Line data Source code
1 : /*-------------------------------------------------------------------------
2 : *
3 : * selfuncs.c
4 : * Selectivity functions and index cost estimation functions for
5 : * standard operators and index access methods.
6 : *
7 : * Selectivity routines are registered in the pg_operator catalog
8 : * in the "oprrest" and "oprjoin" attributes.
9 : *
10 : * Index cost functions are located via the index AM's API struct,
11 : * which is obtained from the handler function registered in pg_am.
12 : *
13 : * Portions Copyright (c) 1996-2021, PostgreSQL Global Development Group
14 : * Portions Copyright (c) 1994, Regents of the University of California
15 : *
16 : *
17 : * IDENTIFICATION
18 : * src/backend/utils/adt/selfuncs.c
19 : *
20 : *-------------------------------------------------------------------------
21 : */
22 :
23 : /*----------
24 : * Operator selectivity estimation functions are called to estimate the
25 : * selectivity of WHERE clauses whose top-level operator is their operator.
26 : * We divide the problem into two cases:
27 : * Restriction clause estimation: the clause involves vars of just
28 : * one relation.
29 : * Join clause estimation: the clause involves vars of multiple rels.
30 : * Join selectivity estimation is far more difficult and usually less accurate
31 : * than restriction estimation.
32 : *
33 : * When dealing with the inner scan of a nestloop join, we consider the
34 : * join's joinclauses as restriction clauses for the inner relation, and
35 : * treat vars of the outer relation as parameters (a/k/a constants of unknown
36 : * values). So, restriction estimators need to be able to accept an argument
37 : * telling which relation is to be treated as the variable.
38 : *
39 : * The call convention for a restriction estimator (oprrest function) is
40 : *
41 : * Selectivity oprrest (PlannerInfo *root,
42 : * Oid operator,
43 : * List *args,
44 : * int varRelid);
45 : *
46 : * root: general information about the query (rtable and RelOptInfo lists
47 : * are particularly important for the estimator).
48 : * operator: OID of the specific operator in question.
49 : * args: argument list from the operator clause.
50 : * varRelid: if not zero, the relid (rtable index) of the relation to
51 : * be treated as the variable relation. May be zero if the args list
52 : * is known to contain vars of only one relation.
53 : *
54 : * This is represented at the SQL level (in pg_proc) as
55 : *
56 : * float8 oprrest (internal, oid, internal, int4);
57 : *
58 : * The result is a selectivity, that is, a fraction (0 to 1) of the rows
59 : * of the relation that are expected to produce a TRUE result for the
60 : * given operator.
61 : *
62 : * The call convention for a join estimator (oprjoin function) is similar
63 : * except that varRelid is not needed, and instead join information is
64 : * supplied:
65 : *
66 : * Selectivity oprjoin (PlannerInfo *root,
67 : * Oid operator,
68 : * List *args,
69 : * JoinType jointype,
70 : * SpecialJoinInfo *sjinfo);
71 : *
72 : * float8 oprjoin (internal, oid, internal, int2, internal);
73 : *
74 : * (Before Postgres 8.4, join estimators had only the first four of these
75 : * parameters. That signature is still allowed, but deprecated.) The
76 : * relationship between jointype and sjinfo is explained in the comments for
77 : * clause_selectivity() --- the short version is that jointype is usually
78 : * best ignored in favor of examining sjinfo.
79 : *
80 : * Join selectivity for regular inner and outer joins is defined as the
81 : * fraction (0 to 1) of the cross product of the relations that is expected
82 : * to produce a TRUE result for the given operator. For both semi and anti
83 : * joins, however, the selectivity is defined as the fraction of the left-hand
84 : * side relation's rows that are expected to have a match (ie, at least one
85 : * row with a TRUE result) in the right-hand side.
86 : *
87 : * For both oprrest and oprjoin functions, the operator's input collation OID
88 : * (if any) is passed using the standard fmgr mechanism, so that the estimator
89 : * function can fetch it with PG_GET_COLLATION(). Note, however, that all
90 : * statistics in pg_statistic are currently built using the relevant column's
91 : * collation.
92 : *----------
93 : */
94 :
95 : #include "postgres.h"
96 :
97 : #include <ctype.h>
98 : #include <math.h>
99 :
100 : #include "access/brin.h"
101 : #include "access/brin_page.h"
102 : #include "access/gin.h"
103 : #include "access/table.h"
104 : #include "access/tableam.h"
105 : #include "access/visibilitymap.h"
106 : #include "catalog/pg_am.h"
107 : #include "catalog/pg_collation.h"
108 : #include "catalog/pg_operator.h"
109 : #include "catalog/pg_statistic.h"
110 : #include "catalog/pg_statistic_ext.h"
111 : #include "executor/nodeAgg.h"
112 : #include "miscadmin.h"
113 : #include "nodes/makefuncs.h"
114 : #include "nodes/nodeFuncs.h"
115 : #include "optimizer/clauses.h"
116 : #include "optimizer/cost.h"
117 : #include "optimizer/optimizer.h"
118 : #include "optimizer/pathnode.h"
119 : #include "optimizer/paths.h"
120 : #include "optimizer/plancat.h"
121 : #include "parser/parse_clause.h"
122 : #include "parser/parsetree.h"
123 : #include "statistics/statistics.h"
124 : #include "storage/bufmgr.h"
125 : #include "utils/acl.h"
126 : #include "utils/builtins.h"
127 : #include "utils/date.h"
128 : #include "utils/datum.h"
129 : #include "utils/fmgroids.h"
130 : #include "utils/index_selfuncs.h"
131 : #include "utils/lsyscache.h"
132 : #include "utils/memutils.h"
133 : #include "utils/pg_locale.h"
134 : #include "utils/rel.h"
135 : #include "utils/selfuncs.h"
136 : #include "utils/snapmgr.h"
137 : #include "utils/spccache.h"
138 : #include "utils/syscache.h"
139 : #include "utils/timestamp.h"
140 : #include "utils/typcache.h"
141 :
142 :
143 : /* Hooks for plugins to get control when we ask for stats */
144 : get_relation_stats_hook_type get_relation_stats_hook = NULL;
145 : get_index_stats_hook_type get_index_stats_hook = NULL;
146 :
147 : static double eqsel_internal(PG_FUNCTION_ARGS, bool negate);
148 : static double eqjoinsel_inner(Oid opfuncoid, Oid collation,
149 : VariableStatData *vardata1, VariableStatData *vardata2,
150 : double nd1, double nd2,
151 : bool isdefault1, bool isdefault2,
152 : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
153 : Form_pg_statistic stats1, Form_pg_statistic stats2,
154 : bool have_mcvs1, bool have_mcvs2);
155 : static double eqjoinsel_semi(Oid opfuncoid, Oid collation,
156 : VariableStatData *vardata1, VariableStatData *vardata2,
157 : double nd1, double nd2,
158 : bool isdefault1, bool isdefault2,
159 : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
160 : Form_pg_statistic stats1, Form_pg_statistic stats2,
161 : bool have_mcvs1, bool have_mcvs2,
162 : RelOptInfo *inner_rel);
163 : static bool estimate_multivariate_ndistinct(PlannerInfo *root,
164 : RelOptInfo *rel, List **varinfos, double *ndistinct);
165 : static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
166 : double *scaledvalue,
167 : Datum lobound, Datum hibound, Oid boundstypid,
168 : double *scaledlobound, double *scaledhibound);
169 : static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
170 : static void convert_string_to_scalar(char *value,
171 : double *scaledvalue,
172 : char *lobound,
173 : double *scaledlobound,
174 : char *hibound,
175 : double *scaledhibound);
176 : static void convert_bytea_to_scalar(Datum value,
177 : double *scaledvalue,
178 : Datum lobound,
179 : double *scaledlobound,
180 : Datum hibound,
181 : double *scaledhibound);
182 : static double convert_one_string_to_scalar(char *value,
183 : int rangelo, int rangehi);
184 : static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
185 : int rangelo, int rangehi);
186 : static char *convert_string_datum(Datum value, Oid typid, Oid collid,
187 : bool *failure);
188 : static double convert_timevalue_to_scalar(Datum value, Oid typid,
189 : bool *failure);
190 : static void examine_simple_variable(PlannerInfo *root, Var *var,
191 : VariableStatData *vardata);
192 : static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
193 : Oid sortop, Oid collation,
194 : Datum *min, Datum *max);
195 : static void get_stats_slot_range(AttStatsSlot *sslot,
196 : Oid opfuncoid, FmgrInfo *opproc,
197 : Oid collation, int16 typLen, bool typByVal,
198 : Datum *min, Datum *max, bool *p_have_data);
199 : static bool get_actual_variable_range(PlannerInfo *root,
200 : VariableStatData *vardata,
201 : Oid sortop, Oid collation,
202 : Datum *min, Datum *max);
203 : static bool get_actual_variable_endpoint(Relation heapRel,
204 : Relation indexRel,
205 : ScanDirection indexscandir,
206 : ScanKey scankeys,
207 : int16 typLen,
208 : bool typByVal,
209 : TupleTableSlot *tableslot,
210 : MemoryContext outercontext,
211 : Datum *endpointDatum);
212 : static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
213 :
214 :
215 : /*
216 : * eqsel - Selectivity of "=" for any data types.
217 : *
218 : * Note: this routine is also used to estimate selectivity for some
219 : * operators that are not "=" but have comparable selectivity behavior,
220 : * such as "~=" (geometric approximate-match). Even for "=", we must
221 : * keep in mind that the left and right datatypes may differ.
222 : */
223 : Datum
224 332524 : eqsel(PG_FUNCTION_ARGS)
225 : {
226 332524 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
227 : }
228 :
229 : /*
230 : * Common code for eqsel() and neqsel()
231 : */
232 : static double
233 345756 : eqsel_internal(PG_FUNCTION_ARGS, bool negate)
234 : {
235 345756 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
236 345756 : Oid operator = PG_GETARG_OID(1);
237 345756 : List *args = (List *) PG_GETARG_POINTER(2);
238 345756 : int varRelid = PG_GETARG_INT32(3);
239 345756 : Oid collation = PG_GET_COLLATION();
240 : VariableStatData vardata;
241 : Node *other;
242 : bool varonleft;
243 : double selec;
244 :
245 : /*
246 : * When asked about <>, we do the estimation using the corresponding =
247 : * operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
248 : */
249 345756 : if (negate)
250 : {
251 13232 : operator = get_negator(operator);
252 13232 : if (!OidIsValid(operator))
253 : {
254 : /* Use default selectivity (should we raise an error instead?) */
255 0 : return 1.0 - DEFAULT_EQ_SEL;
256 : }
257 : }
258 :
259 : /*
260 : * If expression is not variable = something or something = variable, then
261 : * punt and return a default estimate.
262 : */
263 345756 : if (!get_restriction_variable(root, args, varRelid,
264 : &vardata, &other, &varonleft))
265 1686 : return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
266 :
267 : /*
268 : * We can do a lot better if the something is a constant. (Note: the
269 : * Const might result from estimation rather than being a simple constant
270 : * in the query.)
271 : */
272 344070 : if (IsA(other, Const))
273 477972 : selec = var_eq_const(&vardata, operator, collation,
274 159324 : ((Const *) other)->constvalue,
275 159324 : ((Const *) other)->constisnull,
276 : varonleft, negate);
277 : else
278 184746 : selec = var_eq_non_const(&vardata, operator, collation, other,
279 : varonleft, negate);
280 :
281 344070 : ReleaseVariableStats(vardata);
282 :
283 344070 : return selec;
284 : }
285 :
286 : /*
287 : * var_eq_const --- eqsel for var = const case
288 : *
289 : * This is exported so that some other estimation functions can use it.
290 : */
291 : double
292 174114 : var_eq_const(VariableStatData *vardata, Oid operator, Oid collation,
293 : Datum constval, bool constisnull,
294 : bool varonleft, bool negate)
295 : {
296 : double selec;
297 174114 : double nullfrac = 0.0;
298 : bool isdefault;
299 : Oid opfuncoid;
300 :
301 : /*
302 : * If the constant is NULL, assume operator is strict and return zero, ie,
303 : * operator will never return TRUE. (It's zero even for a negator op.)
304 : */
305 174114 : if (constisnull)
306 86 : return 0.0;
307 :
308 : /*
309 : * Grab the nullfrac for use below. Note we allow use of nullfrac
310 : * regardless of security check.
311 : */
312 174028 : if (HeapTupleIsValid(vardata->statsTuple))
313 : {
314 : Form_pg_statistic stats;
315 :
316 121172 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
317 121172 : nullfrac = stats->stanullfrac;
318 : }
319 :
320 : /*
321 : * If we matched the var to a unique index or DISTINCT clause, assume
322 : * there is exactly one match regardless of anything else. (This is
323 : * slightly bogus, since the index or clause's equality operator might be
324 : * different from ours, but it's much more likely to be right than
325 : * ignoring the information.)
326 : */
327 174028 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
328 : {
329 33106 : selec = 1.0 / vardata->rel->tuples;
330 : }
331 241202 : else if (HeapTupleIsValid(vardata->statsTuple) &&
332 100280 : statistic_proc_security_check(vardata,
333 100280 : (opfuncoid = get_opcode(operator))))
334 100280 : {
335 : AttStatsSlot sslot;
336 100280 : bool match = false;
337 : int i;
338 :
339 : /*
340 : * Is the constant "=" to any of the column's most common values?
341 : * (Although the given operator may not really be "=", we will assume
342 : * that seeing whether it returns TRUE is an appropriate test. If you
343 : * don't like this, maybe you shouldn't be using eqsel for your
344 : * operator...)
345 : */
346 100280 : if (get_attstatsslot(&sslot, vardata->statsTuple,
347 : STATISTIC_KIND_MCV, InvalidOid,
348 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
349 : {
350 90664 : LOCAL_FCINFO(fcinfo, 2);
351 : FmgrInfo eqproc;
352 :
353 90664 : fmgr_info(opfuncoid, &eqproc);
354 :
355 : /*
356 : * Save a few cycles by setting up the fcinfo struct just once.
357 : * Using FunctionCallInvoke directly also avoids failure if the
358 : * eqproc returns NULL, though really equality functions should
359 : * never do that.
360 : */
361 90664 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
362 : NULL, NULL);
363 90664 : fcinfo->args[0].isnull = false;
364 90664 : fcinfo->args[1].isnull = false;
365 : /* be careful to apply operator right way 'round */
366 90664 : if (varonleft)
367 90648 : fcinfo->args[1].value = constval;
368 : else
369 16 : fcinfo->args[0].value = constval;
370 :
371 1686236 : for (i = 0; i < sslot.nvalues; i++)
372 : {
373 : Datum fresult;
374 :
375 1641490 : if (varonleft)
376 1641458 : fcinfo->args[0].value = sslot.values[i];
377 : else
378 32 : fcinfo->args[1].value = sslot.values[i];
379 1641490 : fcinfo->isnull = false;
380 1641490 : fresult = FunctionCallInvoke(fcinfo);
381 1641490 : if (!fcinfo->isnull && DatumGetBool(fresult))
382 : {
383 45918 : match = true;
384 45918 : break;
385 : }
386 : }
387 : }
388 : else
389 : {
390 : /* no most-common-value info available */
391 9616 : i = 0; /* keep compiler quiet */
392 : }
393 :
394 100280 : 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 45918 : 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 54362 : double sumcommon = 0.0;
410 : double otherdistinct;
411 :
412 1488962 : for (i = 0; i < sslot.nnumbers; i++)
413 1434600 : sumcommon += sslot.numbers[i];
414 54362 : selec = 1.0 - sumcommon - nullfrac;
415 54362 : 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 54362 : otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
423 54362 : sslot.nnumbers;
424 54362 : if (otherdistinct > 1)
425 32754 : 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 54362 : if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
432 0 : selec = sslot.numbers[sslot.nnumbers - 1];
433 : }
434 :
435 100280 : 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 40642 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
445 : }
446 :
447 : /* now adjust if we wanted <> rather than = */
448 174028 : if (negate)
449 9378 : selec = 1.0 - selec - nullfrac;
450 :
451 : /* result should be in range, but make sure... */
452 174028 : CLAMP_PROBABILITY(selec);
453 :
454 174028 : return selec;
455 : }
456 :
457 : /*
458 : * var_eq_non_const --- eqsel for var = something-other-than-const case
459 : *
460 : * This is exported so that some other estimation functions can use it.
461 : */
462 : double
463 184746 : var_eq_non_const(VariableStatData *vardata, Oid operator, Oid collation,
464 : Node *other,
465 : bool varonleft, bool negate)
466 : {
467 : double selec;
468 184746 : double nullfrac = 0.0;
469 : bool isdefault;
470 :
471 : /*
472 : * Grab the nullfrac for use below.
473 : */
474 184746 : if (HeapTupleIsValid(vardata->statsTuple))
475 : {
476 : Form_pg_statistic stats;
477 :
478 154454 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
479 154454 : nullfrac = stats->stanullfrac;
480 : }
481 :
482 : /*
483 : * If we matched the var to a unique index or DISTINCT clause, assume
484 : * there is exactly one match regardless of anything else. (This is
485 : * slightly bogus, since the index or clause's equality operator might be
486 : * different from ours, but it's much more likely to be right than
487 : * ignoring the information.)
488 : */
489 184746 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
490 : {
491 86856 : selec = 1.0 / vardata->rel->tuples;
492 : }
493 97890 : else if (HeapTupleIsValid(vardata->statsTuple))
494 : {
495 : double ndistinct;
496 : AttStatsSlot sslot;
497 :
498 : /*
499 : * Search is for a value that we do not know a priori, but we will
500 : * assume it is not NULL. Estimate the selectivity as non-null
501 : * fraction divided by number of distinct values, so that we get a
502 : * result averaged over all possible values whether common or
503 : * uncommon. (Essentially, we are assuming that the not-yet-known
504 : * comparison value is equally likely to be any of the possible
505 : * values, regardless of their frequency in the table. Is that a good
506 : * idea?)
507 : */
508 75634 : selec = 1.0 - nullfrac;
509 75634 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
510 75634 : if (ndistinct > 1)
511 69798 : selec /= ndistinct;
512 :
513 : /*
514 : * Cross-check: selectivity should never be estimated as more than the
515 : * most common value's.
516 : */
517 75634 : if (get_attstatsslot(&sslot, vardata->statsTuple,
518 : STATISTIC_KIND_MCV, InvalidOid,
519 : ATTSTATSSLOT_NUMBERS))
520 : {
521 65110 : if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
522 140 : selec = sslot.numbers[0];
523 65110 : free_attstatsslot(&sslot);
524 : }
525 : }
526 : else
527 : {
528 : /*
529 : * No ANALYZE stats available, so make a guess using estimated number
530 : * of distinct values and assuming they are equally common. (The guess
531 : * is unlikely to be very good, but we do know a few special cases.)
532 : */
533 22256 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
534 : }
535 :
536 : /* now adjust if we wanted <> rather than = */
537 184746 : if (negate)
538 2902 : selec = 1.0 - selec - nullfrac;
539 :
540 : /* result should be in range, but make sure... */
541 184746 : CLAMP_PROBABILITY(selec);
542 :
543 184746 : return selec;
544 : }
545 :
546 : /*
547 : * neqsel - Selectivity of "!=" for any data types.
548 : *
549 : * This routine is also used for some operators that are not "!="
550 : * but have comparable selectivity behavior. See above comments
551 : * for eqsel().
552 : */
553 : Datum
554 13232 : neqsel(PG_FUNCTION_ARGS)
555 : {
556 13232 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
557 : }
558 :
559 : /*
560 : * scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
561 : *
562 : * This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
563 : * The isgt and iseq flags distinguish which of the four cases apply.
564 : *
565 : * The caller has commuted the clause, if necessary, so that we can treat
566 : * the variable as being on the left. The caller must also make sure that
567 : * the other side of the clause is a non-null Const, and dissect that into
568 : * a value and datatype. (This definition simplifies some callers that
569 : * want to estimate against a computed value instead of a Const node.)
570 : *
571 : * This routine works for any datatype (or pair of datatypes) known to
572 : * convert_to_scalar(). If it is applied to some other datatype,
573 : * it will return an approximate estimate based on assuming that the constant
574 : * value falls in the middle of the bin identified by binary search.
575 : */
576 : static double
577 194762 : scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
578 : Oid collation,
579 : VariableStatData *vardata, Datum constval, Oid consttype)
580 : {
581 : Form_pg_statistic stats;
582 : FmgrInfo opproc;
583 : double mcv_selec,
584 : hist_selec,
585 : sumcommon;
586 : double selec;
587 :
588 194762 : if (!HeapTupleIsValid(vardata->statsTuple))
589 : {
590 : /*
591 : * No stats are available. Typically this means we have to fall back
592 : * on the default estimate; but if the variable is CTID then we can
593 : * make an estimate based on comparing the constant to the table size.
594 : */
595 11896 : if (vardata->var && IsA(vardata->var, Var) &&
596 8796 : ((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
597 : {
598 : ItemPointer itemptr;
599 : double block;
600 : double density;
601 :
602 : /*
603 : * If the relation's empty, we're going to include all of it.
604 : * (This is mostly to avoid divide-by-zero below.)
605 : */
606 4 : if (vardata->rel->pages == 0)
607 0 : return 1.0;
608 :
609 4 : itemptr = (ItemPointer) DatumGetPointer(constval);
610 4 : block = ItemPointerGetBlockNumberNoCheck(itemptr);
611 :
612 : /*
613 : * Determine the average number of tuples per page (density).
614 : *
615 : * Since the last page will, on average, be only half full, we can
616 : * estimate it to have half as many tuples as earlier pages. So
617 : * give it half the weight of a regular page.
618 : */
619 4 : density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
620 :
621 : /* If target is the last page, use half the density. */
622 4 : if (block >= vardata->rel->pages - 1)
623 0 : density *= 0.5;
624 :
625 : /*
626 : * Using the average tuples per page, calculate how far into the
627 : * page the itemptr is likely to be and adjust block accordingly,
628 : * by adding that fraction of a whole block (but never more than a
629 : * whole block, no matter how high the itemptr's offset is). Here
630 : * we are ignoring the possibility of dead-tuple line pointers,
631 : * which is fairly bogus, but we lack the info to do better.
632 : */
633 4 : if (density > 0.0)
634 : {
635 4 : OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
636 :
637 4 : block += Min(offset / density, 1.0);
638 : }
639 :
640 : /*
641 : * Convert relative block number to selectivity. Again, the last
642 : * page has only half weight.
643 : */
644 4 : selec = block / (vardata->rel->pages - 0.5);
645 :
646 : /*
647 : * The calculation so far gave us a selectivity for the "<=" case.
648 : * We'll have one less tuple for "<" and one additional tuple for
649 : * ">=", the latter of which we'll reverse the selectivity for
650 : * below, so we can simply subtract one tuple for both cases. The
651 : * cases that need this adjustment can be identified by iseq being
652 : * equal to isgt.
653 : */
654 4 : if (iseq == isgt && vardata->rel->tuples >= 1.0)
655 0 : selec -= (1.0 / vardata->rel->tuples);
656 :
657 : /* Finally, reverse the selectivity for the ">", ">=" cases. */
658 4 : if (isgt)
659 4 : selec = 1.0 - selec;
660 :
661 4 : CLAMP_PROBABILITY(selec);
662 4 : return selec;
663 : }
664 :
665 : /* no stats available, so default result */
666 11892 : return DEFAULT_INEQ_SEL;
667 : }
668 182866 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
669 :
670 182866 : fmgr_info(get_opcode(operator), &opproc);
671 :
672 : /*
673 : * If we have most-common-values info, add up the fractions of the MCV
674 : * entries that satisfy MCV OP CONST. These fractions contribute directly
675 : * to the result selectivity. Also add up the total fraction represented
676 : * by MCV entries.
677 : */
678 182866 : mcv_selec = mcv_selectivity(vardata, &opproc, collation, constval, true,
679 : &sumcommon);
680 :
681 : /*
682 : * If there is a histogram, determine which bin the constant falls in, and
683 : * compute the resulting contribution to selectivity.
684 : */
685 182866 : hist_selec = ineq_histogram_selectivity(root, vardata,
686 : operator, &opproc, isgt, iseq,
687 : collation,
688 : constval, consttype);
689 :
690 : /*
691 : * Now merge the results from the MCV and histogram calculations,
692 : * realizing that the histogram covers only the non-null values that are
693 : * not listed in MCV.
694 : */
695 182866 : selec = 1.0 - stats->stanullfrac - sumcommon;
696 :
697 182866 : if (hist_selec >= 0.0)
698 150816 : selec *= hist_selec;
699 : else
700 : {
701 : /*
702 : * If no histogram but there are values not accounted for by MCV,
703 : * arbitrarily assume half of them will match.
704 : */
705 32050 : selec *= 0.5;
706 : }
707 :
708 182866 : selec += mcv_selec;
709 :
710 : /* result should be in range, but make sure... */
711 182866 : CLAMP_PROBABILITY(selec);
712 :
713 182866 : return selec;
714 : }
715 :
716 : /*
717 : * mcv_selectivity - Examine the MCV list for selectivity estimates
718 : *
719 : * Determine the fraction of the variable's MCV population that satisfies
720 : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
721 : * compute the fraction of the total column population represented by the MCV
722 : * list. This code will work for any boolean-returning predicate operator.
723 : *
724 : * The function result is the MCV selectivity, and the fraction of the
725 : * total population is returned into *sumcommonp. Zeroes are returned
726 : * if there is no MCV list.
727 : */
728 : double
729 186318 : mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc, Oid collation,
730 : Datum constval, bool varonleft,
731 : double *sumcommonp)
732 : {
733 : double mcv_selec,
734 : sumcommon;
735 : AttStatsSlot sslot;
736 : int i;
737 :
738 186318 : mcv_selec = 0.0;
739 186318 : sumcommon = 0.0;
740 :
741 370778 : if (HeapTupleIsValid(vardata->statsTuple) &&
742 368864 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
743 184404 : get_attstatsslot(&sslot, vardata->statsTuple,
744 : STATISTIC_KIND_MCV, InvalidOid,
745 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
746 : {
747 92136 : LOCAL_FCINFO(fcinfo, 2);
748 :
749 : /*
750 : * We invoke the opproc "by hand" so that we won't fail on NULL
751 : * results. Such cases won't arise for normal comparison functions,
752 : * but generic_restriction_selectivity could perhaps be used with
753 : * operators that can return NULL. A small side benefit is to not
754 : * need to re-initialize the fcinfo struct from scratch each time.
755 : */
756 92136 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
757 : NULL, NULL);
758 92136 : fcinfo->args[0].isnull = false;
759 92136 : fcinfo->args[1].isnull = false;
760 : /* be careful to apply operator right way 'round */
761 92136 : if (varonleft)
762 92136 : fcinfo->args[1].value = constval;
763 : else
764 0 : fcinfo->args[0].value = constval;
765 :
766 3331372 : for (i = 0; i < sslot.nvalues; i++)
767 : {
768 : Datum fresult;
769 :
770 3239236 : if (varonleft)
771 3239236 : fcinfo->args[0].value = sslot.values[i];
772 : else
773 0 : fcinfo->args[1].value = sslot.values[i];
774 3239236 : fcinfo->isnull = false;
775 3239236 : fresult = FunctionCallInvoke(fcinfo);
776 3239236 : if (!fcinfo->isnull && DatumGetBool(fresult))
777 1508406 : mcv_selec += sslot.numbers[i];
778 3239236 : sumcommon += sslot.numbers[i];
779 : }
780 92136 : free_attstatsslot(&sslot);
781 : }
782 :
783 186318 : *sumcommonp = sumcommon;
784 186318 : return mcv_selec;
785 : }
786 :
787 : /*
788 : * histogram_selectivity - Examine the histogram for selectivity estimates
789 : *
790 : * Determine the fraction of the variable's histogram entries that satisfy
791 : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
792 : *
793 : * This code will work for any boolean-returning predicate operator, whether
794 : * or not it has anything to do with the histogram sort operator. We are
795 : * essentially using the histogram just as a representative sample. However,
796 : * small histograms are unlikely to be all that representative, so the caller
797 : * should be prepared to fall back on some other estimation approach when the
798 : * histogram is missing or very small. It may also be prudent to combine this
799 : * approach with another one when the histogram is small.
800 : *
801 : * If the actual histogram size is not at least min_hist_size, we won't bother
802 : * to do the calculation at all. Also, if the n_skip parameter is > 0, we
803 : * ignore the first and last n_skip histogram elements, on the grounds that
804 : * they are outliers and hence not very representative. Typical values for
805 : * these parameters are 10 and 1.
806 : *
807 : * The function result is the selectivity, or -1 if there is no histogram
808 : * or it's smaller than min_hist_size.
809 : *
810 : * The output parameter *hist_size receives the actual histogram size,
811 : * or zero if no histogram. Callers may use this number to decide how
812 : * much faith to put in the function result.
813 : *
814 : * Note that the result disregards both the most-common-values (if any) and
815 : * null entries. The caller is expected to combine this result with
816 : * statistics for those portions of the column population. It may also be
817 : * prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
818 : */
819 : double
820 3452 : histogram_selectivity(VariableStatData *vardata,
821 : FmgrInfo *opproc, Oid collation,
822 : Datum constval, bool varonleft,
823 : int min_hist_size, int n_skip,
824 : int *hist_size)
825 : {
826 : double result;
827 : AttStatsSlot sslot;
828 :
829 : /* check sanity of parameters */
830 : Assert(n_skip >= 0);
831 : Assert(min_hist_size > 2 * n_skip);
832 :
833 5046 : if (HeapTupleIsValid(vardata->statsTuple) &&
834 3184 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
835 1590 : get_attstatsslot(&sslot, vardata->statsTuple,
836 : STATISTIC_KIND_HISTOGRAM, InvalidOid,
837 : ATTSTATSSLOT_VALUES))
838 : {
839 1546 : *hist_size = sslot.nvalues;
840 1546 : if (sslot.nvalues >= min_hist_size)
841 : {
842 1106 : LOCAL_FCINFO(fcinfo, 2);
843 1106 : int nmatch = 0;
844 : int i;
845 :
846 : /*
847 : * We invoke the opproc "by hand" so that we won't fail on NULL
848 : * results. Such cases won't arise for normal comparison
849 : * functions, but generic_restriction_selectivity could perhaps be
850 : * used with operators that can return NULL. A small side benefit
851 : * is to not need to re-initialize the fcinfo struct from scratch
852 : * each time.
853 : */
854 1106 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
855 : NULL, NULL);
856 1106 : fcinfo->args[0].isnull = false;
857 1106 : fcinfo->args[1].isnull = false;
858 : /* be careful to apply operator right way 'round */
859 1106 : if (varonleft)
860 1106 : fcinfo->args[1].value = constval;
861 : else
862 0 : fcinfo->args[0].value = constval;
863 :
864 106228 : for (i = n_skip; i < sslot.nvalues - n_skip; i++)
865 : {
866 : Datum fresult;
867 :
868 105122 : if (varonleft)
869 105122 : fcinfo->args[0].value = sslot.values[i];
870 : else
871 0 : fcinfo->args[1].value = sslot.values[i];
872 105122 : fcinfo->isnull = false;
873 105122 : fresult = FunctionCallInvoke(fcinfo);
874 105122 : if (!fcinfo->isnull && DatumGetBool(fresult))
875 5602 : nmatch++;
876 : }
877 1106 : result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
878 : }
879 : else
880 440 : result = -1;
881 1546 : free_attstatsslot(&sslot);
882 : }
883 : else
884 : {
885 1906 : *hist_size = 0;
886 1906 : result = -1;
887 : }
888 :
889 3452 : return result;
890 : }
891 :
892 : /*
893 : * generic_restriction_selectivity - Selectivity for almost anything
894 : *
895 : * This function estimates selectivity for operators that we don't have any
896 : * special knowledge about, but are on data types that we collect standard
897 : * MCV and/or histogram statistics for. (Additional assumptions are that
898 : * the operator is strict and immutable, or at least stable.)
899 : *
900 : * If we have "VAR OP CONST" or "CONST OP VAR", selectivity is estimated by
901 : * applying the operator to each element of the column's MCV and/or histogram
902 : * stats, and merging the results using the assumption that the histogram is
903 : * a reasonable random sample of the column's non-MCV population. Note that
904 : * if the operator's semantics are related to the histogram ordering, this
905 : * might not be such a great assumption; other functions such as
906 : * scalarineqsel() are probably a better match in such cases.
907 : *
908 : * Otherwise, fall back to the default selectivity provided by the caller.
909 : */
910 : double
911 810 : generic_restriction_selectivity(PlannerInfo *root, Oid oproid, Oid collation,
912 : List *args, int varRelid,
913 : double default_selectivity)
914 : {
915 : double selec;
916 : VariableStatData vardata;
917 : Node *other;
918 : bool varonleft;
919 :
920 : /*
921 : * If expression is not variable OP something or something OP variable,
922 : * then punt and return the default estimate.
923 : */
924 810 : if (!get_restriction_variable(root, args, varRelid,
925 : &vardata, &other, &varonleft))
926 0 : return default_selectivity;
927 :
928 : /*
929 : * If the something is a NULL constant, assume operator is strict and
930 : * return zero, ie, operator will never return TRUE.
931 : */
932 810 : if (IsA(other, Const) &&
933 810 : ((Const *) other)->constisnull)
934 : {
935 0 : ReleaseVariableStats(vardata);
936 0 : return 0.0;
937 : }
938 :
939 810 : if (IsA(other, Const))
940 : {
941 : /* Variable is being compared to a known non-null constant */
942 810 : Datum constval = ((Const *) other)->constvalue;
943 : FmgrInfo opproc;
944 : double mcvsum;
945 : double mcvsel;
946 : double nullfrac;
947 : int hist_size;
948 :
949 810 : fmgr_info(get_opcode(oproid), &opproc);
950 :
951 : /*
952 : * Calculate the selectivity for the column's most common values.
953 : */
954 810 : mcvsel = mcv_selectivity(&vardata, &opproc, collation,
955 : constval, varonleft,
956 : &mcvsum);
957 :
958 : /*
959 : * If the histogram is large enough, see what fraction of it matches
960 : * the query, and assume that's representative of the non-MCV
961 : * population. Otherwise use the default selectivity for the non-MCV
962 : * population.
963 : */
964 810 : selec = histogram_selectivity(&vardata, &opproc, collation,
965 : constval, varonleft,
966 : 10, 1, &hist_size);
967 810 : if (selec < 0)
968 : {
969 : /* Nope, fall back on default */
970 410 : selec = default_selectivity;
971 : }
972 400 : else if (hist_size < 100)
973 : {
974 : /*
975 : * For histogram sizes from 10 to 100, we combine the histogram
976 : * and default selectivities, putting increasingly more trust in
977 : * the histogram for larger sizes.
978 : */
979 0 : double hist_weight = hist_size / 100.0;
980 :
981 0 : selec = selec * hist_weight +
982 0 : default_selectivity * (1.0 - hist_weight);
983 : }
984 :
985 : /* In any case, don't believe extremely small or large estimates. */
986 810 : if (selec < 0.0001)
987 236 : selec = 0.0001;
988 574 : else if (selec > 0.9999)
989 32 : selec = 0.9999;
990 :
991 : /* Don't forget to account for nulls. */
992 810 : if (HeapTupleIsValid(vardata.statsTuple))
993 440 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
994 : else
995 370 : nullfrac = 0.0;
996 :
997 : /*
998 : * Now merge the results from the MCV and histogram calculations,
999 : * realizing that the histogram covers only the non-null values that
1000 : * are not listed in MCV.
1001 : */
1002 810 : selec *= 1.0 - nullfrac - mcvsum;
1003 810 : selec += mcvsel;
1004 : }
1005 : else
1006 : {
1007 : /* Comparison value is not constant, so we can't do anything */
1008 0 : selec = default_selectivity;
1009 : }
1010 :
1011 810 : ReleaseVariableStats(vardata);
1012 :
1013 : /* result should be in range, but make sure... */
1014 810 : CLAMP_PROBABILITY(selec);
1015 :
1016 810 : return selec;
1017 : }
1018 :
1019 : /*
1020 : * ineq_histogram_selectivity - Examine the histogram for scalarineqsel
1021 : *
1022 : * Determine the fraction of the variable's histogram population that
1023 : * satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
1024 : * The isgt and iseq flags distinguish which of the four cases apply.
1025 : *
1026 : * While opproc could be looked up from the operator OID, common callers
1027 : * also need to call it separately, so we make the caller pass both.
1028 : *
1029 : * Returns -1 if there is no histogram (valid results will always be >= 0).
1030 : *
1031 : * Note that the result disregards both the most-common-values (if any) and
1032 : * null entries. The caller is expected to combine this result with
1033 : * statistics for those portions of the column population.
1034 : *
1035 : * This is exported so that some other estimation functions can use it.
1036 : */
1037 : double
1038 184854 : ineq_histogram_selectivity(PlannerInfo *root,
1039 : VariableStatData *vardata,
1040 : Oid opoid, FmgrInfo *opproc, bool isgt, bool iseq,
1041 : Oid collation,
1042 : Datum constval, Oid consttype)
1043 : {
1044 : double hist_selec;
1045 : AttStatsSlot sslot;
1046 :
1047 184854 : hist_selec = -1.0;
1048 :
1049 : /*
1050 : * Someday, ANALYZE might store more than one histogram per rel/att,
1051 : * corresponding to more than one possible sort ordering defined for the
1052 : * column type. Right now, we know there is only one, so just grab it and
1053 : * see if it matches the query.
1054 : *
1055 : * Note that we can't use opoid as search argument; the staop appearing in
1056 : * pg_statistic will be for the relevant '<' operator, but what we have
1057 : * might be some other inequality operator such as '>='. (Even if opoid
1058 : * is a '<' operator, it could be cross-type.) Hence we must use
1059 : * comparison_ops_are_compatible() to see if the operators match.
1060 : */
1061 368690 : if (HeapTupleIsValid(vardata->statsTuple) &&
1062 367620 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
1063 183784 : get_attstatsslot(&sslot, vardata->statsTuple,
1064 : STATISTIC_KIND_HISTOGRAM, InvalidOid,
1065 : ATTSTATSSLOT_VALUES))
1066 : {
1067 151784 : if (sslot.nvalues > 1 &&
1068 303540 : sslot.stacoll == collation &&
1069 151756 : comparison_ops_are_compatible(sslot.staop, opoid))
1070 151684 : {
1071 : /*
1072 : * Use binary search to find the desired location, namely the
1073 : * right end of the histogram bin containing the comparison value,
1074 : * which is the leftmost entry for which the comparison operator
1075 : * succeeds (if isgt) or fails (if !isgt).
1076 : *
1077 : * In this loop, we pay no attention to whether the operator iseq
1078 : * or not; that detail will be mopped up below. (We cannot tell,
1079 : * anyway, whether the operator thinks the values are equal.)
1080 : *
1081 : * If the binary search accesses the first or last histogram
1082 : * entry, we try to replace that endpoint with the true column min
1083 : * or max as found by get_actual_variable_range(). This
1084 : * ameliorates misestimates when the min or max is moving as a
1085 : * result of changes since the last ANALYZE. Note that this could
1086 : * result in effectively including MCVs into the histogram that
1087 : * weren't there before, but we don't try to correct for that.
1088 : */
1089 : double histfrac;
1090 151684 : int lobound = 0; /* first possible slot to search */
1091 151684 : int hibound = sslot.nvalues; /* last+1 slot to search */
1092 151684 : bool have_end = false;
1093 :
1094 : /*
1095 : * If there are only two histogram entries, we'll want up-to-date
1096 : * values for both. (If there are more than two, we need at most
1097 : * one of them to be updated, so we deal with that within the
1098 : * loop.)
1099 : */
1100 151684 : if (sslot.nvalues == 2)
1101 568 : have_end = get_actual_variable_range(root,
1102 : vardata,
1103 : sslot.staop,
1104 : collation,
1105 : &sslot.values[0],
1106 568 : &sslot.values[1]);
1107 :
1108 1008648 : while (lobound < hibound)
1109 : {
1110 856964 : int probe = (lobound + hibound) / 2;
1111 : bool ltcmp;
1112 :
1113 : /*
1114 : * If we find ourselves about to compare to the first or last
1115 : * histogram entry, first try to replace it with the actual
1116 : * current min or max (unless we already did so above).
1117 : */
1118 856964 : if (probe == 0 && sslot.nvalues > 2)
1119 75510 : have_end = get_actual_variable_range(root,
1120 : vardata,
1121 : sslot.staop,
1122 : collation,
1123 : &sslot.values[0],
1124 : NULL);
1125 781454 : else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
1126 49158 : have_end = get_actual_variable_range(root,
1127 : vardata,
1128 : sslot.staop,
1129 : collation,
1130 : NULL,
1131 49158 : &sslot.values[probe]);
1132 :
1133 856964 : ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
1134 : collation,
1135 : sslot.values[probe],
1136 : constval));
1137 856964 : if (isgt)
1138 61002 : ltcmp = !ltcmp;
1139 856964 : if (ltcmp)
1140 309450 : lobound = probe + 1;
1141 : else
1142 547514 : hibound = probe;
1143 : }
1144 :
1145 151684 : if (lobound <= 0)
1146 : {
1147 : /*
1148 : * Constant is below lower histogram boundary. More
1149 : * precisely, we have found that no entry in the histogram
1150 : * satisfies the inequality clause (if !isgt) or they all do
1151 : * (if isgt). We estimate that that's true of the entire
1152 : * table, so set histfrac to 0.0 (which we'll flip to 1.0
1153 : * below, if isgt).
1154 : */
1155 69364 : histfrac = 0.0;
1156 : }
1157 82320 : else if (lobound >= sslot.nvalues)
1158 : {
1159 : /*
1160 : * Inverse case: constant is above upper histogram boundary.
1161 : */
1162 25568 : histfrac = 1.0;
1163 : }
1164 : else
1165 : {
1166 : /* We have values[i-1] <= constant <= values[i]. */
1167 56752 : int i = lobound;
1168 56752 : double eq_selec = 0;
1169 : double val,
1170 : high,
1171 : low;
1172 : double binfrac;
1173 :
1174 : /*
1175 : * In the cases where we'll need it below, obtain an estimate
1176 : * of the selectivity of "x = constval". We use a calculation
1177 : * similar to what var_eq_const() does for a non-MCV constant,
1178 : * ie, estimate that all distinct non-MCV values occur equally
1179 : * often. But multiplication by "1.0 - sumcommon - nullfrac"
1180 : * will be done by our caller, so we shouldn't do that here.
1181 : * Therefore we can't try to clamp the estimate by reference
1182 : * to the least common MCV; the result would be too small.
1183 : *
1184 : * Note: since this is effectively assuming that constval
1185 : * isn't an MCV, it's logically dubious if constval in fact is
1186 : * one. But we have to apply *some* correction for equality,
1187 : * and anyway we cannot tell if constval is an MCV, since we
1188 : * don't have a suitable equality operator at hand.
1189 : */
1190 56752 : if (i == 1 || isgt == iseq)
1191 : {
1192 : double otherdistinct;
1193 : bool isdefault;
1194 : AttStatsSlot mcvslot;
1195 :
1196 : /* Get estimated number of distinct values */
1197 17322 : otherdistinct = get_variable_numdistinct(vardata,
1198 : &isdefault);
1199 :
1200 : /* Subtract off the number of known MCVs */
1201 17322 : if (get_attstatsslot(&mcvslot, vardata->statsTuple,
1202 : STATISTIC_KIND_MCV, InvalidOid,
1203 : ATTSTATSSLOT_NUMBERS))
1204 : {
1205 5646 : otherdistinct -= mcvslot.nnumbers;
1206 5646 : free_attstatsslot(&mcvslot);
1207 : }
1208 :
1209 : /* If result doesn't seem sane, leave eq_selec at 0 */
1210 17322 : if (otherdistinct > 1)
1211 17322 : eq_selec = 1.0 / otherdistinct;
1212 : }
1213 :
1214 : /*
1215 : * Convert the constant and the two nearest bin boundary
1216 : * values to a uniform comparison scale, and do a linear
1217 : * interpolation within this bin.
1218 : */
1219 56752 : if (convert_to_scalar(constval, consttype, collation,
1220 : &val,
1221 56752 : sslot.values[i - 1], sslot.values[i],
1222 : vardata->vartype,
1223 : &low, &high))
1224 : {
1225 56748 : if (high <= low)
1226 : {
1227 : /* cope if bin boundaries appear identical */
1228 0 : binfrac = 0.5;
1229 : }
1230 56748 : else if (val <= low)
1231 7530 : binfrac = 0.0;
1232 49218 : else if (val >= high)
1233 1404 : binfrac = 1.0;
1234 : else
1235 : {
1236 47814 : binfrac = (val - low) / (high - low);
1237 :
1238 : /*
1239 : * Watch out for the possibility that we got a NaN or
1240 : * Infinity from the division. This can happen
1241 : * despite the previous checks, if for example "low"
1242 : * is -Infinity.
1243 : */
1244 47814 : if (isnan(binfrac) ||
1245 47814 : binfrac < 0.0 || binfrac > 1.0)
1246 0 : binfrac = 0.5;
1247 : }
1248 : }
1249 : else
1250 : {
1251 : /*
1252 : * Ideally we'd produce an error here, on the grounds that
1253 : * the given operator shouldn't have scalarXXsel
1254 : * registered as its selectivity func unless we can deal
1255 : * with its operand types. But currently, all manner of
1256 : * stuff is invoking scalarXXsel, so give a default
1257 : * estimate until that can be fixed.
1258 : */
1259 4 : binfrac = 0.5;
1260 : }
1261 :
1262 : /*
1263 : * Now, compute the overall selectivity across the values
1264 : * represented by the histogram. We have i-1 full bins and
1265 : * binfrac partial bin below the constant.
1266 : */
1267 56752 : histfrac = (double) (i - 1) + binfrac;
1268 56752 : histfrac /= (double) (sslot.nvalues - 1);
1269 :
1270 : /*
1271 : * At this point, histfrac is an estimate of the fraction of
1272 : * the population represented by the histogram that satisfies
1273 : * "x <= constval". Somewhat remarkably, this statement is
1274 : * true regardless of which operator we were doing the probes
1275 : * with, so long as convert_to_scalar() delivers reasonable
1276 : * results. If the probe constant is equal to some histogram
1277 : * entry, we would have considered the bin to the left of that
1278 : * entry if probing with "<" or ">=", or the bin to the right
1279 : * if probing with "<=" or ">"; but binfrac would have come
1280 : * out as 1.0 in the first case and 0.0 in the second, leading
1281 : * to the same histfrac in either case. For probe constants
1282 : * between histogram entries, we find the same bin and get the
1283 : * same estimate with any operator.
1284 : *
1285 : * The fact that the estimate corresponds to "x <= constval"
1286 : * and not "x < constval" is because of the way that ANALYZE
1287 : * constructs the histogram: each entry is, effectively, the
1288 : * rightmost value in its sample bucket. So selectivity
1289 : * values that are exact multiples of 1/(histogram_size-1)
1290 : * should be understood as estimates including a histogram
1291 : * entry plus everything to its left.
1292 : *
1293 : * However, that breaks down for the first histogram entry,
1294 : * which necessarily is the leftmost value in its sample
1295 : * bucket. That means the first histogram bin is slightly
1296 : * narrower than the rest, by an amount equal to eq_selec.
1297 : * Another way to say that is that we want "x <= leftmost" to
1298 : * be estimated as eq_selec not zero. So, if we're dealing
1299 : * with the first bin (i==1), rescale to make that true while
1300 : * adjusting the rest of that bin linearly.
1301 : */
1302 56752 : if (i == 1)
1303 6496 : histfrac += eq_selec * (1.0 - binfrac);
1304 :
1305 : /*
1306 : * "x <= constval" is good if we want an estimate for "<=" or
1307 : * ">", but if we are estimating for "<" or ">=", we now need
1308 : * to decrease the estimate by eq_selec.
1309 : */
1310 56752 : if (isgt == iseq)
1311 14868 : histfrac -= eq_selec;
1312 : }
1313 :
1314 : /*
1315 : * Now the estimate is finished for "<" and "<=" cases. If we are
1316 : * estimating for ">" or ">=", flip it.
1317 : */
1318 151684 : hist_selec = isgt ? (1.0 - histfrac) : histfrac;
1319 :
1320 : /*
1321 : * The histogram boundaries are only approximate to begin with,
1322 : * and may well be out of date anyway. Therefore, don't believe
1323 : * extremely small or large selectivity estimates --- unless we
1324 : * got actual current endpoint values from the table, in which
1325 : * case just do the usual sanity clamp. Somewhat arbitrarily, we
1326 : * set the cutoff for other cases at a hundredth of the histogram
1327 : * resolution.
1328 : */
1329 151684 : if (have_end)
1330 78256 : CLAMP_PROBABILITY(hist_selec);
1331 : else
1332 : {
1333 73428 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1334 :
1335 73428 : if (hist_selec < cutoff)
1336 21096 : hist_selec = cutoff;
1337 52332 : else if (hist_selec > 1.0 - cutoff)
1338 23178 : hist_selec = 1.0 - cutoff;
1339 : }
1340 : }
1341 100 : else if (sslot.nvalues > 1)
1342 : {
1343 : /*
1344 : * If we get here, we have a histogram but it's not sorted the way
1345 : * we want. Do a brute-force search to see how many of the
1346 : * entries satisfy the comparison condition, and take that
1347 : * fraction as our estimate. (This is identical to the inner loop
1348 : * of histogram_selectivity; maybe share code?)
1349 : */
1350 100 : LOCAL_FCINFO(fcinfo, 2);
1351 100 : int nmatch = 0;
1352 :
1353 100 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
1354 : NULL, NULL);
1355 100 : fcinfo->args[0].isnull = false;
1356 100 : fcinfo->args[1].isnull = false;
1357 100 : fcinfo->args[1].value = constval;
1358 641148 : for (int i = 0; i < sslot.nvalues; i++)
1359 : {
1360 : Datum fresult;
1361 :
1362 641048 : fcinfo->args[0].value = sslot.values[i];
1363 641048 : fcinfo->isnull = false;
1364 641048 : fresult = FunctionCallInvoke(fcinfo);
1365 641048 : if (!fcinfo->isnull && DatumGetBool(fresult))
1366 1204 : nmatch++;
1367 : }
1368 100 : hist_selec = ((double) nmatch) / ((double) sslot.nvalues);
1369 :
1370 : /*
1371 : * As above, clamp to a hundredth of the histogram resolution.
1372 : * This case is surely even less trustworthy than the normal one,
1373 : * so we shouldn't believe exact 0 or 1 selectivity. (Maybe the
1374 : * clamp should be more restrictive in this case?)
1375 : */
1376 : {
1377 100 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1378 :
1379 100 : if (hist_selec < cutoff)
1380 0 : hist_selec = cutoff;
1381 100 : else if (hist_selec > 1.0 - cutoff)
1382 0 : hist_selec = 1.0 - cutoff;
1383 : }
1384 : }
1385 :
1386 151784 : free_attstatsslot(&sslot);
1387 : }
1388 :
1389 184854 : return hist_selec;
1390 : }
1391 :
1392 : /*
1393 : * Common wrapper function for the selectivity estimators that simply
1394 : * invoke scalarineqsel().
1395 : */
1396 : static Datum
1397 30382 : scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
1398 : {
1399 30382 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
1400 30382 : Oid operator = PG_GETARG_OID(1);
1401 30382 : List *args = (List *) PG_GETARG_POINTER(2);
1402 30382 : int varRelid = PG_GETARG_INT32(3);
1403 30382 : Oid collation = PG_GET_COLLATION();
1404 : VariableStatData vardata;
1405 : Node *other;
1406 : bool varonleft;
1407 : Datum constval;
1408 : Oid consttype;
1409 : double selec;
1410 :
1411 : /*
1412 : * If expression is not variable op something or something op variable,
1413 : * then punt and return a default estimate.
1414 : */
1415 30382 : if (!get_restriction_variable(root, args, varRelid,
1416 : &vardata, &other, &varonleft))
1417 200 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1418 :
1419 : /*
1420 : * Can't do anything useful if the something is not a constant, either.
1421 : */
1422 30182 : if (!IsA(other, Const))
1423 : {
1424 1428 : ReleaseVariableStats(vardata);
1425 1428 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1426 : }
1427 :
1428 : /*
1429 : * If the constant is NULL, assume operator is strict and return zero, ie,
1430 : * operator will never return TRUE.
1431 : */
1432 28754 : if (((Const *) other)->constisnull)
1433 : {
1434 24 : ReleaseVariableStats(vardata);
1435 24 : PG_RETURN_FLOAT8(0.0);
1436 : }
1437 28730 : constval = ((Const *) other)->constvalue;
1438 28730 : consttype = ((Const *) other)->consttype;
1439 :
1440 : /*
1441 : * Force the var to be on the left to simplify logic in scalarineqsel.
1442 : */
1443 28730 : if (!varonleft)
1444 : {
1445 168 : operator = get_commutator(operator);
1446 168 : if (!operator)
1447 : {
1448 : /* Use default selectivity (should we raise an error instead?) */
1449 0 : ReleaseVariableStats(vardata);
1450 0 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1451 : }
1452 168 : isgt = !isgt;
1453 : }
1454 :
1455 : /* The rest of the work is done by scalarineqsel(). */
1456 28730 : selec = scalarineqsel(root, operator, isgt, iseq, collation,
1457 : &vardata, constval, consttype);
1458 :
1459 28730 : ReleaseVariableStats(vardata);
1460 :
1461 28730 : PG_RETURN_FLOAT8((float8) selec);
1462 : }
1463 :
1464 : /*
1465 : * scalarltsel - Selectivity of "<" for scalars.
1466 : */
1467 : Datum
1468 8658 : scalarltsel(PG_FUNCTION_ARGS)
1469 : {
1470 8658 : return scalarineqsel_wrapper(fcinfo, false, false);
1471 : }
1472 :
1473 : /*
1474 : * scalarlesel - Selectivity of "<=" for scalars.
1475 : */
1476 : Datum
1477 2232 : scalarlesel(PG_FUNCTION_ARGS)
1478 : {
1479 2232 : return scalarineqsel_wrapper(fcinfo, false, true);
1480 : }
1481 :
1482 : /*
1483 : * scalargtsel - Selectivity of ">" for scalars.
1484 : */
1485 : Datum
1486 12656 : scalargtsel(PG_FUNCTION_ARGS)
1487 : {
1488 12656 : return scalarineqsel_wrapper(fcinfo, true, false);
1489 : }
1490 :
1491 : /*
1492 : * scalargesel - Selectivity of ">=" for scalars.
1493 : */
1494 : Datum
1495 6836 : scalargesel(PG_FUNCTION_ARGS)
1496 : {
1497 6836 : return scalarineqsel_wrapper(fcinfo, true, true);
1498 : }
1499 :
1500 : /*
1501 : * boolvarsel - Selectivity of Boolean variable.
1502 : *
1503 : * This can actually be called on any boolean-valued expression. If it
1504 : * involves only Vars of the specified relation, and if there are statistics
1505 : * about the Var or expression (the latter is possible if it's indexed) then
1506 : * we'll produce a real estimate; otherwise it's just a default.
1507 : */
1508 : Selectivity
1509 15202 : boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
1510 : {
1511 : VariableStatData vardata;
1512 : double selec;
1513 :
1514 15202 : examine_variable(root, arg, varRelid, &vardata);
1515 15202 : if (HeapTupleIsValid(vardata.statsTuple))
1516 : {
1517 : /*
1518 : * A boolean variable V is equivalent to the clause V = 't', so we
1519 : * compute the selectivity as if that is what we have.
1520 : */
1521 11886 : selec = var_eq_const(&vardata, BooleanEqualOperator, InvalidOid,
1522 : BoolGetDatum(true), false, true, false);
1523 : }
1524 : else
1525 : {
1526 : /* Otherwise, the default estimate is 0.5 */
1527 3316 : selec = 0.5;
1528 : }
1529 15202 : ReleaseVariableStats(vardata);
1530 15202 : return selec;
1531 : }
1532 :
1533 : /*
1534 : * booltestsel - Selectivity of BooleanTest Node.
1535 : */
1536 : Selectivity
1537 132 : booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
1538 : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1539 : {
1540 : VariableStatData vardata;
1541 : double selec;
1542 :
1543 132 : examine_variable(root, arg, varRelid, &vardata);
1544 :
1545 132 : if (HeapTupleIsValid(vardata.statsTuple))
1546 : {
1547 : Form_pg_statistic stats;
1548 : double freq_null;
1549 : AttStatsSlot sslot;
1550 :
1551 0 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
1552 0 : freq_null = stats->stanullfrac;
1553 :
1554 0 : if (get_attstatsslot(&sslot, vardata.statsTuple,
1555 : STATISTIC_KIND_MCV, InvalidOid,
1556 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
1557 0 : && sslot.nnumbers > 0)
1558 0 : {
1559 : double freq_true;
1560 : double freq_false;
1561 :
1562 : /*
1563 : * Get first MCV frequency and derive frequency for true.
1564 : */
1565 0 : if (DatumGetBool(sslot.values[0]))
1566 0 : freq_true = sslot.numbers[0];
1567 : else
1568 0 : freq_true = 1.0 - sslot.numbers[0] - freq_null;
1569 :
1570 : /*
1571 : * Next derive frequency for false. Then use these as appropriate
1572 : * to derive frequency for each case.
1573 : */
1574 0 : freq_false = 1.0 - freq_true - freq_null;
1575 :
1576 0 : switch (booltesttype)
1577 : {
1578 0 : case IS_UNKNOWN:
1579 : /* select only NULL values */
1580 0 : selec = freq_null;
1581 0 : break;
1582 0 : case IS_NOT_UNKNOWN:
1583 : /* select non-NULL values */
1584 0 : selec = 1.0 - freq_null;
1585 0 : break;
1586 0 : case IS_TRUE:
1587 : /* select only TRUE values */
1588 0 : selec = freq_true;
1589 0 : break;
1590 0 : case IS_NOT_TRUE:
1591 : /* select non-TRUE values */
1592 0 : selec = 1.0 - freq_true;
1593 0 : break;
1594 0 : case IS_FALSE:
1595 : /* select only FALSE values */
1596 0 : selec = freq_false;
1597 0 : break;
1598 0 : case IS_NOT_FALSE:
1599 : /* select non-FALSE values */
1600 0 : selec = 1.0 - freq_false;
1601 0 : break;
1602 0 : default:
1603 0 : elog(ERROR, "unrecognized booltesttype: %d",
1604 : (int) booltesttype);
1605 : selec = 0.0; /* Keep compiler quiet */
1606 : break;
1607 : }
1608 :
1609 0 : free_attstatsslot(&sslot);
1610 : }
1611 : else
1612 : {
1613 : /*
1614 : * No most-common-value info available. Still have null fraction
1615 : * information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
1616 : * for null fraction and assume a 50-50 split of TRUE and FALSE.
1617 : */
1618 0 : switch (booltesttype)
1619 : {
1620 0 : case IS_UNKNOWN:
1621 : /* select only NULL values */
1622 0 : selec = freq_null;
1623 0 : break;
1624 0 : case IS_NOT_UNKNOWN:
1625 : /* select non-NULL values */
1626 0 : selec = 1.0 - freq_null;
1627 0 : break;
1628 0 : case IS_TRUE:
1629 : case IS_FALSE:
1630 : /* Assume we select half of the non-NULL values */
1631 0 : selec = (1.0 - freq_null) / 2.0;
1632 0 : break;
1633 0 : case IS_NOT_TRUE:
1634 : case IS_NOT_FALSE:
1635 : /* Assume we select NULLs plus half of the non-NULLs */
1636 : /* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
1637 0 : selec = (freq_null + 1.0) / 2.0;
1638 0 : break;
1639 0 : default:
1640 0 : elog(ERROR, "unrecognized booltesttype: %d",
1641 : (int) booltesttype);
1642 : selec = 0.0; /* Keep compiler quiet */
1643 : break;
1644 : }
1645 : }
1646 : }
1647 : else
1648 : {
1649 : /*
1650 : * If we can't get variable statistics for the argument, perhaps
1651 : * clause_selectivity can do something with it. We ignore the
1652 : * possibility of a NULL value when using clause_selectivity, and just
1653 : * assume the value is either TRUE or FALSE.
1654 : */
1655 132 : switch (booltesttype)
1656 : {
1657 12 : case IS_UNKNOWN:
1658 12 : selec = DEFAULT_UNK_SEL;
1659 12 : break;
1660 12 : case IS_NOT_UNKNOWN:
1661 12 : selec = DEFAULT_NOT_UNK_SEL;
1662 12 : break;
1663 32 : case IS_TRUE:
1664 : case IS_NOT_FALSE:
1665 32 : selec = (double) clause_selectivity(root, arg,
1666 : varRelid,
1667 : jointype, sjinfo);
1668 32 : break;
1669 76 : case IS_FALSE:
1670 : case IS_NOT_TRUE:
1671 76 : selec = 1.0 - (double) clause_selectivity(root, arg,
1672 : varRelid,
1673 : jointype, sjinfo);
1674 76 : break;
1675 0 : default:
1676 0 : elog(ERROR, "unrecognized booltesttype: %d",
1677 : (int) booltesttype);
1678 : selec = 0.0; /* Keep compiler quiet */
1679 : break;
1680 : }
1681 : }
1682 :
1683 132 : ReleaseVariableStats(vardata);
1684 :
1685 : /* result should be in range, but make sure... */
1686 132 : CLAMP_PROBABILITY(selec);
1687 :
1688 132 : return (Selectivity) selec;
1689 : }
1690 :
1691 : /*
1692 : * nulltestsel - Selectivity of NullTest Node.
1693 : */
1694 : Selectivity
1695 17064 : nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
1696 : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1697 : {
1698 : VariableStatData vardata;
1699 : double selec;
1700 :
1701 17064 : examine_variable(root, arg, varRelid, &vardata);
1702 :
1703 17064 : if (HeapTupleIsValid(vardata.statsTuple))
1704 : {
1705 : Form_pg_statistic stats;
1706 : double freq_null;
1707 :
1708 7474 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
1709 7474 : freq_null = stats->stanullfrac;
1710 :
1711 7474 : switch (nulltesttype)
1712 : {
1713 3862 : case IS_NULL:
1714 :
1715 : /*
1716 : * Use freq_null directly.
1717 : */
1718 3862 : selec = freq_null;
1719 3862 : break;
1720 3612 : case IS_NOT_NULL:
1721 :
1722 : /*
1723 : * Select not unknown (not null) values. Calculate from
1724 : * freq_null.
1725 : */
1726 3612 : selec = 1.0 - freq_null;
1727 3612 : break;
1728 0 : default:
1729 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1730 : (int) nulltesttype);
1731 : return (Selectivity) 0; /* keep compiler quiet */
1732 : }
1733 : }
1734 9590 : else if (vardata.var && IsA(vardata.var, Var) &&
1735 6790 : ((Var *) vardata.var)->varattno < 0)
1736 : {
1737 : /*
1738 : * There are no stats for system columns, but we know they are never
1739 : * NULL.
1740 : */
1741 16 : selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
1742 : }
1743 : else
1744 : {
1745 : /*
1746 : * No ANALYZE stats available, so make a guess
1747 : */
1748 9574 : switch (nulltesttype)
1749 : {
1750 1032 : case IS_NULL:
1751 1032 : selec = DEFAULT_UNK_SEL;
1752 1032 : break;
1753 8542 : case IS_NOT_NULL:
1754 8542 : selec = DEFAULT_NOT_UNK_SEL;
1755 8542 : break;
1756 0 : default:
1757 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1758 : (int) nulltesttype);
1759 : return (Selectivity) 0; /* keep compiler quiet */
1760 : }
1761 : }
1762 :
1763 17064 : ReleaseVariableStats(vardata);
1764 :
1765 : /* result should be in range, but make sure... */
1766 17064 : CLAMP_PROBABILITY(selec);
1767 :
1768 17064 : return (Selectivity) selec;
1769 : }
1770 :
1771 : /*
1772 : * strip_array_coercion - strip binary-compatible relabeling from an array expr
1773 : *
1774 : * For array values, the parser normally generates ArrayCoerceExpr conversions,
1775 : * but it seems possible that RelabelType might show up. Also, the planner
1776 : * is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
1777 : * so we need to be ready to deal with more than one level.
1778 : */
1779 : static Node *
1780 73516 : strip_array_coercion(Node *node)
1781 : {
1782 : for (;;)
1783 : {
1784 73516 : if (node && IsA(node, ArrayCoerceExpr))
1785 12 : {
1786 388 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
1787 :
1788 : /*
1789 : * If the per-element expression is just a RelabelType on top of
1790 : * CaseTestExpr, then we know it's a binary-compatible relabeling.
1791 : */
1792 388 : if (IsA(acoerce->elemexpr, RelabelType) &&
1793 12 : IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
1794 12 : node = (Node *) acoerce->arg;
1795 : else
1796 : break;
1797 : }
1798 73128 : else if (node && IsA(node, RelabelType))
1799 : {
1800 : /* We don't really expect this case, but may as well cope */
1801 0 : node = (Node *) ((RelabelType *) node)->arg;
1802 : }
1803 : else
1804 : break;
1805 : }
1806 73504 : return node;
1807 : }
1808 :
1809 : /*
1810 : * scalararraysel - Selectivity of ScalarArrayOpExpr Node.
1811 : */
1812 : Selectivity
1813 14934 : scalararraysel(PlannerInfo *root,
1814 : ScalarArrayOpExpr *clause,
1815 : bool is_join_clause,
1816 : int varRelid,
1817 : JoinType jointype,
1818 : SpecialJoinInfo *sjinfo)
1819 : {
1820 14934 : Oid operator = clause->opno;
1821 14934 : bool useOr = clause->useOr;
1822 14934 : bool isEquality = false;
1823 14934 : bool isInequality = false;
1824 : Node *leftop;
1825 : Node *rightop;
1826 : Oid nominal_element_type;
1827 : Oid nominal_element_collation;
1828 : TypeCacheEntry *typentry;
1829 : RegProcedure oprsel;
1830 : FmgrInfo oprselproc;
1831 : Selectivity s1;
1832 : Selectivity s1disjoint;
1833 :
1834 : /* First, deconstruct the expression */
1835 : Assert(list_length(clause->args) == 2);
1836 14934 : leftop = (Node *) linitial(clause->args);
1837 14934 : rightop = (Node *) lsecond(clause->args);
1838 :
1839 : /* aggressively reduce both sides to constants */
1840 14934 : leftop = estimate_expression_value(root, leftop);
1841 14934 : rightop = estimate_expression_value(root, rightop);
1842 :
1843 : /* get nominal (after relabeling) element type of rightop */
1844 14934 : nominal_element_type = get_base_element_type(exprType(rightop));
1845 14934 : if (!OidIsValid(nominal_element_type))
1846 0 : return (Selectivity) 0.5; /* probably shouldn't happen */
1847 : /* get nominal collation, too, for generating constants */
1848 14934 : nominal_element_collation = exprCollation(rightop);
1849 :
1850 : /* look through any binary-compatible relabeling of rightop */
1851 14934 : rightop = strip_array_coercion(rightop);
1852 :
1853 : /*
1854 : * Detect whether the operator is the default equality or inequality
1855 : * operator of the array element type.
1856 : */
1857 14934 : typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
1858 14934 : if (OidIsValid(typentry->eq_opr))
1859 : {
1860 14934 : if (operator == typentry->eq_opr)
1861 13858 : isEquality = true;
1862 1076 : else if (get_negator(operator) == typentry->eq_opr)
1863 832 : isInequality = true;
1864 : }
1865 :
1866 : /*
1867 : * If it is equality or inequality, we might be able to estimate this as a
1868 : * form of array containment; for instance "const = ANY(column)" can be
1869 : * treated as "ARRAY[const] <@ column". scalararraysel_containment tries
1870 : * that, and returns the selectivity estimate if successful, or -1 if not.
1871 : */
1872 14934 : if ((isEquality || isInequality) && !is_join_clause)
1873 : {
1874 14690 : s1 = scalararraysel_containment(root, leftop, rightop,
1875 : nominal_element_type,
1876 : isEquality, useOr, varRelid);
1877 14690 : if (s1 >= 0.0)
1878 70 : return s1;
1879 : }
1880 :
1881 : /*
1882 : * Look up the underlying operator's selectivity estimator. Punt if it
1883 : * hasn't got one.
1884 : */
1885 14864 : if (is_join_clause)
1886 0 : oprsel = get_oprjoin(operator);
1887 : else
1888 14864 : oprsel = get_oprrest(operator);
1889 14864 : if (!oprsel)
1890 0 : return (Selectivity) 0.5;
1891 14864 : fmgr_info(oprsel, &oprselproc);
1892 :
1893 : /*
1894 : * In the array-containment check above, we must only believe that an
1895 : * operator is equality or inequality if it is the default btree equality
1896 : * operator (or its negator) for the element type, since those are the
1897 : * operators that array containment will use. But in what follows, we can
1898 : * be a little laxer, and also believe that any operators using eqsel() or
1899 : * neqsel() as selectivity estimator act like equality or inequality.
1900 : */
1901 14864 : if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
1902 13880 : isEquality = true;
1903 984 : else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
1904 776 : isInequality = true;
1905 :
1906 : /*
1907 : * We consider three cases:
1908 : *
1909 : * 1. rightop is an Array constant: deconstruct the array, apply the
1910 : * operator's selectivity function for each array element, and merge the
1911 : * results in the same way that clausesel.c does for AND/OR combinations.
1912 : *
1913 : * 2. rightop is an ARRAY[] construct: apply the operator's selectivity
1914 : * function for each element of the ARRAY[] construct, and merge.
1915 : *
1916 : * 3. otherwise, make a guess ...
1917 : */
1918 14864 : if (rightop && IsA(rightop, Const))
1919 7800 : {
1920 7812 : Datum arraydatum = ((Const *) rightop)->constvalue;
1921 7812 : bool arrayisnull = ((Const *) rightop)->constisnull;
1922 : ArrayType *arrayval;
1923 : int16 elmlen;
1924 : bool elmbyval;
1925 : char elmalign;
1926 : int num_elems;
1927 : Datum *elem_values;
1928 : bool *elem_nulls;
1929 : int i;
1930 :
1931 7812 : if (arrayisnull) /* qual can't succeed if null array */
1932 12 : return (Selectivity) 0.0;
1933 7800 : arrayval = DatumGetArrayTypeP(arraydatum);
1934 7800 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
1935 : &elmlen, &elmbyval, &elmalign);
1936 7800 : deconstruct_array(arrayval,
1937 : ARR_ELEMTYPE(arrayval),
1938 : elmlen, elmbyval, elmalign,
1939 : &elem_values, &elem_nulls, &num_elems);
1940 :
1941 : /*
1942 : * For generic operators, we assume the probability of success is
1943 : * independent for each array element. But for "= ANY" or "<> ALL",
1944 : * if the array elements are distinct (which'd typically be the case)
1945 : * then the probabilities are disjoint, and we should just sum them.
1946 : *
1947 : * If we were being really tense we would try to confirm that the
1948 : * elements are all distinct, but that would be expensive and it
1949 : * doesn't seem to be worth the cycles; it would amount to penalizing
1950 : * well-written queries in favor of poorly-written ones. However, we
1951 : * do protect ourselves a little bit by checking whether the
1952 : * disjointness assumption leads to an impossible (out of range)
1953 : * probability; if so, we fall back to the normal calculation.
1954 : */
1955 7800 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
1956 :
1957 32808 : for (i = 0; i < num_elems; i++)
1958 : {
1959 : List *args;
1960 : Selectivity s2;
1961 :
1962 25008 : args = list_make2(leftop,
1963 : makeConst(nominal_element_type,
1964 : -1,
1965 : nominal_element_collation,
1966 : elmlen,
1967 : elem_values[i],
1968 : elem_nulls[i],
1969 : elmbyval));
1970 25008 : if (is_join_clause)
1971 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
1972 : clause->inputcollid,
1973 : PointerGetDatum(root),
1974 : ObjectIdGetDatum(operator),
1975 : PointerGetDatum(args),
1976 : Int16GetDatum(jointype),
1977 : PointerGetDatum(sjinfo)));
1978 : else
1979 25008 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
1980 : clause->inputcollid,
1981 : PointerGetDatum(root),
1982 : ObjectIdGetDatum(operator),
1983 : PointerGetDatum(args),
1984 : Int32GetDatum(varRelid)));
1985 :
1986 25008 : if (useOr)
1987 : {
1988 22622 : s1 = s1 + s2 - s1 * s2;
1989 22622 : if (isEquality)
1990 22226 : s1disjoint += s2;
1991 : }
1992 : else
1993 : {
1994 2386 : s1 = s1 * s2;
1995 2386 : if (isInequality)
1996 2242 : s1disjoint += s2 - 1.0;
1997 : }
1998 : }
1999 :
2000 : /* accept disjoint-probability estimate if in range */
2001 7800 : if ((useOr ? isEquality : isInequality) &&
2002 7524 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2003 7504 : s1 = s1disjoint;
2004 : }
2005 7052 : else if (rightop && IsA(rightop, ArrayExpr) &&
2006 70 : !((ArrayExpr *) rightop)->multidims)
2007 70 : {
2008 70 : ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
2009 : int16 elmlen;
2010 : bool elmbyval;
2011 : ListCell *l;
2012 :
2013 70 : get_typlenbyval(arrayexpr->element_typeid,
2014 : &elmlen, &elmbyval);
2015 :
2016 : /*
2017 : * We use the assumption of disjoint probabilities here too, although
2018 : * the odds of equal array elements are rather higher if the elements
2019 : * are not all constants (which they won't be, else constant folding
2020 : * would have reduced the ArrayExpr to a Const). In this path it's
2021 : * critical to have the sanity check on the s1disjoint estimate.
2022 : */
2023 70 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
2024 :
2025 248 : foreach(l, arrayexpr->elements)
2026 : {
2027 178 : Node *elem = (Node *) lfirst(l);
2028 : List *args;
2029 : Selectivity s2;
2030 :
2031 : /*
2032 : * Theoretically, if elem isn't of nominal_element_type we should
2033 : * insert a RelabelType, but it seems unlikely that any operator
2034 : * estimation function would really care ...
2035 : */
2036 178 : args = list_make2(leftop, elem);
2037 178 : if (is_join_clause)
2038 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2039 : clause->inputcollid,
2040 : PointerGetDatum(root),
2041 : ObjectIdGetDatum(operator),
2042 : PointerGetDatum(args),
2043 : Int16GetDatum(jointype),
2044 : PointerGetDatum(sjinfo)));
2045 : else
2046 178 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2047 : clause->inputcollid,
2048 : PointerGetDatum(root),
2049 : ObjectIdGetDatum(operator),
2050 : PointerGetDatum(args),
2051 : Int32GetDatum(varRelid)));
2052 :
2053 178 : if (useOr)
2054 : {
2055 178 : s1 = s1 + s2 - s1 * s2;
2056 178 : if (isEquality)
2057 178 : s1disjoint += s2;
2058 : }
2059 : else
2060 : {
2061 0 : s1 = s1 * s2;
2062 0 : if (isInequality)
2063 0 : s1disjoint += s2 - 1.0;
2064 : }
2065 : }
2066 :
2067 : /* accept disjoint-probability estimate if in range */
2068 70 : if ((useOr ? isEquality : isInequality) &&
2069 70 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2070 70 : s1 = s1disjoint;
2071 : }
2072 : else
2073 : {
2074 : CaseTestExpr *dummyexpr;
2075 : List *args;
2076 : Selectivity s2;
2077 : int i;
2078 :
2079 : /*
2080 : * We need a dummy rightop to pass to the operator selectivity
2081 : * routine. It can be pretty much anything that doesn't look like a
2082 : * constant; CaseTestExpr is a convenient choice.
2083 : */
2084 6982 : dummyexpr = makeNode(CaseTestExpr);
2085 6982 : dummyexpr->typeId = nominal_element_type;
2086 6982 : dummyexpr->typeMod = -1;
2087 6982 : dummyexpr->collation = clause->inputcollid;
2088 6982 : args = list_make2(leftop, dummyexpr);
2089 6982 : if (is_join_clause)
2090 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2091 : clause->inputcollid,
2092 : PointerGetDatum(root),
2093 : ObjectIdGetDatum(operator),
2094 : PointerGetDatum(args),
2095 : Int16GetDatum(jointype),
2096 : PointerGetDatum(sjinfo)));
2097 : else
2098 6982 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2099 : clause->inputcollid,
2100 : PointerGetDatum(root),
2101 : ObjectIdGetDatum(operator),
2102 : PointerGetDatum(args),
2103 : Int32GetDatum(varRelid)));
2104 6982 : s1 = useOr ? 0.0 : 1.0;
2105 :
2106 : /*
2107 : * Arbitrarily assume 10 elements in the eventual array value (see
2108 : * also estimate_array_length). We don't risk an assumption of
2109 : * disjoint probabilities here.
2110 : */
2111 76802 : for (i = 0; i < 10; i++)
2112 : {
2113 69820 : if (useOr)
2114 69820 : s1 = s1 + s2 - s1 * s2;
2115 : else
2116 0 : s1 = s1 * s2;
2117 : }
2118 : }
2119 :
2120 : /* result should be in range, but make sure... */
2121 14852 : CLAMP_PROBABILITY(s1);
2122 :
2123 14852 : return s1;
2124 : }
2125 :
2126 : /*
2127 : * Estimate number of elements in the array yielded by an expression.
2128 : *
2129 : * It's important that this agree with scalararraysel.
2130 : */
2131 : int
2132 58570 : estimate_array_length(Node *arrayexpr)
2133 : {
2134 : /* look through any binary-compatible relabeling of arrayexpr */
2135 58570 : arrayexpr = strip_array_coercion(arrayexpr);
2136 :
2137 58570 : if (arrayexpr && IsA(arrayexpr, Const))
2138 : {
2139 13568 : Datum arraydatum = ((Const *) arrayexpr)->constvalue;
2140 13568 : bool arrayisnull = ((Const *) arrayexpr)->constisnull;
2141 : ArrayType *arrayval;
2142 :
2143 13568 : if (arrayisnull)
2144 12 : return 0;
2145 13556 : arrayval = DatumGetArrayTypeP(arraydatum);
2146 13556 : return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
2147 : }
2148 45002 : else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
2149 112 : !((ArrayExpr *) arrayexpr)->multidims)
2150 : {
2151 112 : return list_length(((ArrayExpr *) arrayexpr)->elements);
2152 : }
2153 : else
2154 : {
2155 : /* default guess --- see also scalararraysel */
2156 44890 : return 10;
2157 : }
2158 : }
2159 :
2160 : /*
2161 : * rowcomparesel - Selectivity of RowCompareExpr Node.
2162 : *
2163 : * We estimate RowCompare selectivity by considering just the first (high
2164 : * order) columns, which makes it equivalent to an ordinary OpExpr. While
2165 : * this estimate could be refined by considering additional columns, it
2166 : * seems unlikely that we could do a lot better without multi-column
2167 : * statistics.
2168 : */
2169 : Selectivity
2170 104 : rowcomparesel(PlannerInfo *root,
2171 : RowCompareExpr *clause,
2172 : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
2173 : {
2174 : Selectivity s1;
2175 104 : Oid opno = linitial_oid(clause->opnos);
2176 104 : Oid inputcollid = linitial_oid(clause->inputcollids);
2177 : List *opargs;
2178 : bool is_join_clause;
2179 :
2180 : /* Build equivalent arg list for single operator */
2181 104 : opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
2182 :
2183 : /*
2184 : * Decide if it's a join clause. This should match clausesel.c's
2185 : * treat_as_join_clause(), except that we intentionally consider only the
2186 : * leading columns and not the rest of the clause.
2187 : */
2188 104 : if (varRelid != 0)
2189 : {
2190 : /*
2191 : * Caller is forcing restriction mode (eg, because we are examining an
2192 : * inner indexscan qual).
2193 : */
2194 36 : is_join_clause = false;
2195 : }
2196 68 : else if (sjinfo == NULL)
2197 : {
2198 : /*
2199 : * It must be a restriction clause, since it's being evaluated at a
2200 : * scan node.
2201 : */
2202 60 : is_join_clause = false;
2203 : }
2204 : else
2205 : {
2206 : /*
2207 : * Otherwise, it's a join if there's more than one relation used.
2208 : */
2209 8 : is_join_clause = (NumRelids(root, (Node *) opargs) > 1);
2210 : }
2211 :
2212 104 : if (is_join_clause)
2213 : {
2214 : /* Estimate selectivity for a join clause. */
2215 8 : s1 = join_selectivity(root, opno,
2216 : opargs,
2217 : inputcollid,
2218 : jointype,
2219 : sjinfo);
2220 : }
2221 : else
2222 : {
2223 : /* Estimate selectivity for a restriction clause. */
2224 96 : s1 = restriction_selectivity(root, opno,
2225 : opargs,
2226 : inputcollid,
2227 : varRelid);
2228 : }
2229 :
2230 104 : return s1;
2231 : }
2232 :
2233 : /*
2234 : * eqjoinsel - Join selectivity of "="
2235 : */
2236 : Datum
2237 125196 : eqjoinsel(PG_FUNCTION_ARGS)
2238 : {
2239 125196 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
2240 125196 : Oid operator = PG_GETARG_OID(1);
2241 125196 : List *args = (List *) PG_GETARG_POINTER(2);
2242 :
2243 : #ifdef NOT_USED
2244 : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
2245 : #endif
2246 125196 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
2247 125196 : Oid collation = PG_GET_COLLATION();
2248 : double selec;
2249 : double selec_inner;
2250 : VariableStatData vardata1;
2251 : VariableStatData vardata2;
2252 : double nd1;
2253 : double nd2;
2254 : bool isdefault1;
2255 : bool isdefault2;
2256 : Oid opfuncoid;
2257 : AttStatsSlot sslot1;
2258 : AttStatsSlot sslot2;
2259 125196 : Form_pg_statistic stats1 = NULL;
2260 125196 : Form_pg_statistic stats2 = NULL;
2261 125196 : bool have_mcvs1 = false;
2262 125196 : bool have_mcvs2 = false;
2263 : bool join_is_reversed;
2264 : RelOptInfo *inner_rel;
2265 :
2266 125196 : get_join_variables(root, args, sjinfo,
2267 : &vardata1, &vardata2, &join_is_reversed);
2268 :
2269 125196 : nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
2270 125196 : nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
2271 :
2272 125196 : opfuncoid = get_opcode(operator);
2273 :
2274 125196 : memset(&sslot1, 0, sizeof(sslot1));
2275 125196 : memset(&sslot2, 0, sizeof(sslot2));
2276 :
2277 125196 : if (HeapTupleIsValid(vardata1.statsTuple))
2278 : {
2279 : /* note we allow use of nullfrac regardless of security check */
2280 87296 : stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
2281 87296 : if (statistic_proc_security_check(&vardata1, opfuncoid))
2282 87296 : have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
2283 : STATISTIC_KIND_MCV, InvalidOid,
2284 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2285 : }
2286 :
2287 125196 : if (HeapTupleIsValid(vardata2.statsTuple))
2288 : {
2289 : /* note we allow use of nullfrac regardless of security check */
2290 88830 : stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
2291 88830 : if (statistic_proc_security_check(&vardata2, opfuncoid))
2292 88830 : have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
2293 : STATISTIC_KIND_MCV, InvalidOid,
2294 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2295 : }
2296 :
2297 : /* We need to compute the inner-join selectivity in all cases */
2298 125196 : selec_inner = eqjoinsel_inner(opfuncoid, collation,
2299 : &vardata1, &vardata2,
2300 : nd1, nd2,
2301 : isdefault1, isdefault2,
2302 : &sslot1, &sslot2,
2303 : stats1, stats2,
2304 : have_mcvs1, have_mcvs2);
2305 :
2306 125196 : switch (sjinfo->jointype)
2307 : {
2308 114430 : case JOIN_INNER:
2309 : case JOIN_LEFT:
2310 : case JOIN_FULL:
2311 114430 : selec = selec_inner;
2312 114430 : break;
2313 10766 : case JOIN_SEMI:
2314 : case JOIN_ANTI:
2315 :
2316 : /*
2317 : * Look up the join's inner relation. min_righthand is sufficient
2318 : * information because neither SEMI nor ANTI joins permit any
2319 : * reassociation into or out of their RHS, so the righthand will
2320 : * always be exactly that set of rels.
2321 : */
2322 10766 : inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
2323 :
2324 10766 : if (!join_is_reversed)
2325 8002 : selec = eqjoinsel_semi(opfuncoid, collation,
2326 : &vardata1, &vardata2,
2327 : nd1, nd2,
2328 : isdefault1, isdefault2,
2329 : &sslot1, &sslot2,
2330 : stats1, stats2,
2331 : have_mcvs1, have_mcvs2,
2332 : inner_rel);
2333 : else
2334 : {
2335 2764 : Oid commop = get_commutator(operator);
2336 2764 : Oid commopfuncoid = OidIsValid(commop) ? get_opcode(commop) : InvalidOid;
2337 :
2338 2764 : selec = eqjoinsel_semi(commopfuncoid, collation,
2339 : &vardata2, &vardata1,
2340 : nd2, nd1,
2341 : isdefault2, isdefault1,
2342 : &sslot2, &sslot1,
2343 : stats2, stats1,
2344 : have_mcvs2, have_mcvs1,
2345 : inner_rel);
2346 : }
2347 :
2348 : /*
2349 : * We should never estimate the output of a semijoin to be more
2350 : * rows than we estimate for an inner join with the same input
2351 : * rels and join condition; it's obviously impossible for that to
2352 : * happen. The former estimate is N1 * Ssemi while the latter is
2353 : * N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
2354 : * this is worthwhile because of the shakier estimation rules we
2355 : * use in eqjoinsel_semi, particularly in cases where it has to
2356 : * punt entirely.
2357 : */
2358 10766 : selec = Min(selec, inner_rel->rows * selec_inner);
2359 10766 : break;
2360 0 : default:
2361 : /* other values not expected here */
2362 0 : elog(ERROR, "unrecognized join type: %d",
2363 : (int) sjinfo->jointype);
2364 : selec = 0; /* keep compiler quiet */
2365 : break;
2366 : }
2367 :
2368 125196 : free_attstatsslot(&sslot1);
2369 125196 : free_attstatsslot(&sslot2);
2370 :
2371 125196 : ReleaseVariableStats(vardata1);
2372 125196 : ReleaseVariableStats(vardata2);
2373 :
2374 125196 : CLAMP_PROBABILITY(selec);
2375 :
2376 125196 : PG_RETURN_FLOAT8((float8) selec);
2377 : }
2378 :
2379 : /*
2380 : * eqjoinsel_inner --- eqjoinsel for normal inner join
2381 : *
2382 : * We also use this for LEFT/FULL outer joins; it's not presently clear
2383 : * that it's worth trying to distinguish them here.
2384 : */
2385 : static double
2386 125196 : eqjoinsel_inner(Oid opfuncoid, Oid collation,
2387 : VariableStatData *vardata1, VariableStatData *vardata2,
2388 : double nd1, double nd2,
2389 : bool isdefault1, bool isdefault2,
2390 : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2391 : Form_pg_statistic stats1, Form_pg_statistic stats2,
2392 : bool have_mcvs1, bool have_mcvs2)
2393 : {
2394 : double selec;
2395 :
2396 125196 : if (have_mcvs1 && have_mcvs2)
2397 10656 : {
2398 : /*
2399 : * We have most-common-value lists for both relations. Run through
2400 : * the lists to see which MCVs actually join to each other with the
2401 : * given operator. This allows us to determine the exact join
2402 : * selectivity for the portion of the relations represented by the MCV
2403 : * lists. We still have to estimate for the remaining population, but
2404 : * in a skewed distribution this gives us a big leg up in accuracy.
2405 : * For motivation see the analysis in Y. Ioannidis and S.
2406 : * Christodoulakis, "On the propagation of errors in the size of join
2407 : * results", Technical Report 1018, Computer Science Dept., University
2408 : * of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
2409 : */
2410 10656 : LOCAL_FCINFO(fcinfo, 2);
2411 : FmgrInfo eqproc;
2412 : bool *hasmatch1;
2413 : bool *hasmatch2;
2414 10656 : double nullfrac1 = stats1->stanullfrac;
2415 10656 : double nullfrac2 = stats2->stanullfrac;
2416 : double matchprodfreq,
2417 : matchfreq1,
2418 : matchfreq2,
2419 : unmatchfreq1,
2420 : unmatchfreq2,
2421 : otherfreq1,
2422 : otherfreq2,
2423 : totalsel1,
2424 : totalsel2;
2425 : int i,
2426 : nmatches;
2427 :
2428 10656 : fmgr_info(opfuncoid, &eqproc);
2429 :
2430 : /*
2431 : * Save a few cycles by setting up the fcinfo struct just once. Using
2432 : * FunctionCallInvoke directly also avoids failure if the eqproc
2433 : * returns NULL, though really equality functions should never do
2434 : * that.
2435 : */
2436 10656 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
2437 : NULL, NULL);
2438 10656 : fcinfo->args[0].isnull = false;
2439 10656 : fcinfo->args[1].isnull = false;
2440 :
2441 10656 : hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
2442 10656 : hasmatch2 = (bool *) palloc0(sslot2->nvalues * sizeof(bool));
2443 :
2444 : /*
2445 : * Note we assume that each MCV will match at most one member of the
2446 : * other MCV list. If the operator isn't really equality, there could
2447 : * be multiple matches --- but we don't look for them, both for speed
2448 : * and because the math wouldn't add up...
2449 : */
2450 10656 : matchprodfreq = 0.0;
2451 10656 : nmatches = 0;
2452 258576 : for (i = 0; i < sslot1->nvalues; i++)
2453 : {
2454 : int j;
2455 :
2456 247920 : fcinfo->args[0].value = sslot1->values[i];
2457 :
2458 8714624 : for (j = 0; j < sslot2->nvalues; j++)
2459 : {
2460 : Datum fresult;
2461 :
2462 8529048 : if (hasmatch2[j])
2463 1971344 : continue;
2464 6557704 : fcinfo->args[1].value = sslot2->values[j];
2465 6557704 : fcinfo->isnull = false;
2466 6557704 : fresult = FunctionCallInvoke(fcinfo);
2467 6557704 : if (!fcinfo->isnull && DatumGetBool(fresult))
2468 : {
2469 62344 : hasmatch1[i] = hasmatch2[j] = true;
2470 62344 : matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
2471 62344 : nmatches++;
2472 62344 : break;
2473 : }
2474 : }
2475 : }
2476 10656 : CLAMP_PROBABILITY(matchprodfreq);
2477 : /* Sum up frequencies of matched and unmatched MCVs */
2478 10656 : matchfreq1 = unmatchfreq1 = 0.0;
2479 258576 : for (i = 0; i < sslot1->nvalues; i++)
2480 : {
2481 247920 : if (hasmatch1[i])
2482 62344 : matchfreq1 += sslot1->numbers[i];
2483 : else
2484 185576 : unmatchfreq1 += sslot1->numbers[i];
2485 : }
2486 10656 : CLAMP_PROBABILITY(matchfreq1);
2487 10656 : CLAMP_PROBABILITY(unmatchfreq1);
2488 10656 : matchfreq2 = unmatchfreq2 = 0.0;
2489 424332 : for (i = 0; i < sslot2->nvalues; i++)
2490 : {
2491 413676 : if (hasmatch2[i])
2492 62344 : matchfreq2 += sslot2->numbers[i];
2493 : else
2494 351332 : unmatchfreq2 += sslot2->numbers[i];
2495 : }
2496 10656 : CLAMP_PROBABILITY(matchfreq2);
2497 10656 : CLAMP_PROBABILITY(unmatchfreq2);
2498 10656 : pfree(hasmatch1);
2499 10656 : pfree(hasmatch2);
2500 :
2501 : /*
2502 : * Compute total frequency of non-null values that are not in the MCV
2503 : * lists.
2504 : */
2505 10656 : otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
2506 10656 : otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
2507 10656 : CLAMP_PROBABILITY(otherfreq1);
2508 10656 : CLAMP_PROBABILITY(otherfreq2);
2509 :
2510 : /*
2511 : * We can estimate the total selectivity from the point of view of
2512 : * relation 1 as: the known selectivity for matched MCVs, plus
2513 : * unmatched MCVs that are assumed to match against random members of
2514 : * relation 2's non-MCV population, plus non-MCV values that are
2515 : * assumed to match against random members of relation 2's unmatched
2516 : * MCVs plus non-MCV values.
2517 : */
2518 10656 : totalsel1 = matchprodfreq;
2519 10656 : if (nd2 > sslot2->nvalues)
2520 7052 : totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
2521 10656 : if (nd2 > nmatches)
2522 18128 : totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
2523 9064 : (nd2 - nmatches);
2524 : /* Same estimate from the point of view of relation 2. */
2525 10656 : totalsel2 = matchprodfreq;
2526 10656 : if (nd1 > sslot1->nvalues)
2527 6836 : totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
2528 10656 : if (nd1 > nmatches)
2529 16808 : totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
2530 8404 : (nd1 - nmatches);
2531 :
2532 : /*
2533 : * Use the smaller of the two estimates. This can be justified in
2534 : * essentially the same terms as given below for the no-stats case: to
2535 : * a first approximation, we are estimating from the point of view of
2536 : * the relation with smaller nd.
2537 : */
2538 10656 : selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
2539 : }
2540 : else
2541 : {
2542 : /*
2543 : * We do not have MCV lists for both sides. Estimate the join
2544 : * selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
2545 : * is plausible if we assume that the join operator is strict and the
2546 : * non-null values are about equally distributed: a given non-null
2547 : * tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
2548 : * of rel2, so total join rows are at most
2549 : * N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
2550 : * not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
2551 : * is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
2552 : * with MIN() is an upper bound. Using the MIN() means we estimate
2553 : * from the point of view of the relation with smaller nd (since the
2554 : * larger nd is determining the MIN). It is reasonable to assume that
2555 : * most tuples in this rel will have join partners, so the bound is
2556 : * probably reasonably tight and should be taken as-is.
2557 : *
2558 : * XXX Can we be smarter if we have an MCV list for just one side? It
2559 : * seems that if we assume equal distribution for the other side, we
2560 : * end up with the same answer anyway.
2561 : */
2562 114540 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2563 114540 : double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
2564 :
2565 114540 : selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
2566 114540 : if (nd1 > nd2)
2567 41094 : selec /= nd1;
2568 : else
2569 73446 : selec /= nd2;
2570 : }
2571 :
2572 125196 : return selec;
2573 : }
2574 :
2575 : /*
2576 : * eqjoinsel_semi --- eqjoinsel for semi join
2577 : *
2578 : * (Also used for anti join, which we are supposed to estimate the same way.)
2579 : * Caller has ensured that vardata1 is the LHS variable.
2580 : * Unlike eqjoinsel_inner, we have to cope with opfuncoid being InvalidOid.
2581 : */
2582 : static double
2583 10766 : eqjoinsel_semi(Oid opfuncoid, Oid collation,
2584 : VariableStatData *vardata1, VariableStatData *vardata2,
2585 : double nd1, double nd2,
2586 : bool isdefault1, bool isdefault2,
2587 : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2588 : Form_pg_statistic stats1, Form_pg_statistic stats2,
2589 : bool have_mcvs1, bool have_mcvs2,
2590 : RelOptInfo *inner_rel)
2591 : {
2592 : double selec;
2593 :
2594 : /*
2595 : * We clamp nd2 to be not more than what we estimate the inner relation's
2596 : * size to be. This is intuitively somewhat reasonable since obviously
2597 : * there can't be more than that many distinct values coming from the
2598 : * inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
2599 : * likewise) is that this is the only pathway by which restriction clauses
2600 : * applied to the inner rel will affect the join result size estimate,
2601 : * since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
2602 : * only the outer rel's size. If we clamped nd1 we'd be double-counting
2603 : * the selectivity of outer-rel restrictions.
2604 : *
2605 : * We can apply this clamping both with respect to the base relation from
2606 : * which the join variable comes (if there is just one), and to the
2607 : * immediate inner input relation of the current join.
2608 : *
2609 : * If we clamp, we can treat nd2 as being a non-default estimate; it's not
2610 : * great, maybe, but it didn't come out of nowhere either. This is most
2611 : * helpful when the inner relation is empty and consequently has no stats.
2612 : */
2613 10766 : if (vardata2->rel)
2614 : {
2615 10766 : if (nd2 >= vardata2->rel->rows)
2616 : {
2617 9890 : nd2 = vardata2->rel->rows;
2618 9890 : isdefault2 = false;
2619 : }
2620 : }
2621 10766 : if (nd2 >= inner_rel->rows)
2622 : {
2623 9862 : nd2 = inner_rel->rows;
2624 9862 : isdefault2 = false;
2625 : }
2626 :
2627 10766 : if (have_mcvs1 && have_mcvs2 && OidIsValid(opfuncoid))
2628 304 : {
2629 : /*
2630 : * We have most-common-value lists for both relations. Run through
2631 : * the lists to see which MCVs actually join to each other with the
2632 : * given operator. This allows us to determine the exact join
2633 : * selectivity for the portion of the relations represented by the MCV
2634 : * lists. We still have to estimate for the remaining population, but
2635 : * in a skewed distribution this gives us a big leg up in accuracy.
2636 : */
2637 304 : LOCAL_FCINFO(fcinfo, 2);
2638 : FmgrInfo eqproc;
2639 : bool *hasmatch1;
2640 : bool *hasmatch2;
2641 304 : double nullfrac1 = stats1->stanullfrac;
2642 : double matchfreq1,
2643 : uncertainfrac,
2644 : uncertain;
2645 : int i,
2646 : nmatches,
2647 : clamped_nvalues2;
2648 :
2649 : /*
2650 : * The clamping above could have resulted in nd2 being less than
2651 : * sslot2->nvalues; in which case, we assume that precisely the nd2
2652 : * most common values in the relation will appear in the join input,
2653 : * and so compare to only the first nd2 members of the MCV list. Of
2654 : * course this is frequently wrong, but it's the best bet we can make.
2655 : */
2656 304 : clamped_nvalues2 = Min(sslot2->nvalues, nd2);
2657 :
2658 304 : fmgr_info(opfuncoid, &eqproc);
2659 :
2660 : /*
2661 : * Save a few cycles by setting up the fcinfo struct just once. Using
2662 : * FunctionCallInvoke directly also avoids failure if the eqproc
2663 : * returns NULL, though really equality functions should never do
2664 : * that.
2665 : */
2666 304 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
2667 : NULL, NULL);
2668 304 : fcinfo->args[0].isnull = false;
2669 304 : fcinfo->args[1].isnull = false;
2670 :
2671 304 : hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
2672 304 : hasmatch2 = (bool *) palloc0(clamped_nvalues2 * sizeof(bool));
2673 :
2674 : /*
2675 : * Note we assume that each MCV will match at most one member of the
2676 : * other MCV list. If the operator isn't really equality, there could
2677 : * be multiple matches --- but we don't look for them, both for speed
2678 : * and because the math wouldn't add up...
2679 : */
2680 304 : nmatches = 0;
2681 4916 : for (i = 0; i < sslot1->nvalues; i++)
2682 : {
2683 : int j;
2684 :
2685 4612 : fcinfo->args[0].value = sslot1->values[i];
2686 :
2687 136888 : for (j = 0; j < clamped_nvalues2; j++)
2688 : {
2689 : Datum fresult;
2690 :
2691 136288 : if (hasmatch2[j])
2692 123564 : continue;
2693 12724 : fcinfo->args[1].value = sslot2->values[j];
2694 12724 : fcinfo->isnull = false;
2695 12724 : fresult = FunctionCallInvoke(fcinfo);
2696 12724 : if (!fcinfo->isnull && DatumGetBool(fresult))
2697 : {
2698 4012 : hasmatch1[i] = hasmatch2[j] = true;
2699 4012 : nmatches++;
2700 4012 : break;
2701 : }
2702 : }
2703 : }
2704 : /* Sum up frequencies of matched MCVs */
2705 304 : matchfreq1 = 0.0;
2706 4916 : for (i = 0; i < sslot1->nvalues; i++)
2707 : {
2708 4612 : if (hasmatch1[i])
2709 4012 : matchfreq1 += sslot1->numbers[i];
2710 : }
2711 304 : CLAMP_PROBABILITY(matchfreq1);
2712 304 : pfree(hasmatch1);
2713 304 : pfree(hasmatch2);
2714 :
2715 : /*
2716 : * Now we need to estimate the fraction of relation 1 that has at
2717 : * least one join partner. We know for certain that the matched MCVs
2718 : * do, so that gives us a lower bound, but we're really in the dark
2719 : * about everything else. Our crude approach is: if nd1 <= nd2 then
2720 : * assume all non-null rel1 rows have join partners, else assume for
2721 : * the uncertain rows that a fraction nd2/nd1 have join partners. We
2722 : * can discount the known-matched MCVs from the distinct-values counts
2723 : * before doing the division.
2724 : *
2725 : * Crude as the above is, it's completely useless if we don't have
2726 : * reliable ndistinct values for both sides. Hence, if either nd1 or
2727 : * nd2 is default, punt and assume half of the uncertain rows have
2728 : * join partners.
2729 : */
2730 304 : if (!isdefault1 && !isdefault2)
2731 : {
2732 304 : nd1 -= nmatches;
2733 304 : nd2 -= nmatches;
2734 608 : if (nd1 <= nd2 || nd2 < 0)
2735 288 : uncertainfrac = 1.0;
2736 : else
2737 16 : uncertainfrac = nd2 / nd1;
2738 : }
2739 : else
2740 0 : uncertainfrac = 0.5;
2741 304 : uncertain = 1.0 - matchfreq1 - nullfrac1;
2742 304 : CLAMP_PROBABILITY(uncertain);
2743 304 : selec = matchfreq1 + uncertainfrac * uncertain;
2744 : }
2745 : else
2746 : {
2747 : /*
2748 : * Without MCV lists for both sides, we can only use the heuristic
2749 : * about nd1 vs nd2.
2750 : */
2751 10462 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2752 :
2753 10462 : if (!isdefault1 && !isdefault2)
2754 : {
2755 18692 : if (nd1 <= nd2 || nd2 < 0)
2756 7556 : selec = 1.0 - nullfrac1;
2757 : else
2758 1790 : selec = (nd2 / nd1) * (1.0 - nullfrac1);
2759 : }
2760 : else
2761 1116 : selec = 0.5 * (1.0 - nullfrac1);
2762 : }
2763 :
2764 10766 : return selec;
2765 : }
2766 :
2767 : /*
2768 : * neqjoinsel - Join selectivity of "!="
2769 : */
2770 : Datum
2771 1654 : neqjoinsel(PG_FUNCTION_ARGS)
2772 : {
2773 1654 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
2774 1654 : Oid operator = PG_GETARG_OID(1);
2775 1654 : List *args = (List *) PG_GETARG_POINTER(2);
2776 1654 : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
2777 1654 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
2778 1654 : Oid collation = PG_GET_COLLATION();
2779 : float8 result;
2780 :
2781 1654 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
2782 602 : {
2783 : /*
2784 : * For semi-joins, if there is more than one distinct value in the RHS
2785 : * relation then every non-null LHS row must find a row to join since
2786 : * it can only be equal to one of them. We'll assume that there is
2787 : * always more than one distinct RHS value for the sake of stability,
2788 : * though in theory we could have special cases for empty RHS
2789 : * (selectivity = 0) and single-distinct-value RHS (selectivity =
2790 : * fraction of LHS that has the same value as the single RHS value).
2791 : *
2792 : * For anti-joins, if we use the same assumption that there is more
2793 : * than one distinct key in the RHS relation, then every non-null LHS
2794 : * row must be suppressed by the anti-join.
2795 : *
2796 : * So either way, the selectivity estimate should be 1 - nullfrac.
2797 : */
2798 : VariableStatData leftvar;
2799 : VariableStatData rightvar;
2800 : bool reversed;
2801 : HeapTuple statsTuple;
2802 : double nullfrac;
2803 :
2804 602 : get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
2805 602 : statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
2806 602 : if (HeapTupleIsValid(statsTuple))
2807 508 : nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
2808 : else
2809 94 : nullfrac = 0.0;
2810 602 : ReleaseVariableStats(leftvar);
2811 602 : ReleaseVariableStats(rightvar);
2812 :
2813 602 : result = 1.0 - nullfrac;
2814 : }
2815 : else
2816 : {
2817 : /*
2818 : * We want 1 - eqjoinsel() where the equality operator is the one
2819 : * associated with this != operator, that is, its negator.
2820 : */
2821 1052 : Oid eqop = get_negator(operator);
2822 :
2823 1052 : if (eqop)
2824 : {
2825 : result =
2826 1052 : DatumGetFloat8(DirectFunctionCall5Coll(eqjoinsel,
2827 : collation,
2828 : PointerGetDatum(root),
2829 : ObjectIdGetDatum(eqop),
2830 : PointerGetDatum(args),
2831 : Int16GetDatum(jointype),
2832 : PointerGetDatum(sjinfo)));
2833 : }
2834 : else
2835 : {
2836 : /* Use default selectivity (should we raise an error instead?) */
2837 0 : result = DEFAULT_EQ_SEL;
2838 : }
2839 1052 : result = 1.0 - result;
2840 : }
2841 :
2842 1654 : PG_RETURN_FLOAT8(result);
2843 : }
2844 :
2845 : /*
2846 : * scalarltjoinsel - Join selectivity of "<" for scalars
2847 : */
2848 : Datum
2849 200 : scalarltjoinsel(PG_FUNCTION_ARGS)
2850 : {
2851 200 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2852 : }
2853 :
2854 : /*
2855 : * scalarlejoinsel - Join selectivity of "<=" for scalars
2856 : */
2857 : Datum
2858 130 : scalarlejoinsel(PG_FUNCTION_ARGS)
2859 : {
2860 130 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2861 : }
2862 :
2863 : /*
2864 : * scalargtjoinsel - Join selectivity of ">" for scalars
2865 : */
2866 : Datum
2867 140 : scalargtjoinsel(PG_FUNCTION_ARGS)
2868 : {
2869 140 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2870 : }
2871 :
2872 : /*
2873 : * scalargejoinsel - Join selectivity of ">=" for scalars
2874 : */
2875 : Datum
2876 108 : scalargejoinsel(PG_FUNCTION_ARGS)
2877 : {
2878 108 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2879 : }
2880 :
2881 :
2882 : /*
2883 : * mergejoinscansel - Scan selectivity of merge join.
2884 : *
2885 : * A merge join will stop as soon as it exhausts either input stream.
2886 : * Therefore, if we can estimate the ranges of both input variables,
2887 : * we can estimate how much of the input will actually be read. This
2888 : * can have a considerable impact on the cost when using indexscans.
2889 : *
2890 : * Also, we can estimate how much of each input has to be read before the
2891 : * first join pair is found, which will affect the join's startup time.
2892 : *
2893 : * clause should be a clause already known to be mergejoinable. opfamily,
2894 : * strategy, and nulls_first specify the sort ordering being used.
2895 : *
2896 : * The outputs are:
2897 : * *leftstart is set to the fraction of the left-hand variable expected
2898 : * to be scanned before the first join pair is found (0 to 1).
2899 : * *leftend is set to the fraction of the left-hand variable expected
2900 : * to be scanned before the join terminates (0 to 1).
2901 : * *rightstart, *rightend similarly for the right-hand variable.
2902 : */
2903 : void
2904 58908 : mergejoinscansel(PlannerInfo *root, Node *clause,
2905 : Oid opfamily, int strategy, bool nulls_first,
2906 : Selectivity *leftstart, Selectivity *leftend,
2907 : Selectivity *rightstart, Selectivity *rightend)
2908 : {
2909 : Node *left,
2910 : *right;
2911 : VariableStatData leftvar,
2912 : rightvar;
2913 : int op_strategy;
2914 : Oid op_lefttype;
2915 : Oid op_righttype;
2916 : Oid opno,
2917 : collation,
2918 : lsortop,
2919 : rsortop,
2920 : lstatop,
2921 : rstatop,
2922 : ltop,
2923 : leop,
2924 : revltop,
2925 : revleop;
2926 : bool isgt;
2927 : Datum leftmin,
2928 : leftmax,
2929 : rightmin,
2930 : rightmax;
2931 : double selec;
2932 :
2933 : /* Set default results if we can't figure anything out. */
2934 : /* XXX should default "start" fraction be a bit more than 0? */
2935 58908 : *leftstart = *rightstart = 0.0;
2936 58908 : *leftend = *rightend = 1.0;
2937 :
2938 : /* Deconstruct the merge clause */
2939 58908 : if (!is_opclause(clause))
2940 0 : return; /* shouldn't happen */
2941 58908 : opno = ((OpExpr *) clause)->opno;
2942 58908 : collation = ((OpExpr *) clause)->inputcollid;
2943 58908 : left = get_leftop((Expr *) clause);
2944 58908 : right = get_rightop((Expr *) clause);
2945 58908 : if (!right)
2946 0 : return; /* shouldn't happen */
2947 :
2948 : /* Look for stats for the inputs */
2949 58908 : examine_variable(root, left, 0, &leftvar);
2950 58908 : examine_variable(root, right, 0, &rightvar);
2951 :
2952 : /* Extract the operator's declared left/right datatypes */
2953 58908 : get_op_opfamily_properties(opno, opfamily, false,
2954 : &op_strategy,
2955 : &op_lefttype,
2956 : &op_righttype);
2957 : Assert(op_strategy == BTEqualStrategyNumber);
2958 :
2959 : /*
2960 : * Look up the various operators we need. If we don't find them all, it
2961 : * probably means the opfamily is broken, but we just fail silently.
2962 : *
2963 : * Note: we expect that pg_statistic histograms will be sorted by the '<'
2964 : * operator, regardless of which sort direction we are considering.
2965 : */
2966 58908 : switch (strategy)
2967 : {
2968 58888 : case BTLessStrategyNumber:
2969 58888 : isgt = false;
2970 58888 : if (op_lefttype == op_righttype)
2971 : {
2972 : /* easy case */
2973 57680 : ltop = get_opfamily_member(opfamily,
2974 : op_lefttype, op_righttype,
2975 : BTLessStrategyNumber);
2976 57680 : leop = get_opfamily_member(opfamily,
2977 : op_lefttype, op_righttype,
2978 : BTLessEqualStrategyNumber);
2979 57680 : lsortop = ltop;
2980 57680 : rsortop = ltop;
2981 57680 : lstatop = lsortop;
2982 57680 : rstatop = rsortop;
2983 57680 : revltop = ltop;
2984 57680 : revleop = leop;
2985 : }
2986 : else
2987 : {
2988 1208 : ltop = get_opfamily_member(opfamily,
2989 : op_lefttype, op_righttype,
2990 : BTLessStrategyNumber);
2991 1208 : leop = get_opfamily_member(opfamily,
2992 : op_lefttype, op_righttype,
2993 : BTLessEqualStrategyNumber);
2994 1208 : lsortop = get_opfamily_member(opfamily,
2995 : op_lefttype, op_lefttype,
2996 : BTLessStrategyNumber);
2997 1208 : rsortop = get_opfamily_member(opfamily,
2998 : op_righttype, op_righttype,
2999 : BTLessStrategyNumber);
3000 1208 : lstatop = lsortop;
3001 1208 : rstatop = rsortop;
3002 1208 : revltop = get_opfamily_member(opfamily,
3003 : op_righttype, op_lefttype,
3004 : BTLessStrategyNumber);
3005 1208 : revleop = get_opfamily_member(opfamily,
3006 : op_righttype, op_lefttype,
3007 : BTLessEqualStrategyNumber);
3008 : }
3009 58888 : break;
3010 20 : case BTGreaterStrategyNumber:
3011 : /* descending-order case */
3012 20 : isgt = true;
3013 20 : if (op_lefttype == op_righttype)
3014 : {
3015 : /* easy case */
3016 20 : ltop = get_opfamily_member(opfamily,
3017 : op_lefttype, op_righttype,
3018 : BTGreaterStrategyNumber);
3019 20 : leop = get_opfamily_member(opfamily,
3020 : op_lefttype, op_righttype,
3021 : BTGreaterEqualStrategyNumber);
3022 20 : lsortop = ltop;
3023 20 : rsortop = ltop;
3024 20 : lstatop = get_opfamily_member(opfamily,
3025 : op_lefttype, op_lefttype,
3026 : BTLessStrategyNumber);
3027 20 : rstatop = lstatop;
3028 20 : revltop = ltop;
3029 20 : revleop = leop;
3030 : }
3031 : else
3032 : {
3033 0 : ltop = get_opfamily_member(opfamily,
3034 : op_lefttype, op_righttype,
3035 : BTGreaterStrategyNumber);
3036 0 : leop = get_opfamily_member(opfamily,
3037 : op_lefttype, op_righttype,
3038 : BTGreaterEqualStrategyNumber);
3039 0 : lsortop = get_opfamily_member(opfamily,
3040 : op_lefttype, op_lefttype,
3041 : BTGreaterStrategyNumber);
3042 0 : rsortop = get_opfamily_member(opfamily,
3043 : op_righttype, op_righttype,
3044 : BTGreaterStrategyNumber);
3045 0 : lstatop = get_opfamily_member(opfamily,
3046 : op_lefttype, op_lefttype,
3047 : BTLessStrategyNumber);
3048 0 : rstatop = get_opfamily_member(opfamily,
3049 : op_righttype, op_righttype,
3050 : BTLessStrategyNumber);
3051 0 : revltop = get_opfamily_member(opfamily,
3052 : op_righttype, op_lefttype,
3053 : BTGreaterStrategyNumber);
3054 0 : revleop = get_opfamily_member(opfamily,
3055 : op_righttype, op_lefttype,
3056 : BTGreaterEqualStrategyNumber);
3057 : }
3058 20 : break;
3059 0 : default:
3060 0 : goto fail; /* shouldn't get here */
3061 : }
3062 :
3063 58908 : if (!OidIsValid(lsortop) ||
3064 58908 : !OidIsValid(rsortop) ||
3065 58908 : !OidIsValid(lstatop) ||
3066 58908 : !OidIsValid(rstatop) ||
3067 58900 : !OidIsValid(ltop) ||
3068 58900 : !OidIsValid(leop) ||
3069 58900 : !OidIsValid(revltop) ||
3070 : !OidIsValid(revleop))
3071 8 : goto fail; /* insufficient info in catalogs */
3072 :
3073 : /* Try to get ranges of both inputs */
3074 58900 : if (!isgt)
3075 : {
3076 58880 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3077 : &leftmin, &leftmax))
3078 13504 : goto fail; /* no range available from stats */
3079 45376 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3080 : &rightmin, &rightmax))
3081 3868 : goto fail; /* no range available from stats */
3082 : }
3083 : else
3084 : {
3085 : /* need to swap the max and min */
3086 20 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3087 : &leftmax, &leftmin))
3088 20 : goto fail; /* no range available from stats */
3089 0 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3090 : &rightmax, &rightmin))
3091 0 : goto fail; /* no range available from stats */
3092 : }
3093 :
3094 : /*
3095 : * Now, the fraction of the left variable that will be scanned is the
3096 : * fraction that's <= the right-side maximum value. But only believe
3097 : * non-default estimates, else stick with our 1.0.
3098 : */
3099 41508 : selec = scalarineqsel(root, leop, isgt, true, collation, &leftvar,
3100 : rightmax, op_righttype);
3101 41508 : if (selec != DEFAULT_INEQ_SEL)
3102 41504 : *leftend = selec;
3103 :
3104 : /* And similarly for the right variable. */
3105 41508 : selec = scalarineqsel(root, revleop, isgt, true, collation, &rightvar,
3106 : leftmax, op_lefttype);
3107 41508 : if (selec != DEFAULT_INEQ_SEL)
3108 41508 : *rightend = selec;
3109 :
3110 : /*
3111 : * Only one of the two "end" fractions can really be less than 1.0;
3112 : * believe the smaller estimate and reset the other one to exactly 1.0. If
3113 : * we get exactly equal estimates (as can easily happen with self-joins),
3114 : * believe neither.
3115 : */
3116 41508 : if (*leftend > *rightend)
3117 23356 : *leftend = 1.0;
3118 18152 : else if (*leftend < *rightend)
3119 14386 : *rightend = 1.0;
3120 : else
3121 3766 : *leftend = *rightend = 1.0;
3122 :
3123 : /*
3124 : * Also, the fraction of the left variable that will be scanned before the
3125 : * first join pair is found is the fraction that's < the right-side
3126 : * minimum value. But only believe non-default estimates, else stick with
3127 : * our own default.
3128 : */
3129 41508 : selec = scalarineqsel(root, ltop, isgt, false, collation, &leftvar,
3130 : rightmin, op_righttype);
3131 41508 : if (selec != DEFAULT_INEQ_SEL)
3132 41508 : *leftstart = selec;
3133 :
3134 : /* And similarly for the right variable. */
3135 41508 : selec = scalarineqsel(root, revltop, isgt, false, collation, &rightvar,
3136 : leftmin, op_lefttype);
3137 41508 : if (selec != DEFAULT_INEQ_SEL)
3138 41508 : *rightstart = selec;
3139 :
3140 : /*
3141 : * Only one of the two "start" fractions can really be more than zero;
3142 : * believe the larger estimate and reset the other one to exactly 0.0. If
3143 : * we get exactly equal estimates (as can easily happen with self-joins),
3144 : * believe neither.
3145 : */
3146 41508 : if (*leftstart < *rightstart)
3147 10952 : *leftstart = 0.0;
3148 30556 : else if (*leftstart > *rightstart)
3149 20772 : *rightstart = 0.0;
3150 : else
3151 9784 : *leftstart = *rightstart = 0.0;
3152 :
3153 : /*
3154 : * If the sort order is nulls-first, we're going to have to skip over any
3155 : * nulls too. These would not have been counted by scalarineqsel, and we
3156 : * can safely add in this fraction regardless of whether we believe
3157 : * scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
3158 : */
3159 41508 : if (nulls_first)
3160 : {
3161 : Form_pg_statistic stats;
3162 :
3163 0 : if (HeapTupleIsValid(leftvar.statsTuple))
3164 : {
3165 0 : stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
3166 0 : *leftstart += stats->stanullfrac;
3167 0 : CLAMP_PROBABILITY(*leftstart);
3168 0 : *leftend += stats->stanullfrac;
3169 0 : CLAMP_PROBABILITY(*leftend);
3170 : }
3171 0 : if (HeapTupleIsValid(rightvar.statsTuple))
3172 : {
3173 0 : stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple);
3174 0 : *rightstart += stats->stanullfrac;
3175 0 : CLAMP_PROBABILITY(*rightstart);
3176 0 : *rightend += stats->stanullfrac;
3177 0 : CLAMP_PROBABILITY(*rightend);
3178 : }
3179 : }
3180 :
3181 : /* Disbelieve start >= end, just in case that can happen */
3182 41508 : if (*leftstart >= *leftend)
3183 : {
3184 1466 : *leftstart = 0.0;
3185 1466 : *leftend = 1.0;
3186 : }
3187 41508 : if (*rightstart >= *rightend)
3188 : {
3189 1666 : *rightstart = 0.0;
3190 1666 : *rightend = 1.0;
3191 : }
3192 :
3193 39842 : fail:
3194 58908 : ReleaseVariableStats(leftvar);
3195 58908 : ReleaseVariableStats(rightvar);
3196 : }
3197 :
3198 :
3199 : /*
3200 : * matchingsel -- generic matching-operator selectivity support
3201 : *
3202 : * Use these for any operators that (a) are on data types for which we collect
3203 : * standard statistics, and (b) have behavior for which the default estimate
3204 : * (twice DEFAULT_EQ_SEL) is sane. Typically that is good for match-like
3205 : * operators.
3206 : */
3207 :
3208 : Datum
3209 810 : matchingsel(PG_FUNCTION_ARGS)
3210 : {
3211 810 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
3212 810 : Oid operator = PG_GETARG_OID(1);
3213 810 : List *args = (List *) PG_GETARG_POINTER(2);
3214 810 : int varRelid = PG_GETARG_INT32(3);
3215 810 : Oid collation = PG_GET_COLLATION();
3216 : double selec;
3217 :
3218 : /* Use generic restriction selectivity logic. */
3219 810 : selec = generic_restriction_selectivity(root, operator, collation,
3220 : args, varRelid,
3221 : DEFAULT_MATCHING_SEL);
3222 :
3223 810 : PG_RETURN_FLOAT8((float8) selec);
3224 : }
3225 :
3226 : Datum
3227 4 : matchingjoinsel(PG_FUNCTION_ARGS)
3228 : {
3229 : /* Just punt, for the moment. */
3230 4 : PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
3231 : }
3232 :
3233 :
3234 : /*
3235 : * Helper routine for estimate_num_groups: add an item to a list of
3236 : * GroupVarInfos, but only if it's not known equal to any of the existing
3237 : * entries.
3238 : */
3239 : typedef struct
3240 : {
3241 : Node *var; /* might be an expression, not just a Var */
3242 : RelOptInfo *rel; /* relation it belongs to */
3243 : double ndistinct; /* # distinct values */
3244 : } GroupVarInfo;
3245 :
3246 : static List *
3247 12188 : add_unique_group_var(PlannerInfo *root, List *varinfos,
3248 : Node *var, VariableStatData *vardata)
3249 : {
3250 : GroupVarInfo *varinfo;
3251 : double ndistinct;
3252 : bool isdefault;
3253 : ListCell *lc;
3254 :
3255 12188 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
3256 :
3257 17674 : foreach(lc, varinfos)
3258 : {
3259 5682 : varinfo = (GroupVarInfo *) lfirst(lc);
3260 :
3261 : /* Drop exact duplicates */
3262 5682 : if (equal(var, varinfo->var))
3263 196 : return varinfos;
3264 :
3265 : /*
3266 : * Drop known-equal vars, but only if they belong to different
3267 : * relations (see comments for estimate_num_groups)
3268 : */
3269 6104 : if (vardata->rel != varinfo->rel &&
3270 566 : exprs_known_equal(root, var, varinfo->var))
3271 : {
3272 56 : if (varinfo->ndistinct <= ndistinct)
3273 : {
3274 : /* Keep older item, forget new one */
3275 52 : return varinfos;
3276 : }
3277 : else
3278 : {
3279 : /* Delete the older item */
3280 4 : varinfos = foreach_delete_current(varinfos, lc);
3281 : }
3282 : }
3283 : }
3284 :
3285 11992 : varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
3286 :
3287 11992 : varinfo->var = var;
3288 11992 : varinfo->rel = vardata->rel;
3289 11992 : varinfo->ndistinct = ndistinct;
3290 11992 : varinfos = lappend(varinfos, varinfo);
3291 11992 : return varinfos;
3292 : }
3293 :
3294 : /*
3295 : * estimate_num_groups - Estimate number of groups in a grouped query
3296 : *
3297 : * Given a query having a GROUP BY clause, estimate how many groups there
3298 : * will be --- ie, the number of distinct combinations of the GROUP BY
3299 : * expressions.
3300 : *
3301 : * This routine is also used to estimate the number of rows emitted by
3302 : * a DISTINCT filtering step; that is an isomorphic problem. (Note:
3303 : * actually, we only use it for DISTINCT when there's no grouping or
3304 : * aggregation ahead of the DISTINCT.)
3305 : *
3306 : * Inputs:
3307 : * root - the query
3308 : * groupExprs - list of expressions being grouped by
3309 : * input_rows - number of rows estimated to arrive at the group/unique
3310 : * filter step
3311 : * pgset - NULL, or a List** pointing to a grouping set to filter the
3312 : * groupExprs against
3313 : *
3314 : * Given the lack of any cross-correlation statistics in the system, it's
3315 : * impossible to do anything really trustworthy with GROUP BY conditions
3316 : * involving multiple Vars. We should however avoid assuming the worst
3317 : * case (all possible cross-product terms actually appear as groups) since
3318 : * very often the grouped-by Vars are highly correlated. Our current approach
3319 : * is as follows:
3320 : * 1. Expressions yielding boolean are assumed to contribute two groups,
3321 : * independently of their content, and are ignored in the subsequent
3322 : * steps. This is mainly because tests like "col IS NULL" break the
3323 : * heuristic used in step 2 especially badly.
3324 : * 2. Reduce the given expressions to a list of unique Vars used. For
3325 : * example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
3326 : * It is clearly correct not to count the same Var more than once.
3327 : * It is also reasonable to treat f(x) the same as x: f() cannot
3328 : * increase the number of distinct values (unless it is volatile,
3329 : * which we consider unlikely for grouping), but it probably won't
3330 : * reduce the number of distinct values much either.
3331 : * As a special case, if a GROUP BY expression can be matched to an
3332 : * expressional index for which we have statistics, then we treat the
3333 : * whole expression as though it were just a Var.
3334 : * 3. If the list contains Vars of different relations that are known equal
3335 : * due to equivalence classes, then drop all but one of the Vars from each
3336 : * known-equal set, keeping the one with smallest estimated # of values
3337 : * (since the extra values of the others can't appear in joined rows).
3338 : * Note the reason we only consider Vars of different relations is that
3339 : * if we considered ones of the same rel, we'd be double-counting the
3340 : * restriction selectivity of the equality in the next step.
3341 : * 4. For Vars within a single source rel, we multiply together the numbers
3342 : * of values, clamp to the number of rows in the rel (divided by 10 if
3343 : * more than one Var), and then multiply by a factor based on the
3344 : * selectivity of the restriction clauses for that rel. When there's
3345 : * more than one Var, the initial product is probably too high (it's the
3346 : * worst case) but clamping to a fraction of the rel's rows seems to be a
3347 : * helpful heuristic for not letting the estimate get out of hand. (The
3348 : * factor of 10 is derived from pre-Postgres-7.4 practice.) The factor
3349 : * we multiply by to adjust for the restriction selectivity assumes that
3350 : * the restriction clauses are independent of the grouping, which may not
3351 : * be a valid assumption, but it's hard to do better.
3352 : * 5. If there are Vars from multiple rels, we repeat step 4 for each such
3353 : * rel, and multiply the results together.
3354 : * Note that rels not containing grouped Vars are ignored completely, as are
3355 : * join clauses. Such rels cannot increase the number of groups, and we
3356 : * assume such clauses do not reduce the number either (somewhat bogus,
3357 : * but we don't have the info to do better).
3358 : */
3359 : double
3360 9838 : estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
3361 : List **pgset)
3362 : {
3363 9838 : List *varinfos = NIL;
3364 9838 : double srf_multiplier = 1.0;
3365 : double numdistinct;
3366 : ListCell *l;
3367 : int i;
3368 :
3369 : /*
3370 : * We don't ever want to return an estimate of zero groups, as that tends
3371 : * to lead to division-by-zero and other unpleasantness. The input_rows
3372 : * estimate is usually already at least 1, but clamp it just in case it
3373 : * isn't.
3374 : */
3375 9838 : input_rows = clamp_row_est(input_rows);
3376 :
3377 : /*
3378 : * If no grouping columns, there's exactly one group. (This can't happen
3379 : * for normal cases with GROUP BY or DISTINCT, but it is possible for
3380 : * corner cases with set operations.)
3381 : */
3382 9838 : if (groupExprs == NIL || (pgset && list_length(*pgset) < 1))
3383 458 : return 1.0;
3384 :
3385 : /*
3386 : * Count groups derived from boolean grouping expressions. For other
3387 : * expressions, find the unique Vars used, treating an expression as a Var
3388 : * if we can find stats for it. For each one, record the statistical
3389 : * estimate of number of distinct values (total in its table, without
3390 : * regard for filtering).
3391 : */
3392 9380 : numdistinct = 1.0;
3393 :
3394 9380 : i = 0;
3395 22082 : foreach(l, groupExprs)
3396 : {
3397 12706 : Node *groupexpr = (Node *) lfirst(l);
3398 : double this_srf_multiplier;
3399 : VariableStatData vardata;
3400 : List *varshere;
3401 : ListCell *l2;
3402 :
3403 : /* is expression in this grouping set? */
3404 12706 : if (pgset && !list_member_int(*pgset, i++))
3405 7620 : continue;
3406 :
3407 : /*
3408 : * Set-returning functions in grouping columns are a bit problematic.
3409 : * The code below will effectively ignore their SRF nature and come up
3410 : * with a numdistinct estimate as though they were scalar functions.
3411 : * We compensate by scaling up the end result by the largest SRF
3412 : * rowcount estimate. (This will be an overestimate if the SRF
3413 : * produces multiple copies of any output value, but it seems best to
3414 : * assume the SRF's outputs are distinct. In any case, it's probably
3415 : * pointless to worry too much about this without much better
3416 : * estimates for SRF output rowcounts than we have today.)
3417 : */
3418 12346 : this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
3419 12346 : if (srf_multiplier < this_srf_multiplier)
3420 72 : srf_multiplier = this_srf_multiplier;
3421 :
3422 : /* Short-circuit for expressions returning boolean */
3423 12346 : if (exprType(groupexpr) == BOOLOID)
3424 : {
3425 24 : numdistinct *= 2.0;
3426 24 : continue;
3427 : }
3428 :
3429 : /*
3430 : * If examine_variable is able to deduce anything about the GROUP BY
3431 : * expression, treat it as a single variable even if it's really more
3432 : * complicated.
3433 : */
3434 12322 : examine_variable(root, groupexpr, 0, &vardata);
3435 12322 : if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
3436 : {
3437 6930 : varinfos = add_unique_group_var(root, varinfos,
3438 : groupexpr, &vardata);
3439 6930 : ReleaseVariableStats(vardata);
3440 6930 : continue;
3441 : }
3442 5392 : ReleaseVariableStats(vardata);
3443 :
3444 : /*
3445 : * Else pull out the component Vars. Handle PlaceHolderVars by
3446 : * recursing into their arguments (effectively assuming that the
3447 : * PlaceHolderVar doesn't change the number of groups, which boils
3448 : * down to ignoring the possible addition of nulls to the result set).
3449 : */
3450 5392 : varshere = pull_var_clause(groupexpr,
3451 : PVC_RECURSE_AGGREGATES |
3452 : PVC_RECURSE_WINDOWFUNCS |
3453 : PVC_RECURSE_PLACEHOLDERS);
3454 :
3455 : /*
3456 : * If we find any variable-free GROUP BY item, then either it is a
3457 : * constant (and we can ignore it) or it contains a volatile function;
3458 : * in the latter case we punt and assume that each input row will
3459 : * yield a distinct group.
3460 : */
3461 5392 : if (varshere == NIL)
3462 : {
3463 310 : if (contain_volatile_functions(groupexpr))
3464 4 : return input_rows;
3465 306 : continue;
3466 : }
3467 :
3468 : /*
3469 : * Else add variables to varinfos list
3470 : */
3471 10340 : foreach(l2, varshere)
3472 : {
3473 5258 : Node *var = (Node *) lfirst(l2);
3474 :
3475 5258 : examine_variable(root, var, 0, &vardata);
3476 5258 : varinfos = add_unique_group_var(root, varinfos, var, &vardata);
3477 5258 : ReleaseVariableStats(vardata);
3478 : }
3479 : }
3480 :
3481 : /*
3482 : * If now no Vars, we must have an all-constant or all-boolean GROUP BY
3483 : * list.
3484 : */
3485 9376 : if (varinfos == NIL)
3486 : {
3487 : /* Apply SRF multiplier as we would do in the long path */
3488 168 : numdistinct *= srf_multiplier;
3489 : /* Round off */
3490 168 : numdistinct = ceil(numdistinct);
3491 : /* Guard against out-of-range answers */
3492 168 : if (numdistinct > input_rows)
3493 0 : numdistinct = input_rows;
3494 168 : if (numdistinct < 1.0)
3495 0 : numdistinct = 1.0;
3496 168 : return numdistinct;
3497 : }
3498 :
3499 : /*
3500 : * Group Vars by relation and estimate total numdistinct.
3501 : *
3502 : * For each iteration of the outer loop, we process the frontmost Var in
3503 : * varinfos, plus all other Vars in the same relation. We remove these
3504 : * Vars from the newvarinfos list for the next iteration. This is the
3505 : * easiest way to group Vars of same rel together.
3506 : */
3507 : do
3508 : {
3509 9510 : GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
3510 9510 : RelOptInfo *rel = varinfo1->rel;
3511 9510 : double reldistinct = 1;
3512 9510 : double relmaxndistinct = reldistinct;
3513 9510 : int relvarcount = 0;
3514 9510 : List *newvarinfos = NIL;
3515 9510 : List *relvarinfos = NIL;
3516 :
3517 : /*
3518 : * Split the list of varinfos in two - one for the current rel, one
3519 : * for remaining Vars on other rels.
3520 : */
3521 9510 : relvarinfos = lappend(relvarinfos, varinfo1);
3522 12358 : for_each_from(l, varinfos, 1)
3523 : {
3524 2848 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3525 :
3526 2848 : if (varinfo2->rel == varinfo1->rel)
3527 : {
3528 : /* varinfos on current rel */
3529 2478 : relvarinfos = lappend(relvarinfos, varinfo2);
3530 : }
3531 : else
3532 : {
3533 : /* not time to process varinfo2 yet */
3534 370 : newvarinfos = lappend(newvarinfos, varinfo2);
3535 : }
3536 : }
3537 :
3538 : /*
3539 : * Get the numdistinct estimate for the Vars of this rel. We
3540 : * iteratively search for multivariate n-distinct with maximum number
3541 : * of vars; assuming that each var group is independent of the others,
3542 : * we multiply them together. Any remaining relvarinfos after no more
3543 : * multivariate matches are found are assumed independent too, so
3544 : * their individual ndistinct estimates are multiplied also.
3545 : *
3546 : * While iterating, count how many separate numdistinct values we
3547 : * apply. We apply a fudge factor below, but only if we multiplied
3548 : * more than one such values.
3549 : */
3550 19036 : while (relvarinfos)
3551 : {
3552 : double mvndistinct;
3553 :
3554 9526 : if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
3555 : &mvndistinct))
3556 : {
3557 40 : reldistinct *= mvndistinct;
3558 40 : if (relmaxndistinct < mvndistinct)
3559 40 : relmaxndistinct = mvndistinct;
3560 40 : relvarcount++;
3561 : }
3562 : else
3563 : {
3564 21374 : foreach(l, relvarinfos)
3565 : {
3566 11888 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3567 :
3568 11888 : reldistinct *= varinfo2->ndistinct;
3569 11888 : if (relmaxndistinct < varinfo2->ndistinct)
3570 9752 : relmaxndistinct = varinfo2->ndistinct;
3571 11888 : relvarcount++;
3572 : }
3573 :
3574 : /* we're done with this relation */
3575 9486 : relvarinfos = NIL;
3576 : }
3577 : }
3578 :
3579 : /*
3580 : * Sanity check --- don't divide by zero if empty relation.
3581 : */
3582 : Assert(IS_SIMPLE_REL(rel));
3583 9510 : if (rel->tuples > 0)
3584 : {
3585 : /*
3586 : * Clamp to size of rel, or size of rel / 10 if multiple Vars. The
3587 : * fudge factor is because the Vars are probably correlated but we
3588 : * don't know by how much. We should never clamp to less than the
3589 : * largest ndistinct value for any of the Vars, though, since
3590 : * there will surely be at least that many groups.
3591 : */
3592 9482 : double clamp = rel->tuples;
3593 :
3594 9482 : if (relvarcount > 1)
3595 : {
3596 1596 : clamp *= 0.1;
3597 1596 : if (clamp < relmaxndistinct)
3598 : {
3599 966 : clamp = relmaxndistinct;
3600 : /* for sanity in case some ndistinct is too large: */
3601 966 : if (clamp > rel->tuples)
3602 56 : clamp = rel->tuples;
3603 : }
3604 : }
3605 9482 : if (reldistinct > clamp)
3606 1192 : reldistinct = clamp;
3607 :
3608 : /*
3609 : * Update the estimate based on the restriction selectivity,
3610 : * guarding against division by zero when reldistinct is zero.
3611 : * Also skip this if we know that we are returning all rows.
3612 : */
3613 9482 : if (reldistinct > 0 && rel->rows < rel->tuples)
3614 : {
3615 : /*
3616 : * Given a table containing N rows with n distinct values in a
3617 : * uniform distribution, if we select p rows at random then
3618 : * the expected number of distinct values selected is
3619 : *
3620 : * n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
3621 : *
3622 : * = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
3623 : *
3624 : * See "Approximating block accesses in database
3625 : * organizations", S. B. Yao, Communications of the ACM,
3626 : * Volume 20 Issue 4, April 1977 Pages 260-261.
3627 : *
3628 : * Alternatively, re-arranging the terms from the factorials,
3629 : * this may be written as
3630 : *
3631 : * n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
3632 : *
3633 : * This form of the formula is more efficient to compute in
3634 : * the common case where p is larger than N/n. Additionally,
3635 : * as pointed out by Dell'Era, if i << N for all terms in the
3636 : * product, it can be approximated by
3637 : *
3638 : * n * (1 - ((N-p)/N)^(N/n))
3639 : *
3640 : * See "Expected distinct values when selecting from a bag
3641 : * without replacement", Alberto Dell'Era,
3642 : * http://www.adellera.it/investigations/distinct_balls/.
3643 : *
3644 : * The condition i << N is equivalent to n >> 1, so this is a
3645 : * good approximation when the number of distinct values in
3646 : * the table is large. It turns out that this formula also
3647 : * works well even when n is small.
3648 : */
3649 2484 : reldistinct *=
3650 2484 : (1 - pow((rel->tuples - rel->rows) / rel->tuples,
3651 2484 : rel->tuples / reldistinct));
3652 : }
3653 9482 : reldistinct = clamp_row_est(reldistinct);
3654 :
3655 : /*
3656 : * Update estimate of total distinct groups.
3657 : */
3658 9482 : numdistinct *= reldistinct;
3659 : }
3660 :
3661 9510 : varinfos = newvarinfos;
3662 9510 : } while (varinfos != NIL);
3663 :
3664 : /* Now we can account for the effects of any SRFs */
3665 9208 : numdistinct *= srf_multiplier;
3666 :
3667 : /* Round off */
3668 9208 : numdistinct = ceil(numdistinct);
3669 :
3670 : /* Guard against out-of-range answers */
3671 9208 : if (numdistinct > input_rows)
3672 742 : numdistinct = input_rows;
3673 9208 : if (numdistinct < 1.0)
3674 0 : numdistinct = 1.0;
3675 :
3676 9208 : return numdistinct;
3677 : }
3678 :
3679 : /*
3680 : * Estimate hash bucket statistics when the specified expression is used
3681 : * as a hash key for the given number of buckets.
3682 : *
3683 : * This attempts to determine two values:
3684 : *
3685 : * 1. The frequency of the most common value of the expression (returns
3686 : * zero into *mcv_freq if we can't get that).
3687 : *
3688 : * 2. The "bucketsize fraction", ie, average number of entries in a bucket
3689 : * divided by total tuples in relation.
3690 : *
3691 : * XXX This is really pretty bogus since we're effectively assuming that the
3692 : * distribution of hash keys will be the same after applying restriction
3693 : * clauses as it was in the underlying relation. However, we are not nearly
3694 : * smart enough to figure out how the restrict clauses might change the
3695 : * distribution, so this will have to do for now.
3696 : *
3697 : * We are passed the number of buckets the executor will use for the given
3698 : * input relation. If the data were perfectly distributed, with the same
3699 : * number of tuples going into each available bucket, then the bucketsize
3700 : * fraction would be 1/nbuckets. But this happy state of affairs will occur
3701 : * only if (a) there are at least nbuckets distinct data values, and (b)
3702 : * we have a not-too-skewed data distribution. Otherwise the buckets will
3703 : * be nonuniformly occupied. If the other relation in the join has a key
3704 : * distribution similar to this one's, then the most-loaded buckets are
3705 : * exactly those that will be probed most often. Therefore, the "average"
3706 : * bucket size for costing purposes should really be taken as something close
3707 : * to the "worst case" bucket size. We try to estimate this by adjusting the
3708 : * fraction if there are too few distinct data values, and then scaling up
3709 : * by the ratio of the most common value's frequency to the average frequency.
3710 : *
3711 : * If no statistics are available, use a default estimate of 0.1. This will
3712 : * discourage use of a hash rather strongly if the inner relation is large,
3713 : * which is what we want. We do not want to hash unless we know that the
3714 : * inner rel is well-dispersed (or the alternatives seem much worse).
3715 : *
3716 : * The caller should also check that the mcv_freq is not so large that the
3717 : * most common value would by itself require an impractically large bucket.
3718 : * In a hash join, the executor can split buckets if they get too big, but
3719 : * obviously that doesn't help for a bucket that contains many duplicates of
3720 : * the same value.
3721 : */
3722 : void
3723 87808 : estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
3724 : Selectivity *mcv_freq,
3725 : Selectivity *bucketsize_frac)
3726 : {
3727 : VariableStatData vardata;
3728 : double estfract,
3729 : ndistinct,
3730 : stanullfrac,
3731 : avgfreq;
3732 : bool isdefault;
3733 : AttStatsSlot sslot;
3734 :
3735 87808 : examine_variable(root, hashkey, 0, &vardata);
3736 :
3737 : /* Look up the frequency of the most common value, if available */
3738 87808 : *mcv_freq = 0.0;
3739 :
3740 87808 : if (HeapTupleIsValid(vardata.statsTuple))
3741 : {
3742 61146 : if (get_attstatsslot(&sslot, vardata.statsTuple,
3743 : STATISTIC_KIND_MCV, InvalidOid,
3744 : ATTSTATSSLOT_NUMBERS))
3745 : {
3746 : /*
3747 : * The first MCV stat is for the most common value.
3748 : */
3749 32458 : if (sslot.nnumbers > 0)
3750 32458 : *mcv_freq = sslot.numbers[0];
3751 32458 : free_attstatsslot(&sslot);
3752 : }
3753 : }
3754 :
3755 : /* Get number of distinct values */
3756 87808 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
3757 :
3758 : /*
3759 : * If ndistinct isn't real, punt. We normally return 0.1, but if the
3760 : * mcv_freq is known to be even higher than that, use it instead.
3761 : */
3762 87808 : if (isdefault)
3763 : {
3764 8340 : *bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
3765 8340 : ReleaseVariableStats(vardata);
3766 8340 : return;
3767 : }
3768 :
3769 : /* Get fraction that are null */
3770 79468 : if (HeapTupleIsValid(vardata.statsTuple))
3771 : {
3772 : Form_pg_statistic stats;
3773 :
3774 61134 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
3775 61134 : stanullfrac = stats->stanullfrac;
3776 : }
3777 : else
3778 18334 : stanullfrac = 0.0;
3779 :
3780 : /* Compute avg freq of all distinct data values in raw relation */
3781 79468 : avgfreq = (1.0 - stanullfrac) / ndistinct;
3782 :
3783 : /*
3784 : * Adjust ndistinct to account for restriction clauses. Observe we are
3785 : * assuming that the data distribution is affected uniformly by the
3786 : * restriction clauses!
3787 : *
3788 : * XXX Possibly better way, but much more expensive: multiply by
3789 : * selectivity of rel's restriction clauses that mention the target Var.
3790 : */
3791 79468 : if (vardata.rel && vardata.rel->tuples > 0)
3792 : {
3793 79458 : ndistinct *= vardata.rel->rows / vardata.rel->tuples;
3794 79458 : ndistinct = clamp_row_est(ndistinct);
3795 : }
3796 :
3797 : /*
3798 : * Initial estimate of bucketsize fraction is 1/nbuckets as long as the
3799 : * number of buckets is less than the expected number of distinct values;
3800 : * otherwise it is 1/ndistinct.
3801 : */
3802 79468 : if (ndistinct > nbuckets)
3803 36 : estfract = 1.0 / nbuckets;
3804 : else
3805 79432 : estfract = 1.0 / ndistinct;
3806 :
3807 : /*
3808 : * Adjust estimated bucketsize upward to account for skewed distribution.
3809 : */
3810 79468 : if (avgfreq > 0.0 && *mcv_freq > avgfreq)
3811 28716 : estfract *= *mcv_freq / avgfreq;
3812 :
3813 : /*
3814 : * Clamp bucketsize to sane range (the above adjustment could easily
3815 : * produce an out-of-range result). We set the lower bound a little above
3816 : * zero, since zero isn't a very sane result.
3817 : */
3818 79468 : if (estfract < 1.0e-6)
3819 0 : estfract = 1.0e-6;
3820 79468 : else if (estfract > 1.0)
3821 23494 : estfract = 1.0;
3822 :
3823 79468 : *bucketsize_frac = (Selectivity) estfract;
3824 :
3825 79468 : ReleaseVariableStats(vardata);
3826 : }
3827 :
3828 : /*
3829 : * estimate_hashagg_tablesize
3830 : * estimate the number of bytes that a hash aggregate hashtable will
3831 : * require based on the agg_costs, path width and number of groups.
3832 : *
3833 : * We return the result as "double" to forestall any possible overflow
3834 : * problem in the multiplication by dNumGroups.
3835 : *
3836 : * XXX this may be over-estimating the size now that hashagg knows to omit
3837 : * unneeded columns from the hashtable. Also for mixed-mode grouping sets,
3838 : * grouping columns not in the hashed set are counted here even though hashagg
3839 : * won't store them. Is this a problem?
3840 : */
3841 : double
3842 1256 : estimate_hashagg_tablesize(PlannerInfo *root, Path *path,
3843 : const AggClauseCosts *agg_costs, double dNumGroups)
3844 : {
3845 : Size hashentrysize;
3846 :
3847 1256 : hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
3848 1256 : path->pathtarget->width,
3849 : agg_costs->transitionSpace);
3850 :
3851 : /*
3852 : * Note that this disregards the effect of fill-factor and growth policy
3853 : * of the hash table. That's probably ok, given that the default
3854 : * fill-factor is relatively high. It'd be hard to meaningfully factor in
3855 : * "double-in-size" growth policies here.
3856 : */
3857 1256 : return hashentrysize * dNumGroups;
3858 : }
3859 :
3860 :
3861 : /*-------------------------------------------------------------------------
3862 : *
3863 : * Support routines
3864 : *
3865 : *-------------------------------------------------------------------------
3866 : */
3867 :
3868 : /*
3869 : * Find applicable ndistinct statistics for the given list of VarInfos (which
3870 : * must all belong to the given rel), and update *ndistinct to the estimate of
3871 : * the MVNDistinctItem that best matches. If a match it found, *varinfos is
3872 : * updated to remove the list of matched varinfos.
3873 : *
3874 : * Varinfos that aren't for simple Vars are ignored.
3875 : *
3876 : * Return true if we're able to find a match, false otherwise.
3877 : */
3878 : static bool
3879 9526 : estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
3880 : List **varinfos, double *ndistinct)
3881 : {
3882 : ListCell *lc;
3883 9526 : Bitmapset *attnums = NULL;
3884 : int nmatches;
3885 9526 : Oid statOid = InvalidOid;
3886 : MVNDistinct *stats;
3887 9526 : Bitmapset *matched = NULL;
3888 :
3889 : /* bail out immediately if the table has no extended statistics */
3890 9526 : if (!rel->statlist)
3891 9466 : return false;
3892 :
3893 : /* Determine the attnums we're looking for */
3894 200 : foreach(lc, *varinfos)
3895 : {
3896 140 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
3897 : AttrNumber attnum;
3898 :
3899 : Assert(varinfo->rel == rel);
3900 :
3901 140 : if (!IsA(varinfo->var, Var))
3902 0 : continue;
3903 :
3904 140 : attnum = ((Var *) varinfo->var)->varattno;
3905 :
3906 140 : if (!AttrNumberIsForUserDefinedAttr(attnum))
3907 4 : continue;
3908 :
3909 136 : attnums = bms_add_member(attnums, attnum);
3910 : }
3911 :
3912 : /* look for the ndistinct statistics matching the most vars */
3913 60 : nmatches = 1; /* we require at least two matches */
3914 240 : foreach(lc, rel->statlist)
3915 : {
3916 180 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
3917 : Bitmapset *shared;
3918 : int nshared;
3919 :
3920 : /* skip statistics of other kinds */
3921 180 : if (info->kind != STATS_EXT_NDISTINCT)
3922 120 : continue;
3923 :
3924 : /* compute attnums shared by the vars and the statistics object */
3925 60 : shared = bms_intersect(info->keys, attnums);
3926 60 : nshared = bms_num_members(shared);
3927 :
3928 : /*
3929 : * Does this statistics object match more columns than the currently
3930 : * best object? If so, use this one instead.
3931 : *
3932 : * XXX This should break ties using name of the object, or something
3933 : * like that, to make the outcome stable.
3934 : */
3935 60 : if (nshared > nmatches)
3936 : {
3937 40 : statOid = info->statOid;
3938 40 : nmatches = nshared;
3939 40 : matched = shared;
3940 : }
3941 : }
3942 :
3943 : /* No match? */
3944 60 : if (statOid == InvalidOid)
3945 20 : return false;
3946 : Assert(nmatches > 1 && matched != NULL);
3947 :
3948 40 : stats = statext_ndistinct_load(statOid);
3949 :
3950 : /*
3951 : * If we have a match, search it for the specific item that matches (there
3952 : * must be one), and construct the output values.
3953 : */
3954 40 : if (stats)
3955 : {
3956 : int i;
3957 40 : List *newlist = NIL;
3958 40 : MVNDistinctItem *item = NULL;
3959 :
3960 : /* Find the specific item that exactly matches the combination */
3961 112 : for (i = 0; i < stats->nitems; i++)
3962 : {
3963 112 : MVNDistinctItem *tmpitem = &stats->items[i];
3964 :
3965 112 : if (bms_subset_compare(tmpitem->attrs, matched) == BMS_EQUAL)
3966 : {
3967 40 : item = tmpitem;
3968 40 : break;
3969 : }
3970 : }
3971 :
3972 : /* make sure we found an item */
3973 40 : if (!item)
3974 0 : elog(ERROR, "corrupt MVNDistinct entry");
3975 :
3976 : /* Form the output varinfo list, keeping only unmatched ones */
3977 156 : foreach(lc, *varinfos)
3978 : {
3979 116 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
3980 : AttrNumber attnum;
3981 :
3982 116 : if (!IsA(varinfo->var, Var))
3983 : {
3984 0 : newlist = lappend(newlist, varinfo);
3985 0 : continue;
3986 : }
3987 :
3988 116 : attnum = ((Var *) varinfo->var)->varattno;
3989 :
3990 116 : if (!AttrNumberIsForUserDefinedAttr(attnum))
3991 4 : continue;
3992 :
3993 112 : if (!bms_is_member(attnum, matched))
3994 16 : newlist = lappend(newlist, varinfo);
3995 : }
3996 :
3997 40 : *varinfos = newlist;
3998 40 : *ndistinct = item->ndistinct;
3999 40 : return true;
4000 : }
4001 :
4002 0 : return false;
4003 : }
4004 :
4005 : /*
4006 : * convert_to_scalar
4007 : * Convert non-NULL values of the indicated types to the comparison
4008 : * scale needed by scalarineqsel().
4009 : * Returns "true" if successful.
4010 : *
4011 : * XXX this routine is a hack: ideally we should look up the conversion
4012 : * subroutines in pg_type.
4013 : *
4014 : * All numeric datatypes are simply converted to their equivalent
4015 : * "double" values. (NUMERIC values that are outside the range of "double"
4016 : * are clamped to +/- HUGE_VAL.)
4017 : *
4018 : * String datatypes are converted by convert_string_to_scalar(),
4019 : * which is explained below. The reason why this routine deals with
4020 : * three values at a time, not just one, is that we need it for strings.
4021 : *
4022 : * The bytea datatype is just enough different from strings that it has
4023 : * to be treated separately.
4024 : *
4025 : * The several datatypes representing absolute times are all converted
4026 : * to Timestamp, which is actually an int64, and then we promote that to
4027 : * a double. Note this will give correct results even for the "special"
4028 : * values of Timestamp, since those are chosen to compare correctly;
4029 : * see timestamp_cmp.
4030 : *
4031 : * The several datatypes representing relative times (intervals) are all
4032 : * converted to measurements expressed in seconds.
4033 : */
4034 : static bool
4035 56752 : convert_to_scalar(Datum value, Oid valuetypid, Oid collid, double *scaledvalue,
4036 : Datum lobound, Datum hibound, Oid boundstypid,
4037 : double *scaledlobound, double *scaledhibound)
4038 : {
4039 56752 : bool failure = false;
4040 :
4041 : /*
4042 : * Both the valuetypid and the boundstypid should exactly match the
4043 : * declared input type(s) of the operator we are invoked for. However,
4044 : * extensions might try to use scalarineqsel as estimator for operators
4045 : * with input type(s) we don't handle here; in such cases, we want to
4046 : * return false, not fail. In any case, we mustn't assume that valuetypid
4047 : * and boundstypid are identical.
4048 : *
4049 : * XXX The histogram we are interpolating between points of could belong
4050 : * to a column that's only binary-compatible with the declared type. In
4051 : * essence we are assuming that the semantics of binary-compatible types
4052 : * are enough alike that we can use a histogram generated with one type's
4053 : * operators to estimate selectivity for the other's. This is outright
4054 : * wrong in some cases --- in particular signed versus unsigned
4055 : * interpretation could trip us up. But it's useful enough in the
4056 : * majority of cases that we do it anyway. Should think about more
4057 : * rigorous ways to do it.
4058 : */
4059 56752 : switch (valuetypid)
4060 : {
4061 : /*
4062 : * Built-in numeric types
4063 : */
4064 54524 : case BOOLOID:
4065 : case INT2OID:
4066 : case INT4OID:
4067 : case INT8OID:
4068 : case FLOAT4OID:
4069 : case FLOAT8OID:
4070 : case NUMERICOID:
4071 : case OIDOID:
4072 : case REGPROCOID:
4073 : case REGPROCEDUREOID:
4074 : case REGOPEROID:
4075 : case REGOPERATOROID:
4076 : case REGCLASSOID:
4077 : case REGTYPEOID:
4078 : case REGCONFIGOID:
4079 : case REGDICTIONARYOID:
4080 : case REGROLEOID:
4081 : case REGNAMESPACEOID:
4082 54524 : *scaledvalue = convert_numeric_to_scalar(value, valuetypid,
4083 : &failure);
4084 54524 : *scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
4085 : &failure);
4086 54524 : *scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
4087 : &failure);
4088 54524 : return !failure;
4089 :
4090 : /*
4091 : * Built-in string types
4092 : */
4093 2224 : case CHAROID:
4094 : case BPCHAROID:
4095 : case VARCHAROID:
4096 : case TEXTOID:
4097 : case NAMEOID:
4098 : {
4099 2224 : char *valstr = convert_string_datum(value, valuetypid,
4100 : collid, &failure);
4101 2224 : char *lostr = convert_string_datum(lobound, boundstypid,
4102 : collid, &failure);
4103 2224 : char *histr = convert_string_datum(hibound, boundstypid,
4104 : collid, &failure);
4105 :
4106 : /*
4107 : * Bail out if any of the values is not of string type. We
4108 : * might leak converted strings for the other value(s), but
4109 : * that's not worth troubling over.
4110 : */
4111 2224 : if (failure)
4112 0 : return false;
4113 :
4114 2224 : convert_string_to_scalar(valstr, scaledvalue,
4115 : lostr, scaledlobound,
4116 : histr, scaledhibound);
4117 2224 : pfree(valstr);
4118 2224 : pfree(lostr);
4119 2224 : pfree(histr);
4120 2224 : return true;
4121 : }
4122 :
4123 : /*
4124 : * Built-in bytea type
4125 : */
4126 0 : case BYTEAOID:
4127 : {
4128 : /* We only support bytea vs bytea comparison */
4129 0 : if (boundstypid != BYTEAOID)
4130 0 : return false;
4131 0 : convert_bytea_to_scalar(value, scaledvalue,
4132 : lobound, scaledlobound,
4133 : hibound, scaledhibound);
4134 0 : return true;
4135 : }
4136 :
4137 : /*
4138 : * Built-in time types
4139 : */
4140 0 : case TIMESTAMPOID:
4141 : case TIMESTAMPTZOID:
4142 : case DATEOID:
4143 : case INTERVALOID:
4144 : case TIMEOID:
4145 : case TIMETZOID:
4146 0 : *scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
4147 : &failure);
4148 0 : *scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
4149 : &failure);
4150 0 : *scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
4151 : &failure);
4152 0 : return !failure;
4153 :
4154 : /*
4155 : * Built-in network types
4156 : */
4157 0 : case INETOID:
4158 : case CIDROID:
4159 : case MACADDROID:
4160 : case MACADDR8OID:
4161 0 : *scaledvalue = convert_network_to_scalar(value, valuetypid,
4162 : &failure);
4163 0 : *scaledlobound = convert_network_to_scalar(lobound, boundstypid,
4164 : &failure);
4165 0 : *scaledhibound = convert_network_to_scalar(hibound, boundstypid,
4166 : &failure);
4167 0 : return !failure;
4168 : }
4169 : /* Don't know how to convert */
4170 4 : *scaledvalue = *scaledlobound = *scaledhibound = 0;
4171 4 : return false;
4172 : }
4173 :
4174 : /*
4175 : * Do convert_to_scalar()'s work for any numeric data type.
4176 : *
4177 : * On failure (e.g., unsupported typid), set *failure to true;
4178 : * otherwise, that variable is not changed.
4179 : */
4180 : static double
4181 163572 : convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
4182 : {
4183 163572 : switch (typid)
4184 : {
4185 0 : case BOOLOID:
4186 0 : return (double) DatumGetBool(value);
4187 8 : case INT2OID:
4188 8 : return (double) DatumGetInt16(value);
4189 18472 : case INT4OID:
4190 18472 : return (double) DatumGetInt32(value);
4191 0 : case INT8OID:
4192 0 : return (double) DatumGetInt64(value);
4193 0 : case FLOAT4OID:
4194 0 : return (double) DatumGetFloat4(value);
4195 24 : case FLOAT8OID:
4196 24 : return (double) DatumGetFloat8(value);
4197 0 : case NUMERICOID:
4198 : /* Note: out-of-range values will be clamped to +-HUGE_VAL */
4199 0 : return (double)
4200 0 : DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
4201 : value));
4202 145068 : case OIDOID:
4203 : case REGPROCOID:
4204 : case REGPROCEDUREOID:
4205 : case REGOPEROID:
4206 : case REGOPERATOROID:
4207 : case REGCLASSOID:
4208 : case REGTYPEOID:
4209 : case REGCONFIGOID:
4210 : case REGDICTIONARYOID:
4211 : case REGROLEOID:
4212 : case REGNAMESPACEOID:
4213 : /* we can treat OIDs as integers... */
4214 145068 : return (double) DatumGetObjectId(value);
4215 : }
4216 :
4217 0 : *failure = true;
4218 0 : return 0;
4219 : }
4220 :
4221 : /*
4222 : * Do convert_to_scalar()'s work for any character-string data type.
4223 : *
4224 : * String datatypes are converted to a scale that ranges from 0 to 1,
4225 : * where we visualize the bytes of the string as fractional digits.
4226 : *
4227 : * We do not want the base to be 256, however, since that tends to
4228 : * generate inflated selectivity estimates; few databases will have
4229 : * occurrences of all 256 possible byte values at each position.
4230 : * Instead, use the smallest and largest byte values seen in the bounds
4231 : * as the estimated range for each byte, after some fudging to deal with
4232 : * the fact that we probably aren't going to see the full range that way.
4233 : *
4234 : * An additional refinement is that we discard any common prefix of the
4235 : * three strings before computing the scaled values. This allows us to
4236 : * "zoom in" when we encounter a narrow data range. An example is a phone
4237 : * number database where all the values begin with the same area code.
4238 : * (Actually, the bounds will be adjacent histogram-bin-boundary values,
4239 : * so this is more likely to happen than you might think.)
4240 : */
4241 : static void
4242 2224 : convert_string_to_scalar(char *value,
4243 : double *scaledvalue,
4244 : char *lobound,
4245 : double *scaledlobound,
4246 : char *hibound,
4247 : double *scaledhibound)
4248 : {
4249 : int rangelo,
4250 : rangehi;
4251 : char *sptr;
4252 :
4253 2224 : rangelo = rangehi = (unsigned char) hibound[0];
4254 32296 : for (sptr = lobound; *sptr; sptr++)
4255 : {
4256 30072 : if (rangelo > (unsigned char) *sptr)
4257 6088 : rangelo = (unsigned char) *sptr;
4258 30072 : if (rangehi < (unsigned char) *sptr)
4259 2716 : rangehi = (unsigned char) *sptr;
4260 : }
4261 27960 : for (sptr = hibound; *sptr; sptr++)
4262 : {
4263 25736 : if (rangelo > (unsigned char) *sptr)
4264 274 : rangelo = (unsigned char) *sptr;
4265 25736 : if (rangehi < (unsigned char) *sptr)
4266 2864 : rangehi = (unsigned char) *sptr;
4267 : }
4268 : /* If range includes any upper-case ASCII chars, make it include all */
4269 2224 : if (rangelo <= 'Z' && rangehi >= 'A')
4270 : {
4271 846 : if (rangelo > 'A')
4272 56 : rangelo = 'A';
4273 846 : if (rangehi < 'Z')
4274 320 : rangehi = 'Z';
4275 : }
4276 : /* Ditto lower-case */
4277 2224 : if (rangelo <= 'z' && rangehi >= 'a')
4278 : {
4279 1896 : if (rangelo > 'a')
4280 0 : rangelo = 'a';
4281 1896 : if (rangehi < 'z')
4282 1782 : rangehi = 'z';
4283 : }
4284 : /* Ditto digits */
4285 2224 : if (rangelo <= '9' && rangehi >= '0')
4286 : {
4287 478 : if (rangelo > '0')
4288 446 : rangelo = '0';
4289 478 : if (rangehi < '9')
4290 4 : rangehi = '9';
4291 : }
4292 :
4293 : /*
4294 : * If range includes less than 10 chars, assume we have not got enough
4295 : * data, and make it include regular ASCII set.
4296 : */
4297 2224 : if (rangehi - rangelo < 9)
4298 : {
4299 0 : rangelo = ' ';
4300 0 : rangehi = 127;
4301 : }
4302 :
4303 : /*
4304 : * Now strip any common prefix of the three strings.
4305 : */
4306 3510 : while (*lobound)
4307 : {
4308 3510 : if (*lobound != *hibound || *lobound != *value)
4309 : break;
4310 1286 : lobound++, hibound++, value++;
4311 : }
4312 :
4313 : /*
4314 : * Now we can do the conversions.
4315 : */
4316 2224 : *scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
4317 2224 : *scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
4318 2224 : *scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
4319 2224 : }
4320 :
4321 : static double
4322 6672 : convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
4323 : {
4324 6672 : int slen = strlen(value);
4325 : double num,
4326 : denom,
4327 : base;
4328 :
4329 6672 : if (slen <= 0)
4330 0 : return 0.0; /* empty string has scalar value 0 */
4331 :
4332 : /*
4333 : * There seems little point in considering more than a dozen bytes from
4334 : * the string. Since base is at least 10, that will give us nominal
4335 : * resolution of at least 12 decimal digits, which is surely far more
4336 : * precision than this estimation technique has got anyway (especially in
4337 : * non-C locales). Also, even with the maximum possible base of 256, this
4338 : * ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
4339 : * overflow on any known machine.
4340 : */
4341 6672 : if (slen > 12)
4342 1956 : slen = 12;
4343 :
4344 : /* Convert initial characters to fraction */
4345 6672 : base = rangehi - rangelo + 1;
4346 6672 : num = 0.0;
4347 6672 : denom = base;
4348 57834 : while (slen-- > 0)
4349 : {
4350 51162 : int ch = (unsigned char) *value++;
4351 :
4352 51162 : if (ch < rangelo)
4353 24 : ch = rangelo - 1;
4354 51138 : else if (ch > rangehi)
4355 0 : ch = rangehi + 1;
4356 51162 : num += ((double) (ch - rangelo)) / denom;
4357 51162 : denom *= base;
4358 : }
4359 :
4360 6672 : return num;
4361 : }
4362 :
4363 : /*
4364 : * Convert a string-type Datum into a palloc'd, null-terminated string.
4365 : *
4366 : * On failure (e.g., unsupported typid), set *failure to true;
4367 : * otherwise, that variable is not changed. (We'll return NULL on failure.)
4368 : *
4369 : * When using a non-C locale, we must pass the string through strxfrm()
4370 : * before continuing, so as to generate correct locale-specific results.
4371 : */
4372 : static char *
4373 6672 : convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
4374 : {
4375 : char *val;
4376 :
4377 6672 : switch (typid)
4378 : {
4379 0 : case CHAROID:
4380 0 : val = (char *) palloc(2);
4381 0 : val[0] = DatumGetChar(value);
4382 0 : val[1] = '\0';
4383 0 : break;
4384 1968 : case BPCHAROID:
4385 : case VARCHAROID:
4386 : case TEXTOID:
4387 1968 : val = TextDatumGetCString(value);
4388 1968 : break;
4389 4704 : case NAMEOID:
4390 : {
4391 4704 : NameData *nm = (NameData *) DatumGetPointer(value);
4392 :
4393 4704 : val = pstrdup(NameStr(*nm));
4394 4704 : break;
4395 : }
4396 0 : default:
4397 0 : *failure = true;
4398 0 : return NULL;
4399 : }
4400 :
4401 6672 : if (!lc_collate_is_c(collid))
4402 : {
4403 : char *xfrmstr;
4404 : size_t xfrmlen;
4405 : size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
4406 :
4407 : /*
4408 : * XXX: We could guess at a suitable output buffer size and only call
4409 : * strxfrm twice if our guess is too small.
4410 : *
4411 : * XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
4412 : * bogus data or set an error. This is not really a problem unless it
4413 : * crashes since it will only give an estimation error and nothing
4414 : * fatal.
4415 : */
4416 72 : xfrmlen = strxfrm(NULL, val, 0);
4417 : #ifdef WIN32
4418 :
4419 : /*
4420 : * On Windows, strxfrm returns INT_MAX when an error occurs. Instead
4421 : * of trying to allocate this much memory (and fail), just return the
4422 : * original string unmodified as if we were in the C locale.
4423 : */
4424 : if (xfrmlen == INT_MAX)
4425 : return val;
4426 : #endif
4427 72 : xfrmstr = (char *) palloc(xfrmlen + 1);
4428 72 : xfrmlen2 = strxfrm(xfrmstr, val, xfrmlen + 1);
4429 :
4430 : /*
4431 : * Some systems (e.g., glibc) can return a smaller value from the
4432 : * second call than the first; thus the Assert must be <= not ==.
4433 : */
4434 : Assert(xfrmlen2 <= xfrmlen);
4435 72 : pfree(val);
4436 72 : val = xfrmstr;
4437 : }
4438 :
4439 6672 : return val;
4440 : }
4441 :
4442 : /*
4443 : * Do convert_to_scalar()'s work for any bytea data type.
4444 : *
4445 : * Very similar to convert_string_to_scalar except we can't assume
4446 : * null-termination and therefore pass explicit lengths around.
4447 : *
4448 : * Also, assumptions about likely "normal" ranges of characters have been
4449 : * removed - a data range of 0..255 is always used, for now. (Perhaps
4450 : * someday we will add information about actual byte data range to
4451 : * pg_statistic.)
4452 : */
4453 : static void
4454 0 : convert_bytea_to_scalar(Datum value,
4455 : double *scaledvalue,
4456 : Datum lobound,
4457 : double *scaledlobound,
4458 : Datum hibound,
4459 : double *scaledhibound)
4460 : {
4461 0 : bytea *valuep = DatumGetByteaPP(value);
4462 0 : bytea *loboundp = DatumGetByteaPP(lobound);
4463 0 : bytea *hiboundp = DatumGetByteaPP(hibound);
4464 : int rangelo,
4465 : rangehi,
4466 0 : valuelen = VARSIZE_ANY_EXHDR(valuep),
4467 0 : loboundlen = VARSIZE_ANY_EXHDR(loboundp),
4468 0 : hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
4469 : i,
4470 : minlen;
4471 0 : unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
4472 0 : unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
4473 0 : unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
4474 :
4475 : /*
4476 : * Assume bytea data is uniformly distributed across all byte values.
4477 : */
4478 0 : rangelo = 0;
4479 0 : rangehi = 255;
4480 :
4481 : /*
4482 : * Now strip any common prefix of the three strings.
4483 : */
4484 0 : minlen = Min(Min(valuelen, loboundlen), hiboundlen);
4485 0 : for (i = 0; i < minlen; i++)
4486 : {
4487 0 : if (*lostr != *histr || *lostr != *valstr)
4488 : break;
4489 0 : lostr++, histr++, valstr++;
4490 0 : loboundlen--, hiboundlen--, valuelen--;
4491 : }
4492 :
4493 : /*
4494 : * Now we can do the conversions.
4495 : */
4496 0 : *scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
4497 0 : *scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
4498 0 : *scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
4499 0 : }
4500 :
4501 : static double
4502 0 : convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
4503 : int rangelo, int rangehi)
4504 : {
4505 : double num,
4506 : denom,
4507 : base;
4508 :
4509 0 : if (valuelen <= 0)
4510 0 : return 0.0; /* empty string has scalar value 0 */
4511 :
4512 : /*
4513 : * Since base is 256, need not consider more than about 10 chars (even
4514 : * this many seems like overkill)
4515 : */
4516 0 : if (valuelen > 10)
4517 0 : valuelen = 10;
4518 :
4519 : /* Convert initial characters to fraction */
4520 0 : base = rangehi - rangelo + 1;
4521 0 : num = 0.0;
4522 0 : denom = base;
4523 0 : while (valuelen-- > 0)
4524 : {
4525 0 : int ch = *value++;
4526 :
4527 0 : if (ch < rangelo)
4528 0 : ch = rangelo - 1;
4529 0 : else if (ch > rangehi)
4530 0 : ch = rangehi + 1;
4531 0 : num += ((double) (ch - rangelo)) / denom;
4532 0 : denom *= base;
4533 : }
4534 :
4535 0 : return num;
4536 : }
4537 :
4538 : /*
4539 : * Do convert_to_scalar()'s work for any timevalue data type.
4540 : *
4541 : * On failure (e.g., unsupported typid), set *failure to true;
4542 : * otherwise, that variable is not changed.
4543 : */
4544 : static double
4545 0 : convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
4546 : {
4547 0 : switch (typid)
4548 : {
4549 0 : case TIMESTAMPOID:
4550 0 : return DatumGetTimestamp(value);
4551 0 : case TIMESTAMPTZOID:
4552 0 : return DatumGetTimestampTz(value);
4553 0 : case DATEOID:
4554 0 : return date2timestamp_no_overflow(DatumGetDateADT(value));
4555 0 : case INTERVALOID:
4556 : {
4557 0 : Interval *interval = DatumGetIntervalP(value);
4558 :
4559 : /*
4560 : * Convert the month part of Interval to days using assumed
4561 : * average month length of 365.25/12.0 days. Not too
4562 : * accurate, but plenty good enough for our purposes.
4563 : */
4564 0 : return interval->time + interval->day * (double) USECS_PER_DAY +
4565 0 : interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
4566 : }
4567 0 : case TIMEOID:
4568 0 : return DatumGetTimeADT(value);
4569 0 : case TIMETZOID:
4570 : {
4571 0 : TimeTzADT *timetz = DatumGetTimeTzADTP(value);
4572 :
4573 : /* use GMT-equivalent time */
4574 0 : return (double) (timetz->time + (timetz->zone * 1000000.0));
4575 : }
4576 : }
4577 :
4578 0 : *failure = true;
4579 0 : return 0;
4580 : }
4581 :
4582 :
4583 : /*
4584 : * get_restriction_variable
4585 : * Examine the args of a restriction clause to see if it's of the
4586 : * form (variable op pseudoconstant) or (pseudoconstant op variable),
4587 : * where "variable" could be either a Var or an expression in vars of a
4588 : * single relation. If so, extract information about the variable,
4589 : * and also indicate which side it was on and the other argument.
4590 : *
4591 : * Inputs:
4592 : * root: the planner info
4593 : * args: clause argument list
4594 : * varRelid: see specs for restriction selectivity functions
4595 : *
4596 : * Outputs: (these are valid only if true is returned)
4597 : * *vardata: gets information about variable (see examine_variable)
4598 : * *other: gets other clause argument, aggressively reduced to a constant
4599 : * *varonleft: set true if variable is on the left, false if on the right
4600 : *
4601 : * Returns true if a variable is identified, otherwise false.
4602 : *
4603 : * Note: if there are Vars on both sides of the clause, we must fail, because
4604 : * callers are expecting that the other side will act like a pseudoconstant.
4605 : */
4606 : bool
4607 385040 : get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
4608 : VariableStatData *vardata, Node **other,
4609 : bool *varonleft)
4610 : {
4611 : Node *left,
4612 : *right;
4613 : VariableStatData rdata;
4614 :
4615 : /* Fail if not a binary opclause (probably shouldn't happen) */
4616 385040 : if (list_length(args) != 2)
4617 0 : return false;
4618 :
4619 385040 : left = (Node *) linitial(args);
4620 385040 : right = (Node *) lsecond(args);
4621 :
4622 : /*
4623 : * Examine both sides. Note that when varRelid is nonzero, Vars of other
4624 : * relations will be treated as pseudoconstants.
4625 : */
4626 385040 : examine_variable(root, left, varRelid, vardata);
4627 385040 : examine_variable(root, right, varRelid, &rdata);
4628 :
4629 : /*
4630 : * If one side is a variable and the other not, we win.
4631 : */
4632 385040 : if (vardata->rel && rdata.rel == NULL)
4633 : {
4634 321336 : *varonleft = true;
4635 321336 : *other = estimate_expression_value(root, rdata.var);
4636 : /* Assume we need no ReleaseVariableStats(rdata) here */
4637 321336 : return true;
4638 : }
4639 :
4640 63704 : if (vardata->rel == NULL && rdata.rel)
4641 : {
4642 61754 : *varonleft = false;
4643 61754 : *other = estimate_expression_value(root, vardata->var);
4644 : /* Assume we need no ReleaseVariableStats(*vardata) here */
4645 61754 : *vardata = rdata;
4646 61754 : return true;
4647 : }
4648 :
4649 : /* Oops, clause has wrong structure (probably var op var) */
4650 1950 : ReleaseVariableStats(*vardata);
4651 1950 : ReleaseVariableStats(rdata);
4652 :
4653 1950 : return false;
4654 : }
4655 :
4656 : /*
4657 : * get_join_variables
4658 : * Apply examine_variable() to each side of a join clause.
4659 : * Also, attempt to identify whether the join clause has the same
4660 : * or reversed sense compared to the SpecialJoinInfo.
4661 : *
4662 : * We consider the join clause "normal" if it is "lhs_var OP rhs_var",
4663 : * or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
4664 : * where we can't tell for sure, we default to assuming it's normal.
4665 : */
4666 : void
4667 125798 : get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
4668 : VariableStatData *vardata1, VariableStatData *vardata2,
4669 : bool *join_is_reversed)
4670 : {
4671 : Node *left,
4672 : *right;
4673 :
4674 125798 : if (list_length(args) != 2)
4675 0 : elog(ERROR, "join operator should take two arguments");
4676 :
4677 125798 : left = (Node *) linitial(args);
4678 125798 : right = (Node *) lsecond(args);
4679 :
4680 125798 : examine_variable(root, left, 0, vardata1);
4681 125798 : examine_variable(root, right, 0, vardata2);
4682 :
4683 251506 : if (vardata1->rel &&
4684 125708 : bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
4685 20674 : *join_is_reversed = true; /* var1 is on RHS */
4686 210176 : else if (vardata2->rel &&
4687 105052 : bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
4688 80 : *join_is_reversed = true; /* var2 is on LHS */
4689 : else
4690 105044 : *join_is_reversed = false;
4691 125798 : }
4692 :
4693 : /*
4694 : * examine_variable
4695 : * Try to look up statistical data about an expression.
4696 : * Fill in a VariableStatData struct to describe the expression.
4697 : *
4698 : * Inputs:
4699 : * root: the planner info
4700 : * node: the expression tree to examine
4701 : * varRelid: see specs for restriction selectivity functions
4702 : *
4703 : * Outputs: *vardata is filled as follows:
4704 : * var: the input expression (with any binary relabeling stripped, if
4705 : * it is or contains a variable; but otherwise the type is preserved)
4706 : * rel: RelOptInfo for relation containing variable; NULL if expression
4707 : * contains no Vars (NOTE this could point to a RelOptInfo of a
4708 : * subquery, not one in the current query).
4709 : * statsTuple: the pg_statistic entry for the variable, if one exists;
4710 : * otherwise NULL.
4711 : * freefunc: pointer to a function to release statsTuple with.
4712 : * vartype: exposed type of the expression; this should always match
4713 : * the declared input type of the operator we are estimating for.
4714 : * atttype, atttypmod: actual type/typmod of the "var" expression. This is
4715 : * commonly the same as the exposed type of the variable argument,
4716 : * but can be different in binary-compatible-type cases.
4717 : * isunique: true if we were able to match the var to a unique index or a
4718 : * single-column DISTINCT clause, implying its values are unique for
4719 : * this query. (Caution: this should be trusted for statistical
4720 : * purposes only, since we do not check indimmediate nor verify that
4721 : * the exact same definition of equality applies.)
4722 : * acl_ok: true if current user has permission to read the column(s)
4723 : * underlying the pg_statistic entry. This is consulted by
4724 : * statistic_proc_security_check().
4725 : *
4726 : * Caller is responsible for doing ReleaseVariableStats() before exiting.
4727 : */
4728 : void
4729 1291968 : examine_variable(PlannerInfo *root, Node *node, int varRelid,
4730 : VariableStatData *vardata)
4731 : {
4732 : Node *basenode;
4733 : Relids varnos;
4734 : RelOptInfo *onerel;
4735 :
4736 : /* Make sure we don't return dangling pointers in vardata */
4737 9043776 : MemSet(vardata, 0, sizeof(VariableStatData));
4738 :
4739 : /* Save the exposed type of the expression */
4740 1291968 : vardata->vartype = exprType(node);
4741 :
4742 : /* Look inside any binary-compatible relabeling */
4743 :
4744 1291968 : if (IsA(node, RelabelType))
4745 16374 : basenode = (Node *) ((RelabelType *) node)->arg;
4746 : else
4747 1275594 : basenode = node;
4748 :
4749 : /* Fast path for a simple Var */
4750 :
4751 1291968 : if (IsA(basenode, Var) &&
4752 303218 : (varRelid == 0 || varRelid == ((Var *) basenode)->varno))
4753 : {
4754 873254 : Var *var = (Var *) basenode;
4755 :
4756 : /* Set up result fields other than the stats tuple */
4757 873254 : vardata->var = basenode; /* return Var without relabeling */
4758 873254 : vardata->rel = find_base_rel(root, var->varno);
4759 873254 : vardata->atttype = var->vartype;
4760 873254 : vardata->atttypmod = var->vartypmod;
4761 873254 : vardata->isunique = has_unique_index(vardata->rel, var->varattno);
4762 :
4763 : /* Try to locate some stats */
4764 873254 : examine_simple_variable(root, var, vardata);
4765 :
4766 873254 : return;
4767 : }
4768 :
4769 : /*
4770 : * Okay, it's a more complicated expression. Determine variable
4771 : * membership. Note that when varRelid isn't zero, only vars of that
4772 : * relation are considered "real" vars.
4773 : */
4774 418714 : varnos = pull_varnos(root, basenode);
4775 :
4776 418714 : onerel = NULL;
4777 :
4778 418714 : switch (bms_membership(varnos))
4779 : {
4780 251388 : case BMS_EMPTY_SET:
4781 : /* No Vars at all ... must be pseudo-constant clause */
4782 251388 : break;
4783 163578 : case BMS_SINGLETON:
4784 163578 : if (varRelid == 0 || bms_is_member(varRelid, varnos))
4785 : {
4786 28668 : onerel = find_base_rel(root,
4787 13532 : (varRelid ? varRelid : bms_singleton_member(varnos)));
4788 15136 : vardata->rel = onerel;
4789 15136 : node = basenode; /* strip any relabeling */
4790 : }
4791 : /* else treat it as a constant */
4792 163578 : break;
4793 3748 : case BMS_MULTIPLE:
4794 3748 : if (varRelid == 0)
4795 : {
4796 : /* treat it as a variable of a join relation */
4797 3584 : vardata->rel = find_join_rel(root, varnos);
4798 3584 : node = basenode; /* strip any relabeling */
4799 : }
4800 164 : else if (bms_is_member(varRelid, varnos))
4801 : {
4802 : /* ignore the vars belonging to other relations */
4803 48 : vardata->rel = find_base_rel(root, varRelid);
4804 48 : node = basenode; /* strip any relabeling */
4805 : /* note: no point in expressional-index search here */
4806 : }
4807 : /* else treat it as a constant */
4808 3748 : break;
4809 : }
4810 :
4811 418714 : bms_free(varnos);
4812 :
4813 418714 : vardata->var = node;
4814 418714 : vardata->atttype = exprType(node);
4815 418714 : vardata->atttypmod = exprTypmod(node);
4816 :
4817 418714 : if (onerel)
4818 : {
4819 : /*
4820 : * We have an expression in vars of a single relation. Try to match
4821 : * it to expressional index columns, in hopes of finding some
4822 : * statistics.
4823 : *
4824 : * Note that we consider all index columns including INCLUDE columns,
4825 : * since there could be stats for such columns. But the test for
4826 : * uniqueness needs to be warier.
4827 : *
4828 : * XXX it's conceivable that there are multiple matches with different
4829 : * index opfamilies; if so, we need to pick one that matches the
4830 : * operator we are estimating for. FIXME later.
4831 : */
4832 : ListCell *ilist;
4833 :
4834 24538 : foreach(ilist, onerel->indexlist)
4835 : {
4836 11270 : IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
4837 : ListCell *indexpr_item;
4838 : int pos;
4839 :
4840 11270 : indexpr_item = list_head(index->indexprs);
4841 11270 : if (indexpr_item == NULL)
4842 8198 : continue; /* no expressions here... */
4843 :
4844 4324 : for (pos = 0; pos < index->ncolumns; pos++)
4845 : {
4846 3120 : if (index->indexkeys[pos] == 0)
4847 : {
4848 : Node *indexkey;
4849 :
4850 3072 : if (indexpr_item == NULL)
4851 0 : elog(ERROR, "too few entries in indexprs list");
4852 3072 : indexkey = (Node *) lfirst(indexpr_item);
4853 3072 : if (indexkey && IsA(indexkey, RelabelType))
4854 0 : indexkey = (Node *) ((RelabelType *) indexkey)->arg;
4855 3072 : if (equal(node, indexkey))
4856 : {
4857 : /*
4858 : * Found a match ... is it a unique index? Tests here
4859 : * should match has_unique_index().
4860 : */
4861 2252 : if (index->unique &&
4862 260 : index->nkeycolumns == 1 &&
4863 260 : pos == 0 &&
4864 260 : (index->indpred == NIL || index->predOK))
4865 260 : vardata->isunique = true;
4866 :
4867 : /*
4868 : * Has it got stats? We only consider stats for
4869 : * non-partial indexes, since partial indexes probably
4870 : * don't reflect whole-relation statistics; the above
4871 : * check for uniqueness is the only info we take from
4872 : * a partial index.
4873 : *
4874 : * An index stats hook, however, must make its own
4875 : * decisions about what to do with partial indexes.
4876 : */
4877 2252 : if (get_index_stats_hook &&
4878 0 : (*get_index_stats_hook) (root, index->indexoid,
4879 0 : pos + 1, vardata))
4880 : {
4881 : /*
4882 : * The hook took control of acquiring a stats
4883 : * tuple. If it did supply a tuple, it'd better
4884 : * have supplied a freefunc.
4885 : */
4886 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
4887 0 : !vardata->freefunc)
4888 0 : elog(ERROR, "no function provided to release variable stats with");
4889 : }
4890 2252 : else if (index->indpred == NIL)
4891 : {
4892 2252 : vardata->statsTuple =
4893 4504 : SearchSysCache3(STATRELATTINH,
4894 2252 : ObjectIdGetDatum(index->indexoid),
4895 2252 : Int16GetDatum(pos + 1),
4896 : BoolGetDatum(false));
4897 2252 : vardata->freefunc = ReleaseSysCache;
4898 :
4899 2252 : if (HeapTupleIsValid(vardata->statsTuple))
4900 : {
4901 : /* Get index's table for permission check */
4902 : RangeTblEntry *rte;
4903 : Oid userid;
4904 :
4905 1868 : rte = planner_rt_fetch(index->rel->relid, root);
4906 : Assert(rte->rtekind == RTE_RELATION);
4907 :
4908 : /*
4909 : * Use checkAsUser if it's set, in case we're
4910 : * accessing the table via a view.
4911 : */
4912 1868 : userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
4913 :
4914 : /*
4915 : * For simplicity, we insist on the whole
4916 : * table being selectable, rather than trying
4917 : * to identify which column(s) the index
4918 : * depends on. Also require all rows to be
4919 : * selectable --- there must be no
4920 : * securityQuals from security barrier views
4921 : * or RLS policies.
4922 : */
4923 1868 : vardata->acl_ok =
4924 3736 : rte->securityQuals == NIL &&
4925 1868 : (pg_class_aclcheck(rte->relid, userid,
4926 : ACL_SELECT) == ACLCHECK_OK);
4927 :
4928 : /*
4929 : * If the user doesn't have permissions to
4930 : * access an inheritance child relation, check
4931 : * the permissions of the table actually
4932 : * mentioned in the query, since most likely
4933 : * the user does have that permission. Note
4934 : * that whole-table select privilege on the
4935 : * parent doesn't quite guarantee that the
4936 : * user could read all columns of the child.
4937 : * But in practice it's unlikely that any
4938 : * interesting security violation could result
4939 : * from allowing access to the expression
4940 : * index's stats, so we allow it anyway. See
4941 : * similar code in examine_simple_variable()
4942 : * for additional comments.
4943 : */
4944 1868 : if (!vardata->acl_ok &&
4945 12 : root->append_rel_array != NULL)
4946 : {
4947 : AppendRelInfo *appinfo;
4948 8 : Index varno = index->rel->relid;
4949 :
4950 8 : appinfo = root->append_rel_array[varno];
4951 24 : while (appinfo &&
4952 16 : planner_rt_fetch(appinfo->parent_relid,
4953 16 : root)->rtekind == RTE_RELATION)
4954 : {
4955 16 : varno = appinfo->parent_relid;
4956 16 : appinfo = root->append_rel_array[varno];
4957 : }
4958 8 : if (varno != index->rel->relid)
4959 : {
4960 : /* Repeat access check on this rel */
4961 8 : rte = planner_rt_fetch(varno, root);
4962 : Assert(rte->rtekind == RTE_RELATION);
4963 :
4964 8 : userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
4965 :
4966 8 : vardata->acl_ok =
4967 16 : rte->securityQuals == NIL &&
4968 8 : (pg_class_aclcheck(rte->relid,
4969 : userid,
4970 : ACL_SELECT) == ACLCHECK_OK);
4971 : }
4972 : }
4973 : }
4974 : else
4975 : {
4976 : /* suppress leakproofness checks later */
4977 384 : vardata->acl_ok = true;
4978 : }
4979 : }
4980 2252 : if (vardata->statsTuple)
4981 1868 : break;
4982 : }
4983 1204 : indexpr_item = lnext(index->indexprs, indexpr_item);
4984 : }
4985 : }
4986 3072 : if (vardata->statsTuple)
4987 1868 : break;
4988 : }
4989 : }
4990 : }
4991 :
4992 : /*
4993 : * examine_simple_variable
4994 : * Handle a simple Var for examine_variable
4995 : *
4996 : * This is split out as a subroutine so that we can recurse to deal with
4997 : * Vars referencing subqueries.
4998 : *
4999 : * We already filled in all the fields of *vardata except for the stats tuple.
5000 : */
5001 : static void
5002 873998 : examine_simple_variable(PlannerInfo *root, Var *var,
5003 : VariableStatData *vardata)
5004 : {
5005 873998 : RangeTblEntry *rte = root->simple_rte_array[var->varno];
5006 :
5007 : Assert(IsA(rte, RangeTblEntry));
5008 :
5009 873998 : if (get_relation_stats_hook &&
5010 0 : (*get_relation_stats_hook) (root, rte, var->varattno, vardata))
5011 : {
5012 : /*
5013 : * The hook took control of acquiring a stats tuple. If it did supply
5014 : * a tuple, it'd better have supplied a freefunc.
5015 : */
5016 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
5017 0 : !vardata->freefunc)
5018 0 : elog(ERROR, "no function provided to release variable stats with");
5019 : }
5020 873998 : else if (rte->rtekind == RTE_RELATION)
5021 : {
5022 : /*
5023 : * Plain table or parent of an inheritance appendrel, so look up the
5024 : * column in pg_statistic
5025 : */
5026 820212 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
5027 820212 : ObjectIdGetDatum(rte->relid),
5028 820212 : Int16GetDatum(var->varattno),
5029 820212 : BoolGetDatum(rte->inh));
5030 820212 : vardata->freefunc = ReleaseSysCache;
5031 :
5032 820212 : if (HeapTupleIsValid(vardata->statsTuple))
5033 : {
5034 : Oid userid;
5035 :
5036 : /*
5037 : * Check if user has permission to read this column. We require
5038 : * all rows to be accessible, so there must be no securityQuals
5039 : * from security barrier views or RLS policies. Use checkAsUser
5040 : * if it's set, in case we're accessing the table via a view.
5041 : */
5042 638908 : userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
5043 :
5044 638908 : vardata->acl_ok =
5045 1277692 : rte->securityQuals == NIL &&
5046 638608 : ((pg_class_aclcheck(rte->relid, userid,
5047 176 : ACL_SELECT) == ACLCHECK_OK) ||
5048 176 : (pg_attribute_aclcheck(rte->relid, var->varattno, userid,
5049 : ACL_SELECT) == ACLCHECK_OK));
5050 :
5051 : /*
5052 : * If the user doesn't have permissions to access an inheritance
5053 : * child relation or specifically this attribute, check the
5054 : * permissions of the table/column actually mentioned in the
5055 : * query, since most likely the user does have that permission
5056 : * (else the query will fail at runtime), and if the user can read
5057 : * the column there then he can get the values of the child table
5058 : * too. To do that, we must find out which of the root parent's
5059 : * attributes the child relation's attribute corresponds to.
5060 : */
5061 638908 : if (!vardata->acl_ok && var->varattno > 0 &&
5062 328 : root->append_rel_array != NULL)
5063 : {
5064 : AppendRelInfo *appinfo;
5065 8 : Index varno = var->varno;
5066 8 : int varattno = var->varattno;
5067 8 : bool found = false;
5068 :
5069 8 : appinfo = root->append_rel_array[varno];
5070 :
5071 : /*
5072 : * Partitions are mapped to their immediate parent, not the
5073 : * root parent, so must be ready to walk up multiple
5074 : * AppendRelInfos. But stop if we hit a parent that is not
5075 : * RTE_RELATION --- that's a flattened UNION ALL subquery, not
5076 : * an inheritance parent.
5077 : */
5078 24 : while (appinfo &&
5079 16 : planner_rt_fetch(appinfo->parent_relid,
5080 16 : root)->rtekind == RTE_RELATION)
5081 : {
5082 : int parent_varattno;
5083 :
5084 16 : found = false;
5085 16 : if (varattno <= 0 || varattno > appinfo->num_child_cols)
5086 : break; /* safety check */
5087 16 : parent_varattno = appinfo->parent_colnos[varattno - 1];
5088 16 : if (parent_varattno == 0)
5089 0 : break; /* Var is local to child */
5090 :
5091 16 : varno = appinfo->parent_relid;
5092 16 : varattno = parent_varattno;
5093 16 : found = true;
5094 :
5095 : /* If the parent is itself a child, continue up. */
5096 16 : appinfo = root->append_rel_array[varno];
5097 : }
5098 :
5099 : /*
5100 : * In rare cases, the Var may be local to the child table, in
5101 : * which case, we've got to live with having no access to this
5102 : * column's stats.
5103 : */
5104 8 : if (!found)
5105 0 : return;
5106 :
5107 : /* Repeat the access check on this parent rel & column */
5108 8 : rte = planner_rt_fetch(varno, root);
5109 : Assert(rte->rtekind == RTE_RELATION);
5110 :
5111 8 : userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
5112 :
5113 8 : vardata->acl_ok =
5114 20 : rte->securityQuals == NIL &&
5115 8 : ((pg_class_aclcheck(rte->relid, userid,
5116 4 : ACL_SELECT) == ACLCHECK_OK) ||
5117 4 : (pg_attribute_aclcheck(rte->relid, varattno, userid,
5118 : ACL_SELECT) == ACLCHECK_OK));
5119 : }
5120 : }
5121 : else
5122 : {
5123 : /* suppress any possible leakproofness checks later */
5124 181304 : vardata->acl_ok = true;
5125 : }
5126 : }
5127 53786 : else if (rte->rtekind == RTE_SUBQUERY && !rte->inh)
5128 : {
5129 : /*
5130 : * Plain subquery (not one that was converted to an appendrel).
5131 : */
5132 2504 : Query *subquery = rte->subquery;
5133 : RelOptInfo *rel;
5134 : TargetEntry *ste;
5135 :
5136 : /*
5137 : * Punt if it's a whole-row var rather than a plain column reference.
5138 : */
5139 2504 : if (var->varattno == InvalidAttrNumber)
5140 0 : return;
5141 :
5142 : /*
5143 : * Punt if subquery uses set operations or GROUP BY, as these will
5144 : * mash underlying columns' stats beyond recognition. (Set ops are
5145 : * particularly nasty; if we forged ahead, we would return stats
5146 : * relevant to only the leftmost subselect...) DISTINCT is also
5147 : * problematic, but we check that later because there is a possibility
5148 : * of learning something even with it.
5149 : */
5150 2504 : if (subquery->setOperations ||
5151 2396 : subquery->groupClause)
5152 372 : return;
5153 :
5154 : /*
5155 : * OK, fetch RelOptInfo for subquery. Note that we don't change the
5156 : * rel returned in vardata, since caller expects it to be a rel of the
5157 : * caller's query level. Because we might already be recursing, we
5158 : * can't use that rel pointer either, but have to look up the Var's
5159 : * rel afresh.
5160 : */
5161 2132 : rel = find_base_rel(root, var->varno);
5162 :
5163 : /* If the subquery hasn't been planned yet, we have to punt */
5164 2132 : if (rel->subroot == NULL)
5165 0 : return;
5166 : Assert(IsA(rel->subroot, PlannerInfo));
5167 :
5168 : /*
5169 : * Switch our attention to the subquery as mangled by the planner. It
5170 : * was okay to look at the pre-planning version for the tests above,
5171 : * but now we need a Var that will refer to the subroot's live
5172 : * RelOptInfos. For instance, if any subquery pullup happened during
5173 : * planning, Vars in the targetlist might have gotten replaced, and we
5174 : * need to see the replacement expressions.
5175 : */
5176 2132 : subquery = rel->subroot->parse;
5177 : Assert(IsA(subquery, Query));
5178 :
5179 : /* Get the subquery output expression referenced by the upper Var */
5180 2132 : ste = get_tle_by_resno(subquery->targetList, var->varattno);
5181 2132 : if (ste == NULL || ste->resjunk)
5182 0 : elog(ERROR, "subquery %s does not have attribute %d",
5183 : rte->eref->aliasname, var->varattno);
5184 2132 : var = (Var *) ste->expr;
5185 :
5186 : /*
5187 : * If subquery uses DISTINCT, we can't make use of any stats for the
5188 : * variable ... but, if it's the only DISTINCT column, we are entitled
5189 : * to consider it unique. We do the test this way so that it works
5190 : * for cases involving DISTINCT ON.
5191 : */
5192 2132 : if (subquery->distinctClause)
5193 : {
5194 200 : if (list_length(subquery->distinctClause) == 1 &&
5195 68 : targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
5196 68 : vardata->isunique = true;
5197 : /* cannot go further */
5198 132 : return;
5199 : }
5200 :
5201 : /*
5202 : * If the sub-query originated from a view with the security_barrier
5203 : * attribute, we must not look at the variable's statistics, though it
5204 : * seems all right to notice the existence of a DISTINCT clause. So
5205 : * stop here.
5206 : *
5207 : * This is probably a harsher restriction than necessary; it's
5208 : * certainly OK for the selectivity estimator (which is a C function,
5209 : * and therefore omnipotent anyway) to look at the statistics. But
5210 : * many selectivity estimators will happily *invoke the operator
5211 : * function* to try to work out a good estimate - and that's not OK.
5212 : * So for now, don't dig down for stats.
5213 : */
5214 2000 : if (rte->security_barrier)
5215 140 : return;
5216 :
5217 : /* Can only handle a simple Var of subquery's query level */
5218 1860 : if (var && IsA(var, Var) &&
5219 744 : var->varlevelsup == 0)
5220 : {
5221 : /*
5222 : * OK, recurse into the subquery. Note that the original setting
5223 : * of vardata->isunique (which will surely be false) is left
5224 : * unchanged in this situation. That's what we want, since even
5225 : * if the underlying column is unique, the subquery may have
5226 : * joined to other tables in a way that creates duplicates.
5227 : */
5228 744 : examine_simple_variable(rel->subroot, var, vardata);
5229 : }
5230 : }
5231 : else
5232 : {
5233 : /*
5234 : * Otherwise, the Var comes from a FUNCTION, VALUES, or CTE RTE. (We
5235 : * won't see RTE_JOIN here because join alias Vars have already been
5236 : * flattened.) There's not much we can do with function outputs, but
5237 : * maybe someday try to be smarter about VALUES and/or CTEs.
5238 : */
5239 : }
5240 : }
5241 :
5242 : /*
5243 : * Check whether it is permitted to call func_oid passing some of the
5244 : * pg_statistic data in vardata. We allow this either if the user has SELECT
5245 : * privileges on the table or column underlying the pg_statistic data or if
5246 : * the function is marked leak-proof.
5247 : */
5248 : bool
5249 735264 : statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
5250 : {
5251 735264 : if (vardata->acl_ok)
5252 734864 : return true;
5253 :
5254 400 : if (!OidIsValid(func_oid))
5255 0 : return false;
5256 :
5257 400 : if (get_func_leakproof(func_oid))
5258 288 : return true;
5259 :
5260 112 : ereport(DEBUG2,
5261 : (errmsg_internal("not using statistics because function \"%s\" is not leak-proof",
5262 : get_func_name(func_oid))));
5263 112 : return false;
5264 : }
5265 :
5266 : /*
5267 : * get_variable_numdistinct
5268 : * Estimate the number of distinct values of a variable.
5269 : *
5270 : * vardata: results of examine_variable
5271 : * *isdefault: set to true if the result is a default rather than based on
5272 : * anything meaningful.
5273 : *
5274 : * NB: be careful to produce a positive integral result, since callers may
5275 : * compare the result to exact integer counts, or might divide by it.
5276 : */
5277 : double
5278 560604 : get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
5279 : {
5280 : double stadistinct;
5281 560604 : double stanullfrac = 0.0;
5282 : double ntuples;
5283 :
5284 560604 : *isdefault = false;
5285 :
5286 : /*
5287 : * Determine the stadistinct value to use. There are cases where we can
5288 : * get an estimate even without a pg_statistic entry, or can get a better
5289 : * value than is in pg_statistic. Grab stanullfrac too if we can find it
5290 : * (otherwise, assume no nulls, for lack of any better idea).
5291 : */
5292 560604 : if (HeapTupleIsValid(vardata->statsTuple))
5293 : {
5294 : /* Use the pg_statistic entry */
5295 : Form_pg_statistic stats;
5296 :
5297 391916 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
5298 391916 : stadistinct = stats->stadistinct;
5299 391916 : stanullfrac = stats->stanullfrac;
5300 : }
5301 168688 : else if (vardata->vartype == BOOLOID)
5302 : {
5303 : /*
5304 : * Special-case boolean columns: presumably, two distinct values.
5305 : *
5306 : * Are there any other datatypes we should wire in special estimates
5307 : * for?
5308 : */
5309 140 : stadistinct = 2.0;
5310 : }
5311 168548 : else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
5312 : {
5313 : /*
5314 : * If the Var represents a column of a VALUES RTE, assume it's unique.
5315 : * This could of course be very wrong, but it should tend to be true
5316 : * in well-written queries. We could consider examining the VALUES'
5317 : * contents to get some real statistics; but that only works if the
5318 : * entries are all constants, and it would be pretty expensive anyway.
5319 : */
5320 858 : stadistinct = -1.0; /* unique (and all non null) */
5321 : }
5322 : else
5323 : {
5324 : /*
5325 : * We don't keep statistics for system columns, but in some cases we
5326 : * can infer distinctness anyway.
5327 : */
5328 167690 : if (vardata->var && IsA(vardata->var, Var))
5329 : {
5330 321964 : switch (((Var *) vardata->var)->varattno)
5331 : {
5332 496 : case SelfItemPointerAttributeNumber:
5333 496 : stadistinct = -1.0; /* unique (and all non null) */
5334 496 : break;
5335 1986 : case TableOidAttributeNumber:
5336 1986 : stadistinct = 1.0; /* only 1 value */
5337 1986 : break;
5338 158500 : default:
5339 158500 : stadistinct = 0.0; /* means "unknown" */
5340 158500 : break;
5341 : }
5342 : }
5343 : else
5344 6708 : stadistinct = 0.0; /* means "unknown" */
5345 :
5346 : /*
5347 : * XXX consider using estimate_num_groups on expressions?
5348 : */
5349 : }
5350 :
5351 : /*
5352 : * If there is a unique index or DISTINCT clause for the variable, assume
5353 : * it is unique no matter what pg_statistic says; the statistics could be
5354 : * out of date, or we might have found a partial unique index that proves
5355 : * the var is unique for this query. However, we'd better still believe
5356 : * the null-fraction statistic.
5357 : */
5358 560604 : if (vardata->isunique)
5359 123528 : stadistinct = -1.0 * (1.0 - stanullfrac);
5360 :
5361 : /*
5362 : * If we had an absolute estimate, use that.
5363 : */
5364 560604 : if (stadistinct > 0.0)
5365 99778 : return clamp_row_est(stadistinct);
5366 :
5367 : /*
5368 : * Otherwise we need to get the relation size; punt if not available.
5369 : */
5370 460826 : if (vardata->rel == NULL)
5371 : {
5372 162 : *isdefault = true;
5373 162 : return DEFAULT_NUM_DISTINCT;
5374 : }
5375 460664 : ntuples = vardata->rel->tuples;
5376 460664 : if (ntuples <= 0.0)
5377 : {
5378 8956 : *isdefault = true;
5379 8956 : return DEFAULT_NUM_DISTINCT;
5380 : }
5381 :
5382 : /*
5383 : * If we had a relative estimate, use that.
5384 : */
5385 451708 : if (stadistinct < 0.0)
5386 309154 : return clamp_row_est(-stadistinct * ntuples);
5387 :
5388 : /*
5389 : * With no data, estimate ndistinct = ntuples if the table is small, else
5390 : * use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
5391 : * that the behavior isn't discontinuous.
5392 : */
5393 142554 : if (ntuples < DEFAULT_NUM_DISTINCT)
5394 71282 : return clamp_row_est(ntuples);
5395 :
5396 71272 : *isdefault = true;
5397 71272 : return DEFAULT_NUM_DISTINCT;
5398 : }
5399 :
5400 : /*
5401 : * get_variable_range
5402 : * Estimate the minimum and maximum value of the specified variable.
5403 : * If successful, store values in *min and *max, and return true.
5404 : * If no data available, return false.
5405 : *
5406 : * sortop is the "<" comparison operator to use. This should generally
5407 : * be "<" not ">", as only the former is likely to be found in pg_statistic.
5408 : * The collation must be specified too.
5409 : */
5410 : static bool
5411 104276 : get_variable_range(PlannerInfo *root, VariableStatData *vardata,
5412 : Oid sortop, Oid collation,
5413 : Datum *min, Datum *max)
5414 : {
5415 104276 : Datum tmin = 0;
5416 104276 : Datum tmax = 0;
5417 104276 : bool have_data = false;
5418 : int16 typLen;
5419 : bool typByVal;
5420 : Oid opfuncoid;
5421 : FmgrInfo opproc;
5422 : AttStatsSlot sslot;
5423 :
5424 : /*
5425 : * XXX It's very tempting to try to use the actual column min and max, if
5426 : * we can get them relatively-cheaply with an index probe. However, since
5427 : * this function is called many times during join planning, that could
5428 : * have unpleasant effects on planning speed. Need more investigation
5429 : * before enabling this.
5430 : */
5431 : #ifdef NOT_USED
5432 : if (get_actual_variable_range(root, vardata, sortop, collation, min, max))
5433 : return true;
5434 : #endif
5435 :
5436 104276 : if (!HeapTupleIsValid(vardata->statsTuple))
5437 : {
5438 : /* no stats available, so default result */
5439 16904 : return false;
5440 : }
5441 :
5442 : /*
5443 : * If we can't apply the sortop to the stats data, just fail. In
5444 : * principle, if there's a histogram and no MCVs, we could return the
5445 : * histogram endpoints without ever applying the sortop ... but it's
5446 : * probably not worth trying, because whatever the caller wants to do with
5447 : * the endpoints would likely fail the security check too.
5448 : */
5449 87372 : if (!statistic_proc_security_check(vardata,
5450 87372 : (opfuncoid = get_opcode(sortop))))
5451 0 : return false;
5452 :
5453 87372 : opproc.fn_oid = InvalidOid; /* mark this as not looked up yet */
5454 :
5455 87372 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
5456 :
5457 : /*
5458 : * If there is a histogram with the ordering we want, grab the first and
5459 : * last values.
5460 : */
5461 87372 : if (get_attstatsslot(&sslot, vardata->statsTuple,
5462 : STATISTIC_KIND_HISTOGRAM, sortop,
5463 : ATTSTATSSLOT_VALUES))
5464 : {
5465 72312 : if (sslot.stacoll == collation && sslot.nvalues > 0)
5466 : {
5467 72312 : tmin = datumCopy(sslot.values[0], typByVal, typLen);
5468 72312 : tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
5469 72312 : have_data = true;
5470 : }
5471 72312 : free_attstatsslot(&sslot);
5472 : }
5473 :
5474 : /*
5475 : * Otherwise, if there is a histogram with some other ordering, scan it
5476 : * and get the min and max values according to the ordering we want. This
5477 : * of course may not find values that are really extremal according to our
5478 : * ordering, but it beats ignoring available data.
5479 : */
5480 102432 : if (!have_data &&
5481 15060 : get_attstatsslot(&sslot, vardata->statsTuple,
5482 : STATISTIC_KIND_HISTOGRAM, InvalidOid,
5483 : ATTSTATSSLOT_VALUES))
5484 : {
5485 0 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
5486 : collation, typLen, typByVal,
5487 : &tmin, &tmax, &have_data);
5488 0 : free_attstatsslot(&sslot);
5489 : }
5490 :
5491 : /*
5492 : * If we have most-common-values info, look for extreme MCVs. This is
5493 : * needed even if we also have a histogram, since the histogram excludes
5494 : * the MCVs.
5495 : */
5496 87372 : if (get_attstatsslot(&sslot, vardata->statsTuple,
5497 : STATISTIC_KIND_MCV, InvalidOid,
5498 : ATTSTATSSLOT_VALUES))
5499 : {
5500 41190 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
5501 : collation, typLen, typByVal,
5502 : &tmin, &tmax, &have_data);
5503 41190 : free_attstatsslot(&sslot);
5504 : }
5505 :
5506 87372 : *min = tmin;
5507 87372 : *max = tmax;
5508 87372 : return have_data;
5509 : }
5510 :
5511 : /*
5512 : * get_stats_slot_range: scan sslot for min/max values
5513 : *
5514 : * Subroutine for get_variable_range: update min/max/have_data according
5515 : * to what we find in the statistics array.
5516 : */
5517 : static void
5518 41190 : get_stats_slot_range(AttStatsSlot *sslot, Oid opfuncoid, FmgrInfo *opproc,
5519 : Oid collation, int16 typLen, bool typByVal,
5520 : Datum *min, Datum *max, bool *p_have_data)
5521 : {
5522 41190 : Datum tmin = *min;
5523 41190 : Datum tmax = *max;
5524 41190 : bool have_data = *p_have_data;
5525 41190 : bool found_tmin = false;
5526 41190 : bool found_tmax = false;
5527 :
5528 : /* Look up the comparison function, if we didn't already do so */
5529 41190 : if (opproc->fn_oid != opfuncoid)
5530 41190 : fmgr_info(opfuncoid, opproc);
5531 :
5532 : /* Scan all the slot's values */
5533 1387170 : for (int i = 0; i < sslot->nvalues; i++)
5534 : {
5535 1345980 : if (!have_data)
5536 : {
5537 14572 : tmin = tmax = sslot->values[i];
5538 14572 : found_tmin = found_tmax = true;
5539 14572 : *p_have_data = have_data = true;
5540 14572 : continue;
5541 : }
5542 1331408 : if (DatumGetBool(FunctionCall2Coll(opproc,
5543 : collation,
5544 : sslot->values[i], tmin)))
5545 : {
5546 38028 : tmin = sslot->values[i];
5547 38028 : found_tmin = true;
5548 : }
5549 1331408 : if (DatumGetBool(FunctionCall2Coll(opproc,
5550 : collation,
5551 : tmax, sslot->values[i])))
5552 : {
5553 43808 : tmax = sslot->values[i];
5554 43808 : found_tmax = true;
5555 : }
5556 : }
5557 :
5558 : /*
5559 : * Copy the slot's values, if we found new extreme values.
5560 : */
5561 41190 : if (found_tmin)
5562 32658 : *min = datumCopy(tmin, typByVal, typLen);
5563 41190 : if (found_tmax)
5564 17984 : *max = datumCopy(tmax, typByVal, typLen);
5565 41190 : }
5566 :
5567 :
5568 : /*
5569 : * get_actual_variable_range
5570 : * Attempt to identify the current *actual* minimum and/or maximum
5571 : * of the specified variable, by looking for a suitable btree index
5572 : * and fetching its low and/or high values.
5573 : * If successful, store values in *min and *max, and return true.
5574 : * (Either pointer can be NULL if that endpoint isn't needed.)
5575 : * If no data available, return false.
5576 : *
5577 : * sortop is the "<" comparison operator to use.
5578 : * collation is the required collation.
5579 : */
5580 : static bool
5581 125236 : get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
5582 : Oid sortop, Oid collation,
5583 : Datum *min, Datum *max)
5584 : {
5585 125236 : bool have_data = false;
5586 125236 : RelOptInfo *rel = vardata->rel;
5587 : RangeTblEntry *rte;
5588 : ListCell *lc;
5589 :
5590 : /* No hope if no relation or it doesn't have indexes */
5591 125236 : if (rel == NULL || rel->indexlist == NIL)
5592 9054 : return false;
5593 : /* If it has indexes it must be a plain relation */
5594 116182 : rte = root->simple_rte_array[rel->relid];
5595 : Assert(rte->rtekind == RTE_RELATION);
5596 :
5597 : /* Search through the indexes to see if any match our problem */
5598 247500 : foreach(lc, rel->indexlist)
5599 : {
5600 209574 : IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
5601 : ScanDirection indexscandir;
5602 :
5603 : /* Ignore non-btree indexes */
5604 209574 : if (index->relam != BTREE_AM_OID)
5605 0 : continue;
5606 :
5607 : /*
5608 : * Ignore partial indexes --- we only want stats that cover the entire
5609 : * relation.
5610 : */
5611 209574 : if (index->indpred != NIL)
5612 132 : continue;
5613 :
5614 : /*
5615 : * The index list might include hypothetical indexes inserted by a
5616 : * get_relation_info hook --- don't try to access them.
5617 : */
5618 209442 : if (index->hypothetical)
5619 0 : continue;
5620 :
5621 : /*
5622 : * The first index column must match the desired variable, sortop, and
5623 : * collation --- but we can use a descending-order index.
5624 : */
5625 209442 : if (collation != index->indexcollations[0])
5626 34930 : continue; /* test first 'cause it's cheapest */
5627 174512 : if (!match_index_to_operand(vardata->var, 0, index))
5628 96256 : continue;
5629 78256 : switch (get_op_opfamily_strategy(sortop, index->sortopfamily[0]))
5630 : {
5631 78256 : case BTLessStrategyNumber:
5632 78256 : if (index->reverse_sort[0])
5633 0 : indexscandir = BackwardScanDirection;
5634 : else
5635 78256 : indexscandir = ForwardScanDirection;
5636 78256 : break;
5637 0 : case BTGreaterStrategyNumber:
5638 0 : if (index->reverse_sort[0])
5639 0 : indexscandir = ForwardScanDirection;
5640 : else
5641 0 : indexscandir = BackwardScanDirection;
5642 0 : break;
5643 0 : default:
5644 : /* index doesn't match the sortop */
5645 0 : continue;
5646 : }
5647 :
5648 : /*
5649 : * Found a suitable index to extract data from. Set up some data that
5650 : * can be used by both invocations of get_actual_variable_endpoint.
5651 : */
5652 : {
5653 : MemoryContext tmpcontext;
5654 : MemoryContext oldcontext;
5655 : Relation heapRel;
5656 : Relation indexRel;
5657 : TupleTableSlot *slot;
5658 : int16 typLen;
5659 : bool typByVal;
5660 : ScanKeyData scankeys[1];
5661 :
5662 : /* Make sure any cruft gets recycled when we're done */
5663 78256 : tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
5664 : "get_actual_variable_range workspace",
5665 : ALLOCSET_DEFAULT_SIZES);
5666 78256 : oldcontext = MemoryContextSwitchTo(tmpcontext);
5667 :
5668 : /*
5669 : * Open the table and index so we can read from them. We should
5670 : * already have some type of lock on each.
5671 : */
5672 78256 : heapRel = table_open(rte->relid, NoLock);
5673 78256 : indexRel = index_open(index->indexoid, NoLock);
5674 :
5675 : /* build some stuff needed for indexscan execution */
5676 78256 : slot = table_slot_create(heapRel, NULL);
5677 78256 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
5678 :
5679 : /* set up an IS NOT NULL scan key so that we ignore nulls */
5680 78256 : ScanKeyEntryInitialize(&scankeys[0],
5681 : SK_ISNULL | SK_SEARCHNOTNULL,
5682 : 1, /* index col to scan */
5683 : InvalidStrategy, /* no strategy */
5684 : InvalidOid, /* no strategy subtype */
5685 : InvalidOid, /* no collation */
5686 : InvalidOid, /* no reg proc for this */
5687 : (Datum) 0); /* constant */
5688 :
5689 : /* If min is requested ... */
5690 78256 : if (min)
5691 : {
5692 46652 : have_data = get_actual_variable_endpoint(heapRel,
5693 : indexRel,
5694 : indexscandir,
5695 : scankeys,
5696 : typLen,
5697 : typByVal,
5698 : slot,
5699 : oldcontext,
5700 : min);
5701 : }
5702 : else
5703 : {
5704 : /* If min not requested, assume index is nonempty */
5705 31604 : have_data = true;
5706 : }
5707 :
5708 : /* If max is requested, and we didn't find the index is empty */
5709 78256 : if (max && have_data)
5710 : {
5711 : /* scan in the opposite direction; all else is the same */
5712 63736 : have_data = get_actual_variable_endpoint(heapRel,
5713 : indexRel,
5714 31868 : -indexscandir,
5715 : scankeys,
5716 : typLen,
5717 : typByVal,
5718 : slot,
5719 : oldcontext,
5720 : max);
5721 : }
5722 :
5723 : /* Clean everything up */
5724 78256 : ExecDropSingleTupleTableSlot(slot);
5725 :
5726 78256 : index_close(indexRel, NoLock);
5727 78256 : table_close(heapRel, NoLock);
5728 :
5729 78256 : MemoryContextSwitchTo(oldcontext);
5730 78256 : MemoryContextDelete(tmpcontext);
5731 :
5732 : /* And we're done */
5733 78256 : break;
5734 : }
5735 : }
5736 :
5737 116182 : return have_data;
5738 : }
5739 :
5740 : /*
5741 : * Get one endpoint datum (min or max depending on indexscandir) from the
5742 : * specified index. Return true if successful, false if index is empty.
5743 : * On success, endpoint value is stored to *endpointDatum (and copied into
5744 : * outercontext).
5745 : *
5746 : * scankeys is a 1-element scankey array set up to reject nulls.
5747 : * typLen/typByVal describe the datatype of the index's first column.
5748 : * tableslot is a slot suitable to hold table tuples, in case we need
5749 : * to probe the heap.
5750 : * (We could compute these values locally, but that would mean computing them
5751 : * twice when get_actual_variable_range needs both the min and the max.)
5752 : */
5753 : static bool
5754 78520 : get_actual_variable_endpoint(Relation heapRel,
5755 : Relation indexRel,
5756 : ScanDirection indexscandir,
5757 : ScanKey scankeys,
5758 : int16 typLen,
5759 : bool typByVal,
5760 : TupleTableSlot *tableslot,
5761 : MemoryContext outercontext,
5762 : Datum *endpointDatum)
5763 : {
5764 78520 : bool have_data = false;
5765 : SnapshotData SnapshotNonVacuumable;
5766 : IndexScanDesc index_scan;
5767 78520 : Buffer vmbuffer = InvalidBuffer;
5768 : ItemPointer tid;
5769 : Datum values[INDEX_MAX_KEYS];
5770 : bool isnull[INDEX_MAX_KEYS];
5771 : MemoryContext oldcontext;
5772 :
5773 : /*
5774 : * We use the index-only-scan machinery for this. With mostly-static
5775 : * tables that's a win because it avoids a heap visit. It's also a win
5776 : * for dynamic data, but the reason is less obvious; read on for details.
5777 : *
5778 : * In principle, we should scan the index with our current active
5779 : * snapshot, which is the best approximation we've got to what the query
5780 : * will see when executed. But that won't be exact if a new snap is taken
5781 : * before running the query, and it can be very expensive if a lot of
5782 : * recently-dead or uncommitted rows exist at the beginning or end of the
5783 : * index (because we'll laboriously fetch each one and reject it).
5784 : * Instead, we use SnapshotNonVacuumable. That will accept recently-dead
5785 : * and uncommitted rows as well as normal visible rows. On the other
5786 : * hand, it will reject known-dead rows, and thus not give a bogus answer
5787 : * when the extreme value has been deleted (unless the deletion was quite
5788 : * recent); that case motivates not using SnapshotAny here.
5789 : *
5790 : * A crucial point here is that SnapshotNonVacuumable, with
5791 : * GlobalVisTestFor(heapRel) as horizon, yields the inverse of the
5792 : * condition that the indexscan will use to decide that index entries are
5793 : * killable (see heap_hot_search_buffer()). Therefore, if the snapshot
5794 : * rejects a tuple (or more precisely, all tuples of a HOT chain) and we
5795 : * have to continue scanning past it, we know that the indexscan will mark
5796 : * that index entry killed. That means that the next
5797 : * get_actual_variable_endpoint() call will not have to re-consider that
5798 : * index entry. In this way we avoid repetitive work when this function
5799 : * is used a lot during planning.
5800 : *
5801 : * But using SnapshotNonVacuumable creates a hazard of its own. In a
5802 : * recently-created index, some index entries may point at "broken" HOT
5803 : * chains in which not all the tuple versions contain data matching the
5804 : * index entry. The live tuple version(s) certainly do match the index,
5805 : * but SnapshotNonVacuumable can accept recently-dead tuple versions that
5806 : * don't match. Hence, if we took data from the selected heap tuple, we
5807 : * might get a bogus answer that's not close to the index extremal value,
5808 : * or could even be NULL. We avoid this hazard because we take the data
5809 : * from the index entry not the heap.
5810 : */
5811 78520 : InitNonVacuumableSnapshot(SnapshotNonVacuumable,
5812 : GlobalVisTestFor(heapRel));
5813 :
5814 78520 : index_scan = index_beginscan(heapRel, indexRel,
5815 : &SnapshotNonVacuumable,
5816 : 1, 0);
5817 : /* Set it up for index-only scan */
5818 78520 : index_scan->xs_want_itup = true;
5819 78520 : index_rescan(index_scan, scankeys, 1, NULL, 0);
5820 :
5821 : /* Fetch first/next tuple in specified direction */
5822 82282 : while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
5823 : {
5824 82282 : if (!VM_ALL_VISIBLE(heapRel,
5825 : ItemPointerGetBlockNumber(tid),
5826 : &vmbuffer))
5827 : {
5828 : /* Rats, we have to visit the heap to check visibility */
5829 48594 : if (!index_fetch_heap(index_scan, tableslot))
5830 3762 : continue; /* no visible tuple, try next index entry */
5831 :
5832 : /* We don't actually need the heap tuple for anything */
5833 44832 : ExecClearTuple(tableslot);
5834 :
5835 : /*
5836 : * We don't care whether there's more than one visible tuple in
5837 : * the HOT chain; if any are visible, that's good enough.
5838 : */
5839 : }
5840 :
5841 : /*
5842 : * We expect that btree will return data in IndexTuple not HeapTuple
5843 : * format. It's not lossy either.
5844 : */
5845 78520 : if (!index_scan->xs_itup)
5846 0 : elog(ERROR, "no data returned for index-only scan");
5847 78520 : if (index_scan->xs_recheck)
5848 0 : elog(ERROR, "unexpected recheck indication from btree");
5849 :
5850 : /* OK to deconstruct the index tuple */
5851 78520 : index_deform_tuple(index_scan->xs_itup,
5852 : index_scan->xs_itupdesc,
5853 : values, isnull);
5854 :
5855 : /* Shouldn't have got a null, but be careful */
5856 78520 : if (isnull[0])
5857 0 : elog(ERROR, "found unexpected null value in index \"%s\"",
5858 : RelationGetRelationName(indexRel));
5859 :
5860 : /* Copy the index column value out to caller's context */
5861 78520 : oldcontext = MemoryContextSwitchTo(outercontext);
5862 78520 : *endpointDatum = datumCopy(values[0], typByVal, typLen);
5863 78520 : MemoryContextSwitchTo(oldcontext);
5864 78520 : have_data = true;
5865 78520 : break;
5866 : }
5867 :
5868 78520 : if (vmbuffer != InvalidBuffer)
5869 70932 : ReleaseBuffer(vmbuffer);
5870 78520 : index_endscan(index_scan);
5871 :
5872 78520 : return have_data;
5873 : }
5874 :
5875 : /*
5876 : * find_join_input_rel
5877 : * Look up the input relation for a join.
5878 : *
5879 : * We assume that the input relation's RelOptInfo must have been constructed
5880 : * already.
5881 : */
5882 : static RelOptInfo *
5883 10766 : find_join_input_rel(PlannerInfo *root, Relids relids)
5884 : {
5885 10766 : RelOptInfo *rel = NULL;
5886 :
5887 10766 : switch (bms_membership(relids))
5888 : {
5889 0 : case BMS_EMPTY_SET:
5890 : /* should not happen */
5891 0 : break;
5892 10610 : case BMS_SINGLETON:
5893 10610 : rel = find_base_rel(root, bms_singleton_member(relids));
5894 10610 : break;
5895 156 : case BMS_MULTIPLE:
5896 156 : rel = find_join_rel(root, relids);
5897 156 : break;
5898 : }
5899 :
5900 10766 : if (rel == NULL)
5901 0 : elog(ERROR, "could not find RelOptInfo for given relids");
5902 :
5903 10766 : return rel;
5904 : }
5905 :
5906 :
5907 : /*-------------------------------------------------------------------------
5908 : *
5909 : * Index cost estimation functions
5910 : *
5911 : *-------------------------------------------------------------------------
5912 : */
5913 :
5914 : /*
5915 : * Extract the actual indexquals (as RestrictInfos) from an IndexClause list
5916 : */
5917 : List *
5918 404980 : get_quals_from_indexclauses(List *indexclauses)
5919 : {
5920 404980 : List *result = NIL;
5921 : ListCell *lc;
5922 :
5923 747590 : foreach(lc, indexclauses)
5924 : {
5925 342610 : IndexClause *iclause = lfirst_node(IndexClause, lc);
5926 : ListCell *lc2;
5927 :
5928 687086 : foreach(lc2, iclause->indexquals)
5929 : {
5930 344476 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
5931 :
5932 344476 : result = lappend(result, rinfo);
5933 : }
5934 : }
5935 404980 : return result;
5936 : }
5937 :
5938 : /*
5939 : * Compute the total evaluation cost of the comparison operands in a list
5940 : * of index qual expressions. Since we know these will be evaluated just
5941 : * once per scan, there's no need to distinguish startup from per-row cost.
5942 : *
5943 : * This can be used either on the result of get_quals_from_indexclauses(),
5944 : * or directly on an indexorderbys list. In both cases, we expect that the
5945 : * index key expression is on the left side of binary clauses.
5946 : */
5947 : Cost
5948 804526 : index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
5949 : {
5950 804526 : Cost qual_arg_cost = 0;
5951 : ListCell *lc;
5952 :
5953 1149314 : foreach(lc, indexquals)
5954 : {
5955 344788 : Expr *clause = (Expr *) lfirst(lc);
5956 : Node *other_operand;
5957 : QualCost index_qual_cost;
5958 :
5959 : /*
5960 : * Index quals will have RestrictInfos, indexorderbys won't. Look
5961 : * through RestrictInfo if present.
5962 : */
5963 344788 : if (IsA(clause, RestrictInfo))
5964 344468 : clause = ((RestrictInfo *) clause)->clause;
5965 :
5966 344788 : if (IsA(clause, OpExpr))
5967 : {
5968 333466 : OpExpr *op = (OpExpr *) clause;
5969 :
5970 333466 : other_operand = (Node *) lsecond(op->args);
5971 : }
5972 11322 : else if (IsA(clause, RowCompareExpr))
5973 : {
5974 88 : RowCompareExpr *rc = (RowCompareExpr *) clause;
5975 :
5976 88 : other_operand = (Node *) rc->rargs;
5977 : }
5978 11234 : else if (IsA(clause, ScalarArrayOpExpr))
5979 : {
5980 9320 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
5981 :
5982 9320 : other_operand = (Node *) lsecond(saop->args);
5983 : }
5984 1914 : else if (IsA(clause, NullTest))
5985 : {
5986 1914 : other_operand = NULL;
5987 : }
5988 : else
5989 : {
5990 0 : elog(ERROR, "unsupported indexqual type: %d",
5991 : (int) nodeTag(clause));
5992 : other_operand = NULL; /* keep compiler quiet */
5993 : }
5994 :
5995 344788 : cost_qual_eval_node(&index_qual_cost, other_operand, root);
5996 344788 : qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
5997 : }
5998 804526 : return qual_arg_cost;
5999 : }
6000 :
6001 : void
6002 399554 : genericcostestimate(PlannerInfo *root,
6003 : IndexPath *path,
6004 : double loop_count,
6005 : GenericCosts *costs)
6006 : {
6007 399554 : IndexOptInfo *index = path->indexinfo;
6008 399554 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
6009 399554 : List *indexOrderBys = path->indexorderbys;
6010 : Cost indexStartupCost;
6011 : Cost indexTotalCost;
6012 : Selectivity indexSelectivity;
6013 : double indexCorrelation;
6014 : double numIndexPages;
6015 : double numIndexTuples;
6016 : double spc_random_page_cost;
6017 : double num_sa_scans;
6018 : double num_outer_scans;
6019 : double num_scans;
6020 : double qual_op_cost;
6021 : double qual_arg_cost;
6022 : List *selectivityQuals;
6023 : ListCell *l;
6024 :
6025 : /*
6026 : * If the index is partial, AND the index predicate with the explicitly
6027 : * given indexquals to produce a more accurate idea of the index
6028 : * selectivity.
6029 : */
6030 399554 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
6031 :
6032 : /*
6033 : * Check for ScalarArrayOpExpr index quals, and estimate the number of
6034 : * index scans that will be performed.
6035 : */
6036 399554 : num_sa_scans = 1;
6037 738464 : foreach(l, indexQuals)
6038 : {
6039 338910 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
6040 :
6041 338910 : if (IsA(rinfo->clause, ScalarArrayOpExpr))
6042 : {
6043 9316 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
6044 9316 : int alength = estimate_array_length(lsecond(saop->args));
6045 :
6046 9316 : if (alength > 1)
6047 9252 : num_sa_scans *= alength;
6048 : }
6049 : }
6050 :
6051 : /* Estimate the fraction of main-table tuples that will be visited */
6052 399554 : indexSelectivity = clauselist_selectivity(root, selectivityQuals,
6053 399554 : index->rel->relid,
6054 : JOIN_INNER,
6055 : NULL);
6056 :
6057 : /*
6058 : * If caller didn't give us an estimate, estimate the number of index
6059 : * tuples that will be visited. We do it in this rather peculiar-looking
6060 : * way in order to get the right answer for partial indexes.
6061 : */
6062 399554 : numIndexTuples = costs->numIndexTuples;
6063 399554 : if (numIndexTuples <= 0.0)
6064 : {
6065 33046 : numIndexTuples = indexSelectivity * index->rel->tuples;
6066 :
6067 : /*
6068 : * The above calculation counts all the tuples visited across all
6069 : * scans induced by ScalarArrayOpExpr nodes. We want to consider the
6070 : * average per-indexscan number, so adjust. This is a handy place to
6071 : * round to integer, too. (If caller supplied tuple estimate, it's
6072 : * responsible for handling these considerations.)
6073 : */
6074 33046 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
6075 : }
6076 :
6077 : /*
6078 : * We can bound the number of tuples by the index size in any case. Also,
6079 : * always estimate at least one tuple is touched, even when
6080 : * indexSelectivity estimate is tiny.
6081 : */
6082 399554 : if (numIndexTuples > index->tuples)
6083 1418 : numIndexTuples = index->tuples;
6084 399554 : if (numIndexTuples < 1.0)
6085 30856 : numIndexTuples = 1.0;
6086 :
6087 : /*
6088 : * Estimate the number of index pages that will be retrieved.
6089 : *
6090 : * We use the simplistic method of taking a pro-rata fraction of the total
6091 : * number of index pages. In effect, this counts only leaf pages and not
6092 : * any overhead such as index metapage or upper tree levels.
6093 : *
6094 : * In practice access to upper index levels is often nearly free because
6095 : * those tend to stay in cache under load; moreover, the cost involved is
6096 : * highly dependent on index type. We therefore ignore such costs here
6097 : * and leave it to the caller to add a suitable charge if needed.
6098 : */
6099 399554 : if (index->pages > 1 && index->tuples > 1)
6100 385298 : numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
6101 : else
6102 14256 : numIndexPages = 1.0;
6103 :
6104 : /* fetch estimated page cost for tablespace containing index */
6105 399554 : get_tablespace_page_costs(index->reltablespace,
6106 : &spc_random_page_cost,
6107 : NULL);
6108 :
6109 : /*
6110 : * Now compute the disk access costs.
6111 : *
6112 : * The above calculations are all per-index-scan. However, if we are in a
6113 : * nestloop inner scan, we can expect the scan to be repeated (with
6114 : * different search keys) for each row of the outer relation. Likewise,
6115 : * ScalarArrayOpExpr quals result in multiple index scans. This creates
6116 : * the potential for cache effects to reduce the number of disk page
6117 : * fetches needed. We want to estimate the average per-scan I/O cost in
6118 : * the presence of caching.
6119 : *
6120 : * We use the Mackert-Lohman formula (see costsize.c for details) to
6121 : * estimate the total number of page fetches that occur. While this
6122 : * wasn't what it was designed for, it seems a reasonable model anyway.
6123 : * Note that we are counting pages not tuples anymore, so we take N = T =
6124 : * index size, as if there were one "tuple" per page.
6125 : */
6126 399554 : num_outer_scans = loop_count;
6127 399554 : num_scans = num_sa_scans * num_outer_scans;
6128 :
6129 399554 : if (num_scans > 1)
6130 : {
6131 : double pages_fetched;
6132 :
6133 : /* total page fetches ignoring cache effects */
6134 48082 : pages_fetched = numIndexPages * num_scans;
6135 :
6136 : /* use Mackert and Lohman formula to adjust for cache effects */
6137 48082 : pages_fetched = index_pages_fetched(pages_fetched,
6138 : index->pages,
6139 48082 : (double) index->pages,
6140 : root);
6141 :
6142 : /*
6143 : * Now compute the total disk access cost, and then report a pro-rated
6144 : * share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
6145 : * since that's internal to the indexscan.)
6146 : */
6147 48082 : indexTotalCost = (pages_fetched * spc_random_page_cost)
6148 : / num_outer_scans;
6149 : }
6150 : else
6151 : {
6152 : /*
6153 : * For a single index scan, we just charge spc_random_page_cost per
6154 : * page touched.
6155 : */
6156 351472 : indexTotalCost = numIndexPages * spc_random_page_cost;
6157 : }
6158 :
6159 : /*
6160 : * CPU cost: any complex expressions in the indexquals will need to be
6161 : * evaluated once at the start of the scan to reduce them to runtime keys
6162 : * to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
6163 : * CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
6164 : * indexqual operator. Because we have numIndexTuples as a per-scan
6165 : * number, we have to multiply by num_sa_scans to get the correct result
6166 : * for ScalarArrayOpExpr cases. Similarly add in costs for any index
6167 : * ORDER BY expressions.
6168 : *
6169 : * Note: this neglects the possible costs of rechecking lossy operators.
6170 : * Detecting that that might be needed seems more expensive than it's
6171 : * worth, though, considering all the other inaccuracies here ...
6172 : */
6173 399554 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
6174 399554 : index_other_operands_eval_cost(root, indexOrderBys);
6175 399554 : qual_op_cost = cpu_operator_cost *
6176 399554 : (list_length(indexQuals) + list_length(indexOrderBys));
6177 :
6178 399554 : indexStartupCost = qual_arg_cost;
6179 399554 : indexTotalCost += qual_arg_cost;
6180 399554 : indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
6181 :
6182 : /*
6183 : * Generic assumption about index correlation: there isn't any.
6184 : */
6185 399554 : indexCorrelation = 0.0;
6186 :
6187 : /*
6188 : * Return everything to caller.
6189 : */
6190 399554 : costs->indexStartupCost = indexStartupCost;
6191 399554 : costs->indexTotalCost = indexTotalCost;
6192 399554 : costs->indexSelectivity = indexSelectivity;
6193 399554 : costs->indexCorrelation = indexCorrelation;
6194 399554 : costs->numIndexPages = numIndexPages;
6195 399554 : costs->numIndexTuples = numIndexTuples;
6196 399554 : costs->spc_random_page_cost = spc_random_page_cost;
6197 399554 : costs->num_sa_scans = num_sa_scans;
6198 399554 : }
6199 :
6200 : /*
6201 : * If the index is partial, add its predicate to the given qual list.
6202 : *
6203 : * ANDing the index predicate with the explicitly given indexquals produces
6204 : * a more accurate idea of the index's selectivity. However, we need to be
6205 : * careful not to insert redundant clauses, because clauselist_selectivity()
6206 : * is easily fooled into computing a too-low selectivity estimate. Our
6207 : * approach is to add only the predicate clause(s) that cannot be proven to
6208 : * be implied by the given indexquals. This successfully handles cases such
6209 : * as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
6210 : * There are many other cases where we won't detect redundancy, leading to a
6211 : * too-low selectivity estimate, which will bias the system in favor of using
6212 : * partial indexes where possible. That is not necessarily bad though.
6213 : *
6214 : * Note that indexQuals contains RestrictInfo nodes while the indpred
6215 : * does not, so the output list will be mixed. This is OK for both
6216 : * predicate_implied_by() and clauselist_selectivity(), but might be
6217 : * problematic if the result were passed to other things.
6218 : */
6219 : List *
6220 685974 : add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
6221 : {
6222 685974 : List *predExtraQuals = NIL;
6223 : ListCell *lc;
6224 :
6225 685974 : if (index->indpred == NIL)
6226 684546 : return indexQuals;
6227 :
6228 2864 : foreach(lc, index->indpred)
6229 : {
6230 1436 : Node *predQual = (Node *) lfirst(lc);
6231 1436 : List *oneQual = list_make1(predQual);
6232 :
6233 1436 : if (!predicate_implied_by(oneQual, indexQuals, false))
6234 1288 : predExtraQuals = list_concat(predExtraQuals, oneQual);
6235 : }
6236 1428 : return list_concat(predExtraQuals, indexQuals);
6237 : }
6238 :
6239 :
6240 : void
6241 395764 : btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6242 : Cost *indexStartupCost, Cost *indexTotalCost,
6243 : Selectivity *indexSelectivity, double *indexCorrelation,
6244 : double *indexPages)
6245 : {
6246 395764 : IndexOptInfo *index = path->indexinfo;
6247 : GenericCosts costs;
6248 : Oid relid;
6249 : AttrNumber colnum;
6250 : VariableStatData vardata;
6251 : double numIndexTuples;
6252 : Cost descentCost;
6253 : List *indexBoundQuals;
6254 : int indexcol;
6255 : bool eqQualHere;
6256 : bool found_saop;
6257 : bool found_is_null_op;
6258 : double num_sa_scans;
6259 : ListCell *lc;
6260 :
6261 : /*
6262 : * For a btree scan, only leading '=' quals plus inequality quals for the
6263 : * immediately next attribute contribute to index selectivity (these are
6264 : * the "boundary quals" that determine the starting and stopping points of
6265 : * the index scan). Additional quals can suppress visits to the heap, so
6266 : * it's OK to count them in indexSelectivity, but they should not count
6267 : * for estimating numIndexTuples. So we must examine the given indexquals
6268 : * to find out which ones count as boundary quals. We rely on the
6269 : * knowledge that they are given in index column order.
6270 : *
6271 : * For a RowCompareExpr, we consider only the first column, just as
6272 : * rowcomparesel() does.
6273 : *
6274 : * If there's a ScalarArrayOpExpr in the quals, we'll actually perform N
6275 : * index scans not one, but the ScalarArrayOpExpr's operator can be
6276 : * considered to act the same as it normally does.
6277 : */
6278 395764 : indexBoundQuals = NIL;
6279 395764 : indexcol = 0;
6280 395764 : eqQualHere = false;
6281 395764 : found_saop = false;
6282 395764 : found_is_null_op = false;
6283 395764 : num_sa_scans = 1;
6284 713984 : foreach(lc, path->indexclauses)
6285 : {
6286 330072 : IndexClause *iclause = lfirst_node(IndexClause, lc);
6287 : ListCell *lc2;
6288 :
6289 330072 : if (indexcol != iclause->indexcol)
6290 : {
6291 : /* Beginning of a new column's quals */
6292 59452 : if (!eqQualHere)
6293 11212 : break; /* done if no '=' qual for indexcol */
6294 48240 : eqQualHere = false;
6295 48240 : indexcol++;
6296 48240 : if (indexcol != iclause->indexcol)
6297 640 : break; /* no quals at all for indexcol */
6298 : }
6299 :
6300 : /* Examine each indexqual associated with this index clause */
6301 638258 : foreach(lc2, iclause->indexquals)
6302 : {
6303 320038 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
6304 320038 : Expr *clause = rinfo->clause;
6305 320038 : Oid clause_op = InvalidOid;
6306 : int op_strategy;
6307 :
6308 320038 : if (IsA(clause, OpExpr))
6309 : {
6310 309180 : OpExpr *op = (OpExpr *) clause;
6311 :
6312 309180 : clause_op = op->opno;
6313 : }
6314 10858 : else if (IsA(clause, RowCompareExpr))
6315 : {
6316 88 : RowCompareExpr *rc = (RowCompareExpr *) clause;
6317 :
6318 88 : clause_op = linitial_oid(rc->opnos);
6319 : }
6320 10770 : else if (IsA(clause, ScalarArrayOpExpr))
6321 : {
6322 9184 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
6323 9184 : Node *other_operand = (Node *) lsecond(saop->args);
6324 9184 : int alength = estimate_array_length(other_operand);
6325 :
6326 9184 : clause_op = saop->opno;
6327 9184 : found_saop = true;
6328 : /* count number of SA scans induced by indexBoundQuals only */
6329 9184 : if (alength > 1)
6330 9120 : num_sa_scans *= alength;
6331 : }
6332 1586 : else if (IsA(clause, NullTest))
6333 : {
6334 1586 : NullTest *nt = (NullTest *) clause;
6335 :
6336 1586 : if (nt->nulltesttype == IS_NULL)
6337 : {
6338 92 : found_is_null_op = true;
6339 : /* IS NULL is like = for selectivity purposes */
6340 92 : eqQualHere = true;
6341 : }
6342 : }
6343 : else
6344 0 : elog(ERROR, "unsupported indexqual type: %d",
6345 : (int) nodeTag(clause));
6346 :
6347 : /* check for equality operator */
6348 320038 : if (OidIsValid(clause_op))
6349 : {
6350 318452 : op_strategy = get_op_opfamily_strategy(clause_op,
6351 318452 : index->opfamily[indexcol]);
6352 : Assert(op_strategy != 0); /* not a member of opfamily?? */
6353 318452 : if (op_strategy == BTEqualStrategyNumber)
6354 294984 : eqQualHere = true;
6355 : }
6356 :
6357 320038 : indexBoundQuals = lappend(indexBoundQuals, rinfo);
6358 : }
6359 : }
6360 :
6361 : /*
6362 : * If index is unique and we found an '=' clause for each column, we can
6363 : * just assume numIndexTuples = 1 and skip the expensive
6364 : * clauselist_selectivity calculations. However, a ScalarArrayOp or
6365 : * NullTest invalidates that theory, even though it sets eqQualHere.
6366 : */
6367 395764 : if (index->unique &&
6368 331370 : indexcol == index->nkeycolumns - 1 &&
6369 118190 : eqQualHere &&
6370 118190 : !found_saop &&
6371 110818 : !found_is_null_op)
6372 110774 : numIndexTuples = 1.0;
6373 : else
6374 : {
6375 : List *selectivityQuals;
6376 : Selectivity btreeSelectivity;
6377 :
6378 : /*
6379 : * If the index is partial, AND the index predicate with the
6380 : * index-bound quals to produce a more accurate idea of the number of
6381 : * rows covered by the bound conditions.
6382 : */
6383 284990 : selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
6384 :
6385 284990 : btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
6386 284990 : index->rel->relid,
6387 : JOIN_INNER,
6388 : NULL);
6389 284990 : numIndexTuples = btreeSelectivity * index->rel->tuples;
6390 :
6391 : /*
6392 : * As in genericcostestimate(), we have to adjust for any
6393 : * ScalarArrayOpExpr quals included in indexBoundQuals, and then round
6394 : * to integer.
6395 : */
6396 284990 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
6397 : }
6398 :
6399 : /*
6400 : * Now do generic index cost estimation.
6401 : */
6402 3561876 : MemSet(&costs, 0, sizeof(costs));
6403 395764 : costs.numIndexTuples = numIndexTuples;
6404 :
6405 395764 : genericcostestimate(root, path, loop_count, &costs);
6406 :
6407 : /*
6408 : * Add a CPU-cost component to represent the costs of initial btree
6409 : * descent. We don't charge any I/O cost for touching upper btree levels,
6410 : * since they tend to stay in cache, but we still have to do about log2(N)
6411 : * comparisons to descend a btree of N leaf tuples. We charge one
6412 : * cpu_operator_cost per comparison.
6413 : *
6414 : * If there are ScalarArrayOpExprs, charge this once per SA scan. The
6415 : * ones after the first one are not startup cost so far as the overall
6416 : * plan is concerned, so add them only to "total" cost.
6417 : */
6418 395764 : if (index->tuples > 1) /* avoid computing log(0) */
6419 : {
6420 385294 : descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
6421 385294 : costs.indexStartupCost += descentCost;
6422 385294 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6423 : }
6424 :
6425 : /*
6426 : * Even though we're not charging I/O cost for touching upper btree pages,
6427 : * it's still reasonable to charge some CPU cost per page descended
6428 : * through. Moreover, if we had no such charge at all, bloated indexes
6429 : * would appear to have the same search cost as unbloated ones, at least
6430 : * in cases where only a single leaf page is expected to be visited. This
6431 : * cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
6432 : * touched. The number of such pages is btree tree height plus one (ie,
6433 : * we charge for the leaf page too). As above, charge once per SA scan.
6434 : */
6435 395764 : descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
6436 395764 : costs.indexStartupCost += descentCost;
6437 395764 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6438 :
6439 : /*
6440 : * If we can get an estimate of the first column's ordering correlation C
6441 : * from pg_statistic, estimate the index correlation as C for a
6442 : * single-column index, or C * 0.75 for multiple columns. (The idea here
6443 : * is that multiple columns dilute the importance of the first column's
6444 : * ordering, but don't negate it entirely. Before 8.0 we divided the
6445 : * correlation by the number of columns, but that seems too strong.)
6446 : */
6447 2770348 : MemSet(&vardata, 0, sizeof(vardata));
6448 :
6449 395764 : if (index->indexkeys[0] != 0)
6450 : {
6451 : /* Simple variable --- look to stats for the underlying table */
6452 394456 : RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
6453 :
6454 : Assert(rte->rtekind == RTE_RELATION);
6455 394456 : relid = rte->relid;
6456 : Assert(relid != InvalidOid);
6457 394456 : colnum = index->indexkeys[0];
6458 :
6459 394456 : if (get_relation_stats_hook &&
6460 0 : (*get_relation_stats_hook) (root, rte, colnum, &vardata))
6461 : {
6462 : /*
6463 : * The hook took control of acquiring a stats tuple. If it did
6464 : * supply a tuple, it'd better have supplied a freefunc.
6465 : */
6466 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
6467 0 : !vardata.freefunc)
6468 0 : elog(ERROR, "no function provided to release variable stats with");
6469 : }
6470 : else
6471 : {
6472 394456 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
6473 : ObjectIdGetDatum(relid),
6474 : Int16GetDatum(colnum),
6475 394456 : BoolGetDatum(rte->inh));
6476 394456 : vardata.freefunc = ReleaseSysCache;
6477 : }
6478 : }
6479 : else
6480 : {
6481 : /* Expression --- maybe there are stats for the index itself */
6482 1308 : relid = index->indexoid;
6483 1308 : colnum = 1;
6484 :
6485 1308 : if (get_index_stats_hook &&
6486 0 : (*get_index_stats_hook) (root, relid, colnum, &vardata))
6487 : {
6488 : /*
6489 : * The hook took control of acquiring a stats tuple. If it did
6490 : * supply a tuple, it'd better have supplied a freefunc.
6491 : */
6492 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
6493 0 : !vardata.freefunc)
6494 0 : elog(ERROR, "no function provided to release variable stats with");
6495 : }
6496 : else
6497 : {
6498 1308 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
6499 : ObjectIdGetDatum(relid),
6500 : Int16GetDatum(colnum),
6501 : BoolGetDatum(false));
6502 1308 : vardata.freefunc = ReleaseSysCache;
6503 : }
6504 : }
6505 :
6506 395764 : if (HeapTupleIsValid(vardata.statsTuple))
6507 : {
6508 : Oid sortop;
6509 : AttStatsSlot sslot;
6510 :
6511 632480 : sortop = get_opfamily_member(index->opfamily[0],
6512 316240 : index->opcintype[0],
6513 316240 : index->opcintype[0],
6514 : BTLessStrategyNumber);
6515 632480 : if (OidIsValid(sortop) &&
6516 316240 : get_attstatsslot(&sslot, vardata.statsTuple,
6517 : STATISTIC_KIND_CORRELATION, sortop,
6518 : ATTSTATSSLOT_NUMBERS))
6519 : {
6520 : double varCorrelation;
6521 :
6522 : Assert(sslot.nnumbers == 1);
6523 313958 : varCorrelation = sslot.numbers[0];
6524 :
6525 313958 : if (index->reverse_sort[0])
6526 0 : varCorrelation = -varCorrelation;
6527 :
6528 313958 : if (index->nkeycolumns > 1)
6529 131704 : costs.indexCorrelation = varCorrelation * 0.75;
6530 : else
6531 182254 : costs.indexCorrelation = varCorrelation;
6532 :
6533 313958 : free_attstatsslot(&sslot);
6534 : }
6535 : }
6536 :
6537 395764 : ReleaseVariableStats(vardata);
6538 :
6539 395764 : *indexStartupCost = costs.indexStartupCost;
6540 395764 : *indexTotalCost = costs.indexTotalCost;
6541 395764 : *indexSelectivity = costs.indexSelectivity;
6542 395764 : *indexCorrelation = costs.indexCorrelation;
6543 395764 : *indexPages = costs.numIndexPages;
6544 395764 : }
6545 :
6546 : void
6547 318 : hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6548 : Cost *indexStartupCost, Cost *indexTotalCost,
6549 : Selectivity *indexSelectivity, double *indexCorrelation,
6550 : double *indexPages)
6551 : {
6552 : GenericCosts costs;
6553 :
6554 2862 : MemSet(&costs, 0, sizeof(costs));
6555 :
6556 318 : genericcostestimate(root, path, loop_count, &costs);
6557 :
6558 : /*
6559 : * A hash index has no descent costs as such, since the index AM can go
6560 : * directly to the target bucket after computing the hash value. There
6561 : * are a couple of other hash-specific costs that we could conceivably add
6562 : * here, though:
6563 : *
6564 : * Ideally we'd charge spc_random_page_cost for each page in the target
6565 : * bucket, not just the numIndexPages pages that genericcostestimate
6566 : * thought we'd visit. However in most cases we don't know which bucket
6567 : * that will be. There's no point in considering the average bucket size
6568 : * because the hash AM makes sure that's always one page.
6569 : *
6570 : * Likewise, we could consider charging some CPU for each index tuple in
6571 : * the bucket, if we knew how many there were. But the per-tuple cost is
6572 : * just a hash value comparison, not a general datatype-dependent
6573 : * comparison, so any such charge ought to be quite a bit less than
6574 : * cpu_operator_cost; which makes it probably not worth worrying about.
6575 : *
6576 : * A bigger issue is that chance hash-value collisions will result in
6577 : * wasted probes into the heap. We don't currently attempt to model this
6578 : * cost on the grounds that it's rare, but maybe it's not rare enough.
6579 : * (Any fix for this ought to consider the generic lossy-operator problem,
6580 : * though; it's not entirely hash-specific.)
6581 : */
6582 :
6583 318 : *indexStartupCost = costs.indexStartupCost;
6584 318 : *indexTotalCost = costs.indexTotalCost;
6585 318 : *indexSelectivity = costs.indexSelectivity;
6586 318 : *indexCorrelation = costs.indexCorrelation;
6587 318 : *indexPages = costs.numIndexPages;
6588 318 : }
6589 :
6590 : void
6591 2284 : gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6592 : Cost *indexStartupCost, Cost *indexTotalCost,
6593 : Selectivity *indexSelectivity, double *indexCorrelation,
6594 : double *indexPages)
6595 : {
6596 2284 : IndexOptInfo *index = path->indexinfo;
6597 : GenericCosts costs;
6598 : Cost descentCost;
6599 :
6600 20556 : MemSet(&costs, 0, sizeof(costs));
6601 :
6602 2284 : genericcostestimate(root, path, loop_count, &costs);
6603 :
6604 : /*
6605 : * We model index descent costs similarly to those for btree, but to do
6606 : * that we first need an idea of the tree height. We somewhat arbitrarily
6607 : * assume that the fanout is 100, meaning the tree height is at most
6608 : * log100(index->pages).
6609 : *
6610 : * Although this computation isn't really expensive enough to require
6611 : * caching, we might as well use index->tree_height to cache it.
6612 : */
6613 2284 : if (index->tree_height < 0) /* unknown? */
6614 : {
6615 2272 : if (index->pages > 1) /* avoid computing log(0) */
6616 1904 : index->tree_height = (int) (log(index->pages) / log(100.0));
6617 : else
6618 368 : index->tree_height = 0;
6619 : }
6620 :
6621 : /*
6622 : * Add a CPU-cost component to represent the costs of initial descent. We
6623 : * just use log(N) here not log2(N) since the branching factor isn't
6624 : * necessarily two anyway. As for btree, charge once per SA scan.
6625 : */
6626 2284 : if (index->tuples > 1) /* avoid computing log(0) */
6627 : {
6628 2284 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
6629 2284 : costs.indexStartupCost += descentCost;
6630 2284 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6631 : }
6632 :
6633 : /*
6634 : * Likewise add a per-page charge, calculated the same as for btrees.
6635 : */
6636 2284 : descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
6637 2284 : costs.indexStartupCost += descentCost;
6638 2284 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6639 :
6640 2284 : *indexStartupCost = costs.indexStartupCost;
6641 2284 : *indexTotalCost = costs.indexTotalCost;
6642 2284 : *indexSelectivity = costs.indexSelectivity;
6643 2284 : *indexCorrelation = costs.indexCorrelation;
6644 2284 : *indexPages = costs.numIndexPages;
6645 2284 : }
6646 :
6647 : void
6648 1140 : spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6649 : Cost *indexStartupCost, Cost *indexTotalCost,
6650 : Selectivity *indexSelectivity, double *indexCorrelation,
6651 : double *indexPages)
6652 : {
6653 1140 : IndexOptInfo *index = path->indexinfo;
6654 : GenericCosts costs;
6655 : Cost descentCost;
6656 :
6657 10260 : MemSet(&costs, 0, sizeof(costs));
6658 :
6659 1140 : genericcostestimate(root, path, loop_count, &costs);
6660 :
6661 : /*
6662 : * We model index descent costs similarly to those for btree, but to do
6663 : * that we first need an idea of the tree height. We somewhat arbitrarily
6664 : * assume that the fanout is 100, meaning the tree height is at most
6665 : * log100(index->pages).
6666 : *
6667 : * Although this computation isn't really expensive enough to require
6668 : * caching, we might as well use index->tree_height to cache it.
6669 : */
6670 1140 : if (index->tree_height < 0) /* unknown? */
6671 : {
6672 1136 : if (index->pages > 1) /* avoid computing log(0) */
6673 1136 : index->tree_height = (int) (log(index->pages) / log(100.0));
6674 : else
6675 0 : index->tree_height = 0;
6676 : }
6677 :
6678 : /*
6679 : * Add a CPU-cost component to represent the costs of initial descent. We
6680 : * just use log(N) here not log2(N) since the branching factor isn't
6681 : * necessarily two anyway. As for btree, charge once per SA scan.
6682 : */
6683 1140 : if (index->tuples > 1) /* avoid computing log(0) */
6684 : {
6685 1140 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
6686 1140 : costs.indexStartupCost += descentCost;
6687 1140 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6688 : }
6689 :
6690 : /*
6691 : * Likewise add a per-page charge, calculated the same as for btrees.
6692 : */
6693 1140 : descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
6694 1140 : costs.indexStartupCost += descentCost;
6695 1140 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6696 :
6697 1140 : *indexStartupCost = costs.indexStartupCost;
6698 1140 : *indexTotalCost = costs.indexTotalCost;
6699 1140 : *indexSelectivity = costs.indexSelectivity;
6700 1140 : *indexCorrelation = costs.indexCorrelation;
6701 1140 : *indexPages = costs.numIndexPages;
6702 1140 : }
6703 :
6704 :
6705 : /*
6706 : * Support routines for gincostestimate
6707 : */
6708 :
6709 : typedef struct
6710 : {
6711 : bool attHasFullScan[INDEX_MAX_KEYS];
6712 : bool attHasNormalScan[INDEX_MAX_KEYS];
6713 : double partialEntries;
6714 : double exactEntries;
6715 : double searchEntries;
6716 : double arrayScans;
6717 : } GinQualCounts;
6718 :
6719 : /*
6720 : * Estimate the number of index terms that need to be searched for while
6721 : * testing the given GIN query, and increment the counts in *counts
6722 : * appropriately. If the query is unsatisfiable, return false.
6723 : */
6724 : static bool
6725 1574 : gincost_pattern(IndexOptInfo *index, int indexcol,
6726 : Oid clause_op, Datum query,
6727 : GinQualCounts *counts)
6728 : {
6729 : FmgrInfo flinfo;
6730 : Oid extractProcOid;
6731 : Oid collation;
6732 : int strategy_op;
6733 : Oid lefttype,
6734 : righttype;
6735 1574 : int32 nentries = 0;
6736 1574 : bool *partial_matches = NULL;
6737 1574 : Pointer *extra_data = NULL;
6738 1574 : bool *nullFlags = NULL;
6739 1574 : int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
6740 : int32 i;
6741 :
6742 : Assert(indexcol < index->nkeycolumns);
6743 :
6744 : /*
6745 : * Get the operator's strategy number and declared input data types within
6746 : * the index opfamily. (We don't need the latter, but we use
6747 : * get_op_opfamily_properties because it will throw error if it fails to
6748 : * find a matching pg_amop entry.)
6749 : */
6750 1574 : get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
6751 : &strategy_op, &lefttype, &righttype);
6752 :
6753 : /*
6754 : * GIN always uses the "default" support functions, which are those with
6755 : * lefttype == righttype == the opclass' opcintype (see
6756 : * IndexSupportInitialize in relcache.c).
6757 : */
6758 3148 : extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
6759 1574 : index->opcintype[indexcol],
6760 1574 : index->opcintype[indexcol],
6761 : GIN_EXTRACTQUERY_PROC);
6762 :
6763 1574 : if (!OidIsValid(extractProcOid))
6764 : {
6765 : /* should not happen; throw same error as index_getprocinfo */
6766 0 : elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
6767 : GIN_EXTRACTQUERY_PROC, indexcol + 1,
6768 : get_rel_name(index->indexoid));
6769 : }
6770 :
6771 : /*
6772 : * Choose collation to pass to extractProc (should match initGinState).
6773 : */
6774 1574 : if (OidIsValid(index->indexcollations[indexcol]))
6775 326 : collation = index->indexcollations[indexcol];
6776 : else
6777 1248 : collation = DEFAULT_COLLATION_OID;
6778 :
6779 1574 : fmgr_info(extractProcOid, &flinfo);
6780 :
6781 1574 : set_fn_opclass_options(&flinfo, index->opclassoptions[indexcol]);
6782 :
6783 1574 : FunctionCall7Coll(&flinfo,
6784 : collation,
6785 : query,
6786 : PointerGetDatum(&nentries),
6787 : UInt16GetDatum(strategy_op),
6788 : PointerGetDatum(&partial_matches),
6789 : PointerGetDatum(&extra_data),
6790 : PointerGetDatum(&nullFlags),
6791 : PointerGetDatum(&searchMode));
6792 :
6793 1574 : if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
6794 : {
6795 : /* No match is possible */
6796 8 : return false;
6797 : }
6798 :
6799 4744 : for (i = 0; i < nentries; i++)
6800 : {
6801 : /*
6802 : * For partial match we haven't any information to estimate number of
6803 : * matched entries in index, so, we just estimate it as 100
6804 : */
6805 3178 : if (partial_matches && partial_matches[i])
6806 302 : counts->partialEntries += 100;
6807 : else
6808 2876 : counts->exactEntries++;
6809 :
6810 3178 : counts->searchEntries++;
6811 : }
6812 :
6813 1566 : if (searchMode == GIN_SEARCH_MODE_DEFAULT)
6814 : {
6815 1236 : counts->attHasNormalScan[indexcol] = true;
6816 : }
6817 330 : else if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
6818 : {
6819 : /* Treat "include empty" like an exact-match item */
6820 30 : counts->attHasNormalScan[indexcol] = true;
6821 30 : counts->exactEntries++;
6822 30 : counts->searchEntries++;
6823 : }
6824 : else
6825 : {
6826 : /* It's GIN_SEARCH_MODE_ALL */
6827 300 : counts->attHasFullScan[indexcol] = true;
6828 : }
6829 :
6830 1566 : return true;
6831 : }
6832 :
6833 : /*
6834 : * Estimate the number of index terms that need to be searched for while
6835 : * testing the given GIN index clause, and increment the counts in *counts
6836 : * appropriately. If the query is unsatisfiable, return false.
6837 : */
6838 : static bool
6839 1566 : gincost_opexpr(PlannerInfo *root,
6840 : IndexOptInfo *index,
6841 : int indexcol,
6842 : OpExpr *clause,
6843 : GinQualCounts *counts)
6844 : {
6845 1566 : Oid clause_op = clause->opno;
6846 1566 : Node *operand = (Node *) lsecond(clause->args);
6847 :
6848 : /* aggressively reduce to a constant, and look through relabeling */
6849 1566 : operand = estimate_expression_value(root, operand);
6850 :
6851 1566 : if (IsA(operand, RelabelType))
6852 0 : operand = (Node *) ((RelabelType *) operand)->arg;
6853 :
6854 : /*
6855 : * It's impossible to call extractQuery method for unknown operand. So
6856 : * unless operand is a Const we can't do much; just assume there will be
6857 : * one ordinary search entry from the operand at runtime.
6858 : */
6859 1566 : if (!IsA(operand, Const))
6860 : {
6861 0 : counts->exactEntries++;
6862 0 : counts->searchEntries++;
6863 0 : return true;
6864 : }
6865 :
6866 : /* If Const is null, there can be no matches */
6867 1566 : if (((Const *) operand)->constisnull)
6868 0 : return false;
6869 :
6870 : /* Otherwise, apply extractQuery and get the actual term counts */
6871 1566 : return gincost_pattern(index, indexcol, clause_op,
6872 : ((Const *) operand)->constvalue,
6873 : counts);
6874 : }
6875 :
6876 : /*
6877 : * Estimate the number of index terms that need to be searched for while
6878 : * testing the given GIN index clause, and increment the counts in *counts
6879 : * appropriately. If the query is unsatisfiable, return false.
6880 : *
6881 : * A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
6882 : * each of which involves one value from the RHS array, plus all the
6883 : * non-array quals (if any). To model this, we average the counts across
6884 : * the RHS elements, and add the averages to the counts in *counts (which
6885 : * correspond to per-indexscan costs). We also multiply counts->arrayScans
6886 : * by N, causing gincostestimate to scale up its estimates accordingly.
6887 : */
6888 : static bool
6889 4 : gincost_scalararrayopexpr(PlannerInfo *root,
6890 : IndexOptInfo *index,
6891 : int indexcol,
6892 : ScalarArrayOpExpr *clause,
6893 : double numIndexEntries,
6894 : GinQualCounts *counts)
6895 : {
6896 4 : Oid clause_op = clause->opno;
6897 4 : Node *rightop = (Node *) lsecond(clause->args);
6898 : ArrayType *arrayval;
6899 : int16 elmlen;
6900 : bool elmbyval;
6901 : char elmalign;
6902 : int numElems;
6903 : Datum *elemValues;
6904 : bool *elemNulls;
6905 : GinQualCounts arraycounts;
6906 4 : int numPossible = 0;
6907 : int i;
6908 :
6909 : Assert(clause->useOr);
6910 :
6911 : /* aggressively reduce to a constant, and look through relabeling */
6912 4 : rightop = estimate_expression_value(root, rightop);
6913 :
6914 4 : if (IsA(rightop, RelabelType))
6915 0 : rightop = (Node *) ((RelabelType *) rightop)->arg;
6916 :
6917 : /*
6918 : * It's impossible to call extractQuery method for unknown operand. So
6919 : * unless operand is a Const we can't do much; just assume there will be
6920 : * one ordinary search entry from each array entry at runtime, and fall
6921 : * back on a probably-bad estimate of the number of array entries.
6922 : */
6923 4 : if (!IsA(rightop, Const))
6924 : {
6925 0 : counts->exactEntries++;
6926 0 : counts->searchEntries++;
6927 0 : counts->arrayScans *= estimate_array_length(rightop);
6928 0 : return true;
6929 : }
6930 :
6931 : /* If Const is null, there can be no matches */
6932 4 : if (((Const *) rightop)->constisnull)
6933 0 : return false;
6934 :
6935 : /* Otherwise, extract the array elements and iterate over them */
6936 4 : arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
6937 4 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
6938 : &elmlen, &elmbyval, &elmalign);
6939 4 : deconstruct_array(arrayval,
6940 : ARR_ELEMTYPE(arrayval),
6941 : elmlen, elmbyval, elmalign,
6942 : &elemValues, &elemNulls, &numElems);
6943 :
6944 4 : memset(&arraycounts, 0, sizeof(arraycounts));
6945 :
6946 12 : for (i = 0; i < numElems; i++)
6947 : {
6948 : GinQualCounts elemcounts;
6949 :
6950 : /* NULL can't match anything, so ignore, as the executor will */
6951 8 : if (elemNulls[i])
6952 0 : continue;
6953 :
6954 : /* Otherwise, apply extractQuery and get the actual term counts */
6955 8 : memset(&elemcounts, 0, sizeof(elemcounts));
6956 :
6957 8 : if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
6958 : &elemcounts))
6959 : {
6960 : /* We ignore array elements that are unsatisfiable patterns */
6961 8 : numPossible++;
6962 :
6963 8 : if (elemcounts.attHasFullScan[indexcol] &&
6964 0 : !elemcounts.attHasNormalScan[indexcol])
6965 : {
6966 : /*
6967 : * Full index scan will be required. We treat this as if
6968 : * every key in the index had been listed in the query; is
6969 : * that reasonable?
6970 : */
6971 0 : elemcounts.partialEntries = 0;
6972 0 : elemcounts.exactEntries = numIndexEntries;
6973 0 : elemcounts.searchEntries = numIndexEntries;
6974 : }
6975 8 : arraycounts.partialEntries += elemcounts.partialEntries;
6976 8 : arraycounts.exactEntries += elemcounts.exactEntries;
6977 8 : arraycounts.searchEntries += elemcounts.searchEntries;
6978 : }
6979 : }
6980 :
6981 4 : if (numPossible == 0)
6982 : {
6983 : /* No satisfiable patterns in the array */
6984 0 : return false;
6985 : }
6986 :
6987 : /*
6988 : * Now add the averages to the global counts. This will give us an
6989 : * estimate of the average number of terms searched for in each indexscan,
6990 : * including contributions from both array and non-array quals.
6991 : */
6992 4 : counts->partialEntries += arraycounts.partialEntries / numPossible;
6993 4 : counts->exactEntries += arraycounts.exactEntries / numPossible;
6994 4 : counts->searchEntries += arraycounts.searchEntries / numPossible;
6995 :
6996 4 : counts->arrayScans *= numPossible;
6997 :
6998 4 : return true;
6999 : }
7000 :
7001 : /*
7002 : * GIN has search behavior completely different from other index types
7003 : */
7004 : void
7005 1430 : gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7006 : Cost *indexStartupCost, Cost *indexTotalCost,
7007 : Selectivity *indexSelectivity, double *indexCorrelation,
7008 : double *indexPages)
7009 : {
7010 1430 : IndexOptInfo *index = path->indexinfo;
7011 1430 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
7012 : List *selectivityQuals;
7013 1430 : double numPages = index->pages,
7014 1430 : numTuples = index->tuples;
7015 : double numEntryPages,
7016 : numDataPages,
7017 : numPendingPages,
7018 : numEntries;
7019 : GinQualCounts counts;
7020 : bool matchPossible;
7021 : bool fullIndexScan;
7022 : double partialScale;
7023 : double entryPagesFetched,
7024 : dataPagesFetched,
7025 : dataPagesFetchedBySel;
7026 : double qual_op_cost,
7027 : qual_arg_cost,
7028 : spc_random_page_cost,
7029 : outer_scans;
7030 : Relation indexRel;
7031 : GinStatsData ginStats;
7032 : ListCell *lc;
7033 : int i;
7034 :
7035 : /*
7036 : * Obtain statistical information from the meta page, if possible. Else
7037 : * set ginStats to zeroes, and we'll cope below.
7038 : */
7039 1430 : if (!index->hypothetical)
7040 : {
7041 : /* Lock should have already been obtained in plancat.c */
7042 1430 : indexRel = index_open(index->indexoid, NoLock);
7043 1430 : ginGetStats(indexRel, &ginStats);
7044 1430 : index_close(indexRel, NoLock);
7045 : }
7046 : else
7047 : {
7048 0 : memset(&ginStats, 0, sizeof(ginStats));
7049 : }
7050 :
7051 : /*
7052 : * Assuming we got valid (nonzero) stats at all, nPendingPages can be
7053 : * trusted, but the other fields are data as of the last VACUUM. We can
7054 : * scale them up to account for growth since then, but that method only
7055 : * goes so far; in the worst case, the stats might be for a completely
7056 : * empty index, and scaling them will produce pretty bogus numbers.
7057 : * Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
7058 : * it's grown more than that, fall back to estimating things only from the
7059 : * assumed-accurate index size. But we'll trust nPendingPages in any case
7060 : * so long as it's not clearly insane, ie, more than the index size.
7061 : */
7062 1430 : if (ginStats.nPendingPages < numPages)
7063 1430 : numPendingPages = ginStats.nPendingPages;
7064 : else
7065 0 : numPendingPages = 0;
7066 :
7067 1430 : if (numPages > 0 && ginStats.nTotalPages <= numPages &&
7068 1430 : ginStats.nTotalPages > numPages / 4 &&
7069 1402 : ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
7070 1230 : {
7071 : /*
7072 : * OK, the stats seem close enough to sane to be trusted. But we
7073 : * still need to scale them by the ratio numPages / nTotalPages to
7074 : * account for growth since the last VACUUM.
7075 : */
7076 1230 : double scale = numPages / ginStats.nTotalPages;
7077 :
7078 1230 : numEntryPages = ceil(ginStats.nEntryPages * scale);
7079 1230 : numDataPages = ceil(ginStats.nDataPages * scale);
7080 1230 : numEntries = ceil(ginStats.nEntries * scale);
7081 : /* ensure we didn't round up too much */
7082 1230 : numEntryPages = Min(numEntryPages, numPages - numPendingPages);
7083 1230 : numDataPages = Min(numDataPages,
7084 : numPages - numPendingPages - numEntryPages);
7085 : }
7086 : else
7087 : {
7088 : /*
7089 : * We might get here because it's a hypothetical index, or an index
7090 : * created pre-9.1 and never vacuumed since upgrading (in which case
7091 : * its stats would read as zeroes), or just because it's grown too
7092 : * much since the last VACUUM for us to put our faith in scaling.
7093 : *
7094 : * Invent some plausible internal statistics based on the index page
7095 : * count (and clamp that to at least 10 pages, just in case). We
7096 : * estimate that 90% of the index is entry pages, and the rest is data
7097 : * pages. Estimate 100 entries per entry page; this is rather bogus
7098 : * since it'll depend on the size of the keys, but it's more robust
7099 : * than trying to predict the number of entries per heap tuple.
7100 : */
7101 200 : numPages = Max(numPages, 10);
7102 200 : numEntryPages = floor((numPages - numPendingPages) * 0.90);
7103 200 : numDataPages = numPages - numPendingPages - numEntryPages;
7104 200 : numEntries = floor(numEntryPages * 100);
7105 : }
7106 :
7107 : /* In an empty index, numEntries could be zero. Avoid divide-by-zero */
7108 1430 : if (numEntries < 1)
7109 0 : numEntries = 1;
7110 :
7111 : /*
7112 : * If the index is partial, AND the index predicate with the index-bound
7113 : * quals to produce a more accurate idea of the number of rows covered by
7114 : * the bound conditions.
7115 : */
7116 1430 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
7117 :
7118 : /* Estimate the fraction of main-table tuples that will be visited */
7119 2860 : *indexSelectivity = clauselist_selectivity(root, selectivityQuals,
7120 1430 : index->rel->relid,
7121 : JOIN_INNER,
7122 : NULL);
7123 :
7124 : /* fetch estimated page cost for tablespace containing index */
7125 1430 : get_tablespace_page_costs(index->reltablespace,
7126 : &spc_random_page_cost,
7127 : NULL);
7128 :
7129 : /*
7130 : * Generic assumption about index correlation: there isn't any.
7131 : */
7132 1430 : *indexCorrelation = 0.0;
7133 :
7134 : /*
7135 : * Examine quals to estimate number of search entries & partial matches
7136 : */
7137 1430 : memset(&counts, 0, sizeof(counts));
7138 1430 : counts.arrayScans = 1;
7139 1430 : matchPossible = true;
7140 :
7141 3000 : foreach(lc, path->indexclauses)
7142 : {
7143 1570 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7144 : ListCell *lc2;
7145 :
7146 3132 : foreach(lc2, iclause->indexquals)
7147 : {
7148 1570 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7149 1570 : Expr *clause = rinfo->clause;
7150 :
7151 1570 : if (IsA(clause, OpExpr))
7152 : {
7153 1566 : matchPossible = gincost_opexpr(root,
7154 : index,
7155 1566 : iclause->indexcol,
7156 : (OpExpr *) clause,
7157 : &counts);
7158 1566 : if (!matchPossible)
7159 8 : break;
7160 : }
7161 4 : else if (IsA(clause, ScalarArrayOpExpr))
7162 : {
7163 4 : matchPossible = gincost_scalararrayopexpr(root,
7164 : index,
7165 4 : iclause->indexcol,
7166 : (ScalarArrayOpExpr *) clause,
7167 : numEntries,
7168 : &counts);
7169 4 : if (!matchPossible)
7170 0 : break;
7171 : }
7172 : else
7173 : {
7174 : /* shouldn't be anything else for a GIN index */
7175 0 : elog(ERROR, "unsupported GIN indexqual type: %d",
7176 : (int) nodeTag(clause));
7177 : }
7178 : }
7179 : }
7180 :
7181 : /* Fall out if there were any provably-unsatisfiable quals */
7182 1430 : if (!matchPossible)
7183 : {
7184 8 : *indexStartupCost = 0;
7185 8 : *indexTotalCost = 0;
7186 8 : *indexSelectivity = 0;
7187 8 : return;
7188 : }
7189 :
7190 : /*
7191 : * If attribute has a full scan and at the same time doesn't have normal
7192 : * scan, then we'll have to scan all non-null entries of that attribute.
7193 : * Currently, we don't have per-attribute statistics for GIN. Thus, we
7194 : * must assume the whole GIN index has to be scanned in this case.
7195 : */
7196 1422 : fullIndexScan = false;
7197 2764 : for (i = 0; i < index->nkeycolumns; i++)
7198 : {
7199 1574 : if (counts.attHasFullScan[i] && !counts.attHasNormalScan[i])
7200 : {
7201 232 : fullIndexScan = true;
7202 232 : break;
7203 : }
7204 : }
7205 :
7206 1422 : if (fullIndexScan || indexQuals == NIL)
7207 : {
7208 : /*
7209 : * Full index scan will be required. We treat this as if every key in
7210 : * the index had been listed in the query; is that reasonable?
7211 : */
7212 232 : counts.partialEntries = 0;
7213 232 : counts.exactEntries = numEntries;
7214 232 : counts.searchEntries = numEntries;
7215 : }
7216 :
7217 : /* Will we have more than one iteration of a nestloop scan? */
7218 1422 : outer_scans = loop_count;
7219 :
7220 : /*
7221 : * Compute cost to begin scan, first of all, pay attention to pending
7222 : * list.
7223 : */
7224 1422 : entryPagesFetched = numPendingPages;
7225 :
7226 : /*
7227 : * Estimate number of entry pages read. We need to do
7228 : * counts.searchEntries searches. Use a power function as it should be,
7229 : * but tuples on leaf pages usually is much greater. Here we include all
7230 : * searches in entry tree, including search of first entry in partial
7231 : * match algorithm
7232 : */
7233 1422 : entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
7234 :
7235 : /*
7236 : * Add an estimate of entry pages read by partial match algorithm. It's a
7237 : * scan over leaf pages in entry tree. We haven't any useful stats here,
7238 : * so estimate it as proportion. Because counts.partialEntries is really
7239 : * pretty bogus (see code above), it's possible that it is more than
7240 : * numEntries; clamp the proportion to ensure sanity.
7241 : */
7242 1422 : partialScale = counts.partialEntries / numEntries;
7243 1422 : partialScale = Min(partialScale, 1.0);
7244 :
7245 1422 : entryPagesFetched += ceil(numEntryPages * partialScale);
7246 :
7247 : /*
7248 : * Partial match algorithm reads all data pages before doing actual scan,
7249 : * so it's a startup cost. Again, we haven't any useful stats here, so
7250 : * estimate it as proportion.
7251 : */
7252 1422 : dataPagesFetched = ceil(numDataPages * partialScale);
7253 :
7254 : /*
7255 : * Calculate cache effects if more than one scan due to nestloops or array
7256 : * quals. The result is pro-rated per nestloop scan, but the array qual
7257 : * factor shouldn't be pro-rated (compare genericcostestimate).
7258 : */
7259 1422 : if (outer_scans > 1 || counts.arrayScans > 1)
7260 : {
7261 4 : entryPagesFetched *= outer_scans * counts.arrayScans;
7262 4 : entryPagesFetched = index_pages_fetched(entryPagesFetched,
7263 : (BlockNumber) numEntryPages,
7264 : numEntryPages, root);
7265 4 : entryPagesFetched /= outer_scans;
7266 4 : dataPagesFetched *= outer_scans * counts.arrayScans;
7267 4 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
7268 : (BlockNumber) numDataPages,
7269 : numDataPages, root);
7270 4 : dataPagesFetched /= outer_scans;
7271 : }
7272 :
7273 : /*
7274 : * Here we use random page cost because logically-close pages could be far
7275 : * apart on disk.
7276 : */
7277 1422 : *indexStartupCost = (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
7278 :
7279 : /*
7280 : * Now compute the number of data pages fetched during the scan.
7281 : *
7282 : * We assume every entry to have the same number of items, and that there
7283 : * is no overlap between them. (XXX: tsvector and array opclasses collect
7284 : * statistics on the frequency of individual keys; it would be nice to use
7285 : * those here.)
7286 : */
7287 1422 : dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
7288 :
7289 : /*
7290 : * If there is a lot of overlap among the entries, in particular if one of
7291 : * the entries is very frequent, the above calculation can grossly
7292 : * under-estimate. As a simple cross-check, calculate a lower bound based
7293 : * on the overall selectivity of the quals. At a minimum, we must read
7294 : * one item pointer for each matching entry.
7295 : *
7296 : * The width of each item pointer varies, based on the level of
7297 : * compression. We don't have statistics on that, but an average of
7298 : * around 3 bytes per item is fairly typical.
7299 : */
7300 2844 : dataPagesFetchedBySel = ceil(*indexSelectivity *
7301 1422 : (numTuples / (BLCKSZ / 3)));
7302 1422 : if (dataPagesFetchedBySel > dataPagesFetched)
7303 1152 : dataPagesFetched = dataPagesFetchedBySel;
7304 :
7305 : /* Account for cache effects, the same as above */
7306 1422 : if (outer_scans > 1 || counts.arrayScans > 1)
7307 : {
7308 4 : dataPagesFetched *= outer_scans * counts.arrayScans;
7309 4 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
7310 : (BlockNumber) numDataPages,
7311 : numDataPages, root);
7312 4 : dataPagesFetched /= outer_scans;
7313 : }
7314 :
7315 : /* And apply random_page_cost as the cost per page */
7316 2844 : *indexTotalCost = *indexStartupCost +
7317 1422 : dataPagesFetched * spc_random_page_cost;
7318 :
7319 : /*
7320 : * Add on index qual eval costs, much as in genericcostestimate. But we
7321 : * can disregard indexorderbys, since GIN doesn't support those.
7322 : */
7323 1422 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
7324 1422 : qual_op_cost = cpu_operator_cost * list_length(indexQuals);
7325 :
7326 1422 : *indexStartupCost += qual_arg_cost;
7327 1422 : *indexTotalCost += qual_arg_cost;
7328 1422 : *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost + qual_op_cost);
7329 1422 : *indexPages = dataPagesFetched;
7330 : }
7331 :
7332 : /*
7333 : * BRIN has search behavior completely different from other index types
7334 : */
7335 : void
7336 3996 : brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7337 : Cost *indexStartupCost, Cost *indexTotalCost,
7338 : Selectivity *indexSelectivity, double *indexCorrelation,
7339 : double *indexPages)
7340 : {
7341 3996 : IndexOptInfo *index = path->indexinfo;
7342 3996 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
7343 3996 : double numPages = index->pages;
7344 3996 : RelOptInfo *baserel = index->rel;
7345 3996 : RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
7346 : Cost spc_seq_page_cost;
7347 : Cost spc_random_page_cost;
7348 : double qual_arg_cost;
7349 : double qualSelectivity;
7350 : BrinStatsData statsData;
7351 : double indexRanges;
7352 : double minimalRanges;
7353 : double estimatedRanges;
7354 : double selec;
7355 : Relation indexRel;
7356 : ListCell *l;
7357 : VariableStatData vardata;
7358 :
7359 : Assert(rte->rtekind == RTE_RELATION);
7360 :
7361 : /* fetch estimated page cost for the tablespace containing the index */
7362 3996 : get_tablespace_page_costs(index->reltablespace,
7363 : &spc_random_page_cost,
7364 : &spc_seq_page_cost);
7365 :
7366 : /*
7367 : * Obtain some data from the index itself, if possible. Otherwise invent
7368 : * some plausible internal statistics based on the relation page count.
7369 : */
7370 3996 : if (!index->hypothetical)
7371 : {
7372 : /*
7373 : * A lock should have already been obtained on the index in plancat.c.
7374 : */
7375 3996 : indexRel = index_open(index->indexoid, NoLock);
7376 3996 : brinGetStats(indexRel, &statsData);
7377 3996 : index_close(indexRel, NoLock);
7378 :
7379 : /* work out the actual number of ranges in the index */
7380 3996 : indexRanges = Max(ceil((double) baserel->pages /
7381 : statsData.pagesPerRange), 1.0);
7382 : }
7383 : else
7384 : {
7385 : /*
7386 : * Assume default number of pages per range, and estimate the number
7387 : * of ranges based on that.
7388 : */
7389 0 : indexRanges = Max(ceil((double) baserel->pages /
7390 : BRIN_DEFAULT_PAGES_PER_RANGE), 1.0);
7391 :
7392 0 : statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE;
7393 0 : statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1;
7394 : }
7395 :
7396 : /*
7397 : * Compute index correlation
7398 : *
7399 : * Because we can use all index quals equally when scanning, we can use
7400 : * the largest correlation (in absolute value) among columns used by the
7401 : * query. Start at zero, the worst possible case. If we cannot find any
7402 : * correlation statistics, we will keep it as 0.
7403 : */
7404 3996 : *indexCorrelation = 0;
7405 :
7406 7992 : foreach(l, path->indexclauses)
7407 : {
7408 3996 : IndexClause *iclause = lfirst_node(IndexClause, l);
7409 3996 : AttrNumber attnum = index->indexkeys[iclause->indexcol];
7410 :
7411 : /* attempt to lookup stats in relation for this index column */
7412 3996 : if (attnum != 0)
7413 : {
7414 : /* Simple variable -- look to stats for the underlying table */
7415 3996 : if (get_relation_stats_hook &&
7416 0 : (*get_relation_stats_hook) (root, rte, attnum, &vardata))
7417 : {
7418 : /*
7419 : * The hook took control of acquiring a stats tuple. If it
7420 : * did supply a tuple, it'd better have supplied a freefunc.
7421 : */
7422 0 : if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
7423 0 : elog(ERROR,
7424 : "no function provided to release variable stats with");
7425 : }
7426 : else
7427 : {
7428 3996 : vardata.statsTuple =
7429 7992 : SearchSysCache3(STATRELATTINH,
7430 3996 : ObjectIdGetDatum(rte->relid),
7431 : Int16GetDatum(attnum),
7432 : BoolGetDatum(false));
7433 3996 : vardata.freefunc = ReleaseSysCache;
7434 : }
7435 : }
7436 : else
7437 : {
7438 : /*
7439 : * Looks like we've found an expression column in the index. Let's
7440 : * see if there's any stats for it.
7441 : */
7442 :
7443 : /* get the attnum from the 0-based index. */
7444 0 : attnum = iclause->indexcol + 1;
7445 :
7446 0 : if (get_index_stats_hook &&
7447 0 : (*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
7448 : {
7449 : /*
7450 : * The hook took control of acquiring a stats tuple. If it
7451 : * did supply a tuple, it'd better have supplied a freefunc.
7452 : */
7453 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
7454 0 : !vardata.freefunc)
7455 0 : elog(ERROR, "no function provided to release variable stats with");
7456 : }
7457 : else
7458 : {
7459 0 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
7460 0 : ObjectIdGetDatum(index->indexoid),
7461 : Int16GetDatum(attnum),
7462 : BoolGetDatum(false));
7463 0 : vardata.freefunc = ReleaseSysCache;
7464 : }
7465 : }
7466 :
7467 3996 : if (HeapTupleIsValid(vardata.statsTuple))
7468 : {
7469 : AttStatsSlot sslot;
7470 :
7471 8 : if (get_attstatsslot(&sslot, vardata.statsTuple,
7472 : STATISTIC_KIND_CORRELATION, InvalidOid,
7473 : ATTSTATSSLOT_NUMBERS))
7474 : {
7475 8 : double varCorrelation = 0.0;
7476 :
7477 8 : if (sslot.nnumbers > 0)
7478 8 : varCorrelation = Abs(sslot.numbers[0]);
7479 :
7480 8 : if (varCorrelation > *indexCorrelation)
7481 8 : *indexCorrelation = varCorrelation;
7482 :
7483 8 : free_attstatsslot(&sslot);
7484 : }
7485 : }
7486 :
7487 3996 : ReleaseVariableStats(vardata);
7488 : }
7489 :
7490 3996 : qualSelectivity = clauselist_selectivity(root, indexQuals,
7491 3996 : baserel->relid,
7492 : JOIN_INNER, NULL);
7493 :
7494 : /*
7495 : * Now calculate the minimum possible ranges we could match with if all of
7496 : * the rows were in the perfect order in the table's heap.
7497 : */
7498 3996 : minimalRanges = ceil(indexRanges * qualSelectivity);
7499 :
7500 : /*
7501 : * Now estimate the number of ranges that we'll touch by using the
7502 : * indexCorrelation from the stats. Careful not to divide by zero (note
7503 : * we're using the absolute value of the correlation).
7504 : */
7505 3996 : if (*indexCorrelation < 1.0e-10)
7506 3988 : estimatedRanges = indexRanges;
7507 : else
7508 8 : estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
7509 :
7510 : /* we expect to visit this portion of the table */
7511 3996 : selec = estimatedRanges / indexRanges;
7512 :
7513 3996 : CLAMP_PROBABILITY(selec);
7514 :
7515 3996 : *indexSelectivity = selec;
7516 :
7517 : /*
7518 : * Compute the index qual costs, much as in genericcostestimate, to add to
7519 : * the index costs. We can disregard indexorderbys, since BRIN doesn't
7520 : * support those.
7521 : */
7522 3996 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
7523 :
7524 : /*
7525 : * Compute the startup cost as the cost to read the whole revmap
7526 : * sequentially, including the cost to execute the index quals.
7527 : */
7528 3996 : *indexStartupCost =
7529 3996 : spc_seq_page_cost * statsData.revmapNumPages * loop_count;
7530 3996 : *indexStartupCost += qual_arg_cost;
7531 :
7532 : /*
7533 : * To read a BRIN index there might be a bit of back and forth over
7534 : * regular pages, as revmap might point to them out of sequential order;
7535 : * calculate the total cost as reading the whole index in random order.
7536 : */
7537 7992 : *indexTotalCost = *indexStartupCost +
7538 3996 : spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
7539 :
7540 : /*
7541 : * Charge a small amount per range tuple which we expect to match to. This
7542 : * is meant to reflect the costs of manipulating the bitmap. The BRIN scan
7543 : * will set a bit for each page in the range when we find a matching
7544 : * range, so we must multiply the charge by the number of pages in the
7545 : * range.
7546 : */
7547 7992 : *indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
7548 3996 : statsData.pagesPerRange;
7549 :
7550 3996 : *indexPages = index->pages;
7551 3996 : }
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