    4246             :              * Otherwise we inspect the individual vars, and try matching it
    4247             :              * to variables in the item.
    4248             :              */
    4249         306 :             foreach(lc3, matched_info->exprs)
    4250             :             {
    4251         276 :                 Node       *expr = (Node *) lfirst(lc3);
    4252             : 
    4253         276 :                 if (equal(varinfo->var, expr))
    4254             :                 {
    4255         156 :                     found = true;
    4256         156 :                     break;
    4257             :                 }
    4258             :             }
    4259             : 
    4260             :             /* found exact match, skip */
    4261         186 :             if (found)
    4262         156 :                 continue;
    4263             : 
    4264          30 :             newlist = lappend(newlist, varinfo);
    4265             :         }
    4266             : 
    4267         402 :         *varinfos = newlist;
    4268         402 :         *ndistinct = item->ndistinct;
    4269         402 :         return true;
    4270             :     }
    4271             : 
    4272           0 :     return false;
    4273             : }
    4274             : 
    4275             : /*
    4276             :  * convert_to_scalar
    4277             :  *    Convert non-NULL values of the indicated types to the comparison
    4278             :  *    scale needed by scalarineqsel().
    4279             :  *    Returns "true" if successful.
    4280             :  *
    4281             :  * XXX this routine is a hack: ideally we should look up the conversion
    4282             :  * subroutines in pg_type.
    4283             :  *
    4284             :  * All numeric datatypes are simply converted to their equivalent
    4285             :  * "double" values.  (NUMERIC values that are outside the range of "double"
    4286             :  * are clamped to +/- HUGE_VAL.)
    4287             :  *
    4288             :  * String datatypes are converted by convert_string_to_scalar(),
    4289             :  * which is explained below.  The reason why this routine deals with
    4290             :  * three values at a time, not just one, is that we need it for strings.
    4291             :  *
    4292             :  * The bytea datatype is just enough different from strings that it has
    4293             :  * to be treated separately.
    4294             :  *
    4295             :  * The several datatypes representing absolute times are all converted
    4296             :  * to Timestamp, which is actually an int64, and then we promote that to
    4297             :  * a double.  Note this will give correct results even for the "special"
    4298             :  * values of Timestamp, since those are chosen to compare correctly;
    4299             :  * see timestamp_cmp.
    4300             :  *
    4301             :  * The several datatypes representing relative times (intervals) are all
    4302             :  * converted to measurements expressed in seconds.
    4303             :  */
    4304             : static bool
    4305       77052 : convert_to_scalar(Datum value, Oid valuetypid, Oid collid, double *scaledvalue,
    4306             :                   Datum lobound, Datum hibound, Oid boundstypid,
    4307             :                   double *scaledlobound, double *scaledhibound)
    4308             : {
    4309       77052 :     bool        failure = false;
    4310             : 
    4311             :     /*
    4312             :      * Both the valuetypid and the boundstypid should exactly match the
    4313             :      * declared input type(s) of the operator we are invoked for.  However,
    4314             :      * extensions might try to use scalarineqsel as estimator for operators
    4315             :      * with input type(s) we don't handle here; in such cases, we want to
    4316             :      * return false, not fail.  In any case, we mustn't assume that valuetypid
    4317             :      * and boundstypid are identical.
    4318             :      *
    4319             :      * XXX The histogram we are interpolating between points of could belong
    4320             :      * to a column that's only binary-compatible with the declared type. In
    4321             :      * essence we are assuming that the semantics of binary-compatible types
    4322             :      * are enough alike that we can use a histogram generated with one type's
    4323             :      * operators to estimate selectivity for the other's.  This is outright
    4324             :      * wrong in some cases --- in particular signed versus unsigned
    4325             :      * interpretation could trip us up.  But it's useful enough in the
    4326             :      * majority of cases that we do it anyway.  Should think about more
    4327             :      * rigorous ways to do it.
    4328             :      */
    4329       77052 :     switch (valuetypid)
    4330             :     {
    4331             :             /*
    4332             :              * Built-in numeric types
    4333             :              */
    4334       72154 :         case BOOLOID:
    4335             :         case INT2OID:
    4336             :         case INT4OID:
    4337             :         case INT8OID:
    4338             :         case FLOAT4OID:
    4339             :         case FLOAT8OID:
    4340             :         case NUMERICOID:
    4341             :         case OIDOID:
    4342             :         case REGPROCOID:
    4343             :         case REGPROCEDUREOID:
    4344             :         case REGOPEROID:
    4345             :         case REGOPERATOROID:
    4346             :         case REGCLASSOID:
    4347             :         case REGTYPEOID:
    4348             :         case REGCOLLATIONOID:
    4349             :         case REGCONFIGOID:
    4350             :         case REGDICTIONARYOID:
    4351             :         case REGROLEOID:
    4352             :         case REGNAMESPACEOID:
    4353       72154 :             *scaledvalue = convert_numeric_to_scalar(value, valuetypid,
    4354             :                                                      &failure);
    4355       72154 :             *scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
    4356             :                                                        &failure);
    4357       72154 :             *scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
    4358             :                                                        &failure);
    4359       72154 :             return !failure;
    4360             : 
    4361             :             /*
    4362             :              * Built-in string types
    4363             :              */
    4364        4898 :         case CHAROID:
    4365             :         case BPCHAROID:
    4366             :         case VARCHAROID:
    4367             :         case TEXTOID:
    4368             :         case NAMEOID:
    4369             :             {
    4370        4898 :                 char       *valstr = convert_string_datum(value, valuetypid,
    4371             :                                                           collid, &failure);
    4372        4898 :                 char       *lostr = convert_string_datum(lobound, boundstypid,
    4373             :                                                          collid, &failure);
    4374        4898 :                 char       *histr = convert_string_datum(hibound, boundstypid,
    4375             :                                                          collid, &failure);
    4376             : 
    4377             :                 /*
    4378             :                  * Bail out if any of the values is not of string type.  We
    4379             :                  * might leak converted strings for the other value(s), but
    4380             :                  * that's not worth troubling over.
    4381             :                  */
    4382        4898 :                 if (failure)
    4383           0 :                     return false;
    4384             : 
    4385        4898 :                 convert_string_to_scalar(valstr, scaledvalue,
    4386             :                                          lostr, scaledlobound,
    4387             :                                          histr, scaledhibound);
    4388        4898 :                 pfree(valstr);
    4389        4898 :                 pfree(lostr);
    4390        4898 :                 pfree(histr);
    4391        4898 :                 return true;
    4392             :             }
    4393             : 
    4394             :             /*
    4395             :              * Built-in bytea type
    4396             :              */
    4397           0 :         case BYTEAOID:
    4398             :             {
    4399             :                 /* We only support bytea vs bytea comparison */
    4400           0 :                 if (boundstypid != BYTEAOID)
    4401           0 :                     return false;
    4402           0 :                 convert_bytea_to_scalar(value, scaledvalue,
    4403             :                                         lobound, scaledlobound,
    4404             :                                         hibound, scaledhibound);
    4405           0 :                 return true;
    4406             :             }
    4407             : 
    4408             :             /*
    4409             :              * Built-in time types
    4410             :              */
    4411           0 :         case TIMESTAMPOID:
    4412             :         case TIMESTAMPTZOID:
    4413             :         case DATEOID:
    4414             :         case INTERVALOID:
    4415             :         case TIMEOID:
    4416             :         case TIMETZOID:
    4417           0 :             *scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
    4418             :                                                        &failure);
    4419           0 :             *scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
    4420             :                                                          &failure);
    4421           0 :             *scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
    4422             :                                                          &failure);
    4423           0 :             return !failure;
    4424             : 
    4425             :             /*
    4426             :              * Built-in network types
    4427             :              */
    4428           0 :         case INETOID:
    4429             :         case CIDROID:
    4430             :         case MACADDROID:
    4431             :         case MACADDR8OID:
    4432           0 :             *scaledvalue = convert_network_to_scalar(value, valuetypid,
    4433             :                                                      &failure);
    4434           0 :             *scaledlobound = convert_network_to_scalar(lobound, boundstypid,
    4435             :                                                        &failure);
    4436           0 :             *scaledhibound = convert_network_to_scalar(hibound, boundstypid,
    4437             :                                                        &failure);
    4438           0 :             return !failure;
    4439             :     }
    4440             :     /* Don't know how to convert */
    4441           0 :     *scaledvalue = *scaledlobound = *scaledhibound = 0;
    4442           0 :     return false;
    4443             : }
    4444             : 
    4445             : /*
    4446             :  * Do convert_to_scalar()'s work for any numeric data type.
    4447             :  *
    4448             :  * On failure (e.g., unsupported typid), set *failure to true;
    4449             :  * otherwise, that variable is not changed.
    4450             :  */
    4451             : static double
    4452      216462 : convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
    4453             : {
    4454      216462 :     switch (typid)
    4455             :     {
    4456           0 :         case BOOLOID:
    4457           0 :             return (double) DatumGetBool(value);
    4458          12 :         case INT2OID:
    4459          12 :             return (double) DatumGetInt16(value);
    4460       28986 :         case INT4OID:
    4461       28986 :             return (double) DatumGetInt32(value);
    4462           0 :         case INT8OID:
    4463           0 :             return (double) DatumGetInt64(value);
    4464           0 :         case FLOAT4OID:
    4465           0 :             return (double) DatumGetFloat4(value);
    4466          36 :         case FLOAT8OID:
    4467          36 :             return (double) DatumGetFloat8(value);
    4468           0 :         case NUMERICOID:
    4469             :             /* Note: out-of-range values will be clamped to +-HUGE_VAL */
    4470           0 :             return (double)
    4471           0 :                 DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
    4472             :                                                    value));
    4473      187428 :         case OIDOID:
    4474             :         case REGPROCOID:
    4475             :         case REGPROCEDUREOID:
    4476             :         case REGOPEROID:
    4477             :         case REGOPERATOROID:
    4478             :         case REGCLASSOID:
    4479             :         case REGTYPEOID:
    4480             :         case REGCOLLATIONOID:
    4481             :         case REGCONFIGOID:
    4482             :         case REGDICTIONARYOID:
    4483             :         case REGROLEOID:
    4484             :         case REGNAMESPACEOID:
    4485             :             /* we can treat OIDs as integers... */
    4486      187428 :             return (double) DatumGetObjectId(value);
    4487             :     }
    4488             : 
    4489           0 :     *failure = true;
    4490           0 :     return 0;
    4491             : }
    4492             : 
    4493             : /*
    4494             :  * Do convert_to_scalar()'s work for any character-string data type.
    4495             :  *
    4496             :  * String datatypes are converted to a scale that ranges from 0 to 1,
    4497             :  * where we visualize the bytes of the string as fractional digits.
    4498             :  *
    4499             :  * We do not want the base to be 256, however, since that tends to
    4500             :  * generate inflated selectivity estimates; few databases will have
    4501             :  * occurrences of all 256 possible byte values at each position.
    4502             :  * Instead, use the smallest and largest byte values seen in the bounds
    4503             :  * as the estimated range for each byte, after some fudging to deal with
    4504             :  * the fact that we probably aren't going to see the full range that way.
    4505             :  *
    4506             :  * An additional refinement is that we discard any common prefix of the
    4507             :  * three strings before computing the scaled values.  This allows us to
    4508             :  * "zoom in" when we encounter a narrow data range.  An example is a phone
    4509             :  * number database where all the values begin with the same area code.
    4510             :  * (Actually, the bounds will be adjacent histogram-bin-boundary values,
    4511             :  * so this is more likely to happen than you might think.)
    4512             :  */
    4513             : static void
    4514        4898 : convert_string_to_scalar(char *value,
    4515             :                          double *scaledvalue,
    4516             :                          char *lobound,
    4517             :                          double *scaledlobound,
    4518             :                          char *hibound,
    4519             :                          double *scaledhibound)
    4520             : {
    4521             :     int         rangelo,
    4522             :                 rangehi;
    4523             :     char       *sptr;
    4524             : 
    4525        4898 :     rangelo = rangehi = (unsigned char) hibound[0];
    4526       64350 :     for (sptr = lobound; *sptr; sptr++)
    4527             :     {
    4528       59452 :         if (rangelo > (unsigned char) *sptr)
    4529       12138 :             rangelo = (unsigned char) *sptr;
    4530       59452 :         if (rangehi < (unsigned char) *sptr)
    4531        6410 :             rangehi = (unsigned char) *sptr;
    4532             :     }
    4533       68246 :     for (sptr = hibound; *sptr; sptr++)
    4534             :     {
    4535       63348 :         if (rangelo > (unsigned char) *sptr)
    4536         900 :             rangelo = (unsigned char) *sptr;
    4537       63348 :         if (rangehi < (unsigned char) *sptr)
    4538        2444 :             rangehi = (unsigned char) *sptr;
    4539             :     }
    4540             :     /* If range includes any upper-case ASCII chars, make it include all */
    4541        4898 :     if (rangelo <= 'Z' && rangehi >= 'A')
    4542             :     {
    4543         838 :         if (rangelo > 'A')
    4544          90 :             rangelo = 'A';
    4545         838 :         if (rangehi < 'Z')
    4546         480 :             rangehi = 'Z';
    4547             :     }
    4548             :     /* Ditto lower-case */
    4549        4898 :     if (rangelo <= 'z' && rangehi >= 'a')
    4550             :     {
    4551        4396 :         if (rangelo > 'a')
    4552          18 :             rangelo = 'a';
    4553        4396 :         if (rangehi < 'z')
    4554        4346 :             rangehi = 'z';
    4555             :     }
    4556             :     /* Ditto digits */
    4557        4898 :     if (rangelo <= '9' && rangehi >= '0')
    4558             :     {
    4559         290 :         if (rangelo > '0')
    4560         196 :             rangelo = '0';
    4561         290 :         if (rangehi < '9')
    4562          14 :             rangehi = '9';
    4563             :     }
    4564             : 
    4565             :     /*
    4566             :      * If range includes less than 10 chars, assume we have not got enough
    4567             :      * data, and make it include regular ASCII set.
    4568             :      */
    4569        4898 :     if (rangehi - rangelo < 9)
    4570             :     {
    4571           0 :         rangelo = ' ';
    4572           0 :         rangehi = 127;
    4573             :     }
    4574             : 
    4575             :     /*
    4576             :      * Now strip any common prefix of the three strings.
    4577             :      */
    4578        8872 :     while (*lobound)
    4579             :     {
    4580        8872 :         if (*lobound != *hibound || *lobound != *value)
    4581             :             break;
    4582        3974 :         lobound++, hibound++, value++;
    4583             :     }
    4584             : 
    4585             :     /*
    4586             :      * Now we can do the conversions.
    4587             :      */
    4588        4898 :     *scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
    4589        4898 :     *scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
    4590        4898 :     *scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
    4591        4898 : }
    4592             : 
    4593             : static double
    4594       14694 : convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
    4595             : {
    4596       14694 :     int         slen = strlen(value);
    4597             :     double      num,
    4598             :                 denom,
    4599             :                 base;
    4600             : 
    4601       14694 :     if (slen <= 0)
    4602           0 :         return 0.0;             /* empty string has scalar value 0 */
    4603             : 
    4604             :     /*
    4605             :      * There seems little point in considering more than a dozen bytes from
    4606             :      * the string.  Since base is at least 10, that will give us nominal
    4607             :      * resolution of at least 12 decimal digits, which is surely far more
    4608             :      * precision than this estimation technique has got anyway (especially in
    4609             :      * non-C locales).  Also, even with the maximum possible base of 256, this
    4610             :      * ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
    4611             :      * overflow on any known machine.
    4612             :      */
    4613       14694 :     if (slen > 12)
    4614        4370 :         slen = 12;
    4615             : 
    4616             :     /* Convert initial characters to fraction */
    4617       14694 :     base = rangehi - rangelo + 1;
    4618       14694 :     num = 0.0;
    4619       14694 :     denom = base;
    4620      125546 :     while (slen-- > 0)
    4621             :     {
    4622      110852 :         int         ch = (unsigned char) *value++;
    4623             : 
    4624      110852 :         if (ch < rangelo)
    4625         232 :             ch = rangelo - 1;
    4626      110620 :         else if (ch > rangehi)
    4627           0 :             ch = rangehi + 1;
    4628      110852 :         num += ((double) (ch - rangelo)) / denom;
    4629      110852 :         denom *= base;
    4630             :     }
    4631             : 
    4632       14694 :     return num;
    4633             : }
    4634             : 
    4635             : /*
    4636             :  * Convert a string-type Datum into a palloc'd, null-terminated string.
    4637             :  *
    4638             :  * On failure (e.g., unsupported typid), set *failure to true;
    4639             :  * otherwise, that variable is not changed.  (We'll return NULL on failure.)
    4640             :  *
    4641             :  * When using a non-C locale, we must pass the string through strxfrm()
    4642             :  * before continuing, so as to generate correct locale-specific results.
    4643             :  */
    4644             : static char *
    4645       14694 : convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
    4646             : {
    4647             :     char       *val;
    4648             : 
    4649       14694 :     switch (typid)
    4650             :     {
    4651           0 :         case CHAROID:
    4652           0 :             val = (char *) palloc(2);
    4653           0 :             val[0] = DatumGetChar(value);
    4654           0 :             val[1] = '\0';
    4655           0 :             break;
    4656        4494 :         case BPCHAROID:
    4657             :         case VARCHAROID:
    4658             :         case TEXTOID:
    4659        4494 :             val = TextDatumGetCString(value);
    4660        4494 :             break;
    4661       10200 :         case NAMEOID:
    4662             :             {
    4663       10200 :                 NameData   *nm = (NameData *) DatumGetPointer(value);
    4664             : 
    4665       10200 :                 val = pstrdup(NameStr(*nm));
    4666       10200 :                 break;
    4667             :             }
    4668           0 :         default:
    4669           0 :             *failure = true;
    4670           0 :             return NULL;
    4671             :     }
    4672             : 
    4673       14694 :     if (!lc_collate_is_c(collid))
    4674             :     {
    4675             :         char       *xfrmstr;
    4676             :         size_t      xfrmlen;
    4677             :         size_t      xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
    4678             : 
    4679             :         /*
    4680             :          * XXX: We could guess at a suitable output buffer size and only call
    4681             :          * strxfrm twice if our guess is too small.
    4682             :          *
    4683             :          * XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
    4684             :          * bogus data or set an error. This is not really a problem unless it
    4685             :          * crashes since it will only give an estimation error and nothing
    4686             :          * fatal.
    4687             :          */
    4688          72 :         xfrmlen = strxfrm(NULL, val, 0);
    4689             : #ifdef WIN32
    4690             : 
    4691             :         /*
    4692             :          * On Windows, strxfrm returns INT_MAX when an error occurs. Instead
    4693             :          * of trying to allocate this much memory (and fail), just return the
    4694             :          * original string unmodified as if we were in the C locale.
    4695             :          */
    4696             :         if (xfrmlen == INT_MAX)
    4697             :             return val;
    4698             : #endif
    4699          72 :         xfrmstr = (char *) palloc(xfrmlen + 1);
    4700          72 :         xfrmlen2 = strxfrm(xfrmstr, val, xfrmlen + 1);
    4701             : 
    4702             :         /*
    4703             :          * Some systems (e.g., glibc) can return a smaller value from the
    4704             :          * second call than the first; thus the Assert must be <= not ==.
    4705             :          */
    4706             :         Assert(xfrmlen2 <= xfrmlen);
    4707          72 :         pfree(val);
    4708          72 :         val = xfrmstr;
    4709             :     }
    4710             : 
    4711       14694 :     return val;
    4712             : }
    4713             : 
    4714             : /*
    4715             :  * Do convert_to_scalar()'s work for any bytea data type.
    4716             :  *
    4717             :  * Very similar to convert_string_to_scalar except we can't assume
    4718             :  * null-termination and therefore pass explicit lengths around.
    4719             :  *
    4720             :  * Also, assumptions about likely "normal" ranges of characters have been
    4721             :  * removed - a data range of 0..255 is always used, for now.  (Perhaps
    4722             :  * someday we will add information about actual byte data range to
    4723             :  * pg_statistic.)
    4724             :  */
    4725             : static void
    4726           0 : convert_bytea_to_scalar(Datum value,
    4727             :                         double *scaledvalue,
    4728             :                         Datum lobound,
    4729             :                         double *scaledlobound,
    4730             :                         Datum hibound,
    4731             :                         double *scaledhibound)
    4732             : {
    4733           0 :     bytea      *valuep = DatumGetByteaPP(value);
    4734           0 :     bytea      *loboundp = DatumGetByteaPP(lobound);
    4735           0 :     bytea      *hiboundp = DatumGetByteaPP(hibound);
    4736             :     int         rangelo,
    4737             :                 rangehi,
    4738           0 :                 valuelen = VARSIZE_ANY_EXHDR(valuep),
    4739           0 :                 loboundlen = VARSIZE_ANY_EXHDR(loboundp),
    4740           0 :                 hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
    4741             :                 i,
    4742             :                 minlen;
    4743           0 :     unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
    4744           0 :     unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
    4745           0 :     unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
    4746             : 
    4747             :     /*
    4748             :      * Assume bytea data is uniformly distributed across all byte values.
    4749             :      */
    4750           0 :     rangelo = 0;
    4751           0 :     rangehi = 255;
    4752             : 
    4753             :     /*
    4754             :      * Now strip any common prefix of the three strings.
    4755             :      */
    4756           0 :     minlen = Min(Min(valuelen, loboundlen), hiboundlen);
    4757           0 :     for (i = 0; i < minlen; i++)
    4758             :     {
    4759           0 :         if (*lostr != *histr || *lostr != *valstr)
    4760             :             break;
    4761           0 :         lostr++, histr++, valstr++;
    4762           0 :         loboundlen--, hiboundlen--, valuelen--;
    4763             :     }
    4764             : 
    4765             :     /*
    4766             :      * Now we can do the conversions.
    4767             :      */
    4768           0 :     *scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
    4769           0 :     *scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
    4770           0 :     *scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
    4771           0 : }
    4772             : 
    4773             : static double
    4774           0 : convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
    4775             :                             int rangelo, int rangehi)
    4776             : {
    4777             :     double      num,
    4778             :                 denom,
    4779             :                 base;
    4780             : 
    4781           0 :     if (valuelen <= 0)
    4782           0 :         return 0.0;             /* empty string has scalar value 0 */
    4783             : 
    4784             :     /*
    4785             :      * Since base is 256, need not consider more than about 10 chars (even
    4786             :      * this many seems like overkill)
    4787             :      */
    4788           0 :     if (valuelen > 10)
    4789           0 :         valuelen = 10;
    4790             : 
    4791             :     /* Convert initial characters to fraction */
    4792           0 :     base = rangehi - rangelo + 1;
    4793           0 :     num = 0.0;
    4794           0 :     denom = base;
    4795           0 :     while (valuelen-- > 0)
    4796             :     {
    4797           0 :         int         ch = *value++;
    4798             : 
    4799           0 :         if (ch < rangelo)
    4800           0 :             ch = rangelo - 1;
    4801           0 :         else if (ch > rangehi)
    4802           0 :             ch = rangehi + 1;
    4803           0 :         num += ((double) (ch - rangelo)) / denom;
    4804           0 :         denom *= base;
    4805             :     }
    4806             : 
    4807           0 :     return num;
    4808             : }
    4809             : 
    4810             : /*
    4811             :  * Do convert_to_scalar()'s work for any timevalue data type.
    4812             :  *
    4813             :  * On failure (e.g., unsupported typid), set *failure to true;
    4814             :  * otherwise, that variable is not changed.
    4815             :  */
    4816             : static double
    4817           0 : convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
    4818             : {
    4819           0 :     switch (typid)
    4820             :     {
    4821           0 :         case TIMESTAMPOID:
    4822           0 :             return DatumGetTimestamp(value);
    4823           0 :         case TIMESTAMPTZOID:
    4824           0 :             return DatumGetTimestampTz(value);
    4825           0 :         case DATEOID:
    4826           0 :             return date2timestamp_no_overflow(DatumGetDateADT(value));
    4827           0 :         case INTERVALOID:
    4828             :             {
    4829           0 :                 Interval   *interval = DatumGetIntervalP(value);
    4830             : 
    4831             :                 /*
    4832             :                  * Convert the month part of Interval to days using assumed
    4833             :                  * average month length of 365.25/12.0 days.  Not too
    4834             :                  * accurate, but plenty good enough for our purposes.
    4835             :                  *
    4836             :                  * This also works for infinite intervals, which just have all
    4837             :                  * fields set to INT_MIN/INT_MAX, and so will produce a result
    4838             :                  * smaller/larger than any finite interval.
    4839             :                  */
    4840           0 :                 return interval->time + interval->day * (double) USECS_PER_DAY +
    4841           0 :                     interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
    4842             :             }
    4843           0 :         case TIMEOID:
    4844           0 :             return DatumGetTimeADT(value);
    4845           0 :         case TIMETZOID:
    4846             :             {
    4847           0 :                 TimeTzADT  *timetz = DatumGetTimeTzADTP(value);
    4848             : 
    4849             :                 /* use GMT-equivalent time */
    4850           0 :                 return (double) (timetz->time + (timetz->zone * 1000000.0));
    4851             :             }
    4852             :     }
    4853             : 
    4854           0 :     *failure = true;
    4855           0 :     return 0;
    4856             : }
    4857             : 
    4858             : 
    4859             : /*
    4860             :  * get_restriction_variable
    4861             :  *      Examine the args of a restriction clause to see if it's of the
    4862             :  *      form (variable op pseudoconstant) or (pseudoconstant op variable),
    4863             :  *      where "variable" could be either a Var or an expression in vars of a
    4864             :  *      single relation.  If so, extract information about the variable,
    4865             :  *      and also indicate which side it was on and the other argument.
    4866             :  *
    4867             :  * Inputs:
    4868             :  *  root: the planner info
    4869             :  *  args: clause argument list
    4870             :  *  varRelid: see specs for restriction selectivity functions
    4871             :  *
    4872             :  * Outputs: (these are valid only if true is returned)
    4873             :  *  *vardata: gets information about variable (see examine_variable)
    4874             :  *  *other: gets other clause argument, aggressively reduced to a constant
    4875             :  *  *varonleft: set true if variable is on the left, false if on the right
    4876             :  *
    4877             :  * Returns true if a variable is identified, otherwise false.
    4878             :  *
    4879             :  * Note: if there are Vars on both sides of the clause, we must fail, because
    4880             :  * callers are expecting that the other side will act like a pseudoconstant.
    4881             :  */
    4882             : bool
    4883      618642 : get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
    4884             :                          VariableStatData *vardata, Node **other,
    4885             :                          bool *varonleft)
    4886             : {
    4887             :     Node       *left,
    4888             :                *right;
    4889             :     VariableStatData rdata;
    4890             : 
    4891             :     /* Fail if not a binary opclause (probably shouldn't happen) */
    4892      618642 :     if (list_length(args) != 2)
    4893           0 :         return false;
    4894             : 
    4895      618642 :     left = (Node *) linitial(args);
    4896      618642 :     right = (Node *) lsecond(args);
    4897             : 
    4898             :     /*
    4899             :      * Examine both sides.  Note that when varRelid is nonzero, Vars of other
    4900             :      * relations will be treated as pseudoconstants.
    4901             :      */
    4902      618642 :     examine_variable(root, left, varRelid, vardata);
    4903      618642 :     examine_variable(root, right, varRelid, &rdata);
    4904             : 
    4905             :     /*
    4906             :      * If one side is a variable and the other not, we win.
    4907             :      */
    4908      618642 :     if (vardata->rel && rdata.rel == NULL)
    4909             :     {
    4910      551256 :         *varonleft = true;
    4911      551256 :         *other = estimate_expression_value(root, rdata.var);
    4912             :         /* Assume we need no ReleaseVariableStats(rdata) here */
    4913      551256 :         return true;
    4914             :     }
    4915             : 
    4916       67386 :     if (vardata->rel == NULL && rdata.rel)
    4917             :     {
    4918       62554 :         *varonleft = false;
    4919       62554 :         *other = estimate_expression_value(root, vardata->var);
    4920             :         /* Assume we need no ReleaseVariableStats(*vardata) here */
    4921       62554 :         *vardata = rdata;
    4922       62554 :         return true;
    4923             :     }
    4924             : 
    4925             :     /* Oops, clause has wrong structure (probably var op var) */
    4926        4832 :     ReleaseVariableStats(*vardata);
    4927        4832 :     ReleaseVariableStats(rdata);
    4928             : 
    4929        4832 :     return false;
    4930             : }
    4931             : 
    4932             : /*
    4933             :  * get_join_variables
    4934             :  *      Apply examine_variable() to each side of a join clause.
    4935             :  *      Also, attempt to identify whether the join clause has the same
    4936             :  *      or reversed sense compared to the SpecialJoinInfo.
    4937             :  *
    4938             :  * We consider the join clause "normal" if it is "lhs_var OP rhs_var",
    4939             :  * or "reversed" if it is "rhs_var OP lhs_var".  In complicated cases
    4940             :  * where we can't tell for sure, we default to assuming it's normal.
    4941             :  */
    4942             : void
    4943      190366 : get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
    4944             :                    VariableStatData *vardata1, VariableStatData *vardata2,
    4945             :                    bool *join_is_reversed)
    4946             : {
    4947             :     Node       *left,
    4948             :                *right;
    4949             : 
    4950      190366 :     if (list_length(args) != 2)
    4951           0 :         elog(ERROR, "join operator should take two arguments");
    4952             : 
    4953      190366 :     left = (Node *) linitial(args);
    4954      190366 :     right = (Node *) lsecond(args);
    4955             : 
    4956      190366 :     examine_variable(root, left, 0, vardata1);
    4957      190366 :     examine_variable(root, right, 0, vardata2);
    4958             : 
    4959      380562 :     if (vardata1->rel &&
    4960      190196 :         bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
    4961       61122 :         *join_is_reversed = true;   /* var1 is on RHS */
    4962      258318 :     else if (vardata2->rel &&
    4963      129074 :              bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
    4964         140 :         *join_is_reversed = true;   /* var2 is on LHS */
    4965             :     else
    4966      129104 :         *join_is_reversed = false;
    4967      190366 : }
    4968             : 
    4969             : /* statext_expressions_load copies the tuple, so just pfree it. */
    4970             : static void
    4971        1644 : ReleaseDummy(HeapTuple tuple)
    4972             : {
    4973        1644 :     pfree(tuple);
    4974        1644 : }
    4975             : 
    4976             : /*
    4977             :  * examine_variable
    4978             :  *      Try to look up statistical data about an expression.
    4979             :  *      Fill in a VariableStatData struct to describe the expression.
    4980             :  *
    4981             :  * Inputs:
    4982             :  *  root: the planner info
    4983             :  *  node: the expression tree to examine
    4984             :  *  varRelid: see specs for restriction selectivity functions
    4985             :  *
    4986             :  * Outputs: *vardata is filled as follows:
    4987             :  *  var: the input expression (with any binary relabeling stripped, if
    4988             :  *      it is or contains a variable; but otherwise the type is preserved)
    4989             :  *  rel: RelOptInfo for relation containing variable; NULL if expression
    4990             :  *      contains no Vars (NOTE this could point to a RelOptInfo of a
    4991             :  *      subquery, not one in the current query).
    4992             :  *  statsTuple: the pg_statistic entry for the variable, if one exists;
    4993             :  *      otherwise NULL.
    4994             :  *  freefunc: pointer to a function to release statsTuple with.
    4995             :  *  vartype: exposed type of the expression; this should always match
    4996             :  *      the declared input type of the operator we are estimating for.
    4997             :  *  atttype, atttypmod: actual type/typmod of the "var" expression.  This is
    4998             :  *      commonly the same as the exposed type of the variable argument,
    4999             :  *      but can be different in binary-compatible-type cases.
    5000             :  *  isunique: true if we were able to match the var to a unique index or a
    5001             :  *      single-column DISTINCT clause, implying its values are unique for
    5002             :  *      this query.  (Caution: this should be trusted for statistical
    5003             :  *      purposes only, since we do not check indimmediate nor verify that
    5004             :  *      the exact same definition of equality applies.)
    5005             :  *  acl_ok: true if current user has permission to read the column(s)
    5006             :  *      underlying the pg_statistic entry.  This is consulted by
    5007             :  *      statistic_proc_security_check().
    5008             :  *
    5009             :  * Caller is responsible for doing ReleaseVariableStats() before exiting.
    5010             :  */
    5011             : void
    5012     2399960 : examine_variable(PlannerInfo *root, Node *node, int varRelid,
    5013             :                  VariableStatData *vardata)
    5014             : {
    5015             :     Node       *basenode;
    5016             :     Relids      varnos;
    5017             :     RelOptInfo *onerel;
    5018             : 
    5019             :     /* Make sure we don't return dangling pointers in vardata */
    5020    16799720 :     MemSet(vardata, 0, sizeof(VariableStatData));
    5021             : 
    5022             :     /* Save the exposed type of the expression */
    5023     2399960 :     vardata->vartype = exprType(node);
    5024             : 
    5025             :     /* Look inside any binary-compatible relabeling */
    5026             : 
    5027     2399960 :     if (IsA(node, RelabelType))
    5028       22694 :         basenode = (Node *) ((RelabelType *) node)->arg;
    5029             :     else
    5030     2377266 :         basenode = node;
    5031             : 
    5032             :     /* Fast path for a simple Var */
    5033             : 
    5034     2399960 :     if (IsA(basenode, Var) &&
    5035      558360 :         (varRelid == 0 || varRelid == ((Var *) basenode)->varno))
    5036             :     {
    5037     1693836 :         Var        *var = (Var *) basenode;
    5038             : 
    5039             :         /* Set up result fields other than the stats tuple */
    5040     1693836 :         vardata->var = basenode; /* return Var without relabeling */
    5041     1693836 :         vardata->rel = find_base_rel(root, var->varno);
    5042     1693836 :         vardata->atttype = var->vartype;
    5043     1693836 :         vardata->atttypmod = var->vartypmod;
    5044     1693836 :         vardata->isunique = has_unique_index(vardata->rel, var->varattno);
    5045             : 
    5046             :         /* Try to locate some stats */
    5047     1693836 :         examine_simple_variable(root, var, vardata);
    5048             : 
    5049     1693836 :         return;
    5050             :     }
    5051             : 
    5052             :     /*
    5053             :      * Okay, it's a more complicated expression.  Determine variable
    5054             :      * membership.  Note that when varRelid isn't zero, only vars of that
    5055             :      * relation are considered "real" vars.
    5056             :      */
    5057      706124 :     varnos = pull_varnos(root, basenode);
    5058             : 
    5059      706124 :     onerel = NULL;
    5060             : 
    5061      706124 :     if (bms_is_empty(varnos))
    5062             :     {
    5063             :         /* No Vars at all ... must be pseudo-constant clause */
    5064             :     }
    5065             :     else
    5066             :     {
    5067             :         int         relid;
    5068             : 
    5069      334452 :         if (bms_get_singleton_member(varnos, &relid))
    5070             :         {
    5071      328964 :             if (varRelid == 0 || varRelid == relid)
    5072             :             {
    5073       51664 :                 onerel = find_base_rel(root, relid);
    5074       51664 :                 vardata->rel = onerel;
    5075       51664 :                 node = basenode;    /* strip any relabeling */
    5076             :             }
    5077             :             /* else treat it as a constant */
    5078             :         }
    5079             :         else
    5080             :         {
    5081             :             /* varnos has multiple relids */
    5082        5488 :             if (varRelid == 0)
    5083             :             {
    5084             :                 /* treat it as a variable of a join relation */
    5085        4986 :                 vardata->rel = find_join_rel(root, varnos);
    5086        4986 :                 node = basenode;    /* strip any relabeling */
    5087             :             }
    5088         502 :             else if (bms_is_member(varRelid, varnos))
    5089             :             {
    5090             :                 /* ignore the vars belonging to other relations */
    5091          92 :                 vardata->rel = find_base_rel(root, varRelid);
    5092          92 :                 node = basenode;    /* strip any relabeling */
    5093             :                 /* note: no point in expressional-index search here */
    5094             :             }
    5095             :             /* else treat it as a constant */
    5096             :         }
    5097             :     }
    5098             : 
    5099      706124 :     bms_free(varnos);
    5100             : 
    5101      706124 :     vardata->var = node;
    5102      706124 :     vardata->atttype = exprType(node);
    5103      706124 :     vardata->atttypmod = exprTypmod(node);
    5104             : 
    5105      706124 :     if (onerel)
    5106             :     {
    5107             :         /*
    5108             :          * We have an expression in vars of a single relation.  Try to match
    5109             :          * it to expressional index columns, in hopes of finding some
    5110             :          * statistics.
    5111             :          *
    5112             :          * Note that we consider all index columns including INCLUDE columns,
    5113             :          * since there could be stats for such columns.  But the test for
    5114             :          * uniqueness needs to be warier.
    5115             :          *
    5116             :          * XXX it's conceivable that there are multiple matches with different
    5117             :          * index opfamilies; if so, we need to pick one that matches the
    5118             :          * operator we are estimating for.  FIXME later.
    5119             :          */
    5120             :         ListCell   *ilist;
    5121             :         ListCell   *slist;
    5122             :         Oid         userid;
    5123             : 
    5124             :         /*
    5125             :          * Determine the user ID to use for privilege checks: either
    5126             :          * onerel->userid if it's set (e.g., in case we're accessing the table
    5127             :          * via a view), or the current user otherwise.
    5128             :          *
    5129             :          * If we drill down to child relations, we keep using the same userid:
    5130             :          * it's going to be the same anyway, due to how we set up the relation
    5131             :          * tree (q.v. build_simple_rel).
    5132             :          */
    5133       51664 :         userid = OidIsValid(onerel->userid) ? onerel->userid : GetUserId();
    5134             : 
    5135      101154 :         foreach(ilist, onerel->indexlist)
    5136             :         {
    5137       52268 :             IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
    5138             :             ListCell   *indexpr_item;
    5139             :             int         pos;
    5140             : 
    5141       52268 :             indexpr_item = list_head(index->indexprs);
    5142       52268 :             if (indexpr_item == NULL)
    5143       47522 :                 continue;       /* no expressions here... */
    5144             : 
    5145        6786 :             for (pos = 0; pos < index->ncolumns; pos++)
    5146             :             {
    5147        4818 :                 if (index->indexkeys[pos] == 0)
    5148             :                 {
    5149             :                     Node       *indexkey;
    5150             : 
    5151        4746 :                     if (indexpr_item == NULL)
    5152           0 :                         elog(ERROR, "too few entries in indexprs list");
    5153        4746 :                     indexkey = (Node *) lfirst(indexpr_item);
    5154        4746 :                     if (indexkey && IsA(indexkey, RelabelType))
    5155           0 :                         indexkey = (Node *) ((RelabelType *) indexkey)->arg;
    5156        4746 :                     if (equal(node, indexkey))
    5157             :                     {
    5158             :                         /*
    5159             :                          * Found a match ... is it a unique index? Tests here
    5160             :                          * should match has_unique_index().
    5161             :                          */
    5162        3486 :                         if (index->unique &&
    5163         510 :                             index->nkeycolumns == 1 &&
    5164         510 :                             pos == 0 &&
    5165         510 :                             (index->indpred == NIL || index->predOK))
    5166         510 :                             vardata->isunique = true;
    5167             : 
    5168             :                         /*
    5169             :                          * Has it got stats?  We only consider stats for
    5170             :                          * non-partial indexes, since partial indexes probably
    5171             :                          * don't reflect whole-relation statistics; the above
    5172             :                          * check for uniqueness is the only info we take from
    5173             :                          * a partial index.
    5174             :                          *
    5175             :                          * An index stats hook, however, must make its own
    5176             :                          * decisions about what to do with partial indexes.
    5177             :                          */
    5178        3486 :                         if (get_index_stats_hook &&
    5179           0 :                             (*get_index_stats_hook) (root, index->indexoid,
    5180           0 :                                                      pos + 1, vardata))
    5181             :                         {
    5182             :                             /*
    5183             :                              * The hook took control of acquiring a stats
    5184             :                              * tuple.  If it did supply a tuple, it'd better
    5185             :                              * have supplied a freefunc.
    5186             :                              */
    5187           0 :                             if (HeapTupleIsValid(vardata->statsTuple) &&
    5188           0 :                                 !vardata->freefunc)
    5189           0 :                                 elog(ERROR, "no function provided to release variable stats with");
    5190             :                         }
    5191        3486 :                         else if (index->indpred == NIL)
    5192             :                         {
    5193        3486 :                             vardata->statsTuple =
    5194        6972 :                                 SearchSysCache3(STATRELATTINH,
    5195             :                                                 ObjectIdGetDatum(index->indexoid),
    5196        3486 :                                                 Int16GetDatum(pos + 1),
    5197             :                                                 BoolGetDatum(false));
    5198        3486 :                             vardata->freefunc = ReleaseSysCache;
    5199             : 
    5200        3486 :                             if (HeapTupleIsValid(vardata->statsTuple))
    5201             :                             {
    5202             :                                 /* Get index's table for permission check */
    5203             :                                 RangeTblEntry *rte;
    5204             : 
    5205        2778 :                                 rte = planner_rt_fetch(index->rel->relid, root);
    5206             :                                 Assert(rte->rtekind == RTE_RELATION);
    5207             : 
    5208             :                                 /*
    5209             :                                  * For simplicity, we insist on the whole
    5210             :                                  * table being selectable, rather than trying
    5211             :                                  * to identify which column(s) the index
    5212             :                                  * depends on.  Also require all rows to be
    5213             :                                  * selectable --- there must be no
    5214             :                                  * securityQuals from security barrier views
    5215             :                                  * or RLS policies.
    5216             :                                  */
    5217        2778 :                                 vardata->acl_ok =
    5218        5556 :                                     rte->securityQuals == NIL &&
    5219        2778 :                                     (pg_class_aclcheck(rte->relid, userid,
    5220             :                                                        ACL_SELECT) == ACLCHECK_OK);
    5221             : 
    5222             :                                 /*
    5223             :                                  * If the user doesn't have permissions to
    5224             :                                  * access an inheritance child relation, check
    5225             :                                  * the permissions of the table actually
    5226             :                                  * mentioned in the query, since most likely
    5227             :                                  * the user does have that permission.  Note
    5228             :                                  * that whole-table select privilege on the
    5229             :                                  * parent doesn't quite guarantee that the
    5230             :                                  * user could read all columns of the child.
    5231             :                                  * But in practice it's unlikely that any
    5232             :                                  * interesting security violation could result
    5233             :                                  * from allowing access to the expression
    5234             :                                  * index's stats, so we allow it anyway.  See
    5235             :                                  * similar code in examine_simple_variable()
    5236             :                                  * for additional comments.
    5237             :                                  */
    5238        2778 :                                 if (!vardata->acl_ok &&
    5239          18 :                                     root->append_rel_array != NULL)
    5240             :                                 {
    5241             :                                     AppendRelInfo *appinfo;
    5242          12 :                                     Index       varno = index->rel->relid;
    5243             : 
    5244          12 :                                     appinfo = root->append_rel_array[varno];
    5245          36 :                                     while (appinfo &&
    5246          24 :                                            planner_rt_fetch(appinfo->parent_relid,
    5247          24 :                                                             root)->rtekind == RTE_RELATION)
    5248             :                                     {
    5249          24 :                                         varno = appinfo->parent_relid;
    5250          24 :                                         appinfo = root->append_rel_array[varno];
    5251             :                                     }
    5252          12 :                                     if (varno != index->rel->relid)
    5253             :                                     {
    5254             :                                         /* Repeat access check on this rel */
    5255          12 :                                         rte = planner_rt_fetch(varno, root);
    5256             :                                         Assert(rte->rtekind == RTE_RELATION);
    5257             : 
    5258          12 :                                         vardata->acl_ok =
    5259          24 :                                             rte->securityQuals == NIL &&
    5260          12 :                                             (pg_class_aclcheck(rte->relid,
    5261             :                                                                userid,
    5262             :                                                                ACL_SELECT) == ACLCHECK_OK);
    5263             :                                     }
    5264             :                                 }
    5265             :                             }
    5266             :                             else
    5267             :                             {
    5268             :                                 /* suppress leakproofness checks later */
    5269         708 :                                 vardata->acl_ok = true;
    5270             :                             }
    5271             :                         }
    5272        3486 :                         if (vardata->statsTuple)
    5273        2778 :                             break;
    5274             :                     }
    5275        1968 :                     indexpr_item = lnext(index->indexprs, indexpr_item);
    5276             :                 }
    5277             :             }
    5278        4746 :             if (vardata->statsTuple)
    5279        2778 :                 break;
    5280             :         }
    5281             : 
    5282             :         /*
    5283             :          * Search extended statistics for one with a matching expression.
    5284             :          * There might be multiple ones, so just grab the first one. In the
    5285             :          * future, we might consider the statistics target (and pick the most
    5286             :          * accurate statistics) and maybe some other parameters.
    5287             :          */
    5288       55684 :         foreach(slist, onerel->statlist)
    5289             :         {
    5290        4308 :             StatisticExtInfo *info = (StatisticExtInfo *) lfirst(slist);
    5291        4308 :             RangeTblEntry *rte = planner_rt_fetch(onerel->relid, root);
    5292             :             ListCell   *expr_item;
    5293             :             int         pos;
    5294             : 
    5295             :             /*
    5296             :              * Stop once we've found statistics for the expression (either
    5297             :              * from extended stats, or for an index in the preceding loop).
    5298             :              */
    5299        4308 :             if (vardata->statsTuple)
    5300         288 :                 break;
    5301             : 
    5302             :             /* skip stats without per-expression stats */
    5303        4020 :             if (info->kind != STATS_EXT_EXPRESSIONS)
    5304        2016 :                 continue;
    5305             : 
    5306             :             /* skip stats with mismatching stxdinherit value */
    5307        2004 :             if (info->inherit != rte->inh)
    5308           6 :                 continue;
    5309             : 
    5310        1998 :             pos = 0;
    5311        3300 :             foreach(expr_item, info->exprs)
    5312             :             {
    5313        2946 :                 Node       *expr = (Node *) lfirst(expr_item);
    5314             : 
    5315             :                 Assert(expr);
    5316             : 
    5317             :                 /* strip RelabelType before comparing it */
    5318        2946 :                 if (expr && IsA(expr, RelabelType))
    5319           0 :                     expr = (Node *) ((RelabelType *) expr)->arg;
    5320             : 
    5321             :                 /* found a match, see if we can extract pg_statistic row */
    5322        2946 :                 if (equal(node, expr))
    5323             :                 {
    5324             :                     /*
    5325             :                      * XXX Not sure if we should cache the tuple somewhere.
    5326             :                      * Now we just create a new copy every time.
    5327             :                      */
    5328        1644 :                     vardata->statsTuple =
    5329        1644 :                         statext_expressions_load(info->statOid, rte->inh, pos);
    5330             : 
    5331        1644 :                     vardata->freefunc = ReleaseDummy;
    5332             : 
    5333             :                     /*
    5334             :                      * For simplicity, we insist on the whole table being
    5335             :                      * selectable, rather than trying to identify which
    5336             :                      * column(s) the statistics object depends on.  Also
    5337             :                      * require all rows to be selectable --- there must be no
    5338             :                      * securityQuals from security barrier views or RLS
    5339             :                      * policies.
    5340             :                      */
    5341        1644 :                     vardata->acl_ok =
    5342        3288 :                         rte->securityQuals == NIL &&
    5343        1644 :                         (pg_class_aclcheck(rte->relid, userid,
    5344             :                                            ACL_SELECT) == ACLCHECK_OK);
    5345             : 
    5346             :                     /*
    5347             :                      * If the user doesn't have permissions to access an
    5348             :                      * inheritance child relation, check the permissions of
    5349             :                      * the table actually mentioned in the query, since most
    5350             :                      * likely the user does have that permission.  Note that
    5351             :                      * whole-table select privilege on the parent doesn't
    5352             :                      * quite guarantee that the user could read all columns of
    5353             :                      * the child. But in practice it's unlikely that any
    5354             :                      * interesting security violation could result from
    5355             :                      * allowing access to the expression stats, so we allow it
    5356             :                      * anyway.  See similar code in examine_simple_variable()
    5357             :                      * for additional comments.
    5358             :                      */
    5359        1644 :                     if (!vardata->acl_ok &&
    5360           0 :                         root->append_rel_array != NULL)
    5361             :                     {
    5362             :                         AppendRelInfo *appinfo;
    5363           0 :                         Index       varno = onerel->relid;
    5364             : 
    5365           0 :                         appinfo = root->append_rel_array[varno];
    5366           0 :                         while (appinfo &&
    5367           0 :                                planner_rt_fetch(appinfo->parent_relid,
    5368           0 :                                                 root)->rtekind == RTE_RELATION)
    5369             :                         {
    5370           0 :                             varno = appinfo->parent_relid;
    5371           0 :                             appinfo = root->append_rel_array[varno];
    5372             :                         }
    5373           0 :                         if (varno != onerel->relid)
    5374             :                         {
    5375             :                             /* Repeat access check on this rel */
    5376           0 :                             rte = planner_rt_fetch(varno, root);
    5377             :                             Assert(rte->rtekind == RTE_RELATION);
    5378             : 
    5379           0 :                             vardata->acl_ok =
    5380           0 :                                 rte->securityQuals == NIL &&
    5381           0 :                                 (pg_class_aclcheck(rte->relid,
    5382             :                                                    userid,
    5383             :                                                    ACL_SELECT) == ACLCHECK_OK);
    5384             :                         }
    5385             :                     }
    5386             : 
    5387        1644 :                     break;
    5388             :                 }
    5389             : 
    5390        1302 :                 pos++;
    5391             :             }
    5392             :         }
    5393             :     }
    5394             : }
    5395             : 
    5396             : /*
    5397             :  * examine_simple_variable
    5398             :  *      Handle a simple Var for examine_variable
    5399             :  *
    5400             :  * This is split out as a subroutine so that we can recurse to deal with
    5401             :  * Vars referencing subqueries (either sub-SELECT-in-FROM or CTE style).
    5402             :  *
    5403             :  * We already filled in all the fields of *vardata except for the stats tuple.
    5404             :  */
    5405             : static void
    5406     1697988 : examine_simple_variable(PlannerInfo *root, Var *var,
    5407             :                         VariableStatData *vardata)
    5408             : {
    5409     1697988 :     RangeTblEntry *rte = root->simple_rte_array[var->varno];
    5410             : 
    5411             :     Assert(IsA(rte, RangeTblEntry));
    5412             : 
    5413     1697988 :     if (get_relation_stats_hook &&
    5414           0 :         (*get_relation_stats_hook) (root, rte, var->varattno, vardata))
    5415             :     {
    5416             :         /*
    5417             :          * The hook took control of acquiring a stats tuple.  If it did supply
    5418             :          * a tuple, it'd better have supplied a freefunc.
    5419             :          */
    5420           0 :         if (HeapTupleIsValid(vardata->statsTuple) &&
    5421           0 :             !vardata->freefunc)
    5422           0 :             elog(ERROR, "no function provided to release variable stats with");
    5423             :     }
    5424     1697988 :     else if (rte->rtekind == RTE_RELATION)
    5425             :     {
    5426             :         /*
    5427             :          * Plain table or parent of an inheritance appendrel, so look up the
    5428             :          * column in pg_statistic
    5429             :          */
    5430     1624314 :         vardata->statsTuple = SearchSysCache3(STATRELATTINH,
    5431             :                                               ObjectIdGetDatum(rte->relid),
    5432     1624314 :                                               Int16GetDatum(var->varattno),
    5433     1624314 :                                               BoolGetDatum(rte->inh));
    5434     1624314 :         vardata->freefunc = ReleaseSysCache;
    5435             : 
    5436     1624314 :         if (HeapTupleIsValid(vardata->statsTuple))
    5437             :         {
    5438     1205288 :             RelOptInfo *onerel = find_base_rel_noerr(root, var->varno);
    5439             :             Oid         userid;
    5440             : 
    5441             :             /*
    5442             :              * Check if user has permission to read this column.  We require
    5443             :              * all rows to be accessible, so there must be no securityQuals
    5444             :              * from security barrier views or RLS policies.
    5445             :              *
    5446             :              * Normally the Var will have an associated RelOptInfo from which
    5447             :              * we can find out which userid to do the check as; but it might
    5448             :              * not if it's a RETURNING Var for an INSERT target relation.  In
    5449             :              * that case use the RTEPermissionInfo associated with the RTE.
    5450             :              */
    5451     1205288 :             if (onerel)
    5452     1205246 :                 userid = onerel->userid;
    5453             :             else
    5454             :             {
    5455             :                 RTEPermissionInfo *perminfo;
    5456             : 
    5457          42 :                 perminfo = getRTEPermissionInfo(root->parse->rteperminfos, rte);
    5458          42 :                 userid = perminfo->checkAsUser;
    5459             :             }
    5460     1205288 :             if (!OidIsValid(userid))
    5461     1136126 :                 userid = GetUserId();
    5462             : 
    5463     1205288 :             vardata->acl_ok =
    5464     2410824 :                 rte->securityQuals == NIL &&
    5465     1205288 :                 ((pg_class_aclcheck(rte->relid, userid,
    5466         248 :                                     ACL_SELECT) == ACLCHECK_OK) ||
    5467         248 :                  (pg_attribute_aclcheck(rte->relid, var->varattno, userid,
    5468             :                                         ACL_SELECT) == ACLCHECK_OK));
    5469             : 
    5470             :             /*
    5471             :              * If the user doesn't have permissions to access an inheritance
    5472             :              * child relation or specifically this attribute, check the
    5473             :              * permissions of the table/column actually mentioned in the
    5474             :              * query, since most likely the user does have that permission
    5475             :              * (else the query will fail at runtime), and if the user can read
    5476             :              * the column there then he can get the values of the child table
    5477             :              * too.  To do that, we must find out which of the root parent's
    5478             :              * attributes the child relation's attribute corresponds to.
    5479             :              */
    5480     1205288 :             if (!vardata->acl_ok && var->varattno > 0 &&
    5481          48 :                 root->append_rel_array != NULL)
    5482             :             {
    5483             :                 AppendRelInfo *appinfo;
    5484          12 :                 Index       varno = var->varno;
    5485          12 :                 int         varattno = var->varattno;
    5486          12 :                 bool        found = false;
    5487             : 
    5488          12 :                 appinfo = root->append_rel_array[varno];
    5489             : 
    5490             :                 /*
    5491             :                  * Partitions are mapped to their immediate parent, not the
    5492             :                  * root parent, so must be ready to walk up multiple
    5493             :                  * AppendRelInfos.  But stop if we hit a parent that is not
    5494             :                  * RTE_RELATION --- that's a flattened UNION ALL subquery, not
    5495             :                  * an inheritance parent.
    5496             :                  */
    5497          36 :                 while (appinfo &&
    5498          24 :                        planner_rt_fetch(appinfo->parent_relid,
    5499          24 :                                         root)->rtekind == RTE_RELATION)
    5500             :                 {
    5501             :                     int         parent_varattno;
    5502             : 
    5503          24 :                     found = false;
    5504          24 :                     if (varattno <= 0 || varattno > appinfo->num_child_cols)
    5505             :                         break;  /* safety check */
    5506          24 :                     parent_varattno = appinfo->parent_colnos[varattno - 1];
    5507          24 :                     if (parent_varattno == 0)
    5508           0 :                         break;  /* Var is local to child */
    5509             : 
    5510          24 :                     varno = appinfo->parent_relid;
    5511          24 :                     varattno = parent_varattno;
    5512          24 :                     found = true;
    5513             : 
    5514             :                     /* If the parent is itself a child, continue up. */
    5515          24 :                     appinfo = root->append_rel_array[varno];
    5516             :                 }
    5517             : 
    5518             :                 /*
    5519             :                  * In rare cases, the Var may be local to the child table, in
    5520             :                  * which case, we've got to live with having no access to this
    5521             :                  * column's stats.
    5522             :                  */
    5523          12 :                 if (!found)
    5524           0 :                     return;
    5525             : 
    5526             :                 /* Repeat the access check on this parent rel & column */
    5527          12 :                 rte = planner_rt_fetch(varno, root);
    5528             :                 Assert(rte->rtekind == RTE_RELATION);
    5529             : 
    5530             :                 /*
    5531             :                  * Fine to use the same userid as it's the same in all
    5532             :                  * relations of a given inheritance tree.
    5533             :                  */
    5534          12 :                 vardata->acl_ok =
    5535          30 :                     rte->securityQuals == NIL &&
    5536          12 :                     ((pg_class_aclcheck(rte->relid, userid,
    5537           6 :                                         ACL_SELECT) == ACLCHECK_OK) ||
    5538           6 :                      (pg_attribute_aclcheck(rte->relid, varattno, userid,
    5539             :                                             ACL_SELECT) == ACLCHECK_OK));
    5540             :             }
    5541             :         }
    5542             :         else
    5543             :         {
    5544             :             /* suppress any possible leakproofness checks later */
    5545      419026 :             vardata->acl_ok = true;
    5546             :         }
    5547             :     }
    5548       73674 :     else if ((rte->rtekind == RTE_SUBQUERY && !rte->inh) ||
    5549       66466 :              (rte->rtekind == RTE_CTE && !rte->self_reference))
    5550             :     {
    5551             :         /*
    5552             :          * Plain subquery (not one that was converted to an appendrel) or
    5553             :          * non-recursive CTE.  In either case, we can try to find out what the
    5554             :          * Var refers to within the subquery.  We skip this for appendrel and
    5555             :          * recursive-CTE cases because any column stats we did find would
    5556             :          * likely not be very relevant.
    5557             :          */
    5558             :         PlannerInfo *subroot;
    5559             :         Query      *subquery;
    5560             :         List       *subtlist;
    5561             :         TargetEntry *ste;
    5562             : 
    5563             :         /*
    5564             :          * Punt if it's a whole-row var rather than a plain column reference.
    5565             :          */
    5566       12146 :         if (var->varattno == InvalidAttrNumber)
    5567           0 :             return;
    5568             : 
    5569             :         /*
    5570             :          * Otherwise, find the subquery's planner subroot.
    5571             :          */
    5572       12146 :         if (rte->rtekind == RTE_SUBQUERY)
    5573             :         {
    5574             :             RelOptInfo *rel;
    5575             : 
    5576             :             /*
    5577             :              * Fetch RelOptInfo for subquery.  Note that we don't change the
    5578             :              * rel returned in vardata, since caller expects it to be a rel of
    5579             :              * the caller's query level.  Because we might already be
    5580             :              * recursing, we can't use that rel pointer either, but have to
    5581             :              * look up the Var's rel afresh.
    5582             :              */
    5583        7208 :             rel = find_base_rel(root, var->varno);
    5584             : 
    5585        7208 :             subroot = rel->subroot;
    5586             :         }
    5587             :         else
    5588             :         {
    5589             :             /* CTE case is more difficult */
    5590             :             PlannerInfo *cteroot;
    5591             :             Index       levelsup;
    5592             :             int         ndx;
    5593             :             int         plan_id;
    5594             :             ListCell   *lc;
    5595             : 
    5596             :             /*
    5597             :              * Find the referenced CTE, and locate the subroot previously made
    5598             :              * for it.
    5599             :              */
    5600        4938 :             levelsup = rte->ctelevelsup;
    5601        4938 :             cteroot = root;
    5602        9590 :             while (levelsup-- > 0)
    5603             :             {
    5604        4652 :                 cteroot = cteroot->parent_root;
    5605        4652 :                 if (!cteroot)   /* shouldn't happen */
    5606           0 :                     elog(ERROR, "bad levelsup for CTE \"%s\"", rte->ctename);
    5607             :             }
    5608             : 
    5609             :             /*
    5610             :              * Note: cte_plan_ids can be shorter than cteList, if we are still
    5611             :              * working on planning the CTEs (ie, this is a side-reference from
    5612             :              * another CTE).  So we mustn't use forboth here.
    5613             :              */
    5614        4938 :             ndx = 0;
    5615        7192 :             foreach(lc, cteroot->parse->cteList)
    5616             :             {
    5617        7192 :                 CommonTableExpr *cte = (CommonTableExpr *) lfirst(lc);
    5618             : 
    5619        7192 :                 if (strcmp(cte->ctename, rte->ctename) == 0)
    5620        4938 :                     break;
    5621        2254 :                 ndx++;
    5622             :             }
    5623        4938 :             if (lc == NULL)     /* shouldn't happen */
    5624           0 :                 elog(ERROR, "could not find CTE \"%s\"", rte->ctename);
    5625        4938 :             if (ndx >= list_length(cteroot->cte_plan_ids))
    5626           0 :                 elog(ERROR, "could not find plan for CTE \"%s\"", rte->ctename);
    5627        4938 :             plan_id = list_nth_int(cteroot->cte_plan_ids, ndx);
    5628        4938 :             if (plan_id <= 0)
    5629           0 :                 elog(ERROR, "no plan was made for CTE \"%s\"", rte->ctename);
    5630        4938 :             subroot = list_nth(root->glob->subroots, plan_id - 1);
    5631             :         }
    5632             : 
    5633             :         /* If the subquery hasn't been planned yet, we have to punt */
    5634       12146 :         if (subroot == NULL)
    5635           0 :             return;
    5636             :         Assert(IsA(subroot, PlannerInfo));
    5637             : 
    5638             :         /*
    5639             :          * We must use the subquery parsetree as mangled by the planner, not
    5640             :          * the raw version from the RTE, because we need a Var that will refer
    5641             :          * to the subroot's live RelOptInfos.  For instance, if any subquery
    5642             :          * pullup happened during planning, Vars in the targetlist might have
    5643             :          * gotten replaced, and we need to see the replacement expressions.
    5644             :          */
    5645       12146 :         subquery = subroot->parse;
    5646             :         Assert(IsA(subquery, Query));
    5647             : 
    5648             :         /*
    5649             :          * Punt if subquery uses set operations or GROUP BY, as these will
    5650             :          * mash underlying columns' stats beyond recognition.  (Set ops are
    5651             :          * particularly nasty; if we forged ahead, we would return stats
    5652             :          * relevant to only the leftmost subselect...)  DISTINCT is also
    5653             :          * problematic, but we check that later because there is a possibility
    5654             :          * of learning something even with it.
    5655             :          */
    5656       12146 :         if (subquery->setOperations ||
    5657       10870 :             subquery->groupClause ||
    5658       10214 :             subquery->groupingSets)
    5659        1932 :             return;
    5660             : 
    5661             :         /* Get the subquery output expression referenced by the upper Var */
    5662       10214 :         if (subquery->returningList)
    5663         206 :             subtlist = subquery->returningList;
    5664             :         else
    5665       10008 :             subtlist = subquery->targetList;
    5666       10214 :         ste = get_tle_by_resno(subtlist, var->varattno);
    5667       10214 :         if (ste == NULL || ste->resjunk)
    5668           0 :             elog(ERROR, "subquery %s does not have attribute %d",
    5669             :                  rte->eref->aliasname, var->varattno);
    5670       10214 :         var = (Var *) ste->expr;
    5671             : 
    5672             :         /*
    5673             :          * If subquery uses DISTINCT, we can't make use of any stats for the
    5674             :          * variable ... but, if it's the only DISTINCT column, we are entitled
    5675             :          * to consider it unique.  We do the test this way so that it works
    5676             :          * for cases involving DISTINCT ON.
    5677             :          */
    5678       10214 :         if (subquery->distinctClause)
    5679             :         {
    5680        1794 :             if (list_length(subquery->distinctClause) == 1 &&
    5681         606 :                 targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
    5682         280 :                 vardata->isunique = true;
    5683             :             /* cannot go further */
    5684        1188 :             return;
    5685             :         }
    5686             : 
    5687             :         /*
    5688             :          * If the sub-query originated from a view with the security_barrier
    5689             :          * attribute, we must not look at the variable's statistics, though it
    5690             :          * seems all right to notice the existence of a DISTINCT clause. So
    5691             :          * stop here.
    5692             :          *
    5693             :          * This is probably a harsher restriction than necessary; it's
    5694             :          * certainly OK for the selectivity estimator (which is a C function,
    5695             :          * and therefore omnipotent anyway) to look at the statistics.  But
    5696             :          * many selectivity estimators will happily *invoke the operator
    5697             :          * function* to try to work out a good estimate - and that's not OK.
    5698             :          * So for now, don't dig down for stats.
    5699             :          */
    5700        9026 :         if (rte->security_barrier)
    5701         228 :             return;
    5702             : 
    5703             :         /* Can only handle a simple Var of subquery's query level */
    5704        8798 :         if (var && IsA(var, Var) &&
    5705        4152 :             var->varlevelsup == 0)
    5706             :         {
    5707             :             /*
    5708             :              * OK, recurse into the subquery.  Note that the original setting
    5709             :              * of vardata->isunique (which will surely be false) is left
    5710             :              * unchanged in this situation.  That's what we want, since even
    5711             :              * if the underlying column is unique, the subquery may have
    5712             :              * joined to other tables in a way that creates duplicates.
    5713             :              */
    5714        4152 :             examine_simple_variable(subroot, var, vardata);
    5715             :         }
    5716             :     }
    5717             :     else
    5718             :     {
    5719             :         /*
    5720             :          * Otherwise, the Var comes from a FUNCTION or VALUES RTE.  (We won't
    5721             :          * see RTE_JOIN here because join alias Vars have already been
    5722             :          * flattened.)  There's not much we can do with function outputs, but
    5723             :          * maybe someday try to be smarter about VALUES.
    5724             :          */
    5725             :     }
    5726             : }
    5727             : 
    5728             : /*
    5729             :  * Check whether it is permitted to call func_oid passing some of the
    5730             :  * pg_statistic data in vardata.  We allow this either if the user has SELECT
    5731             :  * privileges on the table or column underlying the pg_statistic data or if
    5732             :  * the function is marked leak-proof.
    5733             :  */
    5734             : bool
    5735      829618 : statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
    5736             : {
    5737      829618 :     if (vardata->acl_ok)
    5738      828692 :         return true;
    5739             : 
    5740         926 :     if (!OidIsValid(func_oid))
    5741           0 :         return false;
    5742             : 
    5743         926 :     if (get_func_leakproof(func_oid))
    5744         424 :         return true;
    5745             : 
    5746         502 :     ereport(DEBUG2,
    5747             :             (errmsg_internal("not using statistics because function \"%s\" is not leak-proof",
    5748             :                              get_func_name(func_oid))));
    5749         502 :     return false;
    5750             : }
    5751             : 
    5752             : /*
    5753             :  * get_variable_numdistinct
    5754             :  *    Estimate the number of distinct values of a variable.
    5755             :  *
    5756             :  * vardata: results of examine_variable
    5757             :  * *isdefault: set to true if the result is a default rather than based on
    5758             :  * anything meaningful.
    5759             :  *
    5760             :  * NB: be careful to produce a positive integral result, since callers may
    5761             :  * compare the result to exact integer counts, or might divide by it.
    5762             :  */
    5763             : double
    5764     1151226 : get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
    5765             : {
    5766             :     double      stadistinct;
    5767     1151226 :     double      stanullfrac = 0.0;
    5768             :     double      ntuples;
    5769             : 
    5770     1151226 :     *isdefault = false;
    5771             : 
    5772             :     /*
    5773             :      * Determine the stadistinct value to use.  There are cases where we can
    5774             :      * get an estimate even without a pg_statistic entry, or can get a better
    5775             :      * value than is in pg_statistic.  Grab stanullfrac too if we can find it
    5776             :      * (otherwise, assume no nulls, for lack of any better idea).
    5777             :      */
    5778     1151226 :     if (HeapTupleIsValid(vardata->statsTuple))
    5779             :     {
    5780             :         /* Use the pg_statistic entry */
    5781             :         Form_pg_statistic stats;
    5782             : 
    5783      820012 :         stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
    5784      820012 :         stadistinct = stats->stadistinct;
    5785      820012 :         stanullfrac = stats->stanullfrac;
    5786             :     }
    5787      331214 :     else if (vardata->vartype == BOOLOID)
    5788             :     {
    5789             :         /*
    5790             :          * Special-case boolean columns: presumably, two distinct values.
    5791             :          *
    5792             :          * Are there any other datatypes we should wire in special estimates
    5793             :          * for?
    5794             :          */
    5795         324 :         stadistinct = 2.0;
    5796             :     }
    5797      330890 :     else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
    5798             :     {
    5799             :         /*
    5800             :          * If the Var represents a column of a VALUES RTE, assume it's unique.
    5801             :          * This could of course be very wrong, but it should tend to be true
    5802             :          * in well-written queries.  We could consider examining the VALUES'
    5803             :          * contents to get some real statistics; but that only works if the
    5804             :          * entries are all constants, and it would be pretty expensive anyway.
    5805             :          */
    5806        2806 :         stadistinct = -1.0;     /* unique (and all non null) */
    5807             :     }
    5808             :     else
    5809             :     {
    5810             :         /*
    5811             :          * We don't keep statistics for system columns, but in some cases we
    5812             :          * can infer distinctness anyway.
    5813             :          */
    5814      328084 :         if (vardata->var && IsA(vardata->var, Var))
    5815             :         {
    5816      307206 :             switch (((Var *) vardata->var)->varattno)
    5817             :             {
    5818         990 :                 case SelfItemPointerAttributeNumber:
    5819         990 :                     stadistinct = -1.0; /* unique (and all non null) */
    5820         990 :                     break;
    5821        2518 :                 case TableOidAttributeNumber:
    5822        2518 :                     stadistinct = 1.0;  /* only 1 value */
    5823        2518 :                     break;
    5824      303698 :                 default:
    5825      303698 :                     stadistinct = 0.0;  /* means "unknown" */
    5826      303698 :                     break;
    5827             :             }
    5828             :         }
    5829             :         else
    5830       20878 :             stadistinct = 0.0;  /* means "unknown" */
    5831             : 
    5832             :         /*
    5833             :          * XXX consider using estimate_num_groups on expressions?
    5834             :          */
    5835             :     }
    5836             : 
    5837             :     /*
    5838             :      * If there is a unique index or DISTINCT clause for the variable, assume
    5839             :      * it is unique no matter what pg_statistic says; the statistics could be
    5840             :      * out of date, or we might have found a partial unique index that proves
    5841             :      * the var is unique for this query.  However, we'd better still believe
    5842             :      * the null-fraction statistic.
    5843             :      */
    5844     1151226 :     if (vardata->isunique)
    5845      320084 :         stadistinct = -1.0 * (1.0 - stanullfrac);
    5846             : 
    5847             :     /*
    5848             :      * If we had an absolute estimate, use that.
    5849             :      */
    5850     1151226 :     if (stadistinct > 0.0)
    5851      264406 :         return clamp_row_est(stadistinct);
    5852             : 
    5853             :     /*
    5854             :      * Otherwise we need to get the relation size; punt if not available.
    5855             :      */
    5856      886820 :     if (vardata->rel == NULL)
    5857             :     {
    5858         400 :         *isdefault = true;
    5859         400 :         return DEFAULT_NUM_DISTINCT;
    5860             :     }
    5861      886420 :     ntuples = vardata->rel->tuples;
    5862      886420 :     if (ntuples <= 0.0)
    5863             :     {
    5864       43276 :         *isdefault = true;
    5865       43276 :         return DEFAULT_NUM_DISTINCT;
    5866             :     }
    5867             : 
    5868             :     /*
    5869             :      * If we had a relative estimate, use that.
    5870             :      */
    5871      843144 :     if (stadistinct < 0.0)
    5872      594932 :         return clamp_row_est(-stadistinct * ntuples);
    5873             : 
    5874             :     /*
    5875             :      * With no data, estimate ndistinct = ntuples if the table is small, else
    5876             :      * use default.  We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
    5877             :      * that the behavior isn't discontinuous.
    5878             :      */
    5879      248212 :     if (ntuples < DEFAULT_NUM_DISTINCT)
    5880      122904 :         return clamp_row_est(ntuples);
    5881             : 
    5882      125308 :     *isdefault = true;
    5883      125308 :     return DEFAULT_NUM_DISTINCT;
    5884             : }
    5885             : 
    5886             : /*
    5887             :  * get_variable_range
    5888             :  *      Estimate the minimum and maximum value of the specified variable.
    5889             :  *      If successful, store values in *min and *max, and return true.
    5890             :  *      If no data available, return false.
    5891             :  *
    5892             :  * sortop is the "<" comparison operator to use.  This should generally
    5893             :  * be "<" not ">", as only the former is likely to be found in pg_statistic.
    5894             :  * The collation must be specified too.
    5895             :  */
    5896             : static bool
    5897      172048 : get_variable_range(PlannerInfo *root, VariableStatData *vardata,
    5898             :                    Oid sortop, Oid collation,
    5899             :                    Datum *min, Datum *max)
    5900             : {
    5901      172048 :     Datum       tmin = 0;
    5902      172048 :     Datum       tmax = 0;
    5903      172048 :     bool        have_data = false;
    5904             :     int16       typLen;
    5905             :     bool        typByVal;
    5906             :     Oid         opfuncoid;
    5907             :     FmgrInfo    opproc;
    5908             :     AttStatsSlot sslot;
    5909             : 
    5910             :     /*
    5911             :      * XXX It's very tempting to try to use the actual column min and max, if
    5912             :      * we can get them relatively-cheaply with an index probe.  However, since
    5913             :      * this function is called many times during join planning, that could
    5914             :      * have unpleasant effects on planning speed.  Need more investigation
    5915             :      * before enabling this.
    5916             :      */
    5917             : #ifdef NOT_USED
    5918             :     if (get_actual_variable_range(root, vardata, sortop, collation, min, max))
    5919             :         return true;
    5920             : #endif
    5921             : 
    5922      172048 :     if (!HeapTupleIsValid(vardata->statsTuple))
    5923             :     {
    5924             :         /* no stats available, so default result */
    5925       36720 :         return false;
    5926             :     }
    5927             : 
    5928             :     /*
    5929             :      * If we can't apply the sortop to the stats data, just fail.  In
    5930             :      * principle, if there's a histogram and no MCVs, we could return the
    5931             :      * histogram endpoints without ever applying the sortop ... but it's
    5932             :      * probably not worth trying, because whatever the caller wants to do with
    5933             :      * the endpoints would likely fail the security check too.
    5934             :      */
    5935      135328 :     if (!statistic_proc_security_check(vardata,
    5936      135328 :                                        (opfuncoid = get_opcode(sortop))))
    5937           0 :         return false;
    5938             : 
    5939      135328 :     opproc.fn_oid = InvalidOid; /* mark this as not looked up yet */
    5940             : 
    5941      135328 :     get_typlenbyval(vardata->atttype, &typLen, &typByVal);
    5942             : 
    5943             :     /*
    5944             :      * If there is a histogram with the ordering we want, grab the first and
    5945             :      * last values.
    5946             :      */
    5947      135328 :     if (get_attstatsslot(&sslot, vardata->statsTuple,
    5948             :                          STATISTIC_KIND_HISTOGRAM, sortop,
    5949             :                          ATTSTATSSLOT_VALUES))
    5950             :     {
    5951      107464 :         if (sslot.stacoll == collation && sslot.nvalues > 0)
    5952             :         {
    5953      107464 :             tmin = datumCopy(sslot.values[0], typByVal, typLen);
    5954      107464 :             tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
    5955      107464 :             have_data = true;
    5956             :         }
    5957      107464 :         free_attstatsslot(&sslot);
    5958             :     }
    5959             : 
    5960             :     /*
    5961             :      * Otherwise, if there is a histogram with some other ordering, scan it
    5962             :      * and get the min and max values according to the ordering we want.  This
    5963             :      * of course may not find values that are really extremal according to our
    5964             :      * ordering, but it beats ignoring available data.
    5965             :      */
    5966      163192 :     if (!have_data &&
    5967       27864 :         get_attstatsslot(&sslot, vardata->statsTuple,
    5968             :                          STATISTIC_KIND_HISTOGRAM, InvalidOid,
    5969             :                          ATTSTATSSLOT_VALUES))
    5970             :     {
    5971           0 :         get_stats_slot_range(&sslot, opfuncoid, &opproc,
    5972             :                              collation, typLen, typByVal,
    5973             :                              &tmin, &tmax, &have_data);
    5974           0 :         free_attstatsslot(&sslot);
    5975             :     }
    5976             : 
    5977             :     /*
    5978             :      * If we have most-common-values info, look for extreme MCVs.  This is
    5979             :      * needed even if we also have a histogram, since the histogram excludes
    5980             :      * the MCVs.  However, if we *only* have MCVs and no histogram, we should
    5981             :      * be pretty wary of deciding that that is a full representation of the
    5982             :      * data.  Proceed only if the MCVs represent the whole table (to within
    5983             :      * roundoff error).
    5984             :      */
    5985      135328 :     if (get_attstatsslot(&sslot, vardata->statsTuple,
    5986             :                          STATISTIC_KIND_MCV, InvalidOid,
    5987      135328 :                          have_data ? ATTSTATSSLOT_VALUES :
    5988             :                          (ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)))
    5989             :     {
    5990       61074 :         bool        use_mcvs = have_data;
    5991             : 
    5992       61074 :         if (!have_data)
    5993             :         {
    5994       26752 :             double      sumcommon = 0.0;
    5995             :             double      nullfrac;
    5996             :             int         i;
    5997             : 
    5998      130398 :             for (i = 0; i < sslot.nnumbers; i++)
    5999      103646 :                 sumcommon += sslot.numbers[i];
    6000       26752 :             nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata->statsTuple))->stanullfrac;
    6001       26752 :             if (sumcommon + nullfrac > 0.99999)
    6002       26562 :                 use_mcvs = true;
    6003             :         }
    6004             : 
    6005       61074 :         if (use_mcvs)
    6006       60884 :             get_stats_slot_range(&sslot, opfuncoid, &opproc,
    6007             :                                  collation, typLen, typByVal,
    6008             :                                  &tmin, &tmax, &have_data);
    6009       61074 :         free_attstatsslot(&sslot);
    6010             :     }
    6011             : 
    6012      135328 :     *min = tmin;
    6013      135328 :     *max = tmax;
    6014      135328 :     return have_data;
    6015             : }
    6016             : 
    6017             : /*
    6018             :  * get_stats_slot_range: scan sslot for min/max values
    6019             :  *
    6020             :  * Subroutine for get_variable_range: update min/max/have_data according
    6021             :  * to what we find in the statistics array.
    6022             :  */
    6023             : static void
    6024       60884 : get_stats_slot_range(AttStatsSlot *sslot, Oid opfuncoid, FmgrInfo *opproc,
    6025             :                      Oid collation, int16 typLen, bool typByVal,
    6026             :                      Datum *min, Datum *max, bool *p_have_data)
    6027             : {
    6028       60884 :     Datum       tmin = *min;
    6029       60884 :     Datum       tmax = *max;
    6030       60884 :     bool        have_data = *p_have_data;
    6031       60884 :     bool        found_tmin = false;
    6032       60884 :     bool        found_tmax = false;
    6033             : 
    6034             :     /* Look up the comparison function, if we didn't already do so */
    6035       60884 :     if (opproc->fn_oid != opfuncoid)
    6036       60884 :         fmgr_info(opfuncoid, opproc);
    6037             : 
    6038             :     /* Scan all the slot's values */
    6039     1688296 :     for (int i = 0; i < sslot->nvalues; i++)
    6040             :     {
    6041     1627412 :         if (!have_data)
    6042             :         {
    6043       26562 :             tmin = tmax = sslot->values[i];
    6044       26562 :             found_tmin = found_tmax = true;
    6045       26562 :             *p_have_data = have_data = true;
    6046       26562 :             continue;
    6047             :         }
    6048     1600850 :         if (DatumGetBool(FunctionCall2Coll(opproc,
    6049             :                                            collation,
    6050     1600850 :                                            sslot->values[i], tmin)))
    6051             :         {
    6052       51266 :             tmin = sslot->values[i];
    6053       51266 :             found_tmin = true;
    6054             :         }
    6055     1600850 :         if (DatumGetBool(FunctionCall2Coll(opproc,
    6056             :                                            collation,
    6057     1600850 :                                            tmax, sslot->values[i])))
    6058             :         {
    6059       70154 :             tmax = sslot->values[i];
    6060       70154 :             found_tmax = true;
    6061             :         }
    6062             :     }
    6063             : 
    6064             :     /*
    6065             :      * Copy the slot's values, if we found new extreme values.
    6066             :      */
    6067       60884 :     if (found_tmin)
    6068       51760 :         *min = datumCopy(tmin, typByVal, typLen);
    6069       60884 :     if (found_tmax)
    6070       29714 :         *max = datumCopy(tmax, typByVal, typLen);
    6071       60884 : }
    6072             : 
    6073             : 
    6074             : /*
    6075             :  * get_actual_variable_range
    6076             :  *      Attempt to identify the current *actual* minimum and/or maximum
    6077             :  *      of the specified variable, by looking for a suitable btree index
    6078             :  *      and fetching its low and/or high values.
    6079             :  *      If successful, store values in *min and *max, and return true.
    6080             :  *      (Either pointer can be NULL if that endpoint isn't needed.)
    6081             :  *      If unsuccessful, return false.
    6082             :  *
    6083             :  * sortop is the "<" comparison operator to use.
    6084             :  * collation is the required collation.
    6085             :  */
    6086             : static bool
    6087      163700 : get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
    6088             :                           Oid sortop, Oid collation,
    6089             :                           Datum *min, Datum *max)
    6090             : {
    6091      163700 :     bool        have_data = false;
    6092      163700 :     RelOptInfo *rel = vardata->rel;
    6093             :     RangeTblEntry *rte;
    6094             :     ListCell   *lc;
    6095             : 
    6096             :     /* No hope if no relation or it doesn't have indexes */
    6097      163700 :     if (rel == NULL || rel->indexlist == NIL)
    6098       12748 :         return false;
    6099             :     /* If it has indexes it must be a plain relation */
    6100      150952 :     rte = root->simple_rte_array[rel->relid];
    6101             :     Assert(rte->rtekind == RTE_RELATION);
    6102             : 
    6103             :     /* ignore partitioned tables.  Any indexes here are not real indexes */
    6104      150952 :     if (rte->relkind == RELKIND_PARTITIONED_TABLE)
    6105         828 :         return false;
    6106             : 
    6107             :     /* Search through the indexes to see if any match our problem */
    6108      321040 :     foreach(lc, rel->indexlist)
    6109             :     {
    6110      273258 :         IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
    6111             :         ScanDirection indexscandir;
    6112             : 
    6113             :         /* Ignore non-btree indexes */
    6114      273258 :         if (index->relam != BTREE_AM_OID)
    6115           0 :             continue;
    6116             : 
    6117             :         /*
    6118             :          * Ignore partial indexes --- we only want stats that cover the entire
    6119             :          * relation.
    6120             :          */
    6121      273258 :         if (index->indpred != NIL)
    6122         288 :             continue;
    6123             : 
    6124             :         /*
    6125             :          * The index list might include hypothetical indexes inserted by a
    6126             :          * get_relation_info hook --- don't try to access them.
    6127             :          */
    6128      272970 :         if (index->hypothetical)
    6129           0 :             continue;
    6130             : 
    6131             :         /*
    6132             :          * The first index column must match the desired variable, sortop, and
    6133             :          * collation --- but we can use a descending-order index.
    6134             :          */
    6135      272970 :         if (collation != index->indexcollations[0])
    6136       42006 :             continue;           /* test first 'cause it's cheapest */
    6137      230964 :         if (!match_index_to_operand(vardata->var, 0, index))
    6138      128622 :             continue;
    6139      102342 :         switch (get_op_opfamily_strategy(sortop, index->sortopfamily[0]))
    6140             :         {
    6141      102342 :             case BTLessStrategyNumber:
    6142      102342 :                 if (index->reverse_sort[0])
    6143           0 :                     indexscandir = BackwardScanDirection;
    6144             :                 else
    6145      102342 :                     indexscandir = ForwardScanDirection;
    6146      102342 :                 break;
    6147           0 :             case BTGreaterStrategyNumber:
    6148           0 :                 if (index->reverse_sort[0])
    6149           0 :                     indexscandir = ForwardScanDirection;
    6150             :                 else
    6151           0 :                     indexscandir = BackwardScanDirection;
    6152           0 :                 break;
    6153           0 :             default:
    6154             :                 /* index doesn't match the sortop */
    6155           0 :                 continue;
    6156             :         }
    6157             : 
    6158             :         /*
    6159             :          * Found a suitable index to extract data from.  Set up some data that
    6160             :          * can be used by both invocations of get_actual_variable_endpoint.
    6161             :          */
    6162             :         {
    6163             :             MemoryContext tmpcontext;
    6164             :             MemoryContext oldcontext;
    6165             :             Relation    heapRel;
    6166             :             Relation    indexRel;
    6167             :             TupleTableSlot *slot;
    6168             :             int16       typLen;
    6169             :             bool        typByVal;
    6170             :             ScanKeyData scankeys[1];
    6171             : 
    6172             :             /* Make sure any cruft gets recycled when we're done */
    6173      102342 :             tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
    6174             :                                                "get_actual_variable_range workspace",
    6175             :                                                ALLOCSET_DEFAULT_SIZES);
    6176      102342 :             oldcontext = MemoryContextSwitchTo(tmpcontext);
    6177             : 
    6178             :             /*
    6179             :              * Open the table and index so we can read from them.  We should
    6180             :              * already have some type of lock on each.
    6181             :              */
    6182      102342 :             heapRel = table_open(rte->relid, NoLock);
    6183      102342 :             indexRel = index_open(index->indexoid, NoLock);
    6184             : 
    6185             :             /* build some stuff needed for indexscan execution */
    6186      102342 :             slot = table_slot_create(heapRel, NULL);
    6187      102342 :             get_typlenbyval(vardata->atttype, &typLen, &typByVal);
    6188             : 
    6189             :             /* set up an IS NOT NULL scan key so that we ignore nulls */
    6190      102342 :             ScanKeyEntryInitialize(&scankeys[0],
    6191             :                                    SK_ISNULL | SK_SEARCHNOTNULL,
    6192             :                                    1,   /* index col to scan */
    6193             :                                    InvalidStrategy, /* no strategy */
    6194             :                                    InvalidOid,  /* no strategy subtype */
    6195             :                                    InvalidOid,  /* no collation */
    6196             :                                    InvalidOid,  /* no reg proc for this */
    6197             :                                    (Datum) 0);  /* constant */
    6198             : 
    6199             :             /* If min is requested ... */
    6200      102342 :             if (min)
    6201             :             {
    6202       60790 :                 have_data = get_actual_variable_endpoint(heapRel,
    6203             :                                                          indexRel,
    6204             :                                                          indexscandir,
    6205             :                                                          scankeys,
    6206             :                                                          typLen,
    6207             :                                                          typByVal,
    6208             :                                                          slot,
    6209             :                                                          oldcontext,
    6210             :                                                          min);
    6211             :             }
    6212             :             else
    6213             :             {
    6214             :                 /* If min not requested, still want to fetch max */
    6215       41552 :                 have_data = true;
    6216             :             }
    6217             : 
    6218             :             /* If max is requested, and we didn't already fail ... */
    6219      102342 :             if (max && have_data)
    6220             :             {
    6221             :                 /* scan in the opposite direction; all else is the same */
    6222       42728 :                 have_data = get_actual_variable_endpoint(heapRel,
    6223             :                                                          indexRel,
    6224       42728 :                                                          -indexscandir,
    6225             :                                                          scankeys,
    6226             :                                                          typLen,
    6227             :                                                          typByVal,
    6228             :                                                          slot,
    6229             :                                                          oldcontext,
    6230             :                                                          max);
    6231             :             }
    6232             : 
    6233             :             /* Clean everything up */
    6234      102342 :             ExecDropSingleTupleTableSlot(slot);
    6235             : 
    6236      102342 :             index_close(indexRel, NoLock);
    6237      102342 :             table_close(heapRel, NoLock);
    6238             : 
    6239      102342 :             MemoryContextSwitchTo(oldcontext);
    6240      102342 :             MemoryContextDelete(tmpcontext);
    6241             : 
    6242             :             /* And we're done */
    6243      102342 :             break;
    6244             :         }
    6245             :     }
    6246             : 
    6247      150124 :     return have_data;
    6248             : }
    6249             : 
    6250             : /*
    6251             :  * Get one endpoint datum (min or max depending on indexscandir) from the
    6252             :  * specified index.  Return true if successful, false if not.
    6253             :  * On success, endpoint value is stored to *endpointDatum (and copied into
    6254             :  * outercontext).
    6255             :  *
    6256             :  * scankeys is a 1-element scankey array set up to reject nulls.
    6257             :  * typLen/typByVal describe the datatype of the index's first column.
    6258             :  * tableslot is a slot suitable to hold table tuples, in case we need
    6259             :  * to probe the heap.
    6260             :  * (We could compute these values locally, but that would mean computing them
    6261             :  * twice when get_actual_variable_range needs both the min and the max.)
    6262             :  *
    6263             :  * Failure occurs either when the index is empty, or we decide that it's
    6264             :  * taking too long to find a suitable tuple.
    6265             :  */
    6266             : static bool
    6267      103518 : get_actual_variable_endpoint(Relation heapRel,
    6268             :                              Relation indexRel,
    6269             :                              ScanDirection indexscandir,
    6270             :                              ScanKey scankeys,
    6271             :                              int16 typLen,
    6272             :                              bool typByVal,
    6273             :                              TupleTableSlot *tableslot,
    6274             :                              MemoryContext outercontext,
    6275             :                              Datum *endpointDatum)
    6276             : {
    6277      103518 :     bool        have_data = false;
    6278             :     SnapshotData SnapshotNonVacuumable;
    6279             :     IndexScanDesc index_scan;
    6280      103518 :     Buffer      vmbuffer = InvalidBuffer;
    6281      103518 :     BlockNumber last_heap_block = InvalidBlockNumber;
    6282      103518 :     int         n_visited_heap_pages = 0;
    6283             :     ItemPointer tid;
    6284             :     Datum       values[INDEX_MAX_KEYS];
    6285             :     bool        isnull[INDEX_MAX_KEYS];
    6286             :     MemoryContext oldcontext;
    6287             : 
    6288             :     /*
    6289             :      * We use the index-only-scan machinery for this.  With mostly-static
    6290             :      * tables that's a win because it avoids a heap visit.  It's also a win
    6291             :      * for dynamic data, but the reason is less obvious; read on for details.
    6292             :      *
    6293             :      * In principle, we should scan the index with our current active
    6294             :      * snapshot, which is the best approximation we've got to what the query
    6295             :      * will see when executed.  But that won't be exact if a new snap is taken
    6296             :      * before running the query, and it can be very expensive if a lot of
    6297             :      * recently-dead or uncommitted rows exist at the beginning or end of the
    6298             :      * index (because we'll laboriously fetch each one and reject it).
    6299             :      * Instead, we use SnapshotNonVacuumable.  That will accept recently-dead
    6300             :      * and uncommitted rows as well as normal visible rows.  On the other
    6301             :      * hand, it will reject known-dead rows, and thus not give a bogus answer
    6302             :      * when the extreme value has been deleted (unless the deletion was quite
    6303             :      * recent); that case motivates not using SnapshotAny here.
    6304             :      *
    6305             :      * A crucial point here is that SnapshotNonVacuumable, with
    6306             :      * GlobalVisTestFor(heapRel) as horizon, yields the inverse of the
    6307             :      * condition that the indexscan will use to decide that index entries are
    6308             :      * killable (see heap_hot_search_buffer()).  Therefore, if the snapshot
    6309             :      * rejects a tuple (or more precisely, all tuples of a HOT chain) and we
    6310             :      * have to continue scanning past it, we know that the indexscan will mark
    6311             :      * that index entry killed.  That means that the next
    6312             :      * get_actual_variable_endpoint() call will not have to re-consider that
    6313             :      * index entry.  In this way we avoid repetitive work when this function
    6314             :      * is used a lot during planning.
    6315             :      *
    6316             :      * But using SnapshotNonVacuumable creates a hazard of its own.  In a
    6317             :      * recently-created index, some index entries may point at "broken" HOT
    6318             :      * chains in which not all the tuple versions contain data matching the
    6319             :      * index entry.  The live tuple version(s) certainly do match the index,
    6320             :      * but SnapshotNonVacuumable can accept recently-dead tuple versions that
    6321             :      * don't match.  Hence, if we took data from the selected heap tuple, we
    6322             :      * might get a bogus answer that's not close to the index extremal value,
    6323             :      * or could even be NULL.  We avoid this hazard because we take the data
    6324             :      * from the index entry not the heap.
    6325             :      *
    6326             :      * Despite all this care, there are situations where we might find many
    6327             :      * non-visible tuples near the end of the index.  We don't want to expend
    6328             :      * a huge amount of time here, so we give up once we've read too many heap
    6329             :      * pages.  When we fail for that reason, the caller will end up using
    6330             :      * whatever extremal value is recorded in pg_statistic.
    6331             :      */
    6332      103518 :     InitNonVacuumableSnapshot(SnapshotNonVacuumable,
    6333             :                               GlobalVisTestFor(heapRel));
    6334             : 
    6335      103518 :     index_scan = index_beginscan(heapRel, indexRel,
    6336             :                                  &SnapshotNonVacuumable,
    6337             :                                  1, 0);
    6338             :     /* Set it up for index-only scan */
    6339      103518 :     index_scan->xs_want_itup = true;
    6340      103518 :     index_rescan(index_scan, scankeys, 1, NULL, 0);
    6341             : 
    6342             :     /* Fetch first/next tuple in specified direction */
    6343      118668 :     while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
    6344             :     {
    6345      118668 :         BlockNumber block = ItemPointerGetBlockNumber(tid);
    6346             : 
    6347      118668 :         if (!VM_ALL_VISIBLE(heapRel,
    6348             :                             block,
    6349             :                             &vmbuffer))
    6350             :         {
    6351             :             /* Rats, we have to visit the heap to check visibility */
    6352       79244 :             if (!index_fetch_heap(index_scan, tableslot))
    6353             :             {
    6354             :                 /*
    6355             :                  * No visible tuple for this index entry, so we need to
    6356             :                  * advance to the next entry.  Before doing so, count heap
    6357             :                  * page fetches and give up if we've done too many.
    6358             :                  *
    6359             :                  * We don't charge a page fetch if this is the same heap page
    6360             :                  * as the previous tuple.  This is on the conservative side,
    6361             :                  * since other recently-accessed pages are probably still in
    6362             :                  * buffers too; but it's good enough for this heuristic.
    6363             :                  */
    6364             : #define VISITED_PAGES_LIMIT 100
    6365             : 
    6366       15150 :                 if (block != last_heap_block)
    6367             :                 {
    6368        1472 :                     last_heap_block = block;
    6369        1472 :                     n_visited_heap_pages++;
    6370        1472 :                     if (n_visited_heap_pages > VISITED_PAGES_LIMIT)
    6371           0 :                         break;
    6372             :                 }
    6373             : 
    6374       15150 :                 continue;       /* no visible tuple, try next index entry */
    6375             :             }
    6376             : 
    6377             :             /* We don't actually need the heap tuple for anything */
    6378       64094 :             ExecClearTuple(tableslot);
    6379             : 
    6380             :             /*
    6381             :              * We don't care whether there's more than one visible tuple in
    6382             :              * the HOT chain; if any are visible, that's good enough.
    6383             :              */
    6384             :         }
    6385             : 
    6386             :         /*
    6387             :          * We expect that btree will return data in IndexTuple not HeapTuple
    6388             :          * format.  It's not lossy either.
    6389             :          */
    6390      103518 :         if (!index_scan->xs_itup)
    6391           0 :             elog(ERROR, "no data returned for index-only scan");
    6392      103518 :         if (index_scan->xs_recheck)
    6393           0 :             elog(ERROR, "unexpected recheck indication from btree");
    6394             : 
    6395             :         /* OK to deconstruct the index tuple */
    6396      103518 :         index_deform_tuple(index_scan->xs_itup,
    6397             :                            index_scan->xs_itupdesc,
    6398             :                            values, isnull);
    6399             : 
    6400             :         /* Shouldn't have got a null, but be careful */
    6401      103518 :         if (isnull[0])
    6402           0 :             elog(ERROR, "found unexpected null value in index \"%s\"",
    6403             :                  RelationGetRelationName(indexRel));
    6404             : 
    6405             :         /* Copy the index column value out to caller's context */
    6406      103518 :         oldcontext = MemoryContextSwitchTo(outercontext);
    6407      103518 :         *endpointDatum = datumCopy(values[0], typByVal, typLen);
    6408      103518 :         MemoryContextSwitchTo(oldcontext);
    6409      103518 :         have_data = true;
    6410      103518 :         break;
    6411             :     }
    6412             : 
    6413      103518 :     if (vmbuffer != InvalidBuffer)
    6414       92138 :         ReleaseBuffer(vmbuffer);
    6415      103518 :     index_endscan(index_scan);
    6416             : 
    6417      103518 :     return have_data;
    6418             : }
    6419             : 
    6420             : /*
    6421             :  * find_join_input_rel
    6422             :  *      Look up the input relation for a join.
    6423             :  *
    6424             :  * We assume that the input relation's RelOptInfo must have been constructed
    6425             :  * already.
    6426             :  */
    6427             : static RelOptInfo *
    6428        7040 : find_join_input_rel(PlannerInfo *root, Relids relids)
    6429             : {
    6430        7040 :     RelOptInfo *rel = NULL;
    6431             : 
    6432        7040 :     if (!bms_is_empty(relids))
    6433             :     {
    6434             :         int         relid;
    6435             : 
    6436        7040 :         if (bms_get_singleton_member(relids, &relid))
    6437        6736 :             rel = find_base_rel(root, relid);
    6438             :         else
    6439         304 :             rel = find_join_rel(root, relids);
    6440             :     }
    6441             : 
    6442        7040 :     if (rel == NULL)
    6443           0 :         elog(ERROR, "could not find RelOptInfo for given relids");
    6444             : 
    6445        7040 :     return rel;
    6446             : }
    6447             : 
    6448             : 
    6449             : /*-------------------------------------------------------------------------
    6450             :  *
    6451             :  * Index cost estimation functions
    6452             :  *
    6453             :  *-------------------------------------------------------------------------
    6454             :  */
    6455             : 
    6456             : /*
    6457             :  * Extract the actual indexquals (as RestrictInfos) from an IndexClause list
    6458             :  */
    6459             : List *
    6460      645082 : get_quals_from_indexclauses(List *indexclauses)
    6461             : {
    6462      645082 :     List       *result = NIL;
    6463             :     ListCell   *lc;
    6464             : 
    6465     1135368 :     foreach(lc, indexclauses)
    6466             :     {
    6467      490286 :         IndexClause *iclause = lfirst_node(IndexClause, lc);
    6468             :         ListCell   *lc2;
    6469             : 
    6470      983392 :         foreach(lc2, iclause->indexquals)
    6471             :         {
    6472      493106 :             RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
    6473             : 
    6474      493106 :             result = lappend(result, rinfo);
    6475             :         }
    6476             :     }
    6477      645082 :     return result;
    6478             : }
    6479             : 
    6480             : /*
    6481             :  * Compute the total evaluation cost of the comparison operands in a list
    6482             :  * of index qual expressions.  Since we know these will be evaluated just
    6483             :  * once per scan, there's no need to distinguish startup from per-row cost.
    6484             :  *
    6485             :  * This can be used either on the result of get_quals_from_indexclauses(),
    6486             :  * or directly on an indexorderbys list.  In both cases, we expect that the
    6487             :  * index key expression is on the left side of binary clauses.
    6488             :  */
    6489             : Cost
    6490     1277564 : index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
    6491             : {
    6492     1277564 :     Cost        qual_arg_cost = 0;
    6493             :     ListCell   *lc;
    6494             : 
    6495     1771132 :     foreach(lc, indexquals)
    6496             :     {
    6497      493568 :         Expr       *clause = (Expr *) lfirst(lc);
    6498             :         Node       *other_operand;
    6499             :         QualCost    index_qual_cost;
    6500             : 
    6501             :         /*
    6502             :          * Index quals will have RestrictInfos, indexorderbys won't.  Look
    6503             :          * through RestrictInfo if present.
    6504             :          */
    6505      493568 :         if (IsA(clause, RestrictInfo))
    6506      493094 :             clause = ((RestrictInfo *) clause)->clause;
    6507             : 
    6508      493568 :         if (IsA(clause, OpExpr))
    6509             :         {
    6510      482956 :             OpExpr     *op = (OpExpr *) clause;
    6511             : 
    6512      482956 :             other_operand = (Node *) lsecond(op->args);
    6513             :         }
    6514       10612 :         else if (IsA(clause, RowCompareExpr))
    6515             :         {
    6516         132 :             RowCompareExpr *rc = (RowCompareExpr *) clause;
    6517             : 
    6518         132 :             other_operand = (Node *) rc->rargs;
    6519             :         }
    6520       10480 :         else if (IsA(clause, ScalarArrayOpExpr))
    6521             :         {
    6522        7686 :             ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
    6523             : 
    6524        7686 :             other_operand = (Node *) lsecond(saop->args);
    6525             :         }
    6526        2794 :         else if (IsA(clause, NullTest))
    6527             :         {
    6528        2794 :             other_operand = NULL;
    6529             :         }
    6530             :         else
    6531             :         {
    6532           0 :             elog(ERROR, "unsupported indexqual type: %d",
    6533             :                  (int) nodeTag(clause));
    6534             :             other_operand = NULL;   /* keep compiler quiet */
    6535             :         }
    6536             : 
    6537      493568 :         cost_qual_eval_node(&index_qual_cost, other_operand, root);
    6538      493568 :         qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
    6539             :     }
    6540     1277564 :     return qual_arg_cost;
    6541             : }
    6542             : 
    6543             : void
    6544      632494 : genericcostestimate(PlannerInfo *root,
    6545             :                     IndexPath *path,
    6546             :                     double loop_count,
    6547             :                     GenericCosts *costs)
    6548             : {
    6549      632494 :     IndexOptInfo *index = path->indexinfo;
    6550      632494 :     List       *indexQuals = get_quals_from_indexclauses(path->indexclauses);
    6551      632494 :     List       *indexOrderBys = path->indexorderbys;
    6552             :     Cost        indexStartupCost;
    6553             :     Cost        indexTotalCost;
    6554             :     Selectivity indexSelectivity;
    6555             :     double      indexCorrelation;
    6556             :     double      numIndexPages;
    6557             :     double      numIndexTuples;
    6558             :     double      spc_random_page_cost;
    6559             :     double      num_sa_scans;
    6560             :     double      num_outer_scans;
    6561             :     double      num_scans;
    6562             :     double      qual_op_cost;
    6563             :     double      qual_arg_cost;
    6564             :     List       *selectivityQuals;
    6565             :     ListCell   *l;
    6566             : 
    6567             :     /*
    6568             :      * If the index is partial, AND the index predicate with the explicitly
    6569             :      * given indexquals to produce a more accurate idea of the index
    6570             :      * selectivity.
    6571             :      */
    6572      632494 :     selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
    6573             : 
    6574             :     /*
    6575             :      * Check for ScalarArrayOpExpr index quals, and estimate the number of
    6576             :      * index scans that will be performed.
    6577             :      */
    6578      632494 :     num_sa_scans = 1;
    6579     1112812 :     foreach(l, indexQuals)
    6580             :     {
    6581      480318 :         RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
    6582             : 
    6583      480318 :         if (IsA(rinfo->clause, ScalarArrayOpExpr))
    6584             :         {
    6585        7680 :             ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
    6586        7680 :             double      alength = estimate_array_length(root, lsecond(saop->args));
    6587             : 
    6588        7680 :             if (alength > 1)
    6589        7584 :                 num_sa_scans *= alength;
    6590             :         }
    6591             :     }
    6592             : 
    6593             :     /* Estimate the fraction of main-table tuples that will be visited */
    6594      632494 :     indexSelectivity = clauselist_selectivity(root, selectivityQuals,
    6595      632494 :                                               index->rel->relid,
    6596             :                                               JOIN_INNER,
    6597             :                                               NULL);
    6598             : 
    6599             :     /*
    6600             :      * If caller didn't give us an estimate, estimate the number of index
    6601             :      * tuples that will be visited.  We do it in this rather peculiar-looking
    6602             :      * way in order to get the right answer for partial indexes.
    6603             :      */
    6604      632494 :     numIndexTuples = costs->numIndexTuples;
    6605      632494 :     if (numIndexTuples <= 0.0)
    6606             :     {
    6607       56534 :         numIndexTuples = indexSelectivity * index->rel->tuples;
    6608             : 
    6609             :         /*
    6610             :          * The above calculation counts all the tuples visited across all
    6611             :          * scans induced by ScalarArrayOpExpr nodes.  We want to consider the
    6612             :          * average per-indexscan number, so adjust.  This is a handy place to
    6613             :          * round to integer, too.  (If caller supplied tuple estimate, it's
    6614             :          * responsible for handling these considerations.)
    6615             :          */
    6616       56534 :         numIndexTuples = rint(numIndexTuples / num_sa_scans);
    6617             :     }
    6618             : 
    6619             :     /*
    6620             :      * We can bound the number of tuples by the index size in any case. Also,
    6621             :      * always estimate at least one tuple is touched, even when
    6622             :      * indexSelectivity estimate is tiny.
    6623             :      */
    6624      632494 :     if (numIndexTuples > index->tuples)
    6625        4908 :         numIndexTuples = index->tuples;
    6626      632494 :     if (numIndexTuples < 1.0)
    6627       55784 :         numIndexTuples = 1.0;
    6628             : 
    6629             :     /*
    6630             :      * Estimate the number of index pages that will be retrieved.
    6631             :      *
    6632             :      * We use the simplistic method of taking a pro-rata fraction of the total
    6633             :      * number of index pages.  In effect, this counts only leaf pages and not
    6634             :      * any overhead such as index metapage or upper tree levels.
    6635             :      *
    6636             :      * In practice access to upper index levels is often nearly free because
    6637             :      * those tend to stay in cache under load; moreover, the cost involved is
    6638             :      * highly dependent on index type.  We therefore ignore such costs here
    6639             :      * and leave it to the caller to add a suitable charge if needed.
    6640             :      */
    6641      632494 :     if (index->pages > 1 && index->tuples > 1)
    6642      591124 :         numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
    6643             :     else
    6644       41370 :         numIndexPages = 1.0;
    6645             : 
    6646             :     /* fetch estimated page cost for tablespace containing index */
    6647      632494 :     get_tablespace_page_costs(index->reltablespace,
    6648             :                               &spc_random_page_cost,
    6649             :                               NULL);
    6650             : 
    6651             :     /*
    6652             :      * Now compute the disk access costs.
    6653             :      *
    6654             :      * The above calculations are all per-index-scan.  However, if we are in a
    6655             :      * nestloop inner scan, we can expect the scan to be repeated (with
    6656             :      * different search keys) for each row of the outer relation.  Likewise,
    6657             :      * ScalarArrayOpExpr quals result in multiple index scans.  This creates
    6658             :      * the potential for cache effects to reduce the number of disk page
    6659             :      * fetches needed.  We want to estimate the average per-scan I/O cost in
    6660             :      * the presence of caching.
    6661             :      *
    6662             :      * We use the Mackert-Lohman formula (see costsize.c for details) to
    6663             :      * estimate the total number of page fetches that occur.  While this
    6664             :      * wasn't what it was designed for, it seems a reasonable model anyway.
    6665             :      * Note that we are counting pages not tuples anymore, so we take N = T =
    6666             :      * index size, as if there were one "tuple" per page.
    6667             :      */
    6668      632494 :     num_outer_scans = loop_count;
    6669      632494 :     num_scans = num_sa_scans * num_outer_scans;
    6670             : 
    6671      632494 :     if (num_scans > 1)
    6672             :     {
    6673             :         double      pages_fetched;
    6674             : 
    6675             :         /* total page fetches ignoring cache effects */
    6676       70488 :         pages_fetched = numIndexPages * num_scans;
    6677             : 
    6678             :         /* use Mackert and Lohman formula to adjust for cache effects */
    6679       70488 :         pages_fetched = index_pages_fetched(pages_fetched,
    6680             :                                             index->pages,
    6681       70488 :                                             (double) index->pages,
    6682             :                                             root);
    6683             : 
    6684             :         /*
    6685             :          * Now compute the total disk access cost, and then report a pro-rated
    6686             :          * share for each outer scan.  (Don't pro-rate for ScalarArrayOpExpr,
    6687             :          * since that's internal to the indexscan.)
    6688             :          */
    6689       70488 :         indexTotalCost = (pages_fetched * spc_random_page_cost)
    6690             :             / num_outer_scans;
    6691             :     }
    6692             :     else
    6693             :     {
    6694             :         /*
    6695             :          * For a single index scan, we just charge spc_random_page_cost per
    6696             :          * page touched.
    6697             :          */
    6698      562006 :         indexTotalCost = numIndexPages * spc_random_page_cost;
    6699             :     }
    6700             : 
    6701             :     /*
    6702             :      * CPU cost: any complex expressions in the indexquals will need to be
    6703             :      * evaluated once at the start of the scan to reduce them to runtime keys
    6704             :      * to pass to the index AM (see nodeIndexscan.c).  We model the per-tuple
    6705             :      * CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
    6706             :      * indexqual operator.  Because we have numIndexTuples as a per-scan
    6707             :      * number, we have to multiply by num_sa_scans to get the correct result
    6708             :      * for ScalarArrayOpExpr cases.  Similarly add in costs for any index
    6709             :      * ORDER BY expressions.
    6710             :      *
    6711             :      * Note: this neglects the possible costs of rechecking lossy operators.
    6712             :      * Detecting that that might be needed seems more expensive than it's
    6713             :      * worth, though, considering all the other inaccuracies here ...
    6714             :      */
    6715      632494 :     qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
    6716      632494 :         index_other_operands_eval_cost(root, indexOrderBys);
    6717      632494 :     qual_op_cost = cpu_operator_cost *
    6718      632494 :         (list_length(indexQuals) + list_length(indexOrderBys));
    6719             : 
    6720      632494 :     indexStartupCost = qual_arg_cost;
    6721      632494 :     indexTotalCost += qual_arg_cost;
    6722      632494 :     indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
    6723             : 
    6724             :     /*
    6725             :      * Generic assumption about index correlation: there isn't any.
    6726             :      */
    6727      632494 :     indexCorrelation = 0.0;
    6728             : 
    6729             :     /*
    6730             :      * Return everything to caller.
    6731             :      */
    6732      632494 :     costs->indexStartupCost = indexStartupCost;
    6733      632494 :     costs->indexTotalCost = indexTotalCost;
    6734      632494 :     costs->indexSelectivity = indexSelectivity;
    6735      632494 :     costs->indexCorrelation = indexCorrelation;
    6736      632494 :     costs->numIndexPages = numIndexPages;
    6737      632494 :     costs->numIndexTuples = numIndexTuples;
    6738      632494 :     costs->spc_random_page_cost = spc_random_page_cost;
    6739      632494 :     costs->num_sa_scans = num_sa_scans;
    6740      632494 : }
    6741             : 
    6742             : /*
    6743             :  * If the index is partial, add its predicate to the given qual list.
    6744             :  *
    6745             :  * ANDing the index predicate with the explicitly given indexquals produces
    6746             :  * a more accurate idea of the index's selectivity.  However, we need to be
    6747             :  * careful not to insert redundant clauses, because clauselist_selectivity()
    6748             :  * is easily fooled into computing a too-low selectivity estimate.  Our
    6749             :  * approach is to add only the predicate clause(s) that cannot be proven to
    6750             :  * be implied by the given indexquals.  This successfully handles cases such
    6751             :  * as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
    6752             :  * There are many other cases where we won't detect redundancy, leading to a
    6753             :  * too-low selectivity estimate, which will bias the system in favor of using
    6754             :  * partial indexes where possible.  That is not necessarily bad though.
    6755             :  *
    6756             :  * Note that indexQuals contains RestrictInfo nodes while the indpred
    6757             :  * does not, so the output list will be mixed.  This is OK for both
    6758             :  * predicate_implied_by() and clauselist_selectivity(), but might be
    6759             :  * problematic if the result were passed to other things.
    6760             :  */
    6761             : List *
    6762     1044448 : add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
    6763             : {
    6764     1044448 :     List       *predExtraQuals = NIL;
    6765             :     ListCell   *lc;
    6766             : 
    6767     1044448 :     if (index->indpred == NIL)
    6768     1042446 :         return indexQuals;
    6769             : 
    6770        4016 :     foreach(lc, index->indpred)
    6771             :     {
    6772        2014 :         Node       *predQual = (Node *) lfirst(lc);
    6773        2014 :         List       *oneQual = list_make1(predQual);
    6774             : 
    6775        2014 :         if (!predicate_implied_by(oneQual, indexQuals, false))
    6776        1812 :             predExtraQuals = list_concat(predExtraQuals, oneQual);
    6777             :     }
    6778        2002 :     return list_concat(predExtraQuals, indexQuals);
    6779             : }
    6780             : 
    6781             : 
    6782             : void
    6783      625014 : btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
    6784             :                Cost *indexStartupCost, Cost *indexTotalCost,
    6785             :                Selectivity *indexSelectivity, double *indexCorrelation,
    6786             :                double *indexPages)
    6787             : {
    6788      625014 :     IndexOptInfo *index = path->indexinfo;
    6789      625014 :     GenericCosts costs = {0};
    6790             :     Oid         relid;
    6791             :     AttrNumber  colnum;
    6792      625014 :     VariableStatData vardata = {0};
    6793             :     double      numIndexTuples;
    6794             :     Cost        descentCost;
    6795             :     List       *indexBoundQuals;
    6796             :     int         indexcol;
    6797             :     bool        eqQualHere;
    6798             :     bool        found_saop;
    6799             :     bool        found_is_null_op;
    6800             :     double      num_sa_scans;
    6801             :     ListCell   *lc;
    6802             : 
    6803             :     /*
    6804             :      * For a btree scan, only leading '=' quals plus inequality quals for the
    6805             :      * immediately next attribute contribute to index selectivity (these are
    6806             :      * the "boundary quals" that determine the starting and stopping points of
    6807             :      * the index scan).  Additional quals can suppress visits to the heap, so
    6808             :      * it's OK to count them in indexSelectivity, but they should not count
    6809             :      * for estimating numIndexTuples.  So we must examine the given indexquals
    6810             :      * to find out which ones count as boundary quals.  We rely on the
    6811             :      * knowledge that they are given in index column order.
    6812             :      *
    6813             :      * For a RowCompareExpr, we consider only the first column, just as
    6814             :      * rowcomparesel() does.
    6815             :      *
    6816             :      * If there's a ScalarArrayOpExpr in the quals, we'll actually perform N
    6817             :      * index scans not one, but the ScalarArrayOpExpr's operator can be
    6818             :      * considered to act the same as it normally does.
    6819             :      */
    6820      625014 :     indexBoundQuals = NIL;
    6821      625014 :     indexcol = 0;
    6822      625014 :     eqQualHere = false;
    6823      625014 :     found_saop = false;
    6824      625014 :     found_is_null_op = false;
    6825      625014 :     num_sa_scans = 1;
    6826     1070632 :     foreach(lc, path->indexclauses)
    6827             :     {
    6828      467368 :         IndexClause *iclause = lfirst_node(IndexClause, lc);
    6829             :         ListCell   *lc2;
    6830             : 
    6831      467368 :         if (indexcol != iclause->indexcol)
    6832             :         {
    6833             :             /* Beginning of a new column's quals */
    6834       76852 :             if (!eqQualHere)
    6835       20534 :                 break;          /* done if no '=' qual for indexcol */
    6836       56318 :             eqQualHere = false;
    6837       56318 :             indexcol++;
    6838       56318 :             if (indexcol != iclause->indexcol)
    6839        1216 :                 break;          /* no quals at all for indexcol */
    6840             :         }
    6841             : 
    6842             :         /* Examine each indexqual associated with this index clause */
    6843      893896 :         foreach(lc2, iclause->indexquals)
    6844             :         {
    6845      448278 :             RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
    6846      448278 :             Expr       *clause = rinfo->clause;
    6847      448278 :             Oid         clause_op = InvalidOid;
    6848             :             int         op_strategy;
    6849             : 
    6850      448278 :             if (IsA(clause, OpExpr))
    6851             :             {
    6852      438466 :                 OpExpr     *op = (OpExpr *) clause;
    6853             : 
    6854      438466 :                 clause_op = op->opno;
    6855             :             }
    6856        9812 :             else if (IsA(clause, RowCompareExpr))
    6857             :             {
    6858         132 :                 RowCompareExpr *rc = (RowCompareExpr *) clause;
    6859             : 
    6860         132 :                 clause_op = linitial_oid(rc->opnos);
    6861             :             }
    6862        9680 :             else if (IsA(clause, ScalarArrayOpExpr))
    6863             :             {
    6864        7480 :                 ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
    6865        7480 :                 Node       *other_operand = (Node *) lsecond(saop->args);
    6866        7480 :                 double      alength = estimate_array_length(root, other_operand);
    6867             : 
    6868        7480 :                 clause_op = saop->opno;
    6869        7480 :                 found_saop = true;
    6870             :                 /* count number of SA scans induced by indexBoundQuals only */
    6871        7480 :                 if (alength > 1)
    6872        7384 :                     num_sa_scans *= alength;
    6873             :             }
    6874        2200 :             else if (IsA(clause, NullTest))
    6875             :             {
    6876        2200 :                 NullTest   *nt = (NullTest *) clause;
    6877             : 
    6878        2200 :                 if (nt->nulltesttype == IS_NULL)
    6879             :                 {
    6880         192 :                     found_is_null_op = true;
    6881             :                     /* IS NULL is like = for selectivity purposes */
    6882         192 :                     eqQualHere = true;
    6883             :                 }
    6884             :             }
    6885             :             else
    6886           0 :                 elog(ERROR, "unsupported indexqual type: %d",
    6887             :                      (int) nodeTag(clause));
    6888             : 
    6889             :             /* check for equality operator */
    6890      448278 :             if (OidIsValid(clause_op))
    6891             :             {
    6892      446078 :                 op_strategy = get_op_opfamily_strategy(clause_op,
    6893      446078 :                                                        index->opfamily[indexcol]);
    6894             :                 Assert(op_strategy != 0);   /* not a member of opfamily?? */
    6895      446078 :                 if (op_strategy == BTEqualStrategyNumber)
    6896      422814 :                     eqQualHere = true;
    6897             :             }
    6898             : 
    6899      448278 :             indexBoundQuals = lappend(indexBoundQuals, rinfo);
    6900             :         }
    6901             :     }
    6902             : 
    6903             :     /*
    6904             :      * If index is unique and we found an '=' clause for each column, we can
    6905             :      * just assume numIndexTuples = 1 and skip the expensive
    6906             :      * clauselist_selectivity calculations.  However, a ScalarArrayOp or
    6907             :      * NullTest invalidates that theory, even though it sets eqQualHere.
    6908             :      */
    6909      625014 :     if (index->unique &&
    6910      518712 :         indexcol == index->nkeycolumns - 1 &&
    6911      219250 :         eqQualHere &&
    6912      219250 :         !found_saop &&
    6913      214984 :         !found_is_null_op)
    6914      214918 :         numIndexTuples = 1.0;
    6915             :     else
    6916             :     {
    6917             :         List       *selectivityQuals;
    6918             :         Selectivity btreeSelectivity;
    6919             : 
    6920             :         /*
    6921             :          * If the index is partial, AND the index predicate with the
    6922             :          * index-bound quals to produce a more accurate idea of the number of
    6923             :          * rows covered by the bound conditions.
    6924             :          */
    6925      410096 :         selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
    6926             : 
    6927      410096 :         btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
    6928      410096 :                                                   index->rel->relid,
    6929             :                                                   JOIN_INNER,
    6930             :                                                   NULL);
    6931      410096 :         numIndexTuples = btreeSelectivity * index->rel->tuples;
    6932             : 
    6933             :         /*
    6934             :          * As in genericcostestimate(), we have to adjust for any
    6935             :          * ScalarArrayOpExpr quals included in indexBoundQuals, and then round
    6936             :          * to integer.
    6937             :          */
    6938      410096 :         numIndexTuples = rint(numIndexTuples / num_sa_scans);
    6939             :     }
    6940             : 
    6941             :     /*
    6942             :      * Now do generic index cost estimation.
    6943             :      */
    6944      625014 :     costs.numIndexTuples = numIndexTuples;
    6945             : 
    6946      625014 :     genericcostestimate(root, path, loop_count, &costs);
    6947             : 
    6948             :     /*
    6949             :      * Add a CPU-cost component to represent the costs of initial btree
    6950             :      * descent.  We don't charge any I/O cost for touching upper btree levels,
    6951             :      * since they tend to stay in cache, but we still have to do about log2(N)
    6952             :      * comparisons to descend a btree of N leaf tuples.  We charge one
    6953             :      * cpu_operator_cost per comparison.
    6954             :      *
    6955             :      * If there are ScalarArrayOpExprs, charge this once per SA scan.  The
    6956             :      * ones after the first one are not startup cost so far as the overall
    6957             :      * plan is concerned, so add them only to "total" cost.
    6958             :      */
    6959      625014 :     if (index->tuples > 1)        /* avoid computing log(0) */
    6960             :     {
    6961      591650 :         descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
    6962      591650 :         costs.indexStartupCost += descentCost;
    6963      591650 :         costs.indexTotalCost += costs.num_sa_scans * descentCost;
    6964             :     }
    6965             : 
    6966             :     /*
    6967             :      * Even though we're not charging I/O cost for touching upper btree pages,
    6968             :      * it's still reasonable to charge some CPU cost per page descended
    6969             :      * through.  Moreover, if we had no such charge at all, bloated indexes
    6970             :      * would appear to have the same search cost as unbloated ones, at least
    6971             :      * in cases where only a single leaf page is expected to be visited.  This
    6972             :      * cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
    6973             :      * touched.  The number of such pages is btree tree height plus one (ie,
    6974             :      * we charge for the leaf page too).  As above, charge once per SA scan.
    6975             :      */
    6976      625014 :     descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    6977      625014 :     costs.indexStartupCost += descentCost;
    6978      625014 :     costs.indexTotalCost += costs.num_sa_scans * descentCost;
    6979             : 
    6980             :     /*
    6981             :      * If we can get an estimate of the first column's ordering correlation C
    6982             :      * from pg_statistic, estimate the index correlation as C for a
    6983             :      * single-column index, or C * 0.75 for multiple columns. (The idea here
    6984             :      * is that multiple columns dilute the importance of the first column's
    6985             :      * ordering, but don't negate it entirely.  Before 8.0 we divided the
    6986             :      * correlation by the number of columns, but that seems too strong.)
    6987             :      */
    6988      625014 :     if (index->indexkeys[0] != 0)
    6989             :     {
    6990             :         /* Simple variable --- look to stats for the underlying table */
    6991      622848 :         RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
    6992             : 
    6993             :         Assert(rte->rtekind == RTE_RELATION);
    6994      622848 :         relid = rte->relid;
    6995             :         Assert(relid != InvalidOid);
    6996      622848 :         colnum = index->indexkeys[0];
    6997             : 
    6998      622848 :         if (get_relation_stats_hook &&
    6999           0 :             (*get_relation_stats_hook) (root, rte, colnum, &vardata))
    7000             :         {
    7001             :             /*
    7002             :              * The hook took control of acquiring a stats tuple.  If it did
    7003             :              * supply a tuple, it'd better have supplied a freefunc.
    7004             :              */
    7005           0 :             if (HeapTupleIsValid(vardata.statsTuple) &&
    7006           0 :                 !vardata.freefunc)
    7007           0 :                 elog(ERROR, "no function provided to release variable stats with");
    7008             :         }
    7009             :         else
    7010             :         {
    7011      622848 :             vardata.statsTuple = SearchSysCache3(STATRELATTINH,
    7012             :                                                  ObjectIdGetDatum(relid),
    7013             :                                                  Int16GetDatum(colnum),
    7014      622848 :                                                  BoolGetDatum(rte->inh));
    7015      622848 :             vardata.freefunc = ReleaseSysCache;
    7016             :         }
    7017             :     }
    7018             :     else
    7019             :     {
    7020             :         /* Expression --- maybe there are stats for the index itself */
    7021        2166 :         relid = index->indexoid;
    7022        2166 :         colnum = 1;
    7023             : 
    7024        2166 :         if (get_index_stats_hook &&
    7025           0 :             (*get_index_stats_hook) (root, relid, colnum, &vardata))
    7026             :         {
    7027             :             /*
    7028             :              * The hook took control of acquiring a stats tuple.  If it did
    7029             :              * supply a tuple, it'd better have supplied a freefunc.
    7030             :              */
    7031           0 :             if (HeapTupleIsValid(vardata.statsTuple) &&
    7032           0 :                 !vardata.freefunc)
    7033           0 :                 elog(ERROR, "no function provided to release variable stats with");
    7034             :         }
    7035             :         else
    7036             :         {
    7037        2166 :             vardata.statsTuple = SearchSysCache3(STATRELATTINH,
    7038             :                                                  ObjectIdGetDatum(relid),
    7039             :                                                  Int16GetDatum(colnum),
    7040             :                                                  BoolGetDatum(false));
    7041        2166 :             vardata.freefunc = ReleaseSysCache;
    7042             :         }
    7043             :     }
    7044             : 
    7045      625014 :     if (HeapTupleIsValid(vardata.statsTuple))
    7046             :     {
    7047             :         Oid         sortop;
    7048             :         AttStatsSlot sslot;
    7049             : 
    7050      456782 :         sortop = get_opfamily_member(index->opfamily[0],
    7051      456782 :                                      index->opcintype[0],
    7052      456782 :                                      index->opcintype[0],
    7053             :                                      BTLessStrategyNumber);
    7054      913564 :         if (OidIsValid(sortop) &&
    7055      456782 :             get_attstatsslot(&sslot, vardata.statsTuple,
    7056             :                              STATISTIC_KIND_CORRELATION, sortop,
    7057             :                              ATTSTATSSLOT_NUMBERS))
    7058             :         {
    7059             :             double      varCorrelation;
    7060             : 
    7061             :             Assert(sslot.nnumbers == 1);
    7062      451952 :             varCorrelation = sslot.numbers[0];
    7063             : 
    7064      451952 :             if (index->reverse_sort[0])
    7065           0 :                 varCorrelation = -varCorrelation;
    7066             : 
    7067      451952 :             if (index->nkeycolumns > 1)
    7068      141266 :                 costs.indexCorrelation = varCorrelation * 0.75;
    7069             :             else
    7070      310686 :                 costs.indexCorrelation = varCorrelation;
    7071             : 
    7072      451952 :             free_attstatsslot(&sslot);
    7073             :         }
    7074             :     }
    7075             : 
    7076      625014 :     ReleaseVariableStats(vardata);
    7077             : 
    7078      625014 :     *indexStartupCost = costs.indexStartupCost;
    7079      625014 :     *indexTotalCost = costs.indexTotalCost;
    7080      625014 :     *indexSelectivity = costs.indexSelectivity;
    7081      625014 :     *indexCorrelation = costs.indexCorrelation;
    7082      625014 :     *indexPages = costs.numIndexPages;
    7083      625014 : }
    7084             : 
    7085             : void
    7086         398 : hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
    7087             :                  Cost *indexStartupCost, Cost *indexTotalCost,
    7088             :                  Selectivity *indexSelectivity, double *indexCorrelation,
    7089             :                  double *indexPages)
    7090             : {
    7091         398 :     GenericCosts costs = {0};
    7092             : 
    7093         398 :     genericcostestimate(root, path, loop_count, &costs);
    7094             : 
    7095             :     /*
    7096             :      * A hash index has no descent costs as such, since the index AM can go
    7097             :      * directly to the target bucket after computing the hash value.  There
    7098             :      * are a couple of other hash-specific costs that we could conceivably add
    7099             :      * here, though:
    7100             :      *
    7101             :      * Ideally we'd charge spc_random_page_cost for each page in the target
    7102             :      * bucket, not just the numIndexPages pages that genericcostestimate
    7103             :      * thought we'd visit.  However in most cases we don't know which bucket
    7104             :      * that will be.  There's no point in considering the average bucket size
    7105             :      * because the hash AM makes sure that's always one page.
    7106             :      *
    7107             :      * Likewise, we could consider charging some CPU for each index tuple in
    7108             :      * the bucket, if we knew how many there were.  But the per-tuple cost is
    7109             :      * just a hash value comparison, not a general datatype-dependent
    7110             :      * comparison, so any such charge ought to be quite a bit less than
    7111             :      * cpu_operator_cost; which makes it probably not worth worrying about.
    7112             :      *
    7113             :      * A bigger issue is that chance hash-value collisions will result in
    7114             :      * wasted probes into the heap.  We don't currently attempt to model this
    7115             :      * cost on the grounds that it's rare, but maybe it's not rare enough.
    7116             :      * (Any fix for this ought to consider the generic lossy-operator problem,
    7117             :      * though; it's not entirely hash-specific.)
    7118             :      */
    7119             : 
    7120         398 :     *indexStartupCost = costs.indexStartupCost;
    7121         398 :     *indexTotalCost = costs.indexTotalCost;
    7122         398 :     *indexSelectivity = costs.indexSelectivity;
    7123         398 :     *indexCorrelation = costs.indexCorrelation;
    7124         398 :     *indexPages = costs.numIndexPages;
    7125         398 : }
    7126             : 
    7127             : void
    7128        4486 : gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
    7129             :                  Cost *indexStartupCost, Cost *indexTotalCost,
    7130             :                  Selectivity *indexSelectivity, double *indexCorrelation,
    7131             :                  double *indexPages)
    7132             : {
    7133        4486 :     IndexOptInfo *index = path->indexinfo;
    7134        4486 :     GenericCosts costs = {0};
    7135             :     Cost        descentCost;
    7136             : 
    7137        4486 :     genericcostestimate(root, path, loop_count, &costs);
    7138             : 
    7139             :     /*
    7140             :      * We model index descent costs similarly to those for btree, but to do
    7141             :      * that we first need an idea of the tree height.  We somewhat arbitrarily
    7142             :      * assume that the fanout is 100, meaning the tree height is at most
    7143             :      * log100(index->pages).
    7144             :      *
    7145             :      * Although this computation isn't really expensive enough to require
    7146             :      * caching, we might as well use index->tree_height to cache it.
    7147             :      */
    7148        4486 :     if (index->tree_height < 0) /* unknown? */
    7149             :     {
    7150        4472 :         if (index->pages > 1) /* avoid computing log(0) */
    7151        2704 :             index->tree_height = (int) (log(index->pages) / log(100.0));
    7152             :         else
    7153        1768 :             index->tree_height = 0;
    7154             :     }
    7155             : 
    7156             :     /*
    7157             :      * Add a CPU-cost component to represent the costs of initial descent. We
    7158             :      * just use log(N) here not log2(N) since the branching factor isn't
    7159             :      * necessarily two anyway.  As for btree, charge once per SA scan.
    7160             :      */
    7161        4486 :     if (index->tuples > 1)        /* avoid computing log(0) */
    7162             :     {
    7163        4486 :         descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
    7164        4486 :         costs.indexStartupCost += descentCost;
    7165        4486 :         costs.indexTotalCost += costs.num_sa_scans * descentCost;
    7166             :     }
    7167             : 
    7168             :     /*
    7169             :      * Likewise add a per-page charge, calculated the same as for btrees.
    7170             :      */
    7171        4486 :     descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    7172        4486 :     costs.indexStartupCost += descentCost;
    7173        4486 :     costs.indexTotalCost += costs.num_sa_scans * descentCost;
    7174             : 
    7175        4486 :     *indexStartupCost = costs.indexStartupCost;
    7176        4486 :     *indexTotalCost = costs.indexTotalCost;
    7177        4486 :     *indexSelectivity = costs.indexSelectivity;
    7178        4486 :     *indexCorrelation = costs.indexCorrelation;
    7179        4486 :     *indexPages = costs.numIndexPages;
    7180        4486 : }
    7181             : 
    7182             : void
    7183        1784 : spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
    7184             :                 Cost *indexStartupCost, Cost *indexTotalCost,
    7185             :                 Selectivity *indexSelectivity, double *indexCorrelation,
    7186             :                 double *indexPages)
    7187             : {
    7188        1784 :     IndexOptInfo *index = path->indexinfo;
    7189        1784 :     GenericCosts costs = {0};
    7190             :     Cost        descentCost;
    7191             : 
    7192        1784 :     genericcostestimate(root, path, loop_count, &costs);
    7193             : 
    7194             :     /*
    7195             :      * We model index descent costs similarly to those for btree, but to do
    7196             :      * that we first need an idea of the tree height.  We somewhat arbitrarily
    7197             :      * assume that the fanout is 100, meaning the tree height is at most
    7198             :      * log100(index->pages).
    7199             :      *
    7200             :      * Although this computation isn't really expensive enough to require
    7201             :      * caching, we might as well use index->tree_height to cache it.
    7202             :      */
    7203        1784 :     if (index->tree_height < 0) /* unknown? */
    7204             :     {
    7205        1778 :         if (index->pages > 1) /* avoid computing log(0) */
    7206        1778 :             index->tree_height = (int) (log(index->pages) / log(100.0));
    7207             :         else
    7208           0 :             index->tree_height = 0;
    7209             :     }
    7210             : 
    7211             :     /*
    7212             :      * Add a CPU-cost component to represent the costs of initial descent. We
    7213             :      * just use log(N) here not log2(N) since the branching factor isn't
    7214             :      * necessarily two anyway.  As for btree, charge once per SA scan.
    7215             :      */
    7216        1784 :     if (index->tuples > 1)        /* avoid computing log(0) */
    7217             :     {
    7218        1784 :         descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
    7219        1784 :         costs.indexStartupCost += descentCost;
    7220        1784 :         costs.indexTotalCost += costs.num_sa_scans * descentCost;
    7221             :     }
    7222             : 
    7223             :     /*
    7224             :      * Likewise add a per-page charge, calculated the same as for btrees.
    7225             :      */
    7226        1784 :     descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    7227        1784 :     costs.indexStartupCost += descentCost;
    7228        1784 :     costs.indexTotalCost += costs.num_sa_scans * descentCost;
    7229             : 
    7230        1784 :     *indexStartupCost = costs.indexStartupCost;
    7231        1784 :     *indexTotalCost = costs.indexTotalCost;
    7232        1784 :     *indexSelectivity = costs.indexSelectivity;
    7233        1784 :     *indexCorrelation = costs.indexCorrelation;
    7234        1784 :     *indexPages = costs.numIndexPages;
    7235        1784 : }
    7236             : 
    7237             : 
    7238             : /*
    7239             :  * Support routines for gincostestimate
    7240             :  */
    7241             : 
    7242             : typedef struct
    7243             : {
    7244             :     bool        attHasFullScan[INDEX_MAX_KEYS];
    7245             :     bool        attHasNormalScan[INDEX_MAX_KEYS];
    7246             :     double      partialEntries;
    7247             :     double      exactEntries;
    7248             :     double      searchEntries;
    7249             :     double      arrayScans;
    7250             : } GinQualCounts;
    7251             : 
    7252             : /*
    7253             :  * Estimate the number of index terms that need to be searched for while
    7254             :  * testing the given GIN query, and increment the counts in *counts
    7255             :  * appropriately.  If the query is unsatisfiable, return false.
    7256             :  */
    7257             : static bool
    7258        2062 : gincost_pattern(IndexOptInfo *index, int indexcol,
    7259             :                 Oid clause_op, Datum query,
    7260             :                 GinQualCounts *counts)
    7261             : {
    7262             :     FmgrInfo    flinfo;
    7263             :     Oid         extractProcOid;
    7264             :     Oid         collation;
    7265             :     int         strategy_op;
    7266             :     Oid         lefttype,
    7267             :                 righttype;
    7268        2062 :     int32       nentries = 0;
    7269        2062 :     bool       *partial_matches = NULL;
    7270        2062 :     Pointer    *extra_data = NULL;
    7271        2062 :     bool       *nullFlags = NULL;
    7272        2062 :     int32       searchMode = GIN_SEARCH_MODE_DEFAULT;
    7273             :     int32       i;
    7274             : 
    7275             :     Assert(indexcol < index->nkeycolumns);
    7276             : 
    7277             :     /*
    7278             :      * Get the operator's strategy number and declared input data types within
    7279             :      * the index opfamily.  (We don't need the latter, but we use
    7280             :      * get_op_opfamily_properties because it will throw error if it fails to
    7281             :      * find a matching pg_amop entry.)
    7282             :      */
    7283        2062 :     get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
    7284             :                                &strategy_op, &lefttype, &righttype);
    7285             : 
    7286             :     /*
    7287             :      * GIN always uses the "default" support functions, which are those with
    7288             :      * lefttype == righttype == the opclass' opcintype (see
    7289             :      * IndexSupportInitialize in relcache.c).
    7290             :      */
    7291        2062 :     extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
    7292        2062 :                                        index->opcintype[indexcol],
    7293        2062 :                                        index->opcintype[indexcol],
    7294             :                                        GIN_EXTRACTQUERY_PROC);
    7295             : 
    7296        2062 :     if (!OidIsValid(extractProcOid))
    7297             :     {
    7298             :         /* should not happen; throw same error as index_getprocinfo */
    7299           0 :         elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
    7300             :              GIN_EXTRACTQUERY_PROC, indexcol + 1,
    7301             :              get_rel_name(index->indexoid));
    7302             :     }
    7303             : 
    7304             :     /*
    7305             :      * Choose collation to pass to extractProc (should match initGinState).
    7306             :      */
    7307        2062 :     if (OidIsValid(index->indexcollations[indexcol]))
    7308         364 :         collation = index->indexcollations[indexcol];
    7309             :     else
    7310        1698 :         collation = DEFAULT_COLLATION_OID;
    7311             : 
    7312        2062 :     fmgr_info(extractProcOid, &flinfo);
    7313             : 
    7314        2062 :     set_fn_opclass_options(&flinfo, index->opclassoptions[indexcol]);
    7315             : 
    7316        2062 :     FunctionCall7Coll(&flinfo,
    7317             :                       collation,
    7318             :                       query,
    7319             :                       PointerGetDatum(&nentries),
    7320             :                       UInt16GetDatum(strategy_op),
    7321             :                       PointerGetDatum(&partial_matches),
    7322             :                       PointerGetDatum(&extra_data),
    7323             :                       PointerGetDatum(&nullFlags),
    7324             :                       PointerGetDatum(&searchMode));
    7325             : 
    7326        2062 :     if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
    7327             :     {
    7328             :         /* No match is possible */
    7329          12 :         return false;
    7330             :     }
    7331             : 
    7332        5786 :     for (i = 0; i < nentries; i++)
    7333             :     {
    7334             :         /*
    7335             :          * For partial match we haven't any information to estimate number of
    7336             :          * matched entries in index, so, we just estimate it as 100
    7337             :          */
    7338        3736 :         if (partial_matches && partial_matches[i])
    7339         316 :             counts->partialEntries += 100;
    7340             :         else
    7341        3420 :             counts->exactEntries++;
    7342             : 
    7343        3736 :         counts->searchEntries++;
    7344             :     }
    7345             : 
    7346        2050 :     if (searchMode == GIN_SEARCH_MODE_DEFAULT)
    7347             :     {
    7348        1578 :         counts->attHasNormalScan[indexcol] = true;
    7349             :     }
    7350         472 :     else if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
    7351             :     {
    7352             :         /* Treat "include empty" like an exact-match item */
    7353          44 :         counts->attHasNormalScan[indexcol] = true;
    7354          44 :         counts->exactEntries++;
    7355          44 :         counts->searchEntries++;
    7356             :     }
    7357             :     else
    7358             :     {
    7359             :         /* It's GIN_SEARCH_MODE_ALL */
    7360         428 :         counts->attHasFullScan[indexcol] = true;
    7361             :     }
    7362             : 
    7363        2050 :     return true;
    7364             : }
    7365             : 
    7366             : /*
    7367             :  * Estimate the number of index terms that need to be searched for while
    7368             :  * testing the given GIN index clause, and increment the counts in *counts
    7369             :  * appropriately.  If the query is unsatisfiable, return false.
    7370             :  */
    7371             : static bool
    7372        2050 : gincost_opexpr(PlannerInfo *root,
    7373             :                IndexOptInfo *index,
    7374             :                int indexcol,
    7375             :                OpExpr *clause,
    7376             :                GinQualCounts *counts)
    7377             : {
    7378        2050 :     Oid         clause_op = clause->opno;
    7379        2050 :     Node       *operand = (Node *) lsecond(clause->args);
    7380             : 
    7381             :     /* aggressively reduce to a constant, and look through relabeling */
    7382        2050 :     operand = estimate_expression_value(root, operand);
    7383             : 
    7384        2050 :     if (IsA(operand, RelabelType))
    7385           0 :         operand = (Node *) ((RelabelType *) operand)->arg;
    7386             : 
    7387             :     /*
    7388             :      * It's impossible to call extractQuery method for unknown operand. So
    7389             :      * unless operand is a Const we can't do much; just assume there will be
    7390             :      * one ordinary search entry from the operand at runtime.
    7391             :      */
    7392        2050 :     if (!IsA(operand, Const))
    7393             :     {
    7394           0 :         counts->exactEntries++;
    7395           0 :         counts->searchEntries++;
    7396           0 :         return true;
    7397             :     }
    7398             : 
    7399             :     /* If Const is null, there can be no matches */
    7400        2050 :     if (((Const *) operand)->constisnull)
    7401           0 :         return false;
    7402             : 
    7403             :     /* Otherwise, apply extractQuery and get the actual term counts */
    7404        2050 :     return gincost_pattern(index, indexcol, clause_op,
    7405             :                            ((Const *) operand)->constvalue,
    7406             :                            counts);
    7407             : }
    7408             : 
    7409             : /*
    7410             :  * Estimate the number of index terms that need to be searched for while
    7411             :  * testing the given GIN index clause, and increment the counts in *counts
    7412             :  * appropriately.  If the query is unsatisfiable, return false.
    7413             :  *
    7414             :  * A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
    7415             :  * each of which involves one value from the RHS array, plus all the
    7416             :  * non-array quals (if any).  To model this, we average the counts across
    7417             :  * the RHS elements, and add the averages to the counts in *counts (which
    7418             :  * correspond to per-indexscan costs).  We also multiply counts->arrayScans
    7419             :  * by N, causing gincostestimate to scale up its estimates accordingly.
    7420             :  */
    7421             : static bool
    7422           6 : gincost_scalararrayopexpr(PlannerInfo *root,
    7423             :                           IndexOptInfo *index,
    7424             :                           int indexcol,
    7425             :                           ScalarArrayOpExpr *clause,
    7426             :                           double numIndexEntries,
    7427             :                           GinQualCounts *counts)
    7428             : {
    7429           6 :     Oid         clause_op = clause->opno;
    7430           6 :     Node       *rightop = (Node *) lsecond(clause->args);
    7431             :     ArrayType  *arrayval;
    7432             :     int16       elmlen;
    7433             :     bool        elmbyval;
    7434             :     char        elmalign;
    7435             :     int         numElems;
    7436             :     Datum      *elemValues;
    7437             :     bool       *elemNulls;
    7438             :     GinQualCounts arraycounts;
    7439           6 :     int         numPossible = 0;
    7440             :     int         i;
    7441             : 
    7442             :     Assert(clause->useOr);
    7443             : 
    7444             :     /* aggressively reduce to a constant, and look through relabeling */
    7445           6 :     rightop = estimate_expression_value(root, rightop);
    7446             : 
    7447           6 :     if (IsA(rightop, RelabelType))
    7448           0 :         rightop = (Node *) ((RelabelType *) rightop)->arg;
    7449             : 
    7450             :     /*
    7451             :      * It's impossible to call extractQuery method for unknown operand. So
    7452             :      * unless operand is a Const we can't do much; just assume there will be
    7453             :      * one ordinary search entry from each array entry at runtime, and fall
    7454             :      * back on a probably-bad estimate of the number of array entries.
    7455             :      */
    7456           6 :     if (!IsA(rightop, Const))
    7457             :     {
    7458           0 :         counts->exactEntries++;
    7459           0 :         counts->searchEntries++;
    7460           0 :         counts->arrayScans *= estimate_array_length(root, rightop);
    7461           0 :         return true;
    7462             :     }
    7463             : 
    7464             :     /* If Const is null, there can be no matches */
    7465           6 :     if (((Const *) rightop)->constisnull)
    7466           0 :         return false;
    7467             : 
    7468             :     /* Otherwise, extract the array elements and iterate over them */
    7469           6 :     arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
    7470           6 :     get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
    7471             :                          &elmlen, &elmbyval, &elmalign);
    7472           6 :     deconstruct_array(arrayval,
    7473             :                       ARR_ELEMTYPE(arrayval),
    7474             :                       elmlen, elmbyval, elmalign,
    7475             :                       &elemValues, &elemNulls, &numElems);
    7476             : 
    7477           6 :     memset(&arraycounts, 0, sizeof(arraycounts));
    7478             : 
    7479          18 :     for (i = 0; i < numElems; i++)
    7480             :     {
    7481             :         GinQualCounts elemcounts;
    7482             : 
    7483             :         /* NULL can't match anything, so ignore, as the executor will */
    7484          12 :         if (elemNulls[i])
    7485           0 :             continue;
    7486             : 
    7487             :         /* Otherwise, apply extractQuery and get the actual term counts */
    7488          12 :         memset(&elemcounts, 0, sizeof(elemcounts));
    7489             : 
    7490          12 :         if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
    7491             :                             &elemcounts))
    7492             :         {
    7493             :             /* We ignore array elements that are unsatisfiable patterns */
    7494          12 :             numPossible++;
    7495             : 
    7496          12 :             if (elemcounts.attHasFullScan[indexcol] &&
    7497           0 :                 !elemcounts.attHasNormalScan[indexcol])
    7498             :             {
    7499             :                 /*
    7500             :                  * Full index scan will be required.  We treat this as if
    7501             :                  * every key in the index had been listed in the query; is
    7502             :                  * that reasonable?
    7503             :                  */
    7504           0 :                 elemcounts.partialEntries = 0;
    7505           0 :                 elemcounts.exactEntries = numIndexEntries;
    7506           0 :                 elemcounts.searchEntries = numIndexEntries;
    7507             :             }
    7508          12 :             arraycounts.partialEntries += elemcounts.partialEntries;
    7509          12 :             arraycounts.exactEntries += elemcounts.exactEntries;
    7510          12 :             arraycounts.searchEntries += elemcounts.searchEntries;
    7511             :         }
    7512             :     }
    7513             : 
    7514           6 :     if (numPossible == 0)
    7515             :     {
    7516             :         /* No satisfiable patterns in the array */
    7517           0 :         return false;
    7518             :     }
    7519             : 
    7520             :     /*
    7521             :      * Now add the averages to the global counts.  This will give us an
    7522             :      * estimate of the average number of terms searched for in each indexscan,
    7523             :      * including contributions from both array and non-array quals.
    7524             :      */
    7525           6 :     counts->partialEntries += arraycounts.partialEntries / numPossible;
    7526           6 :     counts->exactEntries += arraycounts.exactEntries / numPossible;
    7527           6 :     counts->searchEntries += arraycounts.searchEntries / numPossible;
    7528             : 
    7529           6 :     counts->arrayScans *= numPossible;
    7530             : 
    7531           6 :     return true;
    7532             : }
    7533             : 
    7534             : /*
    7535             :  * GIN has search behavior completely different from other index types
    7536             :  */
    7537             : void
    7538        1858 : gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
    7539             :                 Cost *indexStartupCost, Cost *indexTotalCost,
    7540             :                 Selectivity *indexSelectivity, double *indexCorrelation,
    7541             :                 double *indexPages)
    7542             : {
    7543        1858 :     IndexOptInfo *index = path->indexinfo;
    7544        1858 :     List       *indexQuals = get_quals_from_indexclauses(path->indexclauses);
    7545             :     List       *selectivityQuals;
    7546        1858 :     double      numPages = index->pages,
    7547        1858 :                 numTuples = index->tuples;
    7548             :     double      numEntryPages,
    7549             :                 numDataPages,
    7550             :                 numPendingPages,
    7551             :                 numEntries;
    7552             :     GinQualCounts counts;
    7553             :     bool        matchPossible;
    7554             :     bool        fullIndexScan;
    7555             :     double      partialScale;
    7556             :     double      entryPagesFetched,
    7557             :                 dataPagesFetched,
    7558             :                 dataPagesFetchedBySel;
    7559             :     double      qual_op_cost,
    7560             :                 qual_arg_cost,
    7561             :                 spc_random_page_cost,
    7562             :                 outer_scans;
    7563             :     Cost        descentCost;
    7564             :     Relation    indexRel;
    7565             :     GinStatsData ginStats;
    7566             :     ListCell   *lc;
    7567             :     int         i;
    7568             : 
    7569             :     /*
    7570             :      * Obtain statistical information from the meta page, if possible.  Else
    7571             :      * set ginStats to zeroes, and we'll cope below.
    7572             :      */
    7573        1858 :     if (!index->hypothetical)
    7574             :     {
    7575             :         /* Lock should have already been obtained in plancat.c */
    7576        1858 :         indexRel = index_open(index->indexoid, NoLock);
    7577        1858 :         ginGetStats(indexRel, &ginStats);
    7578        1858 :         index_close(indexRel, NoLock);
    7579             :     }
    7580             :     else
    7581             :     {
    7582           0 :         memset(&ginStats, 0, sizeof(ginStats));
    7583             :     }
    7584             : 
    7585             :     /*
    7586             :      * Assuming we got valid (nonzero) stats at all, nPendingPages can be
    7587             :      * trusted, but the other fields are data as of the last VACUUM.  We can
    7588             :      * scale them up to account for growth since then, but that method only
    7589             :      * goes so far; in the worst case, the stats might be for a completely
    7590             :      * empty index, and scaling them will produce pretty bogus numbers.
    7591             :      * Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
    7592             :      * it's grown more than that, fall back to estimating things only from the
    7593             :      * assumed-accurate index size.  But we'll trust nPendingPages in any case
    7594             :      * so long as it's not clearly insane, ie, more than the index size.
    7595             :      */
    7596        1858 :     if (ginStats.nPendingPages < numPages)
    7597        1858 :         numPendingPages = ginStats.nPendingPages;
    7598             :     else
    7599           0 :         numPendingPages = 0;
    7600             : 
    7601        1858 :     if (numPages > 0 && ginStats.nTotalPages <= numPages &&
    7602        1858 :         ginStats.nTotalPages > numPages / 4 &&
    7603        1816 :         ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
    7604        1558 :     {
    7605             :         /*
    7606             :          * OK, the stats seem close enough to sane to be trusted.  But we
    7607             :          * still need to scale them by the ratio numPages / nTotalPages to
    7608             :          * account for growth since the last VACUUM.
    7609             :          */
    7610        1558 :         double      scale = numPages / ginStats.nTotalPages;
    7611             : 
    7612        1558 :         numEntryPages = ceil(ginStats.nEntryPages * scale);
    7613        1558 :         numDataPages = ceil(ginStats.nDataPages * scale);
    7614        1558 :         numEntries = ceil(ginStats.nEntries * scale);
    7615             :         /* ensure we didn't round up too much */
    7616        1558 :         numEntryPages = Min(numEntryPages, numPages - numPendingPages);
    7617        1558 :         numDataPages = Min(numDataPages,
    7618             :                            numPages - numPendingPages - numEntryPages);
    7619             :     }
    7620             :     else
    7621             :     {
    7622             :         /*
    7623             :          * We might get here because it's a hypothetical index, or an index
    7624             :          * created pre-9.1 and never vacuumed since upgrading (in which case
    7625             :          * its stats would read as zeroes), or just because it's grown too
    7626             :          * much since the last VACUUM for us to put our faith in scaling.
    7627             :          *
    7628             :          * Invent some plausible internal statistics based on the index page
    7629             :          * count (and clamp that to at least 10 pages, just in case).  We
    7630             :          * estimate that 90% of the index is entry pages, and the rest is data
    7631             :          * pages.  Estimate 100 entries per entry page; this is rather bogus
    7632             :          * since it'll depend on the size of the keys, but it's more robust
    7633             :          * than trying to predict the number of entries per heap tuple.
    7634             :          */
    7635         300 :         numPages = Max(numPages, 10);
    7636         300 :         numEntryPages = floor((numPages - numPendingPages) * 0.90);
    7637         300 :         numDataPages = numPages - numPendingPages - numEntryPages;
    7638         300 :         numEntries = floor(numEntryPages * 100);
    7639             :     }
    7640             : 
    7641             :     /* In an empty index, numEntries could be zero.  Avoid divide-by-zero */
    7642        1858 :     if (numEntries < 1)
    7643           0 :         numEntries = 1;
    7644             : 
    7645             :     /*
    7646             :      * If the index is partial, AND the index predicate with the index-bound
    7647             :      * quals to produce a more accurate idea of the number of rows covered by
    7648             :      * the bound conditions.
    7649             :      */
    7650        1858 :     selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
    7651             : 
    7652             :     /* Estimate the fraction of main-table tuples that will be visited */
    7653        3716 :     *indexSelectivity = clauselist_selectivity(root, selectivityQuals,
    7654        1858 :                                                index->rel->relid,
    7655             :                                                JOIN_INNER,
    7656             :                                                NULL);
    7657             : 
    7658             :     /* fetch estimated page cost for tablespace containing index */
    7659        1858 :     get_tablespace_page_costs(index->reltablespace,
    7660             :                               &spc_random_page_cost,
    7661             :                               NULL);
    7662             : 
    7663             :     /*
    7664             :      * Generic assumption about index correlation: there isn't any.
    7665             :      */
    7666        1858 :     *indexCorrelation = 0.0;
    7667             : 
    7668             :     /*
    7669             :      * Examine quals to estimate number of search entries & partial matches
    7670             :      */
    7671        1858 :     memset(&counts, 0, sizeof(counts));
    7672        1858 :     counts.arrayScans = 1;
    7673        1858 :     matchPossible = true;
    7674             : 
    7675        3914 :     foreach(lc, path->indexclauses)
    7676             :     {
    7677        2056 :         IndexClause *iclause = lfirst_node(IndexClause, lc);
    7678             :         ListCell   *lc2;
    7679             : 
    7680        4100 :         foreach(lc2, iclause->indexquals)
    7681             :         {
    7682        2056 :             RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
    7683        2056 :             Expr       *clause = rinfo->clause;
    7684             : 
    7685        2056 :             if (IsA(clause, OpExpr))
    7686             :             {
    7687        2050 :                 matchPossible = gincost_opexpr(root,
    7688             :                                                index,
    7689        2050 :                                                iclause->indexcol,
    7690             :                                                (OpExpr *) clause,
    7691             :                                                &counts);
    7692        2050 :                 if (!matchPossible)
    7693          12 :                     break;
    7694             :             }
    7695           6 :             else if (IsA(clause, ScalarArrayOpExpr))
    7696             :             {
    7697           6 :                 matchPossible = gincost_scalararrayopexpr(root,
    7698             :                                                           index,
    7699           6 :                                                           iclause->indexcol,
    7700             :                                                           (ScalarArrayOpExpr *) clause,
    7701             :                                                           numEntries,
    7702             :                                                           &counts);
    7703           6 :                 if (!matchPossible)
    7704           0 :                     break;
    7705             :             }
    7706             :             else
    7707             :             {
    7708             :                 /* shouldn't be anything else for a GIN index */
    7709           0 :                 elog(ERROR, "unsupported GIN indexqual type: %d",
    7710             :                      (int) nodeTag(clause));
    7711             :             }
    7712             :         }
    7713             :     }
    7714             : 
    7715             :     /* Fall out if there were any provably-unsatisfiable quals */
    7716        1858 :     if (!matchPossible)
    7717             :     {
    7718          12 :         *indexStartupCost = 0;
    7719          12 :         *indexTotalCost = 0;
    7720          12 :         *indexSelectivity = 0;
    7721          12 :         return;
    7722             :     }
    7723             : 
    7724             :     /*
    7725             :      * If attribute has a full scan and at the same time doesn't have normal
    7726             :      * scan, then we'll have to scan all non-null entries of that attribute.
    7727             :      * Currently, we don't have per-attribute statistics for GIN.  Thus, we
    7728             :      * must assume the whole GIN index has to be scanned in this case.
    7729             :      */
    7730        1846 :     fullIndexScan = false;
    7731        3582 :     for (i = 0; i < index->nkeycolumns; i++)
    7732             :     {
    7733        2074 :         if (counts.attHasFullScan[i] && !counts.attHasNormalScan[i])
    7734             :         {
    7735         338 :             fullIndexScan = true;
    7736         338 :             break;
    7737             :         }
    7738             :     }
    7739             : 
    7740        1846 :     if (fullIndexScan || indexQuals == NIL)
    7741             :     {
    7742             :         /*
    7743             :          * Full index scan will be required.  We treat this as if every key in
    7744             :          * the index had been listed in the query; is that reasonable?
    7745             :          */
    7746         338 :         counts.partialEntries = 0;
    7747         338 :         counts.exactEntries = numEntries;
    7748         338 :         counts.searchEntries = numEntries;
    7749             :     }
    7750             : 
    7751             :     /* Will we have more than one iteration of a nestloop scan? */
    7752        1846 :     outer_scans = loop_count;
    7753             : 
    7754             :     /*
    7755             :      * Compute cost to begin scan, first of all, pay attention to pending
    7756             :      * list.
    7757             :      */
    7758        1846 :     entryPagesFetched = numPendingPages;
    7759             : 
    7760             :     /*
    7761             :      * Estimate number of entry pages read.  We need to do
    7762             :      * counts.searchEntries searches.  Use a power function as it should be,
    7763             :      * but tuples on leaf pages usually is much greater. Here we include all
    7764             :      * searches in entry tree, including search of first entry in partial
    7765             :      * match algorithm
    7766             :      */
    7767        1846 :     entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
    7768             : 
    7769             :     /*
    7770             :      * Add an estimate of entry pages read by partial match algorithm. It's a
    7771             :      * scan over leaf pages in entry tree.  We haven't any useful stats here,
    7772             :      * so estimate it as proportion.  Because counts.partialEntries is really
    7773             :      * pretty bogus (see code above), it's possible that it is more than
    7774             :      * numEntries; clamp the proportion to ensure sanity.
    7775             :      */
    7776        1846 :     partialScale = counts.partialEntries / numEntries;
    7777        1846 :     partialScale = Min(partialScale, 1.0);
    7778             : 
    7779        1846 :     entryPagesFetched += ceil(numEntryPages * partialScale);
    7780             : 
    7781             :     /*
    7782             :      * Partial match algorithm reads all data pages before doing actual scan,
    7783             :      * so it's a startup cost.  Again, we haven't any useful stats here, so
    7784             :      * estimate it as proportion.
    7785             :      */
    7786        1846 :     dataPagesFetched = ceil(numDataPages * partialScale);
    7787             : 
    7788        1846 :     *indexStartupCost = 0;
    7789        1846 :     *indexTotalCost = 0;
    7790             : 
    7791             :     /*
    7792             :      * Add a CPU-cost component to represent the costs of initial entry btree
    7793             :      * descent.  We don't charge any I/O cost for touching upper btree levels,
    7794             :      * since they tend to stay in cache, but we still have to do about log2(N)
    7795             :      * comparisons to descend a btree of N leaf tuples.  We charge one
    7796             :      * cpu_operator_cost per comparison.
    7797             :      *
    7798             :      * If there are ScalarArrayOpExprs, charge this once per SA scan.  The
    7799             :      * ones after the first one are not startup cost so far as the overall
    7800             :      * plan is concerned, so add them only to "total" cost.
    7801             :      */
    7802        1846 :     if (numEntries > 1)          /* avoid computing log(0) */
    7803             :     {
    7804        1846 :         descentCost = ceil(log(numEntries) / log(2.0)) * cpu_operator_cost;
    7805        1846 :         *indexStartupCost += descentCost * counts.searchEntries;
    7806        1846 :         *indexTotalCost += counts.arrayScans * descentCost * counts.searchEntries;
    7807             :     }
    7808             : 
    7809             :     /*
    7810             :      * Add a cpu cost per entry-page fetched. This is not amortized over a
    7811             :      * loop.
    7812             :      */
    7813        1846 :     *indexStartupCost += entryPagesFetched * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    7814        1846 :     *indexTotalCost += entryPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    7815             : 
    7816             :     /*
    7817             :      * Add a cpu cost per data-page fetched. This is also not amortized over a
    7818             :      * loop. Since those are the data pages from the partial match algorithm,
    7819             :      * charge them as startup cost.
    7820             :      */
    7821        1846 :     *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * dataPagesFetched;
    7822             : 
    7823             :     /*
    7824             :      * Since we add the startup cost to the total cost later on, remove the
    7825             :      * initial arrayscan from the total.
    7826             :      */
    7827        1846 :     *indexTotalCost += dataPagesFetched * (counts.arrayScans - 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    7828             : 
    7829             :     /*
    7830             :      * Calculate cache effects if more than one scan due to nestloops or array
    7831             :      * quals.  The result is pro-rated per nestloop scan, but the array qual
    7832             :      * factor shouldn't be pro-rated (compare genericcostestimate).
    7833             :      */
    7834        1846 :     if (outer_scans > 1 || counts.arrayScans > 1)
    7835             :     {
    7836           6 :         entryPagesFetched *= outer_scans * counts.arrayScans;
    7837           6 :         entryPagesFetched = index_pages_fetched(entryPagesFetched,
    7838             :                                                 (BlockNumber) numEntryPages,
    7839             :                                                 numEntryPages, root);
    7840           6 :         entryPagesFetched /= outer_scans;
    7841           6 :         dataPagesFetched *= outer_scans * counts.arrayScans;
    7842           6 :         dataPagesFetched = index_pages_fetched(dataPagesFetched,
    7843             :                                                (BlockNumber) numDataPages,
    7844             :                                                numDataPages, root);
    7845           6 :         dataPagesFetched /= outer_scans;
    7846             :     }
    7847             : 
    7848             :     /*
    7849             :      * Here we use random page cost because logically-close pages could be far
    7850             :      * apart on disk.
    7851             :      */
    7852        1846 :     *indexStartupCost += (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
    7853             : 
    7854             :     /*
    7855             :      * Now compute the number of data pages fetched during the scan.
    7856             :      *
    7857             :      * We assume every entry to have the same number of items, and that there
    7858             :      * is no overlap between them. (XXX: tsvector and array opclasses collect
    7859             :      * statistics on the frequency of individual keys; it would be nice to use
    7860             :      * those here.)
    7861             :      */
    7862        1846 :     dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
    7863             : 
    7864             :     /*
    7865             :      * If there is a lot of overlap among the entries, in particular if one of
    7866             :      * the entries is very frequent, the above calculation can grossly
    7867             :      * under-estimate.  As a simple cross-check, calculate a lower bound based
    7868             :      * on the overall selectivity of the quals.  At a minimum, we must read
    7869             :      * one item pointer for each matching entry.
    7870             :      *
    7871             :      * The width of each item pointer varies, based on the level of
    7872             :      * compression.  We don't have statistics on that, but an average of
    7873             :      * around 3 bytes per item is fairly typical.
    7874             :      */
    7875        1846 :     dataPagesFetchedBySel = ceil(*indexSelectivity *
    7876        1846 :                                  (numTuples / (BLCKSZ / 3)));
    7877        1846 :     if (dataPagesFetchedBySel > dataPagesFetched)
    7878        1472 :         dataPagesFetched = dataPagesFetchedBySel;
    7879             : 
    7880             :     /* Add one page cpu-cost to the startup cost */
    7881        1846 :     *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * counts.searchEntries;
    7882             : 
    7883             :     /*
    7884             :      * Add once again a CPU-cost for those data pages, before amortizing for
    7885             :      * cache.
    7886             :      */
    7887        1846 :     *indexTotalCost += dataPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
    7888             : 
    7889             :     /* Account for cache effects, the same as above */
    7890        1846 :     if (outer_scans > 1 || counts.arrayScans > 1)
    7891             :     {
    7892           6 :         dataPagesFetched *= outer_scans * counts.arrayScans;
    7893           6 :         dataPagesFetched = index_pages_fetched(dataPagesFetched,
    7894             :                                                (BlockNumber) numDataPages,
    7895             :                                                numDataPages, root);
    7896           6 :         dataPagesFetched /= outer_scans;
    7897             :     }
    7898             : 
    7899             :     /* And apply random_page_cost as the cost per page */
    7900        1846 :     *indexTotalCost += *indexStartupCost +
    7901        1846 :         dataPagesFetched * spc_random_page_cost;
    7902             : 
    7903             :     /*
    7904             :      * Add on index qual eval costs, much as in genericcostestimate. We charge
    7905             :      * cpu but we can disregard indexorderbys, since GIN doesn't support
    7906             :      * those.
    7907             :      */
    7908        1846 :     qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
    7909        1846 :     qual_op_cost = cpu_operator_cost * list_length(indexQuals);
    7910             : 
    7911        1846 :     *indexStartupCost += qual_arg_cost;
    7912        1846 :     *indexTotalCost += qual_arg_cost;
    7913             : 
    7914             :     /*
    7915             :      * Add a cpu cost per search entry, corresponding to the actual visited
    7916             :      * entries.
    7917             :      */
    7918        1846 :     *indexTotalCost += (counts.searchEntries * counts.arrayScans) * (qual_op_cost);
    7919             :     /* Now add a cpu cost per tuple in the posting lists / trees */
    7920        1846 :     *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost);
    7921        1846 :     *indexPages = dataPagesFetched;
    7922             : }
    7923             : 
    7924             : /*
    7925             :  * BRIN has search behavior completely different from other index types
    7926             :  */
    7927             : void
    7928       10730 : brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
    7929             :                  Cost *indexStartupCost, Cost *indexTotalCost,
    7930             :                  Selectivity *indexSelectivity, double *indexCorrelation,
    7931             :                  double *indexPages)
    7932             : {
    7933       10730 :     IndexOptInfo *index = path->indexinfo;
    7934       10730 :     List       *indexQuals = get_quals_from_indexclauses(path->indexclauses);
    7935       10730 :     double      numPages = index->pages;
    7936       10730 :     RelOptInfo *baserel = index->rel;
    7937       10730 :     RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
    7938             :     Cost        spc_seq_page_cost;
    7939             :     Cost        spc_random_page_cost;
    7940             :     double      qual_arg_cost;
    7941             :     double      qualSelectivity;
    7942             :     BrinStatsData statsData;
    7943             :     double      indexRanges;
    7944             :     double      minimalRanges;
    7945             :     double      estimatedRanges;
    7946             :     double      selec;
    7947             :     Relation    indexRel;
    7948             :     ListCell   *l;
    7949             :     VariableStatData vardata;
    7950             : 
    7951             :     Assert(rte->rtekind == RTE_RELATION);
    7952             : 
    7953             :     /* fetch estimated page cost for the tablespace containing the index */
    7954       10730 :     get_tablespace_page_costs(index->reltablespace,
    7955             :                               &spc_random_page_cost,
    7956             :                               &spc_seq_page_cost);
    7957             : 
    7958             :     /*
    7959             :      * Obtain some data from the index itself, if possible.  Otherwise invent
    7960             :      * some plausible internal statistics based on the relation page count.
    7961             :      */
    7962       10730 :     if (!index->hypothetical)
    7963             :     {
    7964             :         /*
    7965             :          * A lock should have already been obtained on the index in plancat.c.
    7966             :          */
    7967       10730 :         indexRel = index_open(index->indexoid, NoLock);
    7968       10730 :         brinGetStats(indexRel, &statsData);
    7969       10730 :         index_close(indexRel, NoLock);
    7970             : 
    7971             :         /* work out the actual number of ranges in the index */
    7972       10730 :         indexRanges = Max(ceil((double) baserel->pages /
    7973             :                                statsData.pagesPerRange), 1.0);
    7974             :     }
    7975             :     else
    7976             :     {
    7977             :         /*
    7978             :          * Assume default number of pages per range, and estimate the number
    7979             :          * of ranges based on that.
    7980             :          */
    7981           0 :         indexRanges = Max(ceil((double) baserel->pages /
    7982             :                                BRIN_DEFAULT_PAGES_PER_RANGE), 1.0);
    7983             : 
    7984           0 :         statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE;
    7985           0 :         statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1;
    7986             :     }
    7987             : 
    7988             :     /*
    7989             :      * Compute index correlation
    7990             :      *
    7991             :      * Because we can use all index quals equally when scanning, we can use
    7992             :      * the largest correlation (in absolute value) among columns used by the
    7993             :      * query.  Start at zero, the worst possible case.  If we cannot find any
    7994             :      * correlation statistics, we will keep it as 0.
    7995             :      */
    7996       10730 :     *indexCorrelation = 0;
    7997             : 
    7998       21462 :     foreach(l, path->indexclauses)
    7999             :     {
    8000       10732 :         IndexClause *iclause = lfirst_node(IndexClause, l);
    8001       10732 :         AttrNumber  attnum = index->indexkeys[iclause->indexcol];
    8002             : 
    8003             :         /* attempt to lookup stats in relation for this index column */
    8004       10732 :         if (attnum != 0)
    8005             :         {
    8006             :             /* Simple variable -- look to stats for the underlying table */
    8007       10732 :             if (get_relation_stats_hook &&
    8008           0 :                 (*get_relation_stats_hook) (root, rte, attnum, &vardata))
    8009             :             {
    8010             :                 /*
    8011             :                  * The hook took control of acquiring a stats tuple.  If it
    8012             :                  * did supply a tuple, it'd better have supplied a freefunc.
    8013             :                  */
    8014           0 :                 if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
    8015           0 :                     elog(ERROR,
    8016             :                          "no function provided to release variable stats with");
    8017             :             }
    8018             :             else
    8019             :             {
    8020       10732 :                 vardata.statsTuple =
    8021       10732 :                     SearchSysCache3(STATRELATTINH,
    8022             :                                     ObjectIdGetDatum(rte->relid),
    8023             :                                     Int16GetDatum(attnum),
    8024             :                                     BoolGetDatum(false));
    8025       10732 :                 vardata.freefunc = ReleaseSysCache;
    8026             :             }
    8027             :         }
    8028             :         else
    8029             :         {
    8030             :             /*
    8031             :              * Looks like we've found an expression column in the index. Let's
    8032             :              * see if there's any stats for it.
    8033             :              */
    8034             : 
    8035             :             /* get the attnum from the 0-based index. */
    8036           0 :             attnum = iclause->indexcol + 1;
    8037             : 
    8038           0 :             if (get_index_stats_hook &&
    8039           0 :                 (*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
    8040             :             {
    8041             :                 /*
    8042             :                  * The hook took control of acquiring a stats tuple.  If it
    8043             :                  * did supply a tuple, it'd better have supplied a freefunc.
    8044             :                  */
    8045           0 :                 if (HeapTupleIsValid(vardata.statsTuple) &&
    8046           0 :                     !vardata.freefunc)
    8047           0 :                     elog(ERROR, "no function provided to release variable stats with");
    8048             :             }
    8049             :             else
    8050             :             {
    8051           0 :                 vardata.statsTuple = SearchSysCache3(STATRELATTINH,
    8052             :                                                      ObjectIdGetDatum(index->indexoid),
    8053             :                                                      Int16GetDatum(attnum),
    8054             :                                                      BoolGetDatum(false));
    8055           0 :                 vardata.freefunc = ReleaseSysCache;
    8056             :             }
    8057             :         }
    8058             : 
    8059       10732 :         if (HeapTupleIsValid(vardata.statsTuple))
    8060             :         {
    8061             :             AttStatsSlot sslot;
    8062             : 
    8063          36 :             if (get_attstatsslot(&sslot, vardata.statsTuple,
    8064             :                                  STATISTIC_KIND_CORRELATION, InvalidOid,
    8065             :                                  ATTSTATSSLOT_NUMBERS))
    8066             :             {
    8067          36 :                 double      varCorrelation = 0.0;
    8068             : 
    8069          36 :                 if (sslot.nnumbers > 0)
    8070          36 :                     varCorrelation = fabs(sslot.numbers[0]);
    8071             : 
    8072          36 :                 if (varCorrelation > *indexCorrelation)
    8073          36 :                     *indexCorrelation = varCorrelation;
    8074             : 
    8075          36 :                 free_attstatsslot(&sslot);
    8076             :             }
    8077             :         }
    8078             : 
    8079       10732 :         ReleaseVariableStats(vardata);
    8080             :     }
    8081             : 
    8082       10730 :     qualSelectivity = clauselist_selectivity(root, indexQuals,
    8083       10730 :                                              baserel->relid,
    8084             :                                              JOIN_INNER, NULL);
    8085             : 
    8086             :     /*
    8087             :      * Now calculate the minimum possible ranges we could match with if all of
    8088             :      * the rows were in the perfect order in the table's heap.
    8089             :      */
    8090       10730 :     minimalRanges = ceil(indexRanges * qualSelectivity);
    8091             : 
    8092             :     /*
    8093             :      * Now estimate the number of ranges that we'll touch by using the
    8094             :      * indexCorrelation from the stats. Careful not to divide by zero (note
    8095             :      * we're using the absolute value of the correlation).
    8096             :      */
    8097       10730 :     if (*indexCorrelation < 1.0e-10)
    8098       10694 :         estimatedRanges = indexRanges;
    8099             :     else
    8100          36 :         estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
    8101             : 
    8102             :     /* we expect to visit this portion of the table */
    8103       10730 :     selec = estimatedRanges / indexRanges;
    8104             : 
    8105       10730 :     CLAMP_PROBABILITY(selec);
    8106             : 
    8107       10730 :     *indexSelectivity = selec;
    8108             : 
    8109             :     /*
    8110             :      * Compute the index qual costs, much as in genericcostestimate, to add to
    8111             :      * the index costs.  We can disregard indexorderbys, since BRIN doesn't
    8112             :      * support those.
    8113             :      */
    8114       10730 :     qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
    8115             : 
    8116             :     /*
    8117             :      * Compute the startup cost as the cost to read the whole revmap
    8118             :      * sequentially, including the cost to execute the index quals.
    8119             :      */
    8120       10730 :     *indexStartupCost =
    8121       10730 :         spc_seq_page_cost * statsData.revmapNumPages * loop_count;
    8122       10730 :     *indexStartupCost += qual_arg_cost;
    8123             : 
    8124             :     /*
    8125             :      * To read a BRIN index there might be a bit of back and forth over
    8126             :      * regular pages, as revmap might point to them out of sequential order;
    8127             :      * calculate the total cost as reading the whole index in random order.
    8128             :      */
    8129       10730 :     *indexTotalCost = *indexStartupCost +
    8130       10730 :         spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
    8131             : 
    8132             :     /*
    8133             :      * Charge a small amount per range tuple which we expect to match to. This
    8134             :      * is meant to reflect the costs of manipulating the bitmap. The BRIN scan
    8135             :      * will set a bit for each page in the range when we find a matching
    8136             :      * range, so we must multiply the charge by the number of pages in the
    8137             :      * range.
    8138             :      */
    8139       10730 :     *indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
    8140       10730 :         statsData.pagesPerRange;
    8141             : 
    8142       10730 :     *indexPages = index->pages;
    8143       10730 : }

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