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) 1996-2020, 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. Thus, in most cases where we are looking at statistics, we
92 : * should ignore the operator collation and use the stats entry's collation.
93 : * We expect that the error induced by doing this is usually not large enough
94 : * to justify complicating matters. In any case, doing otherwise would yield
95 : * entirely garbage results for ordered stats data such as histograms.
96 : *----------
97 : */
98 :
99 : #include "postgres.h"
100 :
101 : #include <ctype.h>
102 : #include <math.h>
103 :
104 : #include "access/brin.h"
105 : #include "access/brin_page.h"
106 : #include "access/gin.h"
107 : #include "access/table.h"
108 : #include "access/tableam.h"
109 : #include "access/visibilitymap.h"
110 : #include "catalog/pg_am.h"
111 : #include "catalog/pg_collation.h"
112 : #include "catalog/pg_operator.h"
113 : #include "catalog/pg_statistic.h"
114 : #include "catalog/pg_statistic_ext.h"
115 : #include "executor/nodeAgg.h"
116 : #include "miscadmin.h"
117 : #include "nodes/makefuncs.h"
118 : #include "nodes/nodeFuncs.h"
119 : #include "optimizer/clauses.h"
120 : #include "optimizer/cost.h"
121 : #include "optimizer/optimizer.h"
122 : #include "optimizer/pathnode.h"
123 : #include "optimizer/paths.h"
124 : #include "optimizer/plancat.h"
125 : #include "parser/parse_clause.h"
126 : #include "parser/parsetree.h"
127 : #include "statistics/statistics.h"
128 : #include "storage/bufmgr.h"
129 : #include "utils/acl.h"
130 : #include "utils/builtins.h"
131 : #include "utils/date.h"
132 : #include "utils/datum.h"
133 : #include "utils/fmgroids.h"
134 : #include "utils/index_selfuncs.h"
135 : #include "utils/lsyscache.h"
136 : #include "utils/memutils.h"
137 : #include "utils/pg_locale.h"
138 : #include "utils/rel.h"
139 : #include "utils/selfuncs.h"
140 : #include "utils/snapmgr.h"
141 : #include "utils/spccache.h"
142 : #include "utils/syscache.h"
143 : #include "utils/timestamp.h"
144 : #include "utils/typcache.h"
145 :
146 :
147 : /* Hooks for plugins to get control when we ask for stats */
148 : get_relation_stats_hook_type get_relation_stats_hook = NULL;
149 : get_index_stats_hook_type get_index_stats_hook = NULL;
150 :
151 : static double eqsel_internal(PG_FUNCTION_ARGS, bool negate);
152 : static double eqjoinsel_inner(Oid opfuncoid,
153 : VariableStatData *vardata1, VariableStatData *vardata2,
154 : double nd1, double nd2,
155 : bool isdefault1, bool isdefault2,
156 : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
157 : Form_pg_statistic stats1, Form_pg_statistic stats2,
158 : bool have_mcvs1, bool have_mcvs2);
159 : static double eqjoinsel_semi(Oid opfuncoid,
160 : VariableStatData *vardata1, VariableStatData *vardata2,
161 : double nd1, double nd2,
162 : bool isdefault1, bool isdefault2,
163 : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
164 : Form_pg_statistic stats1, Form_pg_statistic stats2,
165 : bool have_mcvs1, bool have_mcvs2,
166 : RelOptInfo *inner_rel);
167 : static bool estimate_multivariate_ndistinct(PlannerInfo *root,
168 : RelOptInfo *rel, List **varinfos, double *ndistinct);
169 : static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
170 : double *scaledvalue,
171 : Datum lobound, Datum hibound, Oid boundstypid,
172 : double *scaledlobound, double *scaledhibound);
173 : static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
174 : static void convert_string_to_scalar(char *value,
175 : double *scaledvalue,
176 : char *lobound,
177 : double *scaledlobound,
178 : char *hibound,
179 : double *scaledhibound);
180 : static void convert_bytea_to_scalar(Datum value,
181 : double *scaledvalue,
182 : Datum lobound,
183 : double *scaledlobound,
184 : Datum hibound,
185 : double *scaledhibound);
186 : static double convert_one_string_to_scalar(char *value,
187 : int rangelo, int rangehi);
188 : static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
189 : int rangelo, int rangehi);
190 : static char *convert_string_datum(Datum value, Oid typid, Oid collid,
191 : bool *failure);
192 : static double convert_timevalue_to_scalar(Datum value, Oid typid,
193 : bool *failure);
194 : static void examine_simple_variable(PlannerInfo *root, Var *var,
195 : VariableStatData *vardata);
196 : static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
197 : Oid sortop, Datum *min, Datum *max);
198 : static bool get_actual_variable_range(PlannerInfo *root,
199 : VariableStatData *vardata,
200 : Oid sortop,
201 : Datum *min, Datum *max);
202 : static bool get_actual_variable_endpoint(Relation heapRel,
203 : Relation indexRel,
204 : ScanDirection indexscandir,
205 : ScanKey scankeys,
206 : int16 typLen,
207 : bool typByVal,
208 : TupleTableSlot *tableslot,
209 : MemoryContext outercontext,
210 : Datum *endpointDatum);
211 : static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
212 :
213 :
214 : /*
215 : * eqsel - Selectivity of "=" for any data types.
216 : *
217 : * Note: this routine is also used to estimate selectivity for some
218 : * operators that are not "=" but have comparable selectivity behavior,
219 : * such as "~=" (geometric approximate-match). Even for "=", we must
220 : * keep in mind that the left and right datatypes may differ.
221 : */
222 : Datum
223 323068 : eqsel(PG_FUNCTION_ARGS)
224 : {
225 323068 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
226 : }
227 :
228 : /*
229 : * Common code for eqsel() and neqsel()
230 : */
231 : static double
232 338758 : eqsel_internal(PG_FUNCTION_ARGS, bool negate)
233 : {
234 338758 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
235 338758 : Oid operator = PG_GETARG_OID(1);
236 338758 : List *args = (List *) PG_GETARG_POINTER(2);
237 338758 : int varRelid = PG_GETARG_INT32(3);
238 : VariableStatData vardata;
239 : Node *other;
240 : bool varonleft;
241 : double selec;
242 :
243 : /*
244 : * When asked about <>, we do the estimation using the corresponding =
245 : * operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
246 : */
247 338758 : if (negate)
248 : {
249 15690 : operator = get_negator(operator);
250 15690 : if (!OidIsValid(operator))
251 : {
252 : /* Use default selectivity (should we raise an error instead?) */
253 0 : return 1.0 - DEFAULT_EQ_SEL;
254 : }
255 : }
256 :
257 : /*
258 : * If expression is not variable = something or something = variable, then
259 : * punt and return a default estimate.
260 : */
261 338758 : if (!get_restriction_variable(root, args, varRelid,
262 : &vardata, &other, &varonleft))
263 1674 : return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
264 :
265 : /*
266 : * We can do a lot better if the something is a constant. (Note: the
267 : * Const might result from estimation rather than being a simple constant
268 : * in the query.)
269 : */
270 337084 : if (IsA(other, Const))
271 458664 : selec = var_eq_const(&vardata, operator,
272 152888 : ((Const *) other)->constvalue,
273 152888 : ((Const *) other)->constisnull,
274 : varonleft, negate);
275 : else
276 184196 : selec = var_eq_non_const(&vardata, operator, other,
277 : varonleft, negate);
278 :
279 337084 : ReleaseVariableStats(vardata);
280 :
281 337084 : return selec;
282 : }
283 :
284 : /*
285 : * var_eq_const --- eqsel for var = const case
286 : *
287 : * This is exported so that some other estimation functions can use it.
288 : */
289 : double
290 167990 : var_eq_const(VariableStatData *vardata, Oid operator,
291 : Datum constval, bool constisnull,
292 : bool varonleft, bool negate)
293 : {
294 : double selec;
295 167990 : double nullfrac = 0.0;
296 : bool isdefault;
297 : Oid opfuncoid;
298 :
299 : /*
300 : * If the constant is NULL, assume operator is strict and return zero, ie,
301 : * operator will never return TRUE. (It's zero even for a negator op.)
302 : */
303 167990 : if (constisnull)
304 78 : return 0.0;
305 :
306 : /*
307 : * Grab the nullfrac for use below. Note we allow use of nullfrac
308 : * regardless of security check.
309 : */
310 167912 : if (HeapTupleIsValid(vardata->statsTuple))
311 : {
312 : Form_pg_statistic stats;
313 :
314 115016 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
315 115016 : nullfrac = stats->stanullfrac;
316 : }
317 :
318 : /*
319 : * If we matched the var to a unique index or DISTINCT clause, assume
320 : * there is exactly one match regardless of anything else. (This is
321 : * slightly bogus, since the index or clause's equality operator might be
322 : * different from ours, but it's much more likely to be right than
323 : * ignoring the information.)
324 : */
325 167912 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
326 : {
327 33100 : selec = 1.0 / vardata->rel->tuples;
328 : }
329 228688 : else if (HeapTupleIsValid(vardata->statsTuple) &&
330 93876 : statistic_proc_security_check(vardata,
331 93876 : (opfuncoid = get_opcode(operator))))
332 93876 : {
333 : AttStatsSlot sslot;
334 93876 : bool match = false;
335 : int i;
336 :
337 : /*
338 : * Is the constant "=" to any of the column's most common values?
339 : * (Although the given operator may not really be "=", we will assume
340 : * that seeing whether it returns TRUE is an appropriate test. If you
341 : * don't like this, maybe you shouldn't be using eqsel for your
342 : * operator...)
343 : */
344 93876 : if (get_attstatsslot(&sslot, vardata->statsTuple,
345 : STATISTIC_KIND_MCV, InvalidOid,
346 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
347 : {
348 84910 : LOCAL_FCINFO(fcinfo, 2);
349 : FmgrInfo eqproc;
350 :
351 84910 : fmgr_info(opfuncoid, &eqproc);
352 :
353 : /*
354 : * Save a few cycles by setting up the fcinfo struct just once.
355 : * Using FunctionCallInvoke directly also avoids failure if the
356 : * eqproc returns NULL, though really equality functions should
357 : * never do that.
358 : */
359 84910 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, sslot.stacoll,
360 : NULL, NULL);
361 84910 : fcinfo->args[0].isnull = false;
362 84910 : fcinfo->args[1].isnull = false;
363 : /* be careful to apply operator right way 'round */
364 84910 : if (varonleft)
365 84894 : fcinfo->args[1].value = constval;
366 : else
367 16 : fcinfo->args[0].value = constval;
368 :
369 1660304 : for (i = 0; i < sslot.nvalues; i++)
370 : {
371 : Datum fresult;
372 :
373 1618004 : if (varonleft)
374 1617972 : fcinfo->args[0].value = sslot.values[i];
375 : else
376 32 : fcinfo->args[1].value = sslot.values[i];
377 1618004 : fcinfo->isnull = false;
378 1618004 : fresult = FunctionCallInvoke(fcinfo);
379 1618004 : if (!fcinfo->isnull && DatumGetBool(fresult))
380 : {
381 42610 : match = true;
382 42610 : break;
383 : }
384 : }
385 : }
386 : else
387 : {
388 : /* no most-common-value info available */
389 8966 : i = 0; /* keep compiler quiet */
390 : }
391 :
392 93876 : if (match)
393 : {
394 : /*
395 : * Constant is "=" to this common value. We know selectivity
396 : * exactly (or as exactly as ANALYZE could calculate it, anyway).
397 : */
398 42610 : selec = sslot.numbers[i];
399 : }
400 : else
401 : {
402 : /*
403 : * Comparison is against a constant that is neither NULL nor any
404 : * of the common values. Its selectivity cannot be more than
405 : * this:
406 : */
407 51266 : double sumcommon = 0.0;
408 : double otherdistinct;
409 :
410 1481692 : for (i = 0; i < sslot.nnumbers; i++)
411 1430426 : sumcommon += sslot.numbers[i];
412 51266 : selec = 1.0 - sumcommon - nullfrac;
413 51266 : CLAMP_PROBABILITY(selec);
414 :
415 : /*
416 : * and in fact it's probably a good deal less. We approximate that
417 : * all the not-common values share this remaining fraction
418 : * equally, so we divide by the number of other distinct values.
419 : */
420 51266 : otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
421 51266 : sslot.nnumbers;
422 51266 : if (otherdistinct > 1)
423 30514 : selec /= otherdistinct;
424 :
425 : /*
426 : * Another cross-check: selectivity shouldn't be estimated as more
427 : * than the least common "most common value".
428 : */
429 51266 : if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
430 0 : selec = sslot.numbers[sslot.nnumbers - 1];
431 : }
432 :
433 93876 : free_attstatsslot(&sslot);
434 : }
435 : else
436 : {
437 : /*
438 : * No ANALYZE stats available, so make a guess using estimated number
439 : * of distinct values and assuming they are equally common. (The guess
440 : * is unlikely to be very good, but we do know a few special cases.)
441 : */
442 40936 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
443 : }
444 :
445 : /* now adjust if we wanted <> rather than = */
446 167912 : if (negate)
447 11904 : selec = 1.0 - selec - nullfrac;
448 :
449 : /* result should be in range, but make sure... */
450 167912 : CLAMP_PROBABILITY(selec);
451 :
452 167912 : return selec;
453 : }
454 :
455 : /*
456 : * var_eq_non_const --- eqsel for var = something-other-than-const case
457 : *
458 : * This is exported so that some other estimation functions can use it.
459 : */
460 : double
461 184196 : var_eq_non_const(VariableStatData *vardata, Oid operator,
462 : Node *other,
463 : bool varonleft, bool negate)
464 : {
465 : double selec;
466 184196 : double nullfrac = 0.0;
467 : bool isdefault;
468 :
469 : /*
470 : * Grab the nullfrac for use below.
471 : */
472 184196 : if (HeapTupleIsValid(vardata->statsTuple))
473 : {
474 : Form_pg_statistic stats;
475 :
476 139950 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
477 139950 : nullfrac = stats->stanullfrac;
478 : }
479 :
480 : /*
481 : * If we matched the var to a unique index or DISTINCT clause, assume
482 : * there is exactly one match regardless of anything else. (This is
483 : * slightly bogus, since the index or clause's equality operator might be
484 : * different from ours, but it's much more likely to be right than
485 : * ignoring the information.)
486 : */
487 184196 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
488 : {
489 90510 : selec = 1.0 / vardata->rel->tuples;
490 : }
491 93686 : else if (HeapTupleIsValid(vardata->statsTuple))
492 : {
493 : double ndistinct;
494 : AttStatsSlot sslot;
495 :
496 : /*
497 : * Search is for a value that we do not know a priori, but we will
498 : * assume it is not NULL. Estimate the selectivity as non-null
499 : * fraction divided by number of distinct values, so that we get a
500 : * result averaged over all possible values whether common or
501 : * uncommon. (Essentially, we are assuming that the not-yet-known
502 : * comparison value is equally likely to be any of the possible
503 : * values, regardless of their frequency in the table. Is that a good
504 : * idea?)
505 : */
506 66432 : selec = 1.0 - nullfrac;
507 66432 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
508 66432 : if (ndistinct > 1)
509 63594 : selec /= ndistinct;
510 :
511 : /*
512 : * Cross-check: selectivity should never be estimated as more than the
513 : * most common value's.
514 : */
515 66432 : if (get_attstatsslot(&sslot, vardata->statsTuple,
516 : STATISTIC_KIND_MCV, InvalidOid,
517 : ATTSTATSSLOT_NUMBERS))
518 : {
519 56296 : if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
520 140 : selec = sslot.numbers[0];
521 56296 : free_attstatsslot(&sslot);
522 : }
523 : }
524 : else
525 : {
526 : /*
527 : * No ANALYZE stats available, so make a guess using estimated number
528 : * of distinct values and assuming they are equally common. (The guess
529 : * is unlikely to be very good, but we do know a few special cases.)
530 : */
531 27254 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
532 : }
533 :
534 : /* now adjust if we wanted <> rather than = */
535 184196 : if (negate)
536 2842 : selec = 1.0 - selec - nullfrac;
537 :
538 : /* result should be in range, but make sure... */
539 184196 : CLAMP_PROBABILITY(selec);
540 :
541 184196 : return selec;
542 : }
543 :
544 : /*
545 : * neqsel - Selectivity of "!=" for any data types.
546 : *
547 : * This routine is also used for some operators that are not "!="
548 : * but have comparable selectivity behavior. See above comments
549 : * for eqsel().
550 : */
551 : Datum
552 15690 : neqsel(PG_FUNCTION_ARGS)
553 : {
554 15690 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
555 : }
556 :
557 : /*
558 : * scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
559 : *
560 : * This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
561 : * The isgt and iseq flags distinguish which of the four cases apply.
562 : *
563 : * The caller has commuted the clause, if necessary, so that we can treat
564 : * the variable as being on the left. The caller must also make sure that
565 : * the other side of the clause is a non-null Const, and dissect that into
566 : * a value and datatype. (This definition simplifies some callers that
567 : * want to estimate against a computed value instead of a Const node.)
568 : *
569 : * This routine works for any datatype (or pair of datatypes) known to
570 : * convert_to_scalar(). If it is applied to some other datatype,
571 : * it will return an approximate estimate based on assuming that the constant
572 : * value falls in the middle of the bin identified by binary search.
573 : */
574 : static double
575 181620 : scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
576 : VariableStatData *vardata, Datum constval, Oid consttype)
577 : {
578 : Form_pg_statistic stats;
579 : FmgrInfo opproc;
580 : double mcv_selec,
581 : hist_selec,
582 : sumcommon;
583 : double selec;
584 :
585 181620 : if (!HeapTupleIsValid(vardata->statsTuple))
586 : {
587 : /*
588 : * No stats are available. Typically this means we have to fall back
589 : * on the default estimate; but if the variable is CTID then we can
590 : * make an estimate based on comparing the constant to the table size.
591 : */
592 11588 : if (vardata->var && IsA(vardata->var, Var) &&
593 8580 : ((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
594 : {
595 : ItemPointer itemptr;
596 : double block;
597 : double density;
598 :
599 : /*
600 : * If the relation's empty, we're going to include all of it.
601 : * (This is mostly to avoid divide-by-zero below.)
602 : */
603 4 : if (vardata->rel->pages == 0)
604 0 : return 1.0;
605 :
606 4 : itemptr = (ItemPointer) DatumGetPointer(constval);
607 4 : block = ItemPointerGetBlockNumberNoCheck(itemptr);
608 :
609 : /*
610 : * Determine the average number of tuples per page (density).
611 : *
612 : * Since the last page will, on average, be only half full, we can
613 : * estimate it to have half as many tuples as earlier pages. So
614 : * give it half the weight of a regular page.
615 : */
616 4 : density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
617 :
618 : /* If target is the last page, use half the density. */
619 4 : if (block >= vardata->rel->pages - 1)
620 0 : density *= 0.5;
621 :
622 : /*
623 : * Using the average tuples per page, calculate how far into the
624 : * page the itemptr is likely to be and adjust block accordingly,
625 : * by adding that fraction of a whole block (but never more than a
626 : * whole block, no matter how high the itemptr's offset is). Here
627 : * we are ignoring the possibility of dead-tuple line pointers,
628 : * which is fairly bogus, but we lack the info to do better.
629 : */
630 4 : if (density > 0.0)
631 : {
632 4 : OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
633 :
634 4 : block += Min(offset / density, 1.0);
635 : }
636 :
637 : /*
638 : * Convert relative block number to selectivity. Again, the last
639 : * page has only half weight.
640 : */
641 4 : selec = block / (vardata->rel->pages - 0.5);
642 :
643 : /*
644 : * The calculation so far gave us a selectivity for the "<=" case.
645 : * We'll have one less tuple for "<" and one additional tuple for
646 : * ">=", the latter of which we'll reverse the selectivity for
647 : * below, so we can simply subtract one tuple for both cases. The
648 : * cases that need this adjustment can be identified by iseq being
649 : * equal to isgt.
650 : */
651 4 : if (iseq == isgt && vardata->rel->tuples >= 1.0)
652 0 : selec -= (1.0 / vardata->rel->tuples);
653 :
654 : /* Finally, reverse the selectivity for the ">", ">=" cases. */
655 4 : if (isgt)
656 4 : selec = 1.0 - selec;
657 :
658 4 : CLAMP_PROBABILITY(selec);
659 4 : return selec;
660 : }
661 :
662 : /* no stats available, so default result */
663 11584 : return DEFAULT_INEQ_SEL;
664 : }
665 170032 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
666 :
667 170032 : fmgr_info(get_opcode(operator), &opproc);
668 :
669 : /*
670 : * If we have most-common-values info, add up the fractions of the MCV
671 : * entries that satisfy MCV OP CONST. These fractions contribute directly
672 : * to the result selectivity. Also add up the total fraction represented
673 : * by MCV entries.
674 : */
675 170032 : mcv_selec = mcv_selectivity(vardata, &opproc, constval, true,
676 : &sumcommon);
677 :
678 : /*
679 : * If there is a histogram, determine which bin the constant falls in, and
680 : * compute the resulting contribution to selectivity.
681 : */
682 170032 : hist_selec = ineq_histogram_selectivity(root, vardata,
683 : &opproc, isgt, iseq,
684 : constval, consttype);
685 :
686 : /*
687 : * Now merge the results from the MCV and histogram calculations,
688 : * realizing that the histogram covers only the non-null values that are
689 : * not listed in MCV.
690 : */
691 170032 : selec = 1.0 - stats->stanullfrac - sumcommon;
692 :
693 170032 : if (hist_selec >= 0.0)
694 142420 : selec *= hist_selec;
695 : else
696 : {
697 : /*
698 : * If no histogram but there are values not accounted for by MCV,
699 : * arbitrarily assume half of them will match.
700 : */
701 27612 : selec *= 0.5;
702 : }
703 :
704 170032 : selec += mcv_selec;
705 :
706 : /* result should be in range, but make sure... */
707 170032 : CLAMP_PROBABILITY(selec);
708 :
709 170032 : return selec;
710 : }
711 :
712 : /*
713 : * mcv_selectivity - Examine the MCV list for selectivity estimates
714 : *
715 : * Determine the fraction of the variable's MCV population that satisfies
716 : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
717 : * compute the fraction of the total column population represented by the MCV
718 : * list. This code will work for any boolean-returning predicate operator.
719 : *
720 : * The function result is the MCV selectivity, and the fraction of the
721 : * total population is returned into *sumcommonp. Zeroes are returned
722 : * if there is no MCV list.
723 : */
724 : double
725 173340 : mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
726 : Datum constval, bool varonleft,
727 : double *sumcommonp)
728 : {
729 : double mcv_selec,
730 : sumcommon;
731 : AttStatsSlot sslot;
732 : int i;
733 :
734 173340 : mcv_selec = 0.0;
735 173340 : sumcommon = 0.0;
736 :
737 344922 : if (HeapTupleIsValid(vardata->statsTuple) &&
738 343108 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
739 171526 : get_attstatsslot(&sslot, vardata->statsTuple,
740 : STATISTIC_KIND_MCV, InvalidOid,
741 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
742 : {
743 86940 : LOCAL_FCINFO(fcinfo, 2);
744 :
745 : /*
746 : * We invoke the opproc "by hand" so that we won't fail on NULL
747 : * results. Such cases won't arise for normal comparison functions,
748 : * but generic_restriction_selectivity could perhaps be used with
749 : * operators that can return NULL. A small side benefit is to not
750 : * need to re-initialize the fcinfo struct from scratch each time.
751 : */
752 86940 : InitFunctionCallInfoData(*fcinfo, opproc, 2, sslot.stacoll,
753 : NULL, NULL);
754 86940 : fcinfo->args[0].isnull = false;
755 86940 : fcinfo->args[1].isnull = false;
756 : /* be careful to apply operator right way 'round */
757 86940 : if (varonleft)
758 86940 : fcinfo->args[1].value = constval;
759 : else
760 0 : fcinfo->args[0].value = constval;
761 :
762 2953220 : for (i = 0; i < sslot.nvalues; i++)
763 : {
764 : Datum fresult;
765 :
766 2866280 : if (varonleft)
767 2866280 : fcinfo->args[0].value = sslot.values[i];
768 : else
769 0 : fcinfo->args[1].value = sslot.values[i];
770 2866280 : fcinfo->isnull = false;
771 2866280 : fresult = FunctionCallInvoke(fcinfo);
772 2866280 : if (!fcinfo->isnull && DatumGetBool(fresult))
773 1509816 : mcv_selec += sslot.numbers[i];
774 2866280 : sumcommon += sslot.numbers[i];
775 : }
776 86940 : free_attstatsslot(&sslot);
777 : }
778 :
779 173340 : *sumcommonp = sumcommon;
780 173340 : return mcv_selec;
781 : }
782 :
783 : /*
784 : * histogram_selectivity - Examine the histogram for selectivity estimates
785 : *
786 : * Determine the fraction of the variable's histogram entries that satisfy
787 : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
788 : *
789 : * This code will work for any boolean-returning predicate operator, whether
790 : * or not it has anything to do with the histogram sort operator. We are
791 : * essentially using the histogram just as a representative sample. However,
792 : * small histograms are unlikely to be all that representative, so the caller
793 : * should be prepared to fall back on some other estimation approach when the
794 : * histogram is missing or very small. It may also be prudent to combine this
795 : * approach with another one when the histogram is small.
796 : *
797 : * If the actual histogram size is not at least min_hist_size, we won't bother
798 : * to do the calculation at all. Also, if the n_skip parameter is > 0, we
799 : * ignore the first and last n_skip histogram elements, on the grounds that
800 : * they are outliers and hence not very representative. Typical values for
801 : * these parameters are 10 and 1.
802 : *
803 : * The function result is the selectivity, or -1 if there is no histogram
804 : * or it's smaller than min_hist_size.
805 : *
806 : * The output parameter *hist_size receives the actual histogram size,
807 : * or zero if no histogram. Callers may use this number to decide how
808 : * much faith to put in the function result.
809 : *
810 : * Note that the result disregards both the most-common-values (if any) and
811 : * null entries. The caller is expected to combine this result with
812 : * statistics for those portions of the column population. It may also be
813 : * prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
814 : */
815 : double
816 3308 : histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
817 : Datum constval, bool varonleft,
818 : int min_hist_size, int n_skip,
819 : int *hist_size)
820 : {
821 : double result;
822 : AttStatsSlot sslot;
823 :
824 : /* check sanity of parameters */
825 : Assert(n_skip >= 0);
826 : Assert(min_hist_size > 2 * n_skip);
827 :
828 4858 : if (HeapTupleIsValid(vardata->statsTuple) &&
829 3096 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
830 1546 : get_attstatsslot(&sslot, vardata->statsTuple,
831 : STATISTIC_KIND_HISTOGRAM, InvalidOid,
832 : ATTSTATSSLOT_VALUES))
833 : {
834 1542 : *hist_size = sslot.nvalues;
835 1542 : if (sslot.nvalues >= min_hist_size)
836 : {
837 1136 : LOCAL_FCINFO(fcinfo, 2);
838 1136 : int nmatch = 0;
839 : int i;
840 :
841 : /*
842 : * We invoke the opproc "by hand" so that we won't fail on NULL
843 : * results. Such cases won't arise for normal comparison
844 : * functions, but generic_restriction_selectivity could perhaps be
845 : * used with operators that can return NULL. A small side benefit
846 : * is to not need to re-initialize the fcinfo struct from scratch
847 : * each time.
848 : */
849 1136 : InitFunctionCallInfoData(*fcinfo, opproc, 2, sslot.stacoll,
850 : NULL, NULL);
851 1136 : fcinfo->args[0].isnull = false;
852 1136 : fcinfo->args[1].isnull = false;
853 : /* be careful to apply operator right way 'round */
854 1136 : if (varonleft)
855 1136 : fcinfo->args[1].value = constval;
856 : else
857 0 : fcinfo->args[0].value = constval;
858 :
859 107406 : for (i = n_skip; i < sslot.nvalues - n_skip; i++)
860 : {
861 : Datum fresult;
862 :
863 106270 : if (varonleft)
864 106270 : fcinfo->args[0].value = sslot.values[i];
865 : else
866 0 : fcinfo->args[1].value = sslot.values[i];
867 106270 : fcinfo->isnull = false;
868 106270 : fresult = FunctionCallInvoke(fcinfo);
869 106270 : if (!fcinfo->isnull && DatumGetBool(fresult))
870 7056 : nmatch++;
871 : }
872 1136 : result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
873 : }
874 : else
875 406 : result = -1;
876 1542 : free_attstatsslot(&sslot);
877 : }
878 : else
879 : {
880 1766 : *hist_size = 0;
881 1766 : result = -1;
882 : }
883 :
884 3308 : return result;
885 : }
886 :
887 : /*
888 : * generic_restriction_selectivity - Selectivity for almost anything
889 : *
890 : * This function estimates selectivity for operators that we don't have any
891 : * special knowledge about, but are on data types that we collect standard
892 : * MCV and/or histogram statistics for. (Additional assumptions are that
893 : * the operator is strict and immutable, or at least stable.)
894 : *
895 : * If we have "VAR OP CONST" or "CONST OP VAR", selectivity is estimated by
896 : * applying the operator to each element of the column's MCV and/or histogram
897 : * stats, and merging the results using the assumption that the histogram is
898 : * a reasonable random sample of the column's non-MCV population. Note that
899 : * if the operator's semantics are related to the histogram ordering, this
900 : * might not be such a great assumption; other functions such as
901 : * scalarineqsel() are probably a better match in such cases.
902 : *
903 : * Otherwise, fall back to the default selectivity provided by the caller.
904 : */
905 : double
906 782 : generic_restriction_selectivity(PlannerInfo *root, Oid oproid,
907 : List *args, int varRelid,
908 : double default_selectivity)
909 : {
910 : double selec;
911 : VariableStatData vardata;
912 : Node *other;
913 : bool varonleft;
914 :
915 : /*
916 : * If expression is not variable OP something or something OP variable,
917 : * then punt and return the default estimate.
918 : */
919 782 : if (!get_restriction_variable(root, args, varRelid,
920 : &vardata, &other, &varonleft))
921 0 : return default_selectivity;
922 :
923 : /*
924 : * If the something is a NULL constant, assume operator is strict and
925 : * return zero, ie, operator will never return TRUE.
926 : */
927 782 : if (IsA(other, Const) &&
928 782 : ((Const *) other)->constisnull)
929 : {
930 0 : ReleaseVariableStats(vardata);
931 0 : return 0.0;
932 : }
933 :
934 782 : if (IsA(other, Const))
935 : {
936 : /* Variable is being compared to a known non-null constant */
937 782 : Datum constval = ((Const *) other)->constvalue;
938 : FmgrInfo opproc;
939 : double mcvsum;
940 : double mcvsel;
941 : double nullfrac;
942 : int hist_size;
943 :
944 782 : fmgr_info(get_opcode(oproid), &opproc);
945 :
946 : /*
947 : * Calculate the selectivity for the column's most common values.
948 : */
949 782 : mcvsel = mcv_selectivity(&vardata, &opproc, constval, varonleft,
950 : &mcvsum);
951 :
952 : /*
953 : * If the histogram is large enough, see what fraction of it matches
954 : * the query, and assume that's representative of the non-MCV
955 : * population. Otherwise use the default selectivity for the non-MCV
956 : * population.
957 : */
958 782 : selec = histogram_selectivity(&vardata, &opproc,
959 : constval, varonleft,
960 : 10, 1, &hist_size);
961 782 : if (selec < 0)
962 : {
963 : /* Nope, fall back on default */
964 382 : selec = default_selectivity;
965 : }
966 400 : else if (hist_size < 100)
967 : {
968 : /*
969 : * For histogram sizes from 10 to 100, we combine the histogram
970 : * and default selectivities, putting increasingly more trust in
971 : * the histogram for larger sizes.
972 : */
973 0 : double hist_weight = hist_size / 100.0;
974 :
975 0 : selec = selec * hist_weight +
976 0 : default_selectivity * (1.0 - hist_weight);
977 : }
978 :
979 : /* In any case, don't believe extremely small or large estimates. */
980 782 : if (selec < 0.0001)
981 236 : selec = 0.0001;
982 546 : else if (selec > 0.9999)
983 32 : selec = 0.9999;
984 :
985 : /* Don't forget to account for nulls. */
986 782 : if (HeapTupleIsValid(vardata.statsTuple))
987 400 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
988 : else
989 382 : nullfrac = 0.0;
990 :
991 : /*
992 : * Now merge the results from the MCV and histogram calculations,
993 : * realizing that the histogram covers only the non-null values that
994 : * are not listed in MCV.
995 : */
996 782 : selec *= 1.0 - nullfrac - mcvsum;
997 782 : selec += mcvsel;
998 : }
999 : else
1000 : {
1001 : /* Comparison value is not constant, so we can't do anything */
1002 0 : selec = default_selectivity;
1003 : }
1004 :
1005 782 : ReleaseVariableStats(vardata);
1006 :
1007 : /* result should be in range, but make sure... */
1008 782 : CLAMP_PROBABILITY(selec);
1009 :
1010 782 : return selec;
1011 : }
1012 :
1013 : /*
1014 : * ineq_histogram_selectivity - Examine the histogram for scalarineqsel
1015 : *
1016 : * Determine the fraction of the variable's histogram population that
1017 : * satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
1018 : * The isgt and iseq flags distinguish which of the four cases apply.
1019 : *
1020 : * Returns -1 if there is no histogram (valid results will always be >= 0).
1021 : *
1022 : * Note that the result disregards both the most-common-values (if any) and
1023 : * null entries. The caller is expected to combine this result with
1024 : * statistics for those portions of the column population.
1025 : *
1026 : * This is exported so that some other estimation functions can use it.
1027 : */
1028 : double
1029 171908 : ineq_histogram_selectivity(PlannerInfo *root,
1030 : VariableStatData *vardata,
1031 : FmgrInfo *opproc, bool isgt, bool iseq,
1032 : Datum constval, Oid consttype)
1033 : {
1034 : double hist_selec;
1035 : AttStatsSlot sslot;
1036 :
1037 171908 : hist_selec = -1.0;
1038 :
1039 : /*
1040 : * Someday, ANALYZE might store more than one histogram per rel/att,
1041 : * corresponding to more than one possible sort ordering defined for the
1042 : * column type. However, to make that work we will need to figure out
1043 : * which staop to search for --- it's not necessarily the one we have at
1044 : * hand! (For example, we might have a '<=' operator rather than the '<'
1045 : * operator that will appear in staop.) For now, assume that whatever
1046 : * appears in pg_statistic is sorted the same way our operator sorts, or
1047 : * the reverse way if isgt is true.
1048 : */
1049 342902 : if (HeapTupleIsValid(vardata->statsTuple) &&
1050 341936 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
1051 170942 : get_attstatsslot(&sslot, vardata->statsTuple,
1052 : STATISTIC_KIND_HISTOGRAM, InvalidOid,
1053 : ATTSTATSSLOT_VALUES))
1054 : {
1055 143380 : if (sslot.nvalues > 1)
1056 : {
1057 : /*
1058 : * Use binary search to find the desired location, namely the
1059 : * right end of the histogram bin containing the comparison value,
1060 : * which is the leftmost entry for which the comparison operator
1061 : * succeeds (if isgt) or fails (if !isgt). (If the given operator
1062 : * isn't actually sort-compatible with the histogram, you'll get
1063 : * garbage results ... but probably not any more garbage-y than
1064 : * you would have from the old linear search.)
1065 : *
1066 : * In this loop, we pay no attention to whether the operator iseq
1067 : * or not; that detail will be mopped up below. (We cannot tell,
1068 : * anyway, whether the operator thinks the values are equal.)
1069 : *
1070 : * If the binary search accesses the first or last histogram
1071 : * entry, we try to replace that endpoint with the true column min
1072 : * or max as found by get_actual_variable_range(). This
1073 : * ameliorates misestimates when the min or max is moving as a
1074 : * result of changes since the last ANALYZE. Note that this could
1075 : * result in effectively including MCVs into the histogram that
1076 : * weren't there before, but we don't try to correct for that.
1077 : */
1078 : double histfrac;
1079 143380 : int lobound = 0; /* first possible slot to search */
1080 143380 : int hibound = sslot.nvalues; /* last+1 slot to search */
1081 143380 : bool have_end = false;
1082 :
1083 : /*
1084 : * If there are only two histogram entries, we'll want up-to-date
1085 : * values for both. (If there are more than two, we need at most
1086 : * one of them to be updated, so we deal with that within the
1087 : * loop.)
1088 : */
1089 143380 : if (sslot.nvalues == 2)
1090 508 : have_end = get_actual_variable_range(root,
1091 : vardata,
1092 : sslot.staop,
1093 : &sslot.values[0],
1094 508 : &sslot.values[1]);
1095 :
1096 992140 : while (lobound < hibound)
1097 : {
1098 848760 : int probe = (lobound + hibound) / 2;
1099 : bool ltcmp;
1100 :
1101 : /*
1102 : * If we find ourselves about to compare to the first or last
1103 : * histogram entry, first try to replace it with the actual
1104 : * current min or max (unless we already did so above).
1105 : */
1106 848760 : if (probe == 0 && sslot.nvalues > 2)
1107 72246 : have_end = get_actual_variable_range(root,
1108 : vardata,
1109 : sslot.staop,
1110 : &sslot.values[0],
1111 : NULL);
1112 776514 : else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
1113 52870 : have_end = get_actual_variable_range(root,
1114 : vardata,
1115 : sslot.staop,
1116 : NULL,
1117 52870 : &sslot.values[probe]);
1118 :
1119 848760 : ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
1120 : sslot.stacoll,
1121 : sslot.values[probe],
1122 : constval));
1123 848760 : if (isgt)
1124 61484 : ltcmp = !ltcmp;
1125 848760 : if (ltcmp)
1126 318882 : lobound = probe + 1;
1127 : else
1128 529878 : hibound = probe;
1129 : }
1130 :
1131 143380 : if (lobound <= 0)
1132 : {
1133 : /*
1134 : * Constant is below lower histogram boundary. More
1135 : * precisely, we have found that no entry in the histogram
1136 : * satisfies the inequality clause (if !isgt) or they all do
1137 : * (if isgt). We estimate that that's true of the entire
1138 : * table, so set histfrac to 0.0 (which we'll flip to 1.0
1139 : * below, if isgt).
1140 : */
1141 67576 : histfrac = 0.0;
1142 : }
1143 75804 : else if (lobound >= sslot.nvalues)
1144 : {
1145 : /*
1146 : * Inverse case: constant is above upper histogram boundary.
1147 : */
1148 21680 : histfrac = 1.0;
1149 : }
1150 : else
1151 : {
1152 : /* We have values[i-1] <= constant <= values[i]. */
1153 54124 : int i = lobound;
1154 54124 : double eq_selec = 0;
1155 : double val,
1156 : high,
1157 : low;
1158 : double binfrac;
1159 :
1160 : /*
1161 : * In the cases where we'll need it below, obtain an estimate
1162 : * of the selectivity of "x = constval". We use a calculation
1163 : * similar to what var_eq_const() does for a non-MCV constant,
1164 : * ie, estimate that all distinct non-MCV values occur equally
1165 : * often. But multiplication by "1.0 - sumcommon - nullfrac"
1166 : * will be done by our caller, so we shouldn't do that here.
1167 : * Therefore we can't try to clamp the estimate by reference
1168 : * to the least common MCV; the result would be too small.
1169 : *
1170 : * Note: since this is effectively assuming that constval
1171 : * isn't an MCV, it's logically dubious if constval in fact is
1172 : * one. But we have to apply *some* correction for equality,
1173 : * and anyway we cannot tell if constval is an MCV, since we
1174 : * don't have a suitable equality operator at hand.
1175 : */
1176 54124 : if (i == 1 || isgt == iseq)
1177 : {
1178 : double otherdistinct;
1179 : bool isdefault;
1180 : AttStatsSlot mcvslot;
1181 :
1182 : /* Get estimated number of distinct values */
1183 12956 : otherdistinct = get_variable_numdistinct(vardata,
1184 : &isdefault);
1185 :
1186 : /* Subtract off the number of known MCVs */
1187 12956 : if (get_attstatsslot(&mcvslot, vardata->statsTuple,
1188 : STATISTIC_KIND_MCV, InvalidOid,
1189 : ATTSTATSSLOT_NUMBERS))
1190 : {
1191 2632 : otherdistinct -= mcvslot.nnumbers;
1192 2632 : free_attstatsslot(&mcvslot);
1193 : }
1194 :
1195 : /* If result doesn't seem sane, leave eq_selec at 0 */
1196 12956 : if (otherdistinct > 1)
1197 12956 : eq_selec = 1.0 / otherdistinct;
1198 : }
1199 :
1200 : /*
1201 : * Convert the constant and the two nearest bin boundary
1202 : * values to a uniform comparison scale, and do a linear
1203 : * interpolation within this bin.
1204 : */
1205 54124 : if (convert_to_scalar(constval, consttype, sslot.stacoll,
1206 : &val,
1207 54124 : sslot.values[i - 1], sslot.values[i],
1208 : vardata->vartype,
1209 : &low, &high))
1210 : {
1211 54120 : if (high <= low)
1212 : {
1213 : /* cope if bin boundaries appear identical */
1214 0 : binfrac = 0.5;
1215 : }
1216 54120 : else if (val <= low)
1217 6016 : binfrac = 0.0;
1218 48104 : else if (val >= high)
1219 1936 : binfrac = 1.0;
1220 : else
1221 : {
1222 46168 : binfrac = (val - low) / (high - low);
1223 :
1224 : /*
1225 : * Watch out for the possibility that we got a NaN or
1226 : * Infinity from the division. This can happen
1227 : * despite the previous checks, if for example "low"
1228 : * is -Infinity.
1229 : */
1230 46168 : if (isnan(binfrac) ||
1231 46168 : binfrac < 0.0 || binfrac > 1.0)
1232 0 : binfrac = 0.5;
1233 : }
1234 : }
1235 : else
1236 : {
1237 : /*
1238 : * Ideally we'd produce an error here, on the grounds that
1239 : * the given operator shouldn't have scalarXXsel
1240 : * registered as its selectivity func unless we can deal
1241 : * with its operand types. But currently, all manner of
1242 : * stuff is invoking scalarXXsel, so give a default
1243 : * estimate until that can be fixed.
1244 : */
1245 4 : binfrac = 0.5;
1246 : }
1247 :
1248 : /*
1249 : * Now, compute the overall selectivity across the values
1250 : * represented by the histogram. We have i-1 full bins and
1251 : * binfrac partial bin below the constant.
1252 : */
1253 54124 : histfrac = (double) (i - 1) + binfrac;
1254 54124 : histfrac /= (double) (sslot.nvalues - 1);
1255 :
1256 : /*
1257 : * At this point, histfrac is an estimate of the fraction of
1258 : * the population represented by the histogram that satisfies
1259 : * "x <= constval". Somewhat remarkably, this statement is
1260 : * true regardless of which operator we were doing the probes
1261 : * with, so long as convert_to_scalar() delivers reasonable
1262 : * results. If the probe constant is equal to some histogram
1263 : * entry, we would have considered the bin to the left of that
1264 : * entry if probing with "<" or ">=", or the bin to the right
1265 : * if probing with "<=" or ">"; but binfrac would have come
1266 : * out as 1.0 in the first case and 0.0 in the second, leading
1267 : * to the same histfrac in either case. For probe constants
1268 : * between histogram entries, we find the same bin and get the
1269 : * same estimate with any operator.
1270 : *
1271 : * The fact that the estimate corresponds to "x <= constval"
1272 : * and not "x < constval" is because of the way that ANALYZE
1273 : * constructs the histogram: each entry is, effectively, the
1274 : * rightmost value in its sample bucket. So selectivity
1275 : * values that are exact multiples of 1/(histogram_size-1)
1276 : * should be understood as estimates including a histogram
1277 : * entry plus everything to its left.
1278 : *
1279 : * However, that breaks down for the first histogram entry,
1280 : * which necessarily is the leftmost value in its sample
1281 : * bucket. That means the first histogram bin is slightly
1282 : * narrower than the rest, by an amount equal to eq_selec.
1283 : * Another way to say that is that we want "x <= leftmost" to
1284 : * be estimated as eq_selec not zero. So, if we're dealing
1285 : * with the first bin (i==1), rescale to make that true while
1286 : * adjusting the rest of that bin linearly.
1287 : */
1288 54124 : if (i == 1)
1289 4996 : histfrac += eq_selec * (1.0 - binfrac);
1290 :
1291 : /*
1292 : * "x <= constval" is good if we want an estimate for "<=" or
1293 : * ">", but if we are estimating for "<" or ">=", we now need
1294 : * to decrease the estimate by eq_selec.
1295 : */
1296 54124 : if (isgt == iseq)
1297 12026 : histfrac -= eq_selec;
1298 : }
1299 :
1300 : /*
1301 : * Now the estimate is finished for "<" and "<=" cases. If we are
1302 : * estimating for ">" or ">=", flip it.
1303 : */
1304 143380 : hist_selec = isgt ? (1.0 - histfrac) : histfrac;
1305 :
1306 : /*
1307 : * The histogram boundaries are only approximate to begin with,
1308 : * and may well be out of date anyway. Therefore, don't believe
1309 : * extremely small or large selectivity estimates --- unless we
1310 : * got actual current endpoint values from the table, in which
1311 : * case just do the usual sanity clamp. Somewhat arbitrarily, we
1312 : * set the cutoff for other cases at a hundredth of the histogram
1313 : * resolution.
1314 : */
1315 143380 : if (have_end)
1316 75072 : CLAMP_PROBABILITY(hist_selec);
1317 : else
1318 : {
1319 68308 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1320 :
1321 68308 : if (hist_selec < cutoff)
1322 22920 : hist_selec = cutoff;
1323 45388 : else if (hist_selec > 1.0 - cutoff)
1324 20614 : hist_selec = 1.0 - cutoff;
1325 : }
1326 : }
1327 :
1328 143380 : free_attstatsslot(&sslot);
1329 : }
1330 :
1331 171908 : return hist_selec;
1332 : }
1333 :
1334 : /*
1335 : * Common wrapper function for the selectivity estimators that simply
1336 : * invoke scalarineqsel().
1337 : */
1338 : static Datum
1339 29832 : scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
1340 : {
1341 29832 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
1342 29832 : Oid operator = PG_GETARG_OID(1);
1343 29832 : List *args = (List *) PG_GETARG_POINTER(2);
1344 29832 : int varRelid = PG_GETARG_INT32(3);
1345 : VariableStatData vardata;
1346 : Node *other;
1347 : bool varonleft;
1348 : Datum constval;
1349 : Oid consttype;
1350 : double selec;
1351 :
1352 : /*
1353 : * If expression is not variable op something or something op variable,
1354 : * then punt and return a default estimate.
1355 : */
1356 29832 : if (!get_restriction_variable(root, args, varRelid,
1357 : &vardata, &other, &varonleft))
1358 192 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1359 :
1360 : /*
1361 : * Can't do anything useful if the something is not a constant, either.
1362 : */
1363 29640 : if (!IsA(other, Const))
1364 : {
1365 1428 : ReleaseVariableStats(vardata);
1366 1428 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1367 : }
1368 :
1369 : /*
1370 : * If the constant is NULL, assume operator is strict and return zero, ie,
1371 : * operator will never return TRUE.
1372 : */
1373 28212 : if (((Const *) other)->constisnull)
1374 : {
1375 24 : ReleaseVariableStats(vardata);
1376 24 : PG_RETURN_FLOAT8(0.0);
1377 : }
1378 28188 : constval = ((Const *) other)->constvalue;
1379 28188 : consttype = ((Const *) other)->consttype;
1380 :
1381 : /*
1382 : * Force the var to be on the left to simplify logic in scalarineqsel.
1383 : */
1384 28188 : if (!varonleft)
1385 : {
1386 168 : operator = get_commutator(operator);
1387 168 : if (!operator)
1388 : {
1389 : /* Use default selectivity (should we raise an error instead?) */
1390 0 : ReleaseVariableStats(vardata);
1391 0 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1392 : }
1393 168 : isgt = !isgt;
1394 : }
1395 :
1396 : /* The rest of the work is done by scalarineqsel(). */
1397 28188 : selec = scalarineqsel(root, operator, isgt, iseq,
1398 : &vardata, constval, consttype);
1399 :
1400 28188 : ReleaseVariableStats(vardata);
1401 :
1402 28188 : PG_RETURN_FLOAT8((float8) selec);
1403 : }
1404 :
1405 : /*
1406 : * scalarltsel - Selectivity of "<" for scalars.
1407 : */
1408 : Datum
1409 8452 : scalarltsel(PG_FUNCTION_ARGS)
1410 : {
1411 8452 : return scalarineqsel_wrapper(fcinfo, false, false);
1412 : }
1413 :
1414 : /*
1415 : * scalarlesel - Selectivity of "<=" for scalars.
1416 : */
1417 : Datum
1418 2220 : scalarlesel(PG_FUNCTION_ARGS)
1419 : {
1420 2220 : return scalarineqsel_wrapper(fcinfo, false, true);
1421 : }
1422 :
1423 : /*
1424 : * scalargtsel - Selectivity of ">" for scalars.
1425 : */
1426 : Datum
1427 12458 : scalargtsel(PG_FUNCTION_ARGS)
1428 : {
1429 12458 : return scalarineqsel_wrapper(fcinfo, true, false);
1430 : }
1431 :
1432 : /*
1433 : * scalargesel - Selectivity of ">=" for scalars.
1434 : */
1435 : Datum
1436 6702 : scalargesel(PG_FUNCTION_ARGS)
1437 : {
1438 6702 : return scalarineqsel_wrapper(fcinfo, true, true);
1439 : }
1440 :
1441 : /*
1442 : * boolvarsel - Selectivity of Boolean variable.
1443 : *
1444 : * This can actually be called on any boolean-valued expression. If it
1445 : * involves only Vars of the specified relation, and if there are statistics
1446 : * about the Var or expression (the latter is possible if it's indexed) then
1447 : * we'll produce a real estimate; otherwise it's just a default.
1448 : */
1449 : Selectivity
1450 15312 : boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
1451 : {
1452 : VariableStatData vardata;
1453 : double selec;
1454 :
1455 15312 : examine_variable(root, arg, varRelid, &vardata);
1456 15312 : if (HeapTupleIsValid(vardata.statsTuple))
1457 : {
1458 : /*
1459 : * A boolean variable V is equivalent to the clause V = 't', so we
1460 : * compute the selectivity as if that is what we have.
1461 : */
1462 12254 : selec = var_eq_const(&vardata, BooleanEqualOperator,
1463 : BoolGetDatum(true), false, true, false);
1464 : }
1465 : else
1466 : {
1467 : /* Otherwise, the default estimate is 0.5 */
1468 3058 : selec = 0.5;
1469 : }
1470 15312 : ReleaseVariableStats(vardata);
1471 15312 : return selec;
1472 : }
1473 :
1474 : /*
1475 : * booltestsel - Selectivity of BooleanTest Node.
1476 : */
1477 : Selectivity
1478 132 : booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
1479 : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1480 : {
1481 : VariableStatData vardata;
1482 : double selec;
1483 :
1484 132 : examine_variable(root, arg, varRelid, &vardata);
1485 :
1486 132 : if (HeapTupleIsValid(vardata.statsTuple))
1487 : {
1488 : Form_pg_statistic stats;
1489 : double freq_null;
1490 : AttStatsSlot sslot;
1491 :
1492 0 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
1493 0 : freq_null = stats->stanullfrac;
1494 :
1495 0 : if (get_attstatsslot(&sslot, vardata.statsTuple,
1496 : STATISTIC_KIND_MCV, InvalidOid,
1497 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
1498 0 : && sslot.nnumbers > 0)
1499 0 : {
1500 : double freq_true;
1501 : double freq_false;
1502 :
1503 : /*
1504 : * Get first MCV frequency and derive frequency for true.
1505 : */
1506 0 : if (DatumGetBool(sslot.values[0]))
1507 0 : freq_true = sslot.numbers[0];
1508 : else
1509 0 : freq_true = 1.0 - sslot.numbers[0] - freq_null;
1510 :
1511 : /*
1512 : * Next derive frequency for false. Then use these as appropriate
1513 : * to derive frequency for each case.
1514 : */
1515 0 : freq_false = 1.0 - freq_true - freq_null;
1516 :
1517 0 : switch (booltesttype)
1518 : {
1519 0 : case IS_UNKNOWN:
1520 : /* select only NULL values */
1521 0 : selec = freq_null;
1522 0 : break;
1523 0 : case IS_NOT_UNKNOWN:
1524 : /* select non-NULL values */
1525 0 : selec = 1.0 - freq_null;
1526 0 : break;
1527 0 : case IS_TRUE:
1528 : /* select only TRUE values */
1529 0 : selec = freq_true;
1530 0 : break;
1531 0 : case IS_NOT_TRUE:
1532 : /* select non-TRUE values */
1533 0 : selec = 1.0 - freq_true;
1534 0 : break;
1535 0 : case IS_FALSE:
1536 : /* select only FALSE values */
1537 0 : selec = freq_false;
1538 0 : break;
1539 0 : case IS_NOT_FALSE:
1540 : /* select non-FALSE values */
1541 0 : selec = 1.0 - freq_false;
1542 0 : break;
1543 0 : default:
1544 0 : elog(ERROR, "unrecognized booltesttype: %d",
1545 : (int) booltesttype);
1546 : selec = 0.0; /* Keep compiler quiet */
1547 : break;
1548 : }
1549 :
1550 0 : free_attstatsslot(&sslot);
1551 : }
1552 : else
1553 : {
1554 : /*
1555 : * No most-common-value info available. Still have null fraction
1556 : * information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
1557 : * for null fraction and assume a 50-50 split of TRUE and FALSE.
1558 : */
1559 0 : switch (booltesttype)
1560 : {
1561 0 : case IS_UNKNOWN:
1562 : /* select only NULL values */
1563 0 : selec = freq_null;
1564 0 : break;
1565 0 : case IS_NOT_UNKNOWN:
1566 : /* select non-NULL values */
1567 0 : selec = 1.0 - freq_null;
1568 0 : break;
1569 0 : case IS_TRUE:
1570 : case IS_FALSE:
1571 : /* Assume we select half of the non-NULL values */
1572 0 : selec = (1.0 - freq_null) / 2.0;
1573 0 : break;
1574 0 : case IS_NOT_TRUE:
1575 : case IS_NOT_FALSE:
1576 : /* Assume we select NULLs plus half of the non-NULLs */
1577 : /* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
1578 0 : selec = (freq_null + 1.0) / 2.0;
1579 0 : break;
1580 0 : default:
1581 0 : elog(ERROR, "unrecognized booltesttype: %d",
1582 : (int) booltesttype);
1583 : selec = 0.0; /* Keep compiler quiet */
1584 : break;
1585 : }
1586 : }
1587 : }
1588 : else
1589 : {
1590 : /*
1591 : * If we can't get variable statistics for the argument, perhaps
1592 : * clause_selectivity can do something with it. We ignore the
1593 : * possibility of a NULL value when using clause_selectivity, and just
1594 : * assume the value is either TRUE or FALSE.
1595 : */
1596 132 : switch (booltesttype)
1597 : {
1598 12 : case IS_UNKNOWN:
1599 12 : selec = DEFAULT_UNK_SEL;
1600 12 : break;
1601 12 : case IS_NOT_UNKNOWN:
1602 12 : selec = DEFAULT_NOT_UNK_SEL;
1603 12 : break;
1604 32 : case IS_TRUE:
1605 : case IS_NOT_FALSE:
1606 32 : selec = (double) clause_selectivity(root, arg,
1607 : varRelid,
1608 : jointype, sjinfo);
1609 32 : break;
1610 76 : case IS_FALSE:
1611 : case IS_NOT_TRUE:
1612 76 : selec = 1.0 - (double) clause_selectivity(root, arg,
1613 : varRelid,
1614 : jointype, sjinfo);
1615 76 : break;
1616 0 : default:
1617 0 : elog(ERROR, "unrecognized booltesttype: %d",
1618 : (int) booltesttype);
1619 : selec = 0.0; /* Keep compiler quiet */
1620 : break;
1621 : }
1622 : }
1623 :
1624 132 : ReleaseVariableStats(vardata);
1625 :
1626 : /* result should be in range, but make sure... */
1627 132 : CLAMP_PROBABILITY(selec);
1628 :
1629 132 : return (Selectivity) selec;
1630 : }
1631 :
1632 : /*
1633 : * nulltestsel - Selectivity of NullTest Node.
1634 : */
1635 : Selectivity
1636 13312 : nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
1637 : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1638 : {
1639 : VariableStatData vardata;
1640 : double selec;
1641 :
1642 13312 : examine_variable(root, arg, varRelid, &vardata);
1643 :
1644 13312 : if (HeapTupleIsValid(vardata.statsTuple))
1645 : {
1646 : Form_pg_statistic stats;
1647 : double freq_null;
1648 :
1649 4382 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
1650 4382 : freq_null = stats->stanullfrac;
1651 :
1652 4382 : switch (nulltesttype)
1653 : {
1654 3858 : case IS_NULL:
1655 :
1656 : /*
1657 : * Use freq_null directly.
1658 : */
1659 3858 : selec = freq_null;
1660 3858 : break;
1661 524 : case IS_NOT_NULL:
1662 :
1663 : /*
1664 : * Select not unknown (not null) values. Calculate from
1665 : * freq_null.
1666 : */
1667 524 : selec = 1.0 - freq_null;
1668 524 : break;
1669 0 : default:
1670 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1671 : (int) nulltesttype);
1672 : return (Selectivity) 0; /* keep compiler quiet */
1673 : }
1674 : }
1675 8930 : else if (vardata.var && IsA(vardata.var, Var) &&
1676 6298 : ((Var *) vardata.var)->varattno < 0)
1677 : {
1678 : /*
1679 : * There are no stats for system columns, but we know they are never
1680 : * NULL.
1681 : */
1682 16 : selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
1683 : }
1684 : else
1685 : {
1686 : /*
1687 : * No ANALYZE stats available, so make a guess
1688 : */
1689 8914 : switch (nulltesttype)
1690 : {
1691 982 : case IS_NULL:
1692 982 : selec = DEFAULT_UNK_SEL;
1693 982 : break;
1694 7932 : case IS_NOT_NULL:
1695 7932 : selec = DEFAULT_NOT_UNK_SEL;
1696 7932 : break;
1697 0 : default:
1698 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1699 : (int) nulltesttype);
1700 : return (Selectivity) 0; /* keep compiler quiet */
1701 : }
1702 : }
1703 :
1704 13312 : ReleaseVariableStats(vardata);
1705 :
1706 : /* result should be in range, but make sure... */
1707 13312 : CLAMP_PROBABILITY(selec);
1708 :
1709 13312 : return (Selectivity) selec;
1710 : }
1711 :
1712 : /*
1713 : * strip_array_coercion - strip binary-compatible relabeling from an array expr
1714 : *
1715 : * For array values, the parser normally generates ArrayCoerceExpr conversions,
1716 : * but it seems possible that RelabelType might show up. Also, the planner
1717 : * is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
1718 : * so we need to be ready to deal with more than one level.
1719 : */
1720 : static Node *
1721 71136 : strip_array_coercion(Node *node)
1722 : {
1723 : for (;;)
1724 : {
1725 71136 : if (node && IsA(node, ArrayCoerceExpr))
1726 12 : {
1727 328 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
1728 :
1729 : /*
1730 : * If the per-element expression is just a RelabelType on top of
1731 : * CaseTestExpr, then we know it's a binary-compatible relabeling.
1732 : */
1733 328 : if (IsA(acoerce->elemexpr, RelabelType) &&
1734 12 : IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
1735 12 : node = (Node *) acoerce->arg;
1736 : else
1737 : break;
1738 : }
1739 70808 : else if (node && IsA(node, RelabelType))
1740 : {
1741 : /* We don't really expect this case, but may as well cope */
1742 0 : node = (Node *) ((RelabelType *) node)->arg;
1743 : }
1744 : else
1745 : break;
1746 : }
1747 71124 : return node;
1748 : }
1749 :
1750 : /*
1751 : * scalararraysel - Selectivity of ScalarArrayOpExpr Node.
1752 : */
1753 : Selectivity
1754 14512 : scalararraysel(PlannerInfo *root,
1755 : ScalarArrayOpExpr *clause,
1756 : bool is_join_clause,
1757 : int varRelid,
1758 : JoinType jointype,
1759 : SpecialJoinInfo *sjinfo)
1760 : {
1761 14512 : Oid operator = clause->opno;
1762 14512 : bool useOr = clause->useOr;
1763 14512 : bool isEquality = false;
1764 14512 : bool isInequality = false;
1765 : Node *leftop;
1766 : Node *rightop;
1767 : Oid nominal_element_type;
1768 : Oid nominal_element_collation;
1769 : TypeCacheEntry *typentry;
1770 : RegProcedure oprsel;
1771 : FmgrInfo oprselproc;
1772 : Selectivity s1;
1773 : Selectivity s1disjoint;
1774 :
1775 : /* First, deconstruct the expression */
1776 : Assert(list_length(clause->args) == 2);
1777 14512 : leftop = (Node *) linitial(clause->args);
1778 14512 : rightop = (Node *) lsecond(clause->args);
1779 :
1780 : /* aggressively reduce both sides to constants */
1781 14512 : leftop = estimate_expression_value(root, leftop);
1782 14512 : rightop = estimate_expression_value(root, rightop);
1783 :
1784 : /* get nominal (after relabeling) element type of rightop */
1785 14512 : nominal_element_type = get_base_element_type(exprType(rightop));
1786 14512 : if (!OidIsValid(nominal_element_type))
1787 0 : return (Selectivity) 0.5; /* probably shouldn't happen */
1788 : /* get nominal collation, too, for generating constants */
1789 14512 : nominal_element_collation = exprCollation(rightop);
1790 :
1791 : /* look through any binary-compatible relabeling of rightop */
1792 14512 : rightop = strip_array_coercion(rightop);
1793 :
1794 : /*
1795 : * Detect whether the operator is the default equality or inequality
1796 : * operator of the array element type.
1797 : */
1798 14512 : typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
1799 14512 : if (OidIsValid(typentry->eq_opr))
1800 : {
1801 14512 : if (operator == typentry->eq_opr)
1802 13478 : isEquality = true;
1803 1034 : else if (get_negator(operator) == typentry->eq_opr)
1804 790 : isInequality = true;
1805 : }
1806 :
1807 : /*
1808 : * If it is equality or inequality, we might be able to estimate this as a
1809 : * form of array containment; for instance "const = ANY(column)" can be
1810 : * treated as "ARRAY[const] <@ column". scalararraysel_containment tries
1811 : * that, and returns the selectivity estimate if successful, or -1 if not.
1812 : */
1813 14512 : if ((isEquality || isInequality) && !is_join_clause)
1814 : {
1815 14268 : s1 = scalararraysel_containment(root, leftop, rightop,
1816 : nominal_element_type,
1817 : isEquality, useOr, varRelid);
1818 14268 : if (s1 >= 0.0)
1819 62 : return s1;
1820 : }
1821 :
1822 : /*
1823 : * Look up the underlying operator's selectivity estimator. Punt if it
1824 : * hasn't got one.
1825 : */
1826 14450 : if (is_join_clause)
1827 0 : oprsel = get_oprjoin(operator);
1828 : else
1829 14450 : oprsel = get_oprrest(operator);
1830 14450 : if (!oprsel)
1831 0 : return (Selectivity) 0.5;
1832 14450 : fmgr_info(oprsel, &oprselproc);
1833 :
1834 : /*
1835 : * In the array-containment check above, we must only believe that an
1836 : * operator is equality or inequality if it is the default btree equality
1837 : * operator (or its negator) for the element type, since those are the
1838 : * operators that array containment will use. But in what follows, we can
1839 : * be a little laxer, and also believe that any operators using eqsel() or
1840 : * neqsel() as selectivity estimator act like equality or inequality.
1841 : */
1842 14450 : if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
1843 13500 : isEquality = true;
1844 950 : else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
1845 742 : isInequality = true;
1846 :
1847 : /*
1848 : * We consider three cases:
1849 : *
1850 : * 1. rightop is an Array constant: deconstruct the array, apply the
1851 : * operator's selectivity function for each array element, and merge the
1852 : * results in the same way that clausesel.c does for AND/OR combinations.
1853 : *
1854 : * 2. rightop is an ARRAY[] construct: apply the operator's selectivity
1855 : * function for each element of the ARRAY[] construct, and merge.
1856 : *
1857 : * 3. otherwise, make a guess ...
1858 : */
1859 14450 : if (rightop && IsA(rightop, Const))
1860 7556 : {
1861 7568 : Datum arraydatum = ((Const *) rightop)->constvalue;
1862 7568 : bool arrayisnull = ((Const *) rightop)->constisnull;
1863 : ArrayType *arrayval;
1864 : int16 elmlen;
1865 : bool elmbyval;
1866 : char elmalign;
1867 : int num_elems;
1868 : Datum *elem_values;
1869 : bool *elem_nulls;
1870 : int i;
1871 :
1872 7568 : if (arrayisnull) /* qual can't succeed if null array */
1873 12 : return (Selectivity) 0.0;
1874 7556 : arrayval = DatumGetArrayTypeP(arraydatum);
1875 7556 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
1876 : &elmlen, &elmbyval, &elmalign);
1877 7556 : deconstruct_array(arrayval,
1878 : ARR_ELEMTYPE(arrayval),
1879 : elmlen, elmbyval, elmalign,
1880 : &elem_values, &elem_nulls, &num_elems);
1881 :
1882 : /*
1883 : * For generic operators, we assume the probability of success is
1884 : * independent for each array element. But for "= ANY" or "<> ALL",
1885 : * if the array elements are distinct (which'd typically be the case)
1886 : * then the probabilities are disjoint, and we should just sum them.
1887 : *
1888 : * If we were being really tense we would try to confirm that the
1889 : * elements are all distinct, but that would be expensive and it
1890 : * doesn't seem to be worth the cycles; it would amount to penalizing
1891 : * well-written queries in favor of poorly-written ones. However, we
1892 : * do protect ourselves a little bit by checking whether the
1893 : * disjointness assumption leads to an impossible (out of range)
1894 : * probability; if so, we fall back to the normal calculation.
1895 : */
1896 7556 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
1897 :
1898 31676 : for (i = 0; i < num_elems; i++)
1899 : {
1900 : List *args;
1901 : Selectivity s2;
1902 :
1903 24120 : args = list_make2(leftop,
1904 : makeConst(nominal_element_type,
1905 : -1,
1906 : nominal_element_collation,
1907 : elmlen,
1908 : elem_values[i],
1909 : elem_nulls[i],
1910 : elmbyval));
1911 24120 : if (is_join_clause)
1912 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
1913 : clause->inputcollid,
1914 : PointerGetDatum(root),
1915 : ObjectIdGetDatum(operator),
1916 : PointerGetDatum(args),
1917 : Int16GetDatum(jointype),
1918 : PointerGetDatum(sjinfo)));
1919 : else
1920 24120 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
1921 : clause->inputcollid,
1922 : PointerGetDatum(root),
1923 : ObjectIdGetDatum(operator),
1924 : PointerGetDatum(args),
1925 : Int32GetDatum(varRelid)));
1926 :
1927 24120 : if (useOr)
1928 : {
1929 21810 : s1 = s1 + s2 - s1 * s2;
1930 21810 : if (isEquality)
1931 21414 : s1disjoint += s2;
1932 : }
1933 : else
1934 : {
1935 2310 : s1 = s1 * s2;
1936 2310 : if (isInequality)
1937 2166 : s1disjoint += s2 - 1.0;
1938 : }
1939 : }
1940 :
1941 : /* accept disjoint-probability estimate if in range */
1942 7556 : if ((useOr ? isEquality : isInequality) &&
1943 7280 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
1944 7260 : s1 = s1disjoint;
1945 : }
1946 6882 : else if (rightop && IsA(rightop, ArrayExpr) &&
1947 54 : !((ArrayExpr *) rightop)->multidims)
1948 54 : {
1949 54 : ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
1950 : int16 elmlen;
1951 : bool elmbyval;
1952 : ListCell *l;
1953 :
1954 54 : get_typlenbyval(arrayexpr->element_typeid,
1955 : &elmlen, &elmbyval);
1956 :
1957 : /*
1958 : * We use the assumption of disjoint probabilities here too, although
1959 : * the odds of equal array elements are rather higher if the elements
1960 : * are not all constants (which they won't be, else constant folding
1961 : * would have reduced the ArrayExpr to a Const). In this path it's
1962 : * critical to have the sanity check on the s1disjoint estimate.
1963 : */
1964 54 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
1965 :
1966 200 : foreach(l, arrayexpr->elements)
1967 : {
1968 146 : Node *elem = (Node *) lfirst(l);
1969 : List *args;
1970 : Selectivity s2;
1971 :
1972 : /*
1973 : * Theoretically, if elem isn't of nominal_element_type we should
1974 : * insert a RelabelType, but it seems unlikely that any operator
1975 : * estimation function would really care ...
1976 : */
1977 146 : args = list_make2(leftop, elem);
1978 146 : if (is_join_clause)
1979 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
1980 : clause->inputcollid,
1981 : PointerGetDatum(root),
1982 : ObjectIdGetDatum(operator),
1983 : PointerGetDatum(args),
1984 : Int16GetDatum(jointype),
1985 : PointerGetDatum(sjinfo)));
1986 : else
1987 146 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
1988 : clause->inputcollid,
1989 : PointerGetDatum(root),
1990 : ObjectIdGetDatum(operator),
1991 : PointerGetDatum(args),
1992 : Int32GetDatum(varRelid)));
1993 :
1994 146 : if (useOr)
1995 : {
1996 146 : s1 = s1 + s2 - s1 * s2;
1997 146 : if (isEquality)
1998 146 : s1disjoint += s2;
1999 : }
2000 : else
2001 : {
2002 0 : s1 = s1 * s2;
2003 0 : if (isInequality)
2004 0 : s1disjoint += s2 - 1.0;
2005 : }
2006 : }
2007 :
2008 : /* accept disjoint-probability estimate if in range */
2009 54 : if ((useOr ? isEquality : isInequality) &&
2010 54 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2011 54 : s1 = s1disjoint;
2012 : }
2013 : else
2014 : {
2015 : CaseTestExpr *dummyexpr;
2016 : List *args;
2017 : Selectivity s2;
2018 : int i;
2019 :
2020 : /*
2021 : * We need a dummy rightop to pass to the operator selectivity
2022 : * routine. It can be pretty much anything that doesn't look like a
2023 : * constant; CaseTestExpr is a convenient choice.
2024 : */
2025 6828 : dummyexpr = makeNode(CaseTestExpr);
2026 6828 : dummyexpr->typeId = nominal_element_type;
2027 6828 : dummyexpr->typeMod = -1;
2028 6828 : dummyexpr->collation = clause->inputcollid;
2029 6828 : args = list_make2(leftop, dummyexpr);
2030 6828 : if (is_join_clause)
2031 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2032 : clause->inputcollid,
2033 : PointerGetDatum(root),
2034 : ObjectIdGetDatum(operator),
2035 : PointerGetDatum(args),
2036 : Int16GetDatum(jointype),
2037 : PointerGetDatum(sjinfo)));
2038 : else
2039 6828 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2040 : clause->inputcollid,
2041 : PointerGetDatum(root),
2042 : ObjectIdGetDatum(operator),
2043 : PointerGetDatum(args),
2044 : Int32GetDatum(varRelid)));
2045 6828 : s1 = useOr ? 0.0 : 1.0;
2046 :
2047 : /*
2048 : * Arbitrarily assume 10 elements in the eventual array value (see
2049 : * also estimate_array_length). We don't risk an assumption of
2050 : * disjoint probabilities here.
2051 : */
2052 75108 : for (i = 0; i < 10; i++)
2053 : {
2054 68280 : if (useOr)
2055 68280 : s1 = s1 + s2 - s1 * s2;
2056 : else
2057 0 : s1 = s1 * s2;
2058 : }
2059 : }
2060 :
2061 : /* result should be in range, but make sure... */
2062 14438 : CLAMP_PROBABILITY(s1);
2063 :
2064 14438 : return s1;
2065 : }
2066 :
2067 : /*
2068 : * Estimate number of elements in the array yielded by an expression.
2069 : *
2070 : * It's important that this agree with scalararraysel.
2071 : */
2072 : int
2073 56612 : estimate_array_length(Node *arrayexpr)
2074 : {
2075 : /* look through any binary-compatible relabeling of arrayexpr */
2076 56612 : arrayexpr = strip_array_coercion(arrayexpr);
2077 :
2078 56612 : if (arrayexpr && IsA(arrayexpr, Const))
2079 : {
2080 13158 : Datum arraydatum = ((Const *) arrayexpr)->constvalue;
2081 13158 : bool arrayisnull = ((Const *) arrayexpr)->constisnull;
2082 : ArrayType *arrayval;
2083 :
2084 13158 : if (arrayisnull)
2085 12 : return 0;
2086 13146 : arrayval = DatumGetArrayTypeP(arraydatum);
2087 13146 : return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
2088 : }
2089 43454 : else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
2090 96 : !((ArrayExpr *) arrayexpr)->multidims)
2091 : {
2092 96 : return list_length(((ArrayExpr *) arrayexpr)->elements);
2093 : }
2094 : else
2095 : {
2096 : /* default guess --- see also scalararraysel */
2097 43358 : return 10;
2098 : }
2099 : }
2100 :
2101 : /*
2102 : * rowcomparesel - Selectivity of RowCompareExpr Node.
2103 : *
2104 : * We estimate RowCompare selectivity by considering just the first (high
2105 : * order) columns, which makes it equivalent to an ordinary OpExpr. While
2106 : * this estimate could be refined by considering additional columns, it
2107 : * seems unlikely that we could do a lot better without multi-column
2108 : * statistics.
2109 : */
2110 : Selectivity
2111 104 : rowcomparesel(PlannerInfo *root,
2112 : RowCompareExpr *clause,
2113 : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
2114 : {
2115 : Selectivity s1;
2116 104 : Oid opno = linitial_oid(clause->opnos);
2117 104 : Oid inputcollid = linitial_oid(clause->inputcollids);
2118 : List *opargs;
2119 : bool is_join_clause;
2120 :
2121 : /* Build equivalent arg list for single operator */
2122 104 : opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
2123 :
2124 : /*
2125 : * Decide if it's a join clause. This should match clausesel.c's
2126 : * treat_as_join_clause(), except that we intentionally consider only the
2127 : * leading columns and not the rest of the clause.
2128 : */
2129 104 : if (varRelid != 0)
2130 : {
2131 : /*
2132 : * Caller is forcing restriction mode (eg, because we are examining an
2133 : * inner indexscan qual).
2134 : */
2135 36 : is_join_clause = false;
2136 : }
2137 68 : else if (sjinfo == NULL)
2138 : {
2139 : /*
2140 : * It must be a restriction clause, since it's being evaluated at a
2141 : * scan node.
2142 : */
2143 60 : is_join_clause = false;
2144 : }
2145 : else
2146 : {
2147 : /*
2148 : * Otherwise, it's a join if there's more than one relation used.
2149 : */
2150 8 : is_join_clause = (NumRelids((Node *) opargs) > 1);
2151 : }
2152 :
2153 104 : if (is_join_clause)
2154 : {
2155 : /* Estimate selectivity for a join clause. */
2156 8 : s1 = join_selectivity(root, opno,
2157 : opargs,
2158 : inputcollid,
2159 : jointype,
2160 : sjinfo);
2161 : }
2162 : else
2163 : {
2164 : /* Estimate selectivity for a restriction clause. */
2165 96 : s1 = restriction_selectivity(root, opno,
2166 : opargs,
2167 : inputcollid,
2168 : varRelid);
2169 : }
2170 :
2171 104 : return s1;
2172 : }
2173 :
2174 : /*
2175 : * eqjoinsel - Join selectivity of "="
2176 : */
2177 : Datum
2178 126286 : eqjoinsel(PG_FUNCTION_ARGS)
2179 : {
2180 126286 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
2181 126286 : Oid operator = PG_GETARG_OID(1);
2182 126286 : List *args = (List *) PG_GETARG_POINTER(2);
2183 :
2184 : #ifdef NOT_USED
2185 : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
2186 : #endif
2187 126286 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
2188 : double selec;
2189 : double selec_inner;
2190 : VariableStatData vardata1;
2191 : VariableStatData vardata2;
2192 : double nd1;
2193 : double nd2;
2194 : bool isdefault1;
2195 : bool isdefault2;
2196 : Oid opfuncoid;
2197 : AttStatsSlot sslot1;
2198 : AttStatsSlot sslot2;
2199 126286 : Form_pg_statistic stats1 = NULL;
2200 126286 : Form_pg_statistic stats2 = NULL;
2201 126286 : bool have_mcvs1 = false;
2202 126286 : bool have_mcvs2 = false;
2203 : bool join_is_reversed;
2204 : RelOptInfo *inner_rel;
2205 :
2206 126286 : get_join_variables(root, args, sjinfo,
2207 : &vardata1, &vardata2, &join_is_reversed);
2208 :
2209 126286 : nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
2210 126286 : nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
2211 :
2212 126286 : opfuncoid = get_opcode(operator);
2213 :
2214 126286 : memset(&sslot1, 0, sizeof(sslot1));
2215 126286 : memset(&sslot2, 0, sizeof(sslot2));
2216 :
2217 126286 : if (HeapTupleIsValid(vardata1.statsTuple))
2218 : {
2219 : /* note we allow use of nullfrac regardless of security check */
2220 82678 : stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
2221 82678 : if (statistic_proc_security_check(&vardata1, opfuncoid))
2222 82678 : have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
2223 : STATISTIC_KIND_MCV, InvalidOid,
2224 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2225 : }
2226 :
2227 126286 : if (HeapTupleIsValid(vardata2.statsTuple))
2228 : {
2229 : /* note we allow use of nullfrac regardless of security check */
2230 83786 : stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
2231 83786 : if (statistic_proc_security_check(&vardata2, opfuncoid))
2232 83786 : have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
2233 : STATISTIC_KIND_MCV, InvalidOid,
2234 : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2235 : }
2236 :
2237 : /* We need to compute the inner-join selectivity in all cases */
2238 126286 : selec_inner = eqjoinsel_inner(opfuncoid,
2239 : &vardata1, &vardata2,
2240 : nd1, nd2,
2241 : isdefault1, isdefault2,
2242 : &sslot1, &sslot2,
2243 : stats1, stats2,
2244 : have_mcvs1, have_mcvs2);
2245 :
2246 126286 : switch (sjinfo->jointype)
2247 : {
2248 116336 : case JOIN_INNER:
2249 : case JOIN_LEFT:
2250 : case JOIN_FULL:
2251 116336 : selec = selec_inner;
2252 116336 : break;
2253 9950 : case JOIN_SEMI:
2254 : case JOIN_ANTI:
2255 :
2256 : /*
2257 : * Look up the join's inner relation. min_righthand is sufficient
2258 : * information because neither SEMI nor ANTI joins permit any
2259 : * reassociation into or out of their RHS, so the righthand will
2260 : * always be exactly that set of rels.
2261 : */
2262 9950 : inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
2263 :
2264 9950 : if (!join_is_reversed)
2265 7402 : selec = eqjoinsel_semi(opfuncoid,
2266 : &vardata1, &vardata2,
2267 : nd1, nd2,
2268 : isdefault1, isdefault2,
2269 : &sslot1, &sslot2,
2270 : stats1, stats2,
2271 : have_mcvs1, have_mcvs2,
2272 : inner_rel);
2273 : else
2274 : {
2275 2548 : Oid commop = get_commutator(operator);
2276 2548 : Oid commopfuncoid = OidIsValid(commop) ? get_opcode(commop) : InvalidOid;
2277 :
2278 2548 : selec = eqjoinsel_semi(commopfuncoid,
2279 : &vardata2, &vardata1,
2280 : nd2, nd1,
2281 : isdefault2, isdefault1,
2282 : &sslot2, &sslot1,
2283 : stats2, stats1,
2284 : have_mcvs2, have_mcvs1,
2285 : inner_rel);
2286 : }
2287 :
2288 : /*
2289 : * We should never estimate the output of a semijoin to be more
2290 : * rows than we estimate for an inner join with the same input
2291 : * rels and join condition; it's obviously impossible for that to
2292 : * happen. The former estimate is N1 * Ssemi while the latter is
2293 : * N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
2294 : * this is worthwhile because of the shakier estimation rules we
2295 : * use in eqjoinsel_semi, particularly in cases where it has to
2296 : * punt entirely.
2297 : */
2298 9950 : selec = Min(selec, inner_rel->rows * selec_inner);
2299 9950 : break;
2300 0 : default:
2301 : /* other values not expected here */
2302 0 : elog(ERROR, "unrecognized join type: %d",
2303 : (int) sjinfo->jointype);
2304 : selec = 0; /* keep compiler quiet */
2305 : break;
2306 : }
2307 :
2308 126286 : free_attstatsslot(&sslot1);
2309 126286 : free_attstatsslot(&sslot2);
2310 :
2311 126286 : ReleaseVariableStats(vardata1);
2312 126286 : ReleaseVariableStats(vardata2);
2313 :
2314 126286 : CLAMP_PROBABILITY(selec);
2315 :
2316 126286 : PG_RETURN_FLOAT8((float8) selec);
2317 : }
2318 :
2319 : /*
2320 : * eqjoinsel_inner --- eqjoinsel for normal inner join
2321 : *
2322 : * We also use this for LEFT/FULL outer joins; it's not presently clear
2323 : * that it's worth trying to distinguish them here.
2324 : */
2325 : static double
2326 126286 : eqjoinsel_inner(Oid opfuncoid,
2327 : VariableStatData *vardata1, VariableStatData *vardata2,
2328 : double nd1, double nd2,
2329 : bool isdefault1, bool isdefault2,
2330 : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2331 : Form_pg_statistic stats1, Form_pg_statistic stats2,
2332 : bool have_mcvs1, bool have_mcvs2)
2333 : {
2334 : double selec;
2335 :
2336 126286 : if (have_mcvs1 && have_mcvs2)
2337 11292 : {
2338 : /*
2339 : * We have most-common-value lists for both relations. Run through
2340 : * the lists to see which MCVs actually join to each other with the
2341 : * given operator. This allows us to determine the exact join
2342 : * selectivity for the portion of the relations represented by the MCV
2343 : * lists. We still have to estimate for the remaining population, but
2344 : * in a skewed distribution this gives us a big leg up in accuracy.
2345 : * For motivation see the analysis in Y. Ioannidis and S.
2346 : * Christodoulakis, "On the propagation of errors in the size of join
2347 : * results", Technical Report 1018, Computer Science Dept., University
2348 : * of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
2349 : */
2350 11292 : LOCAL_FCINFO(fcinfo, 2);
2351 : FmgrInfo eqproc;
2352 : bool *hasmatch1;
2353 : bool *hasmatch2;
2354 11292 : double nullfrac1 = stats1->stanullfrac;
2355 11292 : double nullfrac2 = stats2->stanullfrac;
2356 : double matchprodfreq,
2357 : matchfreq1,
2358 : matchfreq2,
2359 : unmatchfreq1,
2360 : unmatchfreq2,
2361 : otherfreq1,
2362 : otherfreq2,
2363 : totalsel1,
2364 : totalsel2;
2365 : int i,
2366 : nmatches;
2367 :
2368 11292 : fmgr_info(opfuncoid, &eqproc);
2369 :
2370 : /*
2371 : * Save a few cycles by setting up the fcinfo struct just once. Using
2372 : * FunctionCallInvoke directly also avoids failure if the eqproc
2373 : * returns NULL, though really equality functions should never do
2374 : * that.
2375 : */
2376 11292 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, sslot1->stacoll,
2377 : NULL, NULL);
2378 11292 : fcinfo->args[0].isnull = false;
2379 11292 : fcinfo->args[1].isnull = false;
2380 :
2381 11292 : hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
2382 11292 : hasmatch2 = (bool *) palloc0(sslot2->nvalues * sizeof(bool));
2383 :
2384 : /*
2385 : * Note we assume that each MCV will match at most one member of the
2386 : * other MCV list. If the operator isn't really equality, there could
2387 : * be multiple matches --- but we don't look for them, both for speed
2388 : * and because the math wouldn't add up...
2389 : */
2390 11292 : matchprodfreq = 0.0;
2391 11292 : nmatches = 0;
2392 250508 : for (i = 0; i < sslot1->nvalues; i++)
2393 : {
2394 : int j;
2395 :
2396 239216 : fcinfo->args[0].value = sslot1->values[i];
2397 :
2398 8432010 : for (j = 0; j < sslot2->nvalues; j++)
2399 : {
2400 : Datum fresult;
2401 :
2402 8248886 : if (hasmatch2[j])
2403 1739752 : continue;
2404 6509134 : fcinfo->args[1].value = sslot2->values[j];
2405 6509134 : fcinfo->isnull = false;
2406 6509134 : fresult = FunctionCallInvoke(fcinfo);
2407 6509134 : if (!fcinfo->isnull && DatumGetBool(fresult))
2408 : {
2409 56092 : hasmatch1[i] = hasmatch2[j] = true;
2410 56092 : matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
2411 56092 : nmatches++;
2412 56092 : break;
2413 : }
2414 : }
2415 : }
2416 11292 : CLAMP_PROBABILITY(matchprodfreq);
2417 : /* Sum up frequencies of matched and unmatched MCVs */
2418 11292 : matchfreq1 = unmatchfreq1 = 0.0;
2419 250508 : for (i = 0; i < sslot1->nvalues; i++)
2420 : {
2421 239216 : if (hasmatch1[i])
2422 56092 : matchfreq1 += sslot1->numbers[i];
2423 : else
2424 183124 : unmatchfreq1 += sslot1->numbers[i];
2425 : }
2426 11292 : CLAMP_PROBABILITY(matchfreq1);
2427 11292 : CLAMP_PROBABILITY(unmatchfreq1);
2428 11292 : matchfreq2 = unmatchfreq2 = 0.0;
2429 477180 : for (i = 0; i < sslot2->nvalues; i++)
2430 : {
2431 465888 : if (hasmatch2[i])
2432 56092 : matchfreq2 += sslot2->numbers[i];
2433 : else
2434 409796 : unmatchfreq2 += sslot2->numbers[i];
2435 : }
2436 11292 : CLAMP_PROBABILITY(matchfreq2);
2437 11292 : CLAMP_PROBABILITY(unmatchfreq2);
2438 11292 : pfree(hasmatch1);
2439 11292 : pfree(hasmatch2);
2440 :
2441 : /*
2442 : * Compute total frequency of non-null values that are not in the MCV
2443 : * lists.
2444 : */
2445 11292 : otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
2446 11292 : otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
2447 11292 : CLAMP_PROBABILITY(otherfreq1);
2448 11292 : CLAMP_PROBABILITY(otherfreq2);
2449 :
2450 : /*
2451 : * We can estimate the total selectivity from the point of view of
2452 : * relation 1 as: the known selectivity for matched MCVs, plus
2453 : * unmatched MCVs that are assumed to match against random members of
2454 : * relation 2's non-MCV population, plus non-MCV values that are
2455 : * assumed to match against random members of relation 2's unmatched
2456 : * MCVs plus non-MCV values.
2457 : */
2458 11292 : totalsel1 = matchprodfreq;
2459 11292 : if (nd2 > sslot2->nvalues)
2460 7792 : totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
2461 11292 : if (nd2 > nmatches)
2462 19592 : totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
2463 9796 : (nd2 - nmatches);
2464 : /* Same estimate from the point of view of relation 2. */
2465 11292 : totalsel2 = matchprodfreq;
2466 11292 : if (nd1 > sslot1->nvalues)
2467 7580 : totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
2468 11292 : if (nd1 > nmatches)
2469 18280 : totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
2470 9140 : (nd1 - nmatches);
2471 :
2472 : /*
2473 : * Use the smaller of the two estimates. This can be justified in
2474 : * essentially the same terms as given below for the no-stats case: to
2475 : * a first approximation, we are estimating from the point of view of
2476 : * the relation with smaller nd.
2477 : */
2478 11292 : selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
2479 : }
2480 : else
2481 : {
2482 : /*
2483 : * We do not have MCV lists for both sides. Estimate the join
2484 : * selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
2485 : * is plausible if we assume that the join operator is strict and the
2486 : * non-null values are about equally distributed: a given non-null
2487 : * tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
2488 : * of rel2, so total join rows are at most
2489 : * N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
2490 : * not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
2491 : * is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
2492 : * with MIN() is an upper bound. Using the MIN() means we estimate
2493 : * from the point of view of the relation with smaller nd (since the
2494 : * larger nd is determining the MIN). It is reasonable to assume that
2495 : * most tuples in this rel will have join partners, so the bound is
2496 : * probably reasonably tight and should be taken as-is.
2497 : *
2498 : * XXX Can we be smarter if we have an MCV list for just one side? It
2499 : * seems that if we assume equal distribution for the other side, we
2500 : * end up with the same answer anyway.
2501 : */
2502 114994 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2503 114994 : double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
2504 :
2505 114994 : selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
2506 114994 : if (nd1 > nd2)
2507 38064 : selec /= nd1;
2508 : else
2509 76930 : selec /= nd2;
2510 : }
2511 :
2512 126286 : return selec;
2513 : }
2514 :
2515 : /*
2516 : * eqjoinsel_semi --- eqjoinsel for semi join
2517 : *
2518 : * (Also used for anti join, which we are supposed to estimate the same way.)
2519 : * Caller has ensured that vardata1 is the LHS variable.
2520 : * Unlike eqjoinsel_inner, we have to cope with opfuncoid being InvalidOid.
2521 : */
2522 : static double
2523 9950 : eqjoinsel_semi(Oid opfuncoid,
2524 : VariableStatData *vardata1, VariableStatData *vardata2,
2525 : double nd1, double nd2,
2526 : bool isdefault1, bool isdefault2,
2527 : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2528 : Form_pg_statistic stats1, Form_pg_statistic stats2,
2529 : bool have_mcvs1, bool have_mcvs2,
2530 : RelOptInfo *inner_rel)
2531 : {
2532 : double selec;
2533 :
2534 : /*
2535 : * We clamp nd2 to be not more than what we estimate the inner relation's
2536 : * size to be. This is intuitively somewhat reasonable since obviously
2537 : * there can't be more than that many distinct values coming from the
2538 : * inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
2539 : * likewise) is that this is the only pathway by which restriction clauses
2540 : * applied to the inner rel will affect the join result size estimate,
2541 : * since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
2542 : * only the outer rel's size. If we clamped nd1 we'd be double-counting
2543 : * the selectivity of outer-rel restrictions.
2544 : *
2545 : * We can apply this clamping both with respect to the base relation from
2546 : * which the join variable comes (if there is just one), and to the
2547 : * immediate inner input relation of the current join.
2548 : *
2549 : * If we clamp, we can treat nd2 as being a non-default estimate; it's not
2550 : * great, maybe, but it didn't come out of nowhere either. This is most
2551 : * helpful when the inner relation is empty and consequently has no stats.
2552 : */
2553 9950 : if (vardata2->rel)
2554 : {
2555 9950 : if (nd2 >= vardata2->rel->rows)
2556 : {
2557 9146 : nd2 = vardata2->rel->rows;
2558 9146 : isdefault2 = false;
2559 : }
2560 : }
2561 9950 : if (nd2 >= inner_rel->rows)
2562 : {
2563 9118 : nd2 = inner_rel->rows;
2564 9118 : isdefault2 = false;
2565 : }
2566 :
2567 9950 : if (have_mcvs1 && have_mcvs2 && OidIsValid(opfuncoid))
2568 248 : {
2569 : /*
2570 : * We have most-common-value lists for both relations. Run through
2571 : * the lists to see which MCVs actually join to each other with the
2572 : * given operator. This allows us to determine the exact join
2573 : * selectivity for the portion of the relations represented by the MCV
2574 : * lists. We still have to estimate for the remaining population, but
2575 : * in a skewed distribution this gives us a big leg up in accuracy.
2576 : */
2577 248 : LOCAL_FCINFO(fcinfo, 2);
2578 : FmgrInfo eqproc;
2579 : bool *hasmatch1;
2580 : bool *hasmatch2;
2581 248 : double nullfrac1 = stats1->stanullfrac;
2582 : double matchfreq1,
2583 : uncertainfrac,
2584 : uncertain;
2585 : int i,
2586 : nmatches,
2587 : clamped_nvalues2;
2588 :
2589 : /*
2590 : * The clamping above could have resulted in nd2 being less than
2591 : * sslot2->nvalues; in which case, we assume that precisely the nd2
2592 : * most common values in the relation will appear in the join input,
2593 : * and so compare to only the first nd2 members of the MCV list. Of
2594 : * course this is frequently wrong, but it's the best bet we can make.
2595 : */
2596 248 : clamped_nvalues2 = Min(sslot2->nvalues, nd2);
2597 :
2598 248 : fmgr_info(opfuncoid, &eqproc);
2599 :
2600 : /*
2601 : * Save a few cycles by setting up the fcinfo struct just once. Using
2602 : * FunctionCallInvoke directly also avoids failure if the eqproc
2603 : * returns NULL, though really equality functions should never do
2604 : * that.
2605 : */
2606 248 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, sslot1->stacoll,
2607 : NULL, NULL);
2608 248 : fcinfo->args[0].isnull = false;
2609 248 : fcinfo->args[1].isnull = false;
2610 :
2611 248 : hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
2612 248 : hasmatch2 = (bool *) palloc0(clamped_nvalues2 * sizeof(bool));
2613 :
2614 : /*
2615 : * Note we assume that each MCV will match at most one member of the
2616 : * other MCV list. If the operator isn't really equality, there could
2617 : * be multiple matches --- but we don't look for them, both for speed
2618 : * and because the math wouldn't add up...
2619 : */
2620 248 : nmatches = 0;
2621 3596 : for (i = 0; i < sslot1->nvalues; i++)
2622 : {
2623 : int j;
2624 :
2625 3348 : fcinfo->args[0].value = sslot1->values[i];
2626 :
2627 106348 : for (j = 0; j < clamped_nvalues2; j++)
2628 : {
2629 : Datum fresult;
2630 :
2631 105764 : if (hasmatch2[j])
2632 94408 : continue;
2633 11356 : fcinfo->args[1].value = sslot2->values[j];
2634 11356 : fcinfo->isnull = false;
2635 11356 : fresult = FunctionCallInvoke(fcinfo);
2636 11356 : if (!fcinfo->isnull && DatumGetBool(fresult))
2637 : {
2638 2764 : hasmatch1[i] = hasmatch2[j] = true;
2639 2764 : nmatches++;
2640 2764 : break;
2641 : }
2642 : }
2643 : }
2644 : /* Sum up frequencies of matched MCVs */
2645 248 : matchfreq1 = 0.0;
2646 3596 : for (i = 0; i < sslot1->nvalues; i++)
2647 : {
2648 3348 : if (hasmatch1[i])
2649 2764 : matchfreq1 += sslot1->numbers[i];
2650 : }
2651 248 : CLAMP_PROBABILITY(matchfreq1);
2652 248 : pfree(hasmatch1);
2653 248 : pfree(hasmatch2);
2654 :
2655 : /*
2656 : * Now we need to estimate the fraction of relation 1 that has at
2657 : * least one join partner. We know for certain that the matched MCVs
2658 : * do, so that gives us a lower bound, but we're really in the dark
2659 : * about everything else. Our crude approach is: if nd1 <= nd2 then
2660 : * assume all non-null rel1 rows have join partners, else assume for
2661 : * the uncertain rows that a fraction nd2/nd1 have join partners. We
2662 : * can discount the known-matched MCVs from the distinct-values counts
2663 : * before doing the division.
2664 : *
2665 : * Crude as the above is, it's completely useless if we don't have
2666 : * reliable ndistinct values for both sides. Hence, if either nd1 or
2667 : * nd2 is default, punt and assume half of the uncertain rows have
2668 : * join partners.
2669 : */
2670 248 : if (!isdefault1 && !isdefault2)
2671 : {
2672 248 : nd1 -= nmatches;
2673 248 : nd2 -= nmatches;
2674 496 : if (nd1 <= nd2 || nd2 < 0)
2675 232 : uncertainfrac = 1.0;
2676 : else
2677 16 : uncertainfrac = nd2 / nd1;
2678 : }
2679 : else
2680 0 : uncertainfrac = 0.5;
2681 248 : uncertain = 1.0 - matchfreq1 - nullfrac1;
2682 248 : CLAMP_PROBABILITY(uncertain);
2683 248 : selec = matchfreq1 + uncertainfrac * uncertain;
2684 : }
2685 : else
2686 : {
2687 : /*
2688 : * Without MCV lists for both sides, we can only use the heuristic
2689 : * about nd1 vs nd2.
2690 : */
2691 9702 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2692 :
2693 9702 : if (!isdefault1 && !isdefault2)
2694 : {
2695 17180 : if (nd1 <= nd2 || nd2 < 0)
2696 7176 : selec = 1.0 - nullfrac1;
2697 : else
2698 1414 : selec = (nd2 / nd1) * (1.0 - nullfrac1);
2699 : }
2700 : else
2701 1112 : selec = 0.5 * (1.0 - nullfrac1);
2702 : }
2703 :
2704 9950 : return selec;
2705 : }
2706 :
2707 : /*
2708 : * neqjoinsel - Join selectivity of "!="
2709 : */
2710 : Datum
2711 1630 : neqjoinsel(PG_FUNCTION_ARGS)
2712 : {
2713 1630 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
2714 1630 : Oid operator = PG_GETARG_OID(1);
2715 1630 : List *args = (List *) PG_GETARG_POINTER(2);
2716 1630 : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
2717 1630 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
2718 : float8 result;
2719 :
2720 1630 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
2721 594 : {
2722 : /*
2723 : * For semi-joins, if there is more than one distinct value in the RHS
2724 : * relation then every non-null LHS row must find a row to join since
2725 : * it can only be equal to one of them. We'll assume that there is
2726 : * always more than one distinct RHS value for the sake of stability,
2727 : * though in theory we could have special cases for empty RHS
2728 : * (selectivity = 0) and single-distinct-value RHS (selectivity =
2729 : * fraction of LHS that has the same value as the single RHS value).
2730 : *
2731 : * For anti-joins, if we use the same assumption that there is more
2732 : * than one distinct key in the RHS relation, then every non-null LHS
2733 : * row must be suppressed by the anti-join.
2734 : *
2735 : * So either way, the selectivity estimate should be 1 - nullfrac.
2736 : */
2737 : VariableStatData leftvar;
2738 : VariableStatData rightvar;
2739 : bool reversed;
2740 : HeapTuple statsTuple;
2741 : double nullfrac;
2742 :
2743 594 : get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
2744 594 : statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
2745 594 : if (HeapTupleIsValid(statsTuple))
2746 500 : nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
2747 : else
2748 94 : nullfrac = 0.0;
2749 594 : ReleaseVariableStats(leftvar);
2750 594 : ReleaseVariableStats(rightvar);
2751 :
2752 594 : result = 1.0 - nullfrac;
2753 : }
2754 : else
2755 : {
2756 : /*
2757 : * We want 1 - eqjoinsel() where the equality operator is the one
2758 : * associated with this != operator, that is, its negator.
2759 : */
2760 1036 : Oid eqop = get_negator(operator);
2761 :
2762 1036 : if (eqop)
2763 : {
2764 1036 : result = DatumGetFloat8(DirectFunctionCall5(eqjoinsel,
2765 : PointerGetDatum(root),
2766 : ObjectIdGetDatum(eqop),
2767 : PointerGetDatum(args),
2768 : Int16GetDatum(jointype),
2769 : PointerGetDatum(sjinfo)));
2770 : }
2771 : else
2772 : {
2773 : /* Use default selectivity (should we raise an error instead?) */
2774 0 : result = DEFAULT_EQ_SEL;
2775 : }
2776 1036 : result = 1.0 - result;
2777 : }
2778 :
2779 1630 : PG_RETURN_FLOAT8(result);
2780 : }
2781 :
2782 : /*
2783 : * scalarltjoinsel - Join selectivity of "<" for scalars
2784 : */
2785 : Datum
2786 200 : scalarltjoinsel(PG_FUNCTION_ARGS)
2787 : {
2788 200 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2789 : }
2790 :
2791 : /*
2792 : * scalarlejoinsel - Join selectivity of "<=" for scalars
2793 : */
2794 : Datum
2795 130 : scalarlejoinsel(PG_FUNCTION_ARGS)
2796 : {
2797 130 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2798 : }
2799 :
2800 : /*
2801 : * scalargtjoinsel - Join selectivity of ">" for scalars
2802 : */
2803 : Datum
2804 140 : scalargtjoinsel(PG_FUNCTION_ARGS)
2805 : {
2806 140 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2807 : }
2808 :
2809 : /*
2810 : * scalargejoinsel - Join selectivity of ">=" for scalars
2811 : */
2812 : Datum
2813 108 : scalargejoinsel(PG_FUNCTION_ARGS)
2814 : {
2815 108 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2816 : }
2817 :
2818 :
2819 : /*
2820 : * mergejoinscansel - Scan selectivity of merge join.
2821 : *
2822 : * A merge join will stop as soon as it exhausts either input stream.
2823 : * Therefore, if we can estimate the ranges of both input variables,
2824 : * we can estimate how much of the input will actually be read. This
2825 : * can have a considerable impact on the cost when using indexscans.
2826 : *
2827 : * Also, we can estimate how much of each input has to be read before the
2828 : * first join pair is found, which will affect the join's startup time.
2829 : *
2830 : * clause should be a clause already known to be mergejoinable. opfamily,
2831 : * strategy, and nulls_first specify the sort ordering being used.
2832 : *
2833 : * The outputs are:
2834 : * *leftstart is set to the fraction of the left-hand variable expected
2835 : * to be scanned before the first join pair is found (0 to 1).
2836 : * *leftend is set to the fraction of the left-hand variable expected
2837 : * to be scanned before the join terminates (0 to 1).
2838 : * *rightstart, *rightend similarly for the right-hand variable.
2839 : */
2840 : void
2841 58398 : mergejoinscansel(PlannerInfo *root, Node *clause,
2842 : Oid opfamily, int strategy, bool nulls_first,
2843 : Selectivity *leftstart, Selectivity *leftend,
2844 : Selectivity *rightstart, Selectivity *rightend)
2845 : {
2846 : Node *left,
2847 : *right;
2848 : VariableStatData leftvar,
2849 : rightvar;
2850 : int op_strategy;
2851 : Oid op_lefttype;
2852 : Oid op_righttype;
2853 : Oid opno,
2854 : lsortop,
2855 : rsortop,
2856 : lstatop,
2857 : rstatop,
2858 : ltop,
2859 : leop,
2860 : revltop,
2861 : revleop;
2862 : bool isgt;
2863 : Datum leftmin,
2864 : leftmax,
2865 : rightmin,
2866 : rightmax;
2867 : double selec;
2868 :
2869 : /* Set default results if we can't figure anything out. */
2870 : /* XXX should default "start" fraction be a bit more than 0? */
2871 58398 : *leftstart = *rightstart = 0.0;
2872 58398 : *leftend = *rightend = 1.0;
2873 :
2874 : /* Deconstruct the merge clause */
2875 58398 : if (!is_opclause(clause))
2876 0 : return; /* shouldn't happen */
2877 58398 : opno = ((OpExpr *) clause)->opno;
2878 58398 : left = get_leftop((Expr *) clause);
2879 58398 : right = get_rightop((Expr *) clause);
2880 58398 : if (!right)
2881 0 : return; /* shouldn't happen */
2882 :
2883 : /* Look for stats for the inputs */
2884 58398 : examine_variable(root, left, 0, &leftvar);
2885 58398 : examine_variable(root, right, 0, &rightvar);
2886 :
2887 : /* Extract the operator's declared left/right datatypes */
2888 58398 : get_op_opfamily_properties(opno, opfamily, false,
2889 : &op_strategy,
2890 : &op_lefttype,
2891 : &op_righttype);
2892 : Assert(op_strategy == BTEqualStrategyNumber);
2893 :
2894 : /*
2895 : * Look up the various operators we need. If we don't find them all, it
2896 : * probably means the opfamily is broken, but we just fail silently.
2897 : *
2898 : * Note: we expect that pg_statistic histograms will be sorted by the '<'
2899 : * operator, regardless of which sort direction we are considering.
2900 : */
2901 58398 : switch (strategy)
2902 : {
2903 58378 : case BTLessStrategyNumber:
2904 58378 : isgt = false;
2905 58378 : if (op_lefttype == op_righttype)
2906 : {
2907 : /* easy case */
2908 57212 : ltop = get_opfamily_member(opfamily,
2909 : op_lefttype, op_righttype,
2910 : BTLessStrategyNumber);
2911 57212 : leop = get_opfamily_member(opfamily,
2912 : op_lefttype, op_righttype,
2913 : BTLessEqualStrategyNumber);
2914 57212 : lsortop = ltop;
2915 57212 : rsortop = ltop;
2916 57212 : lstatop = lsortop;
2917 57212 : rstatop = rsortop;
2918 57212 : revltop = ltop;
2919 57212 : revleop = leop;
2920 : }
2921 : else
2922 : {
2923 1166 : ltop = get_opfamily_member(opfamily,
2924 : op_lefttype, op_righttype,
2925 : BTLessStrategyNumber);
2926 1166 : leop = get_opfamily_member(opfamily,
2927 : op_lefttype, op_righttype,
2928 : BTLessEqualStrategyNumber);
2929 1166 : lsortop = get_opfamily_member(opfamily,
2930 : op_lefttype, op_lefttype,
2931 : BTLessStrategyNumber);
2932 1166 : rsortop = get_opfamily_member(opfamily,
2933 : op_righttype, op_righttype,
2934 : BTLessStrategyNumber);
2935 1166 : lstatop = lsortop;
2936 1166 : rstatop = rsortop;
2937 1166 : revltop = get_opfamily_member(opfamily,
2938 : op_righttype, op_lefttype,
2939 : BTLessStrategyNumber);
2940 1166 : revleop = get_opfamily_member(opfamily,
2941 : op_righttype, op_lefttype,
2942 : BTLessEqualStrategyNumber);
2943 : }
2944 58378 : break;
2945 20 : case BTGreaterStrategyNumber:
2946 : /* descending-order case */
2947 20 : isgt = true;
2948 20 : if (op_lefttype == op_righttype)
2949 : {
2950 : /* easy case */
2951 20 : ltop = get_opfamily_member(opfamily,
2952 : op_lefttype, op_righttype,
2953 : BTGreaterStrategyNumber);
2954 20 : leop = get_opfamily_member(opfamily,
2955 : op_lefttype, op_righttype,
2956 : BTGreaterEqualStrategyNumber);
2957 20 : lsortop = ltop;
2958 20 : rsortop = ltop;
2959 20 : lstatop = get_opfamily_member(opfamily,
2960 : op_lefttype, op_lefttype,
2961 : BTLessStrategyNumber);
2962 20 : rstatop = lstatop;
2963 20 : revltop = ltop;
2964 20 : revleop = leop;
2965 : }
2966 : else
2967 : {
2968 0 : ltop = get_opfamily_member(opfamily,
2969 : op_lefttype, op_righttype,
2970 : BTGreaterStrategyNumber);
2971 0 : leop = get_opfamily_member(opfamily,
2972 : op_lefttype, op_righttype,
2973 : BTGreaterEqualStrategyNumber);
2974 0 : lsortop = get_opfamily_member(opfamily,
2975 : op_lefttype, op_lefttype,
2976 : BTGreaterStrategyNumber);
2977 0 : rsortop = get_opfamily_member(opfamily,
2978 : op_righttype, op_righttype,
2979 : BTGreaterStrategyNumber);
2980 0 : lstatop = get_opfamily_member(opfamily,
2981 : op_lefttype, op_lefttype,
2982 : BTLessStrategyNumber);
2983 0 : rstatop = get_opfamily_member(opfamily,
2984 : op_righttype, op_righttype,
2985 : BTLessStrategyNumber);
2986 0 : revltop = get_opfamily_member(opfamily,
2987 : op_righttype, op_lefttype,
2988 : BTGreaterStrategyNumber);
2989 0 : revleop = get_opfamily_member(opfamily,
2990 : op_righttype, op_lefttype,
2991 : BTGreaterEqualStrategyNumber);
2992 : }
2993 20 : break;
2994 0 : default:
2995 0 : goto fail; /* shouldn't get here */
2996 : }
2997 :
2998 58398 : if (!OidIsValid(lsortop) ||
2999 58398 : !OidIsValid(rsortop) ||
3000 58398 : !OidIsValid(lstatop) ||
3001 58398 : !OidIsValid(rstatop) ||
3002 58390 : !OidIsValid(ltop) ||
3003 58390 : !OidIsValid(leop) ||
3004 58390 : !OidIsValid(revltop) ||
3005 : !OidIsValid(revleop))
3006 8 : goto fail; /* insufficient info in catalogs */
3007 :
3008 : /* Try to get ranges of both inputs */
3009 58390 : if (!isgt)
3010 : {
3011 58370 : if (!get_variable_range(root, &leftvar, lstatop,
3012 : &leftmin, &leftmax))
3013 16402 : goto fail; /* no range available from stats */
3014 41968 : if (!get_variable_range(root, &rightvar, rstatop,
3015 : &rightmin, &rightmax))
3016 3610 : goto fail; /* no range available from stats */
3017 : }
3018 : else
3019 : {
3020 : /* need to swap the max and min */
3021 20 : if (!get_variable_range(root, &leftvar, lstatop,
3022 : &leftmax, &leftmin))
3023 20 : goto fail; /* no range available from stats */
3024 0 : if (!get_variable_range(root, &rightvar, rstatop,
3025 : &rightmax, &rightmin))
3026 0 : goto fail; /* no range available from stats */
3027 : }
3028 :
3029 : /*
3030 : * Now, the fraction of the left variable that will be scanned is the
3031 : * fraction that's <= the right-side maximum value. But only believe
3032 : * non-default estimates, else stick with our 1.0.
3033 : */
3034 38358 : selec = scalarineqsel(root, leop, isgt, true, &leftvar,
3035 : rightmax, op_righttype);
3036 38358 : if (selec != DEFAULT_INEQ_SEL)
3037 38354 : *leftend = selec;
3038 :
3039 : /* And similarly for the right variable. */
3040 38358 : selec = scalarineqsel(root, revleop, isgt, true, &rightvar,
3041 : leftmax, op_lefttype);
3042 38358 : if (selec != DEFAULT_INEQ_SEL)
3043 38358 : *rightend = selec;
3044 :
3045 : /*
3046 : * Only one of the two "end" fractions can really be less than 1.0;
3047 : * believe the smaller estimate and reset the other one to exactly 1.0. If
3048 : * we get exactly equal estimates (as can easily happen with self-joins),
3049 : * believe neither.
3050 : */
3051 38358 : if (*leftend > *rightend)
3052 22092 : *leftend = 1.0;
3053 16266 : else if (*leftend < *rightend)
3054 12594 : *rightend = 1.0;
3055 : else
3056 3672 : *leftend = *rightend = 1.0;
3057 :
3058 : /*
3059 : * Also, the fraction of the left variable that will be scanned before the
3060 : * first join pair is found is the fraction that's < the right-side
3061 : * minimum value. But only believe non-default estimates, else stick with
3062 : * our own default.
3063 : */
3064 38358 : selec = scalarineqsel(root, ltop, isgt, false, &leftvar,
3065 : rightmin, op_righttype);
3066 38358 : if (selec != DEFAULT_INEQ_SEL)
3067 38358 : *leftstart = selec;
3068 :
3069 : /* And similarly for the right variable. */
3070 38358 : selec = scalarineqsel(root, revltop, isgt, false, &rightvar,
3071 : leftmin, op_lefttype);
3072 38358 : if (selec != DEFAULT_INEQ_SEL)
3073 38358 : *rightstart = selec;
3074 :
3075 : /*
3076 : * Only one of the two "start" fractions can really be more than zero;
3077 : * believe the larger estimate and reset the other one to exactly 0.0. If
3078 : * we get exactly equal estimates (as can easily happen with self-joins),
3079 : * believe neither.
3080 : */
3081 38358 : if (*leftstart < *rightstart)
3082 9728 : *leftstart = 0.0;
3083 28630 : else if (*leftstart > *rightstart)
3084 20818 : *rightstart = 0.0;
3085 : else
3086 7812 : *leftstart = *rightstart = 0.0;
3087 :
3088 : /*
3089 : * If the sort order is nulls-first, we're going to have to skip over any
3090 : * nulls too. These would not have been counted by scalarineqsel, and we
3091 : * can safely add in this fraction regardless of whether we believe
3092 : * scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
3093 : */
3094 38358 : if (nulls_first)
3095 : {
3096 : Form_pg_statistic stats;
3097 :
3098 0 : if (HeapTupleIsValid(leftvar.statsTuple))
3099 : {
3100 0 : stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
3101 0 : *leftstart += stats->stanullfrac;
3102 0 : CLAMP_PROBABILITY(*leftstart);
3103 0 : *leftend += stats->stanullfrac;
3104 0 : CLAMP_PROBABILITY(*leftend);
3105 : }
3106 0 : if (HeapTupleIsValid(rightvar.statsTuple))
3107 : {
3108 0 : stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple);
3109 0 : *rightstart += stats->stanullfrac;
3110 0 : CLAMP_PROBABILITY(*rightstart);
3111 0 : *rightend += stats->stanullfrac;
3112 0 : CLAMP_PROBABILITY(*rightend);
3113 : }
3114 : }
3115 :
3116 : /* Disbelieve start >= end, just in case that can happen */
3117 38358 : if (*leftstart >= *leftend)
3118 : {
3119 1066 : *leftstart = 0.0;
3120 1066 : *leftend = 1.0;
3121 : }
3122 38358 : if (*rightstart >= *rightend)
3123 : {
3124 1274 : *rightstart = 0.0;
3125 1274 : *rightend = 1.0;
3126 : }
3127 :
3128 37084 : fail:
3129 58398 : ReleaseVariableStats(leftvar);
3130 58398 : ReleaseVariableStats(rightvar);
3131 : }
3132 :
3133 :
3134 : /*
3135 : * matchingsel -- generic matching-operator selectivity support
3136 : *
3137 : * Use these for any operators that (a) are on data types for which we collect
3138 : * standard statistics, and (b) have behavior for which the default estimate
3139 : * (twice DEFAULT_EQ_SEL) is sane. Typically that is good for match-like
3140 : * operators.
3141 : */
3142 :
3143 : Datum
3144 782 : matchingsel(PG_FUNCTION_ARGS)
3145 : {
3146 782 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
3147 782 : Oid operator = PG_GETARG_OID(1);
3148 782 : List *args = (List *) PG_GETARG_POINTER(2);
3149 782 : int varRelid = PG_GETARG_INT32(3);
3150 : double selec;
3151 :
3152 : /* Use generic restriction selectivity logic. */
3153 782 : selec = generic_restriction_selectivity(root, operator,
3154 : args, varRelid,
3155 : DEFAULT_MATCHING_SEL);
3156 :
3157 782 : PG_RETURN_FLOAT8((float8) selec);
3158 : }
3159 :
3160 : Datum
3161 4 : matchingjoinsel(PG_FUNCTION_ARGS)
3162 : {
3163 : /* Just punt, for the moment. */
3164 4 : PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
3165 : }
3166 :
3167 :
3168 : /*
3169 : * Helper routine for estimate_num_groups: add an item to a list of
3170 : * GroupVarInfos, but only if it's not known equal to any of the existing
3171 : * entries.
3172 : */
3173 : typedef struct
3174 : {
3175 : Node *var; /* might be an expression, not just a Var */
3176 : RelOptInfo *rel; /* relation it belongs to */
3177 : double ndistinct; /* # distinct values */
3178 : } GroupVarInfo;
3179 :
3180 : static List *
3181 11870 : add_unique_group_var(PlannerInfo *root, List *varinfos,
3182 : Node *var, VariableStatData *vardata)
3183 : {
3184 : GroupVarInfo *varinfo;
3185 : double ndistinct;
3186 : bool isdefault;
3187 : ListCell *lc;
3188 :
3189 11870 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
3190 :
3191 17456 : foreach(lc, varinfos)
3192 : {
3193 5886 : varinfo = (GroupVarInfo *) lfirst(lc);
3194 :
3195 : /* Drop exact duplicates */
3196 5886 : if (equal(var, varinfo->var))
3197 300 : return varinfos;
3198 :
3199 : /*
3200 : * Drop known-equal vars, but only if they belong to different
3201 : * relations (see comments for estimate_num_groups)
3202 : */
3203 6312 : if (vardata->rel != varinfo->rel &&
3204 674 : exprs_known_equal(root, var, varinfo->var))
3205 : {
3206 56 : if (varinfo->ndistinct <= ndistinct)
3207 : {
3208 : /* Keep older item, forget new one */
3209 52 : return varinfos;
3210 : }
3211 : else
3212 : {
3213 : /* Delete the older item */
3214 4 : varinfos = foreach_delete_current(varinfos, lc);
3215 : }
3216 : }
3217 : }
3218 :
3219 11570 : varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
3220 :
3221 11570 : varinfo->var = var;
3222 11570 : varinfo->rel = vardata->rel;
3223 11570 : varinfo->ndistinct = ndistinct;
3224 11570 : varinfos = lappend(varinfos, varinfo);
3225 11570 : return varinfos;
3226 : }
3227 :
3228 : /*
3229 : * estimate_num_groups - Estimate number of groups in a grouped query
3230 : *
3231 : * Given a query having a GROUP BY clause, estimate how many groups there
3232 : * will be --- ie, the number of distinct combinations of the GROUP BY
3233 : * expressions.
3234 : *
3235 : * This routine is also used to estimate the number of rows emitted by
3236 : * a DISTINCT filtering step; that is an isomorphic problem. (Note:
3237 : * actually, we only use it for DISTINCT when there's no grouping or
3238 : * aggregation ahead of the DISTINCT.)
3239 : *
3240 : * Inputs:
3241 : * root - the query
3242 : * groupExprs - list of expressions being grouped by
3243 : * input_rows - number of rows estimated to arrive at the group/unique
3244 : * filter step
3245 : * pgset - NULL, or a List** pointing to a grouping set to filter the
3246 : * groupExprs against
3247 : *
3248 : * Given the lack of any cross-correlation statistics in the system, it's
3249 : * impossible to do anything really trustworthy with GROUP BY conditions
3250 : * involving multiple Vars. We should however avoid assuming the worst
3251 : * case (all possible cross-product terms actually appear as groups) since
3252 : * very often the grouped-by Vars are highly correlated. Our current approach
3253 : * is as follows:
3254 : * 1. Expressions yielding boolean are assumed to contribute two groups,
3255 : * independently of their content, and are ignored in the subsequent
3256 : * steps. This is mainly because tests like "col IS NULL" break the
3257 : * heuristic used in step 2 especially badly.
3258 : * 2. Reduce the given expressions to a list of unique Vars used. For
3259 : * example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
3260 : * It is clearly correct not to count the same Var more than once.
3261 : * It is also reasonable to treat f(x) the same as x: f() cannot
3262 : * increase the number of distinct values (unless it is volatile,
3263 : * which we consider unlikely for grouping), but it probably won't
3264 : * reduce the number of distinct values much either.
3265 : * As a special case, if a GROUP BY expression can be matched to an
3266 : * expressional index for which we have statistics, then we treat the
3267 : * whole expression as though it were just a Var.
3268 : * 3. If the list contains Vars of different relations that are known equal
3269 : * due to equivalence classes, then drop all but one of the Vars from each
3270 : * known-equal set, keeping the one with smallest estimated # of values
3271 : * (since the extra values of the others can't appear in joined rows).
3272 : * Note the reason we only consider Vars of different relations is that
3273 : * if we considered ones of the same rel, we'd be double-counting the
3274 : * restriction selectivity of the equality in the next step.
3275 : * 4. For Vars within a single source rel, we multiply together the numbers
3276 : * of values, clamp to the number of rows in the rel (divided by 10 if
3277 : * more than one Var), and then multiply by a factor based on the
3278 : * selectivity of the restriction clauses for that rel. When there's
3279 : * more than one Var, the initial product is probably too high (it's the
3280 : * worst case) but clamping to a fraction of the rel's rows seems to be a
3281 : * helpful heuristic for not letting the estimate get out of hand. (The
3282 : * factor of 10 is derived from pre-Postgres-7.4 practice.) The factor
3283 : * we multiply by to adjust for the restriction selectivity assumes that
3284 : * the restriction clauses are independent of the grouping, which may not
3285 : * be a valid assumption, but it's hard to do better.
3286 : * 5. If there are Vars from multiple rels, we repeat step 4 for each such
3287 : * rel, and multiply the results together.
3288 : * Note that rels not containing grouped Vars are ignored completely, as are
3289 : * join clauses. Such rels cannot increase the number of groups, and we
3290 : * assume such clauses do not reduce the number either (somewhat bogus,
3291 : * but we don't have the info to do better).
3292 : */
3293 : double
3294 9462 : estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
3295 : List **pgset)
3296 : {
3297 9462 : List *varinfos = NIL;
3298 9462 : double srf_multiplier = 1.0;
3299 : double numdistinct;
3300 : ListCell *l;
3301 : int i;
3302 :
3303 : /*
3304 : * We don't ever want to return an estimate of zero groups, as that tends
3305 : * to lead to division-by-zero and other unpleasantness. The input_rows
3306 : * estimate is usually already at least 1, but clamp it just in case it
3307 : * isn't.
3308 : */
3309 9462 : input_rows = clamp_row_est(input_rows);
3310 :
3311 : /*
3312 : * If no grouping columns, there's exactly one group. (This can't happen
3313 : * for normal cases with GROUP BY or DISTINCT, but it is possible for
3314 : * corner cases with set operations.)
3315 : */
3316 9462 : if (groupExprs == NIL || (pgset && list_length(*pgset) < 1))
3317 450 : return 1.0;
3318 :
3319 : /*
3320 : * Count groups derived from boolean grouping expressions. For other
3321 : * expressions, find the unique Vars used, treating an expression as a Var
3322 : * if we can find stats for it. For each one, record the statistical
3323 : * estimate of number of distinct values (total in its table, without
3324 : * regard for filtering).
3325 : */
3326 9012 : numdistinct = 1.0;
3327 :
3328 9012 : i = 0;
3329 21330 : foreach(l, groupExprs)
3330 : {
3331 12322 : Node *groupexpr = (Node *) lfirst(l);
3332 : double this_srf_multiplier;
3333 : VariableStatData vardata;
3334 : List *varshere;
3335 : ListCell *l2;
3336 :
3337 : /* is expression in this grouping set? */
3338 12322 : if (pgset && !list_member_int(*pgset, i++))
3339 7260 : continue;
3340 :
3341 : /*
3342 : * Set-returning functions in grouping columns are a bit problematic.
3343 : * The code below will effectively ignore their SRF nature and come up
3344 : * with a numdistinct estimate as though they were scalar functions.
3345 : * We compensate by scaling up the end result by the largest SRF
3346 : * rowcount estimate. (This will be an overestimate if the SRF
3347 : * produces multiple copies of any output value, but it seems best to
3348 : * assume the SRF's outputs are distinct. In any case, it's probably
3349 : * pointless to worry too much about this without much better
3350 : * estimates for SRF output rowcounts than we have today.)
3351 : */
3352 11966 : this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
3353 11966 : if (srf_multiplier < this_srf_multiplier)
3354 72 : srf_multiplier = this_srf_multiplier;
3355 :
3356 : /* Short-circuit for expressions returning boolean */
3357 11966 : if (exprType(groupexpr) == BOOLOID)
3358 : {
3359 26 : numdistinct *= 2.0;
3360 26 : continue;
3361 : }
3362 :
3363 : /*
3364 : * If examine_variable is able to deduce anything about the GROUP BY
3365 : * expression, treat it as a single variable even if it's really more
3366 : * complicated.
3367 : */
3368 11940 : examine_variable(root, groupexpr, 0, &vardata);
3369 11940 : if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
3370 : {
3371 6588 : varinfos = add_unique_group_var(root, varinfos,
3372 : groupexpr, &vardata);
3373 6588 : ReleaseVariableStats(vardata);
3374 6588 : continue;
3375 : }
3376 5352 : ReleaseVariableStats(vardata);
3377 :
3378 : /*
3379 : * Else pull out the component Vars. Handle PlaceHolderVars by
3380 : * recursing into their arguments (effectively assuming that the
3381 : * PlaceHolderVar doesn't change the number of groups, which boils
3382 : * down to ignoring the possible addition of nulls to the result set).
3383 : */
3384 5352 : varshere = pull_var_clause(groupexpr,
3385 : PVC_RECURSE_AGGREGATES |
3386 : PVC_RECURSE_WINDOWFUNCS |
3387 : PVC_RECURSE_PLACEHOLDERS);
3388 :
3389 : /*
3390 : * If we find any variable-free GROUP BY item, then either it is a
3391 : * constant (and we can ignore it) or it contains a volatile function;
3392 : * in the latter case we punt and assume that each input row will
3393 : * yield a distinct group.
3394 : */
3395 5352 : if (varshere == NIL)
3396 : {
3397 294 : if (contain_volatile_functions(groupexpr))
3398 4 : return input_rows;
3399 290 : continue;
3400 : }
3401 :
3402 : /*
3403 : * Else add variables to varinfos list
3404 : */
3405 10340 : foreach(l2, varshere)
3406 : {
3407 5282 : Node *var = (Node *) lfirst(l2);
3408 :
3409 5282 : examine_variable(root, var, 0, &vardata);
3410 5282 : varinfos = add_unique_group_var(root, varinfos, var, &vardata);
3411 5282 : ReleaseVariableStats(vardata);
3412 : }
3413 : }
3414 :
3415 : /*
3416 : * If now no Vars, we must have an all-constant or all-boolean GROUP BY
3417 : * list.
3418 : */
3419 9008 : if (varinfos == NIL)
3420 : {
3421 : /* Apply SRF multiplier as we would do in the long path */
3422 154 : numdistinct *= srf_multiplier;
3423 : /* Round off */
3424 154 : numdistinct = ceil(numdistinct);
3425 : /* Guard against out-of-range answers */
3426 154 : if (numdistinct > input_rows)
3427 0 : numdistinct = input_rows;
3428 154 : if (numdistinct < 1.0)
3429 0 : numdistinct = 1.0;
3430 154 : return numdistinct;
3431 : }
3432 :
3433 : /*
3434 : * Group Vars by relation and estimate total numdistinct.
3435 : *
3436 : * For each iteration of the outer loop, we process the frontmost Var in
3437 : * varinfos, plus all other Vars in the same relation. We remove these
3438 : * Vars from the newvarinfos list for the next iteration. This is the
3439 : * easiest way to group Vars of same rel together.
3440 : */
3441 : do
3442 : {
3443 9160 : GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
3444 9160 : RelOptInfo *rel = varinfo1->rel;
3445 9160 : double reldistinct = 1;
3446 9160 : double relmaxndistinct = reldistinct;
3447 9160 : int relvarcount = 0;
3448 9160 : List *newvarinfos = NIL;
3449 9160 : List *relvarinfos = NIL;
3450 :
3451 : /*
3452 : * Split the list of varinfos in two - one for the current rel, one
3453 : * for remaining Vars on other rels.
3454 : */
3455 9160 : relvarinfos = lappend(relvarinfos, varinfo1);
3456 11980 : for_each_cell(l, varinfos, list_second_cell(varinfos))
3457 : {
3458 2820 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3459 :
3460 2820 : if (varinfo2->rel == varinfo1->rel)
3461 : {
3462 : /* varinfos on current rel */
3463 2406 : relvarinfos = lappend(relvarinfos, varinfo2);
3464 : }
3465 : else
3466 : {
3467 : /* not time to process varinfo2 yet */
3468 414 : newvarinfos = lappend(newvarinfos, varinfo2);
3469 : }
3470 : }
3471 :
3472 : /*
3473 : * Get the numdistinct estimate for the Vars of this rel. We
3474 : * iteratively search for multivariate n-distinct with maximum number
3475 : * of vars; assuming that each var group is independent of the others,
3476 : * we multiply them together. Any remaining relvarinfos after no more
3477 : * multivariate matches are found are assumed independent too, so
3478 : * their individual ndistinct estimates are multiplied also.
3479 : *
3480 : * While iterating, count how many separate numdistinct values we
3481 : * apply. We apply a fudge factor below, but only if we multiplied
3482 : * more than one such values.
3483 : */
3484 18336 : while (relvarinfos)
3485 : {
3486 : double mvndistinct;
3487 :
3488 9176 : if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
3489 : &mvndistinct))
3490 : {
3491 40 : reldistinct *= mvndistinct;
3492 40 : if (relmaxndistinct < mvndistinct)
3493 40 : relmaxndistinct = mvndistinct;
3494 40 : relvarcount++;
3495 : }
3496 : else
3497 : {
3498 20602 : foreach(l, relvarinfos)
3499 : {
3500 11466 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3501 :
3502 11466 : reldistinct *= varinfo2->ndistinct;
3503 11466 : if (relmaxndistinct < varinfo2->ndistinct)
3504 9372 : relmaxndistinct = varinfo2->ndistinct;
3505 11466 : relvarcount++;
3506 : }
3507 :
3508 : /* we're done with this relation */
3509 9136 : relvarinfos = NIL;
3510 : }
3511 : }
3512 :
3513 : /*
3514 : * Sanity check --- don't divide by zero if empty relation.
3515 : */
3516 : Assert(IS_SIMPLE_REL(rel));
3517 9160 : if (rel->tuples > 0)
3518 : {
3519 : /*
3520 : * Clamp to size of rel, or size of rel / 10 if multiple Vars. The
3521 : * fudge factor is because the Vars are probably correlated but we
3522 : * don't know by how much. We should never clamp to less than the
3523 : * largest ndistinct value for any of the Vars, though, since
3524 : * there will surely be at least that many groups.
3525 : */
3526 9118 : double clamp = rel->tuples;
3527 :
3528 9118 : if (relvarcount > 1)
3529 : {
3530 1520 : clamp *= 0.1;
3531 1520 : if (clamp < relmaxndistinct)
3532 : {
3533 894 : clamp = relmaxndistinct;
3534 : /* for sanity in case some ndistinct is too large: */
3535 894 : if (clamp > rel->tuples)
3536 8 : clamp = rel->tuples;
3537 : }
3538 : }
3539 9118 : if (reldistinct > clamp)
3540 1104 : reldistinct = clamp;
3541 :
3542 : /*
3543 : * Update the estimate based on the restriction selectivity,
3544 : * guarding against division by zero when reldistinct is zero.
3545 : * Also skip this if we know that we are returning all rows.
3546 : */
3547 9118 : if (reldistinct > 0 && rel->rows < rel->tuples)
3548 : {
3549 : /*
3550 : * Given a table containing N rows with n distinct values in a
3551 : * uniform distribution, if we select p rows at random then
3552 : * the expected number of distinct values selected is
3553 : *
3554 : * n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
3555 : *
3556 : * = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
3557 : *
3558 : * See "Approximating block accesses in database
3559 : * organizations", S. B. Yao, Communications of the ACM,
3560 : * Volume 20 Issue 4, April 1977 Pages 260-261.
3561 : *
3562 : * Alternatively, re-arranging the terms from the factorials,
3563 : * this may be written as
3564 : *
3565 : * n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
3566 : *
3567 : * This form of the formula is more efficient to compute in
3568 : * the common case where p is larger than N/n. Additionally,
3569 : * as pointed out by Dell'Era, if i << N for all terms in the
3570 : * product, it can be approximated by
3571 : *
3572 : * n * (1 - ((N-p)/N)^(N/n))
3573 : *
3574 : * See "Expected distinct values when selecting from a bag
3575 : * without replacement", Alberto Dell'Era,
3576 : * http://www.adellera.it/investigations/distinct_balls/.
3577 : *
3578 : * The condition i << N is equivalent to n >> 1, so this is a
3579 : * good approximation when the number of distinct values in
3580 : * the table is large. It turns out that this formula also
3581 : * works well even when n is small.
3582 : */
3583 2464 : reldistinct *=
3584 2464 : (1 - pow((rel->tuples - rel->rows) / rel->tuples,
3585 2464 : rel->tuples / reldistinct));
3586 : }
3587 9118 : reldistinct = clamp_row_est(reldistinct);
3588 :
3589 : /*
3590 : * Update estimate of total distinct groups.
3591 : */
3592 9118 : numdistinct *= reldistinct;
3593 : }
3594 :
3595 9160 : varinfos = newvarinfos;
3596 9160 : } while (varinfos != NIL);
3597 :
3598 : /* Now we can account for the effects of any SRFs */
3599 8854 : numdistinct *= srf_multiplier;
3600 :
3601 : /* Round off */
3602 8854 : numdistinct = ceil(numdistinct);
3603 :
3604 : /* Guard against out-of-range answers */
3605 8854 : if (numdistinct > input_rows)
3606 702 : numdistinct = input_rows;
3607 8854 : if (numdistinct < 1.0)
3608 0 : numdistinct = 1.0;
3609 :
3610 8854 : return numdistinct;
3611 : }
3612 :
3613 : /*
3614 : * Estimate hash bucket statistics when the specified expression is used
3615 : * as a hash key for the given number of buckets.
3616 : *
3617 : * This attempts to determine two values:
3618 : *
3619 : * 1. The frequency of the most common value of the expression (returns
3620 : * zero into *mcv_freq if we can't get that).
3621 : *
3622 : * 2. The "bucketsize fraction", ie, average number of entries in a bucket
3623 : * divided by total tuples in relation.
3624 : *
3625 : * XXX This is really pretty bogus since we're effectively assuming that the
3626 : * distribution of hash keys will be the same after applying restriction
3627 : * clauses as it was in the underlying relation. However, we are not nearly
3628 : * smart enough to figure out how the restrict clauses might change the
3629 : * distribution, so this will have to do for now.
3630 : *
3631 : * We are passed the number of buckets the executor will use for the given
3632 : * input relation. If the data were perfectly distributed, with the same
3633 : * number of tuples going into each available bucket, then the bucketsize
3634 : * fraction would be 1/nbuckets. But this happy state of affairs will occur
3635 : * only if (a) there are at least nbuckets distinct data values, and (b)
3636 : * we have a not-too-skewed data distribution. Otherwise the buckets will
3637 : * be nonuniformly occupied. If the other relation in the join has a key
3638 : * distribution similar to this one's, then the most-loaded buckets are
3639 : * exactly those that will be probed most often. Therefore, the "average"
3640 : * bucket size for costing purposes should really be taken as something close
3641 : * to the "worst case" bucket size. We try to estimate this by adjusting the
3642 : * fraction if there are too few distinct data values, and then scaling up
3643 : * by the ratio of the most common value's frequency to the average frequency.
3644 : *
3645 : * If no statistics are available, use a default estimate of 0.1. This will
3646 : * discourage use of a hash rather strongly if the inner relation is large,
3647 : * which is what we want. We do not want to hash unless we know that the
3648 : * inner rel is well-dispersed (or the alternatives seem much worse).
3649 : *
3650 : * The caller should also check that the mcv_freq is not so large that the
3651 : * most common value would by itself require an impractically large bucket.
3652 : * In a hash join, the executor can split buckets if they get too big, but
3653 : * obviously that doesn't help for a bucket that contains many duplicates of
3654 : * the same value.
3655 : */
3656 : void
3657 86920 : estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
3658 : Selectivity *mcv_freq,
3659 : Selectivity *bucketsize_frac)
3660 : {
3661 : VariableStatData vardata;
3662 : double estfract,
3663 : ndistinct,
3664 : stanullfrac,
3665 : avgfreq;
3666 : bool isdefault;
3667 : AttStatsSlot sslot;
3668 :
3669 86920 : examine_variable(root, hashkey, 0, &vardata);
3670 :
3671 : /* Look up the frequency of the most common value, if available */
3672 86920 : *mcv_freq = 0.0;
3673 :
3674 86920 : if (HeapTupleIsValid(vardata.statsTuple))
3675 : {
3676 54842 : if (get_attstatsslot(&sslot, vardata.statsTuple,
3677 : STATISTIC_KIND_MCV, InvalidOid,
3678 : ATTSTATSSLOT_NUMBERS))
3679 : {
3680 : /*
3681 : * The first MCV stat is for the most common value.
3682 : */
3683 29248 : if (sslot.nnumbers > 0)
3684 29248 : *mcv_freq = sslot.numbers[0];
3685 29248 : free_attstatsslot(&sslot);
3686 : }
3687 : }
3688 :
3689 : /* Get number of distinct values */
3690 86920 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
3691 :
3692 : /*
3693 : * If ndistinct isn't real, punt. We normally return 0.1, but if the
3694 : * mcv_freq is known to be even higher than that, use it instead.
3695 : */
3696 86920 : if (isdefault)
3697 : {
3698 12142 : *bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
3699 12142 : ReleaseVariableStats(vardata);
3700 12142 : return;
3701 : }
3702 :
3703 : /* Get fraction that are null */
3704 74778 : if (HeapTupleIsValid(vardata.statsTuple))
3705 : {
3706 : Form_pg_statistic stats;
3707 :
3708 54830 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
3709 54830 : stanullfrac = stats->stanullfrac;
3710 : }
3711 : else
3712 19948 : stanullfrac = 0.0;
3713 :
3714 : /* Compute avg freq of all distinct data values in raw relation */
3715 74778 : avgfreq = (1.0 - stanullfrac) / ndistinct;
3716 :
3717 : /*
3718 : * Adjust ndistinct to account for restriction clauses. Observe we are
3719 : * assuming that the data distribution is affected uniformly by the
3720 : * restriction clauses!
3721 : *
3722 : * XXX Possibly better way, but much more expensive: multiply by
3723 : * selectivity of rel's restriction clauses that mention the target Var.
3724 : */
3725 74778 : if (vardata.rel && vardata.rel->tuples > 0)
3726 : {
3727 74770 : ndistinct *= vardata.rel->rows / vardata.rel->tuples;
3728 74770 : ndistinct = clamp_row_est(ndistinct);
3729 : }
3730 :
3731 : /*
3732 : * Initial estimate of bucketsize fraction is 1/nbuckets as long as the
3733 : * number of buckets is less than the expected number of distinct values;
3734 : * otherwise it is 1/ndistinct.
3735 : */
3736 74778 : if (ndistinct > nbuckets)
3737 40 : estfract = 1.0 / nbuckets;
3738 : else
3739 74738 : estfract = 1.0 / ndistinct;
3740 :
3741 : /*
3742 : * Adjust estimated bucketsize upward to account for skewed distribution.
3743 : */
3744 74778 : if (avgfreq > 0.0 && *mcv_freq > avgfreq)
3745 26274 : estfract *= *mcv_freq / avgfreq;
3746 :
3747 : /*
3748 : * Clamp bucketsize to sane range (the above adjustment could easily
3749 : * produce an out-of-range result). We set the lower bound a little above
3750 : * zero, since zero isn't a very sane result.
3751 : */
3752 74778 : if (estfract < 1.0e-6)
3753 0 : estfract = 1.0e-6;
3754 74778 : else if (estfract > 1.0)
3755 20706 : estfract = 1.0;
3756 :
3757 74778 : *bucketsize_frac = (Selectivity) estfract;
3758 :
3759 74778 : ReleaseVariableStats(vardata);
3760 : }
3761 :
3762 : /*
3763 : * estimate_hashagg_tablesize
3764 : * estimate the number of bytes that a hash aggregate hashtable will
3765 : * require based on the agg_costs, path width and number of groups.
3766 : *
3767 : * We return the result as "double" to forestall any possible overflow
3768 : * problem in the multiplication by dNumGroups.
3769 : *
3770 : * XXX this may be over-estimating the size now that hashagg knows to omit
3771 : * unneeded columns from the hashtable. Also for mixed-mode grouping sets,
3772 : * grouping columns not in the hashed set are counted here even though hashagg
3773 : * won't store them. Is this a problem?
3774 : */
3775 : double
3776 4850 : estimate_hashagg_tablesize(Path *path, const AggClauseCosts *agg_costs,
3777 : double dNumGroups)
3778 : {
3779 9700 : Size hashentrysize = hash_agg_entry_size(agg_costs->numAggs,
3780 4850 : path->pathtarget->width,
3781 : agg_costs->transitionSpace);
3782 :
3783 : /*
3784 : * Note that this disregards the effect of fill-factor and growth policy
3785 : * of the hash table. That's probably ok, given that the default
3786 : * fill-factor is relatively high. It'd be hard to meaningfully factor in
3787 : * "double-in-size" growth policies here.
3788 : */
3789 4850 : return hashentrysize * dNumGroups;
3790 : }
3791 :
3792 :
3793 : /*-------------------------------------------------------------------------
3794 : *
3795 : * Support routines
3796 : *
3797 : *-------------------------------------------------------------------------
3798 : */
3799 :
3800 : /*
3801 : * Find applicable ndistinct statistics for the given list of VarInfos (which
3802 : * must all belong to the given rel), and update *ndistinct to the estimate of
3803 : * the MVNDistinctItem that best matches. If a match it found, *varinfos is
3804 : * updated to remove the list of matched varinfos.
3805 : *
3806 : * Varinfos that aren't for simple Vars are ignored.
3807 : *
3808 : * Return true if we're able to find a match, false otherwise.
3809 : */
3810 : static bool
3811 9176 : estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
3812 : List **varinfos, double *ndistinct)
3813 : {
3814 : ListCell *lc;
3815 9176 : Bitmapset *attnums = NULL;
3816 : int nmatches;
3817 9176 : Oid statOid = InvalidOid;
3818 : MVNDistinct *stats;
3819 9176 : Bitmapset *matched = NULL;
3820 :
3821 : /* bail out immediately if the table has no extended statistics */
3822 9176 : if (!rel->statlist)
3823 9116 : return false;
3824 :
3825 : /* Determine the attnums we're looking for */
3826 200 : foreach(lc, *varinfos)
3827 : {
3828 140 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
3829 : AttrNumber attnum;
3830 :
3831 : Assert(varinfo->rel == rel);
3832 :
3833 140 : if (!IsA(varinfo->var, Var))
3834 0 : continue;
3835 :
3836 140 : attnum = ((Var *) varinfo->var)->varattno;
3837 :
3838 140 : if (!AttrNumberIsForUserDefinedAttr(attnum))
3839 4 : continue;
3840 :
3841 136 : attnums = bms_add_member(attnums, attnum);
3842 : }
3843 :
3844 : /* look for the ndistinct statistics matching the most vars */
3845 60 : nmatches = 1; /* we require at least two matches */
3846 240 : foreach(lc, rel->statlist)
3847 : {
3848 180 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
3849 : Bitmapset *shared;
3850 : int nshared;
3851 :
3852 : /* skip statistics of other kinds */
3853 180 : if (info->kind != STATS_EXT_NDISTINCT)
3854 120 : continue;
3855 :
3856 : /* compute attnums shared by the vars and the statistics object */
3857 60 : shared = bms_intersect(info->keys, attnums);
3858 60 : nshared = bms_num_members(shared);
3859 :
3860 : /*
3861 : * Does this statistics object match more columns than the currently
3862 : * best object? If so, use this one instead.
3863 : *
3864 : * XXX This should break ties using name of the object, or something
3865 : * like that, to make the outcome stable.
3866 : */
3867 60 : if (nshared > nmatches)
3868 : {
3869 40 : statOid = info->statOid;
3870 40 : nmatches = nshared;
3871 40 : matched = shared;
3872 : }
3873 : }
3874 :
3875 : /* No match? */
3876 60 : if (statOid == InvalidOid)
3877 20 : return false;
3878 : Assert(nmatches > 1 && matched != NULL);
3879 :
3880 40 : stats = statext_ndistinct_load(statOid);
3881 :
3882 : /*
3883 : * If we have a match, search it for the specific item that matches (there
3884 : * must be one), and construct the output values.
3885 : */
3886 40 : if (stats)
3887 : {
3888 : int i;
3889 40 : List *newlist = NIL;
3890 40 : MVNDistinctItem *item = NULL;
3891 :
3892 : /* Find the specific item that exactly matches the combination */
3893 112 : for (i = 0; i < stats->nitems; i++)
3894 : {
3895 112 : MVNDistinctItem *tmpitem = &stats->items[i];
3896 :
3897 112 : if (bms_subset_compare(tmpitem->attrs, matched) == BMS_EQUAL)
3898 : {
3899 40 : item = tmpitem;
3900 40 : break;
3901 : }
3902 : }
3903 :
3904 : /* make sure we found an item */
3905 40 : if (!item)
3906 0 : elog(ERROR, "corrupt MVNDistinct entry");
3907 :
3908 : /* Form the output varinfo list, keeping only unmatched ones */
3909 156 : foreach(lc, *varinfos)
3910 : {
3911 116 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
3912 : AttrNumber attnum;
3913 :
3914 116 : if (!IsA(varinfo->var, Var))
3915 : {
3916 0 : newlist = lappend(newlist, varinfo);
3917 0 : continue;
3918 : }
3919 :
3920 116 : attnum = ((Var *) varinfo->var)->varattno;
3921 :
3922 116 : if (!AttrNumberIsForUserDefinedAttr(attnum))
3923 4 : continue;
3924 :
3925 112 : if (!bms_is_member(attnum, matched))
3926 16 : newlist = lappend(newlist, varinfo);
3927 : }
3928 :
3929 40 : *varinfos = newlist;
3930 40 : *ndistinct = item->ndistinct;
3931 40 : return true;
3932 : }
3933 :
3934 0 : return false;
3935 : }
3936 :
3937 : /*
3938 : * convert_to_scalar
3939 : * Convert non-NULL values of the indicated types to the comparison
3940 : * scale needed by scalarineqsel().
3941 : * Returns "true" if successful.
3942 : *
3943 : * XXX this routine is a hack: ideally we should look up the conversion
3944 : * subroutines in pg_type.
3945 : *
3946 : * All numeric datatypes are simply converted to their equivalent
3947 : * "double" values. (NUMERIC values that are outside the range of "double"
3948 : * are clamped to +/- HUGE_VAL.)
3949 : *
3950 : * String datatypes are converted by convert_string_to_scalar(),
3951 : * which is explained below. The reason why this routine deals with
3952 : * three values at a time, not just one, is that we need it for strings.
3953 : *
3954 : * The bytea datatype is just enough different from strings that it has
3955 : * to be treated separately.
3956 : *
3957 : * The several datatypes representing absolute times are all converted
3958 : * to Timestamp, which is actually a double, and then we just use that
3959 : * double value. Note this will give correct results even for the "special"
3960 : * values of Timestamp, since those are chosen to compare correctly;
3961 : * see timestamp_cmp.
3962 : *
3963 : * The several datatypes representing relative times (intervals) are all
3964 : * converted to measurements expressed in seconds.
3965 : */
3966 : static bool
3967 54124 : convert_to_scalar(Datum value, Oid valuetypid, Oid collid, double *scaledvalue,
3968 : Datum lobound, Datum hibound, Oid boundstypid,
3969 : double *scaledlobound, double *scaledhibound)
3970 : {
3971 54124 : bool failure = false;
3972 :
3973 : /*
3974 : * Both the valuetypid and the boundstypid should exactly match the
3975 : * declared input type(s) of the operator we are invoked for. However,
3976 : * extensions might try to use scalarineqsel as estimator for operators
3977 : * with input type(s) we don't handle here; in such cases, we want to
3978 : * return false, not fail. In any case, we mustn't assume that valuetypid
3979 : * and boundstypid are identical.
3980 : *
3981 : * XXX The histogram we are interpolating between points of could belong
3982 : * to a column that's only binary-compatible with the declared type. In
3983 : * essence we are assuming that the semantics of binary-compatible types
3984 : * are enough alike that we can use a histogram generated with one type's
3985 : * operators to estimate selectivity for the other's. This is outright
3986 : * wrong in some cases --- in particular signed versus unsigned
3987 : * interpretation could trip us up. But it's useful enough in the
3988 : * majority of cases that we do it anyway. Should think about more
3989 : * rigorous ways to do it.
3990 : */
3991 54124 : switch (valuetypid)
3992 : {
3993 : /*
3994 : * Built-in numeric types
3995 : */
3996 51884 : case BOOLOID:
3997 : case INT2OID:
3998 : case INT4OID:
3999 : case INT8OID:
4000 : case FLOAT4OID:
4001 : case FLOAT8OID:
4002 : case NUMERICOID:
4003 : case OIDOID:
4004 : case REGPROCOID:
4005 : case REGPROCEDUREOID:
4006 : case REGOPEROID:
4007 : case REGOPERATOROID:
4008 : case REGCLASSOID:
4009 : case REGTYPEOID:
4010 : case REGCONFIGOID:
4011 : case REGDICTIONARYOID:
4012 : case REGROLEOID:
4013 : case REGNAMESPACEOID:
4014 51884 : *scaledvalue = convert_numeric_to_scalar(value, valuetypid,
4015 : &failure);
4016 51884 : *scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
4017 : &failure);
4018 51884 : *scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
4019 : &failure);
4020 51884 : return !failure;
4021 :
4022 : /*
4023 : * Built-in string types
4024 : */
4025 2236 : case CHAROID:
4026 : case BPCHAROID:
4027 : case VARCHAROID:
4028 : case TEXTOID:
4029 : case NAMEOID:
4030 : {
4031 2236 : char *valstr = convert_string_datum(value, valuetypid,
4032 : collid, &failure);
4033 2236 : char *lostr = convert_string_datum(lobound, boundstypid,
4034 : collid, &failure);
4035 2236 : char *histr = convert_string_datum(hibound, boundstypid,
4036 : collid, &failure);
4037 :
4038 : /*
4039 : * Bail out if any of the values is not of string type. We
4040 : * might leak converted strings for the other value(s), but
4041 : * that's not worth troubling over.
4042 : */
4043 2236 : if (failure)
4044 0 : return false;
4045 :
4046 2236 : convert_string_to_scalar(valstr, scaledvalue,
4047 : lostr, scaledlobound,
4048 : histr, scaledhibound);
4049 2236 : pfree(valstr);
4050 2236 : pfree(lostr);
4051 2236 : pfree(histr);
4052 2236 : return true;
4053 : }
4054 :
4055 : /*
4056 : * Built-in bytea type
4057 : */
4058 0 : case BYTEAOID:
4059 : {
4060 : /* We only support bytea vs bytea comparison */
4061 0 : if (boundstypid != BYTEAOID)
4062 0 : return false;
4063 0 : convert_bytea_to_scalar(value, scaledvalue,
4064 : lobound, scaledlobound,
4065 : hibound, scaledhibound);
4066 0 : return true;
4067 : }
4068 :
4069 : /*
4070 : * Built-in time types
4071 : */
4072 0 : case TIMESTAMPOID:
4073 : case TIMESTAMPTZOID:
4074 : case DATEOID:
4075 : case INTERVALOID:
4076 : case TIMEOID:
4077 : case TIMETZOID:
4078 0 : *scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
4079 : &failure);
4080 0 : *scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
4081 : &failure);
4082 0 : *scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
4083 : &failure);
4084 0 : return !failure;
4085 :
4086 : /*
4087 : * Built-in network types
4088 : */
4089 0 : case INETOID:
4090 : case CIDROID:
4091 : case MACADDROID:
4092 : case MACADDR8OID:
4093 0 : *scaledvalue = convert_network_to_scalar(value, valuetypid,
4094 : &failure);
4095 0 : *scaledlobound = convert_network_to_scalar(lobound, boundstypid,
4096 : &failure);
4097 0 : *scaledhibound = convert_network_to_scalar(hibound, boundstypid,
4098 : &failure);
4099 0 : return !failure;
4100 : }
4101 : /* Don't know how to convert */
4102 4 : *scaledvalue = *scaledlobound = *scaledhibound = 0;
4103 4 : return false;
4104 : }
4105 :
4106 : /*
4107 : * Do convert_to_scalar()'s work for any numeric data type.
4108 : *
4109 : * On failure (e.g., unsupported typid), set *failure to true;
4110 : * otherwise, that variable is not changed.
4111 : */
4112 : static double
4113 155652 : convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
4114 : {
4115 155652 : switch (typid)
4116 : {
4117 0 : case BOOLOID:
4118 0 : return (double) DatumGetBool(value);
4119 8 : case INT2OID:
4120 8 : return (double) DatumGetInt16(value);
4121 18568 : case INT4OID:
4122 18568 : return (double) DatumGetInt32(value);
4123 0 : case INT8OID:
4124 0 : return (double) DatumGetInt64(value);
4125 0 : case FLOAT4OID:
4126 0 : return (double) DatumGetFloat4(value);
4127 24 : case FLOAT8OID:
4128 24 : return (double) DatumGetFloat8(value);
4129 0 : case NUMERICOID:
4130 : /* Note: out-of-range values will be clamped to +-HUGE_VAL */
4131 0 : return (double)
4132 0 : DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
4133 : value));
4134 137052 : case OIDOID:
4135 : case REGPROCOID:
4136 : case REGPROCEDUREOID:
4137 : case REGOPEROID:
4138 : case REGOPERATOROID:
4139 : case REGCLASSOID:
4140 : case REGTYPEOID:
4141 : case REGCONFIGOID:
4142 : case REGDICTIONARYOID:
4143 : case REGROLEOID:
4144 : case REGNAMESPACEOID:
4145 : /* we can treat OIDs as integers... */
4146 137052 : return (double) DatumGetObjectId(value);
4147 : }
4148 :
4149 0 : *failure = true;
4150 0 : return 0;
4151 : }
4152 :
4153 : /*
4154 : * Do convert_to_scalar()'s work for any character-string data type.
4155 : *
4156 : * String datatypes are converted to a scale that ranges from 0 to 1,
4157 : * where we visualize the bytes of the string as fractional digits.
4158 : *
4159 : * We do not want the base to be 256, however, since that tends to
4160 : * generate inflated selectivity estimates; few databases will have
4161 : * occurrences of all 256 possible byte values at each position.
4162 : * Instead, use the smallest and largest byte values seen in the bounds
4163 : * as the estimated range for each byte, after some fudging to deal with
4164 : * the fact that we probably aren't going to see the full range that way.
4165 : *
4166 : * An additional refinement is that we discard any common prefix of the
4167 : * three strings before computing the scaled values. This allows us to
4168 : * "zoom in" when we encounter a narrow data range. An example is a phone
4169 : * number database where all the values begin with the same area code.
4170 : * (Actually, the bounds will be adjacent histogram-bin-boundary values,
4171 : * so this is more likely to happen than you might think.)
4172 : */
4173 : static void
4174 2236 : convert_string_to_scalar(char *value,
4175 : double *scaledvalue,
4176 : char *lobound,
4177 : double *scaledlobound,
4178 : char *hibound,
4179 : double *scaledhibound)
4180 : {
4181 : int rangelo,
4182 : rangehi;
4183 : char *sptr;
4184 :
4185 2236 : rangelo = rangehi = (unsigned char) hibound[0];
4186 31296 : for (sptr = lobound; *sptr; sptr++)
4187 : {
4188 29060 : if (rangelo > (unsigned char) *sptr)
4189 6074 : rangelo = (unsigned char) *sptr;
4190 29060 : if (rangehi < (unsigned char) *sptr)
4191 2770 : rangehi = (unsigned char) *sptr;
4192 : }
4193 28482 : for (sptr = hibound; *sptr; sptr++)
4194 : {
4195 26246 : if (rangelo > (unsigned char) *sptr)
4196 390 : rangelo = (unsigned char) *sptr;
4197 26246 : if (rangehi < (unsigned char) *sptr)
4198 2818 : rangehi = (unsigned char) *sptr;
4199 : }
4200 : /* If range includes any upper-case ASCII chars, make it include all */
4201 2236 : if (rangelo <= 'Z' && rangehi >= 'A')
4202 : {
4203 934 : if (rangelo > 'A')
4204 56 : rangelo = 'A';
4205 934 : if (rangehi < 'Z')
4206 328 : rangehi = 'Z';
4207 : }
4208 : /* Ditto lower-case */
4209 2236 : if (rangelo <= 'z' && rangehi >= 'a')
4210 : {
4211 1900 : if (rangelo > 'a')
4212 0 : rangelo = 'a';
4213 1900 : if (rangehi < 'z')
4214 1756 : rangehi = 'z';
4215 : }
4216 : /* Ditto digits */
4217 2236 : if (rangelo <= '9' && rangehi >= '0')
4218 : {
4219 558 : if (rangelo > '0')
4220 514 : rangelo = '0';
4221 558 : if (rangehi < '9')
4222 4 : rangehi = '9';
4223 : }
4224 :
4225 : /*
4226 : * If range includes less than 10 chars, assume we have not got enough
4227 : * data, and make it include regular ASCII set.
4228 : */
4229 2236 : if (rangehi - rangelo < 9)
4230 : {
4231 0 : rangelo = ' ';
4232 0 : rangehi = 127;
4233 : }
4234 :
4235 : /*
4236 : * Now strip any common prefix of the three strings.
4237 : */
4238 3616 : while (*lobound)
4239 : {
4240 3610 : if (*lobound != *hibound || *lobound != *value)
4241 : break;
4242 1380 : lobound++, hibound++, value++;
4243 : }
4244 :
4245 : /*
4246 : * Now we can do the conversions.
4247 : */
4248 2236 : *scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
4249 2236 : *scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
4250 2236 : *scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
4251 2236 : }
4252 :
4253 : static double
4254 6708 : convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
4255 : {
4256 6708 : int slen = strlen(value);
4257 : double num,
4258 : denom,
4259 : base;
4260 :
4261 6708 : if (slen <= 0)
4262 6 : return 0.0; /* empty string has scalar value 0 */
4263 :
4264 : /*
4265 : * There seems little point in considering more than a dozen bytes from
4266 : * the string. Since base is at least 10, that will give us nominal
4267 : * resolution of at least 12 decimal digits, which is surely far more
4268 : * precision than this estimation technique has got anyway (especially in
4269 : * non-C locales). Also, even with the maximum possible base of 256, this
4270 : * ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
4271 : * overflow on any known machine.
4272 : */
4273 6702 : if (slen > 12)
4274 1890 : slen = 12;
4275 :
4276 : /* Convert initial characters to fraction */
4277 6702 : base = rangehi - rangelo + 1;
4278 6702 : num = 0.0;
4279 6702 : denom = base;
4280 56526 : while (slen-- > 0)
4281 : {
4282 49824 : int ch = (unsigned char) *value++;
4283 :
4284 49824 : if (ch < rangelo)
4285 64 : ch = rangelo - 1;
4286 49760 : else if (ch > rangehi)
4287 0 : ch = rangehi + 1;
4288 49824 : num += ((double) (ch - rangelo)) / denom;
4289 49824 : denom *= base;
4290 : }
4291 :
4292 6702 : return num;
4293 : }
4294 :
4295 : /*
4296 : * Convert a string-type Datum into a palloc'd, null-terminated string.
4297 : *
4298 : * On failure (e.g., unsupported typid), set *failure to true;
4299 : * otherwise, that variable is not changed. (We'll return NULL on failure.)
4300 : *
4301 : * When using a non-C locale, we must pass the string through strxfrm()
4302 : * before continuing, so as to generate correct locale-specific results.
4303 : */
4304 : static char *
4305 6708 : convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
4306 : {
4307 : char *val;
4308 :
4309 6708 : switch (typid)
4310 : {
4311 0 : case CHAROID:
4312 0 : val = (char *) palloc(2);
4313 0 : val[0] = DatumGetChar(value);
4314 0 : val[1] = '\0';
4315 0 : break;
4316 1996 : case BPCHAROID:
4317 : case VARCHAROID:
4318 : case TEXTOID:
4319 1996 : val = TextDatumGetCString(value);
4320 1996 : break;
4321 4712 : case NAMEOID:
4322 : {
4323 4712 : NameData *nm = (NameData *) DatumGetPointer(value);
4324 :
4325 4712 : val = pstrdup(NameStr(*nm));
4326 4712 : break;
4327 : }
4328 0 : default:
4329 0 : *failure = true;
4330 0 : return NULL;
4331 : }
4332 :
4333 6708 : if (!lc_collate_is_c(collid))
4334 : {
4335 : char *xfrmstr;
4336 : size_t xfrmlen;
4337 : size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
4338 :
4339 : /*
4340 : * XXX: We could guess at a suitable output buffer size and only call
4341 : * strxfrm twice if our guess is too small.
4342 : *
4343 : * XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
4344 : * bogus data or set an error. This is not really a problem unless it
4345 : * crashes since it will only give an estimation error and nothing
4346 : * fatal.
4347 : */
4348 72 : xfrmlen = strxfrm(NULL, val, 0);
4349 : #ifdef WIN32
4350 :
4351 : /*
4352 : * On Windows, strxfrm returns INT_MAX when an error occurs. Instead
4353 : * of trying to allocate this much memory (and fail), just return the
4354 : * original string unmodified as if we were in the C locale.
4355 : */
4356 : if (xfrmlen == INT_MAX)
4357 : return val;
4358 : #endif
4359 72 : xfrmstr = (char *) palloc(xfrmlen + 1);
4360 72 : xfrmlen2 = strxfrm(xfrmstr, val, xfrmlen + 1);
4361 :
4362 : /*
4363 : * Some systems (e.g., glibc) can return a smaller value from the
4364 : * second call than the first; thus the Assert must be <= not ==.
4365 : */
4366 : Assert(xfrmlen2 <= xfrmlen);
4367 72 : pfree(val);
4368 72 : val = xfrmstr;
4369 : }
4370 :
4371 6708 : return val;
4372 : }
4373 :
4374 : /*
4375 : * Do convert_to_scalar()'s work for any bytea data type.
4376 : *
4377 : * Very similar to convert_string_to_scalar except we can't assume
4378 : * null-termination and therefore pass explicit lengths around.
4379 : *
4380 : * Also, assumptions about likely "normal" ranges of characters have been
4381 : * removed - a data range of 0..255 is always used, for now. (Perhaps
4382 : * someday we will add information about actual byte data range to
4383 : * pg_statistic.)
4384 : */
4385 : static void
4386 0 : convert_bytea_to_scalar(Datum value,
4387 : double *scaledvalue,
4388 : Datum lobound,
4389 : double *scaledlobound,
4390 : Datum hibound,
4391 : double *scaledhibound)
4392 : {
4393 0 : bytea *valuep = DatumGetByteaPP(value);
4394 0 : bytea *loboundp = DatumGetByteaPP(lobound);
4395 0 : bytea *hiboundp = DatumGetByteaPP(hibound);
4396 : int rangelo,
4397 : rangehi,
4398 0 : valuelen = VARSIZE_ANY_EXHDR(valuep),
4399 0 : loboundlen = VARSIZE_ANY_EXHDR(loboundp),
4400 0 : hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
4401 : i,
4402 : minlen;
4403 0 : unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
4404 0 : unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
4405 0 : unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
4406 :
4407 : /*
4408 : * Assume bytea data is uniformly distributed across all byte values.
4409 : */
4410 0 : rangelo = 0;
4411 0 : rangehi = 255;
4412 :
4413 : /*
4414 : * Now strip any common prefix of the three strings.
4415 : */
4416 0 : minlen = Min(Min(valuelen, loboundlen), hiboundlen);
4417 0 : for (i = 0; i < minlen; i++)
4418 : {
4419 0 : if (*lostr != *histr || *lostr != *valstr)
4420 : break;
4421 0 : lostr++, histr++, valstr++;
4422 0 : loboundlen--, hiboundlen--, valuelen--;
4423 : }
4424 :
4425 : /*
4426 : * Now we can do the conversions.
4427 : */
4428 0 : *scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
4429 0 : *scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
4430 0 : *scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
4431 0 : }
4432 :
4433 : static double
4434 0 : convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
4435 : int rangelo, int rangehi)
4436 : {
4437 : double num,
4438 : denom,
4439 : base;
4440 :
4441 0 : if (valuelen <= 0)
4442 0 : return 0.0; /* empty string has scalar value 0 */
4443 :
4444 : /*
4445 : * Since base is 256, need not consider more than about 10 chars (even
4446 : * this many seems like overkill)
4447 : */
4448 0 : if (valuelen > 10)
4449 0 : valuelen = 10;
4450 :
4451 : /* Convert initial characters to fraction */
4452 0 : base = rangehi - rangelo + 1;
4453 0 : num = 0.0;
4454 0 : denom = base;
4455 0 : while (valuelen-- > 0)
4456 : {
4457 0 : int ch = *value++;
4458 :
4459 0 : if (ch < rangelo)
4460 0 : ch = rangelo - 1;
4461 0 : else if (ch > rangehi)
4462 0 : ch = rangehi + 1;
4463 0 : num += ((double) (ch - rangelo)) / denom;
4464 0 : denom *= base;
4465 : }
4466 :
4467 0 : return num;
4468 : }
4469 :
4470 : /*
4471 : * Do convert_to_scalar()'s work for any timevalue data type.
4472 : *
4473 : * On failure (e.g., unsupported typid), set *failure to true;
4474 : * otherwise, that variable is not changed.
4475 : */
4476 : static double
4477 0 : convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
4478 : {
4479 0 : switch (typid)
4480 : {
4481 0 : case TIMESTAMPOID:
4482 0 : return DatumGetTimestamp(value);
4483 0 : case TIMESTAMPTZOID:
4484 0 : return DatumGetTimestampTz(value);
4485 0 : case DATEOID:
4486 0 : return date2timestamp_no_overflow(DatumGetDateADT(value));
4487 0 : case INTERVALOID:
4488 : {
4489 0 : Interval *interval = DatumGetIntervalP(value);
4490 :
4491 : /*
4492 : * Convert the month part of Interval to days using assumed
4493 : * average month length of 365.25/12.0 days. Not too
4494 : * accurate, but plenty good enough for our purposes.
4495 : */
4496 0 : return interval->time + interval->day * (double) USECS_PER_DAY +
4497 0 : interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
4498 : }
4499 0 : case TIMEOID:
4500 0 : return DatumGetTimeADT(value);
4501 0 : case TIMETZOID:
4502 : {
4503 0 : TimeTzADT *timetz = DatumGetTimeTzADTP(value);
4504 :
4505 : /* use GMT-equivalent time */
4506 0 : return (double) (timetz->time + (timetz->zone * 1000000.0));
4507 : }
4508 : }
4509 :
4510 0 : *failure = true;
4511 0 : return 0;
4512 : }
4513 :
4514 :
4515 : /*
4516 : * get_restriction_variable
4517 : * Examine the args of a restriction clause to see if it's of the
4518 : * form (variable op pseudoconstant) or (pseudoconstant op variable),
4519 : * where "variable" could be either a Var or an expression in vars of a
4520 : * single relation. If so, extract information about the variable,
4521 : * and also indicate which side it was on and the other argument.
4522 : *
4523 : * Inputs:
4524 : * root: the planner info
4525 : * args: clause argument list
4526 : * varRelid: see specs for restriction selectivity functions
4527 : *
4528 : * Outputs: (these are valid only if true is returned)
4529 : * *vardata: gets information about variable (see examine_variable)
4530 : * *other: gets other clause argument, aggressively reduced to a constant
4531 : * *varonleft: set true if variable is on the left, false if on the right
4532 : *
4533 : * Returns true if a variable is identified, otherwise false.
4534 : *
4535 : * Note: if there are Vars on both sides of the clause, we must fail, because
4536 : * callers are expecting that the other side will act like a pseudoconstant.
4537 : */
4538 : bool
4539 376816 : get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
4540 : VariableStatData *vardata, Node **other,
4541 : bool *varonleft)
4542 : {
4543 : Node *left,
4544 : *right;
4545 : VariableStatData rdata;
4546 :
4547 : /* Fail if not a binary opclause (probably shouldn't happen) */
4548 376816 : if (list_length(args) != 2)
4549 0 : return false;
4550 :
4551 376816 : left = (Node *) linitial(args);
4552 376816 : right = (Node *) lsecond(args);
4553 :
4554 : /*
4555 : * Examine both sides. Note that when varRelid is nonzero, Vars of other
4556 : * relations will be treated as pseudoconstants.
4557 : */
4558 376816 : examine_variable(root, left, varRelid, vardata);
4559 376816 : examine_variable(root, right, varRelid, &rdata);
4560 :
4561 : /*
4562 : * If one side is a variable and the other not, we win.
4563 : */
4564 376816 : if (vardata->rel && rdata.rel == NULL)
4565 : {
4566 310484 : *varonleft = true;
4567 310484 : *other = estimate_expression_value(root, rdata.var);
4568 : /* Assume we need no ReleaseVariableStats(rdata) here */
4569 310484 : return true;
4570 : }
4571 :
4572 66332 : if (vardata->rel == NULL && rdata.rel)
4573 : {
4574 64402 : *varonleft = false;
4575 64402 : *other = estimate_expression_value(root, vardata->var);
4576 : /* Assume we need no ReleaseVariableStats(*vardata) here */
4577 64402 : *vardata = rdata;
4578 64402 : return true;
4579 : }
4580 :
4581 : /* Oops, clause has wrong structure (probably var op var) */
4582 1930 : ReleaseVariableStats(*vardata);
4583 1930 : ReleaseVariableStats(rdata);
4584 :
4585 1930 : return false;
4586 : }
4587 :
4588 : /*
4589 : * get_join_variables
4590 : * Apply examine_variable() to each side of a join clause.
4591 : * Also, attempt to identify whether the join clause has the same
4592 : * or reversed sense compared to the SpecialJoinInfo.
4593 : *
4594 : * We consider the join clause "normal" if it is "lhs_var OP rhs_var",
4595 : * or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
4596 : * where we can't tell for sure, we default to assuming it's normal.
4597 : */
4598 : void
4599 126880 : get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
4600 : VariableStatData *vardata1, VariableStatData *vardata2,
4601 : bool *join_is_reversed)
4602 : {
4603 : Node *left,
4604 : *right;
4605 :
4606 126880 : if (list_length(args) != 2)
4607 0 : elog(ERROR, "join operator should take two arguments");
4608 :
4609 126880 : left = (Node *) linitial(args);
4610 126880 : right = (Node *) lsecond(args);
4611 :
4612 126880 : examine_variable(root, left, 0, vardata1);
4613 126880 : examine_variable(root, right, 0, vardata2);
4614 :
4615 253678 : if (vardata1->rel &&
4616 126798 : bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
4617 19946 : *join_is_reversed = true; /* var1 is on RHS */
4618 213796 : else if (vardata2->rel &&
4619 106862 : bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
4620 80 : *join_is_reversed = true; /* var2 is on LHS */
4621 : else
4622 106854 : *join_is_reversed = false;
4623 126880 : }
4624 :
4625 : /*
4626 : * examine_variable
4627 : * Try to look up statistical data about an expression.
4628 : * Fill in a VariableStatData struct to describe the expression.
4629 : *
4630 : * Inputs:
4631 : * root: the planner info
4632 : * node: the expression tree to examine
4633 : * varRelid: see specs for restriction selectivity functions
4634 : *
4635 : * Outputs: *vardata is filled as follows:
4636 : * var: the input expression (with any binary relabeling stripped, if
4637 : * it is or contains a variable; but otherwise the type is preserved)
4638 : * rel: RelOptInfo for relation containing variable; NULL if expression
4639 : * contains no Vars (NOTE this could point to a RelOptInfo of a
4640 : * subquery, not one in the current query).
4641 : * statsTuple: the pg_statistic entry for the variable, if one exists;
4642 : * otherwise NULL.
4643 : * freefunc: pointer to a function to release statsTuple with.
4644 : * vartype: exposed type of the expression; this should always match
4645 : * the declared input type of the operator we are estimating for.
4646 : * atttype, atttypmod: actual type/typmod of the "var" expression. This is
4647 : * commonly the same as the exposed type of the variable argument,
4648 : * but can be different in binary-compatible-type cases.
4649 : * isunique: true if we were able to match the var to a unique index or a
4650 : * single-column DISTINCT clause, implying its values are unique for
4651 : * this query. (Caution: this should be trusted for statistical
4652 : * purposes only, since we do not check indimmediate nor verify that
4653 : * the exact same definition of equality applies.)
4654 : * acl_ok: true if current user has permission to read the column(s)
4655 : * underlying the pg_statistic entry. This is consulted by
4656 : * statistic_proc_security_check().
4657 : *
4658 : * Caller is responsible for doing ReleaseVariableStats() before exiting.
4659 : */
4660 : void
4661 1271354 : examine_variable(PlannerInfo *root, Node *node, int varRelid,
4662 : VariableStatData *vardata)
4663 : {
4664 : Node *basenode;
4665 : Relids varnos;
4666 : RelOptInfo *onerel;
4667 :
4668 : /* Make sure we don't return dangling pointers in vardata */
4669 8899478 : MemSet(vardata, 0, sizeof(VariableStatData));
4670 :
4671 : /* Save the exposed type of the expression */
4672 1271354 : vardata->vartype = exprType(node);
4673 :
4674 : /* Look inside any binary-compatible relabeling */
4675 :
4676 1271354 : if (IsA(node, RelabelType))
4677 15562 : basenode = (Node *) ((RelabelType *) node)->arg;
4678 : else
4679 1255792 : basenode = node;
4680 :
4681 : /* Fast path for a simple Var */
4682 :
4683 1271354 : if (IsA(basenode, Var) &&
4684 309802 : (varRelid == 0 || varRelid == ((Var *) basenode)->varno))
4685 : {
4686 861532 : Var *var = (Var *) basenode;
4687 :
4688 : /* Set up result fields other than the stats tuple */
4689 861532 : vardata->var = basenode; /* return Var without relabeling */
4690 861532 : vardata->rel = find_base_rel(root, var->varno);
4691 861532 : vardata->atttype = var->vartype;
4692 861532 : vardata->atttypmod = var->vartypmod;
4693 861532 : vardata->isunique = has_unique_index(vardata->rel, var->varattno);
4694 :
4695 : /* Try to locate some stats */
4696 861532 : examine_simple_variable(root, var, vardata);
4697 :
4698 861532 : return;
4699 : }
4700 :
4701 : /*
4702 : * Okay, it's a more complicated expression. Determine variable
4703 : * membership. Note that when varRelid isn't zero, only vars of that
4704 : * relation are considered "real" vars.
4705 : */
4706 409822 : varnos = pull_varnos(basenode);
4707 :
4708 409822 : onerel = NULL;
4709 :
4710 409822 : switch (bms_membership(varnos))
4711 : {
4712 239414 : case BMS_EMPTY_SET:
4713 : /* No Vars at all ... must be pseudo-constant clause */
4714 239414 : break;
4715 166872 : case BMS_SINGLETON:
4716 166872 : if (varRelid == 0 || bms_is_member(varRelid, varnos))
4717 : {
4718 28592 : onerel = find_base_rel(root,
4719 13494 : (varRelid ? varRelid : bms_singleton_member(varnos)));
4720 15098 : vardata->rel = onerel;
4721 15098 : node = basenode; /* strip any relabeling */
4722 : }
4723 : /* else treat it as a constant */
4724 166872 : break;
4725 3536 : case BMS_MULTIPLE:
4726 3536 : if (varRelid == 0)
4727 : {
4728 : /* treat it as a variable of a join relation */
4729 3396 : vardata->rel = find_join_rel(root, varnos);
4730 3396 : node = basenode; /* strip any relabeling */
4731 : }
4732 140 : else if (bms_is_member(varRelid, varnos))
4733 : {
4734 : /* ignore the vars belonging to other relations */
4735 48 : vardata->rel = find_base_rel(root, varRelid);
4736 48 : node = basenode; /* strip any relabeling */
4737 : /* note: no point in expressional-index search here */
4738 : }
4739 : /* else treat it as a constant */
4740 3536 : break;
4741 : }
4742 :
4743 409822 : bms_free(varnos);
4744 :
4745 409822 : vardata->var = node;
4746 409822 : vardata->atttype = exprType(node);
4747 409822 : vardata->atttypmod = exprTypmod(node);
4748 :
4749 409822 : if (onerel)
4750 : {
4751 : /*
4752 : * We have an expression in vars of a single relation. Try to match
4753 : * it to expressional index columns, in hopes of finding some
4754 : * statistics.
4755 : *
4756 : * Note that we consider all index columns including INCLUDE columns,
4757 : * since there could be stats for such columns. But the test for
4758 : * uniqueness needs to be warier.
4759 : *
4760 : * XXX it's conceivable that there are multiple matches with different
4761 : * index opfamilies; if so, we need to pick one that matches the
4762 : * operator we are estimating for. FIXME later.
4763 : */
4764 : ListCell *ilist;
4765 :
4766 24592 : foreach(ilist, onerel->indexlist)
4767 : {
4768 11362 : IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
4769 : ListCell *indexpr_item;
4770 : int pos;
4771 :
4772 11362 : indexpr_item = list_head(index->indexprs);
4773 11362 : if (indexpr_item == NULL)
4774 8266 : continue; /* no expressions here... */
4775 :
4776 4372 : for (pos = 0; pos < index->ncolumns; pos++)
4777 : {
4778 3144 : if (index->indexkeys[pos] == 0)
4779 : {
4780 : Node *indexkey;
4781 :
4782 3096 : if (indexpr_item == NULL)
4783 0 : elog(ERROR, "too few entries in indexprs list");
4784 3096 : indexkey = (Node *) lfirst(indexpr_item);
4785 3096 : if (indexkey && IsA(indexkey, RelabelType))
4786 0 : indexkey = (Node *) ((RelabelType *) indexkey)->arg;
4787 3096 : if (equal(node, indexkey))
4788 : {
4789 : /*
4790 : * Found a match ... is it a unique index? Tests here
4791 : * should match has_unique_index().
4792 : */
4793 2252 : if (index->unique &&
4794 260 : index->nkeycolumns == 1 &&
4795 260 : pos == 0 &&
4796 260 : (index->indpred == NIL || index->predOK))
4797 260 : vardata->isunique = true;
4798 :
4799 : /*
4800 : * Has it got stats? We only consider stats for
4801 : * non-partial indexes, since partial indexes probably
4802 : * don't reflect whole-relation statistics; the above
4803 : * check for uniqueness is the only info we take from
4804 : * a partial index.
4805 : *
4806 : * An index stats hook, however, must make its own
4807 : * decisions about what to do with partial indexes.
4808 : */
4809 2252 : if (get_index_stats_hook &&
4810 0 : (*get_index_stats_hook) (root, index->indexoid,
4811 0 : pos + 1, vardata))
4812 : {
4813 : /*
4814 : * The hook took control of acquiring a stats
4815 : * tuple. If it did supply a tuple, it'd better
4816 : * have supplied a freefunc.
4817 : */
4818 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
4819 0 : !vardata->freefunc)
4820 0 : elog(ERROR, "no function provided to release variable stats with");
4821 : }
4822 2252 : else if (index->indpred == NIL)
4823 : {
4824 2252 : vardata->statsTuple =
4825 4504 : SearchSysCache3(STATRELATTINH,
4826 2252 : ObjectIdGetDatum(index->indexoid),
4827 2252 : Int16GetDatum(pos + 1),
4828 : BoolGetDatum(false));
4829 2252 : vardata->freefunc = ReleaseSysCache;
4830 :
4831 2252 : if (HeapTupleIsValid(vardata->statsTuple))
4832 : {
4833 : /* Get index's table for permission check */
4834 : RangeTblEntry *rte;
4835 : Oid userid;
4836 :
4837 1868 : rte = planner_rt_fetch(index->rel->relid, root);
4838 : Assert(rte->rtekind == RTE_RELATION);
4839 :
4840 : /*
4841 : * Use checkAsUser if it's set, in case we're
4842 : * accessing the table via a view.
4843 : */
4844 1868 : userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
4845 :
4846 : /*
4847 : * For simplicity, we insist on the whole
4848 : * table being selectable, rather than trying
4849 : * to identify which column(s) the index
4850 : * depends on. Also require all rows to be
4851 : * selectable --- there must be no
4852 : * securityQuals from security barrier views
4853 : * or RLS policies.
4854 : */
4855 1868 : vardata->acl_ok =
4856 3736 : rte->securityQuals == NIL &&
4857 1868 : (pg_class_aclcheck(rte->relid, userid,
4858 : ACL_SELECT) == ACLCHECK_OK);
4859 :
4860 : /*
4861 : * If the user doesn't have permissions to
4862 : * access an inheritance child relation, check
4863 : * the permissions of the table actually
4864 : * mentioned in the query, since most likely
4865 : * the user does have that permission. Note
4866 : * that whole-table select privilege on the
4867 : * parent doesn't quite guarantee that the
4868 : * user could read all columns of the child.
4869 : * But in practice it's unlikely that any
4870 : * interesting security violation could result
4871 : * from allowing access to the expression
4872 : * index's stats, so we allow it anyway. See
4873 : * similar code in examine_simple_variable()
4874 : * for additional comments.
4875 : */
4876 1868 : if (!vardata->acl_ok &&
4877 12 : root->append_rel_array != NULL)
4878 : {
4879 : AppendRelInfo *appinfo;
4880 8 : Index varno = index->rel->relid;
4881 :
4882 8 : appinfo = root->append_rel_array[varno];
4883 24 : while (appinfo &&
4884 16 : planner_rt_fetch(appinfo->parent_relid,
4885 16 : root)->rtekind == RTE_RELATION)
4886 : {
4887 16 : varno = appinfo->parent_relid;
4888 16 : appinfo = root->append_rel_array[varno];
4889 : }
4890 8 : if (varno != index->rel->relid)
4891 : {
4892 : /* Repeat access check on this rel */
4893 8 : rte = planner_rt_fetch(varno, root);
4894 : Assert(rte->rtekind == RTE_RELATION);
4895 :
4896 8 : userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
4897 :
4898 8 : vardata->acl_ok =
4899 16 : rte->securityQuals == NIL &&
4900 8 : (pg_class_aclcheck(rte->relid,
4901 : userid,
4902 : ACL_SELECT) == ACLCHECK_OK);
4903 : }
4904 : }
4905 : }
4906 : else
4907 : {
4908 : /* suppress leakproofness checks later */
4909 384 : vardata->acl_ok = true;
4910 : }
4911 : }
4912 2252 : if (vardata->statsTuple)
4913 1868 : break;
4914 : }
4915 1228 : indexpr_item = lnext(index->indexprs, indexpr_item);
4916 : }
4917 : }
4918 3096 : if (vardata->statsTuple)
4919 1868 : break;
4920 : }
4921 : }
4922 : }
4923 :
4924 : /*
4925 : * examine_simple_variable
4926 : * Handle a simple Var for examine_variable
4927 : *
4928 : * This is split out as a subroutine so that we can recurse to deal with
4929 : * Vars referencing subqueries.
4930 : *
4931 : * We already filled in all the fields of *vardata except for the stats tuple.
4932 : */
4933 : static void
4934 862276 : examine_simple_variable(PlannerInfo *root, Var *var,
4935 : VariableStatData *vardata)
4936 : {
4937 862276 : RangeTblEntry *rte = root->simple_rte_array[var->varno];
4938 :
4939 : Assert(IsA(rte, RangeTblEntry));
4940 :
4941 862276 : if (get_relation_stats_hook &&
4942 0 : (*get_relation_stats_hook) (root, rte, var->varattno, vardata))
4943 : {
4944 : /*
4945 : * The hook took control of acquiring a stats tuple. If it did supply
4946 : * a tuple, it'd better have supplied a freefunc.
4947 : */
4948 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
4949 0 : !vardata->freefunc)
4950 0 : elog(ERROR, "no function provided to release variable stats with");
4951 : }
4952 862276 : else if (rte->rtekind == RTE_RELATION)
4953 : {
4954 : /*
4955 : * Plain table or parent of an inheritance appendrel, so look up the
4956 : * column in pg_statistic
4957 : */
4958 813342 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
4959 813342 : ObjectIdGetDatum(rte->relid),
4960 813342 : Int16GetDatum(var->varattno),
4961 813342 : BoolGetDatum(rte->inh));
4962 813342 : vardata->freefunc = ReleaseSysCache;
4963 :
4964 813342 : if (HeapTupleIsValid(vardata->statsTuple))
4965 : {
4966 : Oid userid;
4967 :
4968 : /*
4969 : * Check if user has permission to read this column. We require
4970 : * all rows to be accessible, so there must be no securityQuals
4971 : * from security barrier views or RLS policies. Use checkAsUser
4972 : * if it's set, in case we're accessing the table via a view.
4973 : */
4974 591008 : userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
4975 :
4976 591008 : vardata->acl_ok =
4977 1181892 : rte->securityQuals == NIL &&
4978 590708 : ((pg_class_aclcheck(rte->relid, userid,
4979 176 : ACL_SELECT) == ACLCHECK_OK) ||
4980 176 : (pg_attribute_aclcheck(rte->relid, var->varattno, userid,
4981 : ACL_SELECT) == ACLCHECK_OK));
4982 :
4983 : /*
4984 : * If the user doesn't have permissions to access an inheritance
4985 : * child relation or specifically this attribute, check the
4986 : * permissions of the table/column actually mentioned in the
4987 : * query, since most likely the user does have that permission
4988 : * (else the query will fail at runtime), and if the user can read
4989 : * the column there then he can get the values of the child table
4990 : * too. To do that, we must find out which of the root parent's
4991 : * attributes the child relation's attribute corresponds to.
4992 : */
4993 591008 : if (!vardata->acl_ok && var->varattno > 0 &&
4994 328 : root->append_rel_array != NULL)
4995 : {
4996 : AppendRelInfo *appinfo;
4997 8 : Index varno = var->varno;
4998 8 : int varattno = var->varattno;
4999 8 : bool found = false;
5000 :
5001 8 : appinfo = root->append_rel_array[varno];
5002 :
5003 : /*
5004 : * Partitions are mapped to their immediate parent, not the
5005 : * root parent, so must be ready to walk up multiple
5006 : * AppendRelInfos. But stop if we hit a parent that is not
5007 : * RTE_RELATION --- that's a flattened UNION ALL subquery, not
5008 : * an inheritance parent.
5009 : */
5010 24 : while (appinfo &&
5011 16 : planner_rt_fetch(appinfo->parent_relid,
5012 16 : root)->rtekind == RTE_RELATION)
5013 : {
5014 : int parent_varattno;
5015 :
5016 16 : found = false;
5017 16 : if (varattno <= 0 || varattno > appinfo->num_child_cols)
5018 : break; /* safety check */
5019 16 : parent_varattno = appinfo->parent_colnos[varattno - 1];
5020 16 : if (parent_varattno == 0)
5021 0 : break; /* Var is local to child */
5022 :
5023 16 : varno = appinfo->parent_relid;
5024 16 : varattno = parent_varattno;
5025 16 : found = true;
5026 :
5027 : /* If the parent is itself a child, continue up. */
5028 16 : appinfo = root->append_rel_array[varno];
5029 : }
5030 :
5031 : /*
5032 : * In rare cases, the Var may be local to the child table, in
5033 : * which case, we've got to live with having no access to this
5034 : * column's stats.
5035 : */
5036 8 : if (!found)
5037 0 : return;
5038 :
5039 : /* Repeat the access check on this parent rel & column */
5040 8 : rte = planner_rt_fetch(varno, root);
5041 : Assert(rte->rtekind == RTE_RELATION);
5042 :
5043 8 : userid = rte->checkAsUser ? rte->checkAsUser : GetUserId();
5044 :
5045 8 : vardata->acl_ok =
5046 20 : rte->securityQuals == NIL &&
5047 8 : ((pg_class_aclcheck(rte->relid, userid,
5048 4 : ACL_SELECT) == ACLCHECK_OK) ||
5049 4 : (pg_attribute_aclcheck(rte->relid, varattno, userid,
5050 : ACL_SELECT) == ACLCHECK_OK));
5051 : }
5052 : }
5053 : else
5054 : {
5055 : /* suppress any possible leakproofness checks later */
5056 222334 : vardata->acl_ok = true;
5057 : }
5058 : }
5059 48934 : else if (rte->rtekind == RTE_SUBQUERY && !rte->inh)
5060 : {
5061 : /*
5062 : * Plain subquery (not one that was converted to an appendrel).
5063 : */
5064 2472 : Query *subquery = rte->subquery;
5065 : RelOptInfo *rel;
5066 : TargetEntry *ste;
5067 :
5068 : /*
5069 : * Punt if it's a whole-row var rather than a plain column reference.
5070 : */
5071 2472 : if (var->varattno == InvalidAttrNumber)
5072 0 : return;
5073 :
5074 : /*
5075 : * Punt if subquery uses set operations or GROUP BY, as these will
5076 : * mash underlying columns' stats beyond recognition. (Set ops are
5077 : * particularly nasty; if we forged ahead, we would return stats
5078 : * relevant to only the leftmost subselect...) DISTINCT is also
5079 : * problematic, but we check that later because there is a possibility
5080 : * of learning something even with it.
5081 : */
5082 2472 : if (subquery->setOperations ||
5083 2364 : subquery->groupClause)
5084 356 : return;
5085 :
5086 : /*
5087 : * OK, fetch RelOptInfo for subquery. Note that we don't change the
5088 : * rel returned in vardata, since caller expects it to be a rel of the
5089 : * caller's query level. Because we might already be recursing, we
5090 : * can't use that rel pointer either, but have to look up the Var's
5091 : * rel afresh.
5092 : */
5093 2116 : rel = find_base_rel(root, var->varno);
5094 :
5095 : /* If the subquery hasn't been planned yet, we have to punt */
5096 2116 : if (rel->subroot == NULL)
5097 0 : return;
5098 : Assert(IsA(rel->subroot, PlannerInfo));
5099 :
5100 : /*
5101 : * Switch our attention to the subquery as mangled by the planner. It
5102 : * was okay to look at the pre-planning version for the tests above,
5103 : * but now we need a Var that will refer to the subroot's live
5104 : * RelOptInfos. For instance, if any subquery pullup happened during
5105 : * planning, Vars in the targetlist might have gotten replaced, and we
5106 : * need to see the replacement expressions.
5107 : */
5108 2116 : subquery = rel->subroot->parse;
5109 : Assert(IsA(subquery, Query));
5110 :
5111 : /* Get the subquery output expression referenced by the upper Var */
5112 2116 : ste = get_tle_by_resno(subquery->targetList, var->varattno);
5113 2116 : if (ste == NULL || ste->resjunk)
5114 0 : elog(ERROR, "subquery %s does not have attribute %d",
5115 : rte->eref->aliasname, var->varattno);
5116 2116 : var = (Var *) ste->expr;
5117 :
5118 : /*
5119 : * If subquery uses DISTINCT, we can't make use of any stats for the
5120 : * variable ... but, if it's the only DISTINCT column, we are entitled
5121 : * to consider it unique. We do the test this way so that it works
5122 : * for cases involving DISTINCT ON.
5123 : */
5124 2116 : if (subquery->distinctClause)
5125 : {
5126 200 : if (list_length(subquery->distinctClause) == 1 &&
5127 68 : targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
5128 68 : vardata->isunique = true;
5129 : /* cannot go further */
5130 132 : return;
5131 : }
5132 :
5133 : /*
5134 : * If the sub-query originated from a view with the security_barrier
5135 : * attribute, we must not look at the variable's statistics, though it
5136 : * seems all right to notice the existence of a DISTINCT clause. So
5137 : * stop here.
5138 : *
5139 : * This is probably a harsher restriction than necessary; it's
5140 : * certainly OK for the selectivity estimator (which is a C function,
5141 : * and therefore omnipotent anyway) to look at the statistics. But
5142 : * many selectivity estimators will happily *invoke the operator
5143 : * function* to try to work out a good estimate - and that's not OK.
5144 : * So for now, don't dig down for stats.
5145 : */
5146 1984 : if (rte->security_barrier)
5147 140 : return;
5148 :
5149 : /* Can only handle a simple Var of subquery's query level */
5150 1844 : if (var && IsA(var, Var) &&
5151 744 : var->varlevelsup == 0)
5152 : {
5153 : /*
5154 : * OK, recurse into the subquery. Note that the original setting
5155 : * of vardata->isunique (which will surely be false) is left
5156 : * unchanged in this situation. That's what we want, since even
5157 : * if the underlying column is unique, the subquery may have
5158 : * joined to other tables in a way that creates duplicates.
5159 : */
5160 744 : examine_simple_variable(rel->subroot, var, vardata);
5161 : }
5162 : }
5163 : else
5164 : {
5165 : /*
5166 : * Otherwise, the Var comes from a FUNCTION, VALUES, or CTE RTE. (We
5167 : * won't see RTE_JOIN here because join alias Vars have already been
5168 : * flattened.) There's not much we can do with function outputs, but
5169 : * maybe someday try to be smarter about VALUES and/or CTEs.
5170 : */
5171 : }
5172 : }
5173 :
5174 : /*
5175 : * Check whether it is permitted to call func_oid passing some of the
5176 : * pg_statistic data in vardata. We allow this either if the user has SELECT
5177 : * privileges on the table or column underlying the pg_statistic data or if
5178 : * the function is marked leak-proof.
5179 : */
5180 : bool
5181 686424 : statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
5182 : {
5183 686424 : if (vardata->acl_ok)
5184 686024 : return true;
5185 :
5186 400 : if (!OidIsValid(func_oid))
5187 0 : return false;
5188 :
5189 400 : if (get_func_leakproof(func_oid))
5190 288 : return true;
5191 :
5192 112 : ereport(DEBUG2,
5193 : (errmsg_internal("not using statistics because function \"%s\" is not leak-proof",
5194 : get_func_name(func_oid))));
5195 112 : return false;
5196 : }
5197 :
5198 : /*
5199 : * get_variable_numdistinct
5200 : * Estimate the number of distinct values of a variable.
5201 : *
5202 : * vardata: results of examine_variable
5203 : * *isdefault: set to true if the result is a default rather than based on
5204 : * anything meaningful.
5205 : *
5206 : * NB: be careful to produce a positive integral result, since callers may
5207 : * compare the result to exact integer counts, or might divide by it.
5208 : */
5209 : double
5210 550206 : get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
5211 : {
5212 : double stadistinct;
5213 550206 : double stanullfrac = 0.0;
5214 : double ntuples;
5215 :
5216 550206 : *isdefault = false;
5217 :
5218 : /*
5219 : * Determine the stadistinct value to use. There are cases where we can
5220 : * get an estimate even without a pg_statistic entry, or can get a better
5221 : * value than is in pg_statistic. Grab stanullfrac too if we can find it
5222 : * (otherwise, assume no nulls, for lack of any better idea).
5223 : */
5224 550206 : if (HeapTupleIsValid(vardata->statsTuple))
5225 : {
5226 : /* Use the pg_statistic entry */
5227 : Form_pg_statistic stats;
5228 :
5229 358994 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
5230 358994 : stadistinct = stats->stadistinct;
5231 358994 : stanullfrac = stats->stanullfrac;
5232 : }
5233 191212 : else if (vardata->vartype == BOOLOID)
5234 : {
5235 : /*
5236 : * Special-case boolean columns: presumably, two distinct values.
5237 : *
5238 : * Are there any other datatypes we should wire in special estimates
5239 : * for?
5240 : */
5241 140 : stadistinct = 2.0;
5242 : }
5243 191072 : else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
5244 : {
5245 : /*
5246 : * If the Var represents a column of a VALUES RTE, assume it's unique.
5247 : * This could of course be very wrong, but it should tend to be true
5248 : * in well-written queries. We could consider examining the VALUES'
5249 : * contents to get some real statistics; but that only works if the
5250 : * entries are all constants, and it would be pretty expensive anyway.
5251 : */
5252 706 : stadistinct = -1.0; /* unique (and all non null) */
5253 : }
5254 : else
5255 : {
5256 : /*
5257 : * We don't keep statistics for system columns, but in some cases we
5258 : * can infer distinctness anyway.
5259 : */
5260 190366 : if (vardata->var && IsA(vardata->var, Var))
5261 : {
5262 367448 : switch (((Var *) vardata->var)->varattno)
5263 : {
5264 500 : case SelfItemPointerAttributeNumber:
5265 500 : stadistinct = -1.0; /* unique (and all non null) */
5266 500 : break;
5267 1966 : case TableOidAttributeNumber:
5268 1966 : stadistinct = 1.0; /* only 1 value */
5269 1966 : break;
5270 181258 : default:
5271 181258 : stadistinct = 0.0; /* means "unknown" */
5272 181258 : break;
5273 : }
5274 : }
5275 : else
5276 6642 : stadistinct = 0.0; /* means "unknown" */
5277 :
5278 : /*
5279 : * XXX consider using estimate_num_groups on expressions?
5280 : */
5281 : }
5282 :
5283 : /*
5284 : * If there is a unique index or DISTINCT clause for the variable, assume
5285 : * it is unique no matter what pg_statistic says; the statistics could be
5286 : * out of date, or we might have found a partial unique index that proves
5287 : * the var is unique for this query. However, we'd better still believe
5288 : * the null-fraction statistic.
5289 : */
5290 550206 : if (vardata->isunique)
5291 123552 : stadistinct = -1.0 * (1.0 - stanullfrac);
5292 :
5293 : /*
5294 : * If we had an absolute estimate, use that.
5295 : */
5296 550206 : if (stadistinct > 0.0)
5297 91538 : return clamp_row_est(stadistinct);
5298 :
5299 : /*
5300 : * Otherwise we need to get the relation size; punt if not available.
5301 : */
5302 458668 : if (vardata->rel == NULL)
5303 : {
5304 154 : *isdefault = true;
5305 154 : return DEFAULT_NUM_DISTINCT;
5306 : }
5307 458514 : ntuples = vardata->rel->tuples;
5308 458514 : if (ntuples <= 0.0)
5309 : {
5310 1906 : *isdefault = true;
5311 1906 : return DEFAULT_NUM_DISTINCT;
5312 : }
5313 :
5314 : /*
5315 : * If we had a relative estimate, use that.
5316 : */
5317 456608 : if (stadistinct < 0.0)
5318 297288 : return clamp_row_est(-stadistinct * ntuples);
5319 :
5320 : /*
5321 : * With no data, estimate ndistinct = ntuples if the table is small, else
5322 : * use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
5323 : * that the behavior isn't discontinuous.
5324 : */
5325 159320 : if (ntuples < DEFAULT_NUM_DISTINCT)
5326 55826 : return clamp_row_est(ntuples);
5327 :
5328 103494 : *isdefault = true;
5329 103494 : return DEFAULT_NUM_DISTINCT;
5330 : }
5331 :
5332 : /*
5333 : * get_variable_range
5334 : * Estimate the minimum and maximum value of the specified variable.
5335 : * If successful, store values in *min and *max, and return true.
5336 : * If no data available, return false.
5337 : *
5338 : * sortop is the "<" comparison operator to use. This should generally
5339 : * be "<" not ">", as only the former is likely to be found in pg_statistic.
5340 : */
5341 : static bool
5342 100358 : get_variable_range(PlannerInfo *root, VariableStatData *vardata, Oid sortop,
5343 : Datum *min, Datum *max)
5344 : {
5345 100358 : Datum tmin = 0;
5346 100358 : Datum tmax = 0;
5347 100358 : bool have_data = false;
5348 : int16 typLen;
5349 : bool typByVal;
5350 : Oid opfuncoid;
5351 : AttStatsSlot sslot;
5352 : int i;
5353 :
5354 : /*
5355 : * XXX It's very tempting to try to use the actual column min and max, if
5356 : * we can get them relatively-cheaply with an index probe. However, since
5357 : * this function is called many times during join planning, that could
5358 : * have unpleasant effects on planning speed. Need more investigation
5359 : * before enabling this.
5360 : */
5361 : #ifdef NOT_USED
5362 : if (get_actual_variable_range(root, vardata, sortop, min, max))
5363 : return true;
5364 : #endif
5365 :
5366 100358 : if (!HeapTupleIsValid(vardata->statsTuple))
5367 : {
5368 : /* no stats available, so default result */
5369 19548 : return false;
5370 : }
5371 :
5372 : /*
5373 : * If we can't apply the sortop to the stats data, just fail. In
5374 : * principle, if there's a histogram and no MCVs, we could return the
5375 : * histogram endpoints without ever applying the sortop ... but it's
5376 : * probably not worth trying, because whatever the caller wants to do with
5377 : * the endpoints would likely fail the security check too.
5378 : */
5379 80810 : if (!statistic_proc_security_check(vardata,
5380 80810 : (opfuncoid = get_opcode(sortop))))
5381 0 : return false;
5382 :
5383 80810 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
5384 :
5385 : /*
5386 : * If there is a histogram, grab the first and last values.
5387 : *
5388 : * If there is a histogram that is sorted with some other operator than
5389 : * the one we want, fail --- this suggests that there is data we can't
5390 : * use.
5391 : */
5392 80810 : if (get_attstatsslot(&sslot, vardata->statsTuple,
5393 : STATISTIC_KIND_HISTOGRAM, sortop,
5394 : ATTSTATSSLOT_VALUES))
5395 : {
5396 68022 : if (sslot.nvalues > 0)
5397 : {
5398 68022 : tmin = datumCopy(sslot.values[0], typByVal, typLen);
5399 68022 : tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
5400 68022 : have_data = true;
5401 : }
5402 68022 : free_attstatsslot(&sslot);
5403 : }
5404 12788 : else if (get_attstatsslot(&sslot, vardata->statsTuple,
5405 : STATISTIC_KIND_HISTOGRAM, InvalidOid,
5406 : 0))
5407 : {
5408 0 : free_attstatsslot(&sslot);
5409 0 : return false;
5410 : }
5411 :
5412 : /*
5413 : * If we have most-common-values info, look for extreme MCVs. This is
5414 : * needed even if we also have a histogram, since the histogram excludes
5415 : * the MCVs. However, usually the MCVs will not be the extreme values, so
5416 : * avoid unnecessary data copying.
5417 : */
5418 80810 : if (get_attstatsslot(&sslot, vardata->statsTuple,
5419 : STATISTIC_KIND_MCV, InvalidOid,
5420 : ATTSTATSSLOT_VALUES))
5421 : {
5422 38424 : bool tmin_is_mcv = false;
5423 38424 : bool tmax_is_mcv = false;
5424 : FmgrInfo opproc;
5425 :
5426 38424 : fmgr_info(opfuncoid, &opproc);
5427 :
5428 1174992 : for (i = 0; i < sslot.nvalues; i++)
5429 : {
5430 1136568 : if (!have_data)
5431 : {
5432 12304 : tmin = tmax = sslot.values[i];
5433 12304 : tmin_is_mcv = tmax_is_mcv = have_data = true;
5434 12304 : continue;
5435 : }
5436 1124264 : if (DatumGetBool(FunctionCall2Coll(&opproc,
5437 : sslot.stacoll,
5438 : sslot.values[i], tmin)))
5439 : {
5440 43410 : tmin = sslot.values[i];
5441 43410 : tmin_is_mcv = true;
5442 : }
5443 1124264 : if (DatumGetBool(FunctionCall2Coll(&opproc,
5444 : sslot.stacoll,
5445 : tmax, sslot.values[i])))
5446 : {
5447 40030 : tmax = sslot.values[i];
5448 40030 : tmax_is_mcv = true;
5449 : }
5450 : }
5451 38424 : if (tmin_is_mcv)
5452 32578 : tmin = datumCopy(tmin, typByVal, typLen);
5453 38424 : if (tmax_is_mcv)
5454 15356 : tmax = datumCopy(tmax, typByVal, typLen);
5455 38424 : free_attstatsslot(&sslot);
5456 : }
5457 :
5458 80810 : *min = tmin;
5459 80810 : *max = tmax;
5460 80810 : return have_data;
5461 : }
5462 :
5463 :
5464 : /*
5465 : * get_actual_variable_range
5466 : * Attempt to identify the current *actual* minimum and/or maximum
5467 : * of the specified variable, by looking for a suitable btree index
5468 : * and fetching its low and/or high values.
5469 : * If successful, store values in *min and *max, and return true.
5470 : * (Either pointer can be NULL if that endpoint isn't needed.)
5471 : * If no data available, return false.
5472 : *
5473 : * sortop is the "<" comparison operator to use.
5474 : */
5475 : static bool
5476 125624 : get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
5477 : Oid sortop,
5478 : Datum *min, Datum *max)
5479 : {
5480 125624 : bool have_data = false;
5481 125624 : RelOptInfo *rel = vardata->rel;
5482 : RangeTblEntry *rte;
5483 : ListCell *lc;
5484 :
5485 : /* No hope if no relation or it doesn't have indexes */
5486 125624 : if (rel == NULL || rel->indexlist == NIL)
5487 9014 : return false;
5488 : /* If it has indexes it must be a plain relation */
5489 116610 : rte = root->simple_rte_array[rel->relid];
5490 : Assert(rte->rtekind == RTE_RELATION);
5491 :
5492 : /* Search through the indexes to see if any match our problem */
5493 263178 : foreach(lc, rel->indexlist)
5494 : {
5495 221640 : IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
5496 : ScanDirection indexscandir;
5497 :
5498 : /* Ignore non-btree indexes */
5499 221640 : if (index->relam != BTREE_AM_OID)
5500 0 : continue;
5501 :
5502 : /*
5503 : * Ignore partial indexes --- we only want stats that cover the entire
5504 : * relation.
5505 : */
5506 221640 : if (index->indpred != NIL)
5507 132 : continue;
5508 :
5509 : /*
5510 : * The index list might include hypothetical indexes inserted by a
5511 : * get_relation_info hook --- don't try to access them.
5512 : */
5513 221508 : if (index->hypothetical)
5514 0 : continue;
5515 :
5516 : /*
5517 : * The first index column must match the desired variable and sort
5518 : * operator --- but we can use a descending-order index.
5519 : */
5520 221508 : if (!match_index_to_operand(vardata->var, 0, index))
5521 146436 : continue;
5522 75072 : switch (get_op_opfamily_strategy(sortop, index->sortopfamily[0]))
5523 : {
5524 75072 : case BTLessStrategyNumber:
5525 75072 : if (index->reverse_sort[0])
5526 0 : indexscandir = BackwardScanDirection;
5527 : else
5528 75072 : indexscandir = ForwardScanDirection;
5529 75072 : break;
5530 0 : case BTGreaterStrategyNumber:
5531 0 : if (index->reverse_sort[0])
5532 0 : indexscandir = ForwardScanDirection;
5533 : else
5534 0 : indexscandir = BackwardScanDirection;
5535 0 : break;
5536 0 : default:
5537 : /* index doesn't match the sortop */
5538 0 : continue;
5539 : }
5540 :
5541 : /*
5542 : * Found a suitable index to extract data from. Set up some data that
5543 : * can be used by both invocations of get_actual_variable_endpoint.
5544 : */
5545 : {
5546 : MemoryContext tmpcontext;
5547 : MemoryContext oldcontext;
5548 : Relation heapRel;
5549 : Relation indexRel;
5550 : TupleTableSlot *slot;
5551 : int16 typLen;
5552 : bool typByVal;
5553 : ScanKeyData scankeys[1];
5554 :
5555 : /* Make sure any cruft gets recycled when we're done */
5556 75072 : tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
5557 : "get_actual_variable_range workspace",
5558 : ALLOCSET_DEFAULT_SIZES);
5559 75072 : oldcontext = MemoryContextSwitchTo(tmpcontext);
5560 :
5561 : /*
5562 : * Open the table and index so we can read from them. We should
5563 : * already have some type of lock on each.
5564 : */
5565 75072 : heapRel = table_open(rte->relid, NoLock);
5566 75072 : indexRel = index_open(index->indexoid, NoLock);
5567 :
5568 : /* build some stuff needed for indexscan execution */
5569 75072 : slot = table_slot_create(heapRel, NULL);
5570 75072 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
5571 :
5572 : /* set up an IS NOT NULL scan key so that we ignore nulls */
5573 75072 : ScanKeyEntryInitialize(&scankeys[0],
5574 : SK_ISNULL | SK_SEARCHNOTNULL,
5575 : 1, /* index col to scan */
5576 : InvalidStrategy, /* no strategy */
5577 : InvalidOid, /* no strategy subtype */
5578 : InvalidOid, /* no collation */
5579 : InvalidOid, /* no reg proc for this */
5580 : (Datum) 0); /* constant */
5581 :
5582 : /* If min is requested ... */
5583 75072 : if (min)
5584 : {
5585 41638 : have_data = get_actual_variable_endpoint(heapRel,
5586 : indexRel,
5587 : indexscandir,
5588 : scankeys,
5589 : typLen,
5590 : typByVal,
5591 : slot,
5592 : oldcontext,
5593 : min);
5594 : }
5595 : else
5596 : {
5597 : /* If min not requested, assume index is nonempty */
5598 33434 : have_data = true;
5599 : }
5600 :
5601 : /* If max is requested, and we didn't find the index is empty */
5602 75072 : if (max && have_data)
5603 : {
5604 : /* scan in the opposite direction; all else is the same */
5605 67460 : have_data = get_actual_variable_endpoint(heapRel,
5606 : indexRel,
5607 33730 : -indexscandir,
5608 : scankeys,
5609 : typLen,
5610 : typByVal,
5611 : slot,
5612 : oldcontext,
5613 : max);
5614 : }
5615 :
5616 : /* Clean everything up */
5617 75072 : ExecDropSingleTupleTableSlot(slot);
5618 :
5619 75072 : index_close(indexRel, NoLock);
5620 75072 : table_close(heapRel, NoLock);
5621 :
5622 75072 : MemoryContextSwitchTo(oldcontext);
5623 75072 : MemoryContextDelete(tmpcontext);
5624 :
5625 : /* And we're done */
5626 75072 : break;
5627 : }
5628 : }
5629 :
5630 116610 : return have_data;
5631 : }
5632 :
5633 : /*
5634 : * Get one endpoint datum (min or max depending on indexscandir) from the
5635 : * specified index. Return true if successful, false if index is empty.
5636 : * On success, endpoint value is stored to *endpointDatum (and copied into
5637 : * outercontext).
5638 : *
5639 : * scankeys is a 1-element scankey array set up to reject nulls.
5640 : * typLen/typByVal describe the datatype of the index's first column.
5641 : * tableslot is a slot suitable to hold table tuples, in case we need
5642 : * to probe the heap.
5643 : * (We could compute these values locally, but that would mean computing them
5644 : * twice when get_actual_variable_range needs both the min and the max.)
5645 : */
5646 : static bool
5647 75368 : get_actual_variable_endpoint(Relation heapRel,
5648 : Relation indexRel,
5649 : ScanDirection indexscandir,
5650 : ScanKey scankeys,
5651 : int16 typLen,
5652 : bool typByVal,
5653 : TupleTableSlot *tableslot,
5654 : MemoryContext outercontext,
5655 : Datum *endpointDatum)
5656 : {
5657 75368 : bool have_data = false;
5658 : SnapshotData SnapshotNonVacuumable;
5659 : IndexScanDesc index_scan;
5660 75368 : Buffer vmbuffer = InvalidBuffer;
5661 : ItemPointer tid;
5662 : Datum values[INDEX_MAX_KEYS];
5663 : bool isnull[INDEX_MAX_KEYS];
5664 : MemoryContext oldcontext;
5665 :
5666 : /*
5667 : * We use the index-only-scan machinery for this. With mostly-static
5668 : * tables that's a win because it avoids a heap visit. It's also a win
5669 : * for dynamic data, but the reason is less obvious; read on for details.
5670 : *
5671 : * In principle, we should scan the index with our current active
5672 : * snapshot, which is the best approximation we've got to what the query
5673 : * will see when executed. But that won't be exact if a new snap is taken
5674 : * before running the query, and it can be very expensive if a lot of
5675 : * recently-dead or uncommitted rows exist at the beginning or end of the
5676 : * index (because we'll laboriously fetch each one and reject it).
5677 : * Instead, we use SnapshotNonVacuumable. That will accept recently-dead
5678 : * and uncommitted rows as well as normal visible rows. On the other
5679 : * hand, it will reject known-dead rows, and thus not give a bogus answer
5680 : * when the extreme value has been deleted (unless the deletion was quite
5681 : * recent); that case motivates not using SnapshotAny here.
5682 : *
5683 : * A crucial point here is that SnapshotNonVacuumable, with
5684 : * RecentGlobalXmin as horizon, yields the inverse of the condition that
5685 : * the indexscan will use to decide that index entries are killable (see
5686 : * heap_hot_search_buffer()). Therefore, if the snapshot rejects a tuple
5687 : * (or more precisely, all tuples of a HOT chain) and we have to continue
5688 : * scanning past it, we know that the indexscan will mark that index entry
5689 : * killed. That means that the next get_actual_variable_endpoint() call
5690 : * will not have to re-consider that index entry. In this way we avoid
5691 : * repetitive work when this function is used a lot during planning.
5692 : *
5693 : * But using SnapshotNonVacuumable creates a hazard of its own. In a
5694 : * recently-created index, some index entries may point at "broken" HOT
5695 : * chains in which not all the tuple versions contain data matching the
5696 : * index entry. The live tuple version(s) certainly do match the index,
5697 : * but SnapshotNonVacuumable can accept recently-dead tuple versions that
5698 : * don't match. Hence, if we took data from the selected heap tuple, we
5699 : * might get a bogus answer that's not close to the index extremal value,
5700 : * or could even be NULL. We avoid this hazard because we take the data
5701 : * from the index entry not the heap.
5702 : */
5703 75368 : InitNonVacuumableSnapshot(SnapshotNonVacuumable, RecentGlobalXmin);
5704 :
5705 75368 : index_scan = index_beginscan(heapRel, indexRel,
5706 : &SnapshotNonVacuumable,
5707 : 1, 0);
5708 : /* Set it up for index-only scan */
5709 75368 : index_scan->xs_want_itup = true;
5710 75368 : index_rescan(index_scan, scankeys, 1, NULL, 0);
5711 :
5712 : /* Fetch first/next tuple in specified direction */
5713 78936 : while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
5714 : {
5715 78936 : if (!VM_ALL_VISIBLE(heapRel,
5716 : ItemPointerGetBlockNumber(tid),
5717 : &vmbuffer))
5718 : {
5719 : /* Rats, we have to visit the heap to check visibility */
5720 46232 : if (!index_fetch_heap(index_scan, tableslot))
5721 3568 : continue; /* no visible tuple, try next index entry */
5722 :
5723 : /* We don't actually need the heap tuple for anything */
5724 42664 : ExecClearTuple(tableslot);
5725 :
5726 : /*
5727 : * We don't care whether there's more than one visible tuple in
5728 : * the HOT chain; if any are visible, that's good enough.
5729 : */
5730 : }
5731 :
5732 : /*
5733 : * We expect that btree will return data in IndexTuple not HeapTuple
5734 : * format. It's not lossy either.
5735 : */
5736 75368 : if (!index_scan->xs_itup)
5737 0 : elog(ERROR, "no data returned for index-only scan");
5738 75368 : if (index_scan->xs_recheck)
5739 0 : elog(ERROR, "unexpected recheck indication from btree");
5740 :
5741 : /* OK to deconstruct the index tuple */
5742 75368 : index_deform_tuple(index_scan->xs_itup,
5743 : index_scan->xs_itupdesc,
5744 : values, isnull);
5745 :
5746 : /* Shouldn't have got a null, but be careful */
5747 75368 : if (isnull[0])
5748 0 : elog(ERROR, "found unexpected null value in index \"%s\"",
5749 : RelationGetRelationName(indexRel));
5750 :
5751 : /* Copy the index column value out to caller's context */
5752 75368 : oldcontext = MemoryContextSwitchTo(outercontext);
5753 75368 : *endpointDatum = datumCopy(values[0], typByVal, typLen);
5754 75368 : MemoryContextSwitchTo(oldcontext);
5755 75368 : have_data = true;
5756 75368 : break;
5757 : }
5758 :
5759 75368 : if (vmbuffer != InvalidBuffer)
5760 67214 : ReleaseBuffer(vmbuffer);
5761 75368 : index_endscan(index_scan);
5762 :
5763 75368 : return have_data;
5764 : }
5765 :
5766 : /*
5767 : * find_join_input_rel
5768 : * Look up the input relation for a join.
5769 : *
5770 : * We assume that the input relation's RelOptInfo must have been constructed
5771 : * already.
5772 : */
5773 : static RelOptInfo *
5774 9950 : find_join_input_rel(PlannerInfo *root, Relids relids)
5775 : {
5776 9950 : RelOptInfo *rel = NULL;
5777 :
5778 9950 : switch (bms_membership(relids))
5779 : {
5780 0 : case BMS_EMPTY_SET:
5781 : /* should not happen */
5782 0 : break;
5783 9794 : case BMS_SINGLETON:
5784 9794 : rel = find_base_rel(root, bms_singleton_member(relids));
5785 9794 : break;
5786 156 : case BMS_MULTIPLE:
5787 156 : rel = find_join_rel(root, relids);
5788 156 : break;
5789 : }
5790 :
5791 9950 : if (rel == NULL)
5792 0 : elog(ERROR, "could not find RelOptInfo for given relids");
5793 :
5794 9950 : return rel;
5795 : }
5796 :
5797 :
5798 : /*-------------------------------------------------------------------------
5799 : *
5800 : * Index cost estimation functions
5801 : *
5802 : *-------------------------------------------------------------------------
5803 : */
5804 :
5805 : /*
5806 : * Extract the actual indexquals (as RestrictInfos) from an IndexClause list
5807 : */
5808 : List *
5809 408196 : get_quals_from_indexclauses(List *indexclauses)
5810 : {
5811 408196 : List *result = NIL;
5812 : ListCell *lc;
5813 :
5814 746726 : foreach(lc, indexclauses)
5815 : {
5816 338530 : IndexClause *iclause = lfirst_node(IndexClause, lc);
5817 : ListCell *lc2;
5818 :
5819 678864 : foreach(lc2, iclause->indexquals)
5820 : {
5821 340334 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
5822 :
5823 340334 : result = lappend(result, rinfo);
5824 : }
5825 : }
5826 408196 : return result;
5827 : }
5828 :
5829 : /*
5830 : * Compute the total evaluation cost of the comparison operands in a list
5831 : * of index qual expressions. Since we know these will be evaluated just
5832 : * once per scan, there's no need to distinguish startup from per-row cost.
5833 : *
5834 : * This can be used either on the result of get_quals_from_indexclauses(),
5835 : * or directly on an indexorderbys list. In both cases, we expect that the
5836 : * index key expression is on the left side of binary clauses.
5837 : */
5838 : Cost
5839 811050 : index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
5840 : {
5841 811050 : Cost qual_arg_cost = 0;
5842 : ListCell *lc;
5843 :
5844 1151696 : foreach(lc, indexquals)
5845 : {
5846 340646 : Expr *clause = (Expr *) lfirst(lc);
5847 : Node *other_operand;
5848 : QualCost index_qual_cost;
5849 :
5850 : /*
5851 : * Index quals will have RestrictInfos, indexorderbys won't. Look
5852 : * through RestrictInfo if present.
5853 : */
5854 340646 : if (IsA(clause, RestrictInfo))
5855 340326 : clause = ((RestrictInfo *) clause)->clause;
5856 :
5857 340646 : if (IsA(clause, OpExpr))
5858 : {
5859 329550 : OpExpr *op = (OpExpr *) clause;
5860 :
5861 329550 : other_operand = (Node *) lsecond(op->args);
5862 : }
5863 11096 : else if (IsA(clause, RowCompareExpr))
5864 : {
5865 88 : RowCompareExpr *rc = (RowCompareExpr *) clause;
5866 :
5867 88 : other_operand = (Node *) rc->rargs;
5868 : }
5869 11008 : else if (IsA(clause, ScalarArrayOpExpr))
5870 : {
5871 9110 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
5872 :
5873 9110 : other_operand = (Node *) lsecond(saop->args);
5874 : }
5875 1898 : else if (IsA(clause, NullTest))
5876 : {
5877 1898 : other_operand = NULL;
5878 : }
5879 : else
5880 : {
5881 0 : elog(ERROR, "unsupported indexqual type: %d",
5882 : (int) nodeTag(clause));
5883 : other_operand = NULL; /* keep compiler quiet */
5884 : }
5885 :
5886 340646 : cost_qual_eval_node(&index_qual_cost, other_operand, root);
5887 340646 : qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
5888 : }
5889 811050 : return qual_arg_cost;
5890 : }
5891 :
5892 : void
5893 402862 : genericcostestimate(PlannerInfo *root,
5894 : IndexPath *path,
5895 : double loop_count,
5896 : GenericCosts *costs)
5897 : {
5898 402862 : IndexOptInfo *index = path->indexinfo;
5899 402862 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
5900 402862 : List *indexOrderBys = path->indexorderbys;
5901 : Cost indexStartupCost;
5902 : Cost indexTotalCost;
5903 : Selectivity indexSelectivity;
5904 : double indexCorrelation;
5905 : double numIndexPages;
5906 : double numIndexTuples;
5907 : double spc_random_page_cost;
5908 : double num_sa_scans;
5909 : double num_outer_scans;
5910 : double num_scans;
5911 : double qual_op_cost;
5912 : double qual_arg_cost;
5913 : List *selectivityQuals;
5914 : ListCell *l;
5915 :
5916 : /*
5917 : * If the index is partial, AND the index predicate with the explicitly
5918 : * given indexquals to produce a more accurate idea of the index
5919 : * selectivity.
5920 : */
5921 402862 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
5922 :
5923 : /*
5924 : * Check for ScalarArrayOpExpr index quals, and estimate the number of
5925 : * index scans that will be performed.
5926 : */
5927 402862 : num_sa_scans = 1;
5928 737722 : foreach(l, indexQuals)
5929 : {
5930 334860 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
5931 :
5932 334860 : if (IsA(rinfo->clause, ScalarArrayOpExpr))
5933 : {
5934 9106 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
5935 9106 : int alength = estimate_array_length(lsecond(saop->args));
5936 :
5937 9106 : if (alength > 1)
5938 9042 : num_sa_scans *= alength;
5939 : }
5940 : }
5941 :
5942 : /* Estimate the fraction of main-table tuples that will be visited */
5943 402862 : indexSelectivity = clauselist_selectivity(root, selectivityQuals,
5944 402862 : index->rel->relid,
5945 : JOIN_INNER,
5946 : NULL);
5947 :
5948 : /*
5949 : * If caller didn't give us an estimate, estimate the number of index
5950 : * tuples that will be visited. We do it in this rather peculiar-looking
5951 : * way in order to get the right answer for partial indexes.
5952 : */
5953 402862 : numIndexTuples = costs->numIndexTuples;
5954 402862 : if (numIndexTuples <= 0.0)
5955 : {
5956 15588 : numIndexTuples = indexSelectivity * index->rel->tuples;
5957 :
5958 : /*
5959 : * The above calculation counts all the tuples visited across all
5960 : * scans induced by ScalarArrayOpExpr nodes. We want to consider the
5961 : * average per-indexscan number, so adjust. This is a handy place to
5962 : * round to integer, too. (If caller supplied tuple estimate, it's
5963 : * responsible for handling these considerations.)
5964 : */
5965 15588 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
5966 : }
5967 :
5968 : /*
5969 : * We can bound the number of tuples by the index size in any case. Also,
5970 : * always estimate at least one tuple is touched, even when
5971 : * indexSelectivity estimate is tiny.
5972 : */
5973 402862 : if (numIndexTuples > index->tuples)
5974 234 : numIndexTuples = index->tuples;
5975 402862 : if (numIndexTuples < 1.0)
5976 12594 : numIndexTuples = 1.0;
5977 :
5978 : /*
5979 : * Estimate the number of index pages that will be retrieved.
5980 : *
5981 : * We use the simplistic method of taking a pro-rata fraction of the total
5982 : * number of index pages. In effect, this counts only leaf pages and not
5983 : * any overhead such as index metapage or upper tree levels.
5984 : *
5985 : * In practice access to upper index levels is often nearly free because
5986 : * those tend to stay in cache under load; moreover, the cost involved is
5987 : * highly dependent on index type. We therefore ignore such costs here
5988 : * and leave it to the caller to add a suitable charge if needed.
5989 : */
5990 402862 : if (index->pages > 1 && index->tuples > 1)
5991 381718 : numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
5992 : else
5993 21144 : numIndexPages = 1.0;
5994 :
5995 : /* fetch estimated page cost for tablespace containing index */
5996 402862 : get_tablespace_page_costs(index->reltablespace,
5997 : &spc_random_page_cost,
5998 : NULL);
5999 :
6000 : /*
6001 : * Now compute the disk access costs.
6002 : *
6003 : * The above calculations are all per-index-scan. However, if we are in a
6004 : * nestloop inner scan, we can expect the scan to be repeated (with
6005 : * different search keys) for each row of the outer relation. Likewise,
6006 : * ScalarArrayOpExpr quals result in multiple index scans. This creates
6007 : * the potential for cache effects to reduce the number of disk page
6008 : * fetches needed. We want to estimate the average per-scan I/O cost in
6009 : * the presence of caching.
6010 : *
6011 : * We use the Mackert-Lohman formula (see costsize.c for details) to
6012 : * estimate the total number of page fetches that occur. While this
6013 : * wasn't what it was designed for, it seems a reasonable model anyway.
6014 : * Note that we are counting pages not tuples anymore, so we take N = T =
6015 : * index size, as if there were one "tuple" per page.
6016 : */
6017 402862 : num_outer_scans = loop_count;
6018 402862 : num_scans = num_sa_scans * num_outer_scans;
6019 :
6020 402862 : if (num_scans > 1)
6021 : {
6022 : double pages_fetched;
6023 :
6024 : /* total page fetches ignoring cache effects */
6025 53690 : pages_fetched = numIndexPages * num_scans;
6026 :
6027 : /* use Mackert and Lohman formula to adjust for cache effects */
6028 53690 : pages_fetched = index_pages_fetched(pages_fetched,
6029 : index->pages,
6030 53690 : (double) index->pages,
6031 : root);
6032 :
6033 : /*
6034 : * Now compute the total disk access cost, and then report a pro-rated
6035 : * share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
6036 : * since that's internal to the indexscan.)
6037 : */
6038 53690 : indexTotalCost = (pages_fetched * spc_random_page_cost)
6039 : / num_outer_scans;
6040 : }
6041 : else
6042 : {
6043 : /*
6044 : * For a single index scan, we just charge spc_random_page_cost per
6045 : * page touched.
6046 : */
6047 349172 : indexTotalCost = numIndexPages * spc_random_page_cost;
6048 : }
6049 :
6050 : /*
6051 : * CPU cost: any complex expressions in the indexquals will need to be
6052 : * evaluated once at the start of the scan to reduce them to runtime keys
6053 : * to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
6054 : * CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
6055 : * indexqual operator. Because we have numIndexTuples as a per-scan
6056 : * number, we have to multiply by num_sa_scans to get the correct result
6057 : * for ScalarArrayOpExpr cases. Similarly add in costs for any index
6058 : * ORDER BY expressions.
6059 : *
6060 : * Note: this neglects the possible costs of rechecking lossy operators.
6061 : * Detecting that that might be needed seems more expensive than it's
6062 : * worth, though, considering all the other inaccuracies here ...
6063 : */
6064 402862 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
6065 402862 : index_other_operands_eval_cost(root, indexOrderBys);
6066 402862 : qual_op_cost = cpu_operator_cost *
6067 402862 : (list_length(indexQuals) + list_length(indexOrderBys));
6068 :
6069 402862 : indexStartupCost = qual_arg_cost;
6070 402862 : indexTotalCost += qual_arg_cost;
6071 402862 : indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
6072 :
6073 : /*
6074 : * Generic assumption about index correlation: there isn't any.
6075 : */
6076 402862 : indexCorrelation = 0.0;
6077 :
6078 : /*
6079 : * Return everything to caller.
6080 : */
6081 402862 : costs->indexStartupCost = indexStartupCost;
6082 402862 : costs->indexTotalCost = indexTotalCost;
6083 402862 : costs->indexSelectivity = indexSelectivity;
6084 402862 : costs->indexCorrelation = indexCorrelation;
6085 402862 : costs->numIndexPages = numIndexPages;
6086 402862 : costs->numIndexTuples = numIndexTuples;
6087 402862 : costs->spc_random_page_cost = spc_random_page_cost;
6088 402862 : costs->num_sa_scans = num_sa_scans;
6089 402862 : }
6090 :
6091 : /*
6092 : * If the index is partial, add its predicate to the given qual list.
6093 : *
6094 : * ANDing the index predicate with the explicitly given indexquals produces
6095 : * a more accurate idea of the index's selectivity. However, we need to be
6096 : * careful not to insert redundant clauses, because clauselist_selectivity()
6097 : * is easily fooled into computing a too-low selectivity estimate. Our
6098 : * approach is to add only the predicate clause(s) that cannot be proven to
6099 : * be implied by the given indexquals. This successfully handles cases such
6100 : * as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
6101 : * There are many other cases where we won't detect redundancy, leading to a
6102 : * too-low selectivity estimate, which will bias the system in favor of using
6103 : * partial indexes where possible. That is not necessarily bad though.
6104 : *
6105 : * Note that indexQuals contains RestrictInfo nodes while the indpred
6106 : * does not, so the output list will be mixed. This is OK for both
6107 : * predicate_implied_by() and clauselist_selectivity(), but might be
6108 : * problematic if the result were passed to other things.
6109 : */
6110 : List *
6111 687470 : add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
6112 : {
6113 687470 : List *predExtraQuals = NIL;
6114 : ListCell *lc;
6115 :
6116 687470 : if (index->indpred == NIL)
6117 686040 : return indexQuals;
6118 :
6119 2868 : foreach(lc, index->indpred)
6120 : {
6121 1438 : Node *predQual = (Node *) lfirst(lc);
6122 1438 : List *oneQual = list_make1(predQual);
6123 :
6124 1438 : if (!predicate_implied_by(oneQual, indexQuals, false))
6125 1288 : predExtraQuals = list_concat(predExtraQuals, oneQual);
6126 : }
6127 1430 : return list_concat(predExtraQuals, indexQuals);
6128 : }
6129 :
6130 :
6131 : void
6132 399448 : btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6133 : Cost *indexStartupCost, Cost *indexTotalCost,
6134 : Selectivity *indexSelectivity, double *indexCorrelation,
6135 : double *indexPages)
6136 : {
6137 399448 : IndexOptInfo *index = path->indexinfo;
6138 : GenericCosts costs;
6139 : Oid relid;
6140 : AttrNumber colnum;
6141 : VariableStatData vardata;
6142 : double numIndexTuples;
6143 : Cost descentCost;
6144 : List *indexBoundQuals;
6145 : int indexcol;
6146 : bool eqQualHere;
6147 : bool found_saop;
6148 : bool found_is_null_op;
6149 : double num_sa_scans;
6150 : ListCell *lc;
6151 :
6152 : /*
6153 : * For a btree scan, only leading '=' quals plus inequality quals for the
6154 : * immediately next attribute contribute to index selectivity (these are
6155 : * the "boundary quals" that determine the starting and stopping points of
6156 : * the index scan). Additional quals can suppress visits to the heap, so
6157 : * it's OK to count them in indexSelectivity, but they should not count
6158 : * for estimating numIndexTuples. So we must examine the given indexquals
6159 : * to find out which ones count as boundary quals. We rely on the
6160 : * knowledge that they are given in index column order.
6161 : *
6162 : * For a RowCompareExpr, we consider only the first column, just as
6163 : * rowcomparesel() does.
6164 : *
6165 : * If there's a ScalarArrayOpExpr in the quals, we'll actually perform N
6166 : * index scans not one, but the ScalarArrayOpExpr's operator can be
6167 : * considered to act the same as it normally does.
6168 : */
6169 399448 : indexBoundQuals = NIL;
6170 399448 : indexcol = 0;
6171 399448 : eqQualHere = false;
6172 399448 : found_saop = false;
6173 399448 : found_is_null_op = false;
6174 399448 : num_sa_scans = 1;
6175 716090 : foreach(lc, path->indexclauses)
6176 : {
6177 326536 : IndexClause *iclause = lfirst_node(IndexClause, lc);
6178 : ListCell *lc2;
6179 :
6180 326536 : if (indexcol != iclause->indexcol)
6181 : {
6182 : /* Beginning of a new column's quals */
6183 53100 : if (!eqQualHere)
6184 9286 : break; /* done if no '=' qual for indexcol */
6185 43814 : eqQualHere = false;
6186 43814 : indexcol++;
6187 43814 : if (indexcol != iclause->indexcol)
6188 608 : break; /* no quals at all for indexcol */
6189 : }
6190 :
6191 : /* Examine each indexqual associated with this index clause */
6192 635040 : foreach(lc2, iclause->indexquals)
6193 : {
6194 318398 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
6195 318398 : Expr *clause = rinfo->clause;
6196 318398 : Oid clause_op = InvalidOid;
6197 : int op_strategy;
6198 :
6199 318398 : if (IsA(clause, OpExpr))
6200 : {
6201 307762 : OpExpr *op = (OpExpr *) clause;
6202 :
6203 307762 : clause_op = op->opno;
6204 : }
6205 10636 : else if (IsA(clause, RowCompareExpr))
6206 : {
6207 88 : RowCompareExpr *rc = (RowCompareExpr *) clause;
6208 :
6209 88 : clause_op = linitial_oid(rc->opnos);
6210 : }
6211 10548 : else if (IsA(clause, ScalarArrayOpExpr))
6212 : {
6213 8974 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
6214 8974 : Node *other_operand = (Node *) lsecond(saop->args);
6215 8974 : int alength = estimate_array_length(other_operand);
6216 :
6217 8974 : clause_op = saop->opno;
6218 8974 : found_saop = true;
6219 : /* count number of SA scans induced by indexBoundQuals only */
6220 8974 : if (alength > 1)
6221 8910 : num_sa_scans *= alength;
6222 : }
6223 1574 : else if (IsA(clause, NullTest))
6224 : {
6225 1574 : NullTest *nt = (NullTest *) clause;
6226 :
6227 1574 : if (nt->nulltesttype == IS_NULL)
6228 : {
6229 88 : found_is_null_op = true;
6230 : /* IS NULL is like = for selectivity purposes */
6231 88 : eqQualHere = true;
6232 : }
6233 : }
6234 : else
6235 0 : elog(ERROR, "unsupported indexqual type: %d",
6236 : (int) nodeTag(clause));
6237 :
6238 : /* check for equality operator */
6239 318398 : if (OidIsValid(clause_op))
6240 : {
6241 316824 : op_strategy = get_op_opfamily_strategy(clause_op,
6242 316824 : index->opfamily[indexcol]);
6243 : Assert(op_strategy != 0); /* not a member of opfamily?? */
6244 316824 : if (op_strategy == BTEqualStrategyNumber)
6245 293890 : eqQualHere = true;
6246 : }
6247 :
6248 318398 : indexBoundQuals = lappend(indexBoundQuals, rinfo);
6249 : }
6250 : }
6251 :
6252 : /*
6253 : * If index is unique and we found an '=' clause for each column, we can
6254 : * just assume numIndexTuples = 1 and skip the expensive
6255 : * clauselist_selectivity calculations. However, a ScalarArrayOp or
6256 : * NullTest invalidates that theory, even though it sets eqQualHere.
6257 : */
6258 399448 : if (index->unique &&
6259 343070 : indexcol == index->nkeycolumns - 1 &&
6260 123436 : eqQualHere &&
6261 123436 : !found_saop &&
6262 116226 : !found_is_null_op)
6263 116186 : numIndexTuples = 1.0;
6264 : else
6265 : {
6266 : List *selectivityQuals;
6267 : Selectivity btreeSelectivity;
6268 :
6269 : /*
6270 : * If the index is partial, AND the index predicate with the
6271 : * index-bound quals to produce a more accurate idea of the number of
6272 : * rows covered by the bound conditions.
6273 : */
6274 283262 : selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
6275 :
6276 283262 : btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
6277 283262 : index->rel->relid,
6278 : JOIN_INNER,
6279 : NULL);
6280 283262 : numIndexTuples = btreeSelectivity * index->rel->tuples;
6281 :
6282 : /*
6283 : * As in genericcostestimate(), we have to adjust for any
6284 : * ScalarArrayOpExpr quals included in indexBoundQuals, and then round
6285 : * to integer.
6286 : */
6287 283262 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
6288 : }
6289 :
6290 : /*
6291 : * Now do generic index cost estimation.
6292 : */
6293 3595032 : MemSet(&costs, 0, sizeof(costs));
6294 399448 : costs.numIndexTuples = numIndexTuples;
6295 :
6296 399448 : genericcostestimate(root, path, loop_count, &costs);
6297 :
6298 : /*
6299 : * Add a CPU-cost component to represent the costs of initial btree
6300 : * descent. We don't charge any I/O cost for touching upper btree levels,
6301 : * since they tend to stay in cache, but we still have to do about log2(N)
6302 : * comparisons to descend a btree of N leaf tuples. We charge one
6303 : * cpu_operator_cost per comparison.
6304 : *
6305 : * If there are ScalarArrayOpExprs, charge this once per SA scan. The
6306 : * ones after the first one are not startup cost so far as the overall
6307 : * plan is concerned, so add them only to "total" cost.
6308 : */
6309 399448 : if (index->tuples > 1) /* avoid computing log(0) */
6310 : {
6311 397252 : descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
6312 397252 : costs.indexStartupCost += descentCost;
6313 397252 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6314 : }
6315 :
6316 : /*
6317 : * Even though we're not charging I/O cost for touching upper btree pages,
6318 : * it's still reasonable to charge some CPU cost per page descended
6319 : * through. Moreover, if we had no such charge at all, bloated indexes
6320 : * would appear to have the same search cost as unbloated ones, at least
6321 : * in cases where only a single leaf page is expected to be visited. This
6322 : * cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
6323 : * touched. The number of such pages is btree tree height plus one (ie,
6324 : * we charge for the leaf page too). As above, charge once per SA scan.
6325 : */
6326 399448 : descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
6327 399448 : costs.indexStartupCost += descentCost;
6328 399448 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6329 :
6330 : /*
6331 : * If we can get an estimate of the first column's ordering correlation C
6332 : * from pg_statistic, estimate the index correlation as C for a
6333 : * single-column index, or C * 0.75 for multiple columns. (The idea here
6334 : * is that multiple columns dilute the importance of the first column's
6335 : * ordering, but don't negate it entirely. Before 8.0 we divided the
6336 : * correlation by the number of columns, but that seems too strong.)
6337 : */
6338 2796136 : MemSet(&vardata, 0, sizeof(vardata));
6339 :
6340 399448 : if (index->indexkeys[0] != 0)
6341 : {
6342 : /* Simple variable --- look to stats for the underlying table */
6343 398140 : RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
6344 :
6345 : Assert(rte->rtekind == RTE_RELATION);
6346 398140 : relid = rte->relid;
6347 : Assert(relid != InvalidOid);
6348 398140 : colnum = index->indexkeys[0];
6349 :
6350 398140 : if (get_relation_stats_hook &&
6351 0 : (*get_relation_stats_hook) (root, rte, colnum, &vardata))
6352 : {
6353 : /*
6354 : * The hook took control of acquiring a stats tuple. If it did
6355 : * supply a tuple, it'd better have supplied a freefunc.
6356 : */
6357 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
6358 0 : !vardata.freefunc)
6359 0 : elog(ERROR, "no function provided to release variable stats with");
6360 : }
6361 : else
6362 : {
6363 398140 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
6364 : ObjectIdGetDatum(relid),
6365 : Int16GetDatum(colnum),
6366 398140 : BoolGetDatum(rte->inh));
6367 398140 : vardata.freefunc = ReleaseSysCache;
6368 : }
6369 : }
6370 : else
6371 : {
6372 : /* Expression --- maybe there are stats for the index itself */
6373 1308 : relid = index->indexoid;
6374 1308 : colnum = 1;
6375 :
6376 1308 : if (get_index_stats_hook &&
6377 0 : (*get_index_stats_hook) (root, relid, colnum, &vardata))
6378 : {
6379 : /*
6380 : * The hook took control of acquiring a stats tuple. If it did
6381 : * supply a tuple, it'd better have supplied a freefunc.
6382 : */
6383 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
6384 0 : !vardata.freefunc)
6385 0 : elog(ERROR, "no function provided to release variable stats with");
6386 : }
6387 : else
6388 : {
6389 1308 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
6390 : ObjectIdGetDatum(relid),
6391 : Int16GetDatum(colnum),
6392 : BoolGetDatum(false));
6393 1308 : vardata.freefunc = ReleaseSysCache;
6394 : }
6395 : }
6396 :
6397 399448 : if (HeapTupleIsValid(vardata.statsTuple))
6398 : {
6399 : Oid sortop;
6400 : AttStatsSlot sslot;
6401 :
6402 606156 : sortop = get_opfamily_member(index->opfamily[0],
6403 303078 : index->opcintype[0],
6404 303078 : index->opcintype[0],
6405 : BTLessStrategyNumber);
6406 606156 : if (OidIsValid(sortop) &&
6407 303078 : get_attstatsslot(&sslot, vardata.statsTuple,
6408 : STATISTIC_KIND_CORRELATION, sortop,
6409 : ATTSTATSSLOT_NUMBERS))
6410 : {
6411 : double varCorrelation;
6412 :
6413 : Assert(sslot.nnumbers == 1);
6414 301132 : varCorrelation = sslot.numbers[0];
6415 :
6416 301132 : if (index->reverse_sort[0])
6417 0 : varCorrelation = -varCorrelation;
6418 :
6419 301132 : if (index->nkeycolumns > 1)
6420 123854 : costs.indexCorrelation = varCorrelation * 0.75;
6421 : else
6422 177278 : costs.indexCorrelation = varCorrelation;
6423 :
6424 301132 : free_attstatsslot(&sslot);
6425 : }
6426 : }
6427 :
6428 399448 : ReleaseVariableStats(vardata);
6429 :
6430 399448 : *indexStartupCost = costs.indexStartupCost;
6431 399448 : *indexTotalCost = costs.indexTotalCost;
6432 399448 : *indexSelectivity = costs.indexSelectivity;
6433 399448 : *indexCorrelation = costs.indexCorrelation;
6434 399448 : *indexPages = costs.numIndexPages;
6435 399448 : }
6436 :
6437 : void
6438 294 : hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6439 : Cost *indexStartupCost, Cost *indexTotalCost,
6440 : Selectivity *indexSelectivity, double *indexCorrelation,
6441 : double *indexPages)
6442 : {
6443 : GenericCosts costs;
6444 :
6445 2646 : MemSet(&costs, 0, sizeof(costs));
6446 :
6447 294 : genericcostestimate(root, path, loop_count, &costs);
6448 :
6449 : /*
6450 : * A hash index has no descent costs as such, since the index AM can go
6451 : * directly to the target bucket after computing the hash value. There
6452 : * are a couple of other hash-specific costs that we could conceivably add
6453 : * here, though:
6454 : *
6455 : * Ideally we'd charge spc_random_page_cost for each page in the target
6456 : * bucket, not just the numIndexPages pages that genericcostestimate
6457 : * thought we'd visit. However in most cases we don't know which bucket
6458 : * that will be. There's no point in considering the average bucket size
6459 : * because the hash AM makes sure that's always one page.
6460 : *
6461 : * Likewise, we could consider charging some CPU for each index tuple in
6462 : * the bucket, if we knew how many there were. But the per-tuple cost is
6463 : * just a hash value comparison, not a general datatype-dependent
6464 : * comparison, so any such charge ought to be quite a bit less than
6465 : * cpu_operator_cost; which makes it probably not worth worrying about.
6466 : *
6467 : * A bigger issue is that chance hash-value collisions will result in
6468 : * wasted probes into the heap. We don't currently attempt to model this
6469 : * cost on the grounds that it's rare, but maybe it's not rare enough.
6470 : * (Any fix for this ought to consider the generic lossy-operator problem,
6471 : * though; it's not entirely hash-specific.)
6472 : */
6473 :
6474 294 : *indexStartupCost = costs.indexStartupCost;
6475 294 : *indexTotalCost = costs.indexTotalCost;
6476 294 : *indexSelectivity = costs.indexSelectivity;
6477 294 : *indexCorrelation = costs.indexCorrelation;
6478 294 : *indexPages = costs.numIndexPages;
6479 294 : }
6480 :
6481 : void
6482 1932 : gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6483 : Cost *indexStartupCost, Cost *indexTotalCost,
6484 : Selectivity *indexSelectivity, double *indexCorrelation,
6485 : double *indexPages)
6486 : {
6487 1932 : IndexOptInfo *index = path->indexinfo;
6488 : GenericCosts costs;
6489 : Cost descentCost;
6490 :
6491 17388 : MemSet(&costs, 0, sizeof(costs));
6492 :
6493 1932 : genericcostestimate(root, path, loop_count, &costs);
6494 :
6495 : /*
6496 : * We model index descent costs similarly to those for btree, but to do
6497 : * that we first need an idea of the tree height. We somewhat arbitrarily
6498 : * assume that the fanout is 100, meaning the tree height is at most
6499 : * log100(index->pages).
6500 : *
6501 : * Although this computation isn't really expensive enough to require
6502 : * caching, we might as well use index->tree_height to cache it.
6503 : */
6504 1932 : if (index->tree_height < 0) /* unknown? */
6505 : {
6506 1920 : if (index->pages > 1) /* avoid computing log(0) */
6507 1580 : index->tree_height = (int) (log(index->pages) / log(100.0));
6508 : else
6509 340 : index->tree_height = 0;
6510 : }
6511 :
6512 : /*
6513 : * Add a CPU-cost component to represent the costs of initial descent. We
6514 : * just use log(N) here not log2(N) since the branching factor isn't
6515 : * necessarily two anyway. As for btree, charge once per SA scan.
6516 : */
6517 1932 : if (index->tuples > 1) /* avoid computing log(0) */
6518 : {
6519 1932 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
6520 1932 : costs.indexStartupCost += descentCost;
6521 1932 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6522 : }
6523 :
6524 : /*
6525 : * Likewise add a per-page charge, calculated the same as for btrees.
6526 : */
6527 1932 : descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
6528 1932 : costs.indexStartupCost += descentCost;
6529 1932 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6530 :
6531 1932 : *indexStartupCost = costs.indexStartupCost;
6532 1932 : *indexTotalCost = costs.indexTotalCost;
6533 1932 : *indexSelectivity = costs.indexSelectivity;
6534 1932 : *indexCorrelation = costs.indexCorrelation;
6535 1932 : *indexPages = costs.numIndexPages;
6536 1932 : }
6537 :
6538 : void
6539 1140 : spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6540 : Cost *indexStartupCost, Cost *indexTotalCost,
6541 : Selectivity *indexSelectivity, double *indexCorrelation,
6542 : double *indexPages)
6543 : {
6544 1140 : IndexOptInfo *index = path->indexinfo;
6545 : GenericCosts costs;
6546 : Cost descentCost;
6547 :
6548 10260 : MemSet(&costs, 0, sizeof(costs));
6549 :
6550 1140 : genericcostestimate(root, path, loop_count, &costs);
6551 :
6552 : /*
6553 : * We model index descent costs similarly to those for btree, but to do
6554 : * that we first need an idea of the tree height. We somewhat arbitrarily
6555 : * assume that the fanout is 100, meaning the tree height is at most
6556 : * log100(index->pages).
6557 : *
6558 : * Although this computation isn't really expensive enough to require
6559 : * caching, we might as well use index->tree_height to cache it.
6560 : */
6561 1140 : if (index->tree_height < 0) /* unknown? */
6562 : {
6563 1136 : if (index->pages > 1) /* avoid computing log(0) */
6564 1136 : index->tree_height = (int) (log(index->pages) / log(100.0));
6565 : else
6566 0 : index->tree_height = 0;
6567 : }
6568 :
6569 : /*
6570 : * Add a CPU-cost component to represent the costs of initial descent. We
6571 : * just use log(N) here not log2(N) since the branching factor isn't
6572 : * necessarily two anyway. As for btree, charge once per SA scan.
6573 : */
6574 1140 : if (index->tuples > 1) /* avoid computing log(0) */
6575 : {
6576 1140 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
6577 1140 : costs.indexStartupCost += descentCost;
6578 1140 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6579 : }
6580 :
6581 : /*
6582 : * Likewise add a per-page charge, calculated the same as for btrees.
6583 : */
6584 1140 : descentCost = (index->tree_height + 1) * 50.0 * cpu_operator_cost;
6585 1140 : costs.indexStartupCost += descentCost;
6586 1140 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
6587 :
6588 1140 : *indexStartupCost = costs.indexStartupCost;
6589 1140 : *indexTotalCost = costs.indexTotalCost;
6590 1140 : *indexSelectivity = costs.indexSelectivity;
6591 1140 : *indexCorrelation = costs.indexCorrelation;
6592 1140 : *indexPages = costs.numIndexPages;
6593 1140 : }
6594 :
6595 :
6596 : /*
6597 : * Support routines for gincostestimate
6598 : */
6599 :
6600 : typedef struct
6601 : {
6602 : bool attHasFullScan[INDEX_MAX_KEYS];
6603 : bool attHasNormalScan[INDEX_MAX_KEYS];
6604 : double partialEntries;
6605 : double exactEntries;
6606 : double searchEntries;
6607 : double arrayScans;
6608 : } GinQualCounts;
6609 :
6610 : /*
6611 : * Estimate the number of index terms that need to be searched for while
6612 : * testing the given GIN query, and increment the counts in *counts
6613 : * appropriately. If the query is unsatisfiable, return false.
6614 : */
6615 : static bool
6616 1490 : gincost_pattern(IndexOptInfo *index, int indexcol,
6617 : Oid clause_op, Datum query,
6618 : GinQualCounts *counts)
6619 : {
6620 : FmgrInfo flinfo;
6621 : Oid extractProcOid;
6622 : Oid collation;
6623 : int strategy_op;
6624 : Oid lefttype,
6625 : righttype;
6626 1490 : int32 nentries = 0;
6627 1490 : bool *partial_matches = NULL;
6628 1490 : Pointer *extra_data = NULL;
6629 1490 : bool *nullFlags = NULL;
6630 1490 : int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
6631 : int32 i;
6632 :
6633 : Assert(indexcol < index->nkeycolumns);
6634 :
6635 : /*
6636 : * Get the operator's strategy number and declared input data types within
6637 : * the index opfamily. (We don't need the latter, but we use
6638 : * get_op_opfamily_properties because it will throw error if it fails to
6639 : * find a matching pg_amop entry.)
6640 : */
6641 1490 : get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
6642 : &strategy_op, &lefttype, &righttype);
6643 :
6644 : /*
6645 : * GIN always uses the "default" support functions, which are those with
6646 : * lefttype == righttype == the opclass' opcintype (see
6647 : * IndexSupportInitialize in relcache.c).
6648 : */
6649 2980 : extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
6650 1490 : index->opcintype[indexcol],
6651 1490 : index->opcintype[indexcol],
6652 : GIN_EXTRACTQUERY_PROC);
6653 :
6654 1490 : if (!OidIsValid(extractProcOid))
6655 : {
6656 : /* should not happen; throw same error as index_getprocinfo */
6657 0 : elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
6658 : GIN_EXTRACTQUERY_PROC, indexcol + 1,
6659 : get_rel_name(index->indexoid));
6660 : }
6661 :
6662 : /*
6663 : * Choose collation to pass to extractProc (should match initGinState).
6664 : */
6665 1490 : if (OidIsValid(index->indexcollations[indexcol]))
6666 298 : collation = index->indexcollations[indexcol];
6667 : else
6668 1192 : collation = DEFAULT_COLLATION_OID;
6669 :
6670 1490 : fmgr_info(extractProcOid, &flinfo);
6671 :
6672 1490 : set_fn_opclass_options(&flinfo, index->opclassoptions[indexcol]);
6673 :
6674 1490 : FunctionCall7Coll(&flinfo,
6675 : collation,
6676 : query,
6677 : PointerGetDatum(&nentries),
6678 : UInt16GetDatum(strategy_op),
6679 : PointerGetDatum(&partial_matches),
6680 : PointerGetDatum(&extra_data),
6681 : PointerGetDatum(&nullFlags),
6682 : PointerGetDatum(&searchMode));
6683 :
6684 1490 : if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
6685 : {
6686 : /* No match is possible */
6687 8 : return false;
6688 : }
6689 :
6690 4424 : for (i = 0; i < nentries; i++)
6691 : {
6692 : /*
6693 : * For partial match we haven't any information to estimate number of
6694 : * matched entries in index, so, we just estimate it as 100
6695 : */
6696 2942 : if (partial_matches && partial_matches[i])
6697 302 : counts->partialEntries += 100;
6698 : else
6699 2640 : counts->exactEntries++;
6700 :
6701 2942 : counts->searchEntries++;
6702 : }
6703 :
6704 1482 : if (searchMode == GIN_SEARCH_MODE_DEFAULT)
6705 : {
6706 1172 : counts->attHasNormalScan[indexcol] = true;
6707 : }
6708 310 : else if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
6709 : {
6710 : /* Treat "include empty" like an exact-match item */
6711 30 : counts->attHasNormalScan[indexcol] = true;
6712 30 : counts->exactEntries++;
6713 30 : counts->searchEntries++;
6714 : }
6715 : else
6716 : {
6717 : /* It's GIN_SEARCH_MODE_ALL */
6718 280 : counts->attHasFullScan[indexcol] = true;
6719 : }
6720 :
6721 1482 : return true;
6722 : }
6723 :
6724 : /*
6725 : * Estimate the number of index terms that need to be searched for while
6726 : * testing the given GIN index clause, and increment the counts in *counts
6727 : * appropriately. If the query is unsatisfiable, return false.
6728 : */
6729 : static bool
6730 1482 : gincost_opexpr(PlannerInfo *root,
6731 : IndexOptInfo *index,
6732 : int indexcol,
6733 : OpExpr *clause,
6734 : GinQualCounts *counts)
6735 : {
6736 1482 : Oid clause_op = clause->opno;
6737 1482 : Node *operand = (Node *) lsecond(clause->args);
6738 :
6739 : /* aggressively reduce to a constant, and look through relabeling */
6740 1482 : operand = estimate_expression_value(root, operand);
6741 :
6742 1482 : if (IsA(operand, RelabelType))
6743 0 : operand = (Node *) ((RelabelType *) operand)->arg;
6744 :
6745 : /*
6746 : * It's impossible to call extractQuery method for unknown operand. So
6747 : * unless operand is a Const we can't do much; just assume there will be
6748 : * one ordinary search entry from the operand at runtime.
6749 : */
6750 1482 : if (!IsA(operand, Const))
6751 : {
6752 0 : counts->exactEntries++;
6753 0 : counts->searchEntries++;
6754 0 : return true;
6755 : }
6756 :
6757 : /* If Const is null, there can be no matches */
6758 1482 : if (((Const *) operand)->constisnull)
6759 0 : return false;
6760 :
6761 : /* Otherwise, apply extractQuery and get the actual term counts */
6762 1482 : return gincost_pattern(index, indexcol, clause_op,
6763 : ((Const *) operand)->constvalue,
6764 : counts);
6765 : }
6766 :
6767 : /*
6768 : * Estimate the number of index terms that need to be searched for while
6769 : * testing the given GIN index clause, and increment the counts in *counts
6770 : * appropriately. If the query is unsatisfiable, return false.
6771 : *
6772 : * A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
6773 : * each of which involves one value from the RHS array, plus all the
6774 : * non-array quals (if any). To model this, we average the counts across
6775 : * the RHS elements, and add the averages to the counts in *counts (which
6776 : * correspond to per-indexscan costs). We also multiply counts->arrayScans
6777 : * by N, causing gincostestimate to scale up its estimates accordingly.
6778 : */
6779 : static bool
6780 4 : gincost_scalararrayopexpr(PlannerInfo *root,
6781 : IndexOptInfo *index,
6782 : int indexcol,
6783 : ScalarArrayOpExpr *clause,
6784 : double numIndexEntries,
6785 : GinQualCounts *counts)
6786 : {
6787 4 : Oid clause_op = clause->opno;
6788 4 : Node *rightop = (Node *) lsecond(clause->args);
6789 : ArrayType *arrayval;
6790 : int16 elmlen;
6791 : bool elmbyval;
6792 : char elmalign;
6793 : int numElems;
6794 : Datum *elemValues;
6795 : bool *elemNulls;
6796 : GinQualCounts arraycounts;
6797 4 : int numPossible = 0;
6798 : int i;
6799 :
6800 : Assert(clause->useOr);
6801 :
6802 : /* aggressively reduce to a constant, and look through relabeling */
6803 4 : rightop = estimate_expression_value(root, rightop);
6804 :
6805 4 : if (IsA(rightop, RelabelType))
6806 0 : rightop = (Node *) ((RelabelType *) rightop)->arg;
6807 :
6808 : /*
6809 : * It's impossible to call extractQuery method for unknown operand. So
6810 : * unless operand is a Const we can't do much; just assume there will be
6811 : * one ordinary search entry from each array entry at runtime, and fall
6812 : * back on a probably-bad estimate of the number of array entries.
6813 : */
6814 4 : if (!IsA(rightop, Const))
6815 : {
6816 0 : counts->exactEntries++;
6817 0 : counts->searchEntries++;
6818 0 : counts->arrayScans *= estimate_array_length(rightop);
6819 0 : return true;
6820 : }
6821 :
6822 : /* If Const is null, there can be no matches */
6823 4 : if (((Const *) rightop)->constisnull)
6824 0 : return false;
6825 :
6826 : /* Otherwise, extract the array elements and iterate over them */
6827 4 : arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
6828 4 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
6829 : &elmlen, &elmbyval, &elmalign);
6830 4 : deconstruct_array(arrayval,
6831 : ARR_ELEMTYPE(arrayval),
6832 : elmlen, elmbyval, elmalign,
6833 : &elemValues, &elemNulls, &numElems);
6834 :
6835 4 : memset(&arraycounts, 0, sizeof(arraycounts));
6836 :
6837 12 : for (i = 0; i < numElems; i++)
6838 : {
6839 : GinQualCounts elemcounts;
6840 :
6841 : /* NULL can't match anything, so ignore, as the executor will */
6842 8 : if (elemNulls[i])
6843 0 : continue;
6844 :
6845 : /* Otherwise, apply extractQuery and get the actual term counts */
6846 8 : memset(&elemcounts, 0, sizeof(elemcounts));
6847 :
6848 8 : if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
6849 : &elemcounts))
6850 : {
6851 : /* We ignore array elements that are unsatisfiable patterns */
6852 8 : numPossible++;
6853 :
6854 8 : if (elemcounts.attHasFullScan[indexcol] &&
6855 0 : !elemcounts.attHasNormalScan[indexcol])
6856 : {
6857 : /*
6858 : * Full index scan will be required. We treat this as if
6859 : * every key in the index had been listed in the query; is
6860 : * that reasonable?
6861 : */
6862 0 : elemcounts.partialEntries = 0;
6863 0 : elemcounts.exactEntries = numIndexEntries;
6864 0 : elemcounts.searchEntries = numIndexEntries;
6865 : }
6866 8 : arraycounts.partialEntries += elemcounts.partialEntries;
6867 8 : arraycounts.exactEntries += elemcounts.exactEntries;
6868 8 : arraycounts.searchEntries += elemcounts.searchEntries;
6869 : }
6870 : }
6871 :
6872 4 : if (numPossible == 0)
6873 : {
6874 : /* No satisfiable patterns in the array */
6875 0 : return false;
6876 : }
6877 :
6878 : /*
6879 : * Now add the averages to the global counts. This will give us an
6880 : * estimate of the average number of terms searched for in each indexscan,
6881 : * including contributions from both array and non-array quals.
6882 : */
6883 4 : counts->partialEntries += arraycounts.partialEntries / numPossible;
6884 4 : counts->exactEntries += arraycounts.exactEntries / numPossible;
6885 4 : counts->searchEntries += arraycounts.searchEntries / numPossible;
6886 :
6887 4 : counts->arrayScans *= numPossible;
6888 :
6889 4 : return true;
6890 : }
6891 :
6892 : /*
6893 : * GIN has search behavior completely different from other index types
6894 : */
6895 : void
6896 1346 : gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
6897 : Cost *indexStartupCost, Cost *indexTotalCost,
6898 : Selectivity *indexSelectivity, double *indexCorrelation,
6899 : double *indexPages)
6900 : {
6901 1346 : IndexOptInfo *index = path->indexinfo;
6902 1346 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
6903 : List *selectivityQuals;
6904 1346 : double numPages = index->pages,
6905 1346 : numTuples = index->tuples;
6906 : double numEntryPages,
6907 : numDataPages,
6908 : numPendingPages,
6909 : numEntries;
6910 : GinQualCounts counts;
6911 : bool matchPossible;
6912 : bool fullIndexScan;
6913 : double partialScale;
6914 : double entryPagesFetched,
6915 : dataPagesFetched,
6916 : dataPagesFetchedBySel;
6917 : double qual_op_cost,
6918 : qual_arg_cost,
6919 : spc_random_page_cost,
6920 : outer_scans;
6921 : Relation indexRel;
6922 : GinStatsData ginStats;
6923 : ListCell *lc;
6924 : int i;
6925 :
6926 : /*
6927 : * Obtain statistical information from the meta page, if possible. Else
6928 : * set ginStats to zeroes, and we'll cope below.
6929 : */
6930 1346 : if (!index->hypothetical)
6931 : {
6932 : /* Lock should have already been obtained in plancat.c */
6933 1346 : indexRel = index_open(index->indexoid, NoLock);
6934 1346 : ginGetStats(indexRel, &ginStats);
6935 1346 : index_close(indexRel, NoLock);
6936 : }
6937 : else
6938 : {
6939 0 : memset(&ginStats, 0, sizeof(ginStats));
6940 : }
6941 :
6942 : /*
6943 : * Assuming we got valid (nonzero) stats at all, nPendingPages can be
6944 : * trusted, but the other fields are data as of the last VACUUM. We can
6945 : * scale them up to account for growth since then, but that method only
6946 : * goes so far; in the worst case, the stats might be for a completely
6947 : * empty index, and scaling them will produce pretty bogus numbers.
6948 : * Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
6949 : * it's grown more than that, fall back to estimating things only from the
6950 : * assumed-accurate index size. But we'll trust nPendingPages in any case
6951 : * so long as it's not clearly insane, ie, more than the index size.
6952 : */
6953 1346 : if (ginStats.nPendingPages < numPages)
6954 1346 : numPendingPages = ginStats.nPendingPages;
6955 : else
6956 0 : numPendingPages = 0;
6957 :
6958 1346 : if (numPages > 0 && ginStats.nTotalPages <= numPages &&
6959 1346 : ginStats.nTotalPages > numPages / 4 &&
6960 1346 : ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
6961 1174 : {
6962 : /*
6963 : * OK, the stats seem close enough to sane to be trusted. But we
6964 : * still need to scale them by the ratio numPages / nTotalPages to
6965 : * account for growth since the last VACUUM.
6966 : */
6967 1174 : double scale = numPages / ginStats.nTotalPages;
6968 :
6969 1174 : numEntryPages = ceil(ginStats.nEntryPages * scale);
6970 1174 : numDataPages = ceil(ginStats.nDataPages * scale);
6971 1174 : numEntries = ceil(ginStats.nEntries * scale);
6972 : /* ensure we didn't round up too much */
6973 1174 : numEntryPages = Min(numEntryPages, numPages - numPendingPages);
6974 1174 : numDataPages = Min(numDataPages,
6975 : numPages - numPendingPages - numEntryPages);
6976 : }
6977 : else
6978 : {
6979 : /*
6980 : * We might get here because it's a hypothetical index, or an index
6981 : * created pre-9.1 and never vacuumed since upgrading (in which case
6982 : * its stats would read as zeroes), or just because it's grown too
6983 : * much since the last VACUUM for us to put our faith in scaling.
6984 : *
6985 : * Invent some plausible internal statistics based on the index page
6986 : * count (and clamp that to at least 10 pages, just in case). We
6987 : * estimate that 90% of the index is entry pages, and the rest is data
6988 : * pages. Estimate 100 entries per entry page; this is rather bogus
6989 : * since it'll depend on the size of the keys, but it's more robust
6990 : * than trying to predict the number of entries per heap tuple.
6991 : */
6992 172 : numPages = Max(numPages, 10);
6993 172 : numEntryPages = floor((numPages - numPendingPages) * 0.90);
6994 172 : numDataPages = numPages - numPendingPages - numEntryPages;
6995 172 : numEntries = floor(numEntryPages * 100);
6996 : }
6997 :
6998 : /* In an empty index, numEntries could be zero. Avoid divide-by-zero */
6999 1346 : if (numEntries < 1)
7000 0 : numEntries = 1;
7001 :
7002 : /*
7003 : * If the index is partial, AND the index predicate with the index-bound
7004 : * quals to produce a more accurate idea of the number of rows covered by
7005 : * the bound conditions.
7006 : */
7007 1346 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
7008 :
7009 : /* Estimate the fraction of main-table tuples that will be visited */
7010 2692 : *indexSelectivity = clauselist_selectivity(root, selectivityQuals,
7011 1346 : index->rel->relid,
7012 : JOIN_INNER,
7013 : NULL);
7014 :
7015 : /* fetch estimated page cost for tablespace containing index */
7016 1346 : get_tablespace_page_costs(index->reltablespace,
7017 : &spc_random_page_cost,
7018 : NULL);
7019 :
7020 : /*
7021 : * Generic assumption about index correlation: there isn't any.
7022 : */
7023 1346 : *indexCorrelation = 0.0;
7024 :
7025 : /*
7026 : * Examine quals to estimate number of search entries & partial matches
7027 : */
7028 1346 : memset(&counts, 0, sizeof(counts));
7029 1346 : counts.arrayScans = 1;
7030 1346 : matchPossible = true;
7031 :
7032 2832 : foreach(lc, path->indexclauses)
7033 : {
7034 1486 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7035 : ListCell *lc2;
7036 :
7037 2964 : foreach(lc2, iclause->indexquals)
7038 : {
7039 1486 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7040 1486 : Expr *clause = rinfo->clause;
7041 :
7042 1486 : if (IsA(clause, OpExpr))
7043 : {
7044 1482 : matchPossible = gincost_opexpr(root,
7045 : index,
7046 1482 : iclause->indexcol,
7047 : (OpExpr *) clause,
7048 : &counts);
7049 1482 : if (!matchPossible)
7050 8 : break;
7051 : }
7052 4 : else if (IsA(clause, ScalarArrayOpExpr))
7053 : {
7054 4 : matchPossible = gincost_scalararrayopexpr(root,
7055 : index,
7056 4 : iclause->indexcol,
7057 : (ScalarArrayOpExpr *) clause,
7058 : numEntries,
7059 : &counts);
7060 4 : if (!matchPossible)
7061 0 : break;
7062 : }
7063 : else
7064 : {
7065 : /* shouldn't be anything else for a GIN index */
7066 0 : elog(ERROR, "unsupported GIN indexqual type: %d",
7067 : (int) nodeTag(clause));
7068 : }
7069 : }
7070 : }
7071 :
7072 : /* Fall out if there were any provably-unsatisfiable quals */
7073 1346 : if (!matchPossible)
7074 : {
7075 8 : *indexStartupCost = 0;
7076 8 : *indexTotalCost = 0;
7077 8 : *indexSelectivity = 0;
7078 8 : return;
7079 : }
7080 :
7081 : /*
7082 : * If attribute has a full scan and at the same time doesn't have normal
7083 : * scan, then we'll have to scan all non-null entries of that attribute.
7084 : * Currently, we don't have per-attribute statistics for GIN. Thus, we
7085 : * must assume the whole GIN index has to be scanned in this case.
7086 : */
7087 1338 : fullIndexScan = false;
7088 2576 : for (i = 0; i < index->nkeycolumns; i++)
7089 : {
7090 1450 : if (counts.attHasFullScan[i] && !counts.attHasNormalScan[i])
7091 : {
7092 212 : fullIndexScan = true;
7093 212 : break;
7094 : }
7095 : }
7096 :
7097 1338 : if (fullIndexScan || indexQuals == NIL)
7098 : {
7099 : /*
7100 : * Full index scan will be required. We treat this as if every key in
7101 : * the index had been listed in the query; is that reasonable?
7102 : */
7103 212 : counts.partialEntries = 0;
7104 212 : counts.exactEntries = numEntries;
7105 212 : counts.searchEntries = numEntries;
7106 : }
7107 :
7108 : /* Will we have more than one iteration of a nestloop scan? */
7109 1338 : outer_scans = loop_count;
7110 :
7111 : /*
7112 : * Compute cost to begin scan, first of all, pay attention to pending
7113 : * list.
7114 : */
7115 1338 : entryPagesFetched = numPendingPages;
7116 :
7117 : /*
7118 : * Estimate number of entry pages read. We need to do
7119 : * counts.searchEntries searches. Use a power function as it should be,
7120 : * but tuples on leaf pages usually is much greater. Here we include all
7121 : * searches in entry tree, including search of first entry in partial
7122 : * match algorithm
7123 : */
7124 1338 : entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
7125 :
7126 : /*
7127 : * Add an estimate of entry pages read by partial match algorithm. It's a
7128 : * scan over leaf pages in entry tree. We haven't any useful stats here,
7129 : * so estimate it as proportion. Because counts.partialEntries is really
7130 : * pretty bogus (see code above), it's possible that it is more than
7131 : * numEntries; clamp the proportion to ensure sanity.
7132 : */
7133 1338 : partialScale = counts.partialEntries / numEntries;
7134 1338 : partialScale = Min(partialScale, 1.0);
7135 :
7136 1338 : entryPagesFetched += ceil(numEntryPages * partialScale);
7137 :
7138 : /*
7139 : * Partial match algorithm reads all data pages before doing actual scan,
7140 : * so it's a startup cost. Again, we haven't any useful stats here, so
7141 : * estimate it as proportion.
7142 : */
7143 1338 : dataPagesFetched = ceil(numDataPages * partialScale);
7144 :
7145 : /*
7146 : * Calculate cache effects if more than one scan due to nestloops or array
7147 : * quals. The result is pro-rated per nestloop scan, but the array qual
7148 : * factor shouldn't be pro-rated (compare genericcostestimate).
7149 : */
7150 1338 : if (outer_scans > 1 || counts.arrayScans > 1)
7151 : {
7152 4 : entryPagesFetched *= outer_scans * counts.arrayScans;
7153 4 : entryPagesFetched = index_pages_fetched(entryPagesFetched,
7154 : (BlockNumber) numEntryPages,
7155 : numEntryPages, root);
7156 4 : entryPagesFetched /= outer_scans;
7157 4 : dataPagesFetched *= outer_scans * counts.arrayScans;
7158 4 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
7159 : (BlockNumber) numDataPages,
7160 : numDataPages, root);
7161 4 : dataPagesFetched /= outer_scans;
7162 : }
7163 :
7164 : /*
7165 : * Here we use random page cost because logically-close pages could be far
7166 : * apart on disk.
7167 : */
7168 1338 : *indexStartupCost = (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
7169 :
7170 : /*
7171 : * Now compute the number of data pages fetched during the scan.
7172 : *
7173 : * We assume every entry to have the same number of items, and that there
7174 : * is no overlap between them. (XXX: tsvector and array opclasses collect
7175 : * statistics on the frequency of individual keys; it would be nice to use
7176 : * those here.)
7177 : */
7178 1338 : dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
7179 :
7180 : /*
7181 : * If there is a lot of overlap among the entries, in particular if one of
7182 : * the entries is very frequent, the above calculation can grossly
7183 : * under-estimate. As a simple cross-check, calculate a lower bound based
7184 : * on the overall selectivity of the quals. At a minimum, we must read
7185 : * one item pointer for each matching entry.
7186 : *
7187 : * The width of each item pointer varies, based on the level of
7188 : * compression. We don't have statistics on that, but an average of
7189 : * around 3 bytes per item is fairly typical.
7190 : */
7191 2676 : dataPagesFetchedBySel = ceil(*indexSelectivity *
7192 1338 : (numTuples / (BLCKSZ / 3)));
7193 1338 : if (dataPagesFetchedBySel > dataPagesFetched)
7194 1096 : dataPagesFetched = dataPagesFetchedBySel;
7195 :
7196 : /* Account for cache effects, the same as above */
7197 1338 : if (outer_scans > 1 || counts.arrayScans > 1)
7198 : {
7199 4 : dataPagesFetched *= outer_scans * counts.arrayScans;
7200 4 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
7201 : (BlockNumber) numDataPages,
7202 : numDataPages, root);
7203 4 : dataPagesFetched /= outer_scans;
7204 : }
7205 :
7206 : /* And apply random_page_cost as the cost per page */
7207 2676 : *indexTotalCost = *indexStartupCost +
7208 1338 : dataPagesFetched * spc_random_page_cost;
7209 :
7210 : /*
7211 : * Add on index qual eval costs, much as in genericcostestimate. But we
7212 : * can disregard indexorderbys, since GIN doesn't support those.
7213 : */
7214 1338 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
7215 1338 : qual_op_cost = cpu_operator_cost * list_length(indexQuals);
7216 :
7217 1338 : *indexStartupCost += qual_arg_cost;
7218 1338 : *indexTotalCost += qual_arg_cost;
7219 1338 : *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost + qual_op_cost);
7220 1338 : *indexPages = dataPagesFetched;
7221 : }
7222 :
7223 : /*
7224 : * BRIN has search behavior completely different from other index types
7225 : */
7226 : void
7227 3988 : brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7228 : Cost *indexStartupCost, Cost *indexTotalCost,
7229 : Selectivity *indexSelectivity, double *indexCorrelation,
7230 : double *indexPages)
7231 : {
7232 3988 : IndexOptInfo *index = path->indexinfo;
7233 3988 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
7234 3988 : double numPages = index->pages;
7235 3988 : RelOptInfo *baserel = index->rel;
7236 3988 : RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
7237 : Cost spc_seq_page_cost;
7238 : Cost spc_random_page_cost;
7239 : double qual_arg_cost;
7240 : double qualSelectivity;
7241 : BrinStatsData statsData;
7242 : double indexRanges;
7243 : double minimalRanges;
7244 : double estimatedRanges;
7245 : double selec;
7246 : Relation indexRel;
7247 : ListCell *l;
7248 : VariableStatData vardata;
7249 :
7250 : Assert(rte->rtekind == RTE_RELATION);
7251 :
7252 : /* fetch estimated page cost for the tablespace containing the index */
7253 3988 : get_tablespace_page_costs(index->reltablespace,
7254 : &spc_random_page_cost,
7255 : &spc_seq_page_cost);
7256 :
7257 : /*
7258 : * Obtain some data from the index itself, if possible. Otherwise invent
7259 : * some plausible internal statistics based on the relation page count.
7260 : */
7261 3988 : if (!index->hypothetical)
7262 : {
7263 : /*
7264 : * A lock should have already been obtained on the index in plancat.c.
7265 : */
7266 3988 : indexRel = index_open(index->indexoid, NoLock);
7267 3988 : brinGetStats(indexRel, &statsData);
7268 3988 : index_close(indexRel, NoLock);
7269 :
7270 : /* work out the actual number of ranges in the index */
7271 3988 : indexRanges = Max(ceil((double) baserel->pages /
7272 : statsData.pagesPerRange), 1.0);
7273 : }
7274 : else
7275 : {
7276 : /*
7277 : * Assume default number of pages per range, and estimate the number
7278 : * of ranges based on that.
7279 : */
7280 0 : indexRanges = Max(ceil((double) baserel->pages /
7281 : BRIN_DEFAULT_PAGES_PER_RANGE), 1.0);
7282 :
7283 0 : statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE;
7284 0 : statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1;
7285 : }
7286 :
7287 : /*
7288 : * Compute index correlation
7289 : *
7290 : * Because we can use all index quals equally when scanning, we can use
7291 : * the largest correlation (in absolute value) among columns used by the
7292 : * query. Start at zero, the worst possible case. If we cannot find any
7293 : * correlation statistics, we will keep it as 0.
7294 : */
7295 3988 : *indexCorrelation = 0;
7296 :
7297 7976 : foreach(l, path->indexclauses)
7298 : {
7299 3988 : IndexClause *iclause = lfirst_node(IndexClause, l);
7300 3988 : AttrNumber attnum = index->indexkeys[iclause->indexcol];
7301 :
7302 : /* attempt to lookup stats in relation for this index column */
7303 3988 : if (attnum != 0)
7304 : {
7305 : /* Simple variable -- look to stats for the underlying table */
7306 3988 : if (get_relation_stats_hook &&
7307 0 : (*get_relation_stats_hook) (root, rte, attnum, &vardata))
7308 : {
7309 : /*
7310 : * The hook took control of acquiring a stats tuple. If it
7311 : * did supply a tuple, it'd better have supplied a freefunc.
7312 : */
7313 0 : if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
7314 0 : elog(ERROR,
7315 : "no function provided to release variable stats with");
7316 : }
7317 : else
7318 : {
7319 3988 : vardata.statsTuple =
7320 7976 : SearchSysCache3(STATRELATTINH,
7321 3988 : ObjectIdGetDatum(rte->relid),
7322 : Int16GetDatum(attnum),
7323 : BoolGetDatum(false));
7324 3988 : vardata.freefunc = ReleaseSysCache;
7325 : }
7326 : }
7327 : else
7328 : {
7329 : /*
7330 : * Looks like we've found an expression column in the index. Let's
7331 : * see if there's any stats for it.
7332 : */
7333 :
7334 : /* get the attnum from the 0-based index. */
7335 0 : attnum = iclause->indexcol + 1;
7336 :
7337 0 : if (get_index_stats_hook &&
7338 0 : (*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
7339 : {
7340 : /*
7341 : * The hook took control of acquiring a stats tuple. If it
7342 : * did supply a tuple, it'd better have supplied a freefunc.
7343 : */
7344 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
7345 0 : !vardata.freefunc)
7346 0 : elog(ERROR, "no function provided to release variable stats with");
7347 : }
7348 : else
7349 : {
7350 0 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
7351 0 : ObjectIdGetDatum(index->indexoid),
7352 : Int16GetDatum(attnum),
7353 : BoolGetDatum(false));
7354 0 : vardata.freefunc = ReleaseSysCache;
7355 : }
7356 : }
7357 :
7358 3988 : if (HeapTupleIsValid(vardata.statsTuple))
7359 : {
7360 : AttStatsSlot sslot;
7361 :
7362 8 : if (get_attstatsslot(&sslot, vardata.statsTuple,
7363 : STATISTIC_KIND_CORRELATION, InvalidOid,
7364 : ATTSTATSSLOT_NUMBERS))
7365 : {
7366 8 : double varCorrelation = 0.0;
7367 :
7368 8 : if (sslot.nnumbers > 0)
7369 8 : varCorrelation = Abs(sslot.numbers[0]);
7370 :
7371 8 : if (varCorrelation > *indexCorrelation)
7372 8 : *indexCorrelation = varCorrelation;
7373 :
7374 8 : free_attstatsslot(&sslot);
7375 : }
7376 : }
7377 :
7378 3988 : ReleaseVariableStats(vardata);
7379 : }
7380 :
7381 3988 : qualSelectivity = clauselist_selectivity(root, indexQuals,
7382 3988 : baserel->relid,
7383 : JOIN_INNER, NULL);
7384 :
7385 : /*
7386 : * Now calculate the minimum possible ranges we could match with if all of
7387 : * the rows were in the perfect order in the table's heap.
7388 : */
7389 3988 : minimalRanges = ceil(indexRanges * qualSelectivity);
7390 :
7391 : /*
7392 : * Now estimate the number of ranges that we'll touch by using the
7393 : * indexCorrelation from the stats. Careful not to divide by zero (note
7394 : * we're using the absolute value of the correlation).
7395 : */
7396 3988 : if (*indexCorrelation < 1.0e-10)
7397 3980 : estimatedRanges = indexRanges;
7398 : else
7399 8 : estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
7400 :
7401 : /* we expect to visit this portion of the table */
7402 3988 : selec = estimatedRanges / indexRanges;
7403 :
7404 3988 : CLAMP_PROBABILITY(selec);
7405 :
7406 3988 : *indexSelectivity = selec;
7407 :
7408 : /*
7409 : * Compute the index qual costs, much as in genericcostestimate, to add to
7410 : * the index costs. We can disregard indexorderbys, since BRIN doesn't
7411 : * support those.
7412 : */
7413 3988 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
7414 :
7415 : /*
7416 : * Compute the startup cost as the cost to read the whole revmap
7417 : * sequentially, including the cost to execute the index quals.
7418 : */
7419 3988 : *indexStartupCost =
7420 3988 : spc_seq_page_cost * statsData.revmapNumPages * loop_count;
7421 3988 : *indexStartupCost += qual_arg_cost;
7422 :
7423 : /*
7424 : * To read a BRIN index there might be a bit of back and forth over
7425 : * regular pages, as revmap might point to them out of sequential order;
7426 : * calculate the total cost as reading the whole index in random order.
7427 : */
7428 7976 : *indexTotalCost = *indexStartupCost +
7429 3988 : spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
7430 :
7431 : /*
7432 : * Charge a small amount per range tuple which we expect to match to. This
7433 : * is meant to reflect the costs of manipulating the bitmap. The BRIN scan
7434 : * will set a bit for each page in the range when we find a matching
7435 : * range, so we must multiply the charge by the number of pages in the
7436 : * range.
7437 : */
7438 7976 : *indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
7439 3988 : statsData.pagesPerRange;
7440 :
7441 3988 : *indexPages = index->pages;
7442 3988 : }
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