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) 1996-2018, 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 database's default 91 * collation. Thus, in most cases where we are looking at statistics, we 92 * should ignore the actual operator collation and use DEFAULT_COLLATION_OID. 93 * We expect that the error induced by doing this is usually not large enough 94 * to justify complicating matters. 95 *---------- 96 */ 97 98 #include "postgres.h" 99 100 #include <ctype.h> 101 #include <float.h> 102 #include <math.h> 103 104 #include "access/brin.h" 105 #include "access/brin_page.h" 106 #include "access/gin.h" 107 #include "access/htup_details.h" 108 #include "access/relscan.h" 109 #include "access/sysattr.h" 110 #include "access/visibilitymap.h" 111 #include "catalog/pg_am.h" 112 #include "catalog/pg_collation.h" 113 #include "catalog/pg_operator.h" 114 #include "catalog/pg_opfamily.h" 115 #include "catalog/pg_statistic.h" 116 #include "catalog/pg_statistic_ext.h" 117 #include "catalog/pg_type.h" 118 #include "mb/pg_wchar.h" 119 #include "miscadmin.h" 120 #include "nodes/makefuncs.h" 121 #include "nodes/nodeFuncs.h" 122 #include "optimizer/clauses.h" 123 #include "optimizer/cost.h" 124 #include "optimizer/pathnode.h" 125 #include "optimizer/paths.h" 126 #include "optimizer/plancat.h" 127 #include "optimizer/predtest.h" 128 #include "optimizer/restrictinfo.h" 129 #include "optimizer/var.h" 130 #include "parser/parse_clause.h" 131 #include "parser/parse_coerce.h" 132 #include "parser/parsetree.h" 133 #include "statistics/statistics.h" 134 #include "storage/bufmgr.h" 135 #include "utils/acl.h" 136 #include "utils/builtins.h" 137 #include "utils/bytea.h" 138 #include "utils/date.h" 139 #include "utils/datum.h" 140 #include "utils/fmgroids.h" 141 #include "utils/index_selfuncs.h" 142 #include "utils/lsyscache.h" 143 #include "utils/memutils.h" 144 #include "utils/nabstime.h" 145 #include "utils/pg_locale.h" 146 #include "utils/rel.h" 147 #include "utils/selfuncs.h" 148 #include "utils/snapmgr.h" 149 #include "utils/spccache.h" 150 #include "utils/syscache.h" 151 #include "utils/timestamp.h" 152 #include "utils/tqual.h" 153 #include "utils/typcache.h" 154 #include "utils/varlena.h" 155 156 157 /* Hooks for plugins to get control when we ask for stats */ 158 get_relation_stats_hook_type get_relation_stats_hook = NULL; 159 get_index_stats_hook_type get_index_stats_hook = NULL; 160 161 static double eqsel_internal(PG_FUNCTION_ARGS, bool negate); 162 static double var_eq_const(VariableStatData *vardata, Oid operator, 163 Datum constval, bool constisnull, 164 bool varonleft, bool negate); 165 static double var_eq_non_const(VariableStatData *vardata, Oid operator, 166 Node *other, 167 bool varonleft, bool negate); 168 static double ineq_histogram_selectivity(PlannerInfo *root, 169 VariableStatData *vardata, 170 FmgrInfo *opproc, bool isgt, bool iseq, 171 Datum constval, Oid consttype); 172 static double eqjoinsel_inner(Oid operator, 173 VariableStatData *vardata1, VariableStatData *vardata2); 174 static double eqjoinsel_semi(Oid operator, 175 VariableStatData *vardata1, VariableStatData *vardata2, 176 RelOptInfo *inner_rel); 177 static bool estimate_multivariate_ndistinct(PlannerInfo *root, 178 RelOptInfo *rel, List **varinfos, double *ndistinct); 179 static bool convert_to_scalar(Datum value, Oid valuetypid, double *scaledvalue, 180 Datum lobound, Datum hibound, Oid boundstypid, 181 double *scaledlobound, double *scaledhibound); 182 static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure); 183 static void convert_string_to_scalar(char *value, 184 double *scaledvalue, 185 char *lobound, 186 double *scaledlobound, 187 char *hibound, 188 double *scaledhibound); 189 static void convert_bytea_to_scalar(Datum value, 190 double *scaledvalue, 191 Datum lobound, 192 double *scaledlobound, 193 Datum hibound, 194 double *scaledhibound); 195 static double convert_one_string_to_scalar(char *value, 196 int rangelo, int rangehi); 197 static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen, 198 int rangelo, int rangehi); 199 static char *convert_string_datum(Datum value, Oid typid, bool *failure); 200 static double convert_timevalue_to_scalar(Datum value, Oid typid, 201 bool *failure); 202 static void examine_simple_variable(PlannerInfo *root, Var *var, 203 VariableStatData *vardata); 204 static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata, 205 Oid sortop, Datum *min, Datum *max); 206 static bool get_actual_variable_range(PlannerInfo *root, 207 VariableStatData *vardata, 208 Oid sortop, 209 Datum *min, Datum *max); 210 static bool get_actual_variable_endpoint(Relation heapRel, 211 Relation indexRel, 212 ScanDirection indexscandir, 213 ScanKey scankeys, 214 int16 typLen, 215 bool typByVal, 216 MemoryContext outercontext, 217 Datum *endpointDatum); 218 static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids); 219 static Selectivity prefix_selectivity(PlannerInfo *root, 220 VariableStatData *vardata, 221 Oid vartype, Oid opfamily, Const *prefixcon); 222 static Selectivity like_selectivity(const char *patt, int pattlen, 223 bool case_insensitive); 224 static Selectivity regex_selectivity(const char *patt, int pattlen, 225 bool case_insensitive, 226 int fixed_prefix_len); 227 static Datum string_to_datum(const char *str, Oid datatype); 228 static Const *string_to_const(const char *str, Oid datatype); 229 static Const *string_to_bytea_const(const char *str, size_t str_len); 230 static List *add_predicate_to_quals(IndexOptInfo *index, List *indexQuals); 231 232 233 /* 234 * eqsel - Selectivity of "=" for any data types. 235 * 236 * Note: this routine is also used to estimate selectivity for some 237 * operators that are not "=" but have comparable selectivity behavior, 238 * such as "~=" (geometric approximate-match). Even for "=", we must 239 * keep in mind that the left and right datatypes may differ. 240 */ 241 Datum 242 eqsel(PG_FUNCTION_ARGS) 243 { 244 PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false)); 245 } 246 247 /* 248 * Common code for eqsel() and neqsel() 249 */ 250 static double 251 eqsel_internal(PG_FUNCTION_ARGS, bool negate) 252 { 253 PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0); 254 Oid operator = PG_GETARG_OID(1); 255 List *args = (List *) PG_GETARG_POINTER(2); 256 int varRelid = PG_GETARG_INT32(3); 257 VariableStatData vardata; 258 Node *other; 259 bool varonleft; 260 double selec; 261 262 /* 263 * When asked about <>, we do the estimation using the corresponding = 264 * operator, then convert to <> via "1.0 - eq_selectivity - nullfrac". 265 */ 266 if (negate) 267 { 268 operator = get_negator(operator); 269 if (!OidIsValid(operator)) 270 { 271 /* Use default selectivity (should we raise an error instead?) */ 272 return 1.0 - DEFAULT_EQ_SEL; 273 } 274 } 275 276 /* 277 * If expression is not variable = something or something = variable, then 278 * punt and return a default estimate. 279 */ 280 if (!get_restriction_variable(root, args, varRelid, 281 &vardata, &other, &varonleft)) 282 return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL; 283 284 /* 285 * We can do a lot better if the something is a constant. (Note: the 286 * Const might result from estimation rather than being a simple constant 287 * in the query.) 288 */ 289 if (IsA(other, Const)) 290 selec = var_eq_const(&vardata, operator, 291 ((Const *) other)->constvalue, 292 ((Const *) other)->constisnull, 293 varonleft, negate); 294 else 295 selec = var_eq_non_const(&vardata, operator, other, 296 varonleft, negate); 297 298 ReleaseVariableStats(vardata); 299 300 return selec; 301 } 302 303 /* 304 * var_eq_const --- eqsel for var = const case 305 * 306 * This is split out so that some other estimation functions can use it. 307 */ 308 static double 309 var_eq_const(VariableStatData *vardata, Oid operator, 310 Datum constval, bool constisnull, 311 bool varonleft, bool negate) 312 { 313 double selec; 314 double nullfrac = 0.0; 315 bool isdefault; 316 Oid opfuncoid; 317 318 /* 319 * If the constant is NULL, assume operator is strict and return zero, ie, 320 * operator will never return TRUE. (It's zero even for a negator op.) 321 */ 322 if (constisnull) 323 return 0.0; 324 325 /* 326 * Grab the nullfrac for use below. Note we allow use of nullfrac 327 * regardless of security check. 328 */ 329 if (HeapTupleIsValid(vardata->statsTuple)) 330 { 331 Form_pg_statistic stats; 332 333 stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple); 334 nullfrac = stats->stanullfrac; 335 } 336 337 /* 338 * If we matched the var to a unique index or DISTINCT clause, assume 339 * there is exactly one match regardless of anything else. (This is 340 * slightly bogus, since the index or clause's equality operator might be 341 * different from ours, but it's much more likely to be right than 342 * ignoring the information.) 343 */ 344 if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0) 345 { 346 selec = 1.0 / vardata->rel->tuples; 347 } 348 else if (HeapTupleIsValid(vardata->statsTuple) && 349 statistic_proc_security_check(vardata, 350 (opfuncoid = get_opcode(operator)))) 351 { 352 AttStatsSlot sslot; 353 bool match = false; 354 int i; 355 356 /* 357 * Is the constant "=" to any of the column's most common values? 358 * (Although the given operator may not really be "=", we will assume 359 * that seeing whether it returns TRUE is an appropriate test. If you 360 * don't like this, maybe you shouldn't be using eqsel for your 361 * operator...) 362 */ 363 if (get_attstatsslot(&sslot, vardata->statsTuple, 364 STATISTIC_KIND_MCV, InvalidOid, 365 ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)) 366 { 367 FmgrInfo eqproc; 368 369 fmgr_info(opfuncoid, &eqproc); 370 371 for (i = 0; i < sslot.nvalues; i++) 372 { 373 /* be careful to apply operator right way 'round */ 374 if (varonleft) 375 match = DatumGetBool(FunctionCall2Coll(&eqproc, 376 DEFAULT_COLLATION_OID, 377 sslot.values[i], 378 constval)); 379 else 380 match = DatumGetBool(FunctionCall2Coll(&eqproc, 381 DEFAULT_COLLATION_OID, 382 constval, 383 sslot.values[i])); 384 if (match) 385 break; 386 } 387 } 388 else 389 { 390 /* no most-common-value info available */ 391 i = 0; /* keep compiler quiet */ 392 } 393 394 if (match) 395 { 396 /* 397 * Constant is "=" to this common value. We know selectivity 398 * exactly (or as exactly as ANALYZE could calculate it, anyway). 399 */ 400 selec = sslot.numbers[i]; 401 } 402 else 403 { 404 /* 405 * Comparison is against a constant that is neither NULL nor any 406 * of the common values. Its selectivity cannot be more than 407 * this: 408 */ 409 double sumcommon = 0.0; 410 double otherdistinct; 411 412 for (i = 0; i < sslot.nnumbers; i++) 413 sumcommon += sslot.numbers[i]; 414 selec = 1.0 - sumcommon - nullfrac; 415 CLAMP_PROBABILITY(selec); 416 417 /* 418 * and in fact it's probably a good deal less. We approximate that 419 * all the not-common values share this remaining fraction 420 * equally, so we divide by the number of other distinct values. 421 */ 422 otherdistinct = get_variable_numdistinct(vardata, &isdefault) - 423 sslot.nnumbers; 424 if (otherdistinct > 1) 425 selec /= otherdistinct; 426 427 /* 428 * Another cross-check: selectivity shouldn't be estimated as more 429 * than the least common "most common value". 430 */ 431 if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1]) 432 selec = sslot.numbers[sslot.nnumbers - 1]; 433 } 434 435 free_attstatsslot(&sslot); 436 } 437 else 438 { 439 /* 440 * No ANALYZE stats available, so make a guess using estimated number 441 * of distinct values and assuming they are equally common. (The guess 442 * is unlikely to be very good, but we do know a few special cases.) 443 */ 444 selec = 1.0 / get_variable_numdistinct(vardata, &isdefault); 445 } 446 447 /* now adjust if we wanted <> rather than = */ 448 if (negate) 449 selec = 1.0 - selec - nullfrac; 450 451 /* result should be in range, but make sure... */ 452 CLAMP_PROBABILITY(selec); 453 454 return selec; 455 } 456 457 /* 458 * var_eq_non_const --- eqsel for var = something-other-than-const case 459 */ 460 static double 461 var_eq_non_const(VariableStatData *vardata, Oid operator, 462 Node *other, 463 bool varonleft, bool negate) 464 { 465 double selec; 466 double nullfrac = 0.0; 467 bool isdefault; 468 469 /* 470 * Grab the nullfrac for use below. 471 */ 472 if (HeapTupleIsValid(vardata->statsTuple)) 473 { 474 Form_pg_statistic stats; 475 476 stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple); 477 nullfrac = stats->stanullfrac; 478 } 479 480 /* 481 * If we matched the var to a unique index or DISTINCT clause, assume 482 * there is exactly one match regardless of anything else. (This is 483 * slightly bogus, since the index or clause's equality operator might be 484 * different from ours, but it's much more likely to be right than 485 * ignoring the information.) 486 */ 487 if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0) 488 { 489 selec = 1.0 / vardata->rel->tuples; 490 } 491 else if (HeapTupleIsValid(vardata->statsTuple)) 492 { 493 double ndistinct; 494 AttStatsSlot sslot; 495 496 /* 497 * Search is for a value that we do not know a priori, but we will 498 * assume it is not NULL. Estimate the selectivity as non-null 499 * fraction divided by number of distinct values, so that we get a 500 * result averaged over all possible values whether common or 501 * uncommon. (Essentially, we are assuming that the not-yet-known 502 * comparison value is equally likely to be any of the possible 503 * values, regardless of their frequency in the table. Is that a good 504 * idea?) 505 */ 506 selec = 1.0 - nullfrac; 507 ndistinct = get_variable_numdistinct(vardata, &isdefault); 508 if (ndistinct > 1) 509 selec /= ndistinct; 510 511 /* 512 * Cross-check: selectivity should never be estimated as more than the 513 * most common value's. 514 */ 515 if (get_attstatsslot(&sslot, vardata->statsTuple, 516 STATISTIC_KIND_MCV, InvalidOid, 517 ATTSTATSSLOT_NUMBERS)) 518 { 519 if (sslot.nnumbers > 0 && selec > sslot.numbers[0]) 520 selec = sslot.numbers[0]; 521 free_attstatsslot(&sslot); 522 } 523 } 524 else 525 { 526 /* 527 * No ANALYZE stats available, so make a guess using estimated number 528 * of distinct values and assuming they are equally common. (The guess 529 * is unlikely to be very good, but we do know a few special cases.) 530 */ 531 selec = 1.0 / get_variable_numdistinct(vardata, &isdefault); 532 } 533 534 /* now adjust if we wanted <> rather than = */ 535 if (negate) 536 selec = 1.0 - selec - nullfrac; 537 538 /* result should be in range, but make sure... */ 539 CLAMP_PROBABILITY(selec); 540 541 return selec; 542 } 543 544 /* 545 * neqsel - Selectivity of "!=" for any data types. 546 * 547 * This routine is also used for some operators that are not "!=" 548 * but have comparable selectivity behavior. See above comments 549 * for eqsel(). 550 */ 551 Datum 552 neqsel(PG_FUNCTION_ARGS) 553 { 554 PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true)); 555 } 556 557 /* 558 * scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars. 559 * 560 * This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel. 561 * The isgt and iseq flags distinguish which of the four cases apply. 562 * 563 * The caller has commuted the clause, if necessary, so that we can treat 564 * the variable as being on the left. The caller must also make sure that 565 * the other side of the clause is a non-null Const, and dissect that into 566 * a value and datatype. (This definition simplifies some callers that 567 * want to estimate against a computed value instead of a Const node.) 568 * 569 * This routine works for any datatype (or pair of datatypes) known to 570 * convert_to_scalar(). If it is applied to some other datatype, 571 * it will return an approximate estimate based on assuming that the constant 572 * value falls in the middle of the bin identified by binary search. 573 */ 574 static double 575 scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq, 576 VariableStatData *vardata, Datum constval, Oid consttype) 577 { 578 Form_pg_statistic stats; 579 FmgrInfo opproc; 580 double mcv_selec, 581 hist_selec, 582 sumcommon; 583 double selec; 584 585 if (!HeapTupleIsValid(vardata->statsTuple)) 586 { 587 /* no stats available, so default result */ 588 return DEFAULT_INEQ_SEL; 589 } 590 stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple); 591 592 fmgr_info(get_opcode(operator), &opproc); 593 594 /* 595 * If we have most-common-values info, add up the fractions of the MCV 596 * entries that satisfy MCV OP CONST. These fractions contribute directly 597 * to the result selectivity. Also add up the total fraction represented 598 * by MCV entries. 599 */ 600 mcv_selec = mcv_selectivity(vardata, &opproc, constval, true, 601 &sumcommon); 602 603 /* 604 * If there is a histogram, determine which bin the constant falls in, and 605 * compute the resulting contribution to selectivity. 606 */ 607 hist_selec = ineq_histogram_selectivity(root, vardata, 608 &opproc, isgt, iseq, 609 constval, consttype); 610 611 /* 612 * Now merge the results from the MCV and histogram calculations, 613 * realizing that the histogram covers only the non-null values that are 614 * not listed in MCV. 615 */ 616 selec = 1.0 - stats->stanullfrac - sumcommon; 617 618 if (hist_selec >= 0.0) 619 selec *= hist_selec; 620 else 621 { 622 /* 623 * If no histogram but there are values not accounted for by MCV, 624 * arbitrarily assume half of them will match. 625 */ 626 selec *= 0.5; 627 } 628 629 selec += mcv_selec; 630 631 /* result should be in range, but make sure... */ 632 CLAMP_PROBABILITY(selec); 633 634 return selec; 635 } 636 637 /* 638 * mcv_selectivity - Examine the MCV list for selectivity estimates 639 * 640 * Determine the fraction of the variable's MCV population that satisfies 641 * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also 642 * compute the fraction of the total column population represented by the MCV 643 * list. This code will work for any boolean-returning predicate operator. 644 * 645 * The function result is the MCV selectivity, and the fraction of the 646 * total population is returned into *sumcommonp. Zeroes are returned 647 * if there is no MCV list. 648 */ 649 double 650 mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc, 651 Datum constval, bool varonleft, 652 double *sumcommonp) 653 { 654 double mcv_selec, 655 sumcommon; 656 AttStatsSlot sslot; 657 int i; 658 659 mcv_selec = 0.0; 660 sumcommon = 0.0; 661 662 if (HeapTupleIsValid(vardata->statsTuple) && 663 statistic_proc_security_check(vardata, opproc->fn_oid) && 664 get_attstatsslot(&sslot, vardata->statsTuple, 665 STATISTIC_KIND_MCV, InvalidOid, 666 ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)) 667 { 668 for (i = 0; i < sslot.nvalues; i++) 669 { 670 if (varonleft ? 671 DatumGetBool(FunctionCall2Coll(opproc, 672 DEFAULT_COLLATION_OID, 673 sslot.values[i], 674 constval)) : 675 DatumGetBool(FunctionCall2Coll(opproc, 676 DEFAULT_COLLATION_OID, 677 constval, 678 sslot.values[i]))) 679 mcv_selec += sslot.numbers[i]; 680 sumcommon += sslot.numbers[i]; 681 } 682 free_attstatsslot(&sslot); 683 } 684 685 *sumcommonp = sumcommon; 686 return mcv_selec; 687 } 688 689 /* 690 * histogram_selectivity - Examine the histogram for selectivity estimates 691 * 692 * Determine the fraction of the variable's histogram entries that satisfy 693 * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. 694 * 695 * This code will work for any boolean-returning predicate operator, whether 696 * or not it has anything to do with the histogram sort operator. We are 697 * essentially using the histogram just as a representative sample. However, 698 * small histograms are unlikely to be all that representative, so the caller 699 * should be prepared to fall back on some other estimation approach when the 700 * histogram is missing or very small. It may also be prudent to combine this 701 * approach with another one when the histogram is small. 702 * 703 * If the actual histogram size is not at least min_hist_size, we won't bother 704 * to do the calculation at all. Also, if the n_skip parameter is > 0, we 705 * ignore the first and last n_skip histogram elements, on the grounds that 706 * they are outliers and hence not very representative. Typical values for 707 * these parameters are 10 and 1. 708 * 709 * The function result is the selectivity, or -1 if there is no histogram 710 * or it's smaller than min_hist_size. 711 * 712 * The output parameter *hist_size receives the actual histogram size, 713 * or zero if no histogram. Callers may use this number to decide how 714 * much faith to put in the function result. 715 * 716 * Note that the result disregards both the most-common-values (if any) and 717 * null entries. The caller is expected to combine this result with 718 * statistics for those portions of the column population. It may also be 719 * prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs. 720 */ 721 double 722 histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc, 723 Datum constval, bool varonleft, 724 int min_hist_size, int n_skip, 725 int *hist_size) 726 { 727 double result; 728 AttStatsSlot sslot; 729 730 /* check sanity of parameters */ 731 Assert(n_skip >= 0); 732 Assert(min_hist_size > 2 * n_skip); 733 734 if (HeapTupleIsValid(vardata->statsTuple) && 735 statistic_proc_security_check(vardata, opproc->fn_oid) && 736 get_attstatsslot(&sslot, vardata->statsTuple, 737 STATISTIC_KIND_HISTOGRAM, InvalidOid, 738 ATTSTATSSLOT_VALUES)) 739 { 740 *hist_size = sslot.nvalues; 741 if (sslot.nvalues >= min_hist_size) 742 { 743 int nmatch = 0; 744 int i; 745 746 for (i = n_skip; i < sslot.nvalues - n_skip; i++) 747 { 748 if (varonleft ? 749 DatumGetBool(FunctionCall2Coll(opproc, 750 DEFAULT_COLLATION_OID, 751 sslot.values[i], 752 constval)) : 753 DatumGetBool(FunctionCall2Coll(opproc, 754 DEFAULT_COLLATION_OID, 755 constval, 756 sslot.values[i]))) 757 nmatch++; 758 } 759 result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip)); 760 } 761 else 762 result = -1; 763 free_attstatsslot(&sslot); 764 } 765 else 766 { 767 *hist_size = 0; 768 result = -1; 769 } 770 771 return result; 772 } 773 774 /* 775 * ineq_histogram_selectivity - Examine the histogram for scalarineqsel 776 * 777 * Determine the fraction of the variable's histogram population that 778 * satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST. 779 * The isgt and iseq flags distinguish which of the four cases apply. 780 * 781 * Returns -1 if there is no histogram (valid results will always be >= 0). 782 * 783 * Note that the result disregards both the most-common-values (if any) and 784 * null entries. The caller is expected to combine this result with 785 * statistics for those portions of the column population. 786 */ 787 static double 788 ineq_histogram_selectivity(PlannerInfo *root, 789 VariableStatData *vardata, 790 FmgrInfo *opproc, bool isgt, bool iseq, 791 Datum constval, Oid consttype) 792 { 793 double hist_selec; 794 AttStatsSlot sslot; 795 796 hist_selec = -1.0; 797 798 /* 799 * Someday, ANALYZE might store more than one histogram per rel/att, 800 * corresponding to more than one possible sort ordering defined for the 801 * column type. However, to make that work we will need to figure out 802 * which staop to search for --- it's not necessarily the one we have at 803 * hand! (For example, we might have a '<=' operator rather than the '<' 804 * operator that will appear in staop.) For now, assume that whatever 805 * appears in pg_statistic is sorted the same way our operator sorts, or 806 * the reverse way if isgt is true. 807 */ 808 if (HeapTupleIsValid(vardata->statsTuple) && 809 statistic_proc_security_check(vardata, opproc->fn_oid) && 810 get_attstatsslot(&sslot, vardata->statsTuple, 811 STATISTIC_KIND_HISTOGRAM, InvalidOid, 812 ATTSTATSSLOT_VALUES)) 813 { 814 if (sslot.nvalues > 1) 815 { 816 /* 817 * Use binary search to find the desired location, namely the 818 * right end of the histogram bin containing the comparison value, 819 * which is the leftmost entry for which the comparison operator 820 * succeeds (if isgt) or fails (if !isgt). (If the given operator 821 * isn't actually sort-compatible with the histogram, you'll get 822 * garbage results ... but probably not any more garbage-y than 823 * you would have from the old linear search.) 824 * 825 * In this loop, we pay no attention to whether the operator iseq 826 * or not; that detail will be mopped up below. (We cannot tell, 827 * anyway, whether the operator thinks the values are equal.) 828 * 829 * If the binary search accesses the first or last histogram 830 * entry, we try to replace that endpoint with the true column min 831 * or max as found by get_actual_variable_range(). This 832 * ameliorates misestimates when the min or max is moving as a 833 * result of changes since the last ANALYZE. Note that this could 834 * result in effectively including MCVs into the histogram that 835 * weren't there before, but we don't try to correct for that. 836 */ 837 double histfrac; 838 int lobound = 0; /* first possible slot to search */ 839 int hibound = sslot.nvalues; /* last+1 slot to search */ 840 bool have_end = false; 841 842 /* 843 * If there are only two histogram entries, we'll want up-to-date 844 * values for both. (If there are more than two, we need at most 845 * one of them to be updated, so we deal with that within the 846 * loop.) 847 */ 848 if (sslot.nvalues == 2) 849 have_end = get_actual_variable_range(root, 850 vardata, 851 sslot.staop, 852 &sslot.values[0], 853 &sslot.values[1]); 854 855 while (lobound < hibound) 856 { 857 int probe = (lobound + hibound) / 2; 858 bool ltcmp; 859 860 /* 861 * If we find ourselves about to compare to the first or last 862 * histogram entry, first try to replace it with the actual 863 * current min or max (unless we already did so above). 864 */ 865 if (probe == 0 && sslot.nvalues > 2) 866 have_end = get_actual_variable_range(root, 867 vardata, 868 sslot.staop, 869 &sslot.values[0], 870 NULL); 871 else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2) 872 have_end = get_actual_variable_range(root, 873 vardata, 874 sslot.staop, 875 NULL, 876 &sslot.values[probe]); 877 878 ltcmp = DatumGetBool(FunctionCall2Coll(opproc, 879 DEFAULT_COLLATION_OID, 880 sslot.values[probe], 881 constval)); 882 if (isgt) 883 ltcmp = !ltcmp; 884 if (ltcmp) 885 lobound = probe + 1; 886 else 887 hibound = probe; 888 } 889 890 if (lobound <= 0) 891 { 892 /* 893 * Constant is below lower histogram boundary. More 894 * precisely, we have found that no entry in the histogram 895 * satisfies the inequality clause (if !isgt) or they all do 896 * (if isgt). We estimate that that's true of the entire 897 * table, so set histfrac to 0.0 (which we'll flip to 1.0 898 * below, if isgt). 899 */ 900 histfrac = 0.0; 901 } 902 else if (lobound >= sslot.nvalues) 903 { 904 /* 905 * Inverse case: constant is above upper histogram boundary. 906 */ 907 histfrac = 1.0; 908 } 909 else 910 { 911 /* We have values[i-1] <= constant <= values[i]. */ 912 int i = lobound; 913 double eq_selec = 0; 914 double val, 915 high, 916 low; 917 double binfrac; 918 919 /* 920 * In the cases where we'll need it below, obtain an estimate 921 * of the selectivity of "x = constval". We use a calculation 922 * similar to what var_eq_const() does for a non-MCV constant, 923 * ie, estimate that all distinct non-MCV values occur equally 924 * often. But multiplication by "1.0 - sumcommon - nullfrac" 925 * will be done by our caller, so we shouldn't do that here. 926 * Therefore we can't try to clamp the estimate by reference 927 * to the least common MCV; the result would be too small. 928 * 929 * Note: since this is effectively assuming that constval 930 * isn't an MCV, it's logically dubious if constval in fact is 931 * one. But we have to apply *some* correction for equality, 932 * and anyway we cannot tell if constval is an MCV, since we 933 * don't have a suitable equality operator at hand. 934 */ 935 if (i == 1 || isgt == iseq) 936 { 937 double otherdistinct; 938 bool isdefault; 939 AttStatsSlot mcvslot; 940 941 /* Get estimated number of distinct values */ 942 otherdistinct = get_variable_numdistinct(vardata, 943 &isdefault); 944 945 /* Subtract off the number of known MCVs */ 946 if (get_attstatsslot(&mcvslot, vardata->statsTuple, 947 STATISTIC_KIND_MCV, InvalidOid, 948 ATTSTATSSLOT_NUMBERS)) 949 { 950 otherdistinct -= mcvslot.nnumbers; 951 free_attstatsslot(&mcvslot); 952 } 953 954 /* If result doesn't seem sane, leave eq_selec at 0 */ 955 if (otherdistinct > 1) 956 eq_selec = 1.0 / otherdistinct; 957 } 958 959 /* 960 * Convert the constant and the two nearest bin boundary 961 * values to a uniform comparison scale, and do a linear 962 * interpolation within this bin. 963 */ 964 if (convert_to_scalar(constval, consttype, &val, 965 sslot.values[i - 1], sslot.values[i], 966 vardata->vartype, 967 &low, &high)) 968 { 969 if (high <= low) 970 { 971 /* cope if bin boundaries appear identical */ 972 binfrac = 0.5; 973 } 974 else if (val <= low) 975 binfrac = 0.0; 976 else if (val >= high) 977 binfrac = 1.0; 978 else 979 { 980 binfrac = (val - low) / (high - low); 981 982 /* 983 * Watch out for the possibility that we got a NaN or 984 * Infinity from the division. This can happen 985 * despite the previous checks, if for example "low" 986 * is -Infinity. 987 */ 988 if (isnan(binfrac) || 989 binfrac < 0.0 || binfrac > 1.0) 990 binfrac = 0.5; 991 } 992 } 993 else 994 { 995 /* 996 * Ideally we'd produce an error here, on the grounds that 997 * the given operator shouldn't have scalarXXsel 998 * registered as its selectivity func unless we can deal 999 * with its operand types. But currently, all manner of 1000 * stuff is invoking scalarXXsel, so give a default 1001 * estimate until that can be fixed. 1002 */ 1003 binfrac = 0.5; 1004 } 1005 1006 /* 1007 * Now, compute the overall selectivity across the values 1008 * represented by the histogram. We have i-1 full bins and 1009 * binfrac partial bin below the constant. 1010 */ 1011 histfrac = (double) (i - 1) + binfrac; 1012 histfrac /= (double) (sslot.nvalues - 1); 1013 1014 /* 1015 * At this point, histfrac is an estimate of the fraction of 1016 * the population represented by the histogram that satisfies 1017 * "x <= constval". Somewhat remarkably, this statement is 1018 * true regardless of which operator we were doing the probes 1019 * with, so long as convert_to_scalar() delivers reasonable 1020 * results. If the probe constant is equal to some histogram 1021 * entry, we would have considered the bin to the left of that 1022 * entry if probing with "<" or ">=", or the bin to the right 1023 * if probing with "<=" or ">"; but binfrac would have come 1024 * out as 1.0 in the first case and 0.0 in the second, leading 1025 * to the same histfrac in either case. For probe constants 1026 * between histogram entries, we find the same bin and get the 1027 * same estimate with any operator. 1028 * 1029 * The fact that the estimate corresponds to "x <= constval" 1030 * and not "x < constval" is because of the way that ANALYZE 1031 * constructs the histogram: each entry is, effectively, the 1032 * rightmost value in its sample bucket. So selectivity 1033 * values that are exact multiples of 1/(histogram_size-1) 1034 * should be understood as estimates including a histogram 1035 * entry plus everything to its left. 1036 * 1037 * However, that breaks down for the first histogram entry, 1038 * which necessarily is the leftmost value in its sample 1039 * bucket. That means the first histogram bin is slightly 1040 * narrower than the rest, by an amount equal to eq_selec. 1041 * Another way to say that is that we want "x <= leftmost" to 1042 * be estimated as eq_selec not zero. So, if we're dealing 1043 * with the first bin (i==1), rescale to make that true while 1044 * adjusting the rest of that bin linearly. 1045 */ 1046 if (i == 1) 1047 histfrac += eq_selec * (1.0 - binfrac); 1048 1049 /* 1050 * "x <= constval" is good if we want an estimate for "<=" or 1051 * ">", but if we are estimating for "<" or ">=", we now need 1052 * to decrease the estimate by eq_selec. 1053 */ 1054 if (isgt == iseq) 1055 histfrac -= eq_selec; 1056 } 1057 1058 /* 1059 * Now the estimate is finished for "<" and "<=" cases. If we are 1060 * estimating for ">" or ">=", flip it. 1061 */ 1062 hist_selec = isgt ? (1.0 - histfrac) : histfrac; 1063 1064 /* 1065 * The histogram boundaries are only approximate to begin with, 1066 * and may well be out of date anyway. Therefore, don't believe 1067 * extremely small or large selectivity estimates --- unless we 1068 * got actual current endpoint values from the table, in which 1069 * case just do the usual sanity clamp. Somewhat arbitrarily, we 1070 * set the cutoff for other cases at a hundredth of the histogram 1071 * resolution. 1072 */ 1073 if (have_end) 1074 CLAMP_PROBABILITY(hist_selec); 1075 else 1076 { 1077 double cutoff = 0.01 / (double) (sslot.nvalues - 1); 1078 1079 if (hist_selec < cutoff) 1080 hist_selec = cutoff; 1081 else if (hist_selec > 1.0 - cutoff) 1082 hist_selec = 1.0 - cutoff; 1083 } 1084 } 1085 1086 free_attstatsslot(&sslot); 1087 } 1088 1089 return hist_selec; 1090 } 1091 1092 /* 1093 * Common wrapper function for the selectivity estimators that simply 1094 * invoke scalarineqsel(). 1095 */ 1096 static Datum 1097 scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq) 1098 { 1099 PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0); 1100 Oid operator = PG_GETARG_OID(1); 1101 List *args = (List *) PG_GETARG_POINTER(2); 1102 int varRelid = PG_GETARG_INT32(3); 1103 VariableStatData vardata; 1104 Node *other; 1105 bool varonleft; 1106 Datum constval; 1107 Oid consttype; 1108 double selec; 1109 1110 /* 1111 * If expression is not variable op something or something op variable, 1112 * then punt and return a default estimate. 1113 */ 1114 if (!get_restriction_variable(root, args, varRelid, 1115 &vardata, &other, &varonleft)) 1116 PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL); 1117 1118 /* 1119 * Can't do anything useful if the something is not a constant, either. 1120 */ 1121 if (!IsA(other, Const)) 1122 { 1123 ReleaseVariableStats(vardata); 1124 PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL); 1125 } 1126 1127 /* 1128 * If the constant is NULL, assume operator is strict and return zero, ie, 1129 * operator will never return TRUE. 1130 */ 1131 if (((Const *) other)->constisnull) 1132 { 1133 ReleaseVariableStats(vardata); 1134 PG_RETURN_FLOAT8(0.0); 1135 } 1136 constval = ((Const *) other)->constvalue; 1137 consttype = ((Const *) other)->consttype; 1138 1139 /* 1140 * Force the var to be on the left to simplify logic in scalarineqsel. 1141 */ 1142 if (!varonleft) 1143 { 1144 operator = get_commutator(operator); 1145 if (!operator) 1146 { 1147 /* Use default selectivity (should we raise an error instead?) */ 1148 ReleaseVariableStats(vardata); 1149 PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL); 1150 } 1151 isgt = !isgt; 1152 } 1153 1154 /* The rest of the work is done by scalarineqsel(). */ 1155 selec = scalarineqsel(root, operator, isgt, iseq, 1156 &vardata, constval, consttype); 1157 1158 ReleaseVariableStats(vardata); 1159 1160 PG_RETURN_FLOAT8((float8) selec); 1161 } 1162 1163 /* 1164 * scalarltsel - Selectivity of "<" for scalars. 1165 */ 1166 Datum 1167 scalarltsel(PG_FUNCTION_ARGS) 1168 { 1169 return scalarineqsel_wrapper(fcinfo, false, false); 1170 } 1171 1172 /* 1173 * scalarlesel - Selectivity of "<=" for scalars. 1174 */ 1175 Datum 1176 scalarlesel(PG_FUNCTION_ARGS) 1177 { 1178 return scalarineqsel_wrapper(fcinfo, false, true); 1179 } 1180 1181 /* 1182 * scalargtsel - Selectivity of ">" for scalars. 1183 */ 1184 Datum 1185 scalargtsel(PG_FUNCTION_ARGS) 1186 { 1187 return scalarineqsel_wrapper(fcinfo, true, false); 1188 } 1189 1190 /* 1191 * scalargesel - Selectivity of ">=" for scalars. 1192 */ 1193 Datum 1194 scalargesel(PG_FUNCTION_ARGS) 1195 { 1196 return scalarineqsel_wrapper(fcinfo, true, true); 1197 } 1198 1199 /* 1200 * patternsel - Generic code for pattern-match selectivity. 1201 */ 1202 static double 1203 patternsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate) 1204 { 1205 PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0); 1206 Oid operator = PG_GETARG_OID(1); 1207 List *args = (List *) PG_GETARG_POINTER(2); 1208 int varRelid = PG_GETARG_INT32(3); 1209 Oid collation = PG_GET_COLLATION(); 1210 VariableStatData vardata; 1211 Node *other; 1212 bool varonleft; 1213 Datum constval; 1214 Oid consttype; 1215 Oid vartype; 1216 Oid opfamily; 1217 Pattern_Prefix_Status pstatus; 1218 Const *patt; 1219 Const *prefix = NULL; 1220 Selectivity rest_selec = 0; 1221 double nullfrac = 0.0; 1222 double result; 1223 1224 /* 1225 * If this is for a NOT LIKE or similar operator, get the corresponding 1226 * positive-match operator and work with that. Set result to the correct 1227 * default estimate, too. 1228 */ 1229 if (negate) 1230 { 1231 operator = get_negator(operator); 1232 if (!OidIsValid(operator)) 1233 elog(ERROR, "patternsel called for operator without a negator"); 1234 result = 1.0 - DEFAULT_MATCH_SEL; 1235 } 1236 else 1237 { 1238 result = DEFAULT_MATCH_SEL; 1239 } 1240 1241 /* 1242 * If expression is not variable op constant, then punt and return a 1243 * default estimate. 1244 */ 1245 if (!get_restriction_variable(root, args, varRelid, 1246 &vardata, &other, &varonleft)) 1247 return result; 1248 if (!varonleft || !IsA(other, Const)) 1249 { 1250 ReleaseVariableStats(vardata); 1251 return result; 1252 } 1253 1254 /* 1255 * If the constant is NULL, assume operator is strict and return zero, ie, 1256 * operator will never return TRUE. (It's zero even for a negator op.) 1257 */ 1258 if (((Const *) other)->constisnull) 1259 { 1260 ReleaseVariableStats(vardata); 1261 return 0.0; 1262 } 1263 constval = ((Const *) other)->constvalue; 1264 consttype = ((Const *) other)->consttype; 1265 1266 /* 1267 * The right-hand const is type text or bytea for all supported operators. 1268 * We do not expect to see binary-compatible types here, since 1269 * const-folding should have relabeled the const to exactly match the 1270 * operator's declared type. 1271 */ 1272 if (consttype != TEXTOID && consttype != BYTEAOID) 1273 { 1274 ReleaseVariableStats(vardata); 1275 return result; 1276 } 1277 1278 /* 1279 * Similarly, the exposed type of the left-hand side should be one of 1280 * those we know. (Do not look at vardata.atttype, which might be 1281 * something binary-compatible but different.) We can use it to choose 1282 * the index opfamily from which we must draw the comparison operators. 1283 * 1284 * NOTE: It would be more correct to use the PATTERN opfamilies than the 1285 * simple ones, but at the moment ANALYZE will not generate statistics for 1286 * the PATTERN operators. But our results are so approximate anyway that 1287 * it probably hardly matters. 1288 */ 1289 vartype = vardata.vartype; 1290 1291 switch (vartype) 1292 { 1293 case TEXTOID: 1294 opfamily = TEXT_BTREE_FAM_OID; 1295 break; 1296 case BPCHAROID: 1297 opfamily = BPCHAR_BTREE_FAM_OID; 1298 break; 1299 case NAMEOID: 1300 opfamily = NAME_BTREE_FAM_OID; 1301 break; 1302 case BYTEAOID: 1303 opfamily = BYTEA_BTREE_FAM_OID; 1304 break; 1305 default: 1306 ReleaseVariableStats(vardata); 1307 return result; 1308 } 1309 1310 /* 1311 * Grab the nullfrac for use below. 1312 */ 1313 if (HeapTupleIsValid(vardata.statsTuple)) 1314 { 1315 Form_pg_statistic stats; 1316 1317 stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple); 1318 nullfrac = stats->stanullfrac; 1319 } 1320 1321 /* 1322 * Pull out any fixed prefix implied by the pattern, and estimate the 1323 * fractional selectivity of the remainder of the pattern. Unlike many of 1324 * the other functions in this file, we use the pattern operator's actual 1325 * collation for this step. This is not because we expect the collation 1326 * to make a big difference in the selectivity estimate (it seldom would), 1327 * but because we want to be sure we cache compiled regexps under the 1328 * right cache key, so that they can be re-used at runtime. 1329 */ 1330 patt = (Const *) other; 1331 pstatus = pattern_fixed_prefix(patt, ptype, collation, 1332 &prefix, &rest_selec); 1333 1334 /* 1335 * If necessary, coerce the prefix constant to the right type. 1336 */ 1337 if (prefix && prefix->consttype != vartype) 1338 { 1339 char *prefixstr; 1340 1341 switch (prefix->consttype) 1342 { 1343 case TEXTOID: 1344 prefixstr = TextDatumGetCString(prefix->constvalue); 1345 break; 1346 case BYTEAOID: 1347 prefixstr = DatumGetCString(DirectFunctionCall1(byteaout, 1348 prefix->constvalue)); 1349 break; 1350 default: 1351 elog(ERROR, "unrecognized consttype: %u", 1352 prefix->consttype); 1353 ReleaseVariableStats(vardata); 1354 return result; 1355 } 1356 prefix = string_to_const(prefixstr, vartype); 1357 pfree(prefixstr); 1358 } 1359 1360 if (pstatus == Pattern_Prefix_Exact) 1361 { 1362 /* 1363 * Pattern specifies an exact match, so pretend operator is '=' 1364 */ 1365 Oid eqopr = get_opfamily_member(opfamily, vartype, vartype, 1366 BTEqualStrategyNumber); 1367 1368 if (eqopr == InvalidOid) 1369 elog(ERROR, "no = operator for opfamily %u", opfamily); 1370 result = var_eq_const(&vardata, eqopr, prefix->constvalue, 1371 false, true, false); 1372 } 1373 else 1374 { 1375 /* 1376 * Not exact-match pattern. If we have a sufficiently large 1377 * histogram, estimate selectivity for the histogram part of the 1378 * population by counting matches in the histogram. If not, estimate 1379 * selectivity of the fixed prefix and remainder of pattern 1380 * separately, then combine the two to get an estimate of the 1381 * selectivity for the part of the column population represented by 1382 * the histogram. (For small histograms, we combine these 1383 * approaches.) 1384 * 1385 * We then add up data for any most-common-values values; these are 1386 * not in the histogram population, and we can get exact answers for 1387 * them by applying the pattern operator, so there's no reason to 1388 * approximate. (If the MCVs cover a significant part of the total 1389 * population, this gives us a big leg up in accuracy.) 1390 */ 1391 Selectivity selec; 1392 int hist_size; 1393 FmgrInfo opproc; 1394 double mcv_selec, 1395 sumcommon; 1396 1397 /* Try to use the histogram entries to get selectivity */ 1398 fmgr_info(get_opcode(operator), &opproc); 1399 1400 selec = histogram_selectivity(&vardata, &opproc, constval, true, 1401 10, 1, &hist_size); 1402 1403 /* If not at least 100 entries, use the heuristic method */ 1404 if (hist_size < 100) 1405 { 1406 Selectivity heursel; 1407 Selectivity prefixsel; 1408 1409 if (pstatus == Pattern_Prefix_Partial) 1410 prefixsel = prefix_selectivity(root, &vardata, vartype, 1411 opfamily, prefix); 1412 else 1413 prefixsel = 1.0; 1414 heursel = prefixsel * rest_selec; 1415 1416 if (selec < 0) /* fewer than 10 histogram entries? */ 1417 selec = heursel; 1418 else 1419 { 1420 /* 1421 * For histogram sizes from 10 to 100, we combine the 1422 * histogram and heuristic selectivities, putting increasingly 1423 * more trust in the histogram for larger sizes. 1424 */ 1425 double hist_weight = hist_size / 100.0; 1426 1427 selec = selec * hist_weight + heursel * (1.0 - hist_weight); 1428 } 1429 } 1430 1431 /* In any case, don't believe extremely small or large estimates. */ 1432 if (selec < 0.0001) 1433 selec = 0.0001; 1434 else if (selec > 0.9999) 1435 selec = 0.9999; 1436 1437 /* 1438 * If we have most-common-values info, add up the fractions of the MCV 1439 * entries that satisfy MCV OP PATTERN. These fractions contribute 1440 * directly to the result selectivity. Also add up the total fraction 1441 * represented by MCV entries. 1442 */ 1443 mcv_selec = mcv_selectivity(&vardata, &opproc, constval, true, 1444 &sumcommon); 1445 1446 /* 1447 * Now merge the results from the MCV and histogram calculations, 1448 * realizing that the histogram covers only the non-null values that 1449 * are not listed in MCV. 1450 */ 1451 selec *= 1.0 - nullfrac - sumcommon; 1452 selec += mcv_selec; 1453 result = selec; 1454 } 1455 1456 /* now adjust if we wanted not-match rather than match */ 1457 if (negate) 1458 result = 1.0 - result - nullfrac; 1459 1460 /* result should be in range, but make sure... */ 1461 CLAMP_PROBABILITY(result); 1462 1463 if (prefix) 1464 { 1465 pfree(DatumGetPointer(prefix->constvalue)); 1466 pfree(prefix); 1467 } 1468 1469 ReleaseVariableStats(vardata); 1470 1471 return result; 1472 } 1473 1474 /* 1475 * regexeqsel - Selectivity of regular-expression pattern match. 1476 */ 1477 Datum 1478 regexeqsel(PG_FUNCTION_ARGS) 1479 { 1480 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex, false)); 1481 } 1482 1483 /* 1484 * icregexeqsel - Selectivity of case-insensitive regex match. 1485 */ 1486 Datum 1487 icregexeqsel(PG_FUNCTION_ARGS) 1488 { 1489 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex_IC, false)); 1490 } 1491 1492 /* 1493 * likesel - Selectivity of LIKE pattern match. 1494 */ 1495 Datum 1496 likesel(PG_FUNCTION_ARGS) 1497 { 1498 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like, false)); 1499 } 1500 1501 /* 1502 * prefixsel - selectivity of prefix operator 1503 */ 1504 Datum 1505 prefixsel(PG_FUNCTION_ARGS) 1506 { 1507 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Prefix, false)); 1508 } 1509 1510 /* 1511 * 1512 * iclikesel - Selectivity of ILIKE pattern match. 1513 */ 1514 Datum 1515 iclikesel(PG_FUNCTION_ARGS) 1516 { 1517 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like_IC, false)); 1518 } 1519 1520 /* 1521 * regexnesel - Selectivity of regular-expression pattern non-match. 1522 */ 1523 Datum 1524 regexnesel(PG_FUNCTION_ARGS) 1525 { 1526 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex, true)); 1527 } 1528 1529 /* 1530 * icregexnesel - Selectivity of case-insensitive regex non-match. 1531 */ 1532 Datum 1533 icregexnesel(PG_FUNCTION_ARGS) 1534 { 1535 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex_IC, true)); 1536 } 1537 1538 /* 1539 * nlikesel - Selectivity of LIKE pattern non-match. 1540 */ 1541 Datum 1542 nlikesel(PG_FUNCTION_ARGS) 1543 { 1544 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like, true)); 1545 } 1546 1547 /* 1548 * icnlikesel - Selectivity of ILIKE pattern non-match. 1549 */ 1550 Datum 1551 icnlikesel(PG_FUNCTION_ARGS) 1552 { 1553 PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like_IC, true)); 1554 } 1555 1556 /* 1557 * boolvarsel - Selectivity of Boolean variable. 1558 * 1559 * This can actually be called on any boolean-valued expression. If it 1560 * involves only Vars of the specified relation, and if there are statistics 1561 * about the Var or expression (the latter is possible if it's indexed) then 1562 * we'll produce a real estimate; otherwise it's just a default. 1563 */ 1564 Selectivity 1565 boolvarsel(PlannerInfo *root, Node *arg, int varRelid) 1566 { 1567 VariableStatData vardata; 1568 double selec; 1569 1570 examine_variable(root, arg, varRelid, &vardata); 1571 if (HeapTupleIsValid(vardata.statsTuple)) 1572 { 1573 /* 1574 * A boolean variable V is equivalent to the clause V = 't', so we 1575 * compute the selectivity as if that is what we have. 1576 */ 1577 selec = var_eq_const(&vardata, BooleanEqualOperator, 1578 BoolGetDatum(true), false, true, false); 1579 } 1580 else if (is_funcclause(arg)) 1581 { 1582 /* 1583 * If we have no stats and it's a function call, estimate 0.3333333. 1584 * This seems a pretty unprincipled choice, but Postgres has been 1585 * using that estimate for function calls since 1992. The hoariness 1586 * of this behavior suggests that we should not be in too much hurry 1587 * to use another value. 1588 */ 1589 selec = 0.3333333; 1590 } 1591 else 1592 { 1593 /* Otherwise, the default estimate is 0.5 */ 1594 selec = 0.5; 1595 } 1596 ReleaseVariableStats(vardata); 1597 return selec; 1598 } 1599 1600 /* 1601 * booltestsel - Selectivity of BooleanTest Node. 1602 */ 1603 Selectivity 1604 booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg, 1605 int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo) 1606 { 1607 VariableStatData vardata; 1608 double selec; 1609 1610 examine_variable(root, arg, varRelid, &vardata); 1611 1612 if (HeapTupleIsValid(vardata.statsTuple)) 1613 { 1614 Form_pg_statistic stats; 1615 double freq_null; 1616 AttStatsSlot sslot; 1617 1618 stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple); 1619 freq_null = stats->stanullfrac; 1620 1621 if (get_attstatsslot(&sslot, vardata.statsTuple, 1622 STATISTIC_KIND_MCV, InvalidOid, 1623 ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS) 1624 && sslot.nnumbers > 0) 1625 { 1626 double freq_true; 1627 double freq_false; 1628 1629 /* 1630 * Get first MCV frequency and derive frequency for true. 1631 */ 1632 if (DatumGetBool(sslot.values[0])) 1633 freq_true = sslot.numbers[0]; 1634 else 1635 freq_true = 1.0 - sslot.numbers[0] - freq_null; 1636 1637 /* 1638 * Next derive frequency for false. Then use these as appropriate 1639 * to derive frequency for each case. 1640 */ 1641 freq_false = 1.0 - freq_true - freq_null; 1642 1643 switch (booltesttype) 1644 { 1645 case IS_UNKNOWN: 1646 /* select only NULL values */ 1647 selec = freq_null; 1648 break; 1649 case IS_NOT_UNKNOWN: 1650 /* select non-NULL values */ 1651 selec = 1.0 - freq_null; 1652 break; 1653 case IS_TRUE: 1654 /* select only TRUE values */ 1655 selec = freq_true; 1656 break; 1657 case IS_NOT_TRUE: 1658 /* select non-TRUE values */ 1659 selec = 1.0 - freq_true; 1660 break; 1661 case IS_FALSE: 1662 /* select only FALSE values */ 1663 selec = freq_false; 1664 break; 1665 case IS_NOT_FALSE: 1666 /* select non-FALSE values */ 1667 selec = 1.0 - freq_false; 1668 break; 1669 default: 1670 elog(ERROR, "unrecognized booltesttype: %d", 1671 (int) booltesttype); 1672 selec = 0.0; /* Keep compiler quiet */ 1673 break; 1674 } 1675 1676 free_attstatsslot(&sslot); 1677 } 1678 else 1679 { 1680 /* 1681 * No most-common-value info available. Still have null fraction 1682 * information, so use it for IS [NOT] UNKNOWN. Otherwise adjust 1683 * for null fraction and assume a 50-50 split of TRUE and FALSE. 1684 */ 1685 switch (booltesttype) 1686 { 1687 case IS_UNKNOWN: 1688 /* select only NULL values */ 1689 selec = freq_null; 1690 break; 1691 case IS_NOT_UNKNOWN: 1692 /* select non-NULL values */ 1693 selec = 1.0 - freq_null; 1694 break; 1695 case IS_TRUE: 1696 case IS_FALSE: 1697 /* Assume we select half of the non-NULL values */ 1698 selec = (1.0 - freq_null) / 2.0; 1699 break; 1700 case IS_NOT_TRUE: 1701 case IS_NOT_FALSE: 1702 /* Assume we select NULLs plus half of the non-NULLs */ 1703 /* equiv. to freq_null + (1.0 - freq_null) / 2.0 */ 1704 selec = (freq_null + 1.0) / 2.0; 1705 break; 1706 default: 1707 elog(ERROR, "unrecognized booltesttype: %d", 1708 (int) booltesttype); 1709 selec = 0.0; /* Keep compiler quiet */ 1710 break; 1711 } 1712 } 1713 } 1714 else 1715 { 1716 /* 1717 * If we can't get variable statistics for the argument, perhaps 1718 * clause_selectivity can do something with it. We ignore the 1719 * possibility of a NULL value when using clause_selectivity, and just 1720 * assume the value is either TRUE or FALSE. 1721 */ 1722 switch (booltesttype) 1723 { 1724 case IS_UNKNOWN: 1725 selec = DEFAULT_UNK_SEL; 1726 break; 1727 case IS_NOT_UNKNOWN: 1728 selec = DEFAULT_NOT_UNK_SEL; 1729 break; 1730 case IS_TRUE: 1731 case IS_NOT_FALSE: 1732 selec = (double) clause_selectivity(root, arg, 1733 varRelid, 1734 jointype, sjinfo); 1735 break; 1736 case IS_FALSE: 1737 case IS_NOT_TRUE: 1738 selec = 1.0 - (double) clause_selectivity(root, arg, 1739 varRelid, 1740 jointype, sjinfo); 1741 break; 1742 default: 1743 elog(ERROR, "unrecognized booltesttype: %d", 1744 (int) booltesttype); 1745 selec = 0.0; /* Keep compiler quiet */ 1746 break; 1747 } 1748 } 1749 1750 ReleaseVariableStats(vardata); 1751 1752 /* result should be in range, but make sure... */ 1753 CLAMP_PROBABILITY(selec); 1754 1755 return (Selectivity) selec; 1756 } 1757 1758 /* 1759 * nulltestsel - Selectivity of NullTest Node. 1760 */ 1761 Selectivity 1762 nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg, 1763 int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo) 1764 { 1765 VariableStatData vardata; 1766 double selec; 1767 1768 examine_variable(root, arg, varRelid, &vardata); 1769 1770 if (HeapTupleIsValid(vardata.statsTuple)) 1771 { 1772 Form_pg_statistic stats; 1773 double freq_null; 1774 1775 stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple); 1776 freq_null = stats->stanullfrac; 1777 1778 switch (nulltesttype) 1779 { 1780 case IS_NULL: 1781 1782 /* 1783 * Use freq_null directly. 1784 */ 1785 selec = freq_null; 1786 break; 1787 case IS_NOT_NULL: 1788 1789 /* 1790 * Select not unknown (not null) values. Calculate from 1791 * freq_null. 1792 */ 1793 selec = 1.0 - freq_null; 1794 break; 1795 default: 1796 elog(ERROR, "unrecognized nulltesttype: %d", 1797 (int) nulltesttype); 1798 return (Selectivity) 0; /* keep compiler quiet */ 1799 } 1800 } 1801 else 1802 { 1803 /* 1804 * No ANALYZE stats available, so make a guess 1805 */ 1806 switch (nulltesttype) 1807 { 1808 case IS_NULL: 1809 selec = DEFAULT_UNK_SEL; 1810 break; 1811 case IS_NOT_NULL: 1812 selec = DEFAULT_NOT_UNK_SEL; 1813 break; 1814 default: 1815 elog(ERROR, "unrecognized nulltesttype: %d", 1816 (int) nulltesttype); 1817 return (Selectivity) 0; /* keep compiler quiet */ 1818 } 1819 } 1820 1821 ReleaseVariableStats(vardata); 1822 1823 /* result should be in range, but make sure... */ 1824 CLAMP_PROBABILITY(selec); 1825 1826 return (Selectivity) selec; 1827 } 1828 1829 /* 1830 * strip_array_coercion - strip binary-compatible relabeling from an array expr 1831 * 1832 * For array values, the parser normally generates ArrayCoerceExpr conversions, 1833 * but it seems possible that RelabelType might show up. Also, the planner 1834 * is not currently tense about collapsing stacked ArrayCoerceExpr nodes, 1835 * so we need to be ready to deal with more than one level. 1836 */ 1837 static Node * 1838 strip_array_coercion(Node *node) 1839 { 1840 for (;;) 1841 { 1842 if (node && IsA(node, ArrayCoerceExpr)) 1843 { 1844 ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node; 1845 1846 /* 1847 * If the per-element expression is just a RelabelType on top of 1848 * CaseTestExpr, then we know it's a binary-compatible relabeling. 1849 */ 1850 if (IsA(acoerce->elemexpr, RelabelType) && 1851 IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr)) 1852 node = (Node *) acoerce->arg; 1853 else 1854 break; 1855 } 1856 else if (node && IsA(node, RelabelType)) 1857 { 1858 /* We don't really expect this case, but may as well cope */ 1859 node = (Node *) ((RelabelType *) node)->arg; 1860 } 1861 else 1862 break; 1863 } 1864 return node; 1865 } 1866 1867 /* 1868 * scalararraysel - Selectivity of ScalarArrayOpExpr Node. 1869 */ 1870 Selectivity 1871 scalararraysel(PlannerInfo *root, 1872 ScalarArrayOpExpr *clause, 1873 bool is_join_clause, 1874 int varRelid, 1875 JoinType jointype, 1876 SpecialJoinInfo *sjinfo) 1877 { 1878 Oid operator = clause->opno; 1879 bool useOr = clause->useOr; 1880 bool isEquality = false; 1881 bool isInequality = false; 1882 Node *leftop; 1883 Node *rightop; 1884 Oid nominal_element_type; 1885 Oid nominal_element_collation; 1886 TypeCacheEntry *typentry; 1887 RegProcedure oprsel; 1888 FmgrInfo oprselproc; 1889 Selectivity s1; 1890 Selectivity s1disjoint; 1891 1892 /* First, deconstruct the expression */ 1893 Assert(list_length(clause->args) == 2); 1894 leftop = (Node *) linitial(clause->args); 1895 rightop = (Node *) lsecond(clause->args); 1896 1897 /* aggressively reduce both sides to constants */ 1898 leftop = estimate_expression_value(root, leftop); 1899 rightop = estimate_expression_value(root, rightop); 1900 1901 /* get nominal (after relabeling) element type of rightop */ 1902 nominal_element_type = get_base_element_type(exprType(rightop)); 1903 if (!OidIsValid(nominal_element_type)) 1904 return (Selectivity) 0.5; /* probably shouldn't happen */ 1905 /* get nominal collation, too, for generating constants */ 1906 nominal_element_collation = exprCollation(rightop); 1907 1908 /* look through any binary-compatible relabeling of rightop */ 1909 rightop = strip_array_coercion(rightop); 1910 1911 /* 1912 * Detect whether the operator is the default equality or inequality 1913 * operator of the array element type. 1914 */ 1915 typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR); 1916 if (OidIsValid(typentry->eq_opr)) 1917 { 1918 if (operator == typentry->eq_opr) 1919 isEquality = true; 1920 else if (get_negator(operator) == typentry->eq_opr) 1921 isInequality = true; 1922 } 1923 1924 /* 1925 * If it is equality or inequality, we might be able to estimate this as a 1926 * form of array containment; for instance "const = ANY(column)" can be 1927 * treated as "ARRAY[const] <@ column". scalararraysel_containment tries 1928 * that, and returns the selectivity estimate if successful, or -1 if not. 1929 */ 1930 if ((isEquality || isInequality) && !is_join_clause) 1931 { 1932 s1 = scalararraysel_containment(root, leftop, rightop, 1933 nominal_element_type, 1934 isEquality, useOr, varRelid); 1935 if (s1 >= 0.0) 1936 return s1; 1937 } 1938 1939 /* 1940 * Look up the underlying operator's selectivity estimator. Punt if it 1941 * hasn't got one. 1942 */ 1943 if (is_join_clause) 1944 oprsel = get_oprjoin(operator); 1945 else 1946 oprsel = get_oprrest(operator); 1947 if (!oprsel) 1948 return (Selectivity) 0.5; 1949 fmgr_info(oprsel, &oprselproc); 1950 1951 /* 1952 * In the array-containment check above, we must only believe that an 1953 * operator is equality or inequality if it is the default btree equality 1954 * operator (or its negator) for the element type, since those are the 1955 * operators that array containment will use. But in what follows, we can 1956 * be a little laxer, and also believe that any operators using eqsel() or 1957 * neqsel() as selectivity estimator act like equality or inequality. 1958 */ 1959 if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL) 1960 isEquality = true; 1961 else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL) 1962 isInequality = true; 1963 1964 /* 1965 * We consider three cases: 1966 * 1967 * 1. rightop is an Array constant: deconstruct the array, apply the 1968 * operator's selectivity function for each array element, and merge the 1969 * results in the same way that clausesel.c does for AND/OR combinations. 1970 * 1971 * 2. rightop is an ARRAY[] construct: apply the operator's selectivity 1972 * function for each element of the ARRAY[] construct, and merge. 1973 * 1974 * 3. otherwise, make a guess ... 1975 */ 1976 if (rightop && IsA(rightop, Const)) 1977 { 1978 Datum arraydatum = ((Const *) rightop)->constvalue; 1979 bool arrayisnull = ((Const *) rightop)->constisnull; 1980 ArrayType *arrayval; 1981 int16 elmlen; 1982 bool elmbyval; 1983 char elmalign; 1984 int num_elems; 1985 Datum *elem_values; 1986 bool *elem_nulls; 1987 int i; 1988 1989 if (arrayisnull) /* qual can't succeed if null array */ 1990 return (Selectivity) 0.0; 1991 arrayval = DatumGetArrayTypeP(arraydatum); 1992 get_typlenbyvalalign(ARR_ELEMTYPE(arrayval), 1993 &elmlen, &elmbyval, &elmalign); 1994 deconstruct_array(arrayval, 1995 ARR_ELEMTYPE(arrayval), 1996 elmlen, elmbyval, elmalign, 1997 &elem_values, &elem_nulls, &num_elems); 1998 1999 /* 2000 * For generic operators, we assume the probability of success is 2001 * independent for each array element. But for "= ANY" or "<> ALL", 2002 * if the array elements are distinct (which'd typically be the case) 2003 * then the probabilities are disjoint, and we should just sum them. 2004 * 2005 * If we were being really tense we would try to confirm that the 2006 * elements are all distinct, but that would be expensive and it 2007 * doesn't seem to be worth the cycles; it would amount to penalizing 2008 * well-written queries in favor of poorly-written ones. However, we 2009 * do protect ourselves a little bit by checking whether the 2010 * disjointness assumption leads to an impossible (out of range) 2011 * probability; if so, we fall back to the normal calculation. 2012 */ 2013 s1 = s1disjoint = (useOr ? 0.0 : 1.0); 2014 2015 for (i = 0; i < num_elems; i++) 2016 { 2017 List *args; 2018 Selectivity s2; 2019 2020 args = list_make2(leftop, 2021 makeConst(nominal_element_type, 2022 -1, 2023 nominal_element_collation, 2024 elmlen, 2025 elem_values[i], 2026 elem_nulls[i], 2027 elmbyval)); 2028 if (is_join_clause) 2029 s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc, 2030 clause->inputcollid, 2031 PointerGetDatum(root), 2032 ObjectIdGetDatum(operator), 2033 PointerGetDatum(args), 2034 Int16GetDatum(jointype), 2035 PointerGetDatum(sjinfo))); 2036 else 2037 s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc, 2038 clause->inputcollid, 2039 PointerGetDatum(root), 2040 ObjectIdGetDatum(operator), 2041 PointerGetDatum(args), 2042 Int32GetDatum(varRelid))); 2043 2044 if (useOr) 2045 { 2046 s1 = s1 + s2 - s1 * s2; 2047 if (isEquality) 2048 s1disjoint += s2; 2049 } 2050 else 2051 { 2052 s1 = s1 * s2; 2053 if (isInequality) 2054 s1disjoint += s2 - 1.0; 2055 } 2056 } 2057 2058 /* accept disjoint-probability estimate if in range */ 2059 if ((useOr ? isEquality : isInequality) && 2060 s1disjoint >= 0.0 && s1disjoint <= 1.0) 2061 s1 = s1disjoint; 2062 } 2063 else if (rightop && IsA(rightop, ArrayExpr) && 2064 !((ArrayExpr *) rightop)->multidims) 2065 { 2066 ArrayExpr *arrayexpr = (ArrayExpr *) rightop; 2067 int16 elmlen; 2068 bool elmbyval; 2069 ListCell *l; 2070 2071 get_typlenbyval(arrayexpr->element_typeid, 2072 &elmlen, &elmbyval); 2073 2074 /* 2075 * We use the assumption of disjoint probabilities here too, although 2076 * the odds of equal array elements are rather higher if the elements 2077 * are not all constants (which they won't be, else constant folding 2078 * would have reduced the ArrayExpr to a Const). In this path it's 2079 * critical to have the sanity check on the s1disjoint estimate. 2080 */ 2081 s1 = s1disjoint = (useOr ? 0.0 : 1.0); 2082 2083 foreach(l, arrayexpr->elements) 2084 { 2085 Node *elem = (Node *) lfirst(l); 2086 List *args; 2087 Selectivity s2; 2088 2089 /* 2090 * Theoretically, if elem isn't of nominal_element_type we should 2091 * insert a RelabelType, but it seems unlikely that any operator 2092 * estimation function would really care ... 2093 */ 2094 args = list_make2(leftop, elem); 2095 if (is_join_clause) 2096 s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc, 2097 clause->inputcollid, 2098 PointerGetDatum(root), 2099 ObjectIdGetDatum(operator), 2100 PointerGetDatum(args), 2101 Int16GetDatum(jointype), 2102 PointerGetDatum(sjinfo))); 2103 else 2104 s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc, 2105 clause->inputcollid, 2106 PointerGetDatum(root), 2107 ObjectIdGetDatum(operator), 2108 PointerGetDatum(args), 2109 Int32GetDatum(varRelid))); 2110 2111 if (useOr) 2112 { 2113 s1 = s1 + s2 - s1 * s2; 2114 if (isEquality) 2115 s1disjoint += s2; 2116 } 2117 else 2118 { 2119 s1 = s1 * s2; 2120 if (isInequality) 2121 s1disjoint += s2 - 1.0; 2122 } 2123 } 2124 2125 /* accept disjoint-probability estimate if in range */ 2126 if ((useOr ? isEquality : isInequality) && 2127 s1disjoint >= 0.0 && s1disjoint <= 1.0) 2128 s1 = s1disjoint; 2129 } 2130 else 2131 { 2132 CaseTestExpr *dummyexpr; 2133 List *args; 2134 Selectivity s2; 2135 int i; 2136 2137 /* 2138 * We need a dummy rightop to pass to the operator selectivity 2139 * routine. It can be pretty much anything that doesn't look like a 2140 * constant; CaseTestExpr is a convenient choice. 2141 */ 2142 dummyexpr = makeNode(CaseTestExpr); 2143 dummyexpr->typeId = nominal_element_type; 2144 dummyexpr->typeMod = -1; 2145 dummyexpr->collation = clause->inputcollid; 2146 args = list_make2(leftop, dummyexpr); 2147 if (is_join_clause) 2148 s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc, 2149 clause->inputcollid, 2150 PointerGetDatum(root), 2151 ObjectIdGetDatum(operator), 2152 PointerGetDatum(args), 2153 Int16GetDatum(jointype), 2154 PointerGetDatum(sjinfo))); 2155 else 2156 s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc, 2157 clause->inputcollid, 2158 PointerGetDatum(root), 2159 ObjectIdGetDatum(operator), 2160 PointerGetDatum(args), 2161 Int32GetDatum(varRelid))); 2162 s1 = useOr ? 0.0 : 1.0; 2163 2164 /* 2165 * Arbitrarily assume 10 elements in the eventual array value (see 2166 * also estimate_array_length). We don't risk an assumption of 2167 * disjoint probabilities here. 2168 */ 2169 for (i = 0; i < 10; i++) 2170 { 2171 if (useOr) 2172 s1 = s1 + s2 - s1 * s2; 2173 else 2174 s1 = s1 * s2; 2175 } 2176 } 2177 2178 /* result should be in range, but make sure... */ 2179 CLAMP_PROBABILITY(s1); 2180 2181 return s1; 2182 } 2183 2184 /* 2185 * Estimate number of elements in the array yielded by an expression. 2186 * 2187 * It's important that this agree with scalararraysel. 2188 */ 2189 int 2190 estimate_array_length(Node *arrayexpr) 2191 { 2192 /* look through any binary-compatible relabeling of arrayexpr */ 2193 arrayexpr = strip_array_coercion(arrayexpr); 2194 2195 if (arrayexpr && IsA(arrayexpr, Const)) 2196 { 2197 Datum arraydatum = ((Const *) arrayexpr)->constvalue; 2198 bool arrayisnull = ((Const *) arrayexpr)->constisnull; 2199 ArrayType *arrayval; 2200 2201 if (arrayisnull) 2202 return 0; 2203 arrayval = DatumGetArrayTypeP(arraydatum); 2204 return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval)); 2205 } 2206 else if (arrayexpr && IsA(arrayexpr, ArrayExpr) && 2207 !((ArrayExpr *) arrayexpr)->multidims) 2208 { 2209 return list_length(((ArrayExpr *) arrayexpr)->elements); 2210 } 2211 else 2212 { 2213 /* default guess --- see also scalararraysel */ 2214 return 10; 2215 } 2216 } 2217 2218 /* 2219 * rowcomparesel - Selectivity of RowCompareExpr Node. 2220 * 2221 * We estimate RowCompare selectivity by considering just the first (high 2222 * order) columns, which makes it equivalent to an ordinary OpExpr. While 2223 * this estimate could be refined by considering additional columns, it 2224 * seems unlikely that we could do a lot better without multi-column 2225 * statistics. 2226 */ 2227 Selectivity 2228 rowcomparesel(PlannerInfo *root, 2229 RowCompareExpr *clause, 2230 int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo) 2231 { 2232 Selectivity s1; 2233 Oid opno = linitial_oid(clause->opnos); 2234 Oid inputcollid = linitial_oid(clause->inputcollids); 2235 List *opargs; 2236 bool is_join_clause; 2237 2238 /* Build equivalent arg list for single operator */ 2239 opargs = list_make2(linitial(clause->largs), linitial(clause->rargs)); 2240 2241 /* 2242 * Decide if it's a join clause. This should match clausesel.c's 2243 * treat_as_join_clause(), except that we intentionally consider only the 2244 * leading columns and not the rest of the clause. 2245 */ 2246 if (varRelid != 0) 2247 { 2248 /* 2249 * Caller is forcing restriction mode (eg, because we are examining an 2250 * inner indexscan qual). 2251 */ 2252 is_join_clause = false; 2253 } 2254 else if (sjinfo == NULL) 2255 { 2256 /* 2257 * It must be a restriction clause, since it's being evaluated at a 2258 * scan node. 2259 */ 2260 is_join_clause = false; 2261 } 2262 else 2263 { 2264 /* 2265 * Otherwise, it's a join if there's more than one relation used. 2266 */ 2267 is_join_clause = (NumRelids((Node *) opargs) > 1); 2268 } 2269 2270 if (is_join_clause) 2271 { 2272 /* Estimate selectivity for a join clause. */ 2273 s1 = join_selectivity(root, opno, 2274 opargs, 2275 inputcollid, 2276 jointype, 2277 sjinfo); 2278 } 2279 else 2280 { 2281 /* Estimate selectivity for a restriction clause. */ 2282 s1 = restriction_selectivity(root, opno, 2283 opargs, 2284 inputcollid, 2285 varRelid); 2286 } 2287 2288 return s1; 2289 } 2290 2291 /* 2292 * eqjoinsel - Join selectivity of "=" 2293 */ 2294 Datum 2295 eqjoinsel(PG_FUNCTION_ARGS) 2296 { 2297 PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0); 2298 Oid operator = PG_GETARG_OID(1); 2299 List *args = (List *) PG_GETARG_POINTER(2); 2300 2301 #ifdef NOT_USED 2302 JoinType jointype = (JoinType) PG_GETARG_INT16(3); 2303 #endif 2304 SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4); 2305 double selec; 2306 VariableStatData vardata1; 2307 VariableStatData vardata2; 2308 bool join_is_reversed; 2309 RelOptInfo *inner_rel; 2310 2311 get_join_variables(root, args, sjinfo, 2312 &vardata1, &vardata2, &join_is_reversed); 2313 2314 switch (sjinfo->jointype) 2315 { 2316 case JOIN_INNER: 2317 case JOIN_LEFT: 2318 case JOIN_FULL: 2319 selec = eqjoinsel_inner(operator, &vardata1, &vardata2); 2320 break; 2321 case JOIN_SEMI: 2322 case JOIN_ANTI: 2323 2324 /* 2325 * Look up the join's inner relation. min_righthand is sufficient 2326 * information because neither SEMI nor ANTI joins permit any 2327 * reassociation into or out of their RHS, so the righthand will 2328 * always be exactly that set of rels. 2329 */ 2330 inner_rel = find_join_input_rel(root, sjinfo->min_righthand); 2331 2332 if (!join_is_reversed) 2333 selec = eqjoinsel_semi(operator, &vardata1, &vardata2, 2334 inner_rel); 2335 else 2336 selec = eqjoinsel_semi(get_commutator(operator), 2337 &vardata2, &vardata1, 2338 inner_rel); 2339 break; 2340 default: 2341 /* other values not expected here */ 2342 elog(ERROR, "unrecognized join type: %d", 2343 (int) sjinfo->jointype); 2344 selec = 0; /* keep compiler quiet */ 2345 break; 2346 } 2347 2348 ReleaseVariableStats(vardata1); 2349 ReleaseVariableStats(vardata2); 2350 2351 CLAMP_PROBABILITY(selec); 2352 2353 PG_RETURN_FLOAT8((float8) selec); 2354 } 2355 2356 /* 2357 * eqjoinsel_inner --- eqjoinsel for normal inner join 2358 * 2359 * We also use this for LEFT/FULL outer joins; it's not presently clear 2360 * that it's worth trying to distinguish them here. 2361 */ 2362 static double 2363 eqjoinsel_inner(Oid operator, 2364 VariableStatData *vardata1, VariableStatData *vardata2) 2365 { 2366 double selec; 2367 double nd1; 2368 double nd2; 2369 bool isdefault1; 2370 bool isdefault2; 2371 Oid opfuncoid; 2372 Form_pg_statistic stats1 = NULL; 2373 Form_pg_statistic stats2 = NULL; 2374 bool have_mcvs1 = false; 2375 bool have_mcvs2 = false; 2376 AttStatsSlot sslot1; 2377 AttStatsSlot sslot2; 2378 2379 nd1 = get_variable_numdistinct(vardata1, &isdefault1); 2380 nd2 = get_variable_numdistinct(vardata2, &isdefault2); 2381 2382 opfuncoid = get_opcode(operator); 2383 2384 memset(&sslot1, 0, sizeof(sslot1)); 2385 memset(&sslot2, 0, sizeof(sslot2)); 2386 2387 if (HeapTupleIsValid(vardata1->statsTuple)) 2388 { 2389 /* note we allow use of nullfrac regardless of security check */ 2390 stats1 = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple); 2391 if (statistic_proc_security_check(vardata1, opfuncoid)) 2392 have_mcvs1 = get_attstatsslot(&sslot1, vardata1->statsTuple, 2393 STATISTIC_KIND_MCV, InvalidOid, 2394 ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS); 2395 } 2396 2397 if (HeapTupleIsValid(vardata2->statsTuple)) 2398 { 2399 /* note we allow use of nullfrac regardless of security check */ 2400 stats2 = (Form_pg_statistic) GETSTRUCT(vardata2->statsTuple); 2401 if (statistic_proc_security_check(vardata2, opfuncoid)) 2402 have_mcvs2 = get_attstatsslot(&sslot2, vardata2->statsTuple, 2403 STATISTIC_KIND_MCV, InvalidOid, 2404 ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS); 2405 } 2406 2407 if (have_mcvs1 && have_mcvs2) 2408 { 2409 /* 2410 * We have most-common-value lists for both relations. Run through 2411 * the lists to see which MCVs actually join to each other with the 2412 * given operator. This allows us to determine the exact join 2413 * selectivity for the portion of the relations represented by the MCV 2414 * lists. We still have to estimate for the remaining population, but 2415 * in a skewed distribution this gives us a big leg up in accuracy. 2416 * For motivation see the analysis in Y. Ioannidis and S. 2417 * Christodoulakis, "On the propagation of errors in the size of join 2418 * results", Technical Report 1018, Computer Science Dept., University 2419 * of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu). 2420 */ 2421 FmgrInfo eqproc; 2422 bool *hasmatch1; 2423 bool *hasmatch2; 2424 double nullfrac1 = stats1->stanullfrac; 2425 double nullfrac2 = stats2->stanullfrac; 2426 double matchprodfreq, 2427 matchfreq1, 2428 matchfreq2, 2429 unmatchfreq1, 2430 unmatchfreq2, 2431 otherfreq1, 2432 otherfreq2, 2433 totalsel1, 2434 totalsel2; 2435 int i, 2436 nmatches; 2437 2438 fmgr_info(opfuncoid, &eqproc); 2439 hasmatch1 = (bool *) palloc0(sslot1.nvalues * sizeof(bool)); 2440 hasmatch2 = (bool *) palloc0(sslot2.nvalues * sizeof(bool)); 2441 2442 /* 2443 * Note we assume that each MCV will match at most one member of the 2444 * other MCV list. If the operator isn't really equality, there could 2445 * be multiple matches --- but we don't look for them, both for speed 2446 * and because the math wouldn't add up... 2447 */ 2448 matchprodfreq = 0.0; 2449 nmatches = 0; 2450 for (i = 0; i < sslot1.nvalues; i++) 2451 { 2452 int j; 2453 2454 for (j = 0; j < sslot2.nvalues; j++) 2455 { 2456 if (hasmatch2[j]) 2457 continue; 2458 if (DatumGetBool(FunctionCall2Coll(&eqproc, 2459 DEFAULT_COLLATION_OID, 2460 sslot1.values[i], 2461 sslot2.values[j]))) 2462 { 2463 hasmatch1[i] = hasmatch2[j] = true; 2464 matchprodfreq += sslot1.numbers[i] * sslot2.numbers[j]; 2465 nmatches++; 2466 break; 2467 } 2468 } 2469 } 2470 CLAMP_PROBABILITY(matchprodfreq); 2471 /* Sum up frequencies of matched and unmatched MCVs */ 2472 matchfreq1 = unmatchfreq1 = 0.0; 2473 for (i = 0; i < sslot1.nvalues; i++) 2474 { 2475 if (hasmatch1[i]) 2476 matchfreq1 += sslot1.numbers[i]; 2477 else 2478 unmatchfreq1 += sslot1.numbers[i]; 2479 } 2480 CLAMP_PROBABILITY(matchfreq1); 2481 CLAMP_PROBABILITY(unmatchfreq1); 2482 matchfreq2 = unmatchfreq2 = 0.0; 2483 for (i = 0; i < sslot2.nvalues; i++) 2484 { 2485 if (hasmatch2[i]) 2486 matchfreq2 += sslot2.numbers[i]; 2487 else 2488 unmatchfreq2 += sslot2.numbers[i]; 2489 } 2490 CLAMP_PROBABILITY(matchfreq2); 2491 CLAMP_PROBABILITY(unmatchfreq2); 2492 pfree(hasmatch1); 2493 pfree(hasmatch2); 2494 2495 /* 2496 * Compute total frequency of non-null values that are not in the MCV 2497 * lists. 2498 */ 2499 otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1; 2500 otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2; 2501 CLAMP_PROBABILITY(otherfreq1); 2502 CLAMP_PROBABILITY(otherfreq2); 2503 2504 /* 2505 * We can estimate the total selectivity from the point of view of 2506 * relation 1 as: the known selectivity for matched MCVs, plus 2507 * unmatched MCVs that are assumed to match against random members of 2508 * relation 2's non-MCV population, plus non-MCV values that are 2509 * assumed to match against random members of relation 2's unmatched 2510 * MCVs plus non-MCV values. 2511 */ 2512 totalsel1 = matchprodfreq; 2513 if (nd2 > sslot2.nvalues) 2514 totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2.nvalues); 2515 if (nd2 > nmatches) 2516 totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) / 2517 (nd2 - nmatches); 2518 /* Same estimate from the point of view of relation 2. */ 2519 totalsel2 = matchprodfreq; 2520 if (nd1 > sslot1.nvalues) 2521 totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1.nvalues); 2522 if (nd1 > nmatches) 2523 totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) / 2524 (nd1 - nmatches); 2525 2526 /* 2527 * Use the smaller of the two estimates. This can be justified in 2528 * essentially the same terms as given below for the no-stats case: to 2529 * a first approximation, we are estimating from the point of view of 2530 * the relation with smaller nd. 2531 */ 2532 selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2; 2533 } 2534 else 2535 { 2536 /* 2537 * We do not have MCV lists for both sides. Estimate the join 2538 * selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This 2539 * is plausible if we assume that the join operator is strict and the 2540 * non-null values are about equally distributed: a given non-null 2541 * tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows 2542 * of rel2, so total join rows are at most 2543 * N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of 2544 * not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it 2545 * is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression 2546 * with MIN() is an upper bound. Using the MIN() means we estimate 2547 * from the point of view of the relation with smaller nd (since the 2548 * larger nd is determining the MIN). It is reasonable to assume that 2549 * most tuples in this rel will have join partners, so the bound is 2550 * probably reasonably tight and should be taken as-is. 2551 * 2552 * XXX Can we be smarter if we have an MCV list for just one side? It 2553 * seems that if we assume equal distribution for the other side, we 2554 * end up with the same answer anyway. 2555 */ 2556 double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0; 2557 double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0; 2558 2559 selec = (1.0 - nullfrac1) * (1.0 - nullfrac2); 2560 if (nd1 > nd2) 2561 selec /= nd1; 2562 else 2563 selec /= nd2; 2564 } 2565 2566 free_attstatsslot(&sslot1); 2567 free_attstatsslot(&sslot2); 2568 2569 return selec; 2570 } 2571 2572 /* 2573 * eqjoinsel_semi --- eqjoinsel for semi join 2574 * 2575 * (Also used for anti join, which we are supposed to estimate the same way.) 2576 * Caller has ensured that vardata1 is the LHS variable. 2577 * Unlike eqjoinsel_inner, we have to cope with operator being InvalidOid. 2578 */ 2579 static double 2580 eqjoinsel_semi(Oid operator, 2581 VariableStatData *vardata1, VariableStatData *vardata2, 2582 RelOptInfo *inner_rel) 2583 { 2584 double selec; 2585 double nd1; 2586 double nd2; 2587 bool isdefault1; 2588 bool isdefault2; 2589 Oid opfuncoid; 2590 Form_pg_statistic stats1 = NULL; 2591 bool have_mcvs1 = false; 2592 bool have_mcvs2 = false; 2593 AttStatsSlot sslot1; 2594 AttStatsSlot sslot2; 2595 2596 nd1 = get_variable_numdistinct(vardata1, &isdefault1); 2597 nd2 = get_variable_numdistinct(vardata2, &isdefault2); 2598 2599 opfuncoid = OidIsValid(operator) ? get_opcode(operator) : InvalidOid; 2600 2601 memset(&sslot1, 0, sizeof(sslot1)); 2602 memset(&sslot2, 0, sizeof(sslot2)); 2603 2604 /* 2605 * We clamp nd2 to be not more than what we estimate the inner relation's 2606 * size to be. This is intuitively somewhat reasonable since obviously 2607 * there can't be more than that many distinct values coming from the 2608 * inner rel. The reason for the asymmetry (ie, that we don't clamp nd1 2609 * likewise) is that this is the only pathway by which restriction clauses 2610 * applied to the inner rel will affect the join result size estimate, 2611 * since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by 2612 * only the outer rel's size. If we clamped nd1 we'd be double-counting 2613 * the selectivity of outer-rel restrictions. 2614 * 2615 * We can apply this clamping both with respect to the base relation from 2616 * which the join variable comes (if there is just one), and to the 2617 * immediate inner input relation of the current join. 2618 * 2619 * If we clamp, we can treat nd2 as being a non-default estimate; it's not 2620 * great, maybe, but it didn't come out of nowhere either. This is most 2621 * helpful when the inner relation is empty and consequently has no stats. 2622 */ 2623 if (vardata2->rel) 2624 { 2625 if (nd2 >= vardata2->rel->rows) 2626 { 2627 nd2 = vardata2->rel->rows; 2628 isdefault2 = false; 2629 } 2630 } 2631 if (nd2 >= inner_rel->rows) 2632 { 2633 nd2 = inner_rel->rows; 2634 isdefault2 = false; 2635 } 2636 2637 if (HeapTupleIsValid(vardata1->statsTuple)) 2638 { 2639 /* note we allow use of nullfrac regardless of security check */ 2640 stats1 = (Form_pg_statistic) GETSTRUCT(vardata1->statsTuple); 2641 if (statistic_proc_security_check(vardata1, opfuncoid)) 2642 have_mcvs1 = get_attstatsslot(&sslot1, vardata1->statsTuple, 2643 STATISTIC_KIND_MCV, InvalidOid, 2644 ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS); 2645 } 2646 2647 if (HeapTupleIsValid(vardata2->statsTuple) && 2648 statistic_proc_security_check(vardata2, opfuncoid)) 2649 { 2650 have_mcvs2 = get_attstatsslot(&sslot2, vardata2->statsTuple, 2651 STATISTIC_KIND_MCV, InvalidOid, 2652 ATTSTATSSLOT_VALUES); 2653 /* note: currently don't need stanumbers from RHS */ 2654 } 2655 2656 if (have_mcvs1 && have_mcvs2 && OidIsValid(operator)) 2657 { 2658 /* 2659 * We have most-common-value lists for both relations. Run through 2660 * the lists to see which MCVs actually join to each other with the 2661 * given operator. This allows us to determine the exact join 2662 * selectivity for the portion of the relations represented by the MCV 2663 * lists. We still have to estimate for the remaining population, but 2664 * in a skewed distribution this gives us a big leg up in accuracy. 2665 */ 2666 FmgrInfo eqproc; 2667 bool *hasmatch1; 2668 bool *hasmatch2; 2669 double nullfrac1 = stats1->stanullfrac; 2670 double matchfreq1, 2671 uncertainfrac, 2672 uncertain; 2673 int i, 2674 nmatches, 2675 clamped_nvalues2; 2676 2677 /* 2678 * The clamping above could have resulted in nd2 being less than 2679 * sslot2.nvalues; in which case, we assume that precisely the nd2 2680 * most common values in the relation will appear in the join input, 2681 * and so compare to only the first nd2 members of the MCV list. Of 2682 * course this is frequently wrong, but it's the best bet we can make. 2683 */ 2684 clamped_nvalues2 = Min(sslot2.nvalues, nd2); 2685 2686 fmgr_info(opfuncoid, &eqproc); 2687 hasmatch1 = (bool *) palloc0(sslot1.nvalues * sizeof(bool)); 2688 hasmatch2 = (bool *) palloc0(clamped_nvalues2 * sizeof(bool)); 2689 2690 /* 2691 * Note we assume that each MCV will match at most one member of the 2692 * other MCV list. If the operator isn't really equality, there could 2693 * be multiple matches --- but we don't look for them, both for speed 2694 * and because the math wouldn't add up... 2695 */ 2696 nmatches = 0; 2697 for (i = 0; i < sslot1.nvalues; i++) 2698 { 2699 int j; 2700 2701 for (j = 0; j < clamped_nvalues2; j++) 2702 { 2703 if (hasmatch2[j]) 2704 continue; 2705 if (DatumGetBool(FunctionCall2Coll(&eqproc, 2706 DEFAULT_COLLATION_OID, 2707 sslot1.values[i], 2708 sslot2.values[j]))) 2709 { 2710 hasmatch1[i] = hasmatch2[j] = true; 2711 nmatches++; 2712 break; 2713 } 2714 } 2715 } 2716 /* Sum up frequencies of matched MCVs */ 2717 matchfreq1 = 0.0; 2718 for (i = 0; i < sslot1.nvalues; i++) 2719 { 2720 if (hasmatch1[i]) 2721 matchfreq1 += sslot1.numbers[i]; 2722 } 2723 CLAMP_PROBABILITY(matchfreq1); 2724 pfree(hasmatch1); 2725 pfree(hasmatch2); 2726 2727 /* 2728 * Now we need to estimate the fraction of relation 1 that has at 2729 * least one join partner. We know for certain that the matched MCVs 2730 * do, so that gives us a lower bound, but we're really in the dark 2731 * about everything else. Our crude approach is: if nd1 <= nd2 then 2732 * assume all non-null rel1 rows have join partners, else assume for 2733 * the uncertain rows that a fraction nd2/nd1 have join partners. We 2734 * can discount the known-matched MCVs from the distinct-values counts 2735 * before doing the division. 2736 * 2737 * Crude as the above is, it's completely useless if we don't have 2738 * reliable ndistinct values for both sides. Hence, if either nd1 or 2739 * nd2 is default, punt and assume half of the uncertain rows have 2740 * join partners. 2741 */ 2742 if (!isdefault1 && !isdefault2) 2743 { 2744 nd1 -= nmatches; 2745 nd2 -= nmatches; 2746 if (nd1 <= nd2 || nd2 < 0) 2747 uncertainfrac = 1.0; 2748 else 2749 uncertainfrac = nd2 / nd1; 2750 } 2751 else 2752 uncertainfrac = 0.5; 2753 uncertain = 1.0 - matchfreq1 - nullfrac1; 2754 CLAMP_PROBABILITY(uncertain); 2755 selec = matchfreq1 + uncertainfrac * uncertain; 2756 } 2757 else 2758 { 2759 /* 2760 * Without MCV lists for both sides, we can only use the heuristic 2761 * about nd1 vs nd2. 2762 */ 2763 double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0; 2764 2765 if (!isdefault1 && !isdefault2) 2766 { 2767 if (nd1 <= nd2 || nd2 < 0) 2768 selec = 1.0 - nullfrac1; 2769 else 2770 selec = (nd2 / nd1) * (1.0 - nullfrac1); 2771 } 2772 else 2773 selec = 0.5 * (1.0 - nullfrac1); 2774 } 2775 2776 free_attstatsslot(&sslot1); 2777 free_attstatsslot(&sslot2); 2778 2779 return selec; 2780 } 2781 2782 /* 2783 * neqjoinsel - Join selectivity of "!=" 2784 */ 2785 Datum 2786 neqjoinsel(PG_FUNCTION_ARGS) 2787 { 2788 PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0); 2789 Oid operator = PG_GETARG_OID(1); 2790 List *args = (List *) PG_GETARG_POINTER(2); 2791 JoinType jointype = (JoinType) PG_GETARG_INT16(3); 2792 SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4); 2793 float8 result; 2794 2795 if (jointype == JOIN_SEMI || jointype == JOIN_ANTI) 2796 { 2797 /* 2798 * For semi-joins, if there is more than one distinct value in the RHS 2799 * relation then every non-null LHS row must find a row to join since 2800 * it can only be equal to one of them. We'll assume that there is 2801 * always more than one distinct RHS value for the sake of stability, 2802 * though in theory we could have special cases for empty RHS 2803 * (selectivity = 0) and single-distinct-value RHS (selectivity = 2804 * fraction of LHS that has the same value as the single RHS value). 2805 * 2806 * For anti-joins, if we use the same assumption that there is more 2807 * than one distinct key in the RHS relation, then every non-null LHS 2808 * row must be suppressed by the anti-join. 2809 * 2810 * So either way, the selectivity estimate should be 1 - nullfrac. 2811 */ 2812 VariableStatData leftvar; 2813 VariableStatData rightvar; 2814 bool reversed; 2815 HeapTuple statsTuple; 2816 double nullfrac; 2817 2818 get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed); 2819 statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple; 2820 if (HeapTupleIsValid(statsTuple)) 2821 nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac; 2822 else 2823 nullfrac = 0.0; 2824 ReleaseVariableStats(leftvar); 2825 ReleaseVariableStats(rightvar); 2826 2827 result = 1.0 - nullfrac; 2828 } 2829 else 2830 { 2831 /* 2832 * We want 1 - eqjoinsel() where the equality operator is the one 2833 * associated with this != operator, that is, its negator. 2834 */ 2835 Oid eqop = get_negator(operator); 2836 2837 if (eqop) 2838 { 2839 result = DatumGetFloat8(DirectFunctionCall5(eqjoinsel, 2840 PointerGetDatum(root), 2841 ObjectIdGetDatum(eqop), 2842 PointerGetDatum(args), 2843 Int16GetDatum(jointype), 2844 PointerGetDatum(sjinfo))); 2845 } 2846 else 2847 { 2848 /* Use default selectivity (should we raise an error instead?) */ 2849 result = DEFAULT_EQ_SEL; 2850 } 2851 result = 1.0 - result; 2852 } 2853 2854 PG_RETURN_FLOAT8(result); 2855 } 2856 2857 /* 2858 * scalarltjoinsel - Join selectivity of "<" for scalars 2859 */ 2860 Datum 2861 scalarltjoinsel(PG_FUNCTION_ARGS) 2862 { 2863 PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL); 2864 } 2865 2866 /* 2867 * scalarlejoinsel - Join selectivity of "<=" for scalars 2868 */ 2869 Datum 2870 scalarlejoinsel(PG_FUNCTION_ARGS) 2871 { 2872 PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL); 2873 } 2874 2875 /* 2876 * scalargtjoinsel - Join selectivity of ">" for scalars 2877 */ 2878 Datum 2879 scalargtjoinsel(PG_FUNCTION_ARGS) 2880 { 2881 PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL); 2882 } 2883 2884 /* 2885 * scalargejoinsel - Join selectivity of ">=" for scalars 2886 */ 2887 Datum 2888 scalargejoinsel(PG_FUNCTION_ARGS) 2889 { 2890 PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL); 2891 } 2892 2893 /* 2894 * patternjoinsel - Generic code for pattern-match join selectivity. 2895 */ 2896 static double 2897 patternjoinsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate) 2898 { 2899 /* For the moment we just punt. */ 2900 return negate ? (1.0 - DEFAULT_MATCH_SEL) : DEFAULT_MATCH_SEL; 2901 } 2902 2903 /* 2904 * regexeqjoinsel - Join selectivity of regular-expression pattern match. 2905 */ 2906 Datum 2907 regexeqjoinsel(PG_FUNCTION_ARGS) 2908 { 2909 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex, false)); 2910 } 2911 2912 /* 2913 * icregexeqjoinsel - Join selectivity of case-insensitive regex match. 2914 */ 2915 Datum 2916 icregexeqjoinsel(PG_FUNCTION_ARGS) 2917 { 2918 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex_IC, false)); 2919 } 2920 2921 /* 2922 * likejoinsel - Join selectivity of LIKE pattern match. 2923 */ 2924 Datum 2925 likejoinsel(PG_FUNCTION_ARGS) 2926 { 2927 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like, false)); 2928 } 2929 2930 /* 2931 * prefixjoinsel - Join selectivity of prefix operator 2932 */ 2933 Datum 2934 prefixjoinsel(PG_FUNCTION_ARGS) 2935 { 2936 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Prefix, false)); 2937 } 2938 2939 /* 2940 * iclikejoinsel - Join selectivity of ILIKE pattern match. 2941 */ 2942 Datum 2943 iclikejoinsel(PG_FUNCTION_ARGS) 2944 { 2945 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like_IC, false)); 2946 } 2947 2948 /* 2949 * regexnejoinsel - Join selectivity of regex non-match. 2950 */ 2951 Datum 2952 regexnejoinsel(PG_FUNCTION_ARGS) 2953 { 2954 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex, true)); 2955 } 2956 2957 /* 2958 * icregexnejoinsel - Join selectivity of case-insensitive regex non-match. 2959 */ 2960 Datum 2961 icregexnejoinsel(PG_FUNCTION_ARGS) 2962 { 2963 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex_IC, true)); 2964 } 2965 2966 /* 2967 * nlikejoinsel - Join selectivity of LIKE pattern non-match. 2968 */ 2969 Datum 2970 nlikejoinsel(PG_FUNCTION_ARGS) 2971 { 2972 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like, true)); 2973 } 2974 2975 /* 2976 * icnlikejoinsel - Join selectivity of ILIKE pattern non-match. 2977 */ 2978 Datum 2979 icnlikejoinsel(PG_FUNCTION_ARGS) 2980 { 2981 PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like_IC, true)); 2982 } 2983 2984 /* 2985 * mergejoinscansel - Scan selectivity of merge join. 2986 * 2987 * A merge join will stop as soon as it exhausts either input stream. 2988 * Therefore, if we can estimate the ranges of both input variables, 2989 * we can estimate how much of the input will actually be read. This 2990 * can have a considerable impact on the cost when using indexscans. 2991 * 2992 * Also, we can estimate how much of each input has to be read before the 2993 * first join pair is found, which will affect the join's startup time. 2994 * 2995 * clause should be a clause already known to be mergejoinable. opfamily, 2996 * strategy, and nulls_first specify the sort ordering being used. 2997 * 2998 * The outputs are: 2999 * *leftstart is set to the fraction of the left-hand variable expected 3000 * to be scanned before the first join pair is found (0 to 1). 3001 * *leftend is set to the fraction of the left-hand variable expected 3002 * to be scanned before the join terminates (0 to 1). 3003 * *rightstart, *rightend similarly for the right-hand variable. 3004 */ 3005 void 3006 mergejoinscansel(PlannerInfo *root, Node *clause, 3007 Oid opfamily, int strategy, bool nulls_first, 3008 Selectivity *leftstart, Selectivity *leftend, 3009 Selectivity *rightstart, Selectivity *rightend) 3010 { 3011 Node *left, 3012 *right; 3013 VariableStatData leftvar, 3014 rightvar; 3015 int op_strategy; 3016 Oid op_lefttype; 3017 Oid op_righttype; 3018 Oid opno, 3019 lsortop, 3020 rsortop, 3021 lstatop, 3022 rstatop, 3023 ltop, 3024 leop, 3025 revltop, 3026 revleop; 3027 bool isgt; 3028 Datum leftmin, 3029 leftmax, 3030 rightmin, 3031 rightmax; 3032 double selec; 3033 3034 /* Set default results if we can't figure anything out. */ 3035 /* XXX should default "start" fraction be a bit more than 0? */ 3036 *leftstart = *rightstart = 0.0; 3037 *leftend = *rightend = 1.0; 3038 3039 /* Deconstruct the merge clause */ 3040 if (!is_opclause(clause)) 3041 return; /* shouldn't happen */ 3042 opno = ((OpExpr *) clause)->opno; 3043 left = get_leftop((Expr *) clause); 3044 right = get_rightop((Expr *) clause); 3045 if (!right) 3046 return; /* shouldn't happen */ 3047 3048 /* Look for stats for the inputs */ 3049 examine_variable(root, left, 0, &leftvar); 3050 examine_variable(root, right, 0, &rightvar); 3051 3052 /* Extract the operator's declared left/right datatypes */ 3053 get_op_opfamily_properties(opno, opfamily, false, 3054 &op_strategy, 3055 &op_lefttype, 3056 &op_righttype); 3057 Assert(op_strategy == BTEqualStrategyNumber); 3058 3059 /* 3060 * Look up the various operators we need. If we don't find them all, it 3061 * probably means the opfamily is broken, but we just fail silently. 3062 * 3063 * Note: we expect that pg_statistic histograms will be sorted by the '<' 3064 * operator, regardless of which sort direction we are considering. 3065 */ 3066 switch (strategy) 3067 { 3068 case BTLessStrategyNumber: 3069 isgt = false; 3070 if (op_lefttype == op_righttype) 3071 { 3072 /* easy case */ 3073 ltop = get_opfamily_member(opfamily, 3074 op_lefttype, op_righttype, 3075 BTLessStrategyNumber); 3076 leop = get_opfamily_member(opfamily, 3077 op_lefttype, op_righttype, 3078 BTLessEqualStrategyNumber); 3079 lsortop = ltop; 3080 rsortop = ltop; 3081 lstatop = lsortop; 3082 rstatop = rsortop; 3083 revltop = ltop; 3084 revleop = leop; 3085 } 3086 else 3087 { 3088 ltop = get_opfamily_member(opfamily, 3089 op_lefttype, op_righttype, 3090 BTLessStrategyNumber); 3091 leop = get_opfamily_member(opfamily, 3092 op_lefttype, op_righttype, 3093 BTLessEqualStrategyNumber); 3094 lsortop = get_opfamily_member(opfamily, 3095 op_lefttype, op_lefttype, 3096 BTLessStrategyNumber); 3097 rsortop = get_opfamily_member(opfamily, 3098 op_righttype, op_righttype, 3099 BTLessStrategyNumber); 3100 lstatop = lsortop; 3101 rstatop = rsortop; 3102 revltop = get_opfamily_member(opfamily, 3103 op_righttype, op_lefttype, 3104 BTLessStrategyNumber); 3105 revleop = get_opfamily_member(opfamily, 3106 op_righttype, op_lefttype, 3107 BTLessEqualStrategyNumber); 3108 } 3109 break; 3110 case BTGreaterStrategyNumber: 3111 /* descending-order case */ 3112 isgt = true; 3113 if (op_lefttype == op_righttype) 3114 { 3115 /* easy case */ 3116 ltop = get_opfamily_member(opfamily, 3117 op_lefttype, op_righttype, 3118 BTGreaterStrategyNumber); 3119 leop = get_opfamily_member(opfamily, 3120 op_lefttype, op_righttype, 3121 BTGreaterEqualStrategyNumber); 3122 lsortop = ltop; 3123 rsortop = ltop; 3124 lstatop = get_opfamily_member(opfamily, 3125 op_lefttype, op_lefttype, 3126 BTLessStrategyNumber); 3127 rstatop = lstatop; 3128 revltop = ltop; 3129 revleop = leop; 3130 } 3131 else 3132 { 3133 ltop = get_opfamily_member(opfamily, 3134 op_lefttype, op_righttype, 3135 BTGreaterStrategyNumber); 3136 leop = get_opfamily_member(opfamily, 3137 op_lefttype, op_righttype, 3138 BTGreaterEqualStrategyNumber); 3139 lsortop = get_opfamily_member(opfamily, 3140 op_lefttype, op_lefttype, 3141 BTGreaterStrategyNumber); 3142 rsortop = get_opfamily_member(opfamily, 3143 op_righttype, op_righttype, 3144 BTGreaterStrategyNumber); 3145 lstatop = get_opfamily_member(opfamily, 3146 op_lefttype, op_lefttype, 3147 BTLessStrategyNumber); 3148 rstatop = get_opfamily_member(opfamily, 3149 op_righttype, op_righttype, 3150 BTLessStrategyNumber); 3151 revltop = get_opfamily_member(opfamily, 3152 op_righttype, op_lefttype, 3153 BTGreaterStrategyNumber); 3154 revleop = get_opfamily_member(opfamily, 3155 op_righttype, op_lefttype, 3156 BTGreaterEqualStrategyNumber); 3157 } 3158 break; 3159 default: 3160 goto fail; /* shouldn't get here */ 3161 } 3162 3163 if (!OidIsValid(lsortop) || 3164 !OidIsValid(rsortop) || 3165 !OidIsValid(lstatop) || 3166 !OidIsValid(rstatop) || 3167 !OidIsValid(ltop) || 3168 !OidIsValid(leop) || 3169 !OidIsValid(revltop) || 3170 !OidIsValid(revleop)) 3171 goto fail; /* insufficient info in catalogs */ 3172 3173 /* Try to get ranges of both inputs */ 3174 if (!isgt) 3175 { 3176 if (!get_variable_range(root, &leftvar, lstatop, 3177 &leftmin, &leftmax)) 3178 goto fail; /* no range available from stats */ 3179 if (!get_variable_range(root, &rightvar, rstatop, 3180 &rightmin, &rightmax)) 3181 goto fail; /* no range available from stats */ 3182 } 3183 else 3184 { 3185 /* need to swap the max and min */ 3186 if (!get_variable_range(root, &leftvar, lstatop, 3187 &leftmax, &leftmin)) 3188 goto fail; /* no range available from stats */ 3189 if (!get_variable_range(root, &rightvar, rstatop, 3190 &rightmax, &rightmin)) 3191 goto fail; /* no range available from stats */ 3192 } 3193 3194 /* 3195 * Now, the fraction of the left variable that will be scanned is the 3196 * fraction that's <= the right-side maximum value. But only believe 3197 * non-default estimates, else stick with our 1.0. 3198 */ 3199 selec = scalarineqsel(root, leop, isgt, true, &leftvar, 3200 rightmax, op_righttype); 3201 if (selec != DEFAULT_INEQ_SEL) 3202 *leftend = selec; 3203 3204 /* And similarly for the right variable. */ 3205 selec = scalarineqsel(root, revleop, isgt, true, &rightvar, 3206 leftmax, op_lefttype); 3207 if (selec != DEFAULT_INEQ_SEL) 3208 *rightend = selec; 3209 3210 /* 3211 * Only one of the two "end" fractions can really be less than 1.0; 3212 * believe the smaller estimate and reset the other one to exactly 1.0. If 3213 * we get exactly equal estimates (as can easily happen with self-joins), 3214 * believe neither. 3215 */ 3216 if (*leftend > *rightend) 3217 *leftend = 1.0; 3218 else if (*leftend < *rightend) 3219 *rightend = 1.0; 3220 else 3221 *leftend = *rightend = 1.0; 3222 3223 /* 3224 * Also, the fraction of the left variable that will be scanned before the 3225 * first join pair is found is the fraction that's < the right-side 3226 * minimum value. But only believe non-default estimates, else stick with 3227 * our own default. 3228 */ 3229 selec = scalarineqsel(root, ltop, isgt, false, &leftvar, 3230 rightmin, op_righttype); 3231 if (selec != DEFAULT_INEQ_SEL) 3232 *leftstart = selec; 3233 3234 /* And similarly for the right variable. */ 3235 selec = scalarineqsel(root, revltop, isgt, false, &rightvar, 3236 leftmin, op_lefttype); 3237 if (selec != DEFAULT_INEQ_SEL) 3238 *rightstart = selec; 3239 3240 /* 3241 * Only one of the two "start" fractions can really be more than zero; 3242 * believe the larger estimate and reset the other one to exactly 0.0. If 3243 * we get exactly equal estimates (as can easily happen with self-joins), 3244 * believe neither. 3245 */ 3246 if (*leftstart < *rightstart) 3247 *leftstart = 0.0; 3248 else if (*leftstart > *rightstart) 3249 *rightstart = 0.0; 3250 else 3251 *leftstart = *rightstart = 0.0; 3252 3253 /* 3254 * If the sort order is nulls-first, we're going to have to skip over any 3255 * nulls too. These would not have been counted by scalarineqsel, and we 3256 * can safely add in this fraction regardless of whether we believe 3257 * scalarineqsel's results or not. But be sure to clamp the sum to 1.0! 3258 */ 3259 if (nulls_first) 3260 { 3261 Form_pg_statistic stats; 3262 3263 if (HeapTupleIsValid(leftvar.statsTuple)) 3264 { 3265 stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple); 3266 *leftstart += stats->stanullfrac; 3267 CLAMP_PROBABILITY(*leftstart); 3268 *leftend += stats->stanullfrac; 3269 CLAMP_PROBABILITY(*leftend); 3270 } 3271 if (HeapTupleIsValid(rightvar.statsTuple)) 3272 { 3273 stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple); 3274 *rightstart += stats->stanullfrac; 3275 CLAMP_PROBABILITY(*rightstart); 3276 *rightend += stats->stanullfrac; 3277 CLAMP_PROBABILITY(*rightend); 3278 } 3279 } 3280 3281 /* Disbelieve start >= end, just in case that can happen */ 3282 if (*leftstart >= *leftend) 3283 { 3284 *leftstart = 0.0; 3285 *leftend = 1.0; 3286 } 3287 if (*rightstart >= *rightend) 3288 { 3289 *rightstart = 0.0; 3290 *rightend = 1.0; 3291 } 3292 3293 fail: 3294 ReleaseVariableStats(leftvar); 3295 ReleaseVariableStats(rightvar); 3296 } 3297 3298 3299 /* 3300 * Helper routine for estimate_num_groups: add an item to a list of 3301 * GroupVarInfos, but only if it's not known equal to any of the existing 3302 * entries. 3303 */ 3304 typedef struct 3305 { 3306 Node *var; /* might be an expression, not just a Var */ 3307 RelOptInfo *rel; /* relation it belongs to */ 3308 double ndistinct; /* # distinct values */ 3309 } GroupVarInfo; 3310 3311 static List * 3312 add_unique_group_var(PlannerInfo *root, List *varinfos, 3313 Node *var, VariableStatData *vardata) 3314 { 3315 GroupVarInfo *varinfo; 3316 double ndistinct; 3317 bool isdefault; 3318 ListCell *lc; 3319 3320 ndistinct = get_variable_numdistinct(vardata, &isdefault); 3321 3322 /* cannot use foreach here because of possible list_delete */ 3323 lc = list_head(varinfos); 3324 while (lc) 3325 { 3326 varinfo = (GroupVarInfo *) lfirst(lc); 3327 3328 /* must advance lc before list_delete possibly pfree's it */ 3329 lc = lnext(lc); 3330 3331 /* Drop exact duplicates */ 3332 if (equal(var, varinfo->var)) 3333 return varinfos; 3334 3335 /* 3336 * Drop known-equal vars, but only if they belong to different 3337 * relations (see comments for estimate_num_groups) 3338 */ 3339 if (vardata->rel != varinfo->rel && 3340 exprs_known_equal(root, var, varinfo->var)) 3341 { 3342 if (varinfo->ndistinct <= ndistinct) 3343 { 3344 /* Keep older item, forget new one */ 3345 return varinfos; 3346 } 3347 else 3348 { 3349 /* Delete the older item */ 3350 varinfos = list_delete_ptr(varinfos, varinfo); 3351 } 3352 } 3353 } 3354 3355 varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo)); 3356 3357 varinfo->var = var; 3358 varinfo->rel = vardata->rel; 3359 varinfo->ndistinct = ndistinct; 3360 varinfos = lappend(varinfos, varinfo); 3361 return varinfos; 3362 } 3363 3364 /* 3365 * estimate_num_groups - Estimate number of groups in a grouped query 3366 * 3367 * Given a query having a GROUP BY clause, estimate how many groups there 3368 * will be --- ie, the number of distinct combinations of the GROUP BY 3369 * expressions. 3370 * 3371 * This routine is also used to estimate the number of rows emitted by 3372 * a DISTINCT filtering step; that is an isomorphic problem. (Note: 3373 * actually, we only use it for DISTINCT when there's no grouping or 3374 * aggregation ahead of the DISTINCT.) 3375 * 3376 * Inputs: 3377 * root - the query 3378 * groupExprs - list of expressions being grouped by 3379 * input_rows - number of rows estimated to arrive at the group/unique 3380 * filter step 3381 * pgset - NULL, or a List** pointing to a grouping set to filter the 3382 * groupExprs against 3383 * 3384 * Given the lack of any cross-correlation statistics in the system, it's 3385 * impossible to do anything really trustworthy with GROUP BY conditions 3386 * involving multiple Vars. We should however avoid assuming the worst 3387 * case (all possible cross-product terms actually appear as groups) since 3388 * very often the grouped-by Vars are highly correlated. Our current approach 3389 * is as follows: 3390 * 1. Expressions yielding boolean are assumed to contribute two groups, 3391 * independently of their content, and are ignored in the subsequent 3392 * steps. This is mainly because tests like "col IS NULL" break the 3393 * heuristic used in step 2 especially badly. 3394 * 2. Reduce the given expressions to a list of unique Vars used. For 3395 * example, GROUP BY a, a + b is treated the same as GROUP BY a, b. 3396 * It is clearly correct not to count the same Var more than once. 3397 * It is also reasonable to treat f(x) the same as x: f() cannot 3398 * increase the number of distinct values (unless it is volatile, 3399 * which we consider unlikely for grouping), but it probably won't 3400 * reduce the number of distinct values much either. 3401 * As a special case, if a GROUP BY expression can be matched to an 3402 * expressional index for which we have statistics, then we treat the 3403 * whole expression as though it were just a Var. 3404 * 3. If the list contains Vars of different relations that are known equal 3405 * due to equivalence classes, then drop all but one of the Vars from each 3406 * known-equal set, keeping the one with smallest estimated # of values 3407 * (since the extra values of the others can't appear in joined rows). 3408 * Note the reason we only consider Vars of different relations is that 3409 * if we considered ones of the same rel, we'd be double-counting the 3410 * restriction selectivity of the equality in the next step. 3411 * 4. For Vars within a single source rel, we multiply together the numbers 3412 * of values, clamp to the number of rows in the rel (divided by 10 if 3413 * more than one Var), and then multiply by a factor based on the 3414 * selectivity of the restriction clauses for that rel. When there's 3415 * more than one Var, the initial product is probably too high (it's the 3416 * worst case) but clamping to a fraction of the rel's rows seems to be a 3417 * helpful heuristic for not letting the estimate get out of hand. (The 3418 * factor of 10 is derived from pre-Postgres-7.4 practice.) The factor 3419 * we multiply by to adjust for the restriction selectivity assumes that 3420 * the restriction clauses are independent of the grouping, which may not 3421 * be a valid assumption, but it's hard to do better. 3422 * 5. If there are Vars from multiple rels, we repeat step 4 for each such 3423 * rel, and multiply the results together. 3424 * Note that rels not containing grouped Vars are ignored completely, as are 3425 * join clauses. Such rels cannot increase the number of groups, and we 3426 * assume such clauses do not reduce the number either (somewhat bogus, 3427 * but we don't have the info to do better). 3428 */ 3429 double 3430 estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows, 3431 List **pgset) 3432 { 3433 List *varinfos = NIL; 3434 double srf_multiplier = 1.0; 3435 double numdistinct; 3436 ListCell *l; 3437 int i; 3438 3439 /* 3440 * We don't ever want to return an estimate of zero groups, as that tends 3441 * to lead to division-by-zero and other unpleasantness. The input_rows 3442 * estimate is usually already at least 1, but clamp it just in case it 3443 * isn't. 3444 */ 3445 input_rows = clamp_row_est(input_rows); 3446 3447 /* 3448 * If no grouping columns, there's exactly one group. (This can't happen 3449 * for normal cases with GROUP BY or DISTINCT, but it is possible for 3450 * corner cases with set operations.) 3451 */ 3452 if (groupExprs == NIL || (pgset && list_length(*pgset) < 1)) 3453 return 1.0; 3454 3455 /* 3456 * Count groups derived from boolean grouping expressions. For other 3457 * expressions, find the unique Vars used, treating an expression as a Var 3458 * if we can find stats for it. For each one, record the statistical 3459 * estimate of number of distinct values (total in its table, without 3460 * regard for filtering). 3461 */ 3462 numdistinct = 1.0; 3463 3464 i = 0; 3465 foreach(l, groupExprs) 3466 { 3467 Node *groupexpr = (Node *) lfirst(l); 3468 double this_srf_multiplier; 3469 VariableStatData vardata; 3470 List *varshere; 3471 ListCell *l2; 3472 3473 /* is expression in this grouping set? */ 3474 if (pgset && !list_member_int(*pgset, i++)) 3475 continue; 3476 3477 /* 3478 * Set-returning functions in grouping columns are a bit problematic. 3479 * The code below will effectively ignore their SRF nature and come up 3480 * with a numdistinct estimate as though they were scalar functions. 3481 * We compensate by scaling up the end result by the largest SRF 3482 * rowcount estimate. (This will be an overestimate if the SRF 3483 * produces multiple copies of any output value, but it seems best to 3484 * assume the SRF's outputs are distinct. In any case, it's probably 3485 * pointless to worry too much about this without much better 3486 * estimates for SRF output rowcounts than we have today.) 3487 */ 3488 this_srf_multiplier = expression_returns_set_rows(groupexpr); 3489 if (srf_multiplier < this_srf_multiplier) 3490 srf_multiplier = this_srf_multiplier; 3491 3492 /* Short-circuit for expressions returning boolean */ 3493 if (exprType(groupexpr) == BOOLOID) 3494 { 3495 numdistinct *= 2.0; 3496 continue; 3497 } 3498 3499 /* 3500 * If examine_variable is able to deduce anything about the GROUP BY 3501 * expression, treat it as a single variable even if it's really more 3502 * complicated. 3503 */ 3504 examine_variable(root, groupexpr, 0, &vardata); 3505 if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique) 3506 { 3507 varinfos = add_unique_group_var(root, varinfos, 3508 groupexpr, &vardata); 3509 ReleaseVariableStats(vardata); 3510 continue; 3511 } 3512 ReleaseVariableStats(vardata); 3513 3514 /* 3515 * Else pull out the component Vars. Handle PlaceHolderVars by 3516 * recursing into their arguments (effectively assuming that the 3517 * PlaceHolderVar doesn't change the number of groups, which boils 3518 * down to ignoring the possible addition of nulls to the result set). 3519 */ 3520 varshere = pull_var_clause(groupexpr, 3521 PVC_RECURSE_AGGREGATES | 3522 PVC_RECURSE_WINDOWFUNCS | 3523 PVC_RECURSE_PLACEHOLDERS); 3524 3525 /* 3526 * If we find any variable-free GROUP BY item, then either it is a 3527 * constant (and we can ignore it) or it contains a volatile function; 3528 * in the latter case we punt and assume that each input row will 3529 * yield a distinct group. 3530 */ 3531 if (varshere == NIL) 3532 { 3533 if (contain_volatile_functions(groupexpr)) 3534 return input_rows; 3535 continue; 3536 } 3537 3538 /* 3539 * Else add variables to varinfos list 3540 */ 3541 foreach(l2, varshere) 3542 { 3543 Node *var = (Node *) lfirst(l2); 3544 3545 examine_variable(root, var, 0, &vardata); 3546 varinfos = add_unique_group_var(root, varinfos, var, &vardata); 3547 ReleaseVariableStats(vardata); 3548 } 3549 } 3550 3551 /* 3552 * If now no Vars, we must have an all-constant or all-boolean GROUP BY 3553 * list. 3554 */ 3555 if (varinfos == NIL) 3556 { 3557 /* Apply SRF multiplier as we would do in the long path */ 3558 numdistinct *= srf_multiplier; 3559 /* Round off */ 3560 numdistinct = ceil(numdistinct); 3561 /* Guard against out-of-range answers */ 3562 if (numdistinct > input_rows) 3563 numdistinct = input_rows; 3564 if (numdistinct < 1.0) 3565 numdistinct = 1.0; 3566 return numdistinct; 3567 } 3568 3569 /* 3570 * Group Vars by relation and estimate total numdistinct. 3571 * 3572 * For each iteration of the outer loop, we process the frontmost Var in 3573 * varinfos, plus all other Vars in the same relation. We remove these 3574 * Vars from the newvarinfos list for the next iteration. This is the 3575 * easiest way to group Vars of same rel together. 3576 */ 3577 do 3578 { 3579 GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos); 3580 RelOptInfo *rel = varinfo1->rel; 3581 double reldistinct = 1; 3582 double relmaxndistinct = reldistinct; 3583 int relvarcount = 0; 3584 List *newvarinfos = NIL; 3585 List *relvarinfos = NIL; 3586 3587 /* 3588 * Split the list of varinfos in two - one for the current rel, one 3589 * for remaining Vars on other rels. 3590 */ 3591 relvarinfos = lcons(varinfo1, relvarinfos); 3592 for_each_cell(l, lnext(list_head(varinfos))) 3593 { 3594 GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l); 3595 3596 if (varinfo2->rel == varinfo1->rel) 3597 { 3598 /* varinfos on current rel */ 3599 relvarinfos = lcons(varinfo2, relvarinfos); 3600 } 3601 else 3602 { 3603 /* not time to process varinfo2 yet */ 3604 newvarinfos = lcons(varinfo2, newvarinfos); 3605 } 3606 } 3607 3608 /* 3609 * Get the numdistinct estimate for the Vars of this rel. We 3610 * iteratively search for multivariate n-distinct with maximum number 3611 * of vars; assuming that each var group is independent of the others, 3612 * we multiply them together. Any remaining relvarinfos after no more 3613 * multivariate matches are found are assumed independent too, so 3614 * their individual ndistinct estimates are multiplied also. 3615 * 3616 * While iterating, count how many separate numdistinct values we 3617 * apply. We apply a fudge factor below, but only if we multiplied 3618 * more than one such values. 3619 */ 3620 while (relvarinfos) 3621 { 3622 double mvndistinct; 3623 3624 if (estimate_multivariate_ndistinct(root, rel, &relvarinfos, 3625 &mvndistinct)) 3626 { 3627 reldistinct *= mvndistinct; 3628 if (relmaxndistinct < mvndistinct) 3629 relmaxndistinct = mvndistinct; 3630 relvarcount++; 3631 } 3632 else 3633 { 3634 foreach(l, relvarinfos) 3635 { 3636 GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l); 3637 3638 reldistinct *= varinfo2->ndistinct; 3639 if (relmaxndistinct < varinfo2->ndistinct) 3640 relmaxndistinct = varinfo2->ndistinct; 3641 relvarcount++; 3642 } 3643 3644 /* we're done with this relation */ 3645 relvarinfos = NIL; 3646 } 3647 } 3648 3649 /* 3650 * Sanity check --- don't divide by zero if empty relation. 3651 */ 3652 Assert(IS_SIMPLE_REL(rel)); 3653 if (rel->tuples > 0) 3654 { 3655 /* 3656 * Clamp to size of rel, or size of rel / 10 if multiple Vars. The 3657 * fudge factor is because the Vars are probably correlated but we 3658 * don't know by how much. We should never clamp to less than the 3659 * largest ndistinct value for any of the Vars, though, since 3660 * there will surely be at least that many groups. 3661 */ 3662 double clamp = rel->tuples; 3663 3664 if (relvarcount > 1) 3665 { 3666 clamp *= 0.1; 3667 if (clamp < relmaxndistinct) 3668 { 3669 clamp = relmaxndistinct; 3670 /* for sanity in case some ndistinct is too large: */ 3671 if (clamp > rel->tuples) 3672 clamp = rel->tuples; 3673 } 3674 } 3675 if (reldistinct > clamp) 3676 reldistinct = clamp; 3677 3678 /* 3679 * Update the estimate based on the restriction selectivity, 3680 * guarding against division by zero when reldistinct is zero. 3681 * Also skip this if we know that we are returning all rows. 3682 */ 3683 if (reldistinct > 0 && rel->rows < rel->tuples) 3684 { 3685 /* 3686 * Given a table containing N rows with n distinct values in a 3687 * uniform distribution, if we select p rows at random then 3688 * the expected number of distinct values selected is 3689 * 3690 * n * (1 - product((N-N/n-i)/(N-i), i=0..p-1)) 3691 * 3692 * = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!) 3693 * 3694 * See "Approximating block accesses in database 3695 * organizations", S. B. Yao, Communications of the ACM, 3696 * Volume 20 Issue 4, April 1977 Pages 260-261. 3697 * 3698 * Alternatively, re-arranging the terms from the factorials, 3699 * this may be written as 3700 * 3701 * n * (1 - product((N-p-i)/(N-i), i=0..N/n-1)) 3702 * 3703 * This form of the formula is more efficient to compute in 3704 * the common case where p is larger than N/n. Additionally, 3705 * as pointed out by Dell'Era, if i << N for all terms in the 3706 * product, it can be approximated by 3707 * 3708 * n * (1 - ((N-p)/N)^(N/n)) 3709 * 3710 * See "Expected distinct values when selecting from a bag 3711 * without replacement", Alberto Dell'Era, 3712 * http://www.adellera.it/investigations/distinct_balls/. 3713 * 3714 * The condition i << N is equivalent to n >> 1, so this is a 3715 * good approximation when the number of distinct values in 3716 * the table is large. It turns out that this formula also 3717 * works well even when n is small. 3718 */ 3719 reldistinct *= 3720 (1 - pow((rel->tuples - rel->rows) / rel->tuples, 3721 rel->tuples / reldistinct)); 3722 } 3723 reldistinct = clamp_row_est(reldistinct); 3724 3725 /* 3726 * Update estimate of total distinct groups. 3727 */ 3728 numdistinct *= reldistinct; 3729 } 3730 3731 varinfos = newvarinfos; 3732 } while (varinfos != NIL); 3733 3734 /* Now we can account for the effects of any SRFs */ 3735 numdistinct *= srf_multiplier; 3736 3737 /* Round off */ 3738 numdistinct = ceil(numdistinct); 3739 3740 /* Guard against out-of-range answers */ 3741 if (numdistinct > input_rows) 3742 numdistinct = input_rows; 3743 if (numdistinct < 1.0) 3744 numdistinct = 1.0; 3745 3746 return numdistinct; 3747 } 3748 3749 /* 3750 * Estimate hash bucket statistics when the specified expression is used 3751 * as a hash key for the given number of buckets. 3752 * 3753 * This attempts to determine two values: 3754 * 3755 * 1. The frequency of the most common value of the expression (returns 3756 * zero into *mcv_freq if we can't get that). 3757 * 3758 * 2. The "bucketsize fraction", ie, average number of entries in a bucket 3759 * divided by total tuples in relation. 3760 * 3761 * XXX This is really pretty bogus since we're effectively assuming that the 3762 * distribution of hash keys will be the same after applying restriction 3763 * clauses as it was in the underlying relation. However, we are not nearly 3764 * smart enough to figure out how the restrict clauses might change the 3765 * distribution, so this will have to do for now. 3766 * 3767 * We are passed the number of buckets the executor will use for the given 3768 * input relation. If the data were perfectly distributed, with the same 3769 * number of tuples going into each available bucket, then the bucketsize 3770 * fraction would be 1/nbuckets. But this happy state of affairs will occur 3771 * only if (a) there are at least nbuckets distinct data values, and (b) 3772 * we have a not-too-skewed data distribution. Otherwise the buckets will 3773 * be nonuniformly occupied. If the other relation in the join has a key 3774 * distribution similar to this one's, then the most-loaded buckets are 3775 * exactly those that will be probed most often. Therefore, the "average" 3776 * bucket size for costing purposes should really be taken as something close 3777 * to the "worst case" bucket size. We try to estimate this by adjusting the 3778 * fraction if there are too few distinct data values, and then scaling up 3779 * by the ratio of the most common value's frequency to the average frequency. 3780 * 3781 * If no statistics are available, use a default estimate of 0.1. This will 3782 * discourage use of a hash rather strongly if the inner relation is large, 3783 * which is what we want. We do not want to hash unless we know that the 3784 * inner rel is well-dispersed (or the alternatives seem much worse). 3785 * 3786 * The caller should also check that the mcv_freq is not so large that the 3787 * most common value would by itself require an impractically large bucket. 3788 * In a hash join, the executor can split buckets if they get too big, but 3789 * obviously that doesn't help for a bucket that contains many duplicates of 3790 * the same value. 3791 */ 3792 void 3793 estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets, 3794 Selectivity *mcv_freq, 3795 Selectivity *bucketsize_frac) 3796 { 3797 VariableStatData vardata; 3798 double estfract, 3799 ndistinct, 3800 stanullfrac, 3801 avgfreq; 3802 bool isdefault; 3803 AttStatsSlot sslot; 3804 3805 examine_variable(root, hashkey, 0, &vardata); 3806 3807 /* Look up the frequency of the most common value, if available */ 3808 *mcv_freq = 0.0; 3809 3810 if (HeapTupleIsValid(vardata.statsTuple)) 3811 { 3812 if (get_attstatsslot(&sslot, vardata.statsTuple, 3813 STATISTIC_KIND_MCV, InvalidOid, 3814 ATTSTATSSLOT_NUMBERS)) 3815 { 3816 /* 3817 * The first MCV stat is for the most common value. 3818 */ 3819 if (sslot.nnumbers > 0) 3820 *mcv_freq = sslot.numbers[0]; 3821 free_attstatsslot(&sslot); 3822 } 3823 } 3824 3825 /* Get number of distinct values */ 3826 ndistinct = get_variable_numdistinct(&vardata, &isdefault); 3827 3828 /* 3829 * If ndistinct isn't real, punt. We normally return 0.1, but if the 3830 * mcv_freq is known to be even higher than that, use it instead. 3831 */ 3832 if (isdefault) 3833 { 3834 *bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq); 3835 ReleaseVariableStats(vardata); 3836 return; 3837 } 3838 3839 /* Get fraction that are null */ 3840 if (HeapTupleIsValid(vardata.statsTuple)) 3841 { 3842 Form_pg_statistic stats; 3843 3844 stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple); 3845 stanullfrac = stats->stanullfrac; 3846 } 3847 else 3848 stanullfrac = 0.0; 3849 3850 /* Compute avg freq of all distinct data values in raw relation */ 3851 avgfreq = (1.0 - stanullfrac) / ndistinct; 3852 3853 /* 3854 * Adjust ndistinct to account for restriction clauses. Observe we are 3855 * assuming that the data distribution is affected uniformly by the 3856 * restriction clauses! 3857 * 3858 * XXX Possibly better way, but much more expensive: multiply by 3859 * selectivity of rel's restriction clauses that mention the target Var. 3860 */ 3861 if (vardata.rel && vardata.rel->tuples > 0) 3862 { 3863 ndistinct *= vardata.rel->rows / vardata.rel->tuples; 3864 ndistinct = clamp_row_est(ndistinct); 3865 } 3866 3867 /* 3868 * Initial estimate of bucketsize fraction is 1/nbuckets as long as the 3869 * number of buckets is less than the expected number of distinct values; 3870 * otherwise it is 1/ndistinct. 3871 */ 3872 if (ndistinct > nbuckets) 3873 estfract = 1.0 / nbuckets; 3874 else 3875 estfract = 1.0 / ndistinct; 3876 3877 /* 3878 * Adjust estimated bucketsize upward to account for skewed distribution. 3879 */ 3880 if (avgfreq > 0.0 && *mcv_freq > avgfreq) 3881 estfract *= *mcv_freq / avgfreq; 3882 3883 /* 3884 * Clamp bucketsize to sane range (the above adjustment could easily 3885 * produce an out-of-range result). We set the lower bound a little above 3886 * zero, since zero isn't a very sane result. 3887 */ 3888 if (estfract < 1.0e-6) 3889 estfract = 1.0e-6; 3890 else if (estfract > 1.0) 3891 estfract = 1.0; 3892 3893 *bucketsize_frac = (Selectivity) estfract; 3894 3895 ReleaseVariableStats(vardata); 3896 } 3897 3898 3899 /*------------------------------------------------------------------------- 3900 * 3901 * Support routines 3902 * 3903 *------------------------------------------------------------------------- 3904 */ 3905 3906 /* 3907 * Find applicable ndistinct statistics for the given list of VarInfos (which 3908 * must all belong to the given rel), and update *ndistinct to the estimate of 3909 * the MVNDistinctItem that best matches. If a match it found, *varinfos is 3910 * updated to remove the list of matched varinfos. 3911 * 3912 * Varinfos that aren't for simple Vars are ignored. 3913 * 3914 * Return true if we're able to find a match, false otherwise. 3915 */ 3916 static bool 3917 estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel, 3918 List **varinfos, double *ndistinct) 3919 { 3920 ListCell *lc; 3921 Bitmapset *attnums = NULL; 3922 int nmatches; 3923 Oid statOid = InvalidOid; 3924 MVNDistinct *stats; 3925 Bitmapset *matched = NULL; 3926 3927 /* bail out immediately if the table has no extended statistics */ 3928 if (!rel->statlist) 3929 return false; 3930 3931 /* Determine the attnums we're looking for */ 3932 foreach(lc, *varinfos) 3933 { 3934 GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc); 3935 AttrNumber attnum; 3936 3937 Assert(varinfo->rel == rel); 3938 3939 if (!IsA(varinfo->var, Var)) 3940 continue; 3941 3942 attnum = ((Var *) varinfo->var)->varattno; 3943 3944 if (!AttrNumberIsForUserDefinedAttr(attnum)) 3945 continue; 3946 3947 attnums = bms_add_member(attnums, attnum); 3948 } 3949 3950 /* look for the ndistinct statistics matching the most vars */ 3951 nmatches = 1; /* we require at least two matches */ 3952 foreach(lc, rel->statlist) 3953 { 3954 StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc); 3955 Bitmapset *shared; 3956 int nshared; 3957 3958 /* skip statistics of other kinds */ 3959 if (info->kind != STATS_EXT_NDISTINCT) 3960 continue; 3961 3962 /* compute attnums shared by the vars and the statistics object */ 3963 shared = bms_intersect(info->keys, attnums); 3964 nshared = bms_num_members(shared); 3965 3966 /* 3967 * Does this statistics object match more columns than the currently 3968 * best object? If so, use this one instead. 3969 * 3970 * XXX This should break ties using name of the object, or something 3971 * like that, to make the outcome stable. 3972 */ 3973 if (nshared > nmatches) 3974 { 3975 statOid = info->statOid; 3976 nmatches = nshared; 3977 matched = shared; 3978 } 3979 } 3980 3981 /* No match? */ 3982 if (statOid == InvalidOid) 3983 return false; 3984 Assert(nmatches > 1 && matched != NULL); 3985 3986 stats = statext_ndistinct_load(statOid); 3987 3988 /* 3989 * If we have a match, search it for the specific item that matches (there 3990 * must be one), and construct the output values. 3991 */ 3992 if (stats) 3993 { 3994 int i; 3995 List *newlist = NIL; 3996 MVNDistinctItem *item = NULL; 3997 3998 /* Find the specific item that exactly matches the combination */ 3999 for (i = 0; i < stats->nitems; i++) 4000 { 4001 MVNDistinctItem *tmpitem = &stats->items[i]; 4002 4003 if (bms_subset_compare(tmpitem->attrs, matched) == BMS_EQUAL) 4004 { 4005 item = tmpitem; 4006 break; 4007 } 4008 } 4009 4010 /* make sure we found an item */ 4011 if (!item) 4012 elog(ERROR, "corrupt MVNDistinct entry"); 4013 4014 /* Form the output varinfo list, keeping only unmatched ones */ 4015 foreach(lc, *varinfos) 4016 { 4017 GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc); 4018 AttrNumber attnum; 4019 4020 if (!IsA(varinfo->var, Var)) 4021 { 4022 newlist = lappend(newlist, varinfo); 4023 continue; 4024 } 4025 4026 attnum = ((Var *) varinfo->var)->varattno; 4027 4028 if (AttrNumberIsForUserDefinedAttr(attnum) && 4029 bms_is_member(attnum, matched)) 4030 continue; 4031 4032 newlist = lappend(newlist, varinfo); 4033 } 4034 4035 *varinfos = newlist; 4036 *ndistinct = item->ndistinct; 4037 return true; 4038 } 4039 4040 return false; 4041 } 4042 4043 /* 4044 * convert_to_scalar 4045 * Convert non-NULL values of the indicated types to the comparison 4046 * scale needed by scalarineqsel(). 4047 * Returns "true" if successful. 4048 * 4049 * XXX this routine is a hack: ideally we should look up the conversion 4050 * subroutines in pg_type. 4051 * 4052 * All numeric datatypes are simply converted to their equivalent 4053 * "double" values. (NUMERIC values that are outside the range of "double" 4054 * are clamped to +/- HUGE_VAL.) 4055 * 4056 * String datatypes are converted by convert_string_to_scalar(), 4057 * which is explained below. The reason why this routine deals with 4058 * three values at a time, not just one, is that we need it for strings. 4059 * 4060 * The bytea datatype is just enough different from strings that it has 4061 * to be treated separately. 4062 * 4063 * The several datatypes representing absolute times are all converted 4064 * to Timestamp, which is actually a double, and then we just use that 4065 * double value. Note this will give correct results even for the "special" 4066 * values of Timestamp, since those are chosen to compare correctly; 4067 * see timestamp_cmp. 4068 * 4069 * The several datatypes representing relative times (intervals) are all 4070 * converted to measurements expressed in seconds. 4071 */ 4072 static bool 4073 convert_to_scalar(Datum value, Oid valuetypid, double *scaledvalue, 4074 Datum lobound, Datum hibound, Oid boundstypid, 4075 double *scaledlobound, double *scaledhibound) 4076 { 4077 bool failure = false; 4078 4079 /* 4080 * Both the valuetypid and the boundstypid should exactly match the 4081 * declared input type(s) of the operator we are invoked for. However, 4082 * extensions might try to use scalarineqsel as estimator for operators 4083 * with input type(s) we don't handle here; in such cases, we want to 4084 * return false, not fail. In any case, we mustn't assume that valuetypid 4085 * and boundstypid are identical. 4086 * 4087 * XXX The histogram we are interpolating between points of could belong 4088 * to a column that's only binary-compatible with the declared type. In 4089 * essence we are assuming that the semantics of binary-compatible types 4090 * are enough alike that we can use a histogram generated with one type's 4091 * operators to estimate selectivity for the other's. This is outright 4092 * wrong in some cases --- in particular signed versus unsigned 4093 * interpretation could trip us up. But it's useful enough in the 4094 * majority of cases that we do it anyway. Should think about more 4095 * rigorous ways to do it. 4096 */ 4097 switch (valuetypid) 4098 { 4099 /* 4100 * Built-in numeric types 4101 */ 4102 case BOOLOID: 4103 case INT2OID: 4104 case INT4OID: 4105 case INT8OID: 4106 case FLOAT4OID: 4107 case FLOAT8OID: 4108 case NUMERICOID: 4109 case OIDOID: 4110 case REGPROCOID: 4111 case REGPROCEDUREOID: 4112 case REGOPEROID: 4113 case REGOPERATOROID: 4114 case REGCLASSOID: 4115 case REGTYPEOID: 4116 case REGCONFIGOID: 4117 case REGDICTIONARYOID: 4118 case REGROLEOID: 4119 case REGNAMESPACEOID: 4120 *scaledvalue = convert_numeric_to_scalar(value, valuetypid, 4121 &failure); 4122 *scaledlobound = convert_numeric_to_scalar(lobound, boundstypid, 4123 &failure); 4124 *scaledhibound = convert_numeric_to_scalar(hibound, boundstypid, 4125 &failure); 4126 return !failure; 4127 4128 /* 4129 * Built-in string types 4130 */ 4131 case CHAROID: 4132 case BPCHAROID: 4133 case VARCHAROID: 4134 case TEXTOID: 4135 case NAMEOID: 4136 { 4137 char *valstr = convert_string_datum(value, valuetypid, 4138 &failure); 4139 char *lostr = convert_string_datum(lobound, boundstypid, 4140 &failure); 4141 char *histr = convert_string_datum(hibound, boundstypid, 4142 &failure); 4143 4144 /* 4145 * Bail out if any of the values is not of string type. We 4146 * might leak converted strings for the other value(s), but 4147 * that's not worth troubling over. 4148 */ 4149 if (failure) 4150 return false; 4151 4152 convert_string_to_scalar(valstr, scaledvalue, 4153 lostr, scaledlobound, 4154 histr, scaledhibound); 4155 pfree(valstr); 4156 pfree(lostr); 4157 pfree(histr); 4158 return true; 4159 } 4160 4161 /* 4162 * Built-in bytea type 4163 */ 4164 case BYTEAOID: 4165 { 4166 /* We only support bytea vs bytea comparison */ 4167 if (boundstypid != BYTEAOID) 4168 return false; 4169 convert_bytea_to_scalar(value, scaledvalue, 4170 lobound, scaledlobound, 4171 hibound, scaledhibound); 4172 return true; 4173 } 4174 4175 /* 4176 * Built-in time types 4177 */ 4178 case TIMESTAMPOID: 4179 case TIMESTAMPTZOID: 4180 case ABSTIMEOID: 4181 case DATEOID: 4182 case INTERVALOID: 4183 case RELTIMEOID: 4184 case TINTERVALOID: 4185 case TIMEOID: 4186 case TIMETZOID: 4187 *scaledvalue = convert_timevalue_to_scalar(value, valuetypid, 4188 &failure); 4189 *scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid, 4190 &failure); 4191 *scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid, 4192 &failure); 4193 return !failure; 4194 4195 /* 4196 * Built-in network types 4197 */ 4198 case INETOID: 4199 case CIDROID: 4200 case MACADDROID: 4201 case MACADDR8OID: 4202 *scaledvalue = convert_network_to_scalar(value, valuetypid, 4203 &failure); 4204 *scaledlobound = convert_network_to_scalar(lobound, boundstypid, 4205 &failure); 4206 *scaledhibound = convert_network_to_scalar(hibound, boundstypid, 4207 &failure); 4208 return !failure; 4209 } 4210 /* Don't know how to convert */ 4211 *scaledvalue = *scaledlobound = *scaledhibound = 0; 4212 return false; 4213 } 4214 4215 /* 4216 * Do convert_to_scalar()'s work for any numeric data type. 4217 * 4218 * On failure (e.g., unsupported typid), set *failure to true; 4219 * otherwise, that variable is not changed. 4220 */ 4221 static double 4222 convert_numeric_to_scalar(Datum value, Oid typid, bool *failure) 4223 { 4224 switch (typid) 4225 { 4226 case BOOLOID: 4227 return (double) DatumGetBool(value); 4228 case INT2OID: 4229 return (double) DatumGetInt16(value); 4230 case INT4OID: 4231 return (double) DatumGetInt32(value); 4232 case INT8OID: 4233 return (double) DatumGetInt64(value); 4234 case FLOAT4OID: 4235 return (double) DatumGetFloat4(value); 4236 case FLOAT8OID: 4237 return (double) DatumGetFloat8(value); 4238 case NUMERICOID: 4239 /* Note: out-of-range values will be clamped to +-HUGE_VAL */ 4240 return (double) 4241 DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow, 4242 value)); 4243 case OIDOID: 4244 case REGPROCOID: 4245 case REGPROCEDUREOID: 4246 case REGOPEROID: 4247 case REGOPERATOROID: 4248 case REGCLASSOID: 4249 case REGTYPEOID: 4250 case REGCONFIGOID: 4251 case REGDICTIONARYOID: 4252 case REGROLEOID: 4253 case REGNAMESPACEOID: 4254 /* we can treat OIDs as integers... */ 4255 return (double) DatumGetObjectId(value); 4256 } 4257 4258 *failure = true; 4259 return 0; 4260 } 4261 4262 /* 4263 * Do convert_to_scalar()'s work for any character-string data type. 4264 * 4265 * String datatypes are converted to a scale that ranges from 0 to 1, 4266 * where we visualize the bytes of the string as fractional digits. 4267 * 4268 * We do not want the base to be 256, however, since that tends to 4269 * generate inflated selectivity estimates; few databases will have 4270 * occurrences of all 256 possible byte values at each position. 4271 * Instead, use the smallest and largest byte values seen in the bounds 4272 * as the estimated range for each byte, after some fudging to deal with 4273 * the fact that we probably aren't going to see the full range that way. 4274 * 4275 * An additional refinement is that we discard any common prefix of the 4276 * three strings before computing the scaled values. This allows us to 4277 * "zoom in" when we encounter a narrow data range. An example is a phone 4278 * number database where all the values begin with the same area code. 4279 * (Actually, the bounds will be adjacent histogram-bin-boundary values, 4280 * so this is more likely to happen than you might think.) 4281 */ 4282 static void 4283 convert_string_to_scalar(char *value, 4284 double *scaledvalue, 4285 char *lobound, 4286 double *scaledlobound, 4287 char *hibound, 4288 double *scaledhibound) 4289 { 4290 int rangelo, 4291 rangehi; 4292 char *sptr; 4293 4294 rangelo = rangehi = (unsigned char) hibound[0]; 4295 for (sptr = lobound; *sptr; sptr++) 4296 { 4297 if (rangelo > (unsigned char) *sptr) 4298 rangelo = (unsigned char) *sptr; 4299 if (rangehi < (unsigned char) *sptr) 4300 rangehi = (unsigned char) *sptr; 4301 } 4302 for (sptr = hibound; *sptr; sptr++) 4303 { 4304 if (rangelo > (unsigned char) *sptr) 4305 rangelo = (unsigned char) *sptr; 4306 if (rangehi < (unsigned char) *sptr) 4307 rangehi = (unsigned char) *sptr; 4308 } 4309 /* If range includes any upper-case ASCII chars, make it include all */ 4310 if (rangelo <= 'Z' && rangehi >= 'A') 4311 { 4312 if (rangelo > 'A') 4313 rangelo = 'A'; 4314 if (rangehi < 'Z') 4315 rangehi = 'Z'; 4316 } 4317 /* Ditto lower-case */ 4318 if (rangelo <= 'z' && rangehi >= 'a') 4319 { 4320 if (rangelo > 'a') 4321 rangelo = 'a'; 4322 if (rangehi < 'z') 4323 rangehi = 'z'; 4324 } 4325 /* Ditto digits */ 4326 if (rangelo <= '9' && rangehi >= '0') 4327 { 4328 if (rangelo > '0') 4329 rangelo = '0'; 4330 if (rangehi < '9') 4331 rangehi = '9'; 4332 } 4333 4334 /* 4335 * If range includes less than 10 chars, assume we have not got enough 4336 * data, and make it include regular ASCII set. 4337 */ 4338 if (rangehi - rangelo < 9) 4339 { 4340 rangelo = ' '; 4341 rangehi = 127; 4342 } 4343 4344 /* 4345 * Now strip any common prefix of the three strings. 4346 */ 4347 while (*lobound) 4348 { 4349 if (*lobound != *hibound || *lobound != *value) 4350 break; 4351 lobound++, hibound++, value++; 4352 } 4353 4354 /* 4355 * Now we can do the conversions. 4356 */ 4357 *scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi); 4358 *scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi); 4359 *scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi); 4360 } 4361 4362 static double 4363 convert_one_string_to_scalar(char *value, int rangelo, int rangehi) 4364 { 4365 int slen = strlen(value); 4366 double num, 4367 denom, 4368 base; 4369 4370 if (slen <= 0) 4371 return 0.0; /* empty string has scalar value 0 */ 4372 4373 /* 4374 * There seems little point in considering more than a dozen bytes from 4375 * the string. Since base is at least 10, that will give us nominal 4376 * resolution of at least 12 decimal digits, which is surely far more 4377 * precision than this estimation technique has got anyway (especially in 4378 * non-C locales). Also, even with the maximum possible base of 256, this 4379 * ensures denom cannot grow larger than 256^13 = 2.03e31, which will not 4380 * overflow on any known machine. 4381 */ 4382 if (slen > 12) 4383 slen = 12; 4384 4385 /* Convert initial characters to fraction */ 4386 base = rangehi - rangelo + 1; 4387 num = 0.0; 4388 denom = base; 4389 while (slen-- > 0) 4390 { 4391 int ch = (unsigned char) *value++; 4392 4393 if (ch < rangelo) 4394 ch = rangelo - 1; 4395 else if (ch > rangehi) 4396 ch = rangehi + 1; 4397 num += ((double) (ch - rangelo)) / denom; 4398 denom *= base; 4399 } 4400 4401 return num; 4402 } 4403 4404 /* 4405 * Convert a string-type Datum into a palloc'd, null-terminated string. 4406 * 4407 * On failure (e.g., unsupported typid), set *failure to true; 4408 * otherwise, that variable is not changed. (We'll return NULL on failure.) 4409 * 4410 * When using a non-C locale, we must pass the string through strxfrm() 4411 * before continuing, so as to generate correct locale-specific results. 4412 */ 4413 static char * 4414 convert_string_datum(Datum value, Oid typid, bool *failure) 4415 { 4416 char *val; 4417 4418 switch (typid) 4419 { 4420 case CHAROID: 4421 val = (char *) palloc(2); 4422 val[0] = DatumGetChar(value); 4423 val[1] = '\0'; 4424 break; 4425 case BPCHAROID: 4426 case VARCHAROID: 4427 case TEXTOID: 4428 val = TextDatumGetCString(value); 4429 break; 4430 case NAMEOID: 4431 { 4432 NameData *nm = (NameData *) DatumGetPointer(value); 4433 4434 val = pstrdup(NameStr(*nm)); 4435 break; 4436 } 4437 default: 4438 *failure = true; 4439 return NULL; 4440 } 4441 4442 if (!lc_collate_is_c(DEFAULT_COLLATION_OID)) 4443 { 4444 char *xfrmstr; 4445 size_t xfrmlen; 4446 size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY; 4447 4448 /* 4449 * XXX: We could guess at a suitable output buffer size and only call 4450 * strxfrm twice if our guess is too small. 4451 * 4452 * XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return 4453 * bogus data or set an error. This is not really a problem unless it 4454 * crashes since it will only give an estimation error and nothing 4455 * fatal. 4456 */ 4457 #if _MSC_VER == 1400 /* VS.Net 2005 */ 4458 4459 /* 4460 * 4461 * http://connect.microsoft.com/VisualStudio/feedback/ViewFeedback.aspx?FeedbackID=99694 4462 */ 4463 { 4464 char x[1]; 4465 4466 xfrmlen = strxfrm(x, val, 0); 4467 } 4468 #else 4469 xfrmlen = strxfrm(NULL, val, 0); 4470 #endif 4471 #ifdef WIN32 4472 4473 /* 4474 * On Windows, strxfrm returns INT_MAX when an error occurs. Instead 4475 * of trying to allocate this much memory (and fail), just return the 4476 * original string unmodified as if we were in the C locale. 4477 */ 4478 if (xfrmlen == INT_MAX) 4479 return val; 4480 #endif 4481 xfrmstr = (char *) palloc(xfrmlen + 1); 4482 xfrmlen2 = strxfrm(xfrmstr, val, xfrmlen + 1); 4483 4484 /* 4485 * Some systems (e.g., glibc) can return a smaller value from the 4486 * second call than the first; thus the Assert must be <= not ==. 4487 */ 4488 Assert(xfrmlen2 <= xfrmlen); 4489 pfree(val); 4490 val = xfrmstr; 4491 } 4492 4493 return val; 4494 } 4495 4496 /* 4497 * Do convert_to_scalar()'s work for any bytea data type. 4498 * 4499 * Very similar to convert_string_to_scalar except we can't assume 4500 * null-termination and therefore pass explicit lengths around. 4501 * 4502 * Also, assumptions about likely "normal" ranges of characters have been 4503 * removed - a data range of 0..255 is always used, for now. (Perhaps 4504 * someday we will add information about actual byte data range to 4505 * pg_statistic.) 4506 */ 4507 static void 4508 convert_bytea_to_scalar(Datum value, 4509 double *scaledvalue, 4510 Datum lobound, 4511 double *scaledlobound, 4512 Datum hibound, 4513 double *scaledhibound) 4514 { 4515 bytea *valuep = DatumGetByteaPP(value); 4516 bytea *loboundp = DatumGetByteaPP(lobound); 4517 bytea *hiboundp = DatumGetByteaPP(hibound); 4518 int rangelo, 4519 rangehi, 4520 valuelen = VARSIZE_ANY_EXHDR(valuep), 4521 loboundlen = VARSIZE_ANY_EXHDR(loboundp), 4522 hiboundlen = VARSIZE_ANY_EXHDR(hiboundp), 4523 i, 4524 minlen; 4525 unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep); 4526 unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp); 4527 unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp); 4528 4529 /* 4530 * Assume bytea data is uniformly distributed across all byte values. 4531 */ 4532 rangelo = 0; 4533 rangehi = 255; 4534 4535 /* 4536 * Now strip any common prefix of the three strings. 4537 */ 4538 minlen = Min(Min(valuelen, loboundlen), hiboundlen); 4539 for (i = 0; i < minlen; i++) 4540 { 4541 if (*lostr != *histr || *lostr != *valstr) 4542 break; 4543 lostr++, histr++, valstr++; 4544 loboundlen--, hiboundlen--, valuelen--; 4545 } 4546 4547 /* 4548 * Now we can do the conversions. 4549 */ 4550 *scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi); 4551 *scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi); 4552 *scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi); 4553 } 4554 4555 static double 4556 convert_one_bytea_to_scalar(unsigned char *value, int valuelen, 4557 int rangelo, int rangehi) 4558 { 4559 double num, 4560 denom, 4561 base; 4562 4563 if (valuelen <= 0) 4564 return 0.0; /* empty string has scalar value 0 */ 4565 4566 /* 4567 * Since base is 256, need not consider more than about 10 chars (even 4568 * this many seems like overkill) 4569 */ 4570 if (valuelen > 10) 4571 valuelen = 10; 4572 4573 /* Convert initial characters to fraction */ 4574 base = rangehi - rangelo + 1; 4575 num = 0.0; 4576 denom = base; 4577 while (valuelen-- > 0) 4578 { 4579 int ch = *value++; 4580 4581 if (ch < rangelo) 4582 ch = rangelo - 1; 4583 else if (ch > rangehi) 4584 ch = rangehi + 1; 4585 num += ((double) (ch - rangelo)) / denom; 4586 denom *= base; 4587 } 4588 4589 return num; 4590 } 4591 4592 /* 4593 * Do convert_to_scalar()'s work for any timevalue data type. 4594 * 4595 * On failure (e.g., unsupported typid), set *failure to true; 4596 * otherwise, that variable is not changed. 4597 */ 4598 static double 4599 convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure) 4600 { 4601 switch (typid) 4602 { 4603 case TIMESTAMPOID: 4604 return DatumGetTimestamp(value); 4605 case TIMESTAMPTZOID: 4606 return DatumGetTimestampTz(value); 4607 case ABSTIMEOID: 4608 return DatumGetTimestamp(DirectFunctionCall1(abstime_timestamp, 4609 value)); 4610 case DATEOID: 4611 return date2timestamp_no_overflow(DatumGetDateADT(value)); 4612 case INTERVALOID: 4613 { 4614 Interval *interval = DatumGetIntervalP(value); 4615 4616 /* 4617 * Convert the month part of Interval to days using assumed 4618 * average month length of 365.25/12.0 days. Not too 4619 * accurate, but plenty good enough for our purposes. 4620 */ 4621 return interval->time + interval->day * (double) USECS_PER_DAY + 4622 interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY); 4623 } 4624 case RELTIMEOID: 4625 return (DatumGetRelativeTime(value) * 1000000.0); 4626 case TINTERVALOID: 4627 { 4628 TimeInterval tinterval = DatumGetTimeInterval(value); 4629 4630 if (tinterval->status != 0) 4631 return ((tinterval->data[1] - tinterval->data[0]) * 1000000.0); 4632 return 0; /* for lack of a better idea */ 4633 } 4634 case TIMEOID: 4635 return DatumGetTimeADT(value); 4636 case TIMETZOID: 4637 { 4638 TimeTzADT *timetz = DatumGetTimeTzADTP(value); 4639 4640 /* use GMT-equivalent time */ 4641 return (double) (timetz->time + (timetz->zone * 1000000.0)); 4642 } 4643 } 4644 4645 *failure = true; 4646 return 0; 4647 } 4648 4649 4650 /* 4651 * get_restriction_variable 4652 * Examine the args of a restriction clause to see if it's of the 4653 * form (variable op pseudoconstant) or (pseudoconstant op variable), 4654 * where "variable" could be either a Var or an expression in vars of a 4655 * single relation. If so, extract information about the variable, 4656 * and also indicate which side it was on and the other argument. 4657 * 4658 * Inputs: 4659 * root: the planner info 4660 * args: clause argument list 4661 * varRelid: see specs for restriction selectivity functions 4662 * 4663 * Outputs: (these are valid only if true is returned) 4664 * *vardata: gets information about variable (see examine_variable) 4665 * *other: gets other clause argument, aggressively reduced to a constant 4666 * *varonleft: set true if variable is on the left, false if on the right 4667 * 4668 * Returns true if a variable is identified, otherwise false. 4669 * 4670 * Note: if there are Vars on both sides of the clause, we must fail, because 4671 * callers are expecting that the other side will act like a pseudoconstant. 4672 */ 4673 bool 4674 get_restriction_variable(PlannerInfo *root, List *args, int varRelid, 4675 VariableStatData *vardata, Node **other, 4676 bool *varonleft) 4677 { 4678 Node *left, 4679 *right; 4680 VariableStatData rdata; 4681 4682 /* Fail if not a binary opclause (probably shouldn't happen) */ 4683 if (list_length(args) != 2) 4684 return false; 4685 4686 left = (Node *) linitial(args); 4687 right = (Node *) lsecond(args); 4688 4689 /* 4690 * Examine both sides. Note that when varRelid is nonzero, Vars of other 4691 * relations will be treated as pseudoconstants. 4692 */ 4693 examine_variable(root, left, varRelid, vardata); 4694 examine_variable(root, right, varRelid, &rdata); 4695 4696 /* 4697 * If one side is a variable and the other not, we win. 4698 */ 4699 if (vardata->rel && rdata.rel == NULL) 4700 { 4701 *varonleft = true; 4702 *other = estimate_expression_value(root, rdata.var); 4703 /* Assume we need no ReleaseVariableStats(rdata) here */ 4704 return true; 4705 } 4706 4707 if (vardata->rel == NULL && rdata.rel) 4708 { 4709 *varonleft = false; 4710 *other = estimate_expression_value(root, vardata->var); 4711 /* Assume we need no ReleaseVariableStats(*vardata) here */ 4712 *vardata = rdata; 4713 return true; 4714 } 4715 4716 /* Oops, clause has wrong structure (probably var op var) */ 4717 ReleaseVariableStats(*vardata); 4718 ReleaseVariableStats(rdata); 4719 4720 return false; 4721 } 4722 4723 /* 4724 * get_join_variables 4725 * Apply examine_variable() to each side of a join clause. 4726 * Also, attempt to identify whether the join clause has the same 4727 * or reversed sense compared to the SpecialJoinInfo. 4728 * 4729 * We consider the join clause "normal" if it is "lhs_var OP rhs_var", 4730 * or "reversed" if it is "rhs_var OP lhs_var". In complicated cases 4731 * where we can't tell for sure, we default to assuming it's normal. 4732 */ 4733 void 4734 get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo, 4735 VariableStatData *vardata1, VariableStatData *vardata2, 4736 bool *join_is_reversed) 4737 { 4738 Node *left, 4739 *right; 4740 4741 if (list_length(args) != 2) 4742 elog(ERROR, "join operator should take two arguments"); 4743 4744 left = (Node *) linitial(args); 4745 right = (Node *) lsecond(args); 4746 4747 examine_variable(root, left, 0, vardata1); 4748 examine_variable(root, right, 0, vardata2); 4749 4750 if (vardata1->rel && 4751 bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand)) 4752 *join_is_reversed = true; /* var1 is on RHS */ 4753 else if (vardata2->rel && 4754 bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand)) 4755 *join_is_reversed = true; /* var2 is on LHS */ 4756 else 4757 *join_is_reversed = false; 4758 } 4759 4760 /* 4761 * examine_variable 4762 * Try to look up statistical data about an expression. 4763 * Fill in a VariableStatData struct to describe the expression. 4764 * 4765 * Inputs: 4766 * root: the planner info 4767 * node: the expression tree to examine 4768 * varRelid: see specs for restriction selectivity functions 4769 * 4770 * Outputs: *vardata is filled as follows: 4771 * var: the input expression (with any binary relabeling stripped, if 4772 * it is or contains a variable; but otherwise the type is preserved) 4773 * rel: RelOptInfo for relation containing variable; NULL if expression 4774 * contains no Vars (NOTE this could point to a RelOptInfo of a 4775 * subquery, not one in the current query). 4776 * statsTuple: the pg_statistic entry for the variable, if one exists; 4777 * otherwise NULL. 4778 * freefunc: pointer to a function to release statsTuple with. 4779 * vartype: exposed type of the expression; this should always match 4780 * the declared input type of the operator we are estimating for. 4781 * atttype, atttypmod: actual type/typmod of the "var" expression. This is 4782 * commonly the same as the exposed type of the variable argument, 4783 * but can be different in binary-compatible-type cases. 4784 * isunique: true if we were able to match the var to a unique index or a 4785 * single-column DISTINCT clause, implying its values are unique for 4786 * this query. (Caution: this should be trusted for statistical 4787 * purposes only, since we do not check indimmediate nor verify that 4788 * the exact same definition of equality applies.) 4789 * acl_ok: true if current user has permission to read the column(s) 4790 * underlying the pg_statistic entry. This is consulted by 4791 * statistic_proc_security_check(). 4792 * 4793 * Caller is responsible for doing ReleaseVariableStats() before exiting. 4794 */ 4795 void 4796 examine_variable(PlannerInfo *root, Node *node, int varRelid, 4797 VariableStatData *vardata) 4798 { 4799 Node *basenode; 4800 Relids varnos; 4801 RelOptInfo *onerel; 4802 4803 /* Make sure we don't return dangling pointers in vardata */ 4804 MemSet(vardata, 0, sizeof(VariableStatData)); 4805 4806 /* Save the exposed type of the expression */ 4807 vardata->vartype = exprType(node); 4808 4809 /* Look inside any binary-compatible relabeling */ 4810 4811 if (IsA(node, RelabelType)) 4812 basenode = (Node *) ((RelabelType *) node)->arg; 4813 else 4814 basenode = node; 4815 4816 /* Fast path for a simple Var */ 4817 4818 if (IsA(basenode, Var) && 4819 (varRelid == 0 || varRelid == ((Var *) basenode)->varno)) 4820 { 4821 Var *var = (Var *) basenode; 4822 4823 /* Set up result fields other than the stats tuple */ 4824 vardata->var = basenode; /* return Var without relabeling */ 4825 vardata->rel = find_base_rel(root, var->varno); 4826 vardata->atttype = var->vartype; 4827 vardata->atttypmod = var->vartypmod; 4828 vardata->isunique = has_unique_index(vardata->rel, var->varattno); 4829 4830 /* Try to locate some stats */ 4831 examine_simple_variable(root, var, vardata); 4832 4833 return; 4834 } 4835 4836 /* 4837 * Okay, it's a more complicated expression. Determine variable 4838 * membership. Note that when varRelid isn't zero, only vars of that 4839 * relation are considered "real" vars. 4840 */ 4841 varnos = pull_varnos(basenode); 4842 4843 onerel = NULL; 4844 4845 switch (bms_membership(varnos)) 4846 { 4847 case BMS_EMPTY_SET: 4848 /* No Vars at all ... must be pseudo-constant clause */ 4849 break; 4850 case BMS_SINGLETON: 4851 if (varRelid == 0 || bms_is_member(varRelid, varnos)) 4852 { 4853 onerel = find_base_rel(root, 4854 (varRelid ? varRelid : bms_singleton_member(varnos))); 4855 vardata->rel = onerel; 4856 node = basenode; /* strip any relabeling */ 4857 } 4858 /* else treat it as a constant */ 4859 break; 4860 case BMS_MULTIPLE: 4861 if (varRelid == 0) 4862 { 4863 /* treat it as a variable of a join relation */ 4864 vardata->rel = find_join_rel(root, varnos); 4865 node = basenode; /* strip any relabeling */ 4866 } 4867 else if (bms_is_member(varRelid, varnos)) 4868 { 4869 /* ignore the vars belonging to other relations */ 4870 vardata->rel = find_base_rel(root, varRelid); 4871 node = basenode; /* strip any relabeling */ 4872 /* note: no point in expressional-index search here */ 4873 } 4874 /* else treat it as a constant */ 4875 break; 4876 } 4877 4878 bms_free(varnos); 4879 4880 vardata->var = node; 4881 vardata->atttype = exprType(node); 4882 vardata->atttypmod = exprTypmod(node); 4883 4884 if (onerel) 4885 { 4886 /* 4887 * We have an expression in vars of a single relation. Try to match 4888 * it to expressional index columns, in hopes of finding some 4889 * statistics. 4890 * 4891 * Note that we consider all index columns including INCLUDE columns, 4892 * since there could be stats for such columns. But the test for 4893 * uniqueness needs to be warier. 4894 * 4895 * XXX it's conceivable that there are multiple matches with different 4896 * index opfamilies; if so, we need to pick one that matches the 4897 * operator we are estimating for. FIXME later. 4898 */ 4899 ListCell *ilist; 4900 4901 foreach(ilist, onerel->indexlist) 4902 { 4903 IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist); 4904 ListCell *indexpr_item; 4905 int pos; 4906 4907 indexpr_item = list_head(index->indexprs); 4908 if (indexpr_item == NULL) 4909 continue; /* no expressions here... */ 4910 4911 for (pos = 0; pos < index->ncolumns; pos++) 4912 { 4913 if (index->indexkeys[pos] == 0) 4914 { 4915 Node *indexkey; 4916 4917 if (indexpr_item == NULL) 4918 elog(ERROR, "too few entries in indexprs list"); 4919 indexkey = (Node *) lfirst(indexpr_item); 4920 if (indexkey && IsA(indexkey, RelabelType)) 4921 indexkey = (Node *) ((RelabelType *) indexkey)->arg; 4922 if (equal(node, indexkey)) 4923 { 4924 /* 4925 * Found a match ... is it a unique index? Tests here 4926 * should match has_unique_index(). 4927 */ 4928 if (index->unique && 4929 index->nkeycolumns == 1 && 4930 pos == 0 && 4931 (index->indpred == NIL || index->predOK)) 4932 vardata->isunique = true; 4933 4934 /* 4935 * Has it got stats? We only consider stats for 4936 * non-partial indexes, since partial indexes probably 4937 * don't reflect whole-relation statistics; the above 4938 * check for uniqueness is the only info we take from 4939 * a partial index. 4940 * 4941 * An index stats hook, however, must make its own 4942 * decisions about what to do with partial indexes. 4943 */ 4944 if (get_index_stats_hook && 4945 (*get_index_stats_hook) (root, index->indexoid, 4946 pos + 1, vardata)) 4947 { 4948 /* 4949 * The hook took control of acquiring a stats 4950 * tuple. If it did supply a tuple, it'd better 4951 * have supplied a freefunc. 4952 */ 4953 if (HeapTupleIsValid(vardata->statsTuple) && 4954 !vardata->freefunc) 4955 elog(ERROR, "no function provided to release variable stats with"); 4956 } 4957 else if (index->indpred == NIL) 4958 { 4959 vardata->statsTuple = 4960 SearchSysCache3(STATRELATTINH, 4961 ObjectIdGetDatum(index->indexoid), 4962 Int16GetDatum(pos + 1), 4963 BoolGetDatum(false)); 4964 vardata->freefunc = ReleaseSysCache; 4965 4966 if (HeapTupleIsValid(vardata->statsTuple)) 4967 { 4968 /* Get index's table for permission check */ 4969 RangeTblEntry *rte; 4970 Oid userid; 4971 4972 rte = planner_rt_fetch(index->rel->relid, root); 4973 Assert(rte->rtekind == RTE_RELATION); 4974 4975 /* 4976 * Use checkAsUser if it's set, in case we're 4977 * accessing the table via a view. 4978 */ 4979 userid = rte->checkAsUser ? rte->checkAsUser : GetUserId(); 4980 4981 /* 4982 * For simplicity, we insist on the whole 4983 * table being selectable, rather than trying 4984 * to identify which column(s) the index 4985 * depends on. Also require all rows to be 4986 * selectable --- there must be no 4987 * securityQuals from security barrier views 4988 * or RLS policies. 4989 */ 4990 vardata->acl_ok = 4991 rte->securityQuals == NIL && 4992 (pg_class_aclcheck(rte->relid, userid, 4993 ACL_SELECT) == ACLCHECK_OK); 4994 4995 /* 4996 * If the user doesn't have permissions to 4997 * access an inheritance child relation, check 4998 * the permissions of the table actually 4999 * mentioned in the query, since most likely 5000 * the user does have that permission. Note 5001 * that whole-table select privilege on the 5002 * parent doesn't quite guarantee that the 5003 * user could read all columns of the child. 5004 * But in practice it's unlikely that any 5005 * interesting security violation could result 5006 * from allowing access to the expression 5007 * index's stats, so we allow it anyway. See 5008 * similar code in examine_simple_variable() 5009 * for additional comments. 5010 */ 5011 if (!vardata->acl_ok && 5012 root->append_rel_array != NULL) 5013 { 5014 AppendRelInfo *appinfo; 5015 Index varno = index->rel->relid; 5016 5017 appinfo = root->append_rel_array[varno]; 5018 while (appinfo && 5019 planner_rt_fetch(appinfo->parent_relid, 5020 root)->rtekind == RTE_RELATION) 5021 { 5022 varno = appinfo->parent_relid; 5023 appinfo = root->append_rel_array[varno]; 5024 } 5025 if (varno != index->rel->relid) 5026 { 5027 /* Repeat access check on this rel */ 5028 rte = planner_rt_fetch(varno, root); 5029 Assert(rte->rtekind == RTE_RELATION); 5030 5031 userid = rte->checkAsUser ? rte->checkAsUser : GetUserId(); 5032 5033 vardata->acl_ok = 5034 rte->securityQuals == NIL && 5035 (pg_class_aclcheck(rte->relid, 5036 userid, 5037 ACL_SELECT) == ACLCHECK_OK); 5038 } 5039 } 5040 } 5041 else 5042 { 5043 /* suppress leakproofness checks later */ 5044 vardata->acl_ok = true; 5045 } 5046 } 5047 if (vardata->statsTuple) 5048 break; 5049 } 5050 indexpr_item = lnext(indexpr_item); 5051 } 5052 } 5053 if (vardata->statsTuple) 5054 break; 5055 } 5056 } 5057 } 5058 5059 /* 5060 * examine_simple_variable 5061 * Handle a simple Var for examine_variable 5062 * 5063 * This is split out as a subroutine so that we can recurse to deal with 5064 * Vars referencing subqueries. 5065 * 5066 * We already filled in all the fields of *vardata except for the stats tuple. 5067 */ 5068 static void 5069 examine_simple_variable(PlannerInfo *root, Var *var, 5070 VariableStatData *vardata) 5071 { 5072 RangeTblEntry *rte = root->simple_rte_array[var->varno]; 5073 5074 Assert(IsA(rte, RangeTblEntry)); 5075 5076 if (get_relation_stats_hook && 5077 (*get_relation_stats_hook) (root, rte, var->varattno, vardata)) 5078 { 5079 /* 5080 * The hook took control of acquiring a stats tuple. If it did supply 5081 * a tuple, it'd better have supplied a freefunc. 5082 */ 5083 if (HeapTupleIsValid(vardata->statsTuple) && 5084 !vardata->freefunc) 5085 elog(ERROR, "no function provided to release variable stats with"); 5086 } 5087 else if (rte->rtekind == RTE_RELATION) 5088 { 5089 /* 5090 * Plain table or parent of an inheritance appendrel, so look up the 5091 * column in pg_statistic 5092 */ 5093 vardata->statsTuple = SearchSysCache3(STATRELATTINH, 5094 ObjectIdGetDatum(rte->relid), 5095 Int16GetDatum(var->varattno), 5096 BoolGetDatum(rte->inh)); 5097 vardata->freefunc = ReleaseSysCache; 5098 5099 if (HeapTupleIsValid(vardata->statsTuple)) 5100 { 5101 Oid userid; 5102 5103 /* 5104 * Check if user has permission to read this column. We require 5105 * all rows to be accessible, so there must be no securityQuals 5106 * from security barrier views or RLS policies. Use checkAsUser 5107 * if it's set, in case we're accessing the table via a view. 5108 */ 5109 userid = rte->checkAsUser ? rte->checkAsUser : GetUserId(); 5110 5111 vardata->acl_ok = 5112 rte->securityQuals == NIL && 5113 ((pg_class_aclcheck(rte->relid, userid, 5114 ACL_SELECT) == ACLCHECK_OK) || 5115 (pg_attribute_aclcheck(rte->relid, var->varattno, userid, 5116 ACL_SELECT) == ACLCHECK_OK)); 5117 5118 /* 5119 * If the user doesn't have permissions to access an inheritance 5120 * child relation or specifically this attribute, check the 5121 * permissions of the table/column actually mentioned in the 5122 * query, since most likely the user does have that permission 5123 * (else the query will fail at runtime), and if the user can read 5124 * the column there then he can get the values of the child table 5125 * too. To do that, we must find out which of the root parent's 5126 * attributes the child relation's attribute corresponds to. 5127 */ 5128 if (!vardata->acl_ok && var->varattno > 0 && 5129 root->append_rel_array != NULL) 5130 { 5131 AppendRelInfo *appinfo; 5132 Index varno = var->varno; 5133 int varattno = var->varattno; 5134 bool found = false; 5135 5136 appinfo = root->append_rel_array[varno]; 5137 5138 /* 5139 * Partitions are mapped to their immediate parent, not the 5140 * root parent, so must be ready to walk up multiple 5141 * AppendRelInfos. But stop if we hit a parent that is not 5142 * RTE_RELATION --- that's a flattened UNION ALL subquery, not 5143 * an inheritance parent. 5144 */ 5145 while (appinfo && 5146 planner_rt_fetch(appinfo->parent_relid, 5147 root)->rtekind == RTE_RELATION) 5148 { 5149 int parent_varattno; 5150 ListCell *l; 5151 5152 parent_varattno = 1; 5153 found = false; 5154 foreach(l, appinfo->translated_vars) 5155 { 5156 Var *childvar = lfirst_node(Var, l); 5157 5158 /* Ignore dropped attributes of the parent. */ 5159 if (childvar != NULL && 5160 varattno == childvar->varattno) 5161 { 5162 found = true; 5163 break; 5164 } 5165 parent_varattno++; 5166 } 5167 5168 if (!found) 5169 break; 5170 5171 varno = appinfo->parent_relid; 5172 varattno = parent_varattno; 5173 5174 /* If the parent is itself a child, continue up. */ 5175 appinfo = root->append_rel_array[varno]; 5176 } 5177 5178 /* 5179 * In rare cases, the Var may be local to the child table, in 5180 * which case, we've got to live with having no access to this 5181 * column's stats. 5182 */ 5183 if (!found) 5184 return; 5185 5186 /* Repeat the access check on this parent rel & column */ 5187 rte = planner_rt_fetch(varno, root); 5188 Assert(rte->rtekind == RTE_RELATION); 5189 5190 userid = rte->checkAsUser ? rte->checkAsUser : GetUserId(); 5191 5192 vardata->acl_ok = 5193 rte->securityQuals == NIL && 5194 ((pg_class_aclcheck(rte->relid, userid, 5195 ACL_SELECT) == ACLCHECK_OK) || 5196 (pg_attribute_aclcheck(rte->relid, varattno, userid, 5197 ACL_SELECT) == ACLCHECK_OK)); 5198 } 5199 } 5200 else 5201 { 5202 /* suppress any possible leakproofness checks later */ 5203 vardata->acl_ok = true; 5204 } 5205 } 5206 else if (rte->rtekind == RTE_SUBQUERY && !rte->inh) 5207 { 5208 /* 5209 * Plain subquery (not one that was converted to an appendrel). 5210 */ 5211 Query *subquery = rte->subquery; 5212 RelOptInfo *rel; 5213 TargetEntry *ste; 5214 5215 /* 5216 * Punt if it's a whole-row var rather than a plain column reference. 5217 */ 5218 if (var->varattno == InvalidAttrNumber) 5219 return; 5220 5221 /* 5222 * Punt if subquery uses set operations or GROUP BY, as these will 5223 * mash underlying columns' stats beyond recognition. (Set ops are 5224 * particularly nasty; if we forged ahead, we would return stats 5225 * relevant to only the leftmost subselect...) DISTINCT is also 5226 * problematic, but we check that later because there is a possibility 5227 * of learning something even with it. 5228 */ 5229 if (subquery->setOperations || 5230 subquery->groupClause || 5231 subquery->groupingSets) 5232 return; 5233 5234 /* 5235 * OK, fetch RelOptInfo for subquery. Note that we don't change the 5236 * rel returned in vardata, since caller expects it to be a rel of the 5237 * caller's query level. Because we might already be recursing, we 5238 * can't use that rel pointer either, but have to look up the Var's 5239 * rel afresh. 5240 */ 5241 rel = find_base_rel(root, var->varno); 5242 5243 /* If the subquery hasn't been planned yet, we have to punt */ 5244 if (rel->subroot == NULL) 5245 return; 5246 Assert(IsA(rel->subroot, PlannerInfo)); 5247 5248 /* 5249 * Switch our attention to the subquery as mangled by the planner. It 5250 * was okay to look at the pre-planning version for the tests above, 5251 * but now we need a Var that will refer to the subroot's live 5252 * RelOptInfos. For instance, if any subquery pullup happened during 5253 * planning, Vars in the targetlist might have gotten replaced, and we 5254 * need to see the replacement expressions. 5255 */ 5256 subquery = rel->subroot->parse; 5257 Assert(IsA(subquery, Query)); 5258 5259 /* Get the subquery output expression referenced by the upper Var */ 5260 ste = get_tle_by_resno(subquery->targetList, var->varattno); 5261 if (ste == NULL || ste->resjunk) 5262 elog(ERROR, "subquery %s does not have attribute %d", 5263 rte->eref->aliasname, var->varattno); 5264 var = (Var *) ste->expr; 5265 5266 /* 5267 * If subquery uses DISTINCT, we can't make use of any stats for the 5268 * variable ... but, if it's the only DISTINCT column, we are entitled 5269 * to consider it unique. We do the test this way so that it works 5270 * for cases involving DISTINCT ON. 5271 */ 5272 if (subquery->distinctClause) 5273 { 5274 if (list_length(subquery->distinctClause) == 1 && 5275 targetIsInSortList(ste, InvalidOid, subquery->distinctClause)) 5276 vardata->isunique = true; 5277 /* cannot go further */ 5278 return; 5279 } 5280 5281 /* 5282 * If the sub-query originated from a view with the security_barrier 5283 * attribute, we must not look at the variable's statistics, though it 5284 * seems all right to notice the existence of a DISTINCT clause. So 5285 * stop here. 5286 * 5287 * This is probably a harsher restriction than necessary; it's 5288 * certainly OK for the selectivity estimator (which is a C function, 5289 * and therefore omnipotent anyway) to look at the statistics. But 5290 * many selectivity estimators will happily *invoke the operator 5291 * function* to try to work out a good estimate - and that's not OK. 5292 * So for now, don't dig down for stats. 5293 */ 5294 if (rte->security_barrier) 5295 return; 5296 5297 /* Can only handle a simple Var of subquery's query level */ 5298 if (var && IsA(var, Var) && 5299 var->varlevelsup == 0) 5300 { 5301 /* 5302 * OK, recurse into the subquery. Note that the original setting 5303 * of vardata->isunique (which will surely be false) is left 5304 * unchanged in this situation. That's what we want, since even 5305 * if the underlying column is unique, the subquery may have 5306 * joined to other tables in a way that creates duplicates. 5307 */ 5308 examine_simple_variable(rel->subroot, var, vardata); 5309 } 5310 } 5311 else 5312 { 5313 /* 5314 * Otherwise, the Var comes from a FUNCTION, VALUES, or CTE RTE. (We 5315 * won't see RTE_JOIN here because join alias Vars have already been 5316 * flattened.) There's not much we can do with function outputs, but 5317 * maybe someday try to be smarter about VALUES and/or CTEs. 5318 */ 5319 } 5320 } 5321 5322 /* 5323 * Check whether it is permitted to call func_oid passing some of the 5324 * pg_statistic data in vardata. We allow this either if the user has SELECT 5325 * privileges on the table or column underlying the pg_statistic data or if 5326 * the function is marked leak-proof. 5327 */ 5328 bool 5329 statistic_proc_security_check(VariableStatData *vardata, Oid func_oid) 5330 { 5331 if (vardata->acl_ok) 5332 return true; 5333 5334 if (!OidIsValid(func_oid)) 5335 return false; 5336 5337 if (get_func_leakproof(func_oid)) 5338 return true; 5339 5340 ereport(DEBUG2, 5341 (errmsg_internal("not using statistics because function \"%s\" is not leak-proof", 5342 get_func_name(func_oid)))); 5343 return false; 5344 } 5345 5346 /* 5347 * get_variable_numdistinct 5348 * Estimate the number of distinct values of a variable. 5349 * 5350 * vardata: results of examine_variable 5351 * *isdefault: set to true if the result is a default rather than based on 5352 * anything meaningful. 5353 * 5354 * NB: be careful to produce a positive integral result, since callers may 5355 * compare the result to exact integer counts, or might divide by it. 5356 */ 5357 double 5358 get_variable_numdistinct(VariableStatData *vardata, bool *isdefault) 5359 { 5360 double stadistinct; 5361 double stanullfrac = 0.0; 5362 double ntuples; 5363 5364 *isdefault = false; 5365 5366 /* 5367 * Determine the stadistinct value to use. There are cases where we can 5368 * get an estimate even without a pg_statistic entry, or can get a better 5369 * value than is in pg_statistic. Grab stanullfrac too if we can find it 5370 * (otherwise, assume no nulls, for lack of any better idea). 5371 */ 5372 if (HeapTupleIsValid(vardata->statsTuple)) 5373 { 5374 /* Use the pg_statistic entry */ 5375 Form_pg_statistic stats; 5376 5377 stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple); 5378 stadistinct = stats->stadistinct; 5379 stanullfrac = stats->stanullfrac; 5380 } 5381 else if (vardata->vartype == BOOLOID) 5382 { 5383 /* 5384 * Special-case boolean columns: presumably, two distinct values. 5385 * 5386 * Are there any other datatypes we should wire in special estimates 5387 * for? 5388 */ 5389 stadistinct = 2.0; 5390 } 5391 else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES) 5392 { 5393 /* 5394 * If the Var represents a column of a VALUES RTE, assume it's unique. 5395 * This could of course be very wrong, but it should tend to be true 5396 * in well-written queries. We could consider examining the VALUES' 5397 * contents to get some real statistics; but that only works if the 5398 * entries are all constants, and it would be pretty expensive anyway. 5399 */ 5400 stadistinct = -1.0; /* unique (and all non null) */ 5401 } 5402 else 5403 { 5404 /* 5405 * We don't keep statistics for system columns, but in some cases we 5406 * can infer distinctness anyway. 5407 */ 5408 if (vardata->var && IsA(vardata->var, Var)) 5409 { 5410 switch (((Var *) vardata->var)->varattno) 5411 { 5412 case ObjectIdAttributeNumber: 5413 case SelfItemPointerAttributeNumber: 5414 stadistinct = -1.0; /* unique (and all non null) */ 5415 break; 5416 case TableOidAttributeNumber: 5417 stadistinct = 1.0; /* only 1 value */ 5418 break; 5419 default: 5420 stadistinct = 0.0; /* means "unknown" */ 5421 break; 5422 } 5423 } 5424 else 5425 stadistinct = 0.0; /* means "unknown" */ 5426 5427 /* 5428 * XXX consider using estimate_num_groups on expressions? 5429 */ 5430 } 5431 5432 /* 5433 * If there is a unique index or DISTINCT clause for the variable, assume 5434 * it is unique no matter what pg_statistic says; the statistics could be 5435 * out of date, or we might have found a partial unique index that proves 5436 * the var is unique for this query. However, we'd better still believe 5437 * the null-fraction statistic. 5438 */ 5439 if (vardata->isunique) 5440 stadistinct = -1.0 * (1.0 - stanullfrac); 5441 5442 /* 5443 * If we had an absolute estimate, use that. 5444 */ 5445 if (stadistinct > 0.0) 5446 return clamp_row_est(stadistinct); 5447 5448 /* 5449 * Otherwise we need to get the relation size; punt if not available. 5450 */ 5451 if (vardata->rel == NULL) 5452 { 5453 *isdefault = true; 5454 return DEFAULT_NUM_DISTINCT; 5455 } 5456 ntuples = vardata->rel->tuples; 5457 if (ntuples <= 0.0) 5458 { 5459 *isdefault = true; 5460 return DEFAULT_NUM_DISTINCT; 5461 } 5462 5463 /* 5464 * If we had a relative estimate, use that. 5465 */ 5466 if (stadistinct < 0.0) 5467 return clamp_row_est(-stadistinct * ntuples); 5468 5469 /* 5470 * With no data, estimate ndistinct = ntuples if the table is small, else 5471 * use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so 5472 * that the behavior isn't discontinuous. 5473 */ 5474 if (ntuples < DEFAULT_NUM_DISTINCT) 5475 return clamp_row_est(ntuples); 5476 5477 *isdefault = true; 5478 return DEFAULT_NUM_DISTINCT; 5479 } 5480 5481 /* 5482 * get_variable_range 5483 * Estimate the minimum and maximum value of the specified variable. 5484 * If successful, store values in *min and *max, and return true. 5485 * If no data available, return false. 5486 * 5487 * sortop is the "<" comparison operator to use. This should generally 5488 * be "<" not ">", as only the former is likely to be found in pg_statistic. 5489 */ 5490 static bool 5491 get_variable_range(PlannerInfo *root, VariableStatData *vardata, Oid sortop, 5492 Datum *min, Datum *max) 5493 { 5494 Datum tmin = 0; 5495 Datum tmax = 0; 5496 bool have_data = false; 5497 int16 typLen; 5498 bool typByVal; 5499 Oid opfuncoid; 5500 AttStatsSlot sslot; 5501 int i; 5502 5503 /* 5504 * XXX It's very tempting to try to use the actual column min and max, if 5505 * we can get them relatively-cheaply with an index probe. However, since 5506 * this function is called many times during join planning, that could 5507 * have unpleasant effects on planning speed. Need more investigation 5508 * before enabling this. 5509 */ 5510 #ifdef NOT_USED 5511 if (get_actual_variable_range(root, vardata, sortop, min, max)) 5512 return true; 5513 #endif 5514 5515 if (!HeapTupleIsValid(vardata->statsTuple)) 5516 { 5517 /* no stats available, so default result */ 5518 return false; 5519 } 5520 5521 /* 5522 * If we can't apply the sortop to the stats data, just fail. In 5523 * principle, if there's a histogram and no MCVs, we could return the 5524 * histogram endpoints without ever applying the sortop ... but it's 5525 * probably not worth trying, because whatever the caller wants to do with 5526 * the endpoints would likely fail the security check too. 5527 */ 5528 if (!statistic_proc_security_check(vardata, 5529 (opfuncoid = get_opcode(sortop)))) 5530 return false; 5531 5532 get_typlenbyval(vardata->atttype, &typLen, &typByVal); 5533 5534 /* 5535 * If there is a histogram, grab the first and last values. 5536 * 5537 * If there is a histogram that is sorted with some other operator than 5538 * the one we want, fail --- this suggests that there is data we can't 5539 * use. 5540 */ 5541 if (get_attstatsslot(&sslot, vardata->statsTuple, 5542 STATISTIC_KIND_HISTOGRAM, sortop, 5543 ATTSTATSSLOT_VALUES)) 5544 { 5545 if (sslot.nvalues > 0) 5546 { 5547 tmin = datumCopy(sslot.values[0], typByVal, typLen); 5548 tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen); 5549 have_data = true; 5550 } 5551 free_attstatsslot(&sslot); 5552 } 5553 else if (get_attstatsslot(&sslot, vardata->statsTuple, 5554 STATISTIC_KIND_HISTOGRAM, InvalidOid, 5555 0)) 5556 { 5557 free_attstatsslot(&sslot); 5558 return false; 5559 } 5560 5561 /* 5562 * If we have most-common-values info, look for extreme MCVs. This is 5563 * needed even if we also have a histogram, since the histogram excludes 5564 * the MCVs. However, if we *only* have MCVs and no histogram, we should 5565 * be pretty wary of deciding that that is a full representation of the 5566 * data. Proceed only if the MCVs represent the whole table (to within 5567 * roundoff error). 5568 */ 5569 if (get_attstatsslot(&sslot, vardata->statsTuple, 5570 STATISTIC_KIND_MCV, InvalidOid, 5571 have_data ? ATTSTATSSLOT_VALUES : 5572 (ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))) 5573 { 5574 bool use_mcvs = have_data; 5575 5576 if (!have_data) 5577 { 5578 double sumcommon = 0.0; 5579 double nullfrac; 5580 int i; 5581 5582 for (i = 0; i < sslot.nnumbers; i++) 5583 sumcommon += sslot.numbers[i]; 5584 nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata->statsTuple))->stanullfrac; 5585 if (sumcommon + nullfrac > 0.99999) 5586 use_mcvs = true; 5587 } 5588 5589 if (use_mcvs) 5590 { 5591 /* 5592 * Usually the MCVs will not be the extreme values, so avoid 5593 * unnecessary data copying. 5594 */ 5595 bool tmin_is_mcv = false; 5596 bool tmax_is_mcv = false; 5597 FmgrInfo opproc; 5598 5599 fmgr_info(opfuncoid, &opproc); 5600 5601 for (i = 0; i < sslot.nvalues; i++) 5602 { 5603 if (!have_data) 5604 { 5605 tmin = tmax = sslot.values[i]; 5606 tmin_is_mcv = tmax_is_mcv = have_data = true; 5607 continue; 5608 } 5609 if (DatumGetBool(FunctionCall2Coll(&opproc, 5610 DEFAULT_COLLATION_OID, 5611 sslot.values[i], tmin))) 5612 { 5613 tmin = sslot.values[i]; 5614 tmin_is_mcv = true; 5615 } 5616 if (DatumGetBool(FunctionCall2Coll(&opproc, 5617 DEFAULT_COLLATION_OID, 5618 tmax, sslot.values[i]))) 5619 { 5620 tmax = sslot.values[i]; 5621 tmax_is_mcv = true; 5622 } 5623 } 5624 if (tmin_is_mcv) 5625 tmin = datumCopy(tmin, typByVal, typLen); 5626 if (tmax_is_mcv) 5627 tmax = datumCopy(tmax, typByVal, typLen); 5628 } 5629 5630 free_attstatsslot(&sslot); 5631 } 5632 5633 *min = tmin; 5634 *max = tmax; 5635 return have_data; 5636 } 5637 5638 5639 /* 5640 * get_actual_variable_range 5641 * Attempt to identify the current *actual* minimum and/or maximum 5642 * of the specified variable, by looking for a suitable btree index 5643 * and fetching its low and/or high values. 5644 * If successful, store values in *min and *max, and return true. 5645 * (Either pointer can be NULL if that endpoint isn't needed.) 5646 * If no data available, return false. 5647 * 5648 * sortop is the "<" comparison operator to use. 5649 */ 5650 static bool 5651 get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata, 5652 Oid sortop, 5653 Datum *min, Datum *max) 5654 { 5655 bool have_data = false; 5656 RelOptInfo *rel = vardata->rel; 5657 RangeTblEntry *rte; 5658 ListCell *lc; 5659 5660 /* No hope if no relation or it doesn't have indexes */ 5661 if (rel == NULL || rel->indexlist == NIL) 5662 return false; 5663 /* If it has indexes it must be a plain relation */ 5664 rte = root->simple_rte_array[rel->relid]; 5665 Assert(rte->rtekind == RTE_RELATION); 5666 5667 /* Search through the indexes to see if any match our problem */ 5668 foreach(lc, rel->indexlist) 5669 { 5670 IndexOptInfo *index = (IndexOptInfo *) lfirst(lc); 5671 ScanDirection indexscandir; 5672 5673 /* Ignore non-btree indexes */ 5674 if (index->relam != BTREE_AM_OID) 5675 continue; 5676 5677 /* 5678 * Ignore partial indexes --- we only want stats that cover the entire 5679 * relation. 5680 */ 5681 if (index->indpred != NIL) 5682 continue; 5683 5684 /* 5685 * The index list might include hypothetical indexes inserted by a 5686 * get_relation_info hook --- don't try to access them. 5687 */ 5688 if (index->hypothetical) 5689 continue; 5690 5691 /* 5692 * The first index column must match the desired variable and sort 5693 * operator --- but we can use a descending-order index. 5694 */ 5695 if (!match_index_to_operand(vardata->var, 0, index)) 5696 continue; 5697 switch (get_op_opfamily_strategy(sortop, index->sortopfamily[0])) 5698 { 5699 case BTLessStrategyNumber: 5700 if (index->reverse_sort[0]) 5701 indexscandir = BackwardScanDirection; 5702 else 5703 indexscandir = ForwardScanDirection; 5704 break; 5705 case BTGreaterStrategyNumber: 5706 if (index->reverse_sort[0]) 5707 indexscandir = ForwardScanDirection; 5708 else 5709 indexscandir = BackwardScanDirection; 5710 break; 5711 default: 5712 /* index doesn't match the sortop */ 5713 continue; 5714 } 5715 5716 /* 5717 * Found a suitable index to extract data from. Set up some data that 5718 * can be used by both invocations of get_actual_variable_endpoint. 5719 */ 5720 { 5721 MemoryContext tmpcontext; 5722 MemoryContext oldcontext; 5723 Relation heapRel; 5724 Relation indexRel; 5725 int16 typLen; 5726 bool typByVal; 5727 ScanKeyData scankeys[1]; 5728 5729 /* Make sure any cruft gets recycled when we're done */ 5730 tmpcontext = AllocSetContextCreate(CurrentMemoryContext, 5731 "get_actual_variable_range workspace", 5732 ALLOCSET_DEFAULT_SIZES); 5733 oldcontext = MemoryContextSwitchTo(tmpcontext); 5734 5735 /* 5736 * Open the table and index so we can read from them. We should 5737 * already have at least AccessShareLock on the table, but not 5738 * necessarily on the index. 5739 */ 5740 heapRel = heap_open(rte->relid, NoLock); 5741 indexRel = index_open(index->indexoid, AccessShareLock); 5742 5743 /* build some stuff needed for indexscan execution */ 5744 get_typlenbyval(vardata->atttype, &typLen, &typByVal); 5745 5746 /* set up an IS NOT NULL scan key so that we ignore nulls */ 5747 ScanKeyEntryInitialize(&scankeys[0], 5748 SK_ISNULL | SK_SEARCHNOTNULL, 5749 1, /* index col to scan */ 5750 InvalidStrategy, /* no strategy */ 5751 InvalidOid, /* no strategy subtype */ 5752 InvalidOid, /* no collation */ 5753 InvalidOid, /* no reg proc for this */ 5754 (Datum) 0); /* constant */ 5755 5756 /* If min is requested ... */ 5757 if (min) 5758 { 5759 have_data = get_actual_variable_endpoint(heapRel, 5760 indexRel, 5761 indexscandir, 5762 scankeys, 5763 typLen, 5764 typByVal, 5765 oldcontext, 5766 min); 5767 } 5768 else 5769 { 5770 /* If min not requested, assume index is nonempty */ 5771 have_data = true; 5772 } 5773 5774 /* If max is requested, and we didn't find the index is empty */ 5775 if (max && have_data) 5776 { 5777 /* scan in the opposite direction; all else is the same */ 5778 have_data = get_actual_variable_endpoint(heapRel, 5779 indexRel, 5780 -indexscandir, 5781 scankeys, 5782 typLen, 5783 typByVal, 5784 oldcontext, 5785 max); 5786 } 5787 5788 /* Clean everything up */ 5789 index_close(indexRel, AccessShareLock); 5790 heap_close(heapRel, NoLock); 5791 5792 MemoryContextSwitchTo(oldcontext); 5793 MemoryContextDelete(tmpcontext); 5794 5795 /* And we're done */ 5796 break; 5797 } 5798 } 5799 5800 return have_data; 5801 } 5802 5803 /* 5804 * Get one endpoint datum (min or max depending on indexscandir) from the 5805 * specified index. Return true if successful, false if index is empty. 5806 * On success, endpoint value is stored to *endpointDatum (and copied into 5807 * outercontext). 5808 * 5809 * scankeys is a 1-element scankey array set up to reject nulls. 5810 * typLen/typByVal describe the datatype of the index's first column. 5811 * (We could compute these values locally, but that would mean computing them 5812 * twice when get_actual_variable_range needs both the min and the max.) 5813 */ 5814 static bool 5815 get_actual_variable_endpoint(Relation heapRel, 5816 Relation indexRel, 5817 ScanDirection indexscandir, 5818 ScanKey scankeys, 5819 int16 typLen, 5820 bool typByVal, 5821 MemoryContext outercontext, 5822 Datum *endpointDatum) 5823 { 5824 bool have_data = false; 5825 SnapshotData SnapshotNonVacuumable; 5826 IndexScanDesc index_scan; 5827 Buffer vmbuffer = InvalidBuffer; 5828 ItemPointer tid; 5829 Datum values[INDEX_MAX_KEYS]; 5830 bool isnull[INDEX_MAX_KEYS]; 5831 MemoryContext oldcontext; 5832 5833 /* 5834 * We use the index-only-scan machinery for this. With mostly-static 5835 * tables that's a win because it avoids a heap visit. It's also a win 5836 * for dynamic data, but the reason is less obvious; read on for details. 5837 * 5838 * In principle, we should scan the index with our current active 5839 * snapshot, which is the best approximation we've got to what the query 5840 * will see when executed. But that won't be exact if a new snap is taken 5841 * before running the query, and it can be very expensive if a lot of 5842 * recently-dead or uncommitted rows exist at the beginning or end of the 5843 * index (because we'll laboriously fetch each one and reject it). 5844 * Instead, we use SnapshotNonVacuumable. That will accept recently-dead 5845 * and uncommitted rows as well as normal visible rows. On the other 5846 * hand, it will reject known-dead rows, and thus not give a bogus answer 5847 * when the extreme value has been deleted (unless the deletion was quite 5848 * recent); that case motivates not using SnapshotAny here. 5849 * 5850 * A crucial point here is that SnapshotNonVacuumable, with 5851 * RecentGlobalXmin as horizon, yields the inverse of the condition that 5852 * the indexscan will use to decide that index entries are killable (see 5853 * heap_hot_search_buffer()). Therefore, if the snapshot rejects a tuple 5854 * (or more precisely, all tuples of a HOT chain) and we have to continue 5855 * scanning past it, we know that the indexscan will mark that index entry 5856 * killed. That means that the next get_actual_variable_endpoint() call 5857 * will not have to re-consider that index entry. In this way we avoid 5858 * repetitive work when this function is used a lot during planning. 5859 * 5860 * But using SnapshotNonVacuumable creates a hazard of its own. In a 5861 * recently-created index, some index entries may point at "broken" HOT 5862 * chains in which not all the tuple versions contain data matching the 5863 * index entry. The live tuple version(s) certainly do match the index, 5864 * but SnapshotNonVacuumable can accept recently-dead tuple versions that 5865 * don't match. Hence, if we took data from the selected heap tuple, we 5866 * might get a bogus answer that's not close to the index extremal value, 5867 * or could even be NULL. We avoid this hazard because we take the data 5868 * from the index entry not the heap. 5869 */ 5870 InitNonVacuumableSnapshot(SnapshotNonVacuumable, RecentGlobalXmin); 5871 5872 index_scan = index_beginscan(heapRel, indexRel, 5873 &SnapshotNonVacuumable, 5874 1, 0); 5875 /* Set it up for index-only scan */ 5876 index_scan->xs_want_itup = true; 5877 index_rescan(index_scan, scankeys, 1, NULL, 0); 5878 5879 /* Fetch first/next tuple in specified direction */ 5880 while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL) 5881 { 5882 if (!VM_ALL_VISIBLE(heapRel, 5883 ItemPointerGetBlockNumber(tid), 5884 &vmbuffer)) 5885 { 5886 /* Rats, we have to visit the heap to check visibility */ 5887 if (index_fetch_heap(index_scan) == NULL) 5888 continue; /* no visible tuple, try next index entry */ 5889 5890 /* 5891 * We don't care whether there's more than one visible tuple in 5892 * the HOT chain; if any are visible, that's good enough. 5893 */ 5894 } 5895 5896 /* 5897 * We expect that btree will return data in IndexTuple not HeapTuple 5898 * format. It's not lossy either. 5899 */ 5900 if (!index_scan->xs_itup) 5901 elog(ERROR, "no data returned for index-only scan"); 5902 if (index_scan->xs_recheck) 5903 elog(ERROR, "unexpected recheck indication from btree"); 5904 5905 /* OK to deconstruct the index tuple */ 5906 index_deform_tuple(index_scan->xs_itup, 5907 index_scan->xs_itupdesc, 5908 values, isnull); 5909 5910 /* Shouldn't have got a null, but be careful */ 5911 if (isnull[0]) 5912 elog(ERROR, "found unexpected null value in index \"%s\"", 5913 RelationGetRelationName(indexRel)); 5914 5915 /* Copy the index column value out to caller's context */ 5916 oldcontext = MemoryContextSwitchTo(outercontext); 5917 *endpointDatum = datumCopy(values[0], typByVal, typLen); 5918 MemoryContextSwitchTo(oldcontext); 5919 have_data = true; 5920 break; 5921 } 5922 5923 if (vmbuffer != InvalidBuffer) 5924 ReleaseBuffer(vmbuffer); 5925 index_endscan(index_scan); 5926 5927 return have_data; 5928 } 5929 5930 /* 5931 * find_join_input_rel 5932 * Look up the input relation for a join. 5933 * 5934 * We assume that the input relation's RelOptInfo must have been constructed 5935 * already. 5936 */ 5937 static RelOptInfo * 5938 find_join_input_rel(PlannerInfo *root, Relids relids) 5939 { 5940 RelOptInfo *rel = NULL; 5941 5942 switch (bms_membership(relids)) 5943 { 5944 case BMS_EMPTY_SET: 5945 /* should not happen */ 5946 break; 5947 case BMS_SINGLETON: 5948 rel = find_base_rel(root, bms_singleton_member(relids)); 5949 break; 5950 case BMS_MULTIPLE: 5951 rel = find_join_rel(root, relids); 5952 break; 5953 } 5954 5955 if (rel == NULL) 5956 elog(ERROR, "could not find RelOptInfo for given relids"); 5957 5958 return rel; 5959 } 5960 5961 5962 /*------------------------------------------------------------------------- 5963 * 5964 * Pattern analysis functions 5965 * 5966 * These routines support analysis of LIKE and regular-expression patterns 5967 * by the planner/optimizer. It's important that they agree with the 5968 * regular-expression code in backend/regex/ and the LIKE code in 5969 * backend/utils/adt/like.c. Also, the computation of the fixed prefix 5970 * must be conservative: if we report a string longer than the true fixed 5971 * prefix, the query may produce actually wrong answers, rather than just 5972 * getting a bad selectivity estimate! 5973 * 5974 * Note that the prefix-analysis functions are called from 5975 * backend/optimizer/path/indxpath.c as well as from routines in this file. 5976 * 5977 *------------------------------------------------------------------------- 5978 */ 5979 5980 /* 5981 * Check whether char is a letter (and, hence, subject to case-folding) 5982 * 5983 * In multibyte character sets or with ICU, we can't use isalpha, and it does 5984 * not seem worth trying to convert to wchar_t to use iswalpha or u_isalpha. 5985 * Instead, just assume any non-ASCII char is potentially case-varying, and 5986 * hard-wire knowledge of which ASCII chars are letters. 5987 */ 5988 static int 5989 pattern_char_isalpha(char c, bool is_multibyte, 5990 pg_locale_t locale, bool locale_is_c) 5991 { 5992 if (locale_is_c) 5993 return (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z'); 5994 else if (is_multibyte && IS_HIGHBIT_SET(c)) 5995 return true; 5996 else if (locale && locale->provider == COLLPROVIDER_ICU) 5997 return IS_HIGHBIT_SET(c) || 5998 (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z'); 5999 #ifdef HAVE_LOCALE_T 6000 else if (locale && locale->provider == COLLPROVIDER_LIBC) 6001 return isalpha_l((unsigned char) c, locale->info.lt); 6002 #endif 6003 else 6004 return isalpha((unsigned char) c); 6005 } 6006 6007 /* 6008 * Extract the fixed prefix, if any, for a pattern. 6009 * 6010 * *prefix is set to a palloc'd prefix string (in the form of a Const node), 6011 * or to NULL if no fixed prefix exists for the pattern. 6012 * If rest_selec is not NULL, *rest_selec is set to an estimate of the 6013 * selectivity of the remainder of the pattern (without any fixed prefix). 6014 * The prefix Const has the same type (TEXT or BYTEA) as the input pattern. 6015 * 6016 * The return value distinguishes no fixed prefix, a partial prefix, 6017 * or an exact-match-only pattern. 6018 */ 6019 6020 static Pattern_Prefix_Status 6021 like_fixed_prefix(Const *patt_const, bool case_insensitive, Oid collation, 6022 Const **prefix_const, Selectivity *rest_selec) 6023 { 6024 char *match; 6025 char *patt; 6026 int pattlen; 6027 Oid typeid = patt_const->consttype; 6028 int pos, 6029 match_pos; 6030 bool is_multibyte = (pg_database_encoding_max_length() > 1); 6031 pg_locale_t locale = 0; 6032 bool locale_is_c = false; 6033 6034 /* the right-hand const is type text or bytea */ 6035 Assert(typeid == BYTEAOID || typeid == TEXTOID); 6036 6037 if (case_insensitive) 6038 { 6039 if (typeid == BYTEAOID) 6040 ereport(ERROR, 6041 (errcode(ERRCODE_FEATURE_NOT_SUPPORTED), 6042 errmsg("case insensitive matching not supported on type bytea"))); 6043 6044 /* If case-insensitive, we need locale info */ 6045 if (lc_ctype_is_c(collation)) 6046 locale_is_c = true; 6047 else if (collation != DEFAULT_COLLATION_OID) 6048 { 6049 if (!OidIsValid(collation)) 6050 { 6051 /* 6052 * This typically means that the parser could not resolve a 6053 * conflict of implicit collations, so report it that way. 6054 */ 6055 ereport(ERROR, 6056 (errcode(ERRCODE_INDETERMINATE_COLLATION), 6057 errmsg("could not determine which collation to use for ILIKE"), 6058 errhint("Use the COLLATE clause to set the collation explicitly."))); 6059 } 6060 locale = pg_newlocale_from_collation(collation); 6061 } 6062 } 6063 6064 if (typeid != BYTEAOID) 6065 { 6066 patt = TextDatumGetCString(patt_const->constvalue); 6067 pattlen = strlen(patt); 6068 } 6069 else 6070 { 6071 bytea *bstr = DatumGetByteaPP(patt_const->constvalue); 6072 6073 pattlen = VARSIZE_ANY_EXHDR(bstr); 6074 patt = (char *) palloc(pattlen); 6075 memcpy(patt, VARDATA_ANY(bstr), pattlen); 6076 Assert((Pointer) bstr == DatumGetPointer(patt_const->constvalue)); 6077 } 6078 6079 match = palloc(pattlen + 1); 6080 match_pos = 0; 6081 for (pos = 0; pos < pattlen; pos++) 6082 { 6083 /* % and _ are wildcard characters in LIKE */ 6084 if (patt[pos] == '%' || 6085 patt[pos] == '_') 6086 break; 6087 6088 /* Backslash escapes the next character */ 6089 if (patt[pos] == '\\') 6090 { 6091 pos++; 6092 if (pos >= pattlen) 6093 break; 6094 } 6095 6096 /* Stop if case-varying character (it's sort of a wildcard) */ 6097 if (case_insensitive && 6098 pattern_char_isalpha(patt[pos], is_multibyte, locale, locale_is_c)) 6099 break; 6100 6101 match[match_pos++] = patt[pos]; 6102 } 6103 6104 match[match_pos] = '\0'; 6105 6106 if (typeid != BYTEAOID) 6107 *prefix_const = string_to_const(match, typeid); 6108 else 6109 *prefix_const = string_to_bytea_const(match, match_pos); 6110 6111 if (rest_selec != NULL) 6112 *rest_selec = like_selectivity(&patt[pos], pattlen - pos, 6113 case_insensitive); 6114 6115 pfree(patt); 6116 pfree(match); 6117 6118 /* in LIKE, an empty pattern is an exact match! */ 6119 if (pos == pattlen) 6120 return Pattern_Prefix_Exact; /* reached end of pattern, so exact */ 6121 6122 if (match_pos > 0) 6123 return Pattern_Prefix_Partial; 6124 6125 return Pattern_Prefix_None; 6126 } 6127 6128 static Pattern_Prefix_Status 6129 regex_fixed_prefix(Const *patt_const, bool case_insensitive, Oid collation, 6130 Const **prefix_const, Selectivity *rest_selec) 6131 { 6132 Oid typeid = patt_const->consttype; 6133 char *prefix; 6134 bool exact; 6135 6136 /* 6137 * Should be unnecessary, there are no bytea regex operators defined. As 6138 * such, it should be noted that the rest of this function has *not* been 6139 * made safe for binary (possibly NULL containing) strings. 6140 */ 6141 if (typeid == BYTEAOID) 6142 ereport(ERROR, 6143 (errcode(ERRCODE_FEATURE_NOT_SUPPORTED), 6144 errmsg("regular-expression matching not supported on type bytea"))); 6145 6146 /* Use the regexp machinery to extract the prefix, if any */ 6147 prefix = regexp_fixed_prefix(DatumGetTextPP(patt_const->constvalue), 6148 case_insensitive, collation, 6149 &exact); 6150 6151 if (prefix == NULL) 6152 { 6153 *prefix_const = NULL; 6154 6155 if (rest_selec != NULL) 6156 { 6157 char *patt = TextDatumGetCString(patt_const->constvalue); 6158 6159 *rest_selec = regex_selectivity(patt, strlen(patt), 6160 case_insensitive, 6161 0); 6162 pfree(patt); 6163 } 6164 6165 return Pattern_Prefix_None; 6166 } 6167 6168 *prefix_const = string_to_const(prefix, typeid); 6169 6170 if (rest_selec != NULL) 6171 { 6172 if (exact) 6173 { 6174 /* Exact match, so there's no additional selectivity */ 6175 *rest_selec = 1.0; 6176 } 6177 else 6178 { 6179 char *patt = TextDatumGetCString(patt_const->constvalue); 6180 6181 *rest_selec = regex_selectivity(patt, strlen(patt), 6182 case_insensitive, 6183 strlen(prefix)); 6184 pfree(patt); 6185 } 6186 } 6187 6188 pfree(prefix); 6189 6190 if (exact) 6191 return Pattern_Prefix_Exact; /* pattern specifies exact match */ 6192 else 6193 return Pattern_Prefix_Partial; 6194 } 6195 6196 Pattern_Prefix_Status 6197 pattern_fixed_prefix(Const *patt, Pattern_Type ptype, Oid collation, 6198 Const **prefix, Selectivity *rest_selec) 6199 { 6200 Pattern_Prefix_Status result; 6201 6202 switch (ptype) 6203 { 6204 case Pattern_Type_Like: 6205 result = like_fixed_prefix(patt, false, collation, 6206 prefix, rest_selec); 6207 break; 6208 case Pattern_Type_Like_IC: 6209 result = like_fixed_prefix(patt, true, collation, 6210 prefix, rest_selec); 6211 break; 6212 case Pattern_Type_Regex: 6213 result = regex_fixed_prefix(patt, false, collation, 6214 prefix, rest_selec); 6215 break; 6216 case Pattern_Type_Regex_IC: 6217 result = regex_fixed_prefix(patt, true, collation, 6218 prefix, rest_selec); 6219 break; 6220 case Pattern_Type_Prefix: 6221 /* Prefix type work is trivial. */ 6222 result = Pattern_Prefix_Partial; 6223 *rest_selec = 1.0; /* all */ 6224 *prefix = makeConst(patt->consttype, 6225 patt->consttypmod, 6226 patt->constcollid, 6227 patt->constlen, 6228 datumCopy(patt->constvalue, 6229 patt->constbyval, 6230 patt->constlen), 6231 patt->constisnull, 6232 patt->constbyval); 6233 break; 6234 default: 6235 elog(ERROR, "unrecognized ptype: %d", (int) ptype); 6236 result = Pattern_Prefix_None; /* keep compiler quiet */ 6237 break; 6238 } 6239 return result; 6240 } 6241 6242 /* 6243 * Estimate the selectivity of a fixed prefix for a pattern match. 6244 * 6245 * A fixed prefix "foo" is estimated as the selectivity of the expression 6246 * "variable >= 'foo' AND variable < 'fop'" (see also indxpath.c). 6247 * 6248 * The selectivity estimate is with respect to the portion of the column 6249 * population represented by the histogram --- the caller must fold this 6250 * together with info about MCVs and NULLs. 6251 * 6252 * We use the >= and < operators from the specified btree opfamily to do the 6253 * estimation. The given variable and Const must be of the associated 6254 * datatype. 6255 * 6256 * XXX Note: we make use of the upper bound to estimate operator selectivity 6257 * even if the locale is such that we cannot rely on the upper-bound string. 6258 * The selectivity only needs to be approximately right anyway, so it seems 6259 * more useful to use the upper-bound code than not. 6260 */ 6261 static Selectivity 6262 prefix_selectivity(PlannerInfo *root, VariableStatData *vardata, 6263 Oid vartype, Oid opfamily, Const *prefixcon) 6264 { 6265 Selectivity prefixsel; 6266 Oid cmpopr; 6267 FmgrInfo opproc; 6268 Const *greaterstrcon; 6269 Selectivity eq_sel; 6270 6271 cmpopr = get_opfamily_member(opfamily, vartype, vartype, 6272 BTGreaterEqualStrategyNumber); 6273 if (cmpopr == InvalidOid) 6274 elog(ERROR, "no >= operator for opfamily %u", opfamily); 6275 fmgr_info(get_opcode(cmpopr), &opproc); 6276 6277 prefixsel = ineq_histogram_selectivity(root, vardata, 6278 &opproc, true, true, 6279 prefixcon->constvalue, 6280 prefixcon->consttype); 6281 6282 if (prefixsel < 0.0) 6283 { 6284 /* No histogram is present ... return a suitable default estimate */ 6285 return DEFAULT_MATCH_SEL; 6286 } 6287 6288 /*------- 6289 * If we can create a string larger than the prefix, say 6290 * "x < greaterstr". 6291 *------- 6292 */ 6293 cmpopr = get_opfamily_member(opfamily, vartype, vartype, 6294 BTLessStrategyNumber); 6295 if (cmpopr == InvalidOid) 6296 elog(ERROR, "no < operator for opfamily %u", opfamily); 6297 fmgr_info(get_opcode(cmpopr), &opproc); 6298 greaterstrcon = make_greater_string(prefixcon, &opproc, 6299 DEFAULT_COLLATION_OID); 6300 if (greaterstrcon) 6301 { 6302 Selectivity topsel; 6303 6304 topsel = ineq_histogram_selectivity(root, vardata, 6305 &opproc, false, false, 6306 greaterstrcon->constvalue, 6307 greaterstrcon->consttype); 6308 6309 /* ineq_histogram_selectivity worked before, it shouldn't fail now */ 6310 Assert(topsel >= 0.0); 6311 6312 /* 6313 * Merge the two selectivities in the same way as for a range query 6314 * (see clauselist_selectivity()). Note that we don't need to worry 6315 * about double-exclusion of nulls, since ineq_histogram_selectivity 6316 * doesn't count those anyway. 6317 */ 6318 prefixsel = topsel + prefixsel - 1.0; 6319 } 6320 6321 /* 6322 * If the prefix is long then the two bounding values might be too close 6323 * together for the histogram to distinguish them usefully, resulting in a 6324 * zero estimate (plus or minus roundoff error). To avoid returning a 6325 * ridiculously small estimate, compute the estimated selectivity for 6326 * "variable = 'foo'", and clamp to that. (Obviously, the resultant 6327 * estimate should be at least that.) 6328 * 6329 * We apply this even if we couldn't make a greater string. That case 6330 * suggests that the prefix is near the maximum possible, and thus 6331 * probably off the end of the histogram, and thus we probably got a very 6332 * small estimate from the >= condition; so we still need to clamp. 6333 */ 6334 cmpopr = get_opfamily_member(opfamily, vartype, vartype, 6335 BTEqualStrategyNumber); 6336 if (cmpopr == InvalidOid) 6337 elog(ERROR, "no = operator for opfamily %u", opfamily); 6338 eq_sel = var_eq_const(vardata, cmpopr, prefixcon->constvalue, 6339 false, true, false); 6340 6341 prefixsel = Max(prefixsel, eq_sel); 6342 6343 return prefixsel; 6344 } 6345 6346 6347 /* 6348 * Estimate the selectivity of a pattern of the specified type. 6349 * Note that any fixed prefix of the pattern will have been removed already, 6350 * so actually we may be looking at just a fragment of the pattern. 6351 * 6352 * For now, we use a very simplistic approach: fixed characters reduce the 6353 * selectivity a good deal, character ranges reduce it a little, 6354 * wildcards (such as % for LIKE or .* for regex) increase it. 6355 */ 6356 6357 #define FIXED_CHAR_SEL 0.20 /* about 1/5 */ 6358 #define CHAR_RANGE_SEL 0.25 6359 #define ANY_CHAR_SEL 0.9 /* not 1, since it won't match end-of-string */ 6360 #define FULL_WILDCARD_SEL 5.0 6361 #define PARTIAL_WILDCARD_SEL 2.0 6362 6363 static Selectivity 6364 like_selectivity(const char *patt, int pattlen, bool case_insensitive) 6365 { 6366 Selectivity sel = 1.0; 6367 int pos; 6368 6369 /* Skip any leading wildcard; it's already factored into initial sel */ 6370 for (pos = 0; pos < pattlen; pos++) 6371 { 6372 if (patt[pos] != '%' && patt[pos] != '_') 6373 break; 6374 } 6375 6376 for (; pos < pattlen; pos++) 6377 { 6378 /* % and _ are wildcard characters in LIKE */ 6379 if (patt[pos] == '%') 6380 sel *= FULL_WILDCARD_SEL; 6381 else if (patt[pos] == '_') 6382 sel *= ANY_CHAR_SEL; 6383 else if (patt[pos] == '\\') 6384 { 6385 /* Backslash quotes the next character */ 6386 pos++; 6387 if (pos >= pattlen) 6388 break; 6389 sel *= FIXED_CHAR_SEL; 6390 } 6391 else 6392 sel *= FIXED_CHAR_SEL; 6393 } 6394 /* Could get sel > 1 if multiple wildcards */ 6395 if (sel > 1.0) 6396 sel = 1.0; 6397 return sel; 6398 } 6399 6400 static Selectivity 6401 regex_selectivity_sub(const char *patt, int pattlen, bool case_insensitive) 6402 { 6403 Selectivity sel = 1.0; 6404 int paren_depth = 0; 6405 int paren_pos = 0; /* dummy init to keep compiler quiet */ 6406 int pos; 6407 6408 for (pos = 0; pos < pattlen; pos++) 6409 { 6410 if (patt[pos] == '(') 6411 { 6412 if (paren_depth == 0) 6413 paren_pos = pos; /* remember start of parenthesized item */ 6414 paren_depth++; 6415 } 6416 else if (patt[pos] == ')' && paren_depth > 0) 6417 { 6418 paren_depth--; 6419 if (paren_depth == 0) 6420 sel *= regex_selectivity_sub(patt + (paren_pos + 1), 6421 pos - (paren_pos + 1), 6422 case_insensitive); 6423 } 6424 else if (patt[pos] == '|' && paren_depth == 0) 6425 { 6426 /* 6427 * If unquoted | is present at paren level 0 in pattern, we have 6428 * multiple alternatives; sum their probabilities. 6429 */ 6430 sel += regex_selectivity_sub(patt + (pos + 1), 6431 pattlen - (pos + 1), 6432 case_insensitive); 6433 break; /* rest of pattern is now processed */ 6434 } 6435 else if (patt[pos] == '[') 6436 { 6437 bool negclass = false; 6438 6439 if (patt[++pos] == '^') 6440 { 6441 negclass = true; 6442 pos++; 6443 } 6444 if (patt[pos] == ']') /* ']' at start of class is not special */ 6445 pos++; 6446 while (pos < pattlen && patt[pos] != ']') 6447 pos++; 6448 if (paren_depth == 0) 6449 sel *= (negclass ? (1.0 - CHAR_RANGE_SEL) : CHAR_RANGE_SEL); 6450 } 6451 else if (patt[pos] == '.') 6452 { 6453 if (paren_depth == 0) 6454 sel *= ANY_CHAR_SEL; 6455 } 6456 else if (patt[pos] == '*' || 6457 patt[pos] == '?' || 6458 patt[pos] == '+') 6459 { 6460 /* Ought to be smarter about quantifiers... */ 6461 if (paren_depth == 0) 6462 sel *= PARTIAL_WILDCARD_SEL; 6463 } 6464 else if (patt[pos] == '{') 6465 { 6466 while (pos < pattlen && patt[pos] != '}') 6467 pos++; 6468 if (paren_depth == 0) 6469 sel *= PARTIAL_WILDCARD_SEL; 6470 } 6471 else if (patt[pos] == '\\') 6472 { 6473 /* backslash quotes the next character */ 6474 pos++; 6475 if (pos >= pattlen) 6476 break; 6477 if (paren_depth == 0) 6478 sel *= FIXED_CHAR_SEL; 6479 } 6480 else 6481 { 6482 if (paren_depth == 0) 6483 sel *= FIXED_CHAR_SEL; 6484 } 6485 } 6486 /* Could get sel > 1 if multiple wildcards */ 6487 if (sel > 1.0) 6488 sel = 1.0; 6489 return sel; 6490 } 6491 6492 static Selectivity 6493 regex_selectivity(const char *patt, int pattlen, bool case_insensitive, 6494 int fixed_prefix_len) 6495 { 6496 Selectivity sel; 6497 6498 /* If patt doesn't end with $, consider it to have a trailing wildcard */ 6499 if (pattlen > 0 && patt[pattlen - 1] == '$' && 6500 (pattlen == 1 || patt[pattlen - 2] != '\\')) 6501 { 6502 /* has trailing $ */ 6503 sel = regex_selectivity_sub(patt, pattlen - 1, case_insensitive); 6504 } 6505 else 6506 { 6507 /* no trailing $ */ 6508 sel = regex_selectivity_sub(patt, pattlen, case_insensitive); 6509 sel *= FULL_WILDCARD_SEL; 6510 } 6511 6512 /* 6513 * If there's a fixed prefix, discount its selectivity. We have to be 6514 * careful here since a very long prefix could result in pow's result 6515 * underflowing to zero (in which case "sel" probably has as well). 6516 */ 6517 if (fixed_prefix_len > 0) 6518 { 6519 double prefixsel = pow(FIXED_CHAR_SEL, fixed_prefix_len); 6520 6521 if (prefixsel > 0.0) 6522 sel /= prefixsel; 6523 } 6524 6525 /* Make sure result stays in range */ 6526 CLAMP_PROBABILITY(sel); 6527 return sel; 6528 } 6529 6530 6531 /* 6532 * For bytea, the increment function need only increment the current byte 6533 * (there are no multibyte characters to worry about). 6534 */ 6535 static bool 6536 byte_increment(unsigned char *ptr, int len) 6537 { 6538 if (*ptr >= 255) 6539 return false; 6540 (*ptr)++; 6541 return true; 6542 } 6543 6544 /* 6545 * Try to generate a string greater than the given string or any 6546 * string it is a prefix of. If successful, return a palloc'd string 6547 * in the form of a Const node; else return NULL. 6548 * 6549 * The caller must provide the appropriate "less than" comparison function 6550 * for testing the strings, along with the collation to use. 6551 * 6552 * The key requirement here is that given a prefix string, say "foo", 6553 * we must be able to generate another string "fop" that is greater than 6554 * all strings "foobar" starting with "foo". We can test that we have 6555 * generated a string greater than the prefix string, but in non-C collations 6556 * that is not a bulletproof guarantee that an extension of the string might 6557 * not sort after it; an example is that "foo " is less than "foo!", but it 6558 * is not clear that a "dictionary" sort ordering will consider "foo!" less 6559 * than "foo bar". CAUTION: Therefore, this function should be used only for 6560 * estimation purposes when working in a non-C collation. 6561 * 6562 * To try to catch most cases where an extended string might otherwise sort 6563 * before the result value, we determine which of the strings "Z", "z", "y", 6564 * and "9" is seen as largest by the collation, and append that to the given 6565 * prefix before trying to find a string that compares as larger. 6566 * 6567 * To search for a greater string, we repeatedly "increment" the rightmost 6568 * character, using an encoding-specific character incrementer function. 6569 * When it's no longer possible to increment the last character, we truncate 6570 * off that character and start incrementing the next-to-rightmost. 6571 * For example, if "z" were the last character in the sort order, then we 6572 * could produce "foo" as a string greater than "fonz". 6573 * 6574 * This could be rather slow in the worst case, but in most cases we 6575 * won't have to try more than one or two strings before succeeding. 6576 * 6577 * Note that it's important for the character incrementer not to be too anal 6578 * about producing every possible character code, since in some cases the only 6579 * way to get a larger string is to increment a previous character position. 6580 * So we don't want to spend too much time trying every possible character 6581 * code at the last position. A good rule of thumb is to be sure that we 6582 * don't try more than 256*K values for a K-byte character (and definitely 6583 * not 256^K, which is what an exhaustive search would approach). 6584 */ 6585 Const * 6586 make_greater_string(const Const *str_const, FmgrInfo *ltproc, Oid collation) 6587 { 6588 Oid datatype = str_const->consttype; 6589 char *workstr; 6590 int len; 6591 Datum cmpstr; 6592 text *cmptxt = NULL; 6593 mbcharacter_incrementer charinc; 6594 6595 /* 6596 * Get a modifiable copy of the prefix string in C-string format, and set 6597 * up the string we will compare to as a Datum. In C locale this can just 6598 * be the given prefix string, otherwise we need to add a suffix. Types 6599 * NAME and BYTEA sort bytewise so they don't need a suffix either. 6600 */ 6601 if (datatype == NAMEOID) 6602 { 6603 workstr = DatumGetCString(DirectFunctionCall1(nameout, 6604 str_const->constvalue)); 6605 len = strlen(workstr); 6606 cmpstr = str_const->constvalue; 6607 } 6608 else if (datatype == BYTEAOID) 6609 { 6610 bytea *bstr = DatumGetByteaPP(str_const->constvalue); 6611 6612 len = VARSIZE_ANY_EXHDR(bstr); 6613 workstr = (char *) palloc(len); 6614 memcpy(workstr, VARDATA_ANY(bstr), len); 6615 Assert((Pointer) bstr == DatumGetPointer(str_const->constvalue)); 6616 cmpstr = str_const->constvalue; 6617 } 6618 else 6619 { 6620 workstr = TextDatumGetCString(str_const->constvalue); 6621 len = strlen(workstr); 6622 if (lc_collate_is_c(collation) || len == 0) 6623 cmpstr = str_const->constvalue; 6624 else 6625 { 6626 /* If first time through, determine the suffix to use */ 6627 static char suffixchar = 0; 6628 static Oid suffixcollation = 0; 6629 6630 if (!suffixchar || suffixcollation != collation) 6631 { 6632 char *best; 6633 6634 best = "Z"; 6635 if (varstr_cmp(best, 1, "z", 1, collation) < 0) 6636 best = "z"; 6637 if (varstr_cmp(best, 1, "y", 1, collation) < 0) 6638 best = "y"; 6639 if (varstr_cmp(best, 1, "9", 1, collation) < 0) 6640 best = "9"; 6641 suffixchar = *best; 6642 suffixcollation = collation; 6643 } 6644 6645 /* And build the string to compare to */ 6646 cmptxt = (text *) palloc(VARHDRSZ + len + 1); 6647 SET_VARSIZE(cmptxt, VARHDRSZ + len + 1); 6648 memcpy(VARDATA(cmptxt), workstr, len); 6649 *(VARDATA(cmptxt) + len) = suffixchar; 6650 cmpstr = PointerGetDatum(cmptxt); 6651 } 6652 } 6653 6654 /* Select appropriate character-incrementer function */ 6655 if (datatype == BYTEAOID) 6656 charinc = byte_increment; 6657 else 6658 charinc = pg_database_encoding_character_incrementer(); 6659 6660 /* And search ... */ 6661 while (len > 0) 6662 { 6663 int charlen; 6664 unsigned char *lastchar; 6665 6666 /* Identify the last character --- for bytea, just the last byte */ 6667 if (datatype == BYTEAOID) 6668 charlen = 1; 6669 else 6670 charlen = len - pg_mbcliplen(workstr, len, len - 1); 6671 lastchar = (unsigned char *) (workstr + len - charlen); 6672 6673 /* 6674 * Try to generate a larger string by incrementing the last character 6675 * (for BYTEA, we treat each byte as a character). 6676 * 6677 * Note: the incrementer function is expected to return true if it's 6678 * generated a valid-per-the-encoding new character, otherwise false. 6679 * The contents of the character on false return are unspecified. 6680 */ 6681 while (charinc(lastchar, charlen)) 6682 { 6683 Const *workstr_const; 6684 6685 if (datatype == BYTEAOID) 6686 workstr_const = string_to_bytea_const(workstr, len); 6687 else 6688 workstr_const = string_to_const(workstr, datatype); 6689 6690 if (DatumGetBool(FunctionCall2Coll(ltproc, 6691 collation, 6692 cmpstr, 6693 workstr_const->constvalue))) 6694 { 6695 /* Successfully made a string larger than cmpstr */ 6696 if (cmptxt) 6697 pfree(cmptxt); 6698 pfree(workstr); 6699 return workstr_const; 6700 } 6701 6702 /* No good, release unusable value and try again */ 6703 pfree(DatumGetPointer(workstr_const->constvalue)); 6704 pfree(workstr_const); 6705 } 6706 6707 /* 6708 * No luck here, so truncate off the last character and try to 6709 * increment the next one. 6710 */ 6711 len -= charlen; 6712 workstr[len] = '\0'; 6713 } 6714 6715 /* Failed... */ 6716 if (cmptxt) 6717 pfree(cmptxt); 6718 pfree(workstr); 6719 6720 return NULL; 6721 } 6722 6723 /* 6724 * Generate a Datum of the appropriate type from a C string. 6725 * Note that all of the supported types are pass-by-ref, so the 6726 * returned value should be pfree'd if no longer needed. 6727 */ 6728 static Datum 6729 string_to_datum(const char *str, Oid datatype) 6730 { 6731 Assert(str != NULL); 6732 6733 /* 6734 * We cheat a little by assuming that CStringGetTextDatum() will do for 6735 * bpchar and varchar constants too... 6736 */ 6737 if (datatype == NAMEOID) 6738 return DirectFunctionCall1(namein, CStringGetDatum(str)); 6739 else if (datatype == BYTEAOID) 6740 return DirectFunctionCall1(byteain, CStringGetDatum(str)); 6741 else 6742 return CStringGetTextDatum(str); 6743 } 6744 6745 /* 6746 * Generate a Const node of the appropriate type from a C string. 6747 */ 6748 static Const * 6749 string_to_const(const char *str, Oid datatype) 6750 { 6751 Datum conval = string_to_datum(str, datatype); 6752 Oid collation; 6753 int constlen; 6754 6755 /* 6756 * We only need to support a few datatypes here, so hard-wire properties 6757 * instead of incurring the expense of catalog lookups. 6758 */ 6759 switch (datatype) 6760 { 6761 case TEXTOID: 6762 case VARCHAROID: 6763 case BPCHAROID: 6764 collation = DEFAULT_COLLATION_OID; 6765 constlen = -1; 6766 break; 6767 6768 case NAMEOID: 6769 collation = InvalidOid; 6770 constlen = NAMEDATALEN; 6771 break; 6772 6773 case BYTEAOID: 6774 collation = InvalidOid; 6775 constlen = -1; 6776 break; 6777 6778 default: 6779 elog(ERROR, "unexpected datatype in string_to_const: %u", 6780 datatype); 6781 return NULL; 6782 } 6783 6784 return makeConst(datatype, -1, collation, constlen, 6785 conval, false, false); 6786 } 6787 6788 /* 6789 * Generate a Const node of bytea type from a binary C string and a length. 6790 */ 6791 static Const * 6792 string_to_bytea_const(const char *str, size_t str_len) 6793 { 6794 bytea *bstr = palloc(VARHDRSZ + str_len); 6795 Datum conval; 6796 6797 memcpy(VARDATA(bstr), str, str_len); 6798 SET_VARSIZE(bstr, VARHDRSZ + str_len); 6799 conval = PointerGetDatum(bstr); 6800 6801 return makeConst(BYTEAOID, -1, InvalidOid, -1, conval, false, false); 6802 } 6803 6804 /*------------------------------------------------------------------------- 6805 * 6806 * Index cost estimation functions 6807 * 6808 *------------------------------------------------------------------------- 6809 */ 6810 6811 List * 6812 deconstruct_indexquals(IndexPath *path) 6813 { 6814 List *result = NIL; 6815 IndexOptInfo *index = path->indexinfo; 6816 ListCell *lcc, 6817 *lci; 6818 6819 forboth(lcc, path->indexquals, lci, path->indexqualcols) 6820 { 6821 RestrictInfo *rinfo = lfirst_node(RestrictInfo, lcc); 6822 int indexcol = lfirst_int(lci); 6823 Expr *clause; 6824 Node *leftop, 6825 *rightop; 6826 IndexQualInfo *qinfo; 6827 6828 clause = rinfo->clause; 6829 6830 qinfo = (IndexQualInfo *) palloc(sizeof(IndexQualInfo)); 6831 qinfo->rinfo = rinfo; 6832 qinfo->indexcol = indexcol; 6833 6834 if (IsA(clause, OpExpr)) 6835 { 6836 qinfo->clause_op = ((OpExpr *) clause)->opno; 6837 leftop = get_leftop(clause); 6838 rightop = get_rightop(clause); 6839 if (match_index_to_operand(leftop, indexcol, index)) 6840 { 6841 qinfo->varonleft = true; 6842 qinfo->other_operand = rightop; 6843 } 6844 else 6845 { 6846 Assert(match_index_to_operand(rightop, indexcol, index)); 6847 qinfo->varonleft = false; 6848 qinfo->other_operand = leftop; 6849 } 6850 } 6851 else if (IsA(clause, RowCompareExpr)) 6852 { 6853 RowCompareExpr *rc = (RowCompareExpr *) clause; 6854 6855 qinfo->clause_op = linitial_oid(rc->opnos); 6856 /* Examine only first columns to determine left/right sides */ 6857 if (match_index_to_operand((Node *) linitial(rc->largs), 6858 indexcol, index)) 6859 { 6860 qinfo->varonleft = true; 6861 qinfo->other_operand = (Node *) rc->rargs; 6862 } 6863 else 6864 { 6865 Assert(match_index_to_operand((Node *) linitial(rc->rargs), 6866 indexcol, index)); 6867 qinfo->varonleft = false; 6868 qinfo->other_operand = (Node *) rc->largs; 6869 } 6870 } 6871 else if (IsA(clause, ScalarArrayOpExpr)) 6872 { 6873 ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause; 6874 6875 qinfo->clause_op = saop->opno; 6876 /* index column is always on the left in this case */ 6877 Assert(match_index_to_operand((Node *) linitial(saop->args), 6878 indexcol, index)); 6879 qinfo->varonleft = true; 6880 qinfo->other_operand = (Node *) lsecond(saop->args); 6881 } 6882 else if (IsA(clause, NullTest)) 6883 { 6884 qinfo->clause_op = InvalidOid; 6885 Assert(match_index_to_operand((Node *) ((NullTest *) clause)->arg, 6886 indexcol, index)); 6887 qinfo->varonleft = true; 6888 qinfo->other_operand = NULL; 6889 } 6890 else 6891 { 6892 elog(ERROR, "unsupported indexqual type: %d", 6893 (int) nodeTag(clause)); 6894 } 6895 6896 result = lappend(result, qinfo); 6897 } 6898 return result; 6899 } 6900 6901 /* 6902 * Simple function to compute the total eval cost of the "other operands" 6903 * in an IndexQualInfo list. Since we know these will be evaluated just 6904 * once per scan, there's no need to distinguish startup from per-row cost. 6905 */ 6906 static Cost 6907 other_operands_eval_cost(PlannerInfo *root, List *qinfos) 6908 { 6909 Cost qual_arg_cost = 0; 6910 ListCell *lc; 6911 6912 foreach(lc, qinfos) 6913 { 6914 IndexQualInfo *qinfo = (IndexQualInfo *) lfirst(lc); 6915 QualCost index_qual_cost; 6916 6917 cost_qual_eval_node(&index_qual_cost, qinfo->other_operand, root); 6918 qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple; 6919 } 6920 return qual_arg_cost; 6921 } 6922 6923 /* 6924 * Get other-operand eval cost for an index orderby list. 6925 * 6926 * Index orderby expressions aren't represented as RestrictInfos (since they 6927 * aren't boolean, usually). So we can't apply deconstruct_indexquals to 6928 * them. However, they are much simpler to deal with since they are always 6929 * OpExprs and the index column is always on the left. 6930 */ 6931 static Cost 6932 orderby_operands_eval_cost(PlannerInfo *root, IndexPath *path) 6933 { 6934 Cost qual_arg_cost = 0; 6935 ListCell *lc; 6936 6937 foreach(lc, path->indexorderbys) 6938 { 6939 Expr *clause = (Expr *) lfirst(lc); 6940 Node *other_operand; 6941 QualCost index_qual_cost; 6942 6943 if (IsA(clause, OpExpr)) 6944 { 6945 other_operand = get_rightop(clause); 6946 } 6947 else 6948 { 6949 elog(ERROR, "unsupported indexorderby type: %d", 6950 (int) nodeTag(clause)); 6951 other_operand = NULL; /* keep compiler quiet */ 6952 } 6953 6954 cost_qual_eval_node(&index_qual_cost, other_operand, root); 6955 qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple; 6956 } 6957 return qual_arg_cost; 6958 } 6959 6960 void 6961 genericcostestimate(PlannerInfo *root, 6962 IndexPath *path, 6963 double loop_count, 6964 List *qinfos, 6965 GenericCosts *costs) 6966 { 6967 IndexOptInfo *index = path->indexinfo; 6968 List *indexQuals = path->indexquals; 6969 List *indexOrderBys = path->indexorderbys; 6970 Cost indexStartupCost; 6971 Cost indexTotalCost; 6972 Selectivity indexSelectivity; 6973 double indexCorrelation; 6974 double numIndexPages; 6975 double numIndexTuples; 6976 double spc_random_page_cost; 6977 double num_sa_scans; 6978 double num_outer_scans; 6979 double num_scans; 6980 double qual_op_cost; 6981 double qual_arg_cost; 6982 List *selectivityQuals; 6983 ListCell *l; 6984 6985 /* 6986 * If the index is partial, AND the index predicate with the explicitly 6987 * given indexquals to produce a more accurate idea of the index 6988 * selectivity. 6989 */ 6990 selectivityQuals = add_predicate_to_quals(index, indexQuals); 6991 6992 /* 6993 * Check for ScalarArrayOpExpr index quals, and estimate the number of 6994 * index scans that will be performed. 6995 */ 6996 num_sa_scans = 1; 6997 foreach(l, indexQuals) 6998 { 6999 RestrictInfo *rinfo = (RestrictInfo *) lfirst(l); 7000 7001 if (IsA(rinfo->clause, ScalarArrayOpExpr)) 7002 { 7003 ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause; 7004 int alength = estimate_array_length(lsecond(saop->args)); 7005 7006 if (alength > 1) 7007 num_sa_scans *= alength; 7008 } 7009 } 7010 7011 /* Estimate the fraction of main-table tuples that will be visited */ 7012 indexSelectivity = clauselist_selectivity(root, selectivityQuals, 7013 index->rel->relid, 7014 JOIN_INNER, 7015 NULL); 7016 7017 /* 7018 * If caller didn't give us an estimate, estimate the number of index 7019 * tuples that will be visited. We do it in this rather peculiar-looking 7020 * way in order to get the right answer for partial indexes. 7021 */ 7022 numIndexTuples = costs->numIndexTuples; 7023 if (numIndexTuples <= 0.0) 7024 { 7025 numIndexTuples = indexSelectivity * index->rel->tuples; 7026 7027 /* 7028 * The above calculation counts all the tuples visited across all 7029 * scans induced by ScalarArrayOpExpr nodes. We want to consider the 7030 * average per-indexscan number, so adjust. This is a handy place to 7031 * round to integer, too. (If caller supplied tuple estimate, it's 7032 * responsible for handling these considerations.) 7033 */ 7034 numIndexTuples = rint(numIndexTuples / num_sa_scans); 7035 } 7036 7037 /* 7038 * We can bound the number of tuples by the index size in any case. Also, 7039 * always estimate at least one tuple is touched, even when 7040 * indexSelectivity estimate is tiny. 7041 */ 7042 if (numIndexTuples > index->tuples) 7043 numIndexTuples = index->tuples; 7044 if (numIndexTuples < 1.0) 7045 numIndexTuples = 1.0; 7046 7047 /* 7048 * Estimate the number of index pages that will be retrieved. 7049 * 7050 * We use the simplistic method of taking a pro-rata fraction of the total 7051 * number of index pages. In effect, this counts only leaf pages and not 7052 * any overhead such as index metapage or upper tree levels. 7053 * 7054 * In practice access to upper index levels is often nearly free because 7055 * those tend to stay in cache under load; moreover, the cost involved is 7056 * highly dependent on index type. We therefore ignore such costs here 7057 * and leave it to the caller to add a suitable charge if needed. 7058 */ 7059 if (index->pages > 1 && index->tuples > 1) 7060 numIndexPages = ceil(numIndexTuples * index->pages / index->tuples); 7061 else 7062 numIndexPages = 1.0; 7063 7064 /* fetch estimated page cost for tablespace containing index */ 7065 get_tablespace_page_costs(index->reltablespace, 7066 &spc_random_page_cost, 7067 NULL); 7068 7069 /* 7070 * Now compute the disk access costs. 7071 * 7072 * The above calculations are all per-index-scan. However, if we are in a 7073 * nestloop inner scan, we can expect the scan to be repeated (with 7074 * different search keys) for each row of the outer relation. Likewise, 7075 * ScalarArrayOpExpr quals result in multiple index scans. This creates 7076 * the potential for cache effects to reduce the number of disk page 7077 * fetches needed. We want to estimate the average per-scan I/O cost in 7078 * the presence of caching. 7079 * 7080 * We use the Mackert-Lohman formula (see costsize.c for details) to 7081 * estimate the total number of page fetches that occur. While this 7082 * wasn't what it was designed for, it seems a reasonable model anyway. 7083 * Note that we are counting pages not tuples anymore, so we take N = T = 7084 * index size, as if there were one "tuple" per page. 7085 */ 7086 num_outer_scans = loop_count; 7087 num_scans = num_sa_scans * num_outer_scans; 7088 7089 if (num_scans > 1) 7090 { 7091 double pages_fetched; 7092 7093 /* total page fetches ignoring cache effects */ 7094 pages_fetched = numIndexPages * num_scans; 7095 7096 /* use Mackert and Lohman formula to adjust for cache effects */ 7097 pages_fetched = index_pages_fetched(pages_fetched, 7098 index->pages, 7099 (double) index->pages, 7100 root); 7101 7102 /* 7103 * Now compute the total disk access cost, and then report a pro-rated 7104 * share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr, 7105 * since that's internal to the indexscan.) 7106 */ 7107 indexTotalCost = (pages_fetched * spc_random_page_cost) 7108 / num_outer_scans; 7109 } 7110 else 7111 { 7112 /* 7113 * For a single index scan, we just charge spc_random_page_cost per 7114 * page touched. 7115 */ 7116 indexTotalCost = numIndexPages * spc_random_page_cost; 7117 } 7118 7119 /* 7120 * CPU cost: any complex expressions in the indexquals will need to be 7121 * evaluated once at the start of the scan to reduce them to runtime keys 7122 * to pass to the index AM (see nodeIndexscan.c). We model the per-tuple 7123 * CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per 7124 * indexqual operator. Because we have numIndexTuples as a per-scan 7125 * number, we have to multiply by num_sa_scans to get the correct result 7126 * for ScalarArrayOpExpr cases. Similarly add in costs for any index 7127 * ORDER BY expressions. 7128 * 7129 * Note: this neglects the possible costs of rechecking lossy operators. 7130 * Detecting that that might be needed seems more expensive than it's 7131 * worth, though, considering all the other inaccuracies here ... 7132 */ 7133 qual_arg_cost = other_operands_eval_cost(root, qinfos) + 7134 orderby_operands_eval_cost(root, path); 7135 qual_op_cost = cpu_operator_cost * 7136 (list_length(indexQuals) + list_length(indexOrderBys)); 7137 7138 indexStartupCost = qual_arg_cost; 7139 indexTotalCost += qual_arg_cost; 7140 indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost); 7141 7142 /* 7143 * Generic assumption about index correlation: there isn't any. 7144 */ 7145 indexCorrelation = 0.0; 7146 7147 /* 7148 * Return everything to caller. 7149 */ 7150 costs->indexStartupCost = indexStartupCost; 7151 costs->indexTotalCost = indexTotalCost; 7152 costs->indexSelectivity = indexSelectivity; 7153 costs->indexCorrelation = indexCorrelation; 7154 costs->numIndexPages = numIndexPages; 7155 costs->numIndexTuples = numIndexTuples; 7156 costs->spc_random_page_cost = spc_random_page_cost; 7157 costs->num_sa_scans = num_sa_scans; 7158 } 7159 7160 /* 7161 * If the index is partial, add its predicate to the given qual list. 7162 * 7163 * ANDing the index predicate with the explicitly given indexquals produces 7164 * a more accurate idea of the index's selectivity. However, we need to be 7165 * careful not to insert redundant clauses, because clauselist_selectivity() 7166 * is easily fooled into computing a too-low selectivity estimate. Our 7167 * approach is to add only the predicate clause(s) that cannot be proven to 7168 * be implied by the given indexquals. This successfully handles cases such 7169 * as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50". 7170 * There are many other cases where we won't detect redundancy, leading to a 7171 * too-low selectivity estimate, which will bias the system in favor of using 7172 * partial indexes where possible. That is not necessarily bad though. 7173 * 7174 * Note that indexQuals contains RestrictInfo nodes while the indpred 7175 * does not, so the output list will be mixed. This is OK for both 7176 * predicate_implied_by() and clauselist_selectivity(), but might be 7177 * problematic if the result were passed to other things. 7178 */ 7179 static List * 7180 add_predicate_to_quals(IndexOptInfo *index, List *indexQuals) 7181 { 7182 List *predExtraQuals = NIL; 7183 ListCell *lc; 7184 7185 if (index->indpred == NIL) 7186 return indexQuals; 7187 7188 foreach(lc, index->indpred) 7189 { 7190 Node *predQual = (Node *) lfirst(lc); 7191 List *oneQual = list_make1(predQual); 7192 7193 if (!predicate_implied_by(oneQual, indexQuals, false)) 7194 predExtraQuals = list_concat(predExtraQuals, oneQual); 7195 } 7196 /* list_concat avoids modifying the passed-in indexQuals list */ 7197 return list_concat(predExtraQuals, indexQuals); 7198 } 7199 7200 7201 void 7202 btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count, 7203 Cost *indexStartupCost, Cost *indexTotalCost, 7204 Selectivity *indexSelectivity, double *indexCorrelation, 7205 double *indexPages) 7206 { 7207 IndexOptInfo *index = path->indexinfo; 7208 List *qinfos; 7209 GenericCosts costs; 7210 Oid relid; 7211 AttrNumber colnum; 7212 VariableStatData vardata; 7213 double numIndexTuples; 7214 Cost descentCost; 7215 List *indexBoundQuals; 7216 int indexcol; 7217 bool eqQualHere; 7218 bool found_saop; 7219 bool found_is_null_op; 7220 double num_sa_scans; 7221 ListCell *lc; 7222 7223 /* Do preliminary analysis of indexquals */ 7224 qinfos = deconstruct_indexquals(path); 7225 7226 /* 7227 * For a btree scan, only leading '=' quals plus inequality quals for the 7228 * immediately next attribute contribute to index selectivity (these are 7229 * the "boundary quals" that determine the starting and stopping points of 7230 * the index scan). Additional quals can suppress visits to the heap, so 7231 * it's OK to count them in indexSelectivity, but they should not count 7232 * for estimating numIndexTuples. So we must examine the given indexquals 7233 * to find out which ones count as boundary quals. We rely on the 7234 * knowledge that they are given in index column order. 7235 * 7236 * For a RowCompareExpr, we consider only the first column, just as 7237 * rowcomparesel() does. 7238 * 7239 * If there's a ScalarArrayOpExpr in the quals, we'll actually perform N 7240 * index scans not one, but the ScalarArrayOpExpr's operator can be 7241 * considered to act the same as it normally does. 7242 */ 7243 indexBoundQuals = NIL; 7244 indexcol = 0; 7245 eqQualHere = false; 7246 found_saop = false; 7247 found_is_null_op = false; 7248 num_sa_scans = 1; 7249 foreach(lc, qinfos) 7250 { 7251 IndexQualInfo *qinfo = (IndexQualInfo *) lfirst(lc); 7252 RestrictInfo *rinfo = qinfo->rinfo; 7253 Expr *clause = rinfo->clause; 7254 Oid clause_op; 7255 int op_strategy; 7256 7257 if (indexcol != qinfo->indexcol) 7258 { 7259 /* Beginning of a new column's quals */ 7260 if (!eqQualHere) 7261 break; /* done if no '=' qual for indexcol */ 7262 eqQualHere = false; 7263 indexcol++; 7264 if (indexcol != qinfo->indexcol) 7265 break; /* no quals at all for indexcol */ 7266 } 7267 7268 if (IsA(clause, ScalarArrayOpExpr)) 7269 { 7270 int alength = estimate_array_length(qinfo->other_operand); 7271 7272 found_saop = true; 7273 /* count up number of SA scans induced by indexBoundQuals only */ 7274 if (alength > 1) 7275 num_sa_scans *= alength; 7276 } 7277 else if (IsA(clause, NullTest)) 7278 { 7279 NullTest *nt = (NullTest *) clause; 7280 7281 if (nt->nulltesttype == IS_NULL) 7282 { 7283 found_is_null_op = true; 7284 /* IS NULL is like = for selectivity determination purposes */ 7285 eqQualHere = true; 7286 } 7287 } 7288 7289 /* 7290 * We would need to commute the clause_op if not varonleft, except 7291 * that we only care if it's equality or not, so that refinement is 7292 * unnecessary. 7293 */ 7294 clause_op = qinfo->clause_op; 7295 7296 /* check for equality operator */ 7297 if (OidIsValid(clause_op)) 7298 { 7299 op_strategy = get_op_opfamily_strategy(clause_op, 7300 index->opfamily[indexcol]); 7301 Assert(op_strategy != 0); /* not a member of opfamily?? */ 7302 if (op_strategy == BTEqualStrategyNumber) 7303 eqQualHere = true; 7304 } 7305 7306 indexBoundQuals = lappend(indexBoundQuals, rinfo); 7307 } 7308 7309 /* 7310 * If index is unique and we found an '=' clause for each column, we can 7311 * just assume numIndexTuples = 1 and skip the expensive 7312 * clauselist_selectivity calculations. However, a ScalarArrayOp or 7313 * NullTest invalidates that theory, even though it sets eqQualHere. 7314 */ 7315 if (index->unique && 7316 indexcol == index->nkeycolumns - 1 && 7317 eqQualHere && 7318 !found_saop && 7319 !found_is_null_op) 7320 numIndexTuples = 1.0; 7321 else 7322 { 7323 List *selectivityQuals; 7324 Selectivity btreeSelectivity; 7325 7326 /* 7327 * If the index is partial, AND the index predicate with the 7328 * index-bound quals to produce a more accurate idea of the number of 7329 * rows covered by the bound conditions. 7330 */ 7331 selectivityQuals = add_predicate_to_quals(index, indexBoundQuals); 7332 7333 btreeSelectivity = clauselist_selectivity(root, selectivityQuals, 7334 index->rel->relid, 7335 JOIN_INNER, 7336 NULL); 7337 numIndexTuples = btreeSelectivity * index->rel->tuples; 7338 7339 /* 7340 * As in genericcostestimate(), we have to adjust for any 7341 * ScalarArrayOpExpr quals included in indexBoundQuals, and then round 7342 * to integer. 7343 */ 7344 numIndexTuples = rint(numIndexTuples / num_sa_scans); 7345 } 7346 7347 /* 7348 * Now do generic index cost estimation. 7349 */ 7350 MemSet(&costs, 0, sizeof(costs)); 7351 costs.numIndexTuples = numIndexTuples; 7352 7353 genericcostestimate(root, path, loop_count, qinfos, &costs); 7354 7355 /* 7356 * Add a CPU-cost component to represent the costs of initial btree 7357 * descent. We don't charge any I/O cost for touching upper btree levels, 7358 * since they tend to stay in cache, but we still have to do about log2(N) 7359 * comparisons to descend a btree of N leaf tuples. We charge one 7360 * cpu_operator_cost per comparison. 7361 * 7362 * If there are ScalarArrayOpExprs, charge this once per SA scan. The 7363 * ones after the first one are not startup cost so far as the overall 7364 * plan is concerned, so add them only to "total" cost. 7365 */ 7366 if (index->tuples > 1) /* avoid computing log(0) */ 7367 { 7368 descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost; 7369 costs.indexStartupCost += descentCost; 7370 costs.indexTotalCost += costs.num_sa_scans * descentCost; 7371 } 7372 7373 /* 7374 * Even though we're not charging I/O cost for touching upper btree pages, 7375 * it's still reasonable to charge some CPU cost per page descended 7376 * through. Moreover, if we had no such charge at all, bloated indexes 7377 * would appear to have the same search cost as unbloated ones, at least 7378 * in cases where only a single leaf page is expected to be visited. This 7379 * cost is somewhat arbitrarily set at 50x cpu_operator_cost per page 7380 * touched. The number of such pages is btree tree height plus one (ie, 7381 * we charge for the leaf page too). As above, charge once per SA scan. 7382 */ 7383 descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost; 7384 costs.indexStartupCost += descentCost; 7385 costs.indexTotalCost += costs.num_sa_scans * descentCost; 7386 7387 /* 7388 * If we can get an estimate of the first column's ordering correlation C 7389 * from pg_statistic, estimate the index correlation as C for a 7390 * single-column index, or C * 0.75 for multiple columns. (The idea here 7391 * is that multiple columns dilute the importance of the first column's 7392 * ordering, but don't negate it entirely. Before 8.0 we divided the 7393 * correlation by the number of columns, but that seems too strong.) 7394 */ 7395 MemSet(&vardata, 0, sizeof(vardata)); 7396 7397 if (index->indexkeys[0] != 0) 7398 { 7399 /* Simple variable --- look to stats for the underlying table */ 7400 RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root); 7401 7402 Assert(rte->rtekind == RTE_RELATION); 7403 relid = rte->relid; 7404 Assert(relid != InvalidOid); 7405 colnum = index->indexkeys[0]; 7406 7407 if (get_relation_stats_hook && 7408 (*get_relation_stats_hook) (root, rte, colnum, &vardata)) 7409 { 7410 /* 7411 * The hook took control of acquiring a stats tuple. If it did 7412 * supply a tuple, it'd better have supplied a freefunc. 7413 */ 7414 if (HeapTupleIsValid(vardata.statsTuple) && 7415 !vardata.freefunc) 7416 elog(ERROR, "no function provided to release variable stats with"); 7417 } 7418 else 7419 { 7420 vardata.statsTuple = SearchSysCache3(STATRELATTINH, 7421 ObjectIdGetDatum(relid), 7422 Int16GetDatum(colnum), 7423 BoolGetDatum(rte->inh)); 7424 vardata.freefunc = ReleaseSysCache; 7425 } 7426 } 7427 else 7428 { 7429 /* Expression --- maybe there are stats for the index itself */ 7430 relid = index->indexoid; 7431 colnum = 1; 7432 7433 if (get_index_stats_hook && 7434 (*get_index_stats_hook) (root, relid, colnum, &vardata)) 7435 { 7436 /* 7437 * The hook took control of acquiring a stats tuple. If it did 7438 * supply a tuple, it'd better have supplied a freefunc. 7439 */ 7440 if (HeapTupleIsValid(vardata.statsTuple) && 7441 !vardata.freefunc) 7442 elog(ERROR, "no function provided to release variable stats with"); 7443 } 7444 else 7445 { 7446 vardata.statsTuple = SearchSysCache3(STATRELATTINH, 7447 ObjectIdGetDatum(relid), 7448 Int16GetDatum(colnum), 7449 BoolGetDatum(false)); 7450 vardata.freefunc = ReleaseSysCache; 7451 } 7452 } 7453 7454 if (HeapTupleIsValid(vardata.statsTuple)) 7455 { 7456 Oid sortop; 7457 AttStatsSlot sslot; 7458 7459 sortop = get_opfamily_member(index->opfamily[0], 7460 index->opcintype[0], 7461 index->opcintype[0], 7462 BTLessStrategyNumber); 7463 if (OidIsValid(sortop) && 7464 get_attstatsslot(&sslot, vardata.statsTuple, 7465 STATISTIC_KIND_CORRELATION, sortop, 7466 ATTSTATSSLOT_NUMBERS)) 7467 { 7468 double varCorrelation; 7469 7470 Assert(sslot.nnumbers == 1); 7471 varCorrelation = sslot.numbers[0]; 7472 7473 if (index->reverse_sort[0]) 7474 varCorrelation = -varCorrelation; 7475 7476 if (index->nkeycolumns > 1) 7477 costs.indexCorrelation = varCorrelation * 0.75; 7478 else 7479 costs.indexCorrelation = varCorrelation; 7480 7481 free_attstatsslot(&sslot); 7482 } 7483 } 7484 7485 ReleaseVariableStats(vardata); 7486 7487 *indexStartupCost = costs.indexStartupCost; 7488 *indexTotalCost = costs.indexTotalCost; 7489 *indexSelectivity = costs.indexSelectivity; 7490 *indexCorrelation = costs.indexCorrelation; 7491 *indexPages = costs.numIndexPages; 7492 } 7493 7494 void 7495 hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count, 7496 Cost *indexStartupCost, Cost *indexTotalCost, 7497 Selectivity *indexSelectivity, double *indexCorrelation, 7498 double *indexPages) 7499 { 7500 List *qinfos; 7501 GenericCosts costs; 7502 7503 /* Do preliminary analysis of indexquals */ 7504 qinfos = deconstruct_indexquals(path); 7505 7506 MemSet(&costs, 0, sizeof(costs)); 7507 7508 genericcostestimate(root, path, loop_count, qinfos, &costs); 7509 7510 /* 7511 * A hash index has no descent costs as such, since the index AM can go 7512 * directly to the target bucket after computing the hash value. There 7513 * are a couple of other hash-specific costs that we could conceivably add 7514 * here, though: 7515 * 7516 * Ideally we'd charge spc_random_page_cost for each page in the target 7517 * bucket, not just the numIndexPages pages that genericcostestimate 7518 * thought we'd visit. However in most cases we don't know which bucket 7519 * that will be. There's no point in considering the average bucket size 7520 * because the hash AM makes sure that's always one page. 7521 * 7522 * Likewise, we could consider charging some CPU for each index tuple in 7523 * the bucket, if we knew how many there were. But the per-tuple cost is 7524 * just a hash value comparison, not a general datatype-dependent 7525 * comparison, so any such charge ought to be quite a bit less than 7526 * cpu_operator_cost; which makes it probably not worth worrying about. 7527 * 7528 * A bigger issue is that chance hash-value collisions will result in 7529 * wasted probes into the heap. We don't currently attempt to model this 7530 * cost on the grounds that it's rare, but maybe it's not rare enough. 7531 * (Any fix for this ought to consider the generic lossy-operator problem, 7532 * though; it's not entirely hash-specific.) 7533 */ 7534 7535 *indexStartupCost = costs.indexStartupCost; 7536 *indexTotalCost = costs.indexTotalCost; 7537 *indexSelectivity = costs.indexSelectivity; 7538 *indexCorrelation = costs.indexCorrelation; 7539 *indexPages = costs.numIndexPages; 7540 } 7541 7542 void 7543 gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count, 7544 Cost *indexStartupCost, Cost *indexTotalCost, 7545 Selectivity *indexSelectivity, double *indexCorrelation, 7546 double *indexPages) 7547 { 7548 IndexOptInfo *index = path->indexinfo; 7549 List *qinfos; 7550 GenericCosts costs; 7551 Cost descentCost; 7552 7553 /* Do preliminary analysis of indexquals */ 7554 qinfos = deconstruct_indexquals(path); 7555 7556 MemSet(&costs, 0, sizeof(costs)); 7557 7558 genericcostestimate(root, path, loop_count, qinfos, &costs); 7559 7560 /* 7561 * We model index descent costs similarly to those for btree, but to do 7562 * that we first need an idea of the tree height. We somewhat arbitrarily 7563 * assume that the fanout is 100, meaning the tree height is at most 7564 * log100(index->pages). 7565 * 7566 * Although this computation isn't really expensive enough to require 7567 * caching, we might as well use index->tree_height to cache it. 7568 */ 7569 if (index->tree_height < 0) /* unknown? */ 7570 { 7571 if (index->pages > 1) /* avoid computing log(0) */ 7572 index->tree_height = (int) (log(index->pages) / log(100.0)); 7573 else 7574 index->tree_height = 0; 7575 } 7576 7577 /* 7578 * Add a CPU-cost component to represent the costs of initial descent. We 7579 * just use log(N) here not log2(N) since the branching factor isn't 7580 * necessarily two anyway. As for btree, charge once per SA scan. 7581 */ 7582 if (index->tuples > 1) /* avoid computing log(0) */ 7583 { 7584 descentCost = ceil(log(index->tuples)) * cpu_operator_cost; 7585 costs.indexStartupCost += descentCost; 7586 costs.indexTotalCost += costs.num_sa_scans * descentCost; 7587 } 7588 7589 /* 7590 * Likewise add a per-page charge, calculated the same as for btrees. 7591 */ 7592 descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost; 7593 costs.indexStartupCost += descentCost; 7594 costs.indexTotalCost += costs.num_sa_scans * descentCost; 7595 7596 *indexStartupCost = costs.indexStartupCost; 7597 *indexTotalCost = costs.indexTotalCost; 7598 *indexSelectivity = costs.indexSelectivity; 7599 *indexCorrelation = costs.indexCorrelation; 7600 *indexPages = costs.numIndexPages; 7601 } 7602 7603 void 7604 spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count, 7605 Cost *indexStartupCost, Cost *indexTotalCost, 7606 Selectivity *indexSelectivity, double *indexCorrelation, 7607 double *indexPages) 7608 { 7609 IndexOptInfo *index = path->indexinfo; 7610 List *qinfos; 7611 GenericCosts costs; 7612 Cost descentCost; 7613 7614 /* Do preliminary analysis of indexquals */ 7615 qinfos = deconstruct_indexquals(path); 7616 7617 MemSet(&costs, 0, sizeof(costs)); 7618 7619 genericcostestimate(root, path, loop_count, qinfos, &costs); 7620 7621 /* 7622 * We model index descent costs similarly to those for btree, but to do 7623 * that we first need an idea of the tree height. We somewhat arbitrarily 7624 * assume that the fanout is 100, meaning the tree height is at most 7625 * log100(index->pages). 7626 * 7627 * Although this computation isn't really expensive enough to require 7628 * caching, we might as well use index->tree_height to cache it. 7629 */ 7630 if (index->tree_height < 0) /* unknown? */ 7631 { 7632 if (index->pages > 1) /* avoid computing log(0) */ 7633 index->tree_height = (int) (log(index->pages) / log(100.0)); 7634 else 7635 index->tree_height = 0; 7636 } 7637 7638 /* 7639 * Add a CPU-cost component to represent the costs of initial descent. We 7640 * just use log(N) here not log2(N) since the branching factor isn't 7641 * necessarily two anyway. As for btree, charge once per SA scan. 7642 */ 7643 if (index->tuples > 1) /* avoid computing log(0) */ 7644 { 7645 descentCost = ceil(log(index->tuples)) * cpu_operator_cost; 7646 costs.indexStartupCost += descentCost; 7647 costs.indexTotalCost += costs.num_sa_scans * descentCost; 7648 } 7649 7650 /* 7651 * Likewise add a per-page charge, calculated the same as for btrees. 7652 */ 7653 descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost; 7654 costs.indexStartupCost += descentCost; 7655 costs.indexTotalCost += costs.num_sa_scans * descentCost; 7656 7657 *indexStartupCost = costs.indexStartupCost; 7658 *indexTotalCost = costs.indexTotalCost; 7659 *indexSelectivity = costs.indexSelectivity; 7660 *indexCorrelation = costs.indexCorrelation; 7661 *indexPages = costs.numIndexPages; 7662 } 7663 7664 7665 /* 7666 * Support routines for gincostestimate 7667 */ 7668 7669 typedef struct 7670 { 7671 bool haveFullScan; 7672 double partialEntries; 7673 double exactEntries; 7674 double searchEntries; 7675 double arrayScans; 7676 } GinQualCounts; 7677 7678 /* 7679 * Estimate the number of index terms that need to be searched for while 7680 * testing the given GIN query, and increment the counts in *counts 7681 * appropriately. If the query is unsatisfiable, return false. 7682 */ 7683 static bool 7684 gincost_pattern(IndexOptInfo *index, int indexcol, 7685 Oid clause_op, Datum query, 7686 GinQualCounts *counts) 7687 { 7688 Oid extractProcOid; 7689 Oid collation; 7690 int strategy_op; 7691 Oid lefttype, 7692 righttype; 7693 int32 nentries = 0; 7694 bool *partial_matches = NULL; 7695 Pointer *extra_data = NULL; 7696 bool *nullFlags = NULL; 7697 int32 searchMode = GIN_SEARCH_MODE_DEFAULT; 7698 int32 i; 7699 7700 Assert(indexcol < index->nkeycolumns); 7701 7702 /* 7703 * Get the operator's strategy number and declared input data types within 7704 * the index opfamily. (We don't need the latter, but we use 7705 * get_op_opfamily_properties because it will throw error if it fails to 7706 * find a matching pg_amop entry.) 7707 */ 7708 get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false, 7709 &strategy_op, &lefttype, &righttype); 7710 7711 /* 7712 * GIN always uses the "default" support functions, which are those with 7713 * lefttype == righttype == the opclass' opcintype (see 7714 * IndexSupportInitialize in relcache.c). 7715 */ 7716 extractProcOid = get_opfamily_proc(index->opfamily[indexcol], 7717 index->opcintype[indexcol], 7718 index->opcintype[indexcol], 7719 GIN_EXTRACTQUERY_PROC); 7720 7721 if (!OidIsValid(extractProcOid)) 7722 { 7723 /* should not happen; throw same error as index_getprocinfo */ 7724 elog(ERROR, "missing support function %d for attribute %d of index \"%s\"", 7725 GIN_EXTRACTQUERY_PROC, indexcol + 1, 7726 get_rel_name(index->indexoid)); 7727 } 7728 7729 /* 7730 * Choose collation to pass to extractProc (should match initGinState). 7731 */ 7732 if (OidIsValid(index->indexcollations[indexcol])) 7733 collation = index->indexcollations[indexcol]; 7734 else 7735 collation = DEFAULT_COLLATION_OID; 7736 7737 OidFunctionCall7Coll(extractProcOid, 7738 collation, 7739 query, 7740 PointerGetDatum(&nentries), 7741 UInt16GetDatum(strategy_op), 7742 PointerGetDatum(&partial_matches), 7743 PointerGetDatum(&extra_data), 7744 PointerGetDatum(&nullFlags), 7745 PointerGetDatum(&searchMode)); 7746 7747 if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT) 7748 { 7749 /* No match is possible */ 7750 return false; 7751 } 7752 7753 for (i = 0; i < nentries; i++) 7754 { 7755 /* 7756 * For partial match we haven't any information to estimate number of 7757 * matched entries in index, so, we just estimate it as 100 7758 */ 7759 if (partial_matches && partial_matches[i]) 7760 counts->partialEntries += 100; 7761 else 7762 counts->exactEntries++; 7763 7764 counts->searchEntries++; 7765 } 7766 7767 if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY) 7768 { 7769 /* Treat "include empty" like an exact-match item */ 7770 counts->exactEntries++; 7771 counts->searchEntries++; 7772 } 7773 else if (searchMode != GIN_SEARCH_MODE_DEFAULT) 7774 { 7775 /* It's GIN_SEARCH_MODE_ALL */ 7776 counts->haveFullScan = true; 7777 } 7778 7779 return true; 7780 } 7781 7782 /* 7783 * Estimate the number of index terms that need to be searched for while 7784 * testing the given GIN index clause, and increment the counts in *counts 7785 * appropriately. If the query is unsatisfiable, return false. 7786 */ 7787 static bool 7788 gincost_opexpr(PlannerInfo *root, 7789 IndexOptInfo *index, 7790 IndexQualInfo *qinfo, 7791 GinQualCounts *counts) 7792 { 7793 int indexcol = qinfo->indexcol; 7794 Oid clause_op = qinfo->clause_op; 7795 Node *operand = qinfo->other_operand; 7796 7797 if (!qinfo->varonleft) 7798 { 7799 /* must commute the operator */ 7800 clause_op = get_commutator(clause_op); 7801 } 7802 7803 /* aggressively reduce to a constant, and look through relabeling */ 7804 operand = estimate_expression_value(root, operand); 7805 7806 if (IsA(operand, RelabelType)) 7807 operand = (Node *) ((RelabelType *) operand)->arg; 7808 7809 /* 7810 * It's impossible to call extractQuery method for unknown operand. So 7811 * unless operand is a Const we can't do much; just assume there will be 7812 * one ordinary search entry from the operand at runtime. 7813 */ 7814 if (!IsA(operand, Const)) 7815 { 7816 counts->exactEntries++; 7817 counts->searchEntries++; 7818 return true; 7819 } 7820 7821 /* If Const is null, there can be no matches */ 7822 if (((Const *) operand)->constisnull) 7823 return false; 7824 7825 /* Otherwise, apply extractQuery and get the actual term counts */ 7826 return gincost_pattern(index, indexcol, clause_op, 7827 ((Const *) operand)->constvalue, 7828 counts); 7829 } 7830 7831 /* 7832 * Estimate the number of index terms that need to be searched for while 7833 * testing the given GIN index clause, and increment the counts in *counts 7834 * appropriately. If the query is unsatisfiable, return false. 7835 * 7836 * A ScalarArrayOpExpr will give rise to N separate indexscans at runtime, 7837 * each of which involves one value from the RHS array, plus all the 7838 * non-array quals (if any). To model this, we average the counts across 7839 * the RHS elements, and add the averages to the counts in *counts (which 7840 * correspond to per-indexscan costs). We also multiply counts->arrayScans 7841 * by N, causing gincostestimate to scale up its estimates accordingly. 7842 */ 7843 static bool 7844 gincost_scalararrayopexpr(PlannerInfo *root, 7845 IndexOptInfo *index, 7846 IndexQualInfo *qinfo, 7847 double numIndexEntries, 7848 GinQualCounts *counts) 7849 { 7850 int indexcol = qinfo->indexcol; 7851 Oid clause_op = qinfo->clause_op; 7852 Node *rightop = qinfo->other_operand; 7853 ArrayType *arrayval; 7854 int16 elmlen; 7855 bool elmbyval; 7856 char elmalign; 7857 int numElems; 7858 Datum *elemValues; 7859 bool *elemNulls; 7860 GinQualCounts arraycounts; 7861 int numPossible = 0; 7862 int i; 7863 7864 Assert(((ScalarArrayOpExpr *) qinfo->rinfo->clause)->useOr); 7865 7866 /* aggressively reduce to a constant, and look through relabeling */ 7867 rightop = estimate_expression_value(root, rightop); 7868 7869 if (IsA(rightop, RelabelType)) 7870 rightop = (Node *) ((RelabelType *) rightop)->arg; 7871 7872 /* 7873 * It's impossible to call extractQuery method for unknown operand. So 7874 * unless operand is a Const we can't do much; just assume there will be 7875 * one ordinary search entry from each array entry at runtime, and fall 7876 * back on a probably-bad estimate of the number of array entries. 7877 */ 7878 if (!IsA(rightop, Const)) 7879 { 7880 counts->exactEntries++; 7881 counts->searchEntries++; 7882 counts->arrayScans *= estimate_array_length(rightop); 7883 return true; 7884 } 7885 7886 /* If Const is null, there can be no matches */ 7887 if (((Const *) rightop)->constisnull) 7888 return false; 7889 7890 /* Otherwise, extract the array elements and iterate over them */ 7891 arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue); 7892 get_typlenbyvalalign(ARR_ELEMTYPE(arrayval), 7893 &elmlen, &elmbyval, &elmalign); 7894 deconstruct_array(arrayval, 7895 ARR_ELEMTYPE(arrayval), 7896 elmlen, elmbyval, elmalign, 7897 &elemValues, &elemNulls, &numElems); 7898 7899 memset(&arraycounts, 0, sizeof(arraycounts)); 7900 7901 for (i = 0; i < numElems; i++) 7902 { 7903 GinQualCounts elemcounts; 7904 7905 /* NULL can't match anything, so ignore, as the executor will */ 7906 if (elemNulls[i]) 7907 continue; 7908 7909 /* Otherwise, apply extractQuery and get the actual term counts */ 7910 memset(&elemcounts, 0, sizeof(elemcounts)); 7911 7912 if (gincost_pattern(index, indexcol, clause_op, elemValues[i], 7913 &elemcounts)) 7914 { 7915 /* We ignore array elements that are unsatisfiable patterns */ 7916 numPossible++; 7917 7918 if (elemcounts.haveFullScan) 7919 { 7920 /* 7921 * Full index scan will be required. We treat this as if 7922 * every key in the index had been listed in the query; is 7923 * that reasonable? 7924 */ 7925 elemcounts.partialEntries = 0; 7926 elemcounts.exactEntries = numIndexEntries; 7927 elemcounts.searchEntries = numIndexEntries; 7928 } 7929 arraycounts.partialEntries += elemcounts.partialEntries; 7930 arraycounts.exactEntries += elemcounts.exactEntries; 7931 arraycounts.searchEntries += elemcounts.searchEntries; 7932 } 7933 } 7934 7935 if (numPossible == 0) 7936 { 7937 /* No satisfiable patterns in the array */ 7938 return false; 7939 } 7940 7941 /* 7942 * Now add the averages to the global counts. This will give us an 7943 * estimate of the average number of terms searched for in each indexscan, 7944 * including contributions from both array and non-array quals. 7945 */ 7946 counts->partialEntries += arraycounts.partialEntries / numPossible; 7947 counts->exactEntries += arraycounts.exactEntries / numPossible; 7948 counts->searchEntries += arraycounts.searchEntries / numPossible; 7949 7950 counts->arrayScans *= numPossible; 7951 7952 return true; 7953 } 7954 7955 /* 7956 * GIN has search behavior completely different from other index types 7957 */ 7958 void 7959 gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count, 7960 Cost *indexStartupCost, Cost *indexTotalCost, 7961 Selectivity *indexSelectivity, double *indexCorrelation, 7962 double *indexPages) 7963 { 7964 IndexOptInfo *index = path->indexinfo; 7965 List *indexQuals = path->indexquals; 7966 List *indexOrderBys = path->indexorderbys; 7967 List *qinfos; 7968 ListCell *l; 7969 List *selectivityQuals; 7970 double numPages = index->pages, 7971 numTuples = index->tuples; 7972 double numEntryPages, 7973 numDataPages, 7974 numPendingPages, 7975 numEntries; 7976 GinQualCounts counts; 7977 bool matchPossible; 7978 double partialScale; 7979 double entryPagesFetched, 7980 dataPagesFetched, 7981 dataPagesFetchedBySel; 7982 double qual_op_cost, 7983 qual_arg_cost, 7984 spc_random_page_cost, 7985 outer_scans; 7986 Relation indexRel; 7987 GinStatsData ginStats; 7988 7989 /* Do preliminary analysis of indexquals */ 7990 qinfos = deconstruct_indexquals(path); 7991 7992 /* 7993 * Obtain statistical information from the meta page, if possible. Else 7994 * set ginStats to zeroes, and we'll cope below. 7995 */ 7996 if (!index->hypothetical) 7997 { 7998 indexRel = index_open(index->indexoid, AccessShareLock); 7999 ginGetStats(indexRel, &ginStats); 8000 index_close(indexRel, AccessShareLock); 8001 } 8002 else 8003 { 8004 memset(&ginStats, 0, sizeof(ginStats)); 8005 } 8006 8007 /* 8008 * Assuming we got valid (nonzero) stats at all, nPendingPages can be 8009 * trusted, but the other fields are data as of the last VACUUM. We can 8010 * scale them up to account for growth since then, but that method only 8011 * goes so far; in the worst case, the stats might be for a completely 8012 * empty index, and scaling them will produce pretty bogus numbers. 8013 * Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if 8014 * it's grown more than that, fall back to estimating things only from the 8015 * assumed-accurate index size. But we'll trust nPendingPages in any case 8016 * so long as it's not clearly insane, ie, more than the index size. 8017 */ 8018 if (ginStats.nPendingPages < numPages) 8019 numPendingPages = ginStats.nPendingPages; 8020 else 8021 numPendingPages = 0; 8022 8023 if (numPages > 0 && ginStats.nTotalPages <= numPages && 8024 ginStats.nTotalPages > numPages / 4 && 8025 ginStats.nEntryPages > 0 && ginStats.nEntries > 0) 8026 { 8027 /* 8028 * OK, the stats seem close enough to sane to be trusted. But we 8029 * still need to scale them by the ratio numPages / nTotalPages to 8030 * account for growth since the last VACUUM. 8031 */ 8032 double scale = numPages / ginStats.nTotalPages; 8033 8034 numEntryPages = ceil(ginStats.nEntryPages * scale); 8035 numDataPages = ceil(ginStats.nDataPages * scale); 8036 numEntries = ceil(ginStats.nEntries * scale); 8037 /* ensure we didn't round up too much */ 8038 numEntryPages = Min(numEntryPages, numPages - numPendingPages); 8039 numDataPages = Min(numDataPages, 8040 numPages - numPendingPages - numEntryPages); 8041 } 8042 else 8043 { 8044 /* 8045 * We might get here because it's a hypothetical index, or an index 8046 * created pre-9.1 and never vacuumed since upgrading (in which case 8047 * its stats would read as zeroes), or just because it's grown too 8048 * much since the last VACUUM for us to put our faith in scaling. 8049 * 8050 * Invent some plausible internal statistics based on the index page 8051 * count (and clamp that to at least 10 pages, just in case). We 8052 * estimate that 90% of the index is entry pages, and the rest is data 8053 * pages. Estimate 100 entries per entry page; this is rather bogus 8054 * since it'll depend on the size of the keys, but it's more robust 8055 * than trying to predict the number of entries per heap tuple. 8056 */ 8057 numPages = Max(numPages, 10); 8058 numEntryPages = floor((numPages - numPendingPages) * 0.90); 8059 numDataPages = numPages - numPendingPages - numEntryPages; 8060 numEntries = floor(numEntryPages * 100); 8061 } 8062 8063 /* In an empty index, numEntries could be zero. Avoid divide-by-zero */ 8064 if (numEntries < 1) 8065 numEntries = 1; 8066 8067 /* 8068 * Include predicate in selectivityQuals (should match 8069 * genericcostestimate) 8070 */ 8071 if (index->indpred != NIL) 8072 { 8073 List *predExtraQuals = NIL; 8074 8075 foreach(l, index->indpred) 8076 { 8077 Node *predQual = (Node *) lfirst(l); 8078 List *oneQual = list_make1(predQual); 8079 8080 if (!predicate_implied_by(oneQual, indexQuals, false)) 8081 predExtraQuals = list_concat(predExtraQuals, oneQual); 8082 } 8083 /* list_concat avoids modifying the passed-in indexQuals list */ 8084 selectivityQuals = list_concat(predExtraQuals, indexQuals); 8085 } 8086 else 8087 selectivityQuals = indexQuals; 8088 8089 /* Estimate the fraction of main-table tuples that will be visited */ 8090 *indexSelectivity = clauselist_selectivity(root, selectivityQuals, 8091 index->rel->relid, 8092 JOIN_INNER, 8093 NULL); 8094 8095 /* fetch estimated page cost for tablespace containing index */ 8096 get_tablespace_page_costs(index->reltablespace, 8097 &spc_random_page_cost, 8098 NULL); 8099 8100 /* 8101 * Generic assumption about index correlation: there isn't any. 8102 */ 8103 *indexCorrelation = 0.0; 8104 8105 /* 8106 * Examine quals to estimate number of search entries & partial matches 8107 */ 8108 memset(&counts, 0, sizeof(counts)); 8109 counts.arrayScans = 1; 8110 matchPossible = true; 8111 8112 foreach(l, qinfos) 8113 { 8114 IndexQualInfo *qinfo = (IndexQualInfo *) lfirst(l); 8115 Expr *clause = qinfo->rinfo->clause; 8116 8117 if (IsA(clause, OpExpr)) 8118 { 8119 matchPossible = gincost_opexpr(root, 8120 index, 8121 qinfo, 8122 &counts); 8123 if (!matchPossible) 8124 break; 8125 } 8126 else if (IsA(clause, ScalarArrayOpExpr)) 8127 { 8128 matchPossible = gincost_scalararrayopexpr(root, 8129 index, 8130 qinfo, 8131 numEntries, 8132 &counts); 8133 if (!matchPossible) 8134 break; 8135 } 8136 else 8137 { 8138 /* shouldn't be anything else for a GIN index */ 8139 elog(ERROR, "unsupported GIN indexqual type: %d", 8140 (int) nodeTag(clause)); 8141 } 8142 } 8143 8144 /* Fall out if there were any provably-unsatisfiable quals */ 8145 if (!matchPossible) 8146 { 8147 *indexStartupCost = 0; 8148 *indexTotalCost = 0; 8149 *indexSelectivity = 0; 8150 return; 8151 } 8152 8153 if (counts.haveFullScan || indexQuals == NIL) 8154 { 8155 /* 8156 * Full index scan will be required. We treat this as if every key in 8157 * the index had been listed in the query; is that reasonable? 8158 */ 8159 counts.partialEntries = 0; 8160 counts.exactEntries = numEntries; 8161 counts.searchEntries = numEntries; 8162 } 8163 8164 /* Will we have more than one iteration of a nestloop scan? */ 8165 outer_scans = loop_count; 8166 8167 /* 8168 * Compute cost to begin scan, first of all, pay attention to pending 8169 * list. 8170 */ 8171 entryPagesFetched = numPendingPages; 8172 8173 /* 8174 * Estimate number of entry pages read. We need to do 8175 * counts.searchEntries searches. Use a power function as it should be, 8176 * but tuples on leaf pages usually is much greater. Here we include all 8177 * searches in entry tree, including search of first entry in partial 8178 * match algorithm 8179 */ 8180 entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15))); 8181 8182 /* 8183 * Add an estimate of entry pages read by partial match algorithm. It's a 8184 * scan over leaf pages in entry tree. We haven't any useful stats here, 8185 * so estimate it as proportion. Because counts.partialEntries is really 8186 * pretty bogus (see code above), it's possible that it is more than 8187 * numEntries; clamp the proportion to ensure sanity. 8188 */ 8189 partialScale = counts.partialEntries / numEntries; 8190 partialScale = Min(partialScale, 1.0); 8191 8192 entryPagesFetched += ceil(numEntryPages * partialScale); 8193 8194 /* 8195 * Partial match algorithm reads all data pages before doing actual scan, 8196 * so it's a startup cost. Again, we haven't any useful stats here, so 8197 * estimate it as proportion. 8198 */ 8199 dataPagesFetched = ceil(numDataPages * partialScale); 8200 8201 /* 8202 * Calculate cache effects if more than one scan due to nestloops or array 8203 * quals. The result is pro-rated per nestloop scan, but the array qual 8204 * factor shouldn't be pro-rated (compare genericcostestimate). 8205 */ 8206 if (outer_scans > 1 || counts.arrayScans > 1) 8207 { 8208 entryPagesFetched *= outer_scans * counts.arrayScans; 8209 entryPagesFetched = index_pages_fetched(entryPagesFetched, 8210 (BlockNumber) numEntryPages, 8211 numEntryPages, root); 8212 entryPagesFetched /= outer_scans; 8213 dataPagesFetched *= outer_scans * counts.arrayScans; 8214 dataPagesFetched = index_pages_fetched(dataPagesFetched, 8215 (BlockNumber) numDataPages, 8216 numDataPages, root); 8217 dataPagesFetched /= outer_scans; 8218 } 8219 8220 /* 8221 * Here we use random page cost because logically-close pages could be far 8222 * apart on disk. 8223 */ 8224 *indexStartupCost = (entryPagesFetched + dataPagesFetched) * spc_random_page_cost; 8225 8226 /* 8227 * Now compute the number of data pages fetched during the scan. 8228 * 8229 * We assume every entry to have the same number of items, and that there 8230 * is no overlap between them. (XXX: tsvector and array opclasses collect 8231 * statistics on the frequency of individual keys; it would be nice to use 8232 * those here.) 8233 */ 8234 dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries); 8235 8236 /* 8237 * If there is a lot of overlap among the entries, in particular if one of 8238 * the entries is very frequent, the above calculation can grossly 8239 * under-estimate. As a simple cross-check, calculate a lower bound based 8240 * on the overall selectivity of the quals. At a minimum, we must read 8241 * one item pointer for each matching entry. 8242 * 8243 * The width of each item pointer varies, based on the level of 8244 * compression. We don't have statistics on that, but an average of 8245 * around 3 bytes per item is fairly typical. 8246 */ 8247 dataPagesFetchedBySel = ceil(*indexSelectivity * 8248 (numTuples / (BLCKSZ / 3))); 8249 if (dataPagesFetchedBySel > dataPagesFetched) 8250 dataPagesFetched = dataPagesFetchedBySel; 8251 8252 /* Account for cache effects, the same as above */ 8253 if (outer_scans > 1 || counts.arrayScans > 1) 8254 { 8255 dataPagesFetched *= outer_scans * counts.arrayScans; 8256 dataPagesFetched = index_pages_fetched(dataPagesFetched, 8257 (BlockNumber) numDataPages, 8258 numDataPages, root); 8259 dataPagesFetched /= outer_scans; 8260 } 8261 8262 /* And apply random_page_cost as the cost per page */ 8263 *indexTotalCost = *indexStartupCost + 8264 dataPagesFetched * spc_random_page_cost; 8265 8266 /* 8267 * Add on index qual eval costs, much as in genericcostestimate 8268 */ 8269 qual_arg_cost = other_operands_eval_cost(root, qinfos) + 8270 orderby_operands_eval_cost(root, path); 8271 qual_op_cost = cpu_operator_cost * 8272 (list_length(indexQuals) + list_length(indexOrderBys)); 8273 8274 *indexStartupCost += qual_arg_cost; 8275 *indexTotalCost += qual_arg_cost; 8276 *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost + qual_op_cost); 8277 *indexPages = dataPagesFetched; 8278 } 8279 8280 /* 8281 * BRIN has search behavior completely different from other index types 8282 */ 8283 void 8284 brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count, 8285 Cost *indexStartupCost, Cost *indexTotalCost, 8286 Selectivity *indexSelectivity, double *indexCorrelation, 8287 double *indexPages) 8288 { 8289 IndexOptInfo *index = path->indexinfo; 8290 List *indexQuals = path->indexquals; 8291 double numPages = index->pages; 8292 RelOptInfo *baserel = index->rel; 8293 RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root); 8294 List *qinfos; 8295 Cost spc_seq_page_cost; 8296 Cost spc_random_page_cost; 8297 double qual_arg_cost; 8298 double qualSelectivity; 8299 BrinStatsData statsData; 8300 double indexRanges; 8301 double minimalRanges; 8302 double estimatedRanges; 8303 double selec; 8304 Relation indexRel; 8305 ListCell *l; 8306 VariableStatData vardata; 8307 8308 Assert(rte->rtekind == RTE_RELATION); 8309 8310 /* fetch estimated page cost for the tablespace containing the index */ 8311 get_tablespace_page_costs(index->reltablespace, 8312 &spc_random_page_cost, 8313 &spc_seq_page_cost); 8314 8315 /* 8316 * Obtain some data from the index itself, if possible. Otherwise invent 8317 * some plausible internal statistics based on the relation page count. 8318 */ 8319 if (!index->hypothetical) 8320 { 8321 indexRel = index_open(index->indexoid, AccessShareLock); 8322 brinGetStats(indexRel, &statsData); 8323 index_close(indexRel, AccessShareLock); 8324 8325 /* work out the actual number of ranges in the index */ 8326 indexRanges = Max(ceil((double) baserel->pages / 8327 statsData.pagesPerRange), 1.0); 8328 } 8329 else 8330 { 8331 /* 8332 * Assume default number of pages per range, and estimate the number 8333 * of ranges based on that. 8334 */ 8335 indexRanges = Max(ceil((double) baserel->pages / 8336 BRIN_DEFAULT_PAGES_PER_RANGE), 1.0); 8337 8338 statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE; 8339 statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1; 8340 } 8341 8342 /* 8343 * Compute index correlation 8344 * 8345 * Because we can use all index quals equally when scanning, we can use 8346 * the largest correlation (in absolute value) among columns used by the 8347 * query. Start at zero, the worst possible case. If we cannot find any 8348 * correlation statistics, we will keep it as 0. 8349 */ 8350 *indexCorrelation = 0; 8351 8352 qinfos = deconstruct_indexquals(path); 8353 foreach(l, qinfos) 8354 { 8355 IndexQualInfo *qinfo = (IndexQualInfo *) lfirst(l); 8356 AttrNumber attnum = index->indexkeys[qinfo->indexcol]; 8357 8358 /* attempt to lookup stats in relation for this index column */ 8359 if (attnum != 0) 8360 { 8361 /* Simple variable -- look to stats for the underlying table */ 8362 if (get_relation_stats_hook && 8363 (*get_relation_stats_hook) (root, rte, attnum, &vardata)) 8364 { 8365 /* 8366 * The hook took control of acquiring a stats tuple. If it 8367 * did supply a tuple, it'd better have supplied a freefunc. 8368 */ 8369 if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc) 8370 elog(ERROR, 8371 "no function provided to release variable stats with"); 8372 } 8373 else 8374 { 8375 vardata.statsTuple = 8376 SearchSysCache3(STATRELATTINH, 8377 ObjectIdGetDatum(rte->relid), 8378 Int16GetDatum(attnum), 8379 BoolGetDatum(false)); 8380 vardata.freefunc = ReleaseSysCache; 8381 } 8382 } 8383 else 8384 { 8385 /* 8386 * Looks like we've found an expression column in the index. Let's 8387 * see if there's any stats for it. 8388 */ 8389 8390 /* get the attnum from the 0-based index. */ 8391 attnum = qinfo->indexcol + 1; 8392 8393 if (get_index_stats_hook && 8394 (*get_index_stats_hook) (root, index->indexoid, attnum, &vardata)) 8395 { 8396 /* 8397 * The hook took control of acquiring a stats tuple. If it 8398 * did supply a tuple, it'd better have supplied a freefunc. 8399 */ 8400 if (HeapTupleIsValid(vardata.statsTuple) && 8401 !vardata.freefunc) 8402 elog(ERROR, "no function provided to release variable stats with"); 8403 } 8404 else 8405 { 8406 vardata.statsTuple = SearchSysCache3(STATRELATTINH, 8407 ObjectIdGetDatum(index->indexoid), 8408 Int16GetDatum(attnum), 8409 BoolGetDatum(false)); 8410 vardata.freefunc = ReleaseSysCache; 8411 } 8412 } 8413 8414 if (HeapTupleIsValid(vardata.statsTuple)) 8415 { 8416 AttStatsSlot sslot; 8417 8418 if (get_attstatsslot(&sslot, vardata.statsTuple, 8419 STATISTIC_KIND_CORRELATION, InvalidOid, 8420 ATTSTATSSLOT_NUMBERS)) 8421 { 8422 double varCorrelation = 0.0; 8423 8424 if (sslot.nnumbers > 0) 8425 varCorrelation = Abs(sslot.numbers[0]); 8426 8427 if (varCorrelation > *indexCorrelation) 8428 *indexCorrelation = varCorrelation; 8429 8430 free_attstatsslot(&sslot); 8431 } 8432 } 8433 8434 ReleaseVariableStats(vardata); 8435 } 8436 8437 qualSelectivity = clauselist_selectivity(root, indexQuals, 8438 baserel->relid, 8439 JOIN_INNER, NULL); 8440 8441 /* 8442 * Now calculate the minimum possible ranges we could match with if all of 8443 * the rows were in the perfect order in the table's heap. 8444 */ 8445 minimalRanges = ceil(indexRanges * qualSelectivity); 8446 8447 /* 8448 * Now estimate the number of ranges that we'll touch by using the 8449 * indexCorrelation from the stats. Careful not to divide by zero (note 8450 * we're using the absolute value of the correlation). 8451 */ 8452 if (*indexCorrelation < 1.0e-10) 8453 estimatedRanges = indexRanges; 8454 else 8455 estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges); 8456 8457 /* we expect to visit this portion of the table */ 8458 selec = estimatedRanges / indexRanges; 8459 8460 CLAMP_PROBABILITY(selec); 8461 8462 *indexSelectivity = selec; 8463 8464 /* 8465 * Compute the index qual costs, much as in genericcostestimate, to add to 8466 * the index costs. 8467 */ 8468 qual_arg_cost = other_operands_eval_cost(root, qinfos) + 8469 orderby_operands_eval_cost(root, path); 8470 8471 /* 8472 * Compute the startup cost as the cost to read the whole revmap 8473 * sequentially, including the cost to execute the index quals. 8474 */ 8475 *indexStartupCost = 8476 spc_seq_page_cost * statsData.revmapNumPages * loop_count; 8477 *indexStartupCost += qual_arg_cost; 8478 8479 /* 8480 * To read a BRIN index there might be a bit of back and forth over 8481 * regular pages, as revmap might point to them out of sequential order; 8482 * calculate the total cost as reading the whole index in random order. 8483 */ 8484 *indexTotalCost = *indexStartupCost + 8485 spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count; 8486 8487 /* 8488 * Charge a small amount per range tuple which we expect to match to. This 8489 * is meant to reflect the costs of manipulating the bitmap. The BRIN scan 8490 * will set a bit for each page in the range when we find a matching 8491 * range, so we must multiply the charge by the number of pages in the 8492 * range. 8493 */ 8494 *indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges * 8495 statsData.pagesPerRange; 8496 8497 *indexPages = index->pages; 8498 } 8499