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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) 1996-2026, PostgreSQL Global Development Group
14 : : * Portions Copyright (c) 1994, Regents of the University of California
15 : : *
16 : : *
17 : : * IDENTIFICATION
18 : : * src/backend/utils/adt/selfuncs.c
19 : : *
20 : : *-------------------------------------------------------------------------
21 : : */
22 : :
23 : : /*----------
24 : : * Operator selectivity estimation functions are called to estimate the
25 : : * selectivity of WHERE clauses whose top-level operator is their operator.
26 : : * We divide the problem into two cases:
27 : : * Restriction clause estimation: the clause involves vars of just
28 : : * one relation.
29 : : * Join clause estimation: the clause involves vars of multiple rels.
30 : : * Join selectivity estimation is far more difficult and usually less accurate
31 : : * than restriction estimation.
32 : : *
33 : : * When dealing with the inner scan of a nestloop join, we consider the
34 : : * join's joinclauses as restriction clauses for the inner relation, and
35 : : * treat vars of the outer relation as parameters (a/k/a constants of unknown
36 : : * values). So, restriction estimators need to be able to accept an argument
37 : : * telling which relation is to be treated as the variable.
38 : : *
39 : : * The call convention for a restriction estimator (oprrest function) is
40 : : *
41 : : * Selectivity oprrest (PlannerInfo *root,
42 : : * Oid operator,
43 : : * List *args,
44 : : * int varRelid);
45 : : *
46 : : * root: general information about the query (rtable and RelOptInfo lists
47 : : * are particularly important for the estimator).
48 : : * operator: OID of the specific operator in question.
49 : : * args: argument list from the operator clause.
50 : : * varRelid: if not zero, the relid (rtable index) of the relation to
51 : : * be treated as the variable relation. May be zero if the args list
52 : : * is known to contain vars of only one relation.
53 : : *
54 : : * This is represented at the SQL level (in pg_proc) as
55 : : *
56 : : * float8 oprrest (internal, oid, internal, int4);
57 : : *
58 : : * The result is a selectivity, that is, a fraction (0 to 1) of the rows
59 : : * of the relation that are expected to produce a TRUE result for the
60 : : * given operator.
61 : : *
62 : : * The call convention for a join estimator (oprjoin function) is similar
63 : : * except that varRelid is not needed, and instead join information is
64 : : * supplied:
65 : : *
66 : : * Selectivity oprjoin (PlannerInfo *root,
67 : : * Oid operator,
68 : : * List *args,
69 : : * JoinType jointype,
70 : : * SpecialJoinInfo *sjinfo);
71 : : *
72 : : * float8 oprjoin (internal, oid, internal, int2, internal);
73 : : *
74 : : * (Before Postgres 8.4, join estimators had only the first four of these
75 : : * parameters. That signature is still allowed, but deprecated.) The
76 : : * relationship between jointype and sjinfo is explained in the comments for
77 : : * clause_selectivity() --- the short version is that jointype is usually
78 : : * best ignored in favor of examining sjinfo.
79 : : *
80 : : * Join selectivity for regular inner and outer joins is defined as the
81 : : * fraction (0 to 1) of the cross product of the relations that is expected
82 : : * to produce a TRUE result for the given operator. For both semi and anti
83 : : * joins, however, the selectivity is defined as the fraction of the left-hand
84 : : * side relation's rows that are expected to have a match (ie, at least one
85 : : * row with a TRUE result) in the right-hand side.
86 : : *
87 : : * For both oprrest and oprjoin functions, the operator's input collation OID
88 : : * (if any) is passed using the standard fmgr mechanism, so that the estimator
89 : : * function can fetch it with PG_GET_COLLATION(). Note, however, that all
90 : : * statistics in pg_statistic are currently built using the relevant column's
91 : : * collation.
92 : : *----------
93 : : */
94 : :
95 : : #include "postgres.h"
96 : :
97 : : #include <ctype.h>
98 : : #include <math.h>
99 : :
100 : : #include "access/brin.h"
101 : : #include "access/brin_page.h"
102 : : #include "access/gin.h"
103 : : #include "access/table.h"
104 : : #include "access/tableam.h"
105 : : #include "access/visibilitymap.h"
106 : : #include "catalog/pg_collation.h"
107 : : #include "catalog/pg_operator.h"
108 : : #include "catalog/pg_statistic.h"
109 : : #include "catalog/pg_statistic_ext.h"
110 : : #include "executor/nodeAgg.h"
111 : : #include "miscadmin.h"
112 : : #include "nodes/makefuncs.h"
113 : : #include "nodes/nodeFuncs.h"
114 : : #include "optimizer/clauses.h"
115 : : #include "optimizer/cost.h"
116 : : #include "optimizer/optimizer.h"
117 : : #include "optimizer/pathnode.h"
118 : : #include "optimizer/paths.h"
119 : : #include "optimizer/plancat.h"
120 : : #include "parser/parse_clause.h"
121 : : #include "parser/parse_relation.h"
122 : : #include "parser/parsetree.h"
123 : : #include "rewrite/rewriteManip.h"
124 : : #include "statistics/statistics.h"
125 : : #include "storage/bufmgr.h"
126 : : #include "utils/acl.h"
127 : : #include "utils/array.h"
128 : : #include "utils/builtins.h"
129 : : #include "utils/date.h"
130 : : #include "utils/datum.h"
131 : : #include "utils/fmgroids.h"
132 : : #include "utils/index_selfuncs.h"
133 : : #include "utils/lsyscache.h"
134 : : #include "utils/memutils.h"
135 : : #include "utils/pg_locale.h"
136 : : #include "utils/rel.h"
137 : : #include "utils/selfuncs.h"
138 : : #include "utils/snapmgr.h"
139 : : #include "utils/spccache.h"
140 : : #include "utils/syscache.h"
141 : : #include "utils/timestamp.h"
142 : : #include "utils/typcache.h"
143 : :
144 : : #define DEFAULT_PAGE_CPU_MULTIPLIER 50.0
145 : :
146 : : /*
147 : : * In production builds, switch to hash-based MCV matching when the lists are
148 : : * large enough to amortize hash setup cost. (This threshold is compared to
149 : : * the sum of the lengths of the two MCV lists. This is simplistic but seems
150 : : * to work well enough.) In debug builds, we use a smaller threshold so that
151 : : * the regression tests cover both paths well.
152 : : */
153 : : #ifndef USE_ASSERT_CHECKING
154 : : #define EQJOINSEL_MCV_HASH_THRESHOLD 200
155 : : #else
156 : : #define EQJOINSEL_MCV_HASH_THRESHOLD 20
157 : : #endif
158 : :
159 : : /* Entries in the simplehash hash table used by eqjoinsel_find_matches */
160 : : typedef struct MCVHashEntry
161 : : {
162 : : Datum value; /* the value represented by this entry */
163 : : int index; /* its index in the relevant AttStatsSlot */
164 : : uint32 hash; /* hash code for the Datum */
165 : : char status; /* status code used by simplehash.h */
166 : : } MCVHashEntry;
167 : :
168 : : /* private_data for the simplehash hash table */
169 : : typedef struct MCVHashContext
170 : : {
171 : : FunctionCallInfo equal_fcinfo; /* the equality join operator */
172 : : FunctionCallInfo hash_fcinfo; /* the hash function to use */
173 : : bool op_is_reversed; /* equality compares hash type to probe type */
174 : : bool insert_mode; /* doing inserts or lookups? */
175 : : bool hash_typbyval; /* typbyval of hashed data type */
176 : : int16 hash_typlen; /* typlen of hashed data type */
177 : : } MCVHashContext;
178 : :
179 : : /* forward reference */
180 : : typedef struct MCVHashTable_hash MCVHashTable_hash;
181 : :
182 : : /* Hooks for plugins to get control when we ask for stats */
183 : : get_relation_stats_hook_type get_relation_stats_hook = NULL;
184 : : get_index_stats_hook_type get_index_stats_hook = NULL;
185 : :
186 : : static double eqsel_internal(PG_FUNCTION_ARGS, bool negate);
187 : : static double eqjoinsel_inner(FmgrInfo *eqproc, Oid collation,
188 : : Oid hashLeft, Oid hashRight,
189 : : VariableStatData *vardata1, VariableStatData *vardata2,
190 : : double nd1, double nd2,
191 : : bool isdefault1, bool isdefault2,
192 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
193 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
194 : : bool have_mcvs1, bool have_mcvs2,
195 : : bool *hasmatch1, bool *hasmatch2,
196 : : int *p_nmatches);
197 : : static double eqjoinsel_semi(FmgrInfo *eqproc, Oid collation,
198 : : Oid hashLeft, Oid hashRight,
199 : : bool op_is_reversed,
200 : : VariableStatData *vardata1, VariableStatData *vardata2,
201 : : double nd1, double nd2,
202 : : bool isdefault1, bool isdefault2,
203 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
204 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
205 : : bool have_mcvs1, bool have_mcvs2,
206 : : bool *hasmatch1, bool *hasmatch2,
207 : : int *p_nmatches,
208 : : RelOptInfo *inner_rel);
209 : : static void eqjoinsel_find_matches(FmgrInfo *eqproc, Oid collation,
210 : : Oid hashLeft, Oid hashRight,
211 : : bool op_is_reversed,
212 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
213 : : int nvalues1, int nvalues2,
214 : : bool *hasmatch1, bool *hasmatch2,
215 : : int *p_nmatches, double *p_matchprodfreq);
216 : : static uint32 hash_mcv(MCVHashTable_hash *tab, Datum key);
217 : : static bool mcvs_equal(MCVHashTable_hash *tab, Datum key0, Datum key1);
218 : : static bool estimate_multivariate_ndistinct(PlannerInfo *root,
219 : : RelOptInfo *rel, List **varinfos, double *ndistinct);
220 : : static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
221 : : double *scaledvalue,
222 : : Datum lobound, Datum hibound, Oid boundstypid,
223 : : double *scaledlobound, double *scaledhibound);
224 : : static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
225 : : static void convert_string_to_scalar(char *value,
226 : : double *scaledvalue,
227 : : char *lobound,
228 : : double *scaledlobound,
229 : : char *hibound,
230 : : double *scaledhibound);
231 : : static void convert_bytea_to_scalar(Datum value,
232 : : double *scaledvalue,
233 : : Datum lobound,
234 : : double *scaledlobound,
235 : : Datum hibound,
236 : : double *scaledhibound);
237 : : static double convert_one_string_to_scalar(char *value,
238 : : int rangelo, int rangehi);
239 : : static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
240 : : int rangelo, int rangehi);
241 : : static char *convert_string_datum(Datum value, Oid typid, Oid collid,
242 : : bool *failure);
243 : : static double convert_timevalue_to_scalar(Datum value, Oid typid,
244 : : bool *failure);
245 : : static Node *strip_all_phvs_deep(PlannerInfo *root, Node *node);
246 : : static bool contain_placeholder_walker(Node *node, void *context);
247 : : static Node *strip_all_phvs_mutator(Node *node, void *context);
248 : : static void examine_simple_variable(PlannerInfo *root, Var *var,
249 : : VariableStatData *vardata);
250 : : static void examine_indexcol_variable(PlannerInfo *root, IndexOptInfo *index,
251 : : int indexcol, VariableStatData *vardata);
252 : : static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
253 : : Oid sortop, Oid collation,
254 : : Datum *min, Datum *max);
255 : : static void get_stats_slot_range(AttStatsSlot *sslot,
256 : : Oid opfuncoid, FmgrInfo *opproc,
257 : : Oid collation, int16 typLen, bool typByVal,
258 : : Datum *min, Datum *max, bool *p_have_data);
259 : : static bool get_actual_variable_range(PlannerInfo *root,
260 : : VariableStatData *vardata,
261 : : Oid sortop, Oid collation,
262 : : Datum *min, Datum *max);
263 : : static bool get_actual_variable_endpoint(Relation heapRel,
264 : : Relation indexRel,
265 : : ScanDirection indexscandir,
266 : : ScanKey scankeys,
267 : : int16 typLen,
268 : : bool typByVal,
269 : : TupleTableSlot *tableslot,
270 : : MemoryContext outercontext,
271 : : Datum *endpointDatum);
272 : : static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
273 : : static double btcost_correlation(IndexOptInfo *index,
274 : : VariableStatData *vardata);
275 : :
276 : : /* Define support routines for MCV hash tables */
277 : : #define SH_PREFIX MCVHashTable
278 : : #define SH_ELEMENT_TYPE MCVHashEntry
279 : : #define SH_KEY_TYPE Datum
280 : : #define SH_KEY value
281 : : #define SH_HASH_KEY(tab,key) hash_mcv(tab, key)
282 : : #define SH_EQUAL(tab,key0,key1) mcvs_equal(tab, key0, key1)
283 : : #define SH_SCOPE static inline
284 : : #define SH_STORE_HASH
285 : : #define SH_GET_HASH(tab,ent) (ent)->hash
286 : : #define SH_DEFINE
287 : : #define SH_DECLARE
288 : : #include "lib/simplehash.h"
289 : :
290 : :
291 : : /*
292 : : * eqsel - Selectivity of "=" for any data types.
293 : : *
294 : : * Note: this routine is also used to estimate selectivity for some
295 : : * operators that are not "=" but have comparable selectivity behavior,
296 : : * such as "~=" (geometric approximate-match). Even for "=", we must
297 : : * keep in mind that the left and right datatypes may differ.
298 : : */
299 : : Datum
300 : 62051 : eqsel(PG_FUNCTION_ARGS)
301 : : {
302 : 62051 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
303 : : }
304 : :
305 : : /*
306 : : * Common code for eqsel() and neqsel()
307 : : */
308 : : static double
309 : 64797 : eqsel_internal(PG_FUNCTION_ARGS, bool negate)
310 : : {
311 : 64797 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
312 : 64797 : Oid operator = PG_GETARG_OID(1);
313 : 64797 : List *args = (List *) PG_GETARG_POINTER(2);
314 : 64797 : int varRelid = PG_GETARG_INT32(3);
315 : 64797 : Oid collation = PG_GET_COLLATION();
316 : 64797 : VariableStatData vardata;
317 : 64797 : Node *other;
318 : 64797 : bool varonleft;
319 : 64797 : double selec;
320 : :
321 : : /*
322 : : * When asked about <>, we do the estimation using the corresponding =
323 : : * operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
324 : : */
325 [ + + ]: 64797 : if (negate)
326 : : {
327 : 2747 : operator = get_negator(operator);
328 [ + - ]: 2747 : if (!OidIsValid(operator))
329 : : {
330 : : /* Use default selectivity (should we raise an error instead?) */
331 : 0 : return 1.0 - DEFAULT_EQ_SEL;
332 : : }
333 : 2747 : }
334 : :
335 : : /*
336 : : * If expression is not variable = something or something = variable, then
337 : : * punt and return a default estimate.
338 : : */
339 [ + + ]: 64797 : if (!get_restriction_variable(root, args, varRelid,
340 : : &vardata, &other, &varonleft))
341 : 335 : return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
342 : :
343 : : /*
344 : : * We can do a lot better if the something is a constant. (Note: the
345 : : * Const might result from estimation rather than being a simple constant
346 : : * in the query.)
347 : : */
348 [ + + ]: 64462 : if (IsA(other, Const))
349 : 48206 : selec = var_eq_const(&vardata, operator, collation,
350 : 24103 : ((Const *) other)->constvalue,
351 : 24103 : ((Const *) other)->constisnull,
352 : 24103 : varonleft, negate);
353 : : else
354 : 80718 : selec = var_eq_non_const(&vardata, operator, collation, other,
355 : 40359 : varonleft, negate);
356 : :
357 [ + + ]: 64462 : ReleaseVariableStats(vardata);
358 : :
359 : 64462 : return selec;
360 : 64797 : }
361 : :
362 : : /*
363 : : * var_eq_const --- eqsel for var = const case
364 : : *
365 : : * This is exported so that some other estimation functions can use it.
366 : : */
367 : : double
368 : 26333 : var_eq_const(VariableStatData *vardata, Oid oproid, Oid collation,
369 : : Datum constval, bool constisnull,
370 : : bool varonleft, bool negate)
371 : : {
372 : 26333 : double selec;
373 : 26333 : double nullfrac = 0.0;
374 : 26333 : bool isdefault;
375 : 26333 : Oid opfuncoid;
376 : :
377 : : /*
378 : : * If the constant is NULL, assume operator is strict and return zero, ie,
379 : : * operator will never return TRUE. (It's zero even for a negator op.)
380 : : */
381 [ + + ]: 26333 : if (constisnull)
382 : 37 : return 0.0;
383 : :
384 : : /*
385 : : * Grab the nullfrac for use below. Note we allow use of nullfrac
386 : : * regardless of security check.
387 : : */
388 [ + + ]: 26296 : if (HeapTupleIsValid(vardata->statsTuple))
389 : : {
390 : 17231 : Form_pg_statistic stats;
391 : :
392 : 17231 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
393 : 17231 : nullfrac = stats->stanullfrac;
394 : 17231 : }
395 : :
396 : : /*
397 : : * If we matched the var to a unique index, DISTINCT or GROUP-BY clause,
398 : : * assume there is exactly one match regardless of anything else. (This
399 : : * is slightly bogus, since the index or clause's equality operator might
400 : : * be different from ours, but it's much more likely to be right than
401 : : * ignoring the information.)
402 : : */
403 [ + + + - : 26296 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
+ + ]
404 : : {
405 : 4097 : selec = 1.0 / vardata->rel->tuples;
406 : 4097 : }
407 [ + + - + ]: 22199 : else if (HeapTupleIsValid(vardata->statsTuple) &&
408 : 28392 : statistic_proc_security_check(vardata,
409 : 14196 : (opfuncoid = get_opcode(oproid))))
410 : : {
411 : 14196 : AttStatsSlot sslot;
412 : 14196 : bool match = false;
413 : 14196 : int i;
414 : :
415 : : /*
416 : : * Is the constant "=" to any of the column's most common values?
417 : : * (Although the given operator may not really be "=", we will assume
418 : : * that seeing whether it returns TRUE is an appropriate test. If you
419 : : * don't like this, maybe you shouldn't be using eqsel for your
420 : : * operator...)
421 : : */
422 [ + + ]: 14196 : if (get_attstatsslot(&sslot, vardata->statsTuple,
423 : : STATISTIC_KIND_MCV, InvalidOid,
424 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
425 : : {
426 : 12393 : LOCAL_FCINFO(fcinfo, 2);
427 : 12393 : FmgrInfo eqproc;
428 : :
429 : 12393 : fmgr_info(opfuncoid, &eqproc);
430 : :
431 : : /*
432 : : * Save a few cycles by setting up the fcinfo struct just once.
433 : : * Using FunctionCallInvoke directly also avoids failure if the
434 : : * eqproc returns NULL, though really equality functions should
435 : : * never do that.
436 : : */
437 : 12393 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
438 : : NULL, NULL);
439 : 12393 : fcinfo->args[0].isnull = false;
440 : 12393 : fcinfo->args[1].isnull = false;
441 : : /* be careful to apply operator right way 'round */
442 [ + + ]: 12393 : if (varonleft)
443 : 12388 : fcinfo->args[1].value = constval;
444 : : else
445 : 5 : fcinfo->args[0].value = constval;
446 : :
447 [ + + ]: 242799 : for (i = 0; i < sslot.nvalues; i++)
448 : : {
449 : 235746 : Datum fresult;
450 : :
451 [ + + ]: 235746 : if (varonleft)
452 : 235737 : fcinfo->args[0].value = sslot.values[i];
453 : : else
454 : 9 : fcinfo->args[1].value = sslot.values[i];
455 : 235746 : fcinfo->isnull = false;
456 : 235746 : fresult = FunctionCallInvoke(fcinfo);
457 [ + - + + ]: 235746 : if (!fcinfo->isnull && DatumGetBool(fresult))
458 : : {
459 : 5340 : match = true;
460 : 5340 : break;
461 : : }
462 [ - + + ]: 235746 : }
463 : 12393 : }
464 : : else
465 : : {
466 : : /* no most-common-value info available */
467 : 1803 : i = 0; /* keep compiler quiet */
468 : : }
469 : :
470 [ + + ]: 14196 : if (match)
471 : : {
472 : : /*
473 : : * Constant is "=" to this common value. We know selectivity
474 : : * exactly (or as exactly as ANALYZE could calculate it, anyway).
475 : : */
476 : 5340 : selec = sslot.numbers[i];
477 : 5340 : }
478 : : else
479 : : {
480 : : /*
481 : : * Comparison is against a constant that is neither NULL nor any
482 : : * of the common values. Its selectivity cannot be more than
483 : : * this:
484 : : */
485 : 8856 : double sumcommon = 0.0;
486 : 8856 : double otherdistinct;
487 : :
488 [ + + ]: 213148 : for (i = 0; i < sslot.nnumbers; i++)
489 : 204292 : sumcommon += sslot.numbers[i];
490 : 8856 : selec = 1.0 - sumcommon - nullfrac;
491 [ + + + - ]: 16172 : CLAMP_PROBABILITY(selec);
492 : :
493 : : /*
494 : : * and in fact it's probably a good deal less. We approximate that
495 : : * all the not-common values share this remaining fraction
496 : : * equally, so we divide by the number of other distinct values.
497 : : */
498 : 17712 : otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
499 : 8856 : sslot.nnumbers;
500 [ + + ]: 8856 : if (otherdistinct > 1)
501 : 4600 : selec /= otherdistinct;
502 : :
503 : : /*
504 : : * Another cross-check: selectivity shouldn't be estimated as more
505 : : * than the least common "most common value".
506 : : */
507 [ + + + - ]: 8856 : if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
508 : 0 : selec = sslot.numbers[sslot.nnumbers - 1];
509 : 8856 : }
510 : :
511 : 14196 : free_attstatsslot(&sslot);
512 : 14196 : }
513 : : else
514 : : {
515 : : /*
516 : : * No ANALYZE stats available, so make a guess using estimated number
517 : : * of distinct values and assuming they are equally common. (The guess
518 : : * is unlikely to be very good, but we do know a few special cases.)
519 : : */
520 : 8003 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
521 : : }
522 : :
523 : : /* now adjust if we wanted <> rather than = */
524 [ + + ]: 26296 : if (negate)
525 : 1948 : selec = 1.0 - selec - nullfrac;
526 : :
527 : : /* result should be in range, but make sure... */
528 [ - + + - ]: 52592 : CLAMP_PROBABILITY(selec);
529 : :
530 : 26296 : return selec;
531 : 26333 : }
532 : :
533 : : /*
534 : : * var_eq_non_const --- eqsel for var = something-other-than-const case
535 : : *
536 : : * This is exported so that some other estimation functions can use it.
537 : : */
538 : : double
539 : 40359 : var_eq_non_const(VariableStatData *vardata, Oid oproid, Oid collation,
540 : : Node *other,
541 : : bool varonleft, bool negate)
542 : : {
543 : 40359 : double selec;
544 : 40359 : double nullfrac = 0.0;
545 : 40359 : bool isdefault;
546 : :
547 : : /*
548 : : * Grab the nullfrac for use below.
549 : : */
550 [ + + ]: 40359 : if (HeapTupleIsValid(vardata->statsTuple))
551 : : {
552 : 24219 : Form_pg_statistic stats;
553 : :
554 : 24219 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
555 : 24219 : nullfrac = stats->stanullfrac;
556 : 24219 : }
557 : :
558 : : /*
559 : : * If we matched the var to a unique index, DISTINCT or GROUP-BY clause,
560 : : * assume there is exactly one match regardless of anything else. (This
561 : : * is slightly bogus, since the index or clause's equality operator might
562 : : * be different from ours, but it's much more likely to be right than
563 : : * ignoring the information.)
564 : : */
565 [ + + + - : 40359 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
+ + ]
566 : : {
567 : 14585 : selec = 1.0 / vardata->rel->tuples;
568 : 14585 : }
569 [ + + ]: 25774 : else if (HeapTupleIsValid(vardata->statsTuple))
570 : : {
571 : 11905 : double ndistinct;
572 : 11905 : AttStatsSlot sslot;
573 : :
574 : : /*
575 : : * Search is for a value that we do not know a priori, but we will
576 : : * assume it is not NULL. Estimate the selectivity as non-null
577 : : * fraction divided by number of distinct values, so that we get a
578 : : * result averaged over all possible values whether common or
579 : : * uncommon. (Essentially, we are assuming that the not-yet-known
580 : : * comparison value is equally likely to be any of the possible
581 : : * values, regardless of their frequency in the table. Is that a good
582 : : * idea?)
583 : : */
584 : 11905 : selec = 1.0 - nullfrac;
585 : 11905 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
586 [ + + ]: 11905 : if (ndistinct > 1)
587 : 11615 : selec /= ndistinct;
588 : :
589 : : /*
590 : : * Cross-check: selectivity should never be estimated as more than the
591 : : * most common value's.
592 : : */
593 [ + + ]: 11905 : if (get_attstatsslot(&sslot, vardata->statsTuple,
594 : : STATISTIC_KIND_MCV, InvalidOid,
595 : : ATTSTATSSLOT_NUMBERS))
596 : : {
597 [ + - + + ]: 9169 : if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
598 : 97 : selec = sslot.numbers[0];
599 : 9169 : free_attstatsslot(&sslot);
600 : 9169 : }
601 : 11905 : }
602 : : else
603 : : {
604 : : /*
605 : : * No ANALYZE stats available, so make a guess using estimated number
606 : : * of distinct values and assuming they are equally common. (The guess
607 : : * is unlikely to be very good, but we do know a few special cases.)
608 : : */
609 : 13869 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
610 : : }
611 : :
612 : : /* now adjust if we wanted <> rather than = */
613 [ + + ]: 40359 : if (negate)
614 : 692 : selec = 1.0 - selec - nullfrac;
615 : :
616 : : /* result should be in range, but make sure... */
617 [ - + + - ]: 80718 : CLAMP_PROBABILITY(selec);
618 : :
619 : 80718 : return selec;
620 : 40359 : }
621 : :
622 : : /*
623 : : * neqsel - Selectivity of "!=" for any data types.
624 : : *
625 : : * This routine is also used for some operators that are not "!="
626 : : * but have comparable selectivity behavior. See above comments
627 : : * for eqsel().
628 : : */
629 : : Datum
630 : 2747 : neqsel(PG_FUNCTION_ARGS)
631 : : {
632 : 2747 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
633 : : }
634 : :
635 : : /*
636 : : * scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
637 : : *
638 : : * This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
639 : : * The isgt and iseq flags distinguish which of the four cases apply.
640 : : *
641 : : * The caller has commuted the clause, if necessary, so that we can treat
642 : : * the variable as being on the left. The caller must also make sure that
643 : : * the other side of the clause is a non-null Const, and dissect that into
644 : : * a value and datatype. (This definition simplifies some callers that
645 : : * want to estimate against a computed value instead of a Const node.)
646 : : *
647 : : * This routine works for any datatype (or pair of datatypes) known to
648 : : * convert_to_scalar(). If it is applied to some other datatype,
649 : : * it will return an approximate estimate based on assuming that the constant
650 : : * value falls in the middle of the bin identified by binary search.
651 : : */
652 : : static double
653 : 36346 : scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
654 : : Oid collation,
655 : : VariableStatData *vardata, Datum constval, Oid consttype)
656 : : {
657 : 36346 : Form_pg_statistic stats;
658 : 36346 : FmgrInfo opproc;
659 : 36346 : double mcv_selec,
660 : : hist_selec,
661 : : sumcommon;
662 : 36346 : double selec;
663 : :
664 [ + + ]: 36346 : if (!HeapTupleIsValid(vardata->statsTuple))
665 : : {
666 : : /*
667 : : * No stats are available. Typically this means we have to fall back
668 : : * on the default estimate; but if the variable is CTID then we can
669 : : * make an estimate based on comparing the constant to the table size.
670 : : */
671 [ + - + + : 3409 : if (vardata->var && IsA(vardata->var, Var) &&
+ + ]
672 : 2640 : ((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
673 : : {
674 : 340 : ItemPointer itemptr;
675 : 340 : double block;
676 : 340 : double density;
677 : :
678 : : /*
679 : : * If the relation's empty, we're going to include all of it.
680 : : * (This is mostly to avoid divide-by-zero below.)
681 : : */
682 [ + - ]: 340 : if (vardata->rel->pages == 0)
683 : 0 : return 1.0;
684 : :
685 : 340 : itemptr = (ItemPointer) DatumGetPointer(constval);
686 : 340 : block = ItemPointerGetBlockNumberNoCheck(itemptr);
687 : :
688 : : /*
689 : : * Determine the average number of tuples per page (density).
690 : : *
691 : : * Since the last page will, on average, be only half full, we can
692 : : * estimate it to have half as many tuples as earlier pages. So
693 : : * give it half the weight of a regular page.
694 : : */
695 : 340 : density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
696 : :
697 : : /* If target is the last page, use half the density. */
698 [ + + ]: 340 : if (block >= vardata->rel->pages - 1)
699 : 5 : density *= 0.5;
700 : :
701 : : /*
702 : : * Using the average tuples per page, calculate how far into the
703 : : * page the itemptr is likely to be and adjust block accordingly,
704 : : * by adding that fraction of a whole block (but never more than a
705 : : * whole block, no matter how high the itemptr's offset is). Here
706 : : * we are ignoring the possibility of dead-tuple line pointers,
707 : : * which is fairly bogus, but we lack the info to do better.
708 : : */
709 [ - + ]: 340 : if (density > 0.0)
710 : : {
711 : 340 : OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
712 : :
713 [ + + ]: 340 : block += Min(offset / density, 1.0);
714 : 340 : }
715 : :
716 : : /*
717 : : * Convert relative block number to selectivity. Again, the last
718 : : * page has only half weight.
719 : : */
720 : 340 : selec = block / (vardata->rel->pages - 0.5);
721 : :
722 : : /*
723 : : * The calculation so far gave us a selectivity for the "<=" case.
724 : : * We'll have one fewer tuple for "<" and one additional tuple for
725 : : * ">=", the latter of which we'll reverse the selectivity for
726 : : * below, so we can simply subtract one tuple for both cases. The
727 : : * cases that need this adjustment can be identified by iseq being
728 : : * equal to isgt.
729 : : */
730 [ + + - + ]: 340 : if (iseq == isgt && vardata->rel->tuples >= 1.0)
731 : 315 : selec -= (1.0 / vardata->rel->tuples);
732 : :
733 : : /* Finally, reverse the selectivity for the ">", ">=" cases. */
734 [ + + ]: 340 : if (isgt)
735 : 312 : selec = 1.0 - selec;
736 : :
737 [ + + + - ]: 673 : CLAMP_PROBABILITY(selec);
738 : 340 : return selec;
739 : 340 : }
740 : :
741 : : /* no stats available, so default result */
742 : 3069 : return DEFAULT_INEQ_SEL;
743 : : }
744 : 32937 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
745 : :
746 : 32937 : fmgr_info(get_opcode(operator), &opproc);
747 : :
748 : : /*
749 : : * If we have most-common-values info, add up the fractions of the MCV
750 : : * entries that satisfy MCV OP CONST. These fractions contribute directly
751 : : * to the result selectivity. Also add up the total fraction represented
752 : : * by MCV entries.
753 : : */
754 : 32937 : mcv_selec = mcv_selectivity(vardata, &opproc, collation, constval, true,
755 : : &sumcommon);
756 : :
757 : : /*
758 : : * If there is a histogram, determine which bin the constant falls in, and
759 : : * compute the resulting contribution to selectivity.
760 : : */
761 : 65874 : hist_selec = ineq_histogram_selectivity(root, vardata,
762 : 32937 : operator, &opproc, isgt, iseq,
763 : 32937 : collation,
764 : 32937 : constval, consttype);
765 : :
766 : : /*
767 : : * Now merge the results from the MCV and histogram calculations,
768 : : * realizing that the histogram covers only the non-null values that are
769 : : * not listed in MCV.
770 : : */
771 : 32937 : selec = 1.0 - stats->stanullfrac - sumcommon;
772 : :
773 [ + + ]: 32937 : if (hist_selec >= 0.0)
774 : 16564 : selec *= hist_selec;
775 : : else
776 : : {
777 : : /*
778 : : * If no histogram but there are values not accounted for by MCV,
779 : : * arbitrarily assume half of them will match.
780 : : */
781 : 16373 : selec *= 0.5;
782 : : }
783 : :
784 : 32937 : selec += mcv_selec;
785 : :
786 : : /* result should be in range, but make sure... */
787 [ + + + + ]: 60189 : CLAMP_PROBABILITY(selec);
788 : :
789 : 32937 : return selec;
790 : 36346 : }
791 : :
792 : : /*
793 : : * mcv_selectivity - Examine the MCV list for selectivity estimates
794 : : *
795 : : * Determine the fraction of the variable's MCV population that satisfies
796 : : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
797 : : * compute the fraction of the total column population represented by the MCV
798 : : * list. This code will work for any boolean-returning predicate operator.
799 : : *
800 : : * The function result is the MCV selectivity, and the fraction of the
801 : : * total population is returned into *sumcommonp. Zeroes are returned
802 : : * if there is no MCV list.
803 : : */
804 : : double
805 : 33512 : mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc, Oid collation,
806 : : Datum constval, bool varonleft,
807 : : double *sumcommonp)
808 : : {
809 : 33512 : double mcv_selec,
810 : : sumcommon;
811 : 33512 : AttStatsSlot sslot;
812 : 33512 : int i;
813 : :
814 : 33512 : mcv_selec = 0.0;
815 : 33512 : sumcommon = 0.0;
816 : :
817 [ + + ]: 33512 : if (HeapTupleIsValid(vardata->statsTuple) &&
818 [ + + + + ]: 33205 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
819 : 33150 : get_attstatsslot(&sslot, vardata->statsTuple,
820 : : STATISTIC_KIND_MCV, InvalidOid,
821 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
822 : : {
823 : 20078 : LOCAL_FCINFO(fcinfo, 2);
824 : :
825 : : /*
826 : : * We invoke the opproc "by hand" so that we won't fail on NULL
827 : : * results. Such cases won't arise for normal comparison functions,
828 : : * but generic_restriction_selectivity could perhaps be used with
829 : : * operators that can return NULL. A small side benefit is to not
830 : : * need to re-initialize the fcinfo struct from scratch each time.
831 : : */
832 : 20078 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
833 : : NULL, NULL);
834 : 20078 : fcinfo->args[0].isnull = false;
835 : 20078 : fcinfo->args[1].isnull = false;
836 : : /* be careful to apply operator right way 'round */
837 [ + - ]: 20078 : if (varonleft)
838 : 20078 : fcinfo->args[1].value = constval;
839 : : else
840 : 0 : fcinfo->args[0].value = constval;
841 : :
842 [ + + ]: 301869 : for (i = 0; i < sslot.nvalues; i++)
843 : : {
844 : 281791 : Datum fresult;
845 : :
846 [ + - ]: 281791 : if (varonleft)
847 : 281791 : fcinfo->args[0].value = sslot.values[i];
848 : : else
849 : 0 : fcinfo->args[1].value = sslot.values[i];
850 : 281791 : fcinfo->isnull = false;
851 : 281791 : fresult = FunctionCallInvoke(fcinfo);
852 [ + - + + ]: 281791 : if (!fcinfo->isnull && DatumGetBool(fresult))
853 : 148154 : mcv_selec += sslot.numbers[i];
854 : 281791 : sumcommon += sslot.numbers[i];
855 : 281791 : }
856 : 20078 : free_attstatsslot(&sslot);
857 : 20078 : }
858 : :
859 : 33512 : *sumcommonp = sumcommon;
860 : 67024 : return mcv_selec;
861 : 33512 : }
862 : :
863 : : /*
864 : : * histogram_selectivity - Examine the histogram for selectivity estimates
865 : : *
866 : : * Determine the fraction of the variable's histogram entries that satisfy
867 : : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
868 : : *
869 : : * This code will work for any boolean-returning predicate operator, whether
870 : : * or not it has anything to do with the histogram sort operator. We are
871 : : * essentially using the histogram just as a representative sample. However,
872 : : * small histograms are unlikely to be all that representative, so the caller
873 : : * should be prepared to fall back on some other estimation approach when the
874 : : * histogram is missing or very small. It may also be prudent to combine this
875 : : * approach with another one when the histogram is small.
876 : : *
877 : : * If the actual histogram size is not at least min_hist_size, we won't bother
878 : : * to do the calculation at all. Also, if the n_skip parameter is > 0, we
879 : : * ignore the first and last n_skip histogram elements, on the grounds that
880 : : * they are outliers and hence not very representative. Typical values for
881 : : * these parameters are 10 and 1.
882 : : *
883 : : * The function result is the selectivity, or -1 if there is no histogram
884 : : * or it's smaller than min_hist_size.
885 : : *
886 : : * The output parameter *hist_size receives the actual histogram size,
887 : : * or zero if no histogram. Callers may use this number to decide how
888 : : * much faith to put in the function result.
889 : : *
890 : : * Note that the result disregards both the most-common-values (if any) and
891 : : * null entries. The caller is expected to combine this result with
892 : : * statistics for those portions of the column population. It may also be
893 : : * prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
894 : : */
895 : : double
896 : 575 : histogram_selectivity(VariableStatData *vardata,
897 : : FmgrInfo *opproc, Oid collation,
898 : : Datum constval, bool varonleft,
899 : : int min_hist_size, int n_skip,
900 : : int *hist_size)
901 : : {
902 : 575 : double result;
903 : 575 : AttStatsSlot sslot;
904 : :
905 : : /* check sanity of parameters */
906 [ + - ]: 575 : Assert(n_skip >= 0);
907 [ + - ]: 575 : Assert(min_hist_size > 2 * n_skip);
908 : :
909 [ + + ]: 575 : if (HeapTupleIsValid(vardata->statsTuple) &&
910 [ + + + + ]: 268 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
911 : 267 : get_attstatsslot(&sslot, vardata->statsTuple,
912 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
913 : : ATTSTATSSLOT_VALUES))
914 : : {
915 : 252 : *hist_size = sslot.nvalues;
916 [ + + ]: 252 : if (sslot.nvalues >= min_hist_size)
917 : : {
918 : 213 : LOCAL_FCINFO(fcinfo, 2);
919 : 213 : int nmatch = 0;
920 : 213 : int i;
921 : :
922 : : /*
923 : : * We invoke the opproc "by hand" so that we won't fail on NULL
924 : : * results. Such cases won't arise for normal comparison
925 : : * functions, but generic_restriction_selectivity could perhaps be
926 : : * used with operators that can return NULL. A small side benefit
927 : : * is to not need to re-initialize the fcinfo struct from scratch
928 : : * each time.
929 : : */
930 : 213 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
931 : : NULL, NULL);
932 : 213 : fcinfo->args[0].isnull = false;
933 : 213 : fcinfo->args[1].isnull = false;
934 : : /* be careful to apply operator right way 'round */
935 [ + - ]: 213 : if (varonleft)
936 : 213 : fcinfo->args[1].value = constval;
937 : : else
938 : 0 : fcinfo->args[0].value = constval;
939 : :
940 [ + + ]: 20570 : for (i = n_skip; i < sslot.nvalues - n_skip; i++)
941 : : {
942 : 20357 : Datum fresult;
943 : :
944 [ + - ]: 20357 : if (varonleft)
945 : 20357 : fcinfo->args[0].value = sslot.values[i];
946 : : else
947 : 0 : fcinfo->args[1].value = sslot.values[i];
948 : 20357 : fcinfo->isnull = false;
949 : 20357 : fresult = FunctionCallInvoke(fcinfo);
950 [ + - + + ]: 20357 : if (!fcinfo->isnull && DatumGetBool(fresult))
951 : 166 : nmatch++;
952 : 20357 : }
953 : 213 : result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
954 : 213 : }
955 : : else
956 : 39 : result = -1;
957 : 252 : free_attstatsslot(&sslot);
958 : 252 : }
959 : : else
960 : : {
961 : 323 : *hist_size = 0;
962 : 323 : result = -1;
963 : : }
964 : :
965 : 1150 : return result;
966 : 575 : }
967 : :
968 : : /*
969 : : * generic_restriction_selectivity - Selectivity for almost anything
970 : : *
971 : : * This function estimates selectivity for operators that we don't have any
972 : : * special knowledge about, but are on data types that we collect standard
973 : : * MCV and/or histogram statistics for. (Additional assumptions are that
974 : : * the operator is strict and immutable, or at least stable.)
975 : : *
976 : : * If we have "VAR OP CONST" or "CONST OP VAR", selectivity is estimated by
977 : : * applying the operator to each element of the column's MCV and/or histogram
978 : : * stats, and merging the results using the assumption that the histogram is
979 : : * a reasonable random sample of the column's non-MCV population. Note that
980 : : * if the operator's semantics are related to the histogram ordering, this
981 : : * might not be such a great assumption; other functions such as
982 : : * scalarineqsel() are probably a better match in such cases.
983 : : *
984 : : * Otherwise, fall back to the default selectivity provided by the caller.
985 : : */
986 : : double
987 : 140 : generic_restriction_selectivity(PlannerInfo *root, Oid oproid, Oid collation,
988 : : List *args, int varRelid,
989 : : double default_selectivity)
990 : : {
991 : 140 : double selec;
992 : 140 : VariableStatData vardata;
993 : 140 : Node *other;
994 : 140 : bool varonleft;
995 : :
996 : : /*
997 : : * If expression is not variable OP something or something OP variable,
998 : : * then punt and return the default estimate.
999 : : */
1000 [ - + ]: 140 : if (!get_restriction_variable(root, args, varRelid,
1001 : : &vardata, &other, &varonleft))
1002 : 0 : return default_selectivity;
1003 : :
1004 : : /*
1005 : : * If the something is a NULL constant, assume operator is strict and
1006 : : * return zero, ie, operator will never return TRUE.
1007 : : */
1008 [ + - + - ]: 140 : if (IsA(other, Const) &&
1009 : 140 : ((Const *) other)->constisnull)
1010 : : {
1011 [ # # ]: 0 : ReleaseVariableStats(vardata);
1012 : 0 : return 0.0;
1013 : : }
1014 : :
1015 [ + - ]: 140 : if (IsA(other, Const))
1016 : : {
1017 : : /* Variable is being compared to a known non-null constant */
1018 : 140 : Datum constval = ((Const *) other)->constvalue;
1019 : 140 : FmgrInfo opproc;
1020 : 140 : double mcvsum;
1021 : 140 : double mcvsel;
1022 : 140 : double nullfrac;
1023 : 140 : int hist_size;
1024 : :
1025 : 140 : fmgr_info(get_opcode(oproid), &opproc);
1026 : :
1027 : : /*
1028 : : * Calculate the selectivity for the column's most common values.
1029 : : */
1030 : 280 : mcvsel = mcv_selectivity(&vardata, &opproc, collation,
1031 : 140 : constval, varonleft,
1032 : : &mcvsum);
1033 : :
1034 : : /*
1035 : : * If the histogram is large enough, see what fraction of it matches
1036 : : * the query, and assume that's representative of the non-MCV
1037 : : * population. Otherwise use the default selectivity for the non-MCV
1038 : : * population.
1039 : : */
1040 : 280 : selec = histogram_selectivity(&vardata, &opproc, collation,
1041 : 140 : constval, varonleft,
1042 : : 10, 1, &hist_size);
1043 [ + - ]: 140 : if (selec < 0)
1044 : : {
1045 : : /* Nope, fall back on default */
1046 : 140 : selec = default_selectivity;
1047 : 140 : }
1048 [ # # ]: 0 : else if (hist_size < 100)
1049 : : {
1050 : : /*
1051 : : * For histogram sizes from 10 to 100, we combine the histogram
1052 : : * and default selectivities, putting increasingly more trust in
1053 : : * the histogram for larger sizes.
1054 : : */
1055 : 0 : double hist_weight = hist_size / 100.0;
1056 : :
1057 : 0 : selec = selec * hist_weight +
1058 : 0 : default_selectivity * (1.0 - hist_weight);
1059 : 0 : }
1060 : :
1061 : : /* In any case, don't believe extremely small or large estimates. */
1062 [ - + ]: 140 : if (selec < 0.0001)
1063 : 0 : selec = 0.0001;
1064 [ + - ]: 140 : else if (selec > 0.9999)
1065 : 0 : selec = 0.9999;
1066 : :
1067 : : /* Don't forget to account for nulls. */
1068 [ + + ]: 140 : if (HeapTupleIsValid(vardata.statsTuple))
1069 : 14 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
1070 : : else
1071 : 126 : nullfrac = 0.0;
1072 : :
1073 : : /*
1074 : : * Now merge the results from the MCV and histogram calculations,
1075 : : * realizing that the histogram covers only the non-null values that
1076 : : * are not listed in MCV.
1077 : : */
1078 : 140 : selec *= 1.0 - nullfrac - mcvsum;
1079 : 140 : selec += mcvsel;
1080 : 140 : }
1081 : : else
1082 : : {
1083 : : /* Comparison value is not constant, so we can't do anything */
1084 : 0 : selec = default_selectivity;
1085 : : }
1086 : :
1087 [ + + ]: 140 : ReleaseVariableStats(vardata);
1088 : :
1089 : : /* result should be in range, but make sure... */
1090 [ - + + - ]: 280 : CLAMP_PROBABILITY(selec);
1091 : :
1092 : 140 : return selec;
1093 : 140 : }
1094 : :
1095 : : /*
1096 : : * ineq_histogram_selectivity - Examine the histogram for scalarineqsel
1097 : : *
1098 : : * Determine the fraction of the variable's histogram population that
1099 : : * satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
1100 : : * The isgt and iseq flags distinguish which of the four cases apply.
1101 : : *
1102 : : * While opproc could be looked up from the operator OID, common callers
1103 : : * also need to call it separately, so we make the caller pass both.
1104 : : *
1105 : : * Returns -1 if there is no histogram (valid results will always be >= 0).
1106 : : *
1107 : : * Note that the result disregards both the most-common-values (if any) and
1108 : : * null entries. The caller is expected to combine this result with
1109 : : * statistics for those portions of the column population.
1110 : : *
1111 : : * This is exported so that some other estimation functions can use it.
1112 : : */
1113 : : double
1114 : 33114 : ineq_histogram_selectivity(PlannerInfo *root,
1115 : : VariableStatData *vardata,
1116 : : Oid opoid, FmgrInfo *opproc, bool isgt, bool iseq,
1117 : : Oid collation,
1118 : : Datum constval, Oid consttype)
1119 : : {
1120 : 33114 : double hist_selec;
1121 : 33114 : AttStatsSlot sslot;
1122 : :
1123 : 33114 : hist_selec = -1.0;
1124 : :
1125 : : /*
1126 : : * Someday, ANALYZE might store more than one histogram per rel/att,
1127 : : * corresponding to more than one possible sort ordering defined for the
1128 : : * column type. Right now, we know there is only one, so just grab it and
1129 : : * see if it matches the query.
1130 : : *
1131 : : * Note that we can't use opoid as search argument; the staop appearing in
1132 : : * pg_statistic will be for the relevant '<' operator, but what we have
1133 : : * might be some other inequality operator such as '>='. (Even if opoid
1134 : : * is a '<' operator, it could be cross-type.) Hence we must use
1135 : : * comparison_ops_are_compatible() to see if the operators match.
1136 : : */
1137 [ + + ]: 33114 : if (HeapTupleIsValid(vardata->statsTuple) &&
1138 [ + + + + ]: 33023 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
1139 : 32969 : get_attstatsslot(&sslot, vardata->statsTuple,
1140 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
1141 : : ATTSTATSSLOT_VALUES))
1142 : : {
1143 [ + - ]: 16650 : if (sslot.nvalues > 1 &&
1144 [ + + + + ]: 16650 : sslot.stacoll == collation &&
1145 : 16638 : comparison_ops_are_compatible(sslot.staop, opoid))
1146 : : {
1147 : : /*
1148 : : * Use binary search to find the desired location, namely the
1149 : : * right end of the histogram bin containing the comparison value,
1150 : : * which is the leftmost entry for which the comparison operator
1151 : : * succeeds (if isgt) or fails (if !isgt).
1152 : : *
1153 : : * In this loop, we pay no attention to whether the operator iseq
1154 : : * or not; that detail will be mopped up below. (We cannot tell,
1155 : : * anyway, whether the operator thinks the values are equal.)
1156 : : *
1157 : : * If the binary search accesses the first or last histogram
1158 : : * entry, we try to replace that endpoint with the true column min
1159 : : * or max as found by get_actual_variable_range(). This
1160 : : * ameliorates misestimates when the min or max is moving as a
1161 : : * result of changes since the last ANALYZE. Note that this could
1162 : : * result in effectively including MCVs into the histogram that
1163 : : * weren't there before, but we don't try to correct for that.
1164 : : */
1165 : 16620 : double histfrac;
1166 : 16620 : int lobound = 0; /* first possible slot to search */
1167 : 16620 : int hibound = sslot.nvalues; /* last+1 slot to search */
1168 : 16620 : bool have_end = false;
1169 : :
1170 : : /*
1171 : : * If there are only two histogram entries, we'll want up-to-date
1172 : : * values for both. (If there are more than two, we need at most
1173 : : * one of them to be updated, so we deal with that within the
1174 : : * loop.)
1175 : : */
1176 [ + + ]: 16620 : if (sslot.nvalues == 2)
1177 : 526 : have_end = get_actual_variable_range(root,
1178 : 263 : vardata,
1179 : 263 : sslot.staop,
1180 : 263 : collation,
1181 : 263 : &sslot.values[0],
1182 : 263 : &sslot.values[1]);
1183 : :
1184 [ + + ]: 106101 : while (lobound < hibound)
1185 : : {
1186 : 89481 : int probe = (lobound + hibound) / 2;
1187 : 89481 : bool ltcmp;
1188 : :
1189 : : /*
1190 : : * If we find ourselves about to compare to the first or last
1191 : : * histogram entry, first try to replace it with the actual
1192 : : * current min or max (unless we already did so above).
1193 : : */
1194 [ + + + + ]: 89481 : if (probe == 0 && sslot.nvalues > 2)
1195 : 16220 : have_end = get_actual_variable_range(root,
1196 : 8110 : vardata,
1197 : 8110 : sslot.staop,
1198 : 8110 : collation,
1199 : 8110 : &sslot.values[0],
1200 : : NULL);
1201 [ + + + + ]: 81371 : else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
1202 : 10320 : have_end = get_actual_variable_range(root,
1203 : 5160 : vardata,
1204 : 5160 : sslot.staop,
1205 : 5160 : collation,
1206 : : NULL,
1207 : 5160 : &sslot.values[probe]);
1208 : :
1209 : 178962 : ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
1210 : 89481 : collation,
1211 : 89481 : sslot.values[probe],
1212 : 89481 : constval));
1213 [ + + ]: 89481 : if (isgt)
1214 : 6792 : ltcmp = !ltcmp;
1215 [ + + ]: 89481 : if (ltcmp)
1216 : 31551 : lobound = probe + 1;
1217 : : else
1218 : 57930 : hibound = probe;
1219 : 89481 : }
1220 : :
1221 [ + + ]: 16620 : if (lobound <= 0)
1222 : : {
1223 : : /*
1224 : : * Constant is below lower histogram boundary. More
1225 : : * precisely, we have found that no entry in the histogram
1226 : : * satisfies the inequality clause (if !isgt) or they all do
1227 : : * (if isgt). We estimate that that's true of the entire
1228 : : * table, so set histfrac to 0.0 (which we'll flip to 1.0
1229 : : * below, if isgt).
1230 : : */
1231 : 7460 : histfrac = 0.0;
1232 : 7460 : }
1233 [ + + ]: 9160 : else if (lobound >= sslot.nvalues)
1234 : : {
1235 : : /*
1236 : : * Inverse case: constant is above upper histogram boundary.
1237 : : */
1238 : 2574 : histfrac = 1.0;
1239 : 2574 : }
1240 : : else
1241 : : {
1242 : : /* We have values[i-1] <= constant <= values[i]. */
1243 : 6586 : int i = lobound;
1244 : 6586 : double eq_selec = 0;
1245 : 6586 : double val,
1246 : : high,
1247 : : low;
1248 : 6586 : double binfrac;
1249 : :
1250 : : /*
1251 : : * In the cases where we'll need it below, obtain an estimate
1252 : : * of the selectivity of "x = constval". We use a calculation
1253 : : * similar to what var_eq_const() does for a non-MCV constant,
1254 : : * ie, estimate that all distinct non-MCV values occur equally
1255 : : * often. But multiplication by "1.0 - sumcommon - nullfrac"
1256 : : * will be done by our caller, so we shouldn't do that here.
1257 : : * Therefore we can't try to clamp the estimate by reference
1258 : : * to the least common MCV; the result would be too small.
1259 : : *
1260 : : * Note: since this is effectively assuming that constval
1261 : : * isn't an MCV, it's logically dubious if constval in fact is
1262 : : * one. But we have to apply *some* correction for equality,
1263 : : * and anyway we cannot tell if constval is an MCV, since we
1264 : : * don't have a suitable equality operator at hand.
1265 : : */
1266 [ + + + + ]: 6586 : if (i == 1 || isgt == iseq)
1267 : : {
1268 : 1980 : double otherdistinct;
1269 : 1980 : bool isdefault;
1270 : 1980 : AttStatsSlot mcvslot;
1271 : :
1272 : : /* Get estimated number of distinct values */
1273 : 1980 : otherdistinct = get_variable_numdistinct(vardata,
1274 : : &isdefault);
1275 : :
1276 : : /* Subtract off the number of known MCVs */
1277 [ + + ]: 1980 : if (get_attstatsslot(&mcvslot, vardata->statsTuple,
1278 : : STATISTIC_KIND_MCV, InvalidOid,
1279 : : ATTSTATSSLOT_NUMBERS))
1280 : : {
1281 : 258 : otherdistinct -= mcvslot.nnumbers;
1282 : 258 : free_attstatsslot(&mcvslot);
1283 : 258 : }
1284 : :
1285 : : /* If result doesn't seem sane, leave eq_selec at 0 */
1286 [ - + ]: 1980 : if (otherdistinct > 1)
1287 : 1980 : eq_selec = 1.0 / otherdistinct;
1288 : 1980 : }
1289 : :
1290 : : /*
1291 : : * Convert the constant and the two nearest bin boundary
1292 : : * values to a uniform comparison scale, and do a linear
1293 : : * interpolation within this bin.
1294 : : */
1295 [ + - + - ]: 13172 : if (convert_to_scalar(constval, consttype, collation,
1296 : : &val,
1297 : 6586 : sslot.values[i - 1], sslot.values[i],
1298 : 6586 : vardata->vartype,
1299 : : &low, &high))
1300 : : {
1301 [ - + ]: 6586 : if (high <= low)
1302 : : {
1303 : : /* cope if bin boundaries appear identical */
1304 : 0 : binfrac = 0.5;
1305 : 0 : }
1306 [ + + ]: 6586 : else if (val <= low)
1307 : 1407 : binfrac = 0.0;
1308 [ + + ]: 5179 : else if (val >= high)
1309 : 204 : binfrac = 1.0;
1310 : : else
1311 : : {
1312 : 4975 : binfrac = (val - low) / (high - low);
1313 : :
1314 : : /*
1315 : : * Watch out for the possibility that we got a NaN or
1316 : : * Infinity from the division. This can happen
1317 : : * despite the previous checks, if for example "low"
1318 : : * is -Infinity.
1319 : : */
1320 [ - + + + : 4975 : if (isnan(binfrac) ||
+ - ]
1321 [ + + ]: 4975 : binfrac < 0.0 || binfrac > 1.0)
1322 : 19900 : binfrac = 0.5;
1323 : : }
1324 : 6586 : }
1325 : : else
1326 : : {
1327 : : /*
1328 : : * Ideally we'd produce an error here, on the grounds that
1329 : : * the given operator shouldn't have scalarXXsel
1330 : : * registered as its selectivity func unless we can deal
1331 : : * with its operand types. But currently, all manner of
1332 : : * stuff is invoking scalarXXsel, so give a default
1333 : : * estimate until that can be fixed.
1334 : : */
1335 : 0 : binfrac = 0.5;
1336 : : }
1337 : :
1338 : : /*
1339 : : * Now, compute the overall selectivity across the values
1340 : : * represented by the histogram. We have i-1 full bins and
1341 : : * binfrac partial bin below the constant.
1342 : : */
1343 : 6586 : histfrac = (double) (i - 1) + binfrac;
1344 : 6586 : histfrac /= (double) (sslot.nvalues - 1);
1345 : :
1346 : : /*
1347 : : * At this point, histfrac is an estimate of the fraction of
1348 : : * the population represented by the histogram that satisfies
1349 : : * "x <= constval". Somewhat remarkably, this statement is
1350 : : * true regardless of which operator we were doing the probes
1351 : : * with, so long as convert_to_scalar() delivers reasonable
1352 : : * results. If the probe constant is equal to some histogram
1353 : : * entry, we would have considered the bin to the left of that
1354 : : * entry if probing with "<" or ">=", or the bin to the right
1355 : : * if probing with "<=" or ">"; but binfrac would have come
1356 : : * out as 1.0 in the first case and 0.0 in the second, leading
1357 : : * to the same histfrac in either case. For probe constants
1358 : : * between histogram entries, we find the same bin and get the
1359 : : * same estimate with any operator.
1360 : : *
1361 : : * The fact that the estimate corresponds to "x <= constval"
1362 : : * and not "x < constval" is because of the way that ANALYZE
1363 : : * constructs the histogram: each entry is, effectively, the
1364 : : * rightmost value in its sample bucket. So selectivity
1365 : : * values that are exact multiples of 1/(histogram_size-1)
1366 : : * should be understood as estimates including a histogram
1367 : : * entry plus everything to its left.
1368 : : *
1369 : : * However, that breaks down for the first histogram entry,
1370 : : * which necessarily is the leftmost value in its sample
1371 : : * bucket. That means the first histogram bin is slightly
1372 : : * narrower than the rest, by an amount equal to eq_selec.
1373 : : * Another way to say that is that we want "x <= leftmost" to
1374 : : * be estimated as eq_selec not zero. So, if we're dealing
1375 : : * with the first bin (i==1), rescale to make that true while
1376 : : * adjusting the rest of that bin linearly.
1377 : : */
1378 [ + + ]: 6586 : if (i == 1)
1379 : 818 : histfrac += eq_selec * (1.0 - binfrac);
1380 : :
1381 : : /*
1382 : : * "x <= constval" is good if we want an estimate for "<=" or
1383 : : * ">", but if we are estimating for "<" or ">=", we now need
1384 : : * to decrease the estimate by eq_selec.
1385 : : */
1386 [ + + ]: 6586 : if (isgt == iseq)
1387 : 1768 : histfrac -= eq_selec;
1388 : 6586 : }
1389 : :
1390 : : /*
1391 : : * Now the estimate is finished for "<" and "<=" cases. If we are
1392 : : * estimating for ">" or ">=", flip it.
1393 : : */
1394 [ + + ]: 16620 : hist_selec = isgt ? (1.0 - histfrac) : histfrac;
1395 : :
1396 : : /*
1397 : : * The histogram boundaries are only approximate to begin with,
1398 : : * and may well be out of date anyway. Therefore, don't believe
1399 : : * extremely small or large selectivity estimates --- unless we
1400 : : * got actual current endpoint values from the table, in which
1401 : : * case just do the usual sanity clamp. Somewhat arbitrarily, we
1402 : : * set the cutoff for other cases at a hundredth of the histogram
1403 : : * resolution.
1404 : : */
1405 [ + + ]: 16620 : if (have_end)
1406 [ - + + - ]: 17346 : CLAMP_PROBABILITY(hist_selec);
1407 : : else
1408 : : {
1409 : 7947 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1410 : :
1411 [ + + ]: 7947 : if (hist_selec < cutoff)
1412 : 2058 : hist_selec = cutoff;
1413 [ + + ]: 5889 : else if (hist_selec > 1.0 - cutoff)
1414 : 2449 : hist_selec = 1.0 - cutoff;
1415 : 7947 : }
1416 : 16620 : }
1417 [ - + ]: 30 : else if (sslot.nvalues > 1)
1418 : : {
1419 : : /*
1420 : : * If we get here, we have a histogram but it's not sorted the way
1421 : : * we want. Do a brute-force search to see how many of the
1422 : : * entries satisfy the comparison condition, and take that
1423 : : * fraction as our estimate. (This is identical to the inner loop
1424 : : * of histogram_selectivity; maybe share code?)
1425 : : */
1426 : 30 : LOCAL_FCINFO(fcinfo, 2);
1427 : 30 : int nmatch = 0;
1428 : :
1429 : 30 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
1430 : : NULL, NULL);
1431 : 30 : fcinfo->args[0].isnull = false;
1432 : 30 : fcinfo->args[1].isnull = false;
1433 : 30 : fcinfo->args[1].value = constval;
1434 [ + + ]: 160348 : for (int i = 0; i < sslot.nvalues; i++)
1435 : : {
1436 : 160318 : Datum fresult;
1437 : :
1438 : 160318 : fcinfo->args[0].value = sslot.values[i];
1439 : 160318 : fcinfo->isnull = false;
1440 : 160318 : fresult = FunctionCallInvoke(fcinfo);
1441 [ + - + + ]: 160318 : if (!fcinfo->isnull && DatumGetBool(fresult))
1442 : 322 : nmatch++;
1443 : 160318 : }
1444 : 30 : hist_selec = ((double) nmatch) / ((double) sslot.nvalues);
1445 : :
1446 : : /*
1447 : : * As above, clamp to a hundredth of the histogram resolution.
1448 : : * This case is surely even less trustworthy than the normal one,
1449 : : * so we shouldn't believe exact 0 or 1 selectivity. (Maybe the
1450 : : * clamp should be more restrictive in this case?)
1451 : : */
1452 : : {
1453 : 30 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1454 : :
1455 [ - + ]: 30 : if (hist_selec < cutoff)
1456 : 0 : hist_selec = cutoff;
1457 [ + - ]: 30 : else if (hist_selec > 1.0 - cutoff)
1458 : 0 : hist_selec = 1.0 - cutoff;
1459 : 30 : }
1460 : 30 : }
1461 : :
1462 : 16650 : free_attstatsslot(&sslot);
1463 : 16650 : }
1464 : :
1465 : 66228 : return hist_selec;
1466 : 33114 : }
1467 : :
1468 : : /*
1469 : : * Common wrapper function for the selectivity estimators that simply
1470 : : * invoke scalarineqsel().
1471 : : */
1472 : : static Datum
1473 : 6193 : scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
1474 : : {
1475 : 6193 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
1476 : 6193 : Oid operator = PG_GETARG_OID(1);
1477 : 6193 : List *args = (List *) PG_GETARG_POINTER(2);
1478 : 6193 : int varRelid = PG_GETARG_INT32(3);
1479 : 6193 : Oid collation = PG_GET_COLLATION();
1480 : 6193 : VariableStatData vardata;
1481 : 6193 : Node *other;
1482 : 6193 : bool varonleft;
1483 : 6193 : Datum constval;
1484 : 6193 : Oid consttype;
1485 : 6193 : double selec;
1486 : :
1487 : : /*
1488 : : * If expression is not variable op something or something op variable,
1489 : : * then punt and return a default estimate.
1490 : : */
1491 [ + + ]: 6193 : if (!get_restriction_variable(root, args, varRelid,
1492 : : &vardata, &other, &varonleft))
1493 : 67 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1494 : :
1495 : : /*
1496 : : * Can't do anything useful if the something is not a constant, either.
1497 : : */
1498 [ + + ]: 6126 : if (!IsA(other, Const))
1499 : : {
1500 [ + + ]: 417 : ReleaseVariableStats(vardata);
1501 : 417 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1502 : : }
1503 : :
1504 : : /*
1505 : : * If the constant is NULL, assume operator is strict and return zero, ie,
1506 : : * operator will never return TRUE.
1507 : : */
1508 [ + + ]: 5709 : if (((Const *) other)->constisnull)
1509 : : {
1510 [ + + ]: 11 : ReleaseVariableStats(vardata);
1511 : 11 : PG_RETURN_FLOAT8(0.0);
1512 : : }
1513 : 5698 : constval = ((Const *) other)->constvalue;
1514 : 5698 : consttype = ((Const *) other)->consttype;
1515 : :
1516 : : /*
1517 : : * Force the var to be on the left to simplify logic in scalarineqsel.
1518 : : */
1519 [ + + ]: 5698 : if (!varonleft)
1520 : : {
1521 : 63 : operator = get_commutator(operator);
1522 [ + - ]: 63 : if (!operator)
1523 : : {
1524 : : /* Use default selectivity (should we raise an error instead?) */
1525 [ # # ]: 0 : ReleaseVariableStats(vardata);
1526 : 0 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1527 : : }
1528 : 63 : isgt = !isgt;
1529 : 63 : }
1530 : :
1531 : : /* The rest of the work is done by scalarineqsel(). */
1532 : 11396 : selec = scalarineqsel(root, operator, isgt, iseq, collation,
1533 : 5698 : &vardata, constval, consttype);
1534 : :
1535 [ + + ]: 5698 : ReleaseVariableStats(vardata);
1536 : :
1537 : 5698 : PG_RETURN_FLOAT8((float8) selec);
1538 : 6193 : }
1539 : :
1540 : : /*
1541 : : * scalarltsel - Selectivity of "<" for scalars.
1542 : : */
1543 : : Datum
1544 : 2237 : scalarltsel(PG_FUNCTION_ARGS)
1545 : : {
1546 : 2237 : return scalarineqsel_wrapper(fcinfo, false, false);
1547 : : }
1548 : :
1549 : : /*
1550 : : * scalarlesel - Selectivity of "<=" for scalars.
1551 : : */
1552 : : Datum
1553 : 667 : scalarlesel(PG_FUNCTION_ARGS)
1554 : : {
1555 : 667 : return scalarineqsel_wrapper(fcinfo, false, true);
1556 : : }
1557 : :
1558 : : /*
1559 : : * scalargtsel - Selectivity of ">" for scalars.
1560 : : */
1561 : : Datum
1562 : 2042 : scalargtsel(PG_FUNCTION_ARGS)
1563 : : {
1564 : 2042 : return scalarineqsel_wrapper(fcinfo, true, false);
1565 : : }
1566 : :
1567 : : /*
1568 : : * scalargesel - Selectivity of ">=" for scalars.
1569 : : */
1570 : : Datum
1571 : 1247 : scalargesel(PG_FUNCTION_ARGS)
1572 : : {
1573 : 1247 : return scalarineqsel_wrapper(fcinfo, true, true);
1574 : : }
1575 : :
1576 : : /*
1577 : : * boolvarsel - Selectivity of Boolean variable.
1578 : : *
1579 : : * This can actually be called on any boolean-valued expression. If it
1580 : : * involves only Vars of the specified relation, and if there are statistics
1581 : : * about the Var or expression (the latter is possible if it's indexed) then
1582 : : * we'll produce a real estimate; otherwise it's just a default.
1583 : : */
1584 : : Selectivity
1585 : 4118 : boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
1586 : : {
1587 : 4118 : VariableStatData vardata;
1588 : 4118 : double selec;
1589 : :
1590 : 4118 : examine_variable(root, arg, varRelid, &vardata);
1591 [ + + ]: 4118 : if (HeapTupleIsValid(vardata.statsTuple))
1592 : : {
1593 : : /*
1594 : : * A boolean variable V is equivalent to the clause V = 't', so we
1595 : : * compute the selectivity as if that is what we have.
1596 : : */
1597 : 1122 : selec = var_eq_const(&vardata, BooleanEqualOperator, InvalidOid,
1598 : 1122 : BoolGetDatum(true), false, true, false);
1599 : 1122 : }
1600 [ + + ]: 2996 : else if (is_funcclause(arg))
1601 : : {
1602 : : /*
1603 : : * If we have no stats and it's a function call, estimate 0.3333333.
1604 : : * This seems a pretty unprincipled choice, but Postgres has been
1605 : : * using that estimate for function calls since 1992. The hoariness
1606 : : * of this behavior suggests that we should not be in too much hurry
1607 : : * to use another value.
1608 : : */
1609 : 1847 : selec = 0.3333333;
1610 : 1847 : }
1611 : : else
1612 : : {
1613 : : /* Otherwise, the default estimate is 0.5 */
1614 : 1149 : selec = 0.5;
1615 : : }
1616 [ + + ]: 4118 : ReleaseVariableStats(vardata);
1617 : 8236 : return selec;
1618 : 4118 : }
1619 : :
1620 : : /*
1621 : : * booltestsel - Selectivity of BooleanTest Node.
1622 : : */
1623 : : Selectivity
1624 : 133 : booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
1625 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1626 : : {
1627 : 133 : VariableStatData vardata;
1628 : 133 : double selec;
1629 : :
1630 : 133 : examine_variable(root, arg, varRelid, &vardata);
1631 : :
1632 [ + + ]: 133 : if (HeapTupleIsValid(vardata.statsTuple))
1633 : : {
1634 : 2 : Form_pg_statistic stats;
1635 : 2 : double freq_null;
1636 : 2 : AttStatsSlot sslot;
1637 : :
1638 : 2 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
1639 : 2 : freq_null = stats->stanullfrac;
1640 : :
1641 : 2 : if (get_attstatsslot(&sslot, vardata.statsTuple,
1642 : : STATISTIC_KIND_MCV, InvalidOid,
1643 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
1644 [ + - - + ]: 2 : && sslot.nnumbers > 0)
1645 : : {
1646 : 2 : double freq_true;
1647 : 2 : double freq_false;
1648 : :
1649 : : /*
1650 : : * Get first MCV frequency and derive frequency for true.
1651 : : */
1652 [ - + ]: 2 : if (DatumGetBool(sslot.values[0]))
1653 : 0 : freq_true = sslot.numbers[0];
1654 : : else
1655 : 2 : freq_true = 1.0 - sslot.numbers[0] - freq_null;
1656 : :
1657 : : /*
1658 : : * Next derive frequency for false. Then use these as appropriate
1659 : : * to derive frequency for each case.
1660 : : */
1661 : 2 : freq_false = 1.0 - freq_true - freq_null;
1662 : :
1663 [ - - + - : 2 : switch (booltesttype)
- - - ]
1664 : : {
1665 : : case IS_UNKNOWN:
1666 : : /* select only NULL values */
1667 : 0 : selec = freq_null;
1668 : 0 : break;
1669 : : case IS_NOT_UNKNOWN:
1670 : : /* select non-NULL values */
1671 : 0 : selec = 1.0 - freq_null;
1672 : 0 : break;
1673 : : case IS_TRUE:
1674 : : /* select only TRUE values */
1675 : 2 : selec = freq_true;
1676 : 2 : break;
1677 : : case IS_NOT_TRUE:
1678 : : /* select non-TRUE values */
1679 : 0 : selec = 1.0 - freq_true;
1680 : 0 : break;
1681 : : case IS_FALSE:
1682 : : /* select only FALSE values */
1683 : 0 : selec = freq_false;
1684 : 0 : break;
1685 : : case IS_NOT_FALSE:
1686 : : /* select non-FALSE values */
1687 : 0 : selec = 1.0 - freq_false;
1688 : 0 : break;
1689 : : default:
1690 [ # # # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1691 : : (int) booltesttype);
1692 : 0 : selec = 0.0; /* Keep compiler quiet */
1693 : 0 : break;
1694 : : }
1695 : :
1696 : 2 : free_attstatsslot(&sslot);
1697 : 2 : }
1698 : : else
1699 : : {
1700 : : /*
1701 : : * No most-common-value info available. Still have null fraction
1702 : : * information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
1703 : : * for null fraction and assume a 50-50 split of TRUE and FALSE.
1704 : : */
1705 [ # # # # : 0 : switch (booltesttype)
# ]
1706 : : {
1707 : : case IS_UNKNOWN:
1708 : : /* select only NULL values */
1709 : 0 : selec = freq_null;
1710 : 0 : break;
1711 : : case IS_NOT_UNKNOWN:
1712 : : /* select non-NULL values */
1713 : 0 : selec = 1.0 - freq_null;
1714 : 0 : break;
1715 : : case IS_TRUE:
1716 : : case IS_FALSE:
1717 : : /* Assume we select half of the non-NULL values */
1718 : 0 : selec = (1.0 - freq_null) / 2.0;
1719 : 0 : break;
1720 : : case IS_NOT_TRUE:
1721 : : case IS_NOT_FALSE:
1722 : : /* Assume we select NULLs plus half of the non-NULLs */
1723 : : /* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
1724 : 0 : selec = (freq_null + 1.0) / 2.0;
1725 : 0 : break;
1726 : : default:
1727 [ # # # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1728 : : (int) booltesttype);
1729 : 0 : selec = 0.0; /* Keep compiler quiet */
1730 : 0 : break;
1731 : : }
1732 : : }
1733 : 2 : }
1734 : : else
1735 : : {
1736 : : /*
1737 : : * If we can't get variable statistics for the argument, perhaps
1738 : : * clause_selectivity can do something with it. We ignore the
1739 : : * possibility of a NULL value when using clause_selectivity, and just
1740 : : * assume the value is either TRUE or FALSE.
1741 : : */
1742 [ + + + + : 131 : switch (booltesttype)
- ]
1743 : : {
1744 : : case IS_UNKNOWN:
1745 : 8 : selec = DEFAULT_UNK_SEL;
1746 : 8 : break;
1747 : : case IS_NOT_UNKNOWN:
1748 : 18 : selec = DEFAULT_NOT_UNK_SEL;
1749 : 18 : break;
1750 : : case IS_TRUE:
1751 : : case IS_NOT_FALSE:
1752 : 84 : selec = (double) clause_selectivity(root, arg,
1753 : 42 : varRelid,
1754 : 42 : jointype, sjinfo);
1755 : 42 : break;
1756 : : case IS_FALSE:
1757 : : case IS_NOT_TRUE:
1758 : 126 : selec = 1.0 - (double) clause_selectivity(root, arg,
1759 : 63 : varRelid,
1760 : 63 : jointype, sjinfo);
1761 : 63 : break;
1762 : : default:
1763 [ # # # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1764 : : (int) booltesttype);
1765 : 0 : selec = 0.0; /* Keep compiler quiet */
1766 : 0 : break;
1767 : : }
1768 : : }
1769 : :
1770 [ + + ]: 133 : ReleaseVariableStats(vardata);
1771 : :
1772 : : /* result should be in range, but make sure... */
1773 [ - + + - ]: 266 : CLAMP_PROBABILITY(selec);
1774 : :
1775 : 266 : return (Selectivity) selec;
1776 : 133 : }
1777 : :
1778 : : /*
1779 : : * nulltestsel - Selectivity of NullTest Node.
1780 : : */
1781 : : Selectivity
1782 : 2051 : nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
1783 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1784 : : {
1785 : 2051 : VariableStatData vardata;
1786 : 2051 : double selec;
1787 : :
1788 : 2051 : examine_variable(root, arg, varRelid, &vardata);
1789 : :
1790 [ + + ]: 2051 : if (HeapTupleIsValid(vardata.statsTuple))
1791 : : {
1792 : 1214 : Form_pg_statistic stats;
1793 : 1214 : double freq_null;
1794 : :
1795 : 1214 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
1796 : 1214 : freq_null = stats->stanullfrac;
1797 : :
1798 [ + + - ]: 1214 : switch (nulltesttype)
1799 : : {
1800 : : case IS_NULL:
1801 : :
1802 : : /*
1803 : : * Use freq_null directly.
1804 : : */
1805 : 1068 : selec = freq_null;
1806 : 1068 : break;
1807 : : case IS_NOT_NULL:
1808 : :
1809 : : /*
1810 : : * Select not unknown (not null) values. Calculate from
1811 : : * freq_null.
1812 : : */
1813 : 146 : selec = 1.0 - freq_null;
1814 : 146 : break;
1815 : : default:
1816 [ # # # # ]: 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1817 : : (int) nulltesttype);
1818 : 0 : return (Selectivity) 0; /* keep compiler quiet */
1819 : : }
1820 [ - + ]: 1214 : }
1821 [ + - + + : 837 : else if (vardata.var && IsA(vardata.var, Var) &&
+ + ]
1822 : 780 : ((Var *) vardata.var)->varattno < 0)
1823 : : {
1824 : : /*
1825 : : * There are no stats for system columns, but we know they are never
1826 : : * NULL.
1827 : : */
1828 : 21 : selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
1829 : 21 : }
1830 : : else
1831 : : {
1832 : : /*
1833 : : * No ANALYZE stats available, so make a guess
1834 : : */
1835 [ + + - ]: 816 : switch (nulltesttype)
1836 : : {
1837 : : case IS_NULL:
1838 : 255 : selec = DEFAULT_UNK_SEL;
1839 : 255 : break;
1840 : : case IS_NOT_NULL:
1841 : 561 : selec = DEFAULT_NOT_UNK_SEL;
1842 : 561 : break;
1843 : : default:
1844 [ # # # # ]: 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1845 : : (int) nulltesttype);
1846 : 0 : return (Selectivity) 0; /* keep compiler quiet */
1847 : : }
1848 : : }
1849 : :
1850 [ + + ]: 2051 : ReleaseVariableStats(vardata);
1851 : :
1852 : : /* result should be in range, but make sure... */
1853 [ - + + - ]: 4102 : CLAMP_PROBABILITY(selec);
1854 : :
1855 : 2051 : return (Selectivity) selec;
1856 : 2051 : }
1857 : :
1858 : : /*
1859 : : * strip_array_coercion - strip binary-compatible relabeling from an array expr
1860 : : *
1861 : : * For array values, the parser normally generates ArrayCoerceExpr conversions,
1862 : : * but it seems possible that RelabelType might show up. Also, the planner
1863 : : * is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
1864 : : * so we need to be ready to deal with more than one level.
1865 : : */
1866 : : static Node *
1867 : 14879 : strip_array_coercion(Node *node)
1868 : : {
1869 : 14895 : for (;;)
1870 : : {
1871 [ + - + + ]: 14895 : if (node && IsA(node, ArrayCoerceExpr))
1872 : : {
1873 : 92 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
1874 : :
1875 : : /*
1876 : : * If the per-element expression is just a RelabelType on top of
1877 : : * CaseTestExpr, then we know it's a binary-compatible relabeling.
1878 : : */
1879 [ + + - + ]: 92 : if (IsA(acoerce->elemexpr, RelabelType) &&
1880 : 16 : IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
1881 : 16 : node = (Node *) acoerce->arg;
1882 : : else
1883 : 76 : break;
1884 [ - + + ]: 92 : }
1885 [ + - + - ]: 14803 : else if (node && IsA(node, RelabelType))
1886 : : {
1887 : : /* We don't really expect this case, but may as well cope */
1888 : 0 : node = (Node *) ((RelabelType *) node)->arg;
1889 : 0 : }
1890 : : else
1891 : 14803 : break;
1892 : : }
1893 : 14879 : return node;
1894 : : }
1895 : :
1896 : : /*
1897 : : * scalararraysel - Selectivity of ScalarArrayOpExpr Node.
1898 : : */
1899 : : Selectivity
1900 : 2102 : scalararraysel(PlannerInfo *root,
1901 : : ScalarArrayOpExpr *clause,
1902 : : bool is_join_clause,
1903 : : int varRelid,
1904 : : JoinType jointype,
1905 : : SpecialJoinInfo *sjinfo)
1906 : : {
1907 : 2102 : Oid operator = clause->opno;
1908 : 2102 : bool useOr = clause->useOr;
1909 : 2102 : bool isEquality = false;
1910 : 2102 : bool isInequality = false;
1911 : 2102 : Node *leftop;
1912 : 2102 : Node *rightop;
1913 : 2102 : Oid nominal_element_type;
1914 : 2102 : Oid nominal_element_collation;
1915 : 2102 : TypeCacheEntry *typentry;
1916 : 2102 : RegProcedure oprsel;
1917 : 2102 : FmgrInfo oprselproc;
1918 : 2102 : Selectivity s1;
1919 : 2102 : Selectivity s1disjoint;
1920 : :
1921 : : /* First, deconstruct the expression */
1922 [ + - ]: 2102 : Assert(list_length(clause->args) == 2);
1923 : 2102 : leftop = (Node *) linitial(clause->args);
1924 : 2102 : rightop = (Node *) lsecond(clause->args);
1925 : :
1926 : : /* aggressively reduce both sides to constants */
1927 : 2102 : leftop = estimate_expression_value(root, leftop);
1928 : 2102 : rightop = estimate_expression_value(root, rightop);
1929 : :
1930 : : /* get nominal (after relabeling) element type of rightop */
1931 : 2102 : nominal_element_type = get_base_element_type(exprType(rightop));
1932 [ - + ]: 2102 : if (!OidIsValid(nominal_element_type))
1933 : 0 : return (Selectivity) 0.5; /* probably shouldn't happen */
1934 : : /* get nominal collation, too, for generating constants */
1935 : 2102 : nominal_element_collation = exprCollation(rightop);
1936 : :
1937 : : /* look through any binary-compatible relabeling of rightop */
1938 : 2102 : rightop = strip_array_coercion(rightop);
1939 : :
1940 : : /*
1941 : : * Detect whether the operator is the default equality or inequality
1942 : : * operator of the array element type.
1943 : : */
1944 : 2102 : typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
1945 [ - + ]: 2102 : if (OidIsValid(typentry->eq_opr))
1946 : : {
1947 [ + + ]: 2102 : if (operator == typentry->eq_opr)
1948 : 1940 : isEquality = true;
1949 [ + + ]: 162 : else if (get_negator(operator) == typentry->eq_opr)
1950 : 68 : isInequality = true;
1951 : 2102 : }
1952 : :
1953 : : /*
1954 : : * If it is equality or inequality, we might be able to estimate this as a
1955 : : * form of array containment; for instance "const = ANY(column)" can be
1956 : : * treated as "ARRAY[const] <@ column". scalararraysel_containment tries
1957 : : * that, and returns the selectivity estimate if successful, or -1 if not.
1958 : : */
1959 [ + + + + ]: 2102 : if ((isEquality || isInequality) && !is_join_clause)
1960 : : {
1961 : 4016 : s1 = scalararraysel_containment(root, leftop, rightop,
1962 : 2008 : nominal_element_type,
1963 : 2008 : isEquality, useOr, varRelid);
1964 [ + + ]: 2008 : if (s1 >= 0.0)
1965 : 18 : return s1;
1966 : 1990 : }
1967 : :
1968 : : /*
1969 : : * Look up the underlying operator's selectivity estimator. Punt if it
1970 : : * hasn't got one.
1971 : : */
1972 [ - + ]: 2084 : if (is_join_clause)
1973 : 0 : oprsel = get_oprjoin(operator);
1974 : : else
1975 : 2084 : oprsel = get_oprrest(operator);
1976 [ + - ]: 2084 : if (!oprsel)
1977 : 0 : return (Selectivity) 0.5;
1978 : 2084 : fmgr_info(oprsel, &oprselproc);
1979 : :
1980 : : /*
1981 : : * In the array-containment check above, we must only believe that an
1982 : : * operator is equality or inequality if it is the default btree equality
1983 : : * operator (or its negator) for the element type, since those are the
1984 : : * operators that array containment will use. But in what follows, we can
1985 : : * be a little laxer, and also believe that any operators using eqsel() or
1986 : : * neqsel() as selectivity estimator act like equality or inequality.
1987 : : */
1988 [ + + - + ]: 2084 : if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
1989 : 1954 : isEquality = true;
1990 [ + + - + ]: 130 : else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
1991 : 50 : isInequality = true;
1992 : :
1993 : : /*
1994 : : * We consider three cases:
1995 : : *
1996 : : * 1. rightop is an Array constant: deconstruct the array, apply the
1997 : : * operator's selectivity function for each array element, and merge the
1998 : : * results in the same way that clausesel.c does for AND/OR combinations.
1999 : : *
2000 : : * 2. rightop is an ARRAY[] construct: apply the operator's selectivity
2001 : : * function for each element of the ARRAY[] construct, and merge.
2002 : : *
2003 : : * 3. otherwise, make a guess ...
2004 : : */
2005 [ + - + + ]: 2084 : if (rightop && IsA(rightop, Const))
2006 : : {
2007 : 1489 : Datum arraydatum = ((Const *) rightop)->constvalue;
2008 : 1489 : bool arrayisnull = ((Const *) rightop)->constisnull;
2009 : 1489 : ArrayType *arrayval;
2010 : 1489 : int16 elmlen;
2011 : 1489 : bool elmbyval;
2012 : 1489 : char elmalign;
2013 : 1489 : int num_elems;
2014 : 1489 : Datum *elem_values;
2015 : 1489 : bool *elem_nulls;
2016 : 1489 : int i;
2017 : :
2018 [ + + ]: 1489 : if (arrayisnull) /* qual can't succeed if null array */
2019 : 5 : return (Selectivity) 0.0;
2020 : 1484 : arrayval = DatumGetArrayTypeP(arraydatum);
2021 : 1484 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
2022 : : &elmlen, &elmbyval, &elmalign);
2023 : 2968 : deconstruct_array(arrayval,
2024 : 1484 : ARR_ELEMTYPE(arrayval),
2025 : 1484 : elmlen, elmbyval, elmalign,
2026 : : &elem_values, &elem_nulls, &num_elems);
2027 : :
2028 : : /*
2029 : : * For generic operators, we assume the probability of success is
2030 : : * independent for each array element. But for "= ANY" or "<> ALL",
2031 : : * if the array elements are distinct (which'd typically be the case)
2032 : : * then the probabilities are disjoint, and we should just sum them.
2033 : : *
2034 : : * If we were being really tense we would try to confirm that the
2035 : : * elements are all distinct, but that would be expensive and it
2036 : : * doesn't seem to be worth the cycles; it would amount to penalizing
2037 : : * well-written queries in favor of poorly-written ones. However, we
2038 : : * do protect ourselves a little bit by checking whether the
2039 : : * disjointness assumption leads to an impossible (out of range)
2040 : : * probability; if so, we fall back to the normal calculation.
2041 : : */
2042 : 1484 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
2043 : :
2044 [ + + ]: 5886 : for (i = 0; i < num_elems; i++)
2045 : : {
2046 : 4402 : List *args;
2047 : 4402 : Selectivity s2;
2048 : :
2049 : 4402 : args = list_make2(leftop,
2050 : : makeConst(nominal_element_type,
2051 : : -1,
2052 : : nominal_element_collation,
2053 : : elmlen,
2054 : : elem_values[i],
2055 : : elem_nulls[i],
2056 : : elmbyval));
2057 [ - + ]: 4402 : if (is_join_clause)
2058 : 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2059 : 0 : clause->inputcollid,
2060 : 0 : PointerGetDatum(root),
2061 : 0 : ObjectIdGetDatum(operator),
2062 : 0 : PointerGetDatum(args),
2063 : 0 : Int16GetDatum(jointype),
2064 : 0 : PointerGetDatum(sjinfo)));
2065 : : else
2066 : 4402 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2067 : 4402 : clause->inputcollid,
2068 : 4402 : PointerGetDatum(root),
2069 : 4402 : ObjectIdGetDatum(operator),
2070 : 4402 : PointerGetDatum(args),
2071 : 4402 : Int32GetDatum(varRelid)));
2072 : :
2073 [ + + ]: 4402 : if (useOr)
2074 : : {
2075 : 4165 : s1 = s1 + s2 - s1 * s2;
2076 [ + + ]: 4165 : if (isEquality)
2077 : 3991 : s1disjoint += s2;
2078 : 4165 : }
2079 : : else
2080 : : {
2081 : 237 : s1 = s1 * s2;
2082 [ + + ]: 237 : if (isInequality)
2083 : 185 : s1disjoint += s2 - 1.0;
2084 : : }
2085 : 4402 : }
2086 : :
2087 : : /* accept disjoint-probability estimate if in range */
2088 [ + + + + ]: 1484 : if ((useOr ? isEquality : isInequality) &&
2089 [ + + ]: 1381 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2090 : 1376 : s1 = s1disjoint;
2091 [ + + ]: 1489 : }
2092 [ + - + + : 595 : else if (rightop && IsA(rightop, ArrayExpr) &&
- + ]
2093 : 63 : !((ArrayExpr *) rightop)->multidims)
2094 : : {
2095 : 63 : ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
2096 : 63 : int16 elmlen;
2097 : 63 : bool elmbyval;
2098 : 63 : ListCell *l;
2099 : :
2100 : 63 : get_typlenbyval(arrayexpr->element_typeid,
2101 : : &elmlen, &elmbyval);
2102 : :
2103 : : /*
2104 : : * We use the assumption of disjoint probabilities here too, although
2105 : : * the odds of equal array elements are rather higher if the elements
2106 : : * are not all constants (which they won't be, else constant folding
2107 : : * would have reduced the ArrayExpr to a Const). In this path it's
2108 : : * critical to have the sanity check on the s1disjoint estimate.
2109 : : */
2110 : 63 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
2111 : :
2112 [ + - + + : 228 : foreach(l, arrayexpr->elements)
+ + ]
2113 : : {
2114 : 165 : Node *elem = (Node *) lfirst(l);
2115 : 165 : List *args;
2116 : 165 : Selectivity s2;
2117 : :
2118 : : /*
2119 : : * Theoretically, if elem isn't of nominal_element_type we should
2120 : : * insert a RelabelType, but it seems unlikely that any operator
2121 : : * estimation function would really care ...
2122 : : */
2123 : 165 : args = list_make2(leftop, elem);
2124 [ - + ]: 165 : if (is_join_clause)
2125 : 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2126 : 0 : clause->inputcollid,
2127 : 0 : PointerGetDatum(root),
2128 : 0 : ObjectIdGetDatum(operator),
2129 : 0 : PointerGetDatum(args),
2130 : 0 : Int16GetDatum(jointype),
2131 : 0 : PointerGetDatum(sjinfo)));
2132 : : else
2133 : 165 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2134 : 165 : clause->inputcollid,
2135 : 165 : PointerGetDatum(root),
2136 : 165 : ObjectIdGetDatum(operator),
2137 : 165 : PointerGetDatum(args),
2138 : 165 : Int32GetDatum(varRelid)));
2139 : :
2140 [ + - ]: 165 : if (useOr)
2141 : : {
2142 : 165 : s1 = s1 + s2 - s1 * s2;
2143 [ - + ]: 165 : if (isEquality)
2144 : 165 : s1disjoint += s2;
2145 : 165 : }
2146 : : else
2147 : : {
2148 : 0 : s1 = s1 * s2;
2149 [ # # ]: 0 : if (isInequality)
2150 : 0 : s1disjoint += s2 - 1.0;
2151 : : }
2152 : 165 : }
2153 : :
2154 : : /* accept disjoint-probability estimate if in range */
2155 [ + - + - ]: 63 : if ((useOr ? isEquality : isInequality) &&
2156 [ - + ]: 63 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2157 : 63 : s1 = s1disjoint;
2158 : 63 : }
2159 : : else
2160 : : {
2161 : 532 : CaseTestExpr *dummyexpr;
2162 : 532 : List *args;
2163 : 532 : Selectivity s2;
2164 : 532 : int i;
2165 : :
2166 : : /*
2167 : : * We need a dummy rightop to pass to the operator selectivity
2168 : : * routine. It can be pretty much anything that doesn't look like a
2169 : : * constant; CaseTestExpr is a convenient choice.
2170 : : */
2171 : 532 : dummyexpr = makeNode(CaseTestExpr);
2172 : 532 : dummyexpr->typeId = nominal_element_type;
2173 : 532 : dummyexpr->typeMod = -1;
2174 : 532 : dummyexpr->collation = clause->inputcollid;
2175 : 532 : args = list_make2(leftop, dummyexpr);
2176 [ - + ]: 532 : if (is_join_clause)
2177 : 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2178 : 0 : clause->inputcollid,
2179 : 0 : PointerGetDatum(root),
2180 : 0 : ObjectIdGetDatum(operator),
2181 : 0 : PointerGetDatum(args),
2182 : 0 : Int16GetDatum(jointype),
2183 : 0 : PointerGetDatum(sjinfo)));
2184 : : else
2185 : 532 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2186 : 532 : clause->inputcollid,
2187 : 532 : PointerGetDatum(root),
2188 : 532 : ObjectIdGetDatum(operator),
2189 : 532 : PointerGetDatum(args),
2190 : 532 : Int32GetDatum(varRelid)));
2191 : 532 : s1 = useOr ? 0.0 : 1.0;
2192 : :
2193 : : /*
2194 : : * Arbitrarily assume 10 elements in the eventual array value (see
2195 : : * also estimate_array_length). We don't risk an assumption of
2196 : : * disjoint probabilities here.
2197 : : */
2198 [ + + ]: 5852 : for (i = 0; i < 10; i++)
2199 : : {
2200 [ + - ]: 5320 : if (useOr)
2201 : 5320 : s1 = s1 + s2 - s1 * s2;
2202 : : else
2203 : 0 : s1 = s1 * s2;
2204 : 5320 : }
2205 : 532 : }
2206 : :
2207 : : /* result should be in range, but make sure... */
2208 [ - + + - ]: 4158 : CLAMP_PROBABILITY(s1);
2209 : :
2210 : 2079 : return s1;
2211 : 2102 : }
2212 : :
2213 : : /*
2214 : : * Estimate number of elements in the array yielded by an expression.
2215 : : *
2216 : : * Note: the result is integral, but we use "double" to avoid overflow
2217 : : * concerns. Most callers will use it in double-type expressions anyway.
2218 : : *
2219 : : * Note: in some code paths root can be passed as NULL, resulting in
2220 : : * slightly worse estimates.
2221 : : */
2222 : : double
2223 : 12777 : estimate_array_length(PlannerInfo *root, Node *arrayexpr)
2224 : : {
2225 : : /* look through any binary-compatible relabeling of arrayexpr */
2226 : 12777 : arrayexpr = strip_array_coercion(arrayexpr);
2227 : :
2228 [ + - + + ]: 12777 : if (arrayexpr && IsA(arrayexpr, Const))
2229 : : {
2230 : 4290 : Datum arraydatum = ((Const *) arrayexpr)->constvalue;
2231 : 4290 : bool arrayisnull = ((Const *) arrayexpr)->constisnull;
2232 : 4290 : ArrayType *arrayval;
2233 : :
2234 [ + + ]: 4290 : if (arrayisnull)
2235 : 15 : return 0;
2236 : 4275 : arrayval = DatumGetArrayTypeP(arraydatum);
2237 : 4275 : return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
2238 : 4290 : }
2239 [ + - + + : 8487 : else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
- + ]
2240 : 109 : !((ArrayExpr *) arrayexpr)->multidims)
2241 : : {
2242 : 109 : return list_length(((ArrayExpr *) arrayexpr)->elements);
2243 : : }
2244 [ + - + + ]: 8378 : else if (arrayexpr && root)
2245 : : {
2246 : : /* See if we can find any statistics about it */
2247 : 8374 : VariableStatData vardata;
2248 : 8374 : AttStatsSlot sslot;
2249 : 8374 : double nelem = 0;
2250 : :
2251 : 8374 : examine_variable(root, arrayexpr, 0, &vardata);
2252 [ + + ]: 8374 : if (HeapTupleIsValid(vardata.statsTuple))
2253 : : {
2254 : : /*
2255 : : * Found stats, so use the average element count, which is stored
2256 : : * in the last stanumbers element of the DECHIST statistics.
2257 : : * Actually that is the average count of *distinct* elements;
2258 : : * perhaps we should scale it up somewhat?
2259 : : */
2260 [ + + ]: 135 : if (get_attstatsslot(&sslot, vardata.statsTuple,
2261 : : STATISTIC_KIND_DECHIST, InvalidOid,
2262 : : ATTSTATSSLOT_NUMBERS))
2263 : : {
2264 [ - + ]: 116 : if (sslot.nnumbers > 0)
2265 : 116 : nelem = clamp_row_est(sslot.numbers[sslot.nnumbers - 1]);
2266 : 116 : free_attstatsslot(&sslot);
2267 : 116 : }
2268 : 135 : }
2269 [ + + ]: 8374 : ReleaseVariableStats(vardata);
2270 : :
2271 [ + + ]: 8374 : if (nelem > 0)
2272 : 116 : return nelem;
2273 [ - + + ]: 8374 : }
2274 : :
2275 : : /* Else use a default guess --- this should match scalararraysel */
2276 : 8262 : return 10;
2277 : 12777 : }
2278 : :
2279 : : /*
2280 : : * rowcomparesel - Selectivity of RowCompareExpr Node.
2281 : : *
2282 : : * We estimate RowCompare selectivity by considering just the first (high
2283 : : * order) columns, which makes it equivalent to an ordinary OpExpr. While
2284 : : * this estimate could be refined by considering additional columns, it
2285 : : * seems unlikely that we could do a lot better without multi-column
2286 : : * statistics.
2287 : : */
2288 : : Selectivity
2289 : 42 : rowcomparesel(PlannerInfo *root,
2290 : : RowCompareExpr *clause,
2291 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
2292 : : {
2293 : 42 : Selectivity s1;
2294 : 42 : Oid opno = linitial_oid(clause->opnos);
2295 : 42 : Oid inputcollid = linitial_oid(clause->inputcollids);
2296 : 42 : List *opargs;
2297 : 42 : bool is_join_clause;
2298 : :
2299 : : /* Build equivalent arg list for single operator */
2300 : 42 : opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
2301 : :
2302 : : /*
2303 : : * Decide if it's a join clause. This should match clausesel.c's
2304 : : * treat_as_join_clause(), except that we intentionally consider only the
2305 : : * leading columns and not the rest of the clause.
2306 : : */
2307 [ + + ]: 42 : if (varRelid != 0)
2308 : : {
2309 : : /*
2310 : : * Caller is forcing restriction mode (eg, because we are examining an
2311 : : * inner indexscan qual).
2312 : : */
2313 : 9 : is_join_clause = false;
2314 : 9 : }
2315 [ + + ]: 33 : else if (sjinfo == NULL)
2316 : : {
2317 : : /*
2318 : : * It must be a restriction clause, since it's being evaluated at a
2319 : : * scan node.
2320 : : */
2321 : 31 : is_join_clause = false;
2322 : 31 : }
2323 : : else
2324 : : {
2325 : : /*
2326 : : * Otherwise, it's a join if there's more than one base relation used.
2327 : : */
2328 : 2 : is_join_clause = (NumRelids(root, (Node *) opargs) > 1);
2329 : : }
2330 : :
2331 [ + + ]: 42 : if (is_join_clause)
2332 : : {
2333 : : /* Estimate selectivity for a join clause. */
2334 : 4 : s1 = join_selectivity(root, opno,
2335 : 2 : opargs,
2336 : 2 : inputcollid,
2337 : 2 : jointype,
2338 : 2 : sjinfo);
2339 : 2 : }
2340 : : else
2341 : : {
2342 : : /* Estimate selectivity for a restriction clause. */
2343 : 80 : s1 = restriction_selectivity(root, opno,
2344 : 40 : opargs,
2345 : 40 : inputcollid,
2346 : 40 : varRelid);
2347 : : }
2348 : :
2349 : 84 : return s1;
2350 : 42 : }
2351 : :
2352 : : /*
2353 : : * eqjoinsel - Join selectivity of "="
2354 : : */
2355 : : Datum
2356 : 25042 : eqjoinsel(PG_FUNCTION_ARGS)
2357 : : {
2358 : 25042 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
2359 : 25042 : Oid operator = PG_GETARG_OID(1);
2360 : 25042 : List *args = (List *) PG_GETARG_POINTER(2);
2361 : :
2362 : : #ifdef NOT_USED
2363 : : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
2364 : : #endif
2365 : 25042 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
2366 : 25042 : Oid collation = PG_GET_COLLATION();
2367 : 25042 : double selec;
2368 : 25042 : double selec_inner;
2369 : 25042 : VariableStatData vardata1;
2370 : 25042 : VariableStatData vardata2;
2371 : 25042 : double nd1;
2372 : 25042 : double nd2;
2373 : 25042 : bool isdefault1;
2374 : 25042 : bool isdefault2;
2375 : 25042 : Oid opfuncoid;
2376 : 25042 : FmgrInfo eqproc;
2377 : 25042 : Oid hashLeft = InvalidOid;
2378 : 25042 : Oid hashRight = InvalidOid;
2379 : 25042 : AttStatsSlot sslot1;
2380 : 25042 : AttStatsSlot sslot2;
2381 : 25042 : Form_pg_statistic stats1 = NULL;
2382 : 25042 : Form_pg_statistic stats2 = NULL;
2383 : 25042 : bool have_mcvs1 = false;
2384 : 25042 : bool have_mcvs2 = false;
2385 : 25042 : bool *hasmatch1 = NULL;
2386 : 25042 : bool *hasmatch2 = NULL;
2387 : 25042 : int nmatches = 0;
2388 : 25042 : bool get_mcv_stats;
2389 : 25042 : bool join_is_reversed;
2390 : 25042 : RelOptInfo *inner_rel;
2391 : :
2392 : 25042 : get_join_variables(root, args, sjinfo,
2393 : : &vardata1, &vardata2, &join_is_reversed);
2394 : :
2395 : 25042 : nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
2396 : 25042 : nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
2397 : :
2398 : 25042 : opfuncoid = get_opcode(operator);
2399 : :
2400 : 25042 : memset(&sslot1, 0, sizeof(sslot1));
2401 : 25042 : memset(&sslot2, 0, sizeof(sslot2));
2402 : :
2403 : : /*
2404 : : * There is no use in fetching one side's MCVs if we lack MCVs for the
2405 : : * other side, so do a quick check to verify that both stats exist.
2406 : : */
2407 [ + + ]: 44254 : get_mcv_stats = (HeapTupleIsValid(vardata1.statsTuple) &&
2408 [ + + ]: 19212 : HeapTupleIsValid(vardata2.statsTuple) &&
2409 : 12969 : get_attstatsslot(&sslot1, vardata1.statsTuple,
2410 : : STATISTIC_KIND_MCV, InvalidOid,
2411 [ + + ]: 12969 : 0) &&
2412 : 7683 : get_attstatsslot(&sslot2, vardata2.statsTuple,
2413 : : STATISTIC_KIND_MCV, InvalidOid,
2414 : : 0));
2415 : :
2416 [ + + ]: 25042 : if (HeapTupleIsValid(vardata1.statsTuple))
2417 : : {
2418 : : /* note we allow use of nullfrac regardless of security check */
2419 : 19212 : stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
2420 [ + + - + ]: 19212 : if (get_mcv_stats &&
2421 : 3693 : statistic_proc_security_check(&vardata1, opfuncoid))
2422 : 3693 : have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
2423 : : STATISTIC_KIND_MCV, InvalidOid,
2424 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2425 : 19212 : }
2426 : :
2427 [ + + ]: 25042 : if (HeapTupleIsValid(vardata2.statsTuple))
2428 : : {
2429 : : /* note we allow use of nullfrac regardless of security check */
2430 : 14556 : stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
2431 [ + + - + ]: 14556 : if (get_mcv_stats &&
2432 : 3693 : statistic_proc_security_check(&vardata2, opfuncoid))
2433 : 3693 : have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
2434 : : STATISTIC_KIND_MCV, InvalidOid,
2435 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2436 : 14556 : }
2437 : :
2438 : : /* Prepare info usable by both eqjoinsel_inner and eqjoinsel_semi */
2439 [ + + - + ]: 25042 : if (have_mcvs1 && have_mcvs2)
2440 : : {
2441 : 3693 : fmgr_info(opfuncoid, &eqproc);
2442 : 3693 : hasmatch1 = (bool *) palloc0(sslot1.nvalues * sizeof(bool));
2443 : 3693 : hasmatch2 = (bool *) palloc0(sslot2.nvalues * sizeof(bool));
2444 : :
2445 : : /*
2446 : : * If the MCV lists are long enough to justify hashing, try to look up
2447 : : * hash functions for the join operator.
2448 : : */
2449 [ + + ]: 3693 : if ((sslot1.nvalues + sslot2.nvalues) >= EQJOINSEL_MCV_HASH_THRESHOLD)
2450 : 1245 : (void) get_op_hash_functions(operator, &hashLeft, &hashRight);
2451 : 3693 : }
2452 : : else
2453 : 21349 : memset(&eqproc, 0, sizeof(eqproc)); /* silence uninit-var warnings */
2454 : :
2455 : : /* We need to compute the inner-join selectivity in all cases */
2456 : 50084 : selec_inner = eqjoinsel_inner(&eqproc, collation,
2457 : 25042 : hashLeft, hashRight,
2458 : : &vardata1, &vardata2,
2459 : 25042 : nd1, nd2,
2460 : 25042 : isdefault1, isdefault2,
2461 : : &sslot1, &sslot2,
2462 : 25042 : stats1, stats2,
2463 : 25042 : have_mcvs1, have_mcvs2,
2464 : 25042 : hasmatch1, hasmatch2,
2465 : : &nmatches);
2466 : :
2467 [ + + - ]: 25042 : switch (sjinfo->jointype)
2468 : : {
2469 : : case JOIN_INNER:
2470 : : case JOIN_LEFT:
2471 : : case JOIN_FULL:
2472 : 23662 : selec = selec_inner;
2473 : 23662 : break;
2474 : : case JOIN_SEMI:
2475 : : case JOIN_ANTI:
2476 : :
2477 : : /*
2478 : : * Look up the join's inner relation. min_righthand is sufficient
2479 : : * information because neither SEMI nor ANTI joins permit any
2480 : : * reassociation into or out of their RHS, so the righthand will
2481 : : * always be exactly that set of rels.
2482 : : */
2483 : 1380 : inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
2484 : :
2485 [ + + ]: 1380 : if (!join_is_reversed)
2486 : 1750 : selec = eqjoinsel_semi(&eqproc, collation,
2487 : 875 : hashLeft, hashRight,
2488 : : false,
2489 : : &vardata1, &vardata2,
2490 : 875 : nd1, nd2,
2491 : 875 : isdefault1, isdefault2,
2492 : : &sslot1, &sslot2,
2493 : 875 : stats1, stats2,
2494 : 875 : have_mcvs1, have_mcvs2,
2495 : 875 : hasmatch1, hasmatch2,
2496 : : &nmatches,
2497 : 875 : inner_rel);
2498 : : else
2499 : 1010 : selec = eqjoinsel_semi(&eqproc, collation,
2500 : 505 : hashLeft, hashRight,
2501 : : true,
2502 : : &vardata2, &vardata1,
2503 : 505 : nd2, nd1,
2504 : 505 : isdefault2, isdefault1,
2505 : : &sslot2, &sslot1,
2506 : 505 : stats2, stats1,
2507 : 505 : have_mcvs2, have_mcvs1,
2508 : 505 : hasmatch2, hasmatch1,
2509 : : &nmatches,
2510 : 505 : inner_rel);
2511 : :
2512 : : /*
2513 : : * We should never estimate the output of a semijoin to be more
2514 : : * rows than we estimate for an inner join with the same input
2515 : : * rels and join condition; it's obviously impossible for that to
2516 : : * happen. The former estimate is N1 * Ssemi while the latter is
2517 : : * N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
2518 : : * this is worthwhile because of the shakier estimation rules we
2519 : : * use in eqjoinsel_semi, particularly in cases where it has to
2520 : : * punt entirely.
2521 : : */
2522 [ + + ]: 1380 : selec = Min(selec, inner_rel->rows * selec_inner);
2523 : 1380 : break;
2524 : : default:
2525 : : /* other values not expected here */
2526 [ # # # # ]: 0 : elog(ERROR, "unrecognized join type: %d",
2527 : : (int) sjinfo->jointype);
2528 : 0 : selec = 0; /* keep compiler quiet */
2529 : 0 : break;
2530 : : }
2531 : :
2532 : 25042 : free_attstatsslot(&sslot1);
2533 : 25042 : free_attstatsslot(&sslot2);
2534 : :
2535 [ + + ]: 25042 : ReleaseVariableStats(vardata1);
2536 [ + + ]: 25042 : ReleaseVariableStats(vardata2);
2537 : :
2538 [ + + ]: 25042 : if (hasmatch1)
2539 : 3693 : pfree(hasmatch1);
2540 [ + + ]: 25042 : if (hasmatch2)
2541 : 3693 : pfree(hasmatch2);
2542 : :
2543 [ - + + - ]: 50084 : CLAMP_PROBABILITY(selec);
2544 : :
2545 : 50084 : PG_RETURN_FLOAT8((float8) selec);
2546 : 25042 : }
2547 : :
2548 : : /*
2549 : : * eqjoinsel_inner --- eqjoinsel for normal inner join
2550 : : *
2551 : : * In addition to computing the selectivity estimate, this will fill
2552 : : * hasmatch1[], hasmatch2[], and *p_nmatches (if have_mcvs1 && have_mcvs2).
2553 : : * We may be able to re-use that data in eqjoinsel_semi.
2554 : : *
2555 : : * We also use this for LEFT/FULL outer joins; it's not presently clear
2556 : : * that it's worth trying to distinguish them here.
2557 : : */
2558 : : static double
2559 : 25042 : eqjoinsel_inner(FmgrInfo *eqproc, Oid collation,
2560 : : Oid hashLeft, Oid hashRight,
2561 : : VariableStatData *vardata1, VariableStatData *vardata2,
2562 : : double nd1, double nd2,
2563 : : bool isdefault1, bool isdefault2,
2564 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2565 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
2566 : : bool have_mcvs1, bool have_mcvs2,
2567 : : bool *hasmatch1, bool *hasmatch2,
2568 : : int *p_nmatches)
2569 : : {
2570 : 25042 : double selec;
2571 : :
2572 [ + + - + ]: 25042 : if (have_mcvs1 && have_mcvs2)
2573 : : {
2574 : : /*
2575 : : * We have most-common-value lists for both relations. Run through
2576 : : * the lists to see which MCVs actually join to each other with the
2577 : : * given operator. This allows us to determine the exact join
2578 : : * selectivity for the portion of the relations represented by the MCV
2579 : : * lists. We still have to estimate for the remaining population, but
2580 : : * in a skewed distribution this gives us a big leg up in accuracy.
2581 : : * For motivation see the analysis in Y. Ioannidis and S.
2582 : : * Christodoulakis, "On the propagation of errors in the size of join
2583 : : * results", Technical Report 1018, Computer Science Dept., University
2584 : : * of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
2585 : : */
2586 : 3693 : double nullfrac1 = stats1->stanullfrac;
2587 : 3693 : double nullfrac2 = stats2->stanullfrac;
2588 : 3693 : double matchprodfreq,
2589 : : matchfreq1,
2590 : : matchfreq2,
2591 : : unmatchfreq1,
2592 : : unmatchfreq2,
2593 : : otherfreq1,
2594 : : otherfreq2,
2595 : : totalsel1,
2596 : : totalsel2;
2597 : 3693 : int i,
2598 : : nmatches;
2599 : :
2600 : : /* Fill the match arrays */
2601 : 7386 : eqjoinsel_find_matches(eqproc, collation,
2602 : 3693 : hashLeft, hashRight,
2603 : : false,
2604 : 3693 : sslot1, sslot2,
2605 : 3693 : sslot1->nvalues, sslot2->nvalues,
2606 : 3693 : hasmatch1, hasmatch2,
2607 : 3693 : p_nmatches, &matchprodfreq);
2608 : 3693 : nmatches = *p_nmatches;
2609 [ - + + - ]: 7386 : CLAMP_PROBABILITY(matchprodfreq);
2610 : :
2611 : : /* Sum up frequencies of matched and unmatched MCVs */
2612 : 3693 : matchfreq1 = unmatchfreq1 = 0.0;
2613 [ + + ]: 48831 : for (i = 0; i < sslot1->nvalues; i++)
2614 : : {
2615 [ + + ]: 45138 : if (hasmatch1[i])
2616 : 36080 : matchfreq1 += sslot1->numbers[i];
2617 : : else
2618 : 9058 : unmatchfreq1 += sslot1->numbers[i];
2619 : 45138 : }
2620 [ - + + + ]: 7386 : CLAMP_PROBABILITY(matchfreq1);
2621 [ - + + - ]: 7386 : CLAMP_PROBABILITY(unmatchfreq1);
2622 : 3693 : matchfreq2 = unmatchfreq2 = 0.0;
2623 [ + + ]: 49414 : for (i = 0; i < sslot2->nvalues; i++)
2624 : : {
2625 [ + + ]: 45721 : if (hasmatch2[i])
2626 : 36080 : matchfreq2 += sslot2->numbers[i];
2627 : : else
2628 : 9641 : unmatchfreq2 += sslot2->numbers[i];
2629 : 45721 : }
2630 [ - + + + ]: 7386 : CLAMP_PROBABILITY(matchfreq2);
2631 [ - + + - ]: 7386 : CLAMP_PROBABILITY(unmatchfreq2);
2632 : :
2633 : : /*
2634 : : * Compute total frequency of non-null values that are not in the MCV
2635 : : * lists.
2636 : : */
2637 : 3693 : otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
2638 : 3693 : otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
2639 [ + + + - ]: 7175 : CLAMP_PROBABILITY(otherfreq1);
2640 [ + + + - ]: 7186 : CLAMP_PROBABILITY(otherfreq2);
2641 : :
2642 : : /*
2643 : : * We can estimate the total selectivity from the point of view of
2644 : : * relation 1 as: the known selectivity for matched MCVs, plus
2645 : : * unmatched MCVs that are assumed to match against random members of
2646 : : * relation 2's non-MCV population, plus non-MCV values that are
2647 : : * assumed to match against random members of relation 2's unmatched
2648 : : * MCVs plus non-MCV values.
2649 : : */
2650 : 3693 : totalsel1 = matchprodfreq;
2651 [ + + ]: 3693 : if (nd2 > sslot2->nvalues)
2652 : 352 : totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
2653 [ + + ]: 3693 : if (nd2 > nmatches)
2654 : 1624 : totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
2655 : 812 : (nd2 - nmatches);
2656 : : /* Same estimate from the point of view of relation 2. */
2657 : 3693 : totalsel2 = matchprodfreq;
2658 [ + + ]: 3693 : if (nd1 > sslot1->nvalues)
2659 : 320 : totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
2660 [ + + ]: 3693 : if (nd1 > nmatches)
2661 : 1444 : totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
2662 : 722 : (nd1 - nmatches);
2663 : :
2664 : : /*
2665 : : * Use the smaller of the two estimates. This can be justified in
2666 : : * essentially the same terms as given below for the no-stats case: to
2667 : : * a first approximation, we are estimating from the point of view of
2668 : : * the relation with smaller nd.
2669 : : */
2670 [ + + ]: 3693 : selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
2671 : 3693 : }
2672 : : else
2673 : : {
2674 : : /*
2675 : : * We do not have MCV lists for both sides. Estimate the join
2676 : : * selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
2677 : : * is plausible if we assume that the join operator is strict and the
2678 : : * non-null values are about equally distributed: a given non-null
2679 : : * tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
2680 : : * of rel2, so total join rows are at most
2681 : : * N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
2682 : : * not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
2683 : : * is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
2684 : : * with MIN() is an upper bound. Using the MIN() means we estimate
2685 : : * from the point of view of the relation with smaller nd (since the
2686 : : * larger nd is determining the MIN). It is reasonable to assume that
2687 : : * most tuples in this rel will have join partners, so the bound is
2688 : : * probably reasonably tight and should be taken as-is.
2689 : : *
2690 : : * XXX Can we be smarter if we have an MCV list for just one side? It
2691 : : * seems that if we assume equal distribution for the other side, we
2692 : : * end up with the same answer anyway.
2693 : : */
2694 [ + + ]: 21349 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2695 [ + + ]: 21349 : double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
2696 : :
2697 : 21349 : selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
2698 [ + + ]: 21349 : if (nd1 > nd2)
2699 : 9138 : selec /= nd1;
2700 : : else
2701 : 12211 : selec /= nd2;
2702 : 21349 : }
2703 : :
2704 : 50084 : return selec;
2705 : 25042 : }
2706 : :
2707 : : /*
2708 : : * eqjoinsel_semi --- eqjoinsel for semi join
2709 : : *
2710 : : * (Also used for anti join, which we are supposed to estimate the same way.)
2711 : : * Caller has ensured that vardata1 is the LHS variable; however, eqproc
2712 : : * is for the original join operator, which might now need to have the inputs
2713 : : * swapped in order to apply correctly. Also, if have_mcvs1 && have_mcvs2
2714 : : * then hasmatch1[], hasmatch2[], and *p_nmatches were filled by
2715 : : * eqjoinsel_inner.
2716 : : */
2717 : : static double
2718 : 1380 : eqjoinsel_semi(FmgrInfo *eqproc, Oid collation,
2719 : : Oid hashLeft, Oid hashRight,
2720 : : bool op_is_reversed,
2721 : : VariableStatData *vardata1, VariableStatData *vardata2,
2722 : : double nd1, double nd2,
2723 : : bool isdefault1, bool isdefault2,
2724 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2725 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
2726 : : bool have_mcvs1, bool have_mcvs2,
2727 : : bool *hasmatch1, bool *hasmatch2,
2728 : : int *p_nmatches,
2729 : : RelOptInfo *inner_rel)
2730 : : {
2731 : 1380 : double selec;
2732 : :
2733 : : /*
2734 : : * We clamp nd2 to be not more than what we estimate the inner relation's
2735 : : * size to be. This is intuitively somewhat reasonable since obviously
2736 : : * there can't be more than that many distinct values coming from the
2737 : : * inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
2738 : : * likewise) is that this is the only pathway by which restriction clauses
2739 : : * applied to the inner rel will affect the join result size estimate,
2740 : : * since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
2741 : : * only the outer rel's size. If we clamped nd1 we'd be double-counting
2742 : : * the selectivity of outer-rel restrictions.
2743 : : *
2744 : : * We can apply this clamping both with respect to the base relation from
2745 : : * which the join variable comes (if there is just one), and to the
2746 : : * immediate inner input relation of the current join.
2747 : : *
2748 : : * If we clamp, we can treat nd2 as being a non-default estimate; it's not
2749 : : * great, maybe, but it didn't come out of nowhere either. This is most
2750 : : * helpful when the inner relation is empty and consequently has no stats.
2751 : : */
2752 [ + + ]: 1380 : if (vardata2->rel)
2753 : : {
2754 [ + + ]: 1379 : if (nd2 >= vardata2->rel->rows)
2755 : : {
2756 : 1078 : nd2 = vardata2->rel->rows;
2757 : 1078 : isdefault2 = false;
2758 : 1078 : }
2759 : 1379 : }
2760 [ + + ]: 1380 : if (nd2 >= inner_rel->rows)
2761 : : {
2762 : 1068 : nd2 = inner_rel->rows;
2763 : 1068 : isdefault2 = false;
2764 : 1068 : }
2765 : :
2766 [ + + - + ]: 1380 : if (have_mcvs1 && have_mcvs2)
2767 : : {
2768 : : /*
2769 : : * We have most-common-value lists for both relations. Run through
2770 : : * the lists to see which MCVs actually join to each other with the
2771 : : * given operator. This allows us to determine the exact join
2772 : : * selectivity for the portion of the relations represented by the MCV
2773 : : * lists. We still have to estimate for the remaining population, but
2774 : : * in a skewed distribution this gives us a big leg up in accuracy.
2775 : : */
2776 : 102 : double nullfrac1 = stats1->stanullfrac;
2777 : 102 : double matchprodfreq,
2778 : : matchfreq1,
2779 : : uncertainfrac,
2780 : : uncertain;
2781 : 102 : int i,
2782 : : nmatches,
2783 : : clamped_nvalues2;
2784 : :
2785 : : /*
2786 : : * The clamping above could have resulted in nd2 being less than
2787 : : * sslot2->nvalues; in which case, we assume that precisely the nd2
2788 : : * most common values in the relation will appear in the join input,
2789 : : * and so compare to only the first nd2 members of the MCV list. Of
2790 : : * course this is frequently wrong, but it's the best bet we can make.
2791 : : */
2792 [ + + ]: 102 : clamped_nvalues2 = Min(sslot2->nvalues, nd2);
2793 : :
2794 : : /*
2795 : : * If we did not set clamped_nvalues2 to less than sslot2->nvalues,
2796 : : * then the hasmatch1[] and hasmatch2[] match flags computed by
2797 : : * eqjoinsel_inner are still perfectly applicable, so we need not
2798 : : * re-do the matching work. Note that it does not matter if
2799 : : * op_is_reversed: we'd get the same answers.
2800 : : *
2801 : : * If we did clamp, then a different set of sslot2 values is to be
2802 : : * compared, so we have to re-do the matching.
2803 : : */
2804 [ + - ]: 102 : if (clamped_nvalues2 != sslot2->nvalues)
2805 : : {
2806 : : /* Must re-zero the arrays */
2807 : 0 : memset(hasmatch1, 0, sslot1->nvalues * sizeof(bool));
2808 : 0 : memset(hasmatch2, 0, clamped_nvalues2 * sizeof(bool));
2809 : : /* Re-fill the match arrays */
2810 : 0 : eqjoinsel_find_matches(eqproc, collation,
2811 : 0 : hashLeft, hashRight,
2812 : 0 : op_is_reversed,
2813 : 0 : sslot1, sslot2,
2814 : 0 : sslot1->nvalues, clamped_nvalues2,
2815 : 0 : hasmatch1, hasmatch2,
2816 : 0 : p_nmatches, &matchprodfreq);
2817 : 0 : }
2818 : 102 : nmatches = *p_nmatches;
2819 : :
2820 : : /* Sum up frequencies of matched MCVs */
2821 : 102 : matchfreq1 = 0.0;
2822 [ + + ]: 2199 : for (i = 0; i < sslot1->nvalues; i++)
2823 : : {
2824 [ + + ]: 2097 : if (hasmatch1[i])
2825 : 1920 : matchfreq1 += sslot1->numbers[i];
2826 : 2097 : }
2827 [ - + + + ]: 204 : CLAMP_PROBABILITY(matchfreq1);
2828 : :
2829 : : /*
2830 : : * Now we need to estimate the fraction of relation 1 that has at
2831 : : * least one join partner. We know for certain that the matched MCVs
2832 : : * do, so that gives us a lower bound, but we're really in the dark
2833 : : * about everything else. Our crude approach is: if nd1 <= nd2 then
2834 : : * assume all non-null rel1 rows have join partners, else assume for
2835 : : * the uncertain rows that a fraction nd2/nd1 have join partners. We
2836 : : * can discount the known-matched MCVs from the distinct-values counts
2837 : : * before doing the division.
2838 : : *
2839 : : * Crude as the above is, it's completely useless if we don't have
2840 : : * reliable ndistinct values for both sides. Hence, if either nd1 or
2841 : : * nd2 is default, punt and assume half of the uncertain rows have
2842 : : * join partners.
2843 : : */
2844 [ + - - + ]: 102 : if (!isdefault1 && !isdefault2)
2845 : : {
2846 : 102 : nd1 -= nmatches;
2847 : 102 : nd2 -= nmatches;
2848 [ + + - + ]: 102 : if (nd1 <= nd2 || nd2 < 0)
2849 : 96 : uncertainfrac = 1.0;
2850 : : else
2851 : 6 : uncertainfrac = nd2 / nd1;
2852 : 102 : }
2853 : : else
2854 : 0 : uncertainfrac = 0.5;
2855 : 102 : uncertain = 1.0 - matchfreq1 - nullfrac1;
2856 [ - + + - ]: 204 : CLAMP_PROBABILITY(uncertain);
2857 : 102 : selec = matchfreq1 + uncertainfrac * uncertain;
2858 : 102 : }
2859 : : else
2860 : : {
2861 : : /*
2862 : : * Without MCV lists for both sides, we can only use the heuristic
2863 : : * about nd1 vs nd2.
2864 : : */
2865 [ + + ]: 1278 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2866 : :
2867 [ + + + + ]: 1278 : if (!isdefault1 && !isdefault2)
2868 : : {
2869 [ + + - + ]: 980 : if (nd1 <= nd2 || nd2 < 0)
2870 : 810 : selec = 1.0 - nullfrac1;
2871 : : else
2872 : 170 : selec = (nd2 / nd1) * (1.0 - nullfrac1);
2873 : 980 : }
2874 : : else
2875 : 298 : selec = 0.5 * (1.0 - nullfrac1);
2876 : 1278 : }
2877 : :
2878 : 2760 : return selec;
2879 : 1380 : }
2880 : :
2881 : : /*
2882 : : * Identify matching MCVs for eqjoinsel_inner or eqjoinsel_semi.
2883 : : *
2884 : : * Inputs:
2885 : : * eqproc: FmgrInfo for equality function to use (might be reversed)
2886 : : * collation: OID of collation to use
2887 : : * hashLeft, hashRight: OIDs of hash functions associated with equality op,
2888 : : * or InvalidOid if we're not to use hashing
2889 : : * op_is_reversed: indicates that eqproc compares right type to left type
2890 : : * sslot1, sslot2: MCV values for the lefthand and righthand inputs
2891 : : * nvalues1, nvalues2: number of values to be considered (can be less than
2892 : : * sslotN->nvalues, but not more)
2893 : : * Outputs:
2894 : : * hasmatch1[], hasmatch2[]: pre-zeroed arrays of lengths nvalues1, nvalues2;
2895 : : * entries are set to true if that MCV has a match on the other side
2896 : : * *p_nmatches: receives number of MCV pairs that match
2897 : : * *p_matchprodfreq: receives sum(sslot1->numbers[i] * sslot2->numbers[j])
2898 : : * for matching MCVs
2899 : : *
2900 : : * Note that hashLeft is for the eqproc's left-hand input type, hashRight
2901 : : * for its right, regardless of op_is_reversed.
2902 : : *
2903 : : * Note we assume that each MCV will match at most one member of the other
2904 : : * MCV list. If the operator isn't really equality, there could be multiple
2905 : : * matches --- but we don't look for them, both for speed and because the
2906 : : * math wouldn't add up...
2907 : : */
2908 : : static void
2909 : 3693 : eqjoinsel_find_matches(FmgrInfo *eqproc, Oid collation,
2910 : : Oid hashLeft, Oid hashRight,
2911 : : bool op_is_reversed,
2912 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2913 : : int nvalues1, int nvalues2,
2914 : : bool *hasmatch1, bool *hasmatch2,
2915 : : int *p_nmatches, double *p_matchprodfreq)
2916 : : {
2917 : 3693 : LOCAL_FCINFO(fcinfo, 2);
2918 : 3693 : double matchprodfreq = 0.0;
2919 : 3693 : int nmatches = 0;
2920 : :
2921 : : /*
2922 : : * Save a few cycles by setting up the fcinfo struct just once. Using
2923 : : * FunctionCallInvoke directly also avoids failure if the eqproc returns
2924 : : * NULL, though really equality functions should never do that.
2925 : : */
2926 : 3693 : InitFunctionCallInfoData(*fcinfo, eqproc, 2, collation,
2927 : : NULL, NULL);
2928 : 3693 : fcinfo->args[0].isnull = false;
2929 : 3693 : fcinfo->args[1].isnull = false;
2930 : :
2931 [ + + - + ]: 3693 : if (OidIsValid(hashLeft) && OidIsValid(hashRight))
2932 : : {
2933 : : /* Use a hash table to speed up the matching */
2934 : 1245 : LOCAL_FCINFO(hash_fcinfo, 1);
2935 : 1245 : FmgrInfo hash_proc;
2936 : 1245 : MCVHashContext hashContext;
2937 : 1245 : MCVHashTable_hash *hashTable;
2938 : 1245 : AttStatsSlot *statsProbe;
2939 : 1245 : AttStatsSlot *statsHash;
2940 : 1245 : bool *hasMatchProbe;
2941 : 1245 : bool *hasMatchHash;
2942 : 1245 : int nvaluesProbe;
2943 : 1245 : int nvaluesHash;
2944 : :
2945 : : /* Make sure we build the hash table on the smaller array. */
2946 [ + + ]: 1245 : if (sslot1->nvalues >= sslot2->nvalues)
2947 : : {
2948 : 1107 : statsProbe = sslot1;
2949 : 1107 : statsHash = sslot2;
2950 : 1107 : hasMatchProbe = hasmatch1;
2951 : 1107 : hasMatchHash = hasmatch2;
2952 : 1107 : nvaluesProbe = nvalues1;
2953 : 1107 : nvaluesHash = nvalues2;
2954 : 1107 : }
2955 : : else
2956 : : {
2957 : : /* We'll have to reverse the direction of use of the operator. */
2958 : 138 : op_is_reversed = !op_is_reversed;
2959 : 138 : statsProbe = sslot2;
2960 : 138 : statsHash = sslot1;
2961 : 138 : hasMatchProbe = hasmatch2;
2962 : 138 : hasMatchHash = hasmatch1;
2963 : 138 : nvaluesProbe = nvalues2;
2964 : 138 : nvaluesHash = nvalues1;
2965 : : }
2966 : :
2967 : : /*
2968 : : * Build the hash table on the smaller array, using the appropriate
2969 : : * hash function for its data type.
2970 : : */
2971 [ + + ]: 1245 : fmgr_info(op_is_reversed ? hashLeft : hashRight, &hash_proc);
2972 : 1245 : InitFunctionCallInfoData(*hash_fcinfo, &hash_proc, 1, collation,
2973 : : NULL, NULL);
2974 : 1245 : hash_fcinfo->args[0].isnull = false;
2975 : :
2976 : 1245 : hashContext.equal_fcinfo = fcinfo;
2977 : 1245 : hashContext.hash_fcinfo = hash_fcinfo;
2978 : 1245 : hashContext.op_is_reversed = op_is_reversed;
2979 : 1245 : hashContext.insert_mode = true;
2980 : 2490 : get_typlenbyval(statsHash->valuetype,
2981 : 1245 : &hashContext.hash_typlen,
2982 : 1245 : &hashContext.hash_typbyval);
2983 : :
2984 : 2490 : hashTable = MCVHashTable_create(CurrentMemoryContext,
2985 : 1245 : nvaluesHash,
2986 : : &hashContext);
2987 : :
2988 [ + + ]: 33441 : for (int i = 0; i < nvaluesHash; i++)
2989 : : {
2990 : 32196 : bool found = false;
2991 : 64392 : MCVHashEntry *entry = MCVHashTable_insert(hashTable,
2992 : 32196 : statsHash->values[i],
2993 : : &found);
2994 : :
2995 : : /*
2996 : : * MCVHashTable_insert will only report "found" if the new value
2997 : : * is equal to some previous one per datum_image_eq(). That
2998 : : * probably shouldn't happen, since we're not expecting duplicates
2999 : : * in the MCV list. If we do find a dup, just ignore it, leaving
3000 : : * the hash entry's index pointing at the first occurrence. That
3001 : : * matches the behavior that the non-hashed code path would have.
3002 : : */
3003 [ + - ]: 32196 : if (likely(!found))
3004 : 32196 : entry->index = i;
3005 : 32196 : }
3006 : :
3007 : : /*
3008 : : * Prepare to probe the hash table. If the probe values are of a
3009 : : * different data type, then we need to change hash functions. (This
3010 : : * code relies on the assumption that since we defined SH_STORE_HASH,
3011 : : * simplehash.h will never need to compute hash values for existing
3012 : : * hash table entries.)
3013 : : */
3014 : 1245 : hashContext.insert_mode = false;
3015 [ + + ]: 1245 : if (hashLeft != hashRight)
3016 : : {
3017 [ + - ]: 114 : fmgr_info(op_is_reversed ? hashRight : hashLeft, &hash_proc);
3018 : : /* Resetting hash_fcinfo is probably unnecessary, but be safe */
3019 : 114 : InitFunctionCallInfoData(*hash_fcinfo, &hash_proc, 1, collation,
3020 : : NULL, NULL);
3021 : 114 : hash_fcinfo->args[0].isnull = false;
3022 : 114 : }
3023 : :
3024 : : /* Look up each probe value in turn. */
3025 [ + + ]: 38735 : for (int i = 0; i < nvaluesProbe; i++)
3026 : : {
3027 : 74980 : MCVHashEntry *entry = MCVHashTable_lookup(hashTable,
3028 : 37490 : statsProbe->values[i]);
3029 : :
3030 : : /* As in the other code path, skip already-matched hash entries */
3031 [ + + - + ]: 37490 : if (entry != NULL && !hasMatchHash[entry->index])
3032 : : {
3033 : 26145 : hasMatchHash[entry->index] = hasMatchProbe[i] = true;
3034 : 26145 : nmatches++;
3035 : 26145 : matchprodfreq += statsHash->numbers[entry->index] * statsProbe->numbers[i];
3036 : 26145 : }
3037 : 37490 : }
3038 : :
3039 : 1245 : MCVHashTable_destroy(hashTable);
3040 : 1245 : }
3041 : : else
3042 : : {
3043 : : /* We're not to use hashing, so do it the O(N^2) way */
3044 : 2448 : int index1,
3045 : : index2;
3046 : :
3047 : : /* Set up to supply the values in the order the operator expects */
3048 [ - + ]: 2448 : if (op_is_reversed)
3049 : : {
3050 : 0 : index1 = 1;
3051 : 0 : index2 = 0;
3052 : 0 : }
3053 : : else
3054 : : {
3055 : 2448 : index1 = 0;
3056 : 2448 : index2 = 1;
3057 : : }
3058 : :
3059 [ + + ]: 13014 : for (int i = 0; i < nvalues1; i++)
3060 : : {
3061 : 10566 : fcinfo->args[index1].value = sslot1->values[i];
3062 : :
3063 [ + + ]: 41832 : for (int j = 0; j < nvalues2; j++)
3064 : : {
3065 : 31266 : Datum fresult;
3066 : :
3067 [ + + ]: 31266 : if (hasmatch2[j])
3068 : 18583 : continue;
3069 : 12683 : fcinfo->args[index2].value = sslot2->values[j];
3070 : 12683 : fcinfo->isnull = false;
3071 : 12683 : fresult = FunctionCallInvoke(fcinfo);
3072 [ + - + + ]: 12683 : if (!fcinfo->isnull && DatumGetBool(fresult))
3073 : : {
3074 : 9935 : hasmatch1[i] = hasmatch2[j] = true;
3075 : 9935 : matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
3076 : 9935 : nmatches++;
3077 : 9935 : break;
3078 : : }
3079 [ + + + ]: 31266 : }
3080 : 10566 : }
3081 : 2448 : }
3082 : :
3083 : 3693 : *p_nmatches = nmatches;
3084 : 3693 : *p_matchprodfreq = matchprodfreq;
3085 : 3693 : }
3086 : :
3087 : : /*
3088 : : * Support functions for the hash tables used by eqjoinsel_find_matches
3089 : : */
3090 : : static uint32
3091 : 69686 : hash_mcv(MCVHashTable_hash *tab, Datum key)
3092 : : {
3093 : 69686 : MCVHashContext *context = (MCVHashContext *) tab->private_data;
3094 : 69686 : FunctionCallInfo fcinfo = context->hash_fcinfo;
3095 : 69686 : Datum fresult;
3096 : :
3097 : 69686 : fcinfo->args[0].value = key;
3098 : 69686 : fcinfo->isnull = false;
3099 : 69686 : fresult = FunctionCallInvoke(fcinfo);
3100 [ + - ]: 69686 : Assert(!fcinfo->isnull);
3101 : 139372 : return DatumGetUInt32(fresult);
3102 : 69686 : }
3103 : :
3104 : : static bool
3105 : 26145 : mcvs_equal(MCVHashTable_hash *tab, Datum key0, Datum key1)
3106 : : {
3107 : 26145 : MCVHashContext *context = (MCVHashContext *) tab->private_data;
3108 : :
3109 [ - + ]: 26145 : if (context->insert_mode)
3110 : : {
3111 : : /*
3112 : : * During the insertion step, any comparisons will be between two
3113 : : * Datums of the hash table's data type, so if the given operator is
3114 : : * cross-type it will be the wrong thing to use. Fortunately, we can
3115 : : * use datum_image_eq instead. The MCV values should all be distinct
3116 : : * anyway, so it's mostly pro-forma to compare them at all.
3117 : : */
3118 : 0 : return datum_image_eq(key0, key1,
3119 : 0 : context->hash_typbyval, context->hash_typlen);
3120 : : }
3121 : : else
3122 : : {
3123 : 26145 : FunctionCallInfo fcinfo = context->equal_fcinfo;
3124 : 26145 : Datum fresult;
3125 : :
3126 : : /*
3127 : : * Apply the operator the correct way around. Although simplehash.h
3128 : : * doesn't document this explicitly, during lookups key0 is from the
3129 : : * hash table while key1 is the probe value, so we should compare them
3130 : : * in that order only if op_is_reversed.
3131 : : */
3132 [ + + ]: 26145 : if (context->op_is_reversed)
3133 : : {
3134 : 4148 : fcinfo->args[0].value = key0;
3135 : 4148 : fcinfo->args[1].value = key1;
3136 : 4148 : }
3137 : : else
3138 : : {
3139 : 21997 : fcinfo->args[0].value = key1;
3140 : 21997 : fcinfo->args[1].value = key0;
3141 : : }
3142 : 26145 : fcinfo->isnull = false;
3143 : 26145 : fresult = FunctionCallInvoke(fcinfo);
3144 [ - + ]: 26145 : return (!fcinfo->isnull && DatumGetBool(fresult));
3145 : 26145 : }
3146 : 26145 : }
3147 : :
3148 : : /*
3149 : : * neqjoinsel - Join selectivity of "!="
3150 : : */
3151 : : Datum
3152 : 224 : neqjoinsel(PG_FUNCTION_ARGS)
3153 : : {
3154 : 224 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
3155 : 224 : Oid operator = PG_GETARG_OID(1);
3156 : 224 : List *args = (List *) PG_GETARG_POINTER(2);
3157 : 224 : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
3158 : 224 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
3159 : 224 : Oid collation = PG_GET_COLLATION();
3160 : 224 : float8 result;
3161 : :
3162 [ + + - + ]: 224 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
3163 : : {
3164 : : /*
3165 : : * For semi-joins, if there is more than one distinct value in the RHS
3166 : : * relation then every non-null LHS row must find a row to join since
3167 : : * it can only be equal to one of them. We'll assume that there is
3168 : : * always more than one distinct RHS value for the sake of stability,
3169 : : * though in theory we could have special cases for empty RHS
3170 : : * (selectivity = 0) and single-distinct-value RHS (selectivity =
3171 : : * fraction of LHS that has the same value as the single RHS value).
3172 : : *
3173 : : * For anti-joins, if we use the same assumption that there is more
3174 : : * than one distinct key in the RHS relation, then every non-null LHS
3175 : : * row must be suppressed by the anti-join.
3176 : : *
3177 : : * So either way, the selectivity estimate should be 1 - nullfrac.
3178 : : */
3179 : 91 : VariableStatData leftvar;
3180 : 91 : VariableStatData rightvar;
3181 : 91 : bool reversed;
3182 : 91 : HeapTuple statsTuple;
3183 : 91 : double nullfrac;
3184 : :
3185 : 91 : get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
3186 [ + + ]: 91 : statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
3187 [ + + ]: 91 : if (HeapTupleIsValid(statsTuple))
3188 : 64 : nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
3189 : : else
3190 : 27 : nullfrac = 0.0;
3191 [ + + ]: 91 : ReleaseVariableStats(leftvar);
3192 [ + + ]: 91 : ReleaseVariableStats(rightvar);
3193 : :
3194 : 91 : result = 1.0 - nullfrac;
3195 : 91 : }
3196 : : else
3197 : : {
3198 : : /*
3199 : : * We want 1 - eqjoinsel() where the equality operator is the one
3200 : : * associated with this != operator, that is, its negator.
3201 : : */
3202 : 133 : Oid eqop = get_negator(operator);
3203 : :
3204 [ + - ]: 133 : if (eqop)
3205 : : {
3206 : 133 : result =
3207 : 133 : DatumGetFloat8(DirectFunctionCall5Coll(eqjoinsel,
3208 : 133 : collation,
3209 : 133 : PointerGetDatum(root),
3210 : 133 : ObjectIdGetDatum(eqop),
3211 : 133 : PointerGetDatum(args),
3212 : 133 : Int16GetDatum(jointype),
3213 : 133 : PointerGetDatum(sjinfo)));
3214 : 133 : }
3215 : : else
3216 : : {
3217 : : /* Use default selectivity (should we raise an error instead?) */
3218 : 0 : result = DEFAULT_EQ_SEL;
3219 : : }
3220 : 133 : result = 1.0 - result;
3221 : 133 : }
3222 : :
3223 : 448 : PG_RETURN_FLOAT8(result);
3224 : 224 : }
3225 : :
3226 : : /*
3227 : : * scalarltjoinsel - Join selectivity of "<" for scalars
3228 : : */
3229 : : Datum
3230 : 54 : scalarltjoinsel(PG_FUNCTION_ARGS)
3231 : : {
3232 : 54 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3233 : : }
3234 : :
3235 : : /*
3236 : : * scalarlejoinsel - Join selectivity of "<=" for scalars
3237 : : */
3238 : : Datum
3239 : 30 : scalarlejoinsel(PG_FUNCTION_ARGS)
3240 : : {
3241 : 30 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3242 : : }
3243 : :
3244 : : /*
3245 : : * scalargtjoinsel - Join selectivity of ">" for scalars
3246 : : */
3247 : : Datum
3248 : 46 : scalargtjoinsel(PG_FUNCTION_ARGS)
3249 : : {
3250 : 46 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3251 : : }
3252 : :
3253 : : /*
3254 : : * scalargejoinsel - Join selectivity of ">=" for scalars
3255 : : */
3256 : : Datum
3257 : 30 : scalargejoinsel(PG_FUNCTION_ARGS)
3258 : : {
3259 : 30 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3260 : : }
3261 : :
3262 : :
3263 : : /*
3264 : : * mergejoinscansel - Scan selectivity of merge join.
3265 : : *
3266 : : * A merge join will stop as soon as it exhausts either input stream.
3267 : : * Therefore, if we can estimate the ranges of both input variables,
3268 : : * we can estimate how much of the input will actually be read. This
3269 : : * can have a considerable impact on the cost when using indexscans.
3270 : : *
3271 : : * Also, we can estimate how much of each input has to be read before the
3272 : : * first join pair is found, which will affect the join's startup time.
3273 : : *
3274 : : * clause should be a clause already known to be mergejoinable. opfamily,
3275 : : * cmptype, and nulls_first specify the sort ordering being used.
3276 : : *
3277 : : * The outputs are:
3278 : : * *leftstart is set to the fraction of the left-hand variable expected
3279 : : * to be scanned before the first join pair is found (0 to 1).
3280 : : * *leftend is set to the fraction of the left-hand variable expected
3281 : : * to be scanned before the join terminates (0 to 1).
3282 : : * *rightstart, *rightend similarly for the right-hand variable.
3283 : : */
3284 : : void
3285 : 13800 : mergejoinscansel(PlannerInfo *root, Node *clause,
3286 : : Oid opfamily, CompareType cmptype, bool nulls_first,
3287 : : Selectivity *leftstart, Selectivity *leftend,
3288 : : Selectivity *rightstart, Selectivity *rightend)
3289 : : {
3290 : 13800 : Node *left,
3291 : : *right;
3292 : 13800 : VariableStatData leftvar,
3293 : : rightvar;
3294 : 13800 : Oid opmethod;
3295 : 13800 : int op_strategy;
3296 : 13800 : Oid op_lefttype;
3297 : 13800 : Oid op_righttype;
3298 : 13800 : Oid opno,
3299 : : collation,
3300 : : lsortop,
3301 : : rsortop,
3302 : : lstatop,
3303 : : rstatop,
3304 : : ltop,
3305 : : leop,
3306 : : revltop,
3307 : : revleop;
3308 : 13800 : StrategyNumber ltstrat,
3309 : : lestrat,
3310 : : gtstrat,
3311 : : gestrat;
3312 : 13800 : bool isgt;
3313 : 13800 : Datum leftmin,
3314 : : leftmax,
3315 : : rightmin,
3316 : : rightmax;
3317 : 13800 : double selec;
3318 : :
3319 : : /* Set default results if we can't figure anything out. */
3320 : : /* XXX should default "start" fraction be a bit more than 0? */
3321 : 13800 : *leftstart = *rightstart = 0.0;
3322 : 13800 : *leftend = *rightend = 1.0;
3323 : :
3324 : : /* Deconstruct the merge clause */
3325 [ + - ]: 13800 : if (!is_opclause(clause))
3326 : 0 : return; /* shouldn't happen */
3327 : 13800 : opno = ((OpExpr *) clause)->opno;
3328 : 13800 : collation = ((OpExpr *) clause)->inputcollid;
3329 : 13800 : left = get_leftop((Expr *) clause);
3330 : 13800 : right = get_rightop((Expr *) clause);
3331 [ + - ]: 13800 : if (!right)
3332 : 0 : return; /* shouldn't happen */
3333 : :
3334 : : /* Look for stats for the inputs */
3335 : 13800 : examine_variable(root, left, 0, &leftvar);
3336 : 13800 : examine_variable(root, right, 0, &rightvar);
3337 : :
3338 : 13800 : opmethod = get_opfamily_method(opfamily);
3339 : :
3340 : : /* Extract the operator's declared left/right datatypes */
3341 : 13800 : get_op_opfamily_properties(opno, opfamily, false,
3342 : : &op_strategy,
3343 : : &op_lefttype,
3344 : : &op_righttype);
3345 [ + - ]: 13800 : Assert(IndexAmTranslateStrategy(op_strategy, opmethod, opfamily, true) == COMPARE_EQ);
3346 : :
3347 : : /*
3348 : : * Look up the various operators we need. If we don't find them all, it
3349 : : * probably means the opfamily is broken, but we just fail silently.
3350 : : *
3351 : : * Note: we expect that pg_statistic histograms will be sorted by the '<'
3352 : : * operator, regardless of which sort direction we are considering.
3353 : : */
3354 [ - + + ]: 13800 : switch (cmptype)
3355 : : {
3356 : : case COMPARE_LT:
3357 : 13794 : isgt = false;
3358 : 13794 : ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
3359 : 13794 : lestrat = IndexAmTranslateCompareType(COMPARE_LE, opmethod, opfamily, true);
3360 [ + + ]: 13794 : if (op_lefttype == op_righttype)
3361 : : {
3362 : : /* easy case */
3363 : 27328 : ltop = get_opfamily_member(opfamily,
3364 : 13664 : op_lefttype, op_righttype,
3365 : 13664 : ltstrat);
3366 : 27328 : leop = get_opfamily_member(opfamily,
3367 : 13664 : op_lefttype, op_righttype,
3368 : 13664 : lestrat);
3369 : 13664 : lsortop = ltop;
3370 : 13664 : rsortop = ltop;
3371 : 13664 : lstatop = lsortop;
3372 : 13664 : rstatop = rsortop;
3373 : 13664 : revltop = ltop;
3374 : 13664 : revleop = leop;
3375 : 13664 : }
3376 : : else
3377 : : {
3378 : 260 : ltop = get_opfamily_member(opfamily,
3379 : 130 : op_lefttype, op_righttype,
3380 : 130 : ltstrat);
3381 : 260 : leop = get_opfamily_member(opfamily,
3382 : 130 : op_lefttype, op_righttype,
3383 : 130 : lestrat);
3384 : 260 : lsortop = get_opfamily_member(opfamily,
3385 : 130 : op_lefttype, op_lefttype,
3386 : 130 : ltstrat);
3387 : 260 : rsortop = get_opfamily_member(opfamily,
3388 : 130 : op_righttype, op_righttype,
3389 : 130 : ltstrat);
3390 : 130 : lstatop = lsortop;
3391 : 130 : rstatop = rsortop;
3392 : 260 : revltop = get_opfamily_member(opfamily,
3393 : 130 : op_righttype, op_lefttype,
3394 : 130 : ltstrat);
3395 : 260 : revleop = get_opfamily_member(opfamily,
3396 : 130 : op_righttype, op_lefttype,
3397 : 130 : lestrat);
3398 : : }
3399 : 13794 : break;
3400 : : case COMPARE_GT:
3401 : : /* descending-order case */
3402 : 6 : isgt = true;
3403 : 6 : ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
3404 : 6 : gtstrat = IndexAmTranslateCompareType(COMPARE_GT, opmethod, opfamily, true);
3405 : 6 : gestrat = IndexAmTranslateCompareType(COMPARE_GE, opmethod, opfamily, true);
3406 [ + - ]: 6 : if (op_lefttype == op_righttype)
3407 : : {
3408 : : /* easy case */
3409 : 12 : ltop = get_opfamily_member(opfamily,
3410 : 6 : op_lefttype, op_righttype,
3411 : 6 : gtstrat);
3412 : 12 : leop = get_opfamily_member(opfamily,
3413 : 6 : op_lefttype, op_righttype,
3414 : 6 : gestrat);
3415 : 6 : lsortop = ltop;
3416 : 6 : rsortop = ltop;
3417 : 12 : lstatop = get_opfamily_member(opfamily,
3418 : 6 : op_lefttype, op_lefttype,
3419 : 6 : ltstrat);
3420 : 6 : rstatop = lstatop;
3421 : 6 : revltop = ltop;
3422 : 6 : revleop = leop;
3423 : 6 : }
3424 : : else
3425 : : {
3426 : 0 : ltop = get_opfamily_member(opfamily,
3427 : 0 : op_lefttype, op_righttype,
3428 : 0 : gtstrat);
3429 : 0 : leop = get_opfamily_member(opfamily,
3430 : 0 : op_lefttype, op_righttype,
3431 : 0 : gestrat);
3432 : 0 : lsortop = get_opfamily_member(opfamily,
3433 : 0 : op_lefttype, op_lefttype,
3434 : 0 : gtstrat);
3435 : 0 : rsortop = get_opfamily_member(opfamily,
3436 : 0 : op_righttype, op_righttype,
3437 : 0 : gtstrat);
3438 : 0 : lstatop = get_opfamily_member(opfamily,
3439 : 0 : op_lefttype, op_lefttype,
3440 : 0 : ltstrat);
3441 : 0 : rstatop = get_opfamily_member(opfamily,
3442 : 0 : op_righttype, op_righttype,
3443 : 0 : ltstrat);
3444 : 0 : revltop = get_opfamily_member(opfamily,
3445 : 0 : op_righttype, op_lefttype,
3446 : 0 : gtstrat);
3447 : 0 : revleop = get_opfamily_member(opfamily,
3448 : 0 : op_righttype, op_lefttype,
3449 : 0 : gestrat);
3450 : : }
3451 : 6 : break;
3452 : : default:
3453 : 0 : goto fail; /* shouldn't get here */
3454 : : }
3455 : :
3456 [ + - ]: 13800 : if (!OidIsValid(lsortop) ||
3457 [ + - ]: 13800 : !OidIsValid(rsortop) ||
3458 [ + - ]: 13800 : !OidIsValid(lstatop) ||
3459 [ + - ]: 13800 : !OidIsValid(rstatop) ||
3460 [ + + ]: 13800 : !OidIsValid(ltop) ||
3461 [ + - ]: 13798 : !OidIsValid(leop) ||
3462 [ + - - + ]: 13798 : !OidIsValid(revltop) ||
3463 : 13798 : !OidIsValid(revleop))
3464 : 2 : goto fail; /* insufficient info in catalogs */
3465 : :
3466 : : /* Try to get ranges of both inputs */
3467 [ + + ]: 13798 : if (!isgt)
3468 : : {
3469 [ + + ]: 13792 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3470 : : &leftmin, &leftmax))
3471 : 2903 : goto fail; /* no range available from stats */
3472 [ + + ]: 10889 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3473 : : &rightmin, &rightmax))
3474 : 3228 : goto fail; /* no range available from stats */
3475 : 7661 : }
3476 : : else
3477 : : {
3478 : : /* need to swap the max and min */
3479 [ + + ]: 6 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3480 : : &leftmax, &leftmin))
3481 : 5 : goto fail; /* no range available from stats */
3482 [ + - ]: 1 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3483 : : &rightmax, &rightmin))
3484 : 0 : goto fail; /* no range available from stats */
3485 : : }
3486 : :
3487 : : /*
3488 : : * Now, the fraction of the left variable that will be scanned is the
3489 : : * fraction that's <= the right-side maximum value. But only believe
3490 : : * non-default estimates, else stick with our 1.0.
3491 : : */
3492 : 15324 : selec = scalarineqsel(root, leop, isgt, true, collation, &leftvar,
3493 : 7662 : rightmax, op_righttype);
3494 [ + + ]: 7662 : if (selec != DEFAULT_INEQ_SEL)
3495 : 7661 : *leftend = selec;
3496 : :
3497 : : /* And similarly for the right variable. */
3498 : 15324 : selec = scalarineqsel(root, revleop, isgt, true, collation, &rightvar,
3499 : 7662 : leftmax, op_lefttype);
3500 [ - + ]: 7662 : if (selec != DEFAULT_INEQ_SEL)
3501 : 7662 : *rightend = selec;
3502 : :
3503 : : /*
3504 : : * Only one of the two "end" fractions can really be less than 1.0;
3505 : : * believe the smaller estimate and reset the other one to exactly 1.0. If
3506 : : * we get exactly equal estimates (as can easily happen with self-joins),
3507 : : * believe neither.
3508 : : */
3509 [ + + ]: 7662 : if (*leftend > *rightend)
3510 : 2470 : *leftend = 1.0;
3511 [ + + ]: 5192 : else if (*leftend < *rightend)
3512 : 1925 : *rightend = 1.0;
3513 : : else
3514 : 3267 : *leftend = *rightend = 1.0;
3515 : :
3516 : : /*
3517 : : * Also, the fraction of the left variable that will be scanned before the
3518 : : * first join pair is found is the fraction that's < the right-side
3519 : : * minimum value. But only believe non-default estimates, else stick with
3520 : : * our own default.
3521 : : */
3522 : 15324 : selec = scalarineqsel(root, ltop, isgt, false, collation, &leftvar,
3523 : 7662 : rightmin, op_righttype);
3524 [ - + ]: 7662 : if (selec != DEFAULT_INEQ_SEL)
3525 : 7662 : *leftstart = selec;
3526 : :
3527 : : /* And similarly for the right variable. */
3528 : 15324 : selec = scalarineqsel(root, revltop, isgt, false, collation, &rightvar,
3529 : 7662 : leftmin, op_lefttype);
3530 [ - + ]: 7662 : if (selec != DEFAULT_INEQ_SEL)
3531 : 7662 : *rightstart = selec;
3532 : :
3533 : : /*
3534 : : * Only one of the two "start" fractions can really be more than zero;
3535 : : * believe the larger estimate and reset the other one to exactly 0.0. If
3536 : : * we get exactly equal estimates (as can easily happen with self-joins),
3537 : : * believe neither.
3538 : : */
3539 [ + + ]: 7662 : if (*leftstart < *rightstart)
3540 : 843 : *leftstart = 0.0;
3541 [ + + ]: 6819 : else if (*leftstart > *rightstart)
3542 : 1826 : *rightstart = 0.0;
3543 : : else
3544 : 4993 : *leftstart = *rightstart = 0.0;
3545 : :
3546 : : /*
3547 : : * If the sort order is nulls-first, we're going to have to skip over any
3548 : : * nulls too. These would not have been counted by scalarineqsel, and we
3549 : : * can safely add in this fraction regardless of whether we believe
3550 : : * scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
3551 : : */
3552 [ + + ]: 7662 : if (nulls_first)
3553 : : {
3554 : 1 : Form_pg_statistic stats;
3555 : :
3556 [ - + ]: 1 : if (HeapTupleIsValid(leftvar.statsTuple))
3557 : : {
3558 : 1 : stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
3559 : 1 : *leftstart += stats->stanullfrac;
3560 [ - + + - ]: 2 : CLAMP_PROBABILITY(*leftstart);
3561 : 1 : *leftend += stats->stanullfrac;
3562 [ - + + - ]: 2 : CLAMP_PROBABILITY(*leftend);
3563 : 1 : }
3564 [ - + ]: 1 : if (HeapTupleIsValid(rightvar.statsTuple))
3565 : : {
3566 : 1 : stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple);
3567 : 1 : *rightstart += stats->stanullfrac;
3568 [ - + + - ]: 2 : CLAMP_PROBABILITY(*rightstart);
3569 : 1 : *rightend += stats->stanullfrac;
3570 [ - + + - ]: 2 : CLAMP_PROBABILITY(*rightend);
3571 : 1 : }
3572 : 1 : }
3573 : :
3574 : : /* Disbelieve start >= end, just in case that can happen */
3575 [ + + ]: 7662 : if (*leftstart >= *leftend)
3576 : : {
3577 : 32 : *leftstart = 0.0;
3578 : 32 : *leftend = 1.0;
3579 : 32 : }
3580 [ + + ]: 7694 : if (*rightstart >= *rightend)
3581 : : {
3582 : 32 : *rightstart = 0.0;
3583 : 32 : *rightend = 1.0;
3584 : 32 : }
3585 : :
3586 : : fail:
3587 [ + + ]: 13800 : ReleaseVariableStats(leftvar);
3588 [ + + ]: 13800 : ReleaseVariableStats(rightvar);
3589 [ - + ]: 13800 : }
3590 : :
3591 : :
3592 : : /*
3593 : : * matchingsel -- generic matching-operator selectivity support
3594 : : *
3595 : : * Use these for any operators that (a) are on data types for which we collect
3596 : : * standard statistics, and (b) have behavior for which the default estimate
3597 : : * (twice DEFAULT_EQ_SEL) is sane. Typically that is good for match-like
3598 : : * operators.
3599 : : */
3600 : :
3601 : : Datum
3602 : 140 : matchingsel(PG_FUNCTION_ARGS)
3603 : : {
3604 : 140 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
3605 : 140 : Oid operator = PG_GETARG_OID(1);
3606 : 140 : List *args = (List *) PG_GETARG_POINTER(2);
3607 : 140 : int varRelid = PG_GETARG_INT32(3);
3608 : 140 : Oid collation = PG_GET_COLLATION();
3609 : 140 : double selec;
3610 : :
3611 : : /* Use generic restriction selectivity logic. */
3612 : 280 : selec = generic_restriction_selectivity(root, operator, collation,
3613 : 140 : args, varRelid,
3614 : : DEFAULT_MATCHING_SEL);
3615 : :
3616 : 280 : PG_RETURN_FLOAT8((float8) selec);
3617 : 140 : }
3618 : :
3619 : : Datum
3620 : 1 : matchingjoinsel(PG_FUNCTION_ARGS)
3621 : : {
3622 : : /* Just punt, for the moment. */
3623 : 1 : PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
3624 : : }
3625 : :
3626 : :
3627 : : /*
3628 : : * Helper routine for estimate_num_groups: add an item to a list of
3629 : : * GroupVarInfos, but only if it's not known equal to any of the existing
3630 : : * entries.
3631 : : */
3632 : : typedef struct
3633 : : {
3634 : : Node *var; /* might be an expression, not just a Var */
3635 : : RelOptInfo *rel; /* relation it belongs to */
3636 : : double ndistinct; /* # distinct values */
3637 : : bool isdefault; /* true if DEFAULT_NUM_DISTINCT was used */
3638 : : } GroupVarInfo;
3639 : :
3640 : : static List *
3641 : 28527 : add_unique_group_var(PlannerInfo *root, List *varinfos,
3642 : : Node *var, VariableStatData *vardata)
3643 : : {
3644 : 28527 : GroupVarInfo *varinfo;
3645 : 28527 : double ndistinct;
3646 : 28527 : bool isdefault;
3647 : 28527 : ListCell *lc;
3648 : :
3649 : 28527 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
3650 : :
3651 : : /*
3652 : : * The nullingrels bits within the var could cause the same var to be
3653 : : * counted multiple times if it's marked with different nullingrels. They
3654 : : * could also prevent us from matching the var to the expressions in
3655 : : * extended statistics (see estimate_multivariate_ndistinct). So strip
3656 : : * them out first.
3657 : : */
3658 : 28527 : var = remove_nulling_relids(var, root->outer_join_rels, NULL);
3659 : :
3660 [ + + + + : 36903 : foreach(lc, varinfos)
+ + + + ]
3661 : : {
3662 : 8376 : varinfo = (GroupVarInfo *) lfirst(lc);
3663 : :
3664 : : /* Drop exact duplicates */
3665 [ + + ]: 8376 : if (equal(var, varinfo->var))
3666 : 75 : return varinfos;
3667 : :
3668 : : /*
3669 : : * Drop known-equal vars, but only if they belong to different
3670 : : * relations (see comments for estimate_num_groups). We aren't too
3671 : : * fussy about the semantics of "equal" here.
3672 : : */
3673 [ + + + + ]: 8301 : if (vardata->rel != varinfo->rel &&
3674 : 734 : exprs_known_equal(root, var, varinfo->var, InvalidOid))
3675 : : {
3676 [ + + ]: 50 : if (varinfo->ndistinct <= ndistinct)
3677 : : {
3678 : : /* Keep older item, forget new one */
3679 : 46 : return varinfos;
3680 : : }
3681 : : else
3682 : : {
3683 : : /* Delete the older item */
3684 : 4 : varinfos = foreach_delete_current(varinfos, lc);
3685 : : }
3686 : 4 : }
3687 : 8255 : }
3688 : :
3689 : 28406 : varinfo = palloc_object(GroupVarInfo);
3690 : :
3691 : 28406 : varinfo->var = var;
3692 : 28406 : varinfo->rel = vardata->rel;
3693 : 28406 : varinfo->ndistinct = ndistinct;
3694 : 28406 : varinfo->isdefault = isdefault;
3695 : 28406 : varinfos = lappend(varinfos, varinfo);
3696 : 28406 : return varinfos;
3697 : 28527 : }
3698 : :
3699 : : /*
3700 : : * estimate_num_groups - Estimate number of groups in a grouped query
3701 : : *
3702 : : * Given a query having a GROUP BY clause, estimate how many groups there
3703 : : * will be --- ie, the number of distinct combinations of the GROUP BY
3704 : : * expressions.
3705 : : *
3706 : : * This routine is also used to estimate the number of rows emitted by
3707 : : * a DISTINCT filtering step; that is an isomorphic problem. (Note:
3708 : : * actually, we only use it for DISTINCT when there's no grouping or
3709 : : * aggregation ahead of the DISTINCT.)
3710 : : *
3711 : : * Inputs:
3712 : : * root - the query
3713 : : * groupExprs - list of expressions being grouped by
3714 : : * input_rows - number of rows estimated to arrive at the group/unique
3715 : : * filter step
3716 : : * pgset - NULL, or a List** pointing to a grouping set to filter the
3717 : : * groupExprs against
3718 : : *
3719 : : * Outputs:
3720 : : * estinfo - When passed as non-NULL, the function will set bits in the
3721 : : * "flags" field in order to provide callers with additional information
3722 : : * about the estimation. Currently, we only set the SELFLAG_USED_DEFAULT
3723 : : * bit if we used any default values in the estimation.
3724 : : *
3725 : : * Given the lack of any cross-correlation statistics in the system, it's
3726 : : * impossible to do anything really trustworthy with GROUP BY conditions
3727 : : * involving multiple Vars. We should however avoid assuming the worst
3728 : : * case (all possible cross-product terms actually appear as groups) since
3729 : : * very often the grouped-by Vars are highly correlated. Our current approach
3730 : : * is as follows:
3731 : : * 1. Expressions yielding boolean are assumed to contribute two groups,
3732 : : * independently of their content, and are ignored in the subsequent
3733 : : * steps. This is mainly because tests like "col IS NULL" break the
3734 : : * heuristic used in step 2 especially badly.
3735 : : * 2. Reduce the given expressions to a list of unique Vars used. For
3736 : : * example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
3737 : : * It is clearly correct not to count the same Var more than once.
3738 : : * It is also reasonable to treat f(x) the same as x: f() cannot
3739 : : * increase the number of distinct values (unless it is volatile,
3740 : : * which we consider unlikely for grouping), but it probably won't
3741 : : * reduce the number of distinct values much either.
3742 : : * As a special case, if a GROUP BY expression can be matched to an
3743 : : * expressional index for which we have statistics, then we treat the
3744 : : * whole expression as though it were just a Var.
3745 : : * 3. If the list contains Vars of different relations that are known equal
3746 : : * due to equivalence classes, then drop all but one of the Vars from each
3747 : : * known-equal set, keeping the one with smallest estimated # of values
3748 : : * (since the extra values of the others can't appear in joined rows).
3749 : : * Note the reason we only consider Vars of different relations is that
3750 : : * if we considered ones of the same rel, we'd be double-counting the
3751 : : * restriction selectivity of the equality in the next step.
3752 : : * 4. For Vars within a single source rel, we multiply together the numbers
3753 : : * of values, clamp to the number of rows in the rel (divided by 10 if
3754 : : * more than one Var), and then multiply by a factor based on the
3755 : : * selectivity of the restriction clauses for that rel. When there's
3756 : : * more than one Var, the initial product is probably too high (it's the
3757 : : * worst case) but clamping to a fraction of the rel's rows seems to be a
3758 : : * helpful heuristic for not letting the estimate get out of hand. (The
3759 : : * factor of 10 is derived from pre-Postgres-7.4 practice.) The factor
3760 : : * we multiply by to adjust for the restriction selectivity assumes that
3761 : : * the restriction clauses are independent of the grouping, which may not
3762 : : * be a valid assumption, but it's hard to do better.
3763 : : * 5. If there are Vars from multiple rels, we repeat step 4 for each such
3764 : : * rel, and multiply the results together.
3765 : : * Note that rels not containing grouped Vars are ignored completely, as are
3766 : : * join clauses. Such rels cannot increase the number of groups, and we
3767 : : * assume such clauses do not reduce the number either (somewhat bogus,
3768 : : * but we don't have the info to do better).
3769 : : */
3770 : : double
3771 : 25342 : estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
3772 : : List **pgset, EstimationInfo *estinfo)
3773 : : {
3774 : 25342 : List *varinfos = NIL;
3775 : 25342 : double srf_multiplier = 1.0;
3776 : 25342 : double numdistinct;
3777 : 25342 : ListCell *l;
3778 : 25342 : int i;
3779 : :
3780 : : /* Zero the estinfo output parameter, if non-NULL */
3781 [ + + ]: 25342 : if (estinfo != NULL)
3782 : 18403 : memset(estinfo, 0, sizeof(EstimationInfo));
3783 : :
3784 : : /*
3785 : : * We don't ever want to return an estimate of zero groups, as that tends
3786 : : * to lead to division-by-zero and other unpleasantness. The input_rows
3787 : : * estimate is usually already at least 1, but clamp it just in case it
3788 : : * isn't.
3789 : : */
3790 : 25342 : input_rows = clamp_row_est(input_rows);
3791 : :
3792 : : /*
3793 : : * If no grouping columns, there's exactly one group. (This can't happen
3794 : : * for normal cases with GROUP BY or DISTINCT, but it is possible for
3795 : : * corner cases with set operations.)
3796 : : */
3797 [ + + + + : 25342 : if (groupExprs == NIL || (pgset && *pgset == NIL))
+ + ]
3798 : 153 : return 1.0;
3799 : :
3800 : : /*
3801 : : * Count groups derived from boolean grouping expressions. For other
3802 : : * expressions, find the unique Vars used, treating an expression as a Var
3803 : : * if we can find stats for it. For each one, record the statistical
3804 : : * estimate of number of distinct values (total in its table, without
3805 : : * regard for filtering).
3806 : : */
3807 : 25189 : numdistinct = 1.0;
3808 : :
3809 : 25189 : i = 0;
3810 [ + - + + : 53669 : foreach(l, groupExprs)
+ + + + ]
3811 : : {
3812 : 28480 : Node *groupexpr = (Node *) lfirst(l);
3813 : 28480 : double this_srf_multiplier;
3814 : 28480 : VariableStatData vardata;
3815 : 28480 : List *varshere;
3816 : 28480 : ListCell *l2;
3817 : :
3818 : : /* is expression in this grouping set? */
3819 [ + + + + ]: 28480 : if (pgset && !list_member_int(*pgset, i++))
3820 : 131 : continue;
3821 : :
3822 : : /*
3823 : : * Set-returning functions in grouping columns are a bit problematic.
3824 : : * The code below will effectively ignore their SRF nature and come up
3825 : : * with a numdistinct estimate as though they were scalar functions.
3826 : : * We compensate by scaling up the end result by the largest SRF
3827 : : * rowcount estimate. (This will be an overestimate if the SRF
3828 : : * produces multiple copies of any output value, but it seems best to
3829 : : * assume the SRF's outputs are distinct. In any case, it's probably
3830 : : * pointless to worry too much about this without much better
3831 : : * estimates for SRF output rowcounts than we have today.)
3832 : : */
3833 : 28349 : this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
3834 [ + + ]: 28349 : if (srf_multiplier < this_srf_multiplier)
3835 : 26 : srf_multiplier = this_srf_multiplier;
3836 : :
3837 : : /* Short-circuit for expressions returning boolean */
3838 [ + + ]: 28349 : if (exprType(groupexpr) == BOOLOID)
3839 : : {
3840 : 34 : numdistinct *= 2.0;
3841 : 34 : continue;
3842 : : }
3843 : :
3844 : : /*
3845 : : * If examine_variable is able to deduce anything about the GROUP BY
3846 : : * expression, treat it as a single variable even if it's really more
3847 : : * complicated.
3848 : : *
3849 : : * XXX This has the consequence that if there's a statistics object on
3850 : : * the expression, we don't split it into individual Vars. This
3851 : : * affects our selection of statistics in
3852 : : * estimate_multivariate_ndistinct, because it's probably better to
3853 : : * use more accurate estimate for each expression and treat them as
3854 : : * independent, than to combine estimates for the extracted variables
3855 : : * when we don't know how that relates to the expressions.
3856 : : */
3857 : 28315 : examine_variable(root, groupexpr, 0, &vardata);
3858 [ + + + + ]: 28315 : if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
3859 : : {
3860 : 40380 : varinfos = add_unique_group_var(root, varinfos,
3861 : 20190 : groupexpr, &vardata);
3862 [ + + ]: 20190 : ReleaseVariableStats(vardata);
3863 : 20190 : continue;
3864 : : }
3865 [ + - ]: 8125 : ReleaseVariableStats(vardata);
3866 : :
3867 : : /*
3868 : : * Else pull out the component Vars. Handle PlaceHolderVars by
3869 : : * recursing into their arguments (effectively assuming that the
3870 : : * PlaceHolderVar doesn't change the number of groups, which boils
3871 : : * down to ignoring the possible addition of nulls to the result set).
3872 : : */
3873 : 8125 : varshere = pull_var_clause(groupexpr,
3874 : : PVC_RECURSE_AGGREGATES |
3875 : : PVC_RECURSE_WINDOWFUNCS |
3876 : : PVC_RECURSE_PLACEHOLDERS);
3877 : :
3878 : : /*
3879 : : * If we find any variable-free GROUP BY item, then either it is a
3880 : : * constant (and we can ignore it) or it contains a volatile function;
3881 : : * in the latter case we punt and assume that each input row will
3882 : : * yield a distinct group.
3883 : : */
3884 [ + + ]: 8125 : if (varshere == NIL)
3885 : : {
3886 [ + + ]: 120 : if (contain_volatile_functions(groupexpr))
3887 : 8 : return input_rows;
3888 : 112 : continue;
3889 : : }
3890 : :
3891 : : /*
3892 : : * Else add variables to varinfos list
3893 : : */
3894 [ + - + + : 16342 : foreach(l2, varshere)
+ + ]
3895 : : {
3896 : 8337 : Node *var = (Node *) lfirst(l2);
3897 : :
3898 : 8337 : examine_variable(root, var, 0, &vardata);
3899 : 8337 : varinfos = add_unique_group_var(root, varinfos, var, &vardata);
3900 [ + + ]: 8337 : ReleaseVariableStats(vardata);
3901 : 8337 : }
3902 [ + + + ]: 28480 : }
3903 : :
3904 : : /*
3905 : : * If now no Vars, we must have an all-constant or all-boolean GROUP BY
3906 : : * list.
3907 : : */
3908 [ + + ]: 25181 : if (varinfos == NIL)
3909 : : {
3910 : : /* Apply SRF multiplier as we would do in the long path */
3911 : 76 : numdistinct *= srf_multiplier;
3912 : : /* Round off */
3913 : 76 : numdistinct = ceil(numdistinct);
3914 : : /* Guard against out-of-range answers */
3915 [ + + ]: 76 : if (numdistinct > input_rows)
3916 : 14 : numdistinct = input_rows;
3917 [ + - ]: 76 : if (numdistinct < 1.0)
3918 : 0 : numdistinct = 1.0;
3919 : 76 : return numdistinct;
3920 : : }
3921 : :
3922 : : /*
3923 : : * Group Vars by relation and estimate total numdistinct.
3924 : : *
3925 : : * For each iteration of the outer loop, we process the frontmost Var in
3926 : : * varinfos, plus all other Vars in the same relation. We remove these
3927 : : * Vars from the newvarinfos list for the next iteration. This is the
3928 : : * easiest way to group Vars of same rel together.
3929 : : */
3930 : 25105 : do
3931 : : {
3932 : 25350 : GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
3933 : 25350 : RelOptInfo *rel = varinfo1->rel;
3934 : 25350 : double reldistinct = 1;
3935 : 25350 : double relmaxndistinct = reldistinct;
3936 : 25350 : int relvarcount = 0;
3937 : 25350 : List *newvarinfos = NIL;
3938 : 25350 : List *relvarinfos = NIL;
3939 : :
3940 : : /*
3941 : : * Split the list of varinfos in two - one for the current rel, one
3942 : : * for remaining Vars on other rels.
3943 : : */
3944 : 25350 : relvarinfos = lappend(relvarinfos, varinfo1);
3945 [ + - + + : 28979 : for_each_from(l, varinfos, 1)
+ + ]
3946 : : {
3947 : 3629 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3948 : :
3949 [ + + ]: 3629 : if (varinfo2->rel == varinfo1->rel)
3950 : : {
3951 : : /* varinfos on current rel */
3952 : 3052 : relvarinfos = lappend(relvarinfos, varinfo2);
3953 : 3052 : }
3954 : : else
3955 : : {
3956 : : /* not time to process varinfo2 yet */
3957 : 577 : newvarinfos = lappend(newvarinfos, varinfo2);
3958 : : }
3959 : 3629 : }
3960 : :
3961 : : /*
3962 : : * Get the numdistinct estimate for the Vars of this rel. We
3963 : : * iteratively search for multivariate n-distinct with maximum number
3964 : : * of vars; assuming that each var group is independent of the others,
3965 : : * we multiply them together. Any remaining relvarinfos after no more
3966 : : * multivariate matches are found are assumed independent too, so
3967 : : * their individual ndistinct estimates are multiplied also.
3968 : : *
3969 : : * While iterating, count how many separate numdistinct values we
3970 : : * apply. We apply a fudge factor below, but only if we multiplied
3971 : : * more than one such values.
3972 : : */
3973 [ + + ]: 50721 : while (relvarinfos)
3974 : : {
3975 : 25371 : double mvndistinct;
3976 : :
3977 [ + + ]: 25371 : if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
3978 : : &mvndistinct))
3979 : : {
3980 : 69 : reldistinct *= mvndistinct;
3981 [ + + ]: 69 : if (relmaxndistinct < mvndistinct)
3982 : 67 : relmaxndistinct = mvndistinct;
3983 : 69 : relvarcount++;
3984 : 69 : }
3985 : : else
3986 : : {
3987 [ + - + + : 53558 : foreach(l, relvarinfos)
+ + ]
3988 : : {
3989 : 28256 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3990 : :
3991 : 28256 : reldistinct *= varinfo2->ndistinct;
3992 [ + + ]: 28256 : if (relmaxndistinct < varinfo2->ndistinct)
3993 : 25221 : relmaxndistinct = varinfo2->ndistinct;
3994 : 28256 : relvarcount++;
3995 : :
3996 : : /*
3997 : : * When varinfo2's isdefault is set then we'd better set
3998 : : * the SELFLAG_USED_DEFAULT bit in the EstimationInfo.
3999 : : */
4000 [ + + + + ]: 28256 : if (estinfo != NULL && varinfo2->isdefault)
4001 : 2486 : estinfo->flags |= SELFLAG_USED_DEFAULT;
4002 : 28256 : }
4003 : :
4004 : : /* we're done with this relation */
4005 : 25302 : relvarinfos = NIL;
4006 : : }
4007 : 25371 : }
4008 : :
4009 : : /*
4010 : : * Sanity check --- don't divide by zero if empty relation.
4011 : : */
4012 [ + + + - ]: 25350 : Assert(IS_SIMPLE_REL(rel));
4013 [ + + ]: 25350 : if (rel->tuples > 0)
4014 : : {
4015 : : /*
4016 : : * Clamp to size of rel, or size of rel / 10 if multiple Vars. The
4017 : : * fudge factor is because the Vars are probably correlated but we
4018 : : * don't know by how much. We should never clamp to less than the
4019 : : * largest ndistinct value for any of the Vars, though, since
4020 : : * there will surely be at least that many groups.
4021 : : */
4022 : 25181 : double clamp = rel->tuples;
4023 : :
4024 [ + + ]: 25181 : if (relvarcount > 1)
4025 : : {
4026 : 2265 : clamp *= 0.1;
4027 [ + + ]: 2265 : if (clamp < relmaxndistinct)
4028 : : {
4029 : 1832 : clamp = relmaxndistinct;
4030 : : /* for sanity in case some ndistinct is too large: */
4031 [ + + ]: 1832 : if (clamp > rel->tuples)
4032 : 13 : clamp = rel->tuples;
4033 : 1832 : }
4034 : 2265 : }
4035 [ + + ]: 25181 : if (reldistinct > clamp)
4036 : 1926 : reldistinct = clamp;
4037 : :
4038 : : /*
4039 : : * Update the estimate based on the restriction selectivity,
4040 : : * guarding against division by zero when reldistinct is zero.
4041 : : * Also skip this if we know that we are returning all rows.
4042 : : */
4043 [ + - + + ]: 25181 : if (reldistinct > 0 && rel->rows < rel->tuples)
4044 : : {
4045 : : /*
4046 : : * Given a table containing N rows with n distinct values in a
4047 : : * uniform distribution, if we select p rows at random then
4048 : : * the expected number of distinct values selected is
4049 : : *
4050 : : * n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
4051 : : *
4052 : : * = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
4053 : : *
4054 : : * See "Approximating block accesses in database
4055 : : * organizations", S. B. Yao, Communications of the ACM,
4056 : : * Volume 20 Issue 4, April 1977 Pages 260-261.
4057 : : *
4058 : : * Alternatively, re-arranging the terms from the factorials,
4059 : : * this may be written as
4060 : : *
4061 : : * n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
4062 : : *
4063 : : * This form of the formula is more efficient to compute in
4064 : : * the common case where p is larger than N/n. Additionally,
4065 : : * as pointed out by Dell'Era, if i << N for all terms in the
4066 : : * product, it can be approximated by
4067 : : *
4068 : : * n * (1 - ((N-p)/N)^(N/n))
4069 : : *
4070 : : * See "Expected distinct values when selecting from a bag
4071 : : * without replacement", Alberto Dell'Era,
4072 : : * http://www.adellera.it/investigations/distinct_balls/.
4073 : : *
4074 : : * The condition i << N is equivalent to n >> 1, so this is a
4075 : : * good approximation when the number of distinct values in
4076 : : * the table is large. It turns out that this formula also
4077 : : * works well even when n is small.
4078 : : */
4079 : 5753 : reldistinct *=
4080 : 11506 : (1 - pow((rel->tuples - rel->rows) / rel->tuples,
4081 : 5753 : rel->tuples / reldistinct));
4082 : 5753 : }
4083 : 25181 : reldistinct = clamp_row_est(reldistinct);
4084 : :
4085 : : /*
4086 : : * Update estimate of total distinct groups.
4087 : : */
4088 : 25181 : numdistinct *= reldistinct;
4089 : 25181 : }
4090 : :
4091 : 25350 : varinfos = newvarinfos;
4092 [ + + ]: 25350 : } while (varinfos != NIL);
4093 : :
4094 : : /* Now we can account for the effects of any SRFs */
4095 : 25105 : numdistinct *= srf_multiplier;
4096 : :
4097 : : /* Round off */
4098 : 25105 : numdistinct = ceil(numdistinct);
4099 : :
4100 : : /* Guard against out-of-range answers */
4101 [ + + ]: 25105 : if (numdistinct > input_rows)
4102 : 5207 : numdistinct = input_rows;
4103 [ + - ]: 25105 : if (numdistinct < 1.0)
4104 : 0 : numdistinct = 1.0;
4105 : :
4106 : 25105 : return numdistinct;
4107 : 25342 : }
4108 : :
4109 : : /*
4110 : : * Try to estimate the bucket size of the hash join inner side when the join
4111 : : * condition contains two or more clauses by employing extended statistics.
4112 : : *
4113 : : * The main idea of this approach is that the distinct value generated by
4114 : : * multivariate estimation on two or more columns would provide less bucket size
4115 : : * than estimation on one separate column.
4116 : : *
4117 : : * IMPORTANT: It is crucial to synchronize the approach of combining different
4118 : : * estimations with the caller's method.
4119 : : *
4120 : : * Return a list of clauses that didn't fetch any extended statistics.
4121 : : */
4122 : : List *
4123 : 52403 : estimate_multivariate_bucketsize(PlannerInfo *root, RelOptInfo *inner,
4124 : : List *hashclauses,
4125 : : Selectivity *innerbucketsize)
4126 : : {
4127 : 52403 : List *clauses;
4128 : 52403 : List *otherclauses;
4129 : 52403 : double ndistinct;
4130 : :
4131 [ + + ]: 52403 : if (list_length(hashclauses) <= 1)
4132 : : {
4133 : : /*
4134 : : * Nothing to do for a single clause. Could we employ univariate
4135 : : * extended stat here?
4136 : : */
4137 : 49896 : return hashclauses;
4138 : : }
4139 : :
4140 : : /* "clauses" is the list of hashclauses we've not dealt with yet */
4141 : 2507 : clauses = list_copy(hashclauses);
4142 : : /* "otherclauses" holds clauses we are going to return to caller */
4143 : 2507 : otherclauses = NIL;
4144 : : /* current estimate of ndistinct */
4145 : 2507 : ndistinct = 1.0;
4146 [ + + ]: 5016 : while (clauses != NIL)
4147 : : {
4148 : 2509 : ListCell *lc;
4149 : 2509 : int relid = -1;
4150 : 2509 : List *varinfos = NIL;
4151 : 2509 : List *origin_rinfos = NIL;
4152 : 2509 : double mvndistinct;
4153 : 2509 : List *origin_varinfos;
4154 : 2509 : int group_relid = -1;
4155 : 2509 : RelOptInfo *group_rel = NULL;
4156 : 2509 : ListCell *lc1,
4157 : : *lc2;
4158 : :
4159 : : /*
4160 : : * Find clauses, referencing the same single base relation and try to
4161 : : * estimate such a group with extended statistics. Create varinfo for
4162 : : * an approved clause, push it to otherclauses, if it can't be
4163 : : * estimated here or ignore to process at the next iteration.
4164 : : */
4165 [ + + + + : 7629 : foreach(lc, clauses)
+ + ]
4166 : : {
4167 : 5120 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
4168 : 5120 : Node *expr;
4169 : 5120 : Relids relids;
4170 : 5120 : GroupVarInfo *varinfo;
4171 : :
4172 : : /*
4173 : : * Find the inner side of the join, which we need to estimate the
4174 : : * number of buckets. Use outer_is_left because the
4175 : : * clause_sides_match_join routine has called on hash clauses.
4176 : : */
4177 [ + + ]: 5120 : relids = rinfo->outer_is_left ?
4178 : 5120 : rinfo->right_relids : rinfo->left_relids;
4179 [ + + ]: 5120 : expr = rinfo->outer_is_left ?
4180 : 5120 : get_rightop(rinfo->clause) : get_leftop(rinfo->clause);
4181 : :
4182 [ + + + + ]: 5120 : if (bms_get_singleton_member(relids, &relid) &&
4183 : 5019 : root->simple_rel_array[relid]->statlist != NIL)
4184 : : {
4185 : 10 : bool is_duplicate = false;
4186 : :
4187 : : /*
4188 : : * This inner-side expression references only one relation.
4189 : : * Extended statistics on this clause can exist.
4190 : : */
4191 [ + + ]: 10 : if (group_relid < 0)
4192 : : {
4193 : 5 : RangeTblEntry *rte = root->simple_rte_array[relid];
4194 : :
4195 [ + - - + : 5 : if (!rte || (rte->relkind != RELKIND_RELATION &&
# # ]
4196 [ # # ]: 0 : rte->relkind != RELKIND_MATVIEW &&
4197 [ # # ]: 0 : rte->relkind != RELKIND_FOREIGN_TABLE &&
4198 : 0 : rte->relkind != RELKIND_PARTITIONED_TABLE))
4199 : : {
4200 : : /* Extended statistics can't exist in principle */
4201 : 0 : otherclauses = lappend(otherclauses, rinfo);
4202 : 0 : clauses = foreach_delete_current(clauses, lc);
4203 : 0 : continue;
4204 : : }
4205 : :
4206 : 5 : group_relid = relid;
4207 : 5 : group_rel = root->simple_rel_array[relid];
4208 [ - + ]: 5 : }
4209 [ - + ]: 5 : else if (group_relid != relid)
4210 : : {
4211 : : /*
4212 : : * Being in the group forming state we don't need other
4213 : : * clauses.
4214 : : */
4215 : 0 : continue;
4216 : : }
4217 : :
4218 : : /*
4219 : : * We're going to add the new clause to the varinfos list. We
4220 : : * might re-use add_unique_group_var(), but we don't do so for
4221 : : * two reasons.
4222 : : *
4223 : : * 1) We must keep the origin_rinfos list ordered exactly the
4224 : : * same way as varinfos.
4225 : : *
4226 : : * 2) add_unique_group_var() is designed for
4227 : : * estimate_num_groups(), where a larger number of groups is
4228 : : * worse. While estimating the number of hash buckets, we
4229 : : * have the opposite: a lesser number of groups is worse.
4230 : : * Therefore, we don't have to remove "known equal" vars: the
4231 : : * removed var may valuably contribute to the multivariate
4232 : : * statistics to grow the number of groups.
4233 : : */
4234 : :
4235 : : /*
4236 : : * Clear nullingrels to correctly match hash keys. See
4237 : : * add_unique_group_var()'s comment for details.
4238 : : */
4239 : 10 : expr = remove_nulling_relids(expr, root->outer_join_rels, NULL);
4240 : :
4241 : : /*
4242 : : * Detect and exclude exact duplicates from the list of hash
4243 : : * keys (like add_unique_group_var does).
4244 : : */
4245 [ + + + + : 16 : foreach(lc1, varinfos)
+ + ]
4246 : : {
4247 : 6 : varinfo = (GroupVarInfo *) lfirst(lc1);
4248 : :
4249 [ + + ]: 6 : if (!equal(expr, varinfo->var))
4250 : 4 : continue;
4251 : :
4252 : 2 : is_duplicate = true;
4253 : 2 : break;
4254 : : }
4255 : :
4256 [ + + ]: 10 : if (is_duplicate)
4257 : : {
4258 : : /*
4259 : : * Skip exact duplicates. Adding them to the otherclauses
4260 : : * list also doesn't make sense.
4261 : : */
4262 : 2 : continue;
4263 : : }
4264 : :
4265 : : /*
4266 : : * Initialize GroupVarInfo. We only use it to call
4267 : : * estimate_multivariate_ndistinct(), which doesn't care about
4268 : : * ndistinct and isdefault fields. Thus, skip these fields.
4269 : : */
4270 : 8 : varinfo = palloc0_object(GroupVarInfo);
4271 : 8 : varinfo->var = expr;
4272 : 8 : varinfo->rel = root->simple_rel_array[relid];
4273 : 8 : varinfos = lappend(varinfos, varinfo);
4274 : :
4275 : : /*
4276 : : * Remember the link to RestrictInfo for the case the clause
4277 : : * is failed to be estimated.
4278 : : */
4279 : 8 : origin_rinfos = lappend(origin_rinfos, rinfo);
4280 [ + + ]: 10 : }
4281 : : else
4282 : : {
4283 : : /* This clause can't be estimated with extended statistics */
4284 : 5110 : otherclauses = lappend(otherclauses, rinfo);
4285 : : }
4286 : :
4287 : 5118 : clauses = foreach_delete_current(clauses, lc);
4288 [ + + ]: 5120 : }
4289 : :
4290 [ + + ]: 2509 : if (list_length(varinfos) < 2)
4291 : : {
4292 : : /*
4293 : : * Multivariate statistics doesn't apply to single columns except
4294 : : * for expressions, but it has not been implemented yet.
4295 : : */
4296 : 2507 : otherclauses = list_concat(otherclauses, origin_rinfos);
4297 : 2507 : list_free_deep(varinfos);
4298 : 2507 : list_free(origin_rinfos);
4299 : 2507 : continue;
4300 : : }
4301 : :
4302 [ - + ]: 2 : Assert(group_rel != NULL);
4303 : :
4304 : : /* Employ the extended statistics. */
4305 : 2 : origin_varinfos = varinfos;
4306 : 4 : for (;;)
4307 : : {
4308 : 8 : bool estimated = estimate_multivariate_ndistinct(root,
4309 : 4 : group_rel,
4310 : : &varinfos,
4311 : : &mvndistinct);
4312 : :
4313 [ + + ]: 4 : if (!estimated)
4314 : 2 : break;
4315 : :
4316 : : /*
4317 : : * We've got an estimation. Use ndistinct value in a consistent
4318 : : * way - according to the caller's logic (see
4319 : : * final_cost_hashjoin).
4320 : : */
4321 [ - + ]: 2 : if (ndistinct < mvndistinct)
4322 : 2 : ndistinct = mvndistinct;
4323 [ - + ]: 2 : Assert(ndistinct >= 1.0);
4324 [ + + ]: 4 : }
4325 : :
4326 [ - + ]: 2 : Assert(list_length(origin_varinfos) == list_length(origin_rinfos));
4327 : :
4328 : : /* Collect unmatched clauses as otherclauses. */
4329 [ + - + + : 7 : forboth(lc1, origin_varinfos, lc2, origin_rinfos)
+ - + + +
+ + + ]
4330 : : {
4331 : 5 : GroupVarInfo *vinfo = lfirst(lc1);
4332 : :
4333 [ - + ]: 5 : if (!list_member_ptr(varinfos, vinfo))
4334 : : /* Already estimated */
4335 : 5 : continue;
4336 : :
4337 : : /* Can't be estimated here - push to the returning list */
4338 : 0 : otherclauses = lappend(otherclauses, lfirst(lc2));
4339 [ + - ]: 5 : }
4340 [ + + ]: 2509 : }
4341 : :
4342 : 2507 : *innerbucketsize = 1.0 / ndistinct;
4343 : 2507 : return otherclauses;
4344 : 52403 : }
4345 : :
4346 : : /*
4347 : : * Estimate hash bucket statistics when the specified expression is used
4348 : : * as a hash key for the given number of buckets.
4349 : : *
4350 : : * This attempts to determine two values:
4351 : : *
4352 : : * 1. The frequency of the most common value of the expression (returns
4353 : : * zero into *mcv_freq if we can't get that).
4354 : : *
4355 : : * 2. The "bucketsize fraction", ie, average number of entries in a bucket
4356 : : * divided by total tuples in relation.
4357 : : *
4358 : : * XXX This is really pretty bogus since we're effectively assuming that the
4359 : : * distribution of hash keys will be the same after applying restriction
4360 : : * clauses as it was in the underlying relation. However, we are not nearly
4361 : : * smart enough to figure out how the restrict clauses might change the
4362 : : * distribution, so this will have to do for now.
4363 : : *
4364 : : * We are passed the number of buckets the executor will use for the given
4365 : : * input relation. If the data were perfectly distributed, with the same
4366 : : * number of tuples going into each available bucket, then the bucketsize
4367 : : * fraction would be 1/nbuckets. But this happy state of affairs will occur
4368 : : * only if (a) there are at least nbuckets distinct data values, and (b)
4369 : : * we have a not-too-skewed data distribution. Otherwise the buckets will
4370 : : * be nonuniformly occupied. If the other relation in the join has a key
4371 : : * distribution similar to this one's, then the most-loaded buckets are
4372 : : * exactly those that will be probed most often. Therefore, the "average"
4373 : : * bucket size for costing purposes should really be taken as something close
4374 : : * to the "worst case" bucket size. We try to estimate this by adjusting the
4375 : : * fraction if there are too few distinct data values, and then scaling up
4376 : : * by the ratio of the most common value's frequency to the average frequency.
4377 : : *
4378 : : * If no statistics are available, use a default estimate of 0.1. This will
4379 : : * discourage use of a hash rather strongly if the inner relation is large,
4380 : : * which is what we want. We do not want to hash unless we know that the
4381 : : * inner rel is well-dispersed (or the alternatives seem much worse).
4382 : : *
4383 : : * The caller should also check that the mcv_freq is not so large that the
4384 : : * most common value would by itself require an impractically large bucket.
4385 : : * In a hash join, the executor can split buckets if they get too big, but
4386 : : * obviously that doesn't help for a bucket that contains many duplicates of
4387 : : * the same value.
4388 : : */
4389 : : void
4390 : 21687 : estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
4391 : : Selectivity *mcv_freq,
4392 : : Selectivity *bucketsize_frac)
4393 : : {
4394 : 21687 : VariableStatData vardata;
4395 : 21687 : double estfract,
4396 : : ndistinct,
4397 : : stanullfrac,
4398 : : avgfreq;
4399 : 21687 : bool isdefault;
4400 : 21687 : AttStatsSlot sslot;
4401 : :
4402 : 21687 : examine_variable(root, hashkey, 0, &vardata);
4403 : :
4404 : : /* Initialize *mcv_freq to "unknown" */
4405 : 21687 : *mcv_freq = 0.0;
4406 : :
4407 : : /* Look up the frequency of the most common value, if available */
4408 [ + + ]: 21687 : if (HeapTupleIsValid(vardata.statsTuple))
4409 : : {
4410 [ + + ]: 15339 : if (get_attstatsslot(&sslot, vardata.statsTuple,
4411 : : STATISTIC_KIND_MCV, InvalidOid,
4412 : : ATTSTATSSLOT_NUMBERS))
4413 : : {
4414 : : /*
4415 : : * The first MCV stat is for the most common value.
4416 : : */
4417 [ - + ]: 10159 : if (sslot.nnumbers > 0)
4418 : 10159 : *mcv_freq = sslot.numbers[0];
4419 : 10159 : free_attstatsslot(&sslot);
4420 : 10159 : }
4421 [ + + ]: 5180 : else if (get_attstatsslot(&sslot, vardata.statsTuple,
4422 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
4423 : : 0))
4424 : : {
4425 : : /*
4426 : : * If there are no recorded MCVs, but we do have a histogram, then
4427 : : * assume that ANALYZE determined that the column is unique.
4428 : : */
4429 [ + - + + ]: 5101 : if (vardata.rel && vardata.rel->rows > 0)
4430 : 5098 : *mcv_freq = 1.0 / vardata.rel->rows;
4431 : 5101 : }
4432 : 15339 : }
4433 : :
4434 : : /* Get number of distinct values */
4435 : 21687 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
4436 : :
4437 : : /*
4438 : : * If ndistinct isn't real, punt. We normally return 0.1, but if the
4439 : : * mcv_freq is known to be even higher than that, use it instead.
4440 : : */
4441 [ + + ]: 21687 : if (isdefault)
4442 : : {
4443 [ + - ]: 2965 : *bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
4444 [ + + ]: 2965 : ReleaseVariableStats(vardata);
4445 : 2965 : return;
4446 : : }
4447 : :
4448 : : /* Get fraction that are null */
4449 [ + + ]: 18722 : if (HeapTupleIsValid(vardata.statsTuple))
4450 : : {
4451 : 15336 : Form_pg_statistic stats;
4452 : :
4453 : 15336 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
4454 : 15336 : stanullfrac = stats->stanullfrac;
4455 : 15336 : }
4456 : : else
4457 : 3386 : stanullfrac = 0.0;
4458 : :
4459 : : /* Compute avg freq of all distinct data values in raw relation */
4460 : 18722 : avgfreq = (1.0 - stanullfrac) / ndistinct;
4461 : :
4462 : : /*
4463 : : * Adjust ndistinct to account for restriction clauses. Observe we are
4464 : : * assuming that the data distribution is affected uniformly by the
4465 : : * restriction clauses!
4466 : : *
4467 : : * XXX Possibly better way, but much more expensive: multiply by
4468 : : * selectivity of rel's restriction clauses that mention the target Var.
4469 : : */
4470 [ + - + + ]: 18722 : if (vardata.rel && vardata.rel->tuples > 0)
4471 : : {
4472 : 18720 : ndistinct *= vardata.rel->rows / vardata.rel->tuples;
4473 : 18720 : ndistinct = clamp_row_est(ndistinct);
4474 : 18720 : }
4475 : :
4476 : : /*
4477 : : * Initial estimate of bucketsize fraction is 1/nbuckets as long as the
4478 : : * number of buckets is less than the expected number of distinct values;
4479 : : * otherwise it is 1/ndistinct.
4480 : : */
4481 [ + + ]: 18722 : if (ndistinct > nbuckets)
4482 : 14 : estfract = 1.0 / nbuckets;
4483 : : else
4484 : 18708 : estfract = 1.0 / ndistinct;
4485 : :
4486 : : /*
4487 : : * Adjust estimated bucketsize upward to account for skewed distribution.
4488 : : */
4489 [ + + + + ]: 18722 : if (avgfreq > 0.0 && *mcv_freq > avgfreq)
4490 : 10599 : estfract *= *mcv_freq / avgfreq;
4491 : :
4492 : : /*
4493 : : * Clamp bucketsize to sane range (the above adjustment could easily
4494 : : * produce an out-of-range result). We set the lower bound a little above
4495 : : * zero, since zero isn't a very sane result.
4496 : : */
4497 [ - + ]: 18722 : if (estfract < 1.0e-6)
4498 : 0 : estfract = 1.0e-6;
4499 [ + + ]: 18722 : else if (estfract > 1.0)
4500 : 4794 : estfract = 1.0;
4501 : :
4502 : 18722 : *bucketsize_frac = (Selectivity) estfract;
4503 : :
4504 [ + + ]: 18722 : ReleaseVariableStats(vardata);
4505 [ - + ]: 21687 : }
4506 : :
4507 : : /*
4508 : : * estimate_hashagg_tablesize
4509 : : * estimate the number of bytes that a hash aggregate hashtable will
4510 : : * require based on the agg_costs, path width and number of groups.
4511 : : *
4512 : : * We return the result as "double" to forestall any possible overflow
4513 : : * problem in the multiplication by dNumGroups.
4514 : : *
4515 : : * XXX this may be over-estimating the size now that hashagg knows to omit
4516 : : * unneeded columns from the hashtable. Also for mixed-mode grouping sets,
4517 : : * grouping columns not in the hashed set are counted here even though hashagg
4518 : : * won't store them. Is this a problem?
4519 : : */
4520 : : double
4521 : 425 : estimate_hashagg_tablesize(PlannerInfo *root, Path *path,
4522 : : const AggClauseCosts *agg_costs, double dNumGroups)
4523 : : {
4524 : 425 : Size hashentrysize;
4525 : :
4526 : 850 : hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
4527 : 425 : path->pathtarget->width,
4528 : 425 : agg_costs->transitionSpace);
4529 : :
4530 : : /*
4531 : : * Note that this disregards the effect of fill-factor and growth policy
4532 : : * of the hash table. That's probably ok, given that the default
4533 : : * fill-factor is relatively high. It'd be hard to meaningfully factor in
4534 : : * "double-in-size" growth policies here.
4535 : : */
4536 : 850 : return hashentrysize * dNumGroups;
4537 : 425 : }
4538 : :
4539 : :
4540 : : /*-------------------------------------------------------------------------
4541 : : *
4542 : : * Support routines
4543 : : *
4544 : : *-------------------------------------------------------------------------
4545 : : */
4546 : :
4547 : : /*
4548 : : * Find the best matching ndistinct extended statistics for the given list of
4549 : : * GroupVarInfos.
4550 : : *
4551 : : * Callers must ensure that the given GroupVarInfos all belong to 'rel' and
4552 : : * the GroupVarInfos list does not contain any duplicate Vars or expressions.
4553 : : *
4554 : : * When statistics are found that match > 1 of the given GroupVarInfo, the
4555 : : * *ndistinct parameter is set according to the ndistinct estimate and a new
4556 : : * list is built with the matching GroupVarInfos removed, which is output via
4557 : : * the *varinfos parameter before returning true. When no matching stats are
4558 : : * found, false is returned and the *varinfos and *ndistinct parameters are
4559 : : * left untouched.
4560 : : */
4561 : : static bool
4562 : 25375 : estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
4563 : : List **varinfos, double *ndistinct)
4564 : : {
4565 : 25375 : ListCell *lc;
4566 : 25375 : int nmatches_vars;
4567 : 25375 : int nmatches_exprs;
4568 : 25375 : Oid statOid = InvalidOid;
4569 : 25375 : MVNDistinct *stats;
4570 : 25375 : StatisticExtInfo *matched_info = NULL;
4571 [ - + ]: 25375 : RangeTblEntry *rte = planner_rt_fetch(rel->relid, root);
4572 : :
4573 : : /* bail out immediately if the table has no extended statistics */
4574 [ + + ]: 25375 : if (!rel->statlist)
4575 : 25281 : return false;
4576 : :
4577 : : /* look for the ndistinct statistics object matching the most vars */
4578 : 94 : nmatches_vars = 0; /* we require at least two matches */
4579 : 94 : nmatches_exprs = 0;
4580 [ + - + + : 374 : foreach(lc, rel->statlist)
+ + ]
4581 : : {
4582 : 280 : ListCell *lc2;
4583 : 280 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
4584 : 280 : int nshared_vars = 0;
4585 : 280 : int nshared_exprs = 0;
4586 : :
4587 : : /* skip statistics of other kinds */
4588 [ + + ]: 280 : if (info->kind != STATS_EXT_NDISTINCT)
4589 : 132 : continue;
4590 : :
4591 : : /* skip statistics with mismatching stxdinherit value */
4592 [ + + ]: 148 : if (info->inherit != rte->inh)
4593 : 5 : continue;
4594 : :
4595 : : /*
4596 : : * Determine how many expressions (and variables in non-matched
4597 : : * expressions) match. We'll then use these numbers to pick the
4598 : : * statistics object that best matches the clauses.
4599 : : */
4600 [ + + + + : 453 : foreach(lc2, *varinfos)
+ + ]
4601 : : {
4602 : 310 : ListCell *lc3;
4603 : 310 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
4604 : 310 : AttrNumber attnum;
4605 : :
4606 [ + - ]: 310 : Assert(varinfo->rel == rel);
4607 : :
4608 : : /* simple Var, search in statistics keys directly */
4609 [ + + ]: 310 : if (IsA(varinfo->var, Var))
4610 : : {
4611 : 249 : attnum = ((Var *) varinfo->var)->varattno;
4612 : :
4613 : : /*
4614 : : * Ignore system attributes - we don't support statistics on
4615 : : * them, so can't match them (and it'd fail as the values are
4616 : : * negative).
4617 : : */
4618 [ + + ]: 249 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4619 : 2 : continue;
4620 : :
4621 [ + + ]: 247 : if (bms_is_member(attnum, info->keys))
4622 : 146 : nshared_vars++;
4623 : :
4624 : 247 : continue;
4625 : : }
4626 : :
4627 : : /* expression - see if it's in the statistics object */
4628 [ + + + + : 149 : foreach(lc3, info->exprs)
+ + ]
4629 : : {
4630 : 88 : Node *expr = (Node *) lfirst(lc3);
4631 : :
4632 [ + + ]: 88 : if (equal(varinfo->var, expr))
4633 : : {
4634 : 39 : nshared_exprs++;
4635 : 39 : break;
4636 : : }
4637 [ + + ]: 88 : }
4638 [ + + ]: 310 : }
4639 : :
4640 : : /*
4641 : : * The ndistinct extended statistics contain estimates for a minimum
4642 : : * of pairs of columns which the statistics are defined on and
4643 : : * certainly not single columns. Here we skip unless we managed to
4644 : : * match to at least two columns.
4645 : : */
4646 [ + + ]: 143 : if (nshared_vars + nshared_exprs < 2)
4647 : 66 : continue;
4648 : :
4649 : : /*
4650 : : * Check if these statistics are a better match than the previous best
4651 : : * match and if so, take note of the StatisticExtInfo.
4652 : : *
4653 : : * The statslist is sorted by statOid, so the StatisticExtInfo we
4654 : : * select as the best match is deterministic even when multiple sets
4655 : : * of statistics match equally as well.
4656 : : */
4657 [ + + + + ]: 136 : if ((nshared_exprs > nmatches_exprs) ||
4658 [ + - ]: 59 : (((nshared_exprs == nmatches_exprs)) && (nshared_vars > nmatches_vars)))
4659 : : {
4660 : 73 : statOid = info->statOid;
4661 : 73 : nmatches_vars = nshared_vars;
4662 : 73 : nmatches_exprs = nshared_exprs;
4663 : 73 : matched_info = info;
4664 : 73 : }
4665 [ + + ]: 280 : }
4666 : :
4667 : : /* No match? */
4668 [ + + ]: 94 : if (statOid == InvalidOid)
4669 : 23 : return false;
4670 : :
4671 [ + - ]: 71 : Assert(nmatches_vars + nmatches_exprs > 1);
4672 : :
4673 : 71 : stats = statext_ndistinct_load(statOid, rte->inh);
4674 : :
4675 : : /*
4676 : : * If we have a match, search it for the specific item that matches (there
4677 : : * must be one), and construct the output values.
4678 : : */
4679 [ + - ]: 71 : if (stats)
4680 : : {
4681 : 71 : int i;
4682 : 71 : List *newlist = NIL;
4683 : 71 : MVNDistinctItem *item = NULL;
4684 : 71 : ListCell *lc2;
4685 : 71 : Bitmapset *matched = NULL;
4686 : 71 : AttrNumber attnum_offset;
4687 : :
4688 : : /*
4689 : : * How much we need to offset the attnums? If there are no
4690 : : * expressions, no offset is needed. Otherwise offset enough to move
4691 : : * the lowest one (which is equal to number of expressions) to 1.
4692 : : */
4693 [ + + ]: 71 : if (matched_info->exprs)
4694 : 25 : attnum_offset = (list_length(matched_info->exprs) + 1);
4695 : : else
4696 : 46 : attnum_offset = 0;
4697 : :
4698 : : /* see what actually matched */
4699 [ + - + + : 248 : foreach(lc2, *varinfos)
+ + ]
4700 : : {
4701 : 177 : ListCell *lc3;
4702 : 177 : int idx;
4703 : 177 : bool found = false;
4704 : :
4705 : 177 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
4706 : :
4707 : : /*
4708 : : * Process a simple Var expression, by matching it to keys
4709 : : * directly. If there's a matching expression, we'll try matching
4710 : : * it later.
4711 : : */
4712 [ + + ]: 177 : if (IsA(varinfo->var, Var))
4713 : : {
4714 : 146 : AttrNumber attnum = ((Var *) varinfo->var)->varattno;
4715 : :
4716 : : /*
4717 : : * Ignore expressions on system attributes. Can't rely on the
4718 : : * bms check for negative values.
4719 : : */
4720 [ + + ]: 146 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4721 : 1 : continue;
4722 : :
4723 : : /* Is the variable covered by the statistics object? */
4724 [ + + ]: 145 : if (!bms_is_member(attnum, matched_info->keys))
4725 : 20 : continue;
4726 : :
4727 : 125 : attnum = attnum + attnum_offset;
4728 : :
4729 : : /* ensure sufficient offset */
4730 [ - + ]: 125 : Assert(AttrNumberIsForUserDefinedAttr(attnum));
4731 : :
4732 : 125 : matched = bms_add_member(matched, attnum);
4733 : :
4734 : 125 : found = true;
4735 [ + + ]: 146 : }
4736 : :
4737 : : /*
4738 : : * XXX Maybe we should allow searching the expressions even if we
4739 : : * found an attribute matching the expression? That would handle
4740 : : * trivial expressions like "(a)" but it seems fairly useless.
4741 : : */
4742 [ + + ]: 156 : if (found)
4743 : 125 : continue;
4744 : :
4745 : : /* expression - see if it's in the statistics object */
4746 : 31 : idx = 0;
4747 [ + + + + : 77 : foreach(lc3, matched_info->exprs)
+ + ]
4748 : : {
4749 : 46 : Node *expr = (Node *) lfirst(lc3);
4750 : :
4751 [ + + ]: 46 : if (equal(varinfo->var, expr))
4752 : : {
4753 : 26 : AttrNumber attnum = -(idx + 1);
4754 : :
4755 : 26 : attnum = attnum + attnum_offset;
4756 : :
4757 : : /* ensure sufficient offset */
4758 [ - + ]: 26 : Assert(AttrNumberIsForUserDefinedAttr(attnum));
4759 : :
4760 : 26 : matched = bms_add_member(matched, attnum);
4761 : :
4762 : : /* there should be just one matching expression */
4763 : : break;
4764 : 26 : }
4765 : :
4766 : 20 : idx++;
4767 [ + + ]: 46 : }
4768 [ + + ]: 177 : }
4769 : :
4770 : : /* Find the specific item that exactly matches the combination */
4771 [ - + ]: 144 : for (i = 0; i < stats->nitems; i++)
4772 : : {
4773 : 144 : int j;
4774 : 144 : MVNDistinctItem *tmpitem = &stats->items[i];
4775 : :
4776 [ + + ]: 144 : if (tmpitem->nattributes != bms_num_members(matched))
4777 : 27 : continue;
4778 : :
4779 : : /* assume it's the right item */
4780 : 117 : item = tmpitem;
4781 : :
4782 : : /* check that all item attributes/expressions fit the match */
4783 [ + + ]: 282 : for (j = 0; j < tmpitem->nattributes; j++)
4784 : : {
4785 : 211 : AttrNumber attnum = tmpitem->attributes[j];
4786 : :
4787 : : /*
4788 : : * Thanks to how we constructed the matched bitmap above, we
4789 : : * can just offset all attnums the same way.
4790 : : */
4791 : 211 : attnum = attnum + attnum_offset;
4792 : :
4793 [ + + ]: 211 : if (!bms_is_member(attnum, matched))
4794 : : {
4795 : : /* nah, it's not this item */
4796 : 46 : item = NULL;
4797 : 46 : break;
4798 : : }
4799 [ + + ]: 211 : }
4800 : :
4801 : : /*
4802 : : * If the item has all the matched attributes, we know it's the
4803 : : * right one - there can't be a better one. matching more.
4804 : : */
4805 [ + + ]: 117 : if (item)
4806 : 71 : break;
4807 [ + + + ]: 144 : }
4808 : :
4809 : : /*
4810 : : * Make sure we found an item. There has to be one, because ndistinct
4811 : : * statistics includes all combinations of attributes.
4812 : : */
4813 [ + - ]: 71 : if (!item)
4814 [ # # # # ]: 0 : elog(ERROR, "corrupt MVNDistinct entry");
4815 : :
4816 : : /* Form the output varinfo list, keeping only unmatched ones */
4817 [ + - + + : 248 : foreach(lc, *varinfos)
+ + ]
4818 : : {
4819 : 177 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
4820 : 177 : ListCell *lc3;
4821 : 177 : bool found = false;
4822 : :
4823 : : /*
4824 : : * Let's look at plain variables first, because it's the most
4825 : : * common case and the check is quite cheap. We can simply get the
4826 : : * attnum and check (with an offset) matched bitmap.
4827 : : */
4828 [ + + ]: 177 : if (IsA(varinfo->var, Var))
4829 : : {
4830 : 146 : AttrNumber attnum = ((Var *) varinfo->var)->varattno;
4831 : :
4832 : : /*
4833 : : * If it's a system attribute, we're done. We don't support
4834 : : * extended statistics on system attributes, so it's clearly
4835 : : * not matched. Just keep the expression and continue.
4836 : : */
4837 [ + + ]: 146 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4838 : : {
4839 : 1 : newlist = lappend(newlist, varinfo);
4840 : 1 : continue;
4841 : : }
4842 : :
4843 : : /* apply the same offset as above */
4844 : 145 : attnum += attnum_offset;
4845 : :
4846 : : /* if it's not matched, keep the varinfo */
4847 [ + + ]: 145 : if (!bms_is_member(attnum, matched))
4848 : 20 : newlist = lappend(newlist, varinfo);
4849 : :
4850 : : /* The rest of the loop deals with complex expressions. */
4851 : 145 : continue;
4852 : 146 : }
4853 : :
4854 : : /*
4855 : : * Process complex expressions, not just simple Vars.
4856 : : *
4857 : : * First, we search for an exact match of an expression. If we
4858 : : * find one, we can just discard the whole GroupVarInfo, with all
4859 : : * the variables we extracted from it.
4860 : : *
4861 : : * Otherwise we inspect the individual vars, and try matching it
4862 : : * to variables in the item.
4863 : : */
4864 [ + + + + : 77 : foreach(lc3, matched_info->exprs)
+ + ]
4865 : : {
4866 : 46 : Node *expr = (Node *) lfirst(lc3);
4867 : :
4868 [ + + ]: 46 : if (equal(varinfo->var, expr))
4869 : : {
4870 : 26 : found = true;
4871 : 26 : break;
4872 : : }
4873 [ + + ]: 46 : }
4874 : :
4875 : : /* found exact match, skip */
4876 [ + + ]: 31 : if (found)
4877 : 26 : continue;
4878 : :
4879 : 5 : newlist = lappend(newlist, varinfo);
4880 [ + + ]: 177 : }
4881 : :
4882 : 71 : *varinfos = newlist;
4883 : 71 : *ndistinct = item->ndistinct;
4884 : 71 : return true;
4885 : 71 : }
4886 : :
4887 : 0 : return false;
4888 : 25375 : }
4889 : :
4890 : : /*
4891 : : * convert_to_scalar
4892 : : * Convert non-NULL values of the indicated types to the comparison
4893 : : * scale needed by scalarineqsel().
4894 : : * Returns "true" if successful.
4895 : : *
4896 : : * XXX this routine is a hack: ideally we should look up the conversion
4897 : : * subroutines in pg_type.
4898 : : *
4899 : : * All numeric datatypes are simply converted to their equivalent
4900 : : * "double" values. (NUMERIC values that are outside the range of "double"
4901 : : * are clamped to +/- HUGE_VAL.)
4902 : : *
4903 : : * String datatypes are converted by convert_string_to_scalar(),
4904 : : * which is explained below. The reason why this routine deals with
4905 : : * three values at a time, not just one, is that we need it for strings.
4906 : : *
4907 : : * The bytea datatype is just enough different from strings that it has
4908 : : * to be treated separately.
4909 : : *
4910 : : * The several datatypes representing absolute times are all converted
4911 : : * to Timestamp, which is actually an int64, and then we promote that to
4912 : : * a double. Note this will give correct results even for the "special"
4913 : : * values of Timestamp, since those are chosen to compare correctly;
4914 : : * see timestamp_cmp.
4915 : : *
4916 : : * The several datatypes representing relative times (intervals) are all
4917 : : * converted to measurements expressed in seconds.
4918 : : */
4919 : : static bool
4920 : 6586 : convert_to_scalar(Datum value, Oid valuetypid, Oid collid, double *scaledvalue,
4921 : : Datum lobound, Datum hibound, Oid boundstypid,
4922 : : double *scaledlobound, double *scaledhibound)
4923 : : {
4924 : 6586 : bool failure = false;
4925 : :
4926 : : /*
4927 : : * Both the valuetypid and the boundstypid should exactly match the
4928 : : * declared input type(s) of the operator we are invoked for. However,
4929 : : * extensions might try to use scalarineqsel as estimator for operators
4930 : : * with input type(s) we don't handle here; in such cases, we want to
4931 : : * return false, not fail. In any case, we mustn't assume that valuetypid
4932 : : * and boundstypid are identical.
4933 : : *
4934 : : * XXX The histogram we are interpolating between points of could belong
4935 : : * to a column that's only binary-compatible with the declared type. In
4936 : : * essence we are assuming that the semantics of binary-compatible types
4937 : : * are enough alike that we can use a histogram generated with one type's
4938 : : * operators to estimate selectivity for the other's. This is outright
4939 : : * wrong in some cases --- in particular signed versus unsigned
4940 : : * interpretation could trip us up. But it's useful enough in the
4941 : : * majority of cases that we do it anyway. Should think about more
4942 : : * rigorous ways to do it.
4943 : : */
4944 [ + - + - : 6586 : switch (valuetypid)
- - ]
4945 : : {
4946 : : /*
4947 : : * Built-in numeric types
4948 : : */
4949 : : case BOOLOID:
4950 : : case INT2OID:
4951 : : case INT4OID:
4952 : : case INT8OID:
4953 : : case FLOAT4OID:
4954 : : case FLOAT8OID:
4955 : : case NUMERICOID:
4956 : : case OIDOID:
4957 : : case REGPROCOID:
4958 : : case REGPROCEDUREOID:
4959 : : case REGOPEROID:
4960 : : case REGOPERATOROID:
4961 : : case REGCLASSOID:
4962 : : case REGTYPEOID:
4963 : : case REGCOLLATIONOID:
4964 : : case REGCONFIGOID:
4965 : : case REGDICTIONARYOID:
4966 : : case REGROLEOID:
4967 : : case REGNAMESPACEOID:
4968 : : case REGDATABASEOID:
4969 : 6064 : *scaledvalue = convert_numeric_to_scalar(value, valuetypid,
4970 : : &failure);
4971 : 6064 : *scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
4972 : : &failure);
4973 : 6064 : *scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
4974 : : &failure);
4975 : 6064 : return !failure;
4976 : :
4977 : : /*
4978 : : * Built-in string types
4979 : : */
4980 : : case CHAROID:
4981 : : case BPCHAROID:
4982 : : case VARCHAROID:
4983 : : case TEXTOID:
4984 : : case NAMEOID:
4985 : : {
4986 : 1044 : char *valstr = convert_string_datum(value, valuetypid,
4987 : 522 : collid, &failure);
4988 : 1044 : char *lostr = convert_string_datum(lobound, boundstypid,
4989 : 522 : collid, &failure);
4990 : 1044 : char *histr = convert_string_datum(hibound, boundstypid,
4991 : 522 : collid, &failure);
4992 : :
4993 : : /*
4994 : : * Bail out if any of the values is not of string type. We
4995 : : * might leak converted strings for the other value(s), but
4996 : : * that's not worth troubling over.
4997 : : */
4998 [ - + ]: 522 : if (failure)
4999 : 0 : return false;
5000 : :
5001 : 1044 : convert_string_to_scalar(valstr, scaledvalue,
5002 : 522 : lostr, scaledlobound,
5003 : 522 : histr, scaledhibound);
5004 : 522 : pfree(valstr);
5005 : 522 : pfree(lostr);
5006 : 522 : pfree(histr);
5007 : 522 : return true;
5008 : 522 : }
5009 : :
5010 : : /*
5011 : : * Built-in bytea type
5012 : : */
5013 : : case BYTEAOID:
5014 : : {
5015 : : /* We only support bytea vs bytea comparison */
5016 [ # # ]: 0 : if (boundstypid != BYTEAOID)
5017 : 0 : return false;
5018 : 0 : convert_bytea_to_scalar(value, scaledvalue,
5019 : 0 : lobound, scaledlobound,
5020 : 0 : hibound, scaledhibound);
5021 : 0 : return true;
5022 : : }
5023 : :
5024 : : /*
5025 : : * Built-in time types
5026 : : */
5027 : : case TIMESTAMPOID:
5028 : : case TIMESTAMPTZOID:
5029 : : case DATEOID:
5030 : : case INTERVALOID:
5031 : : case TIMEOID:
5032 : : case TIMETZOID:
5033 : 0 : *scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
5034 : : &failure);
5035 : 0 : *scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
5036 : : &failure);
5037 : 0 : *scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
5038 : : &failure);
5039 : 0 : return !failure;
5040 : :
5041 : : /*
5042 : : * Built-in network types
5043 : : */
5044 : : case INETOID:
5045 : : case CIDROID:
5046 : : case MACADDROID:
5047 : : case MACADDR8OID:
5048 : 0 : *scaledvalue = convert_network_to_scalar(value, valuetypid,
5049 : : &failure);
5050 : 0 : *scaledlobound = convert_network_to_scalar(lobound, boundstypid,
5051 : : &failure);
5052 : 0 : *scaledhibound = convert_network_to_scalar(hibound, boundstypid,
5053 : : &failure);
5054 : 0 : return !failure;
5055 : : }
5056 : : /* Don't know how to convert */
5057 : 0 : *scaledvalue = *scaledlobound = *scaledhibound = 0;
5058 : 0 : return false;
5059 : 6586 : }
5060 : :
5061 : : /*
5062 : : * Do convert_to_scalar()'s work for any numeric data type.
5063 : : *
5064 : : * On failure (e.g., unsupported typid), set *failure to true;
5065 : : * otherwise, that variable is not changed.
5066 : : */
5067 : : static double
5068 : 18192 : convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
5069 : : {
5070 [ - + + - : 18192 : switch (typid)
- + - -
+ ]
5071 : : {
5072 : : case BOOLOID:
5073 : 0 : return (double) DatumGetBool(value);
5074 : : case INT2OID:
5075 : 2 : return (double) DatumGetInt16(value);
5076 : : case INT4OID:
5077 : 4234 : return (double) DatumGetInt32(value);
5078 : : case INT8OID:
5079 : 0 : return (double) DatumGetInt64(value);
5080 : : case FLOAT4OID:
5081 : 0 : return (double) DatumGetFloat4(value);
5082 : : case FLOAT8OID:
5083 : 9 : return (double) DatumGetFloat8(value);
5084 : : case NUMERICOID:
5085 : : /* Note: out-of-range values will be clamped to +-HUGE_VAL */
5086 : 0 : return (double)
5087 : 0 : DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
5088 : : value));
5089 : : case OIDOID:
5090 : : case REGPROCOID:
5091 : : case REGPROCEDUREOID:
5092 : : case REGOPEROID:
5093 : : case REGOPERATOROID:
5094 : : case REGCLASSOID:
5095 : : case REGTYPEOID:
5096 : : case REGCOLLATIONOID:
5097 : : case REGCONFIGOID:
5098 : : case REGDICTIONARYOID:
5099 : : case REGROLEOID:
5100 : : case REGNAMESPACEOID:
5101 : : case REGDATABASEOID:
5102 : : /* we can treat OIDs as integers... */
5103 : 13947 : return (double) DatumGetObjectId(value);
5104 : : }
5105 : :
5106 : 0 : *failure = true;
5107 : 0 : return 0;
5108 : 18192 : }
5109 : :
5110 : : /*
5111 : : * Do convert_to_scalar()'s work for any character-string data type.
5112 : : *
5113 : : * String datatypes are converted to a scale that ranges from 0 to 1,
5114 : : * where we visualize the bytes of the string as fractional digits.
5115 : : *
5116 : : * We do not want the base to be 256, however, since that tends to
5117 : : * generate inflated selectivity estimates; few databases will have
5118 : : * occurrences of all 256 possible byte values at each position.
5119 : : * Instead, use the smallest and largest byte values seen in the bounds
5120 : : * as the estimated range for each byte, after some fudging to deal with
5121 : : * the fact that we probably aren't going to see the full range that way.
5122 : : *
5123 : : * An additional refinement is that we discard any common prefix of the
5124 : : * three strings before computing the scaled values. This allows us to
5125 : : * "zoom in" when we encounter a narrow data range. An example is a phone
5126 : : * number database where all the values begin with the same area code.
5127 : : * (Actually, the bounds will be adjacent histogram-bin-boundary values,
5128 : : * so this is more likely to happen than you might think.)
5129 : : */
5130 : : static void
5131 : 522 : convert_string_to_scalar(char *value,
5132 : : double *scaledvalue,
5133 : : char *lobound,
5134 : : double *scaledlobound,
5135 : : char *hibound,
5136 : : double *scaledhibound)
5137 : : {
5138 : 522 : int rangelo,
5139 : : rangehi;
5140 : 522 : char *sptr;
5141 : :
5142 : 522 : rangelo = rangehi = (unsigned char) hibound[0];
5143 [ + + ]: 8201 : for (sptr = lobound; *sptr; sptr++)
5144 : : {
5145 [ + + ]: 7679 : if (rangelo > (unsigned char) *sptr)
5146 : 1374 : rangelo = (unsigned char) *sptr;
5147 [ + + ]: 7679 : if (rangehi < (unsigned char) *sptr)
5148 : 800 : rangehi = (unsigned char) *sptr;
5149 : 7679 : }
5150 [ + + ]: 8653 : for (sptr = hibound; *sptr; sptr++)
5151 : : {
5152 [ + + ]: 8131 : if (rangelo > (unsigned char) *sptr)
5153 : 105 : rangelo = (unsigned char) *sptr;
5154 [ + + ]: 8131 : if (rangehi < (unsigned char) *sptr)
5155 : 120 : rangehi = (unsigned char) *sptr;
5156 : 8131 : }
5157 : : /* If range includes any upper-case ASCII chars, make it include all */
5158 [ + + + + ]: 522 : if (rangelo <= 'Z' && rangehi >= 'A')
5159 : : {
5160 [ + + ]: 158 : if (rangelo > 'A')
5161 : 37 : rangelo = 'A';
5162 [ + + ]: 158 : if (rangehi < 'Z')
5163 : 80 : rangehi = 'Z';
5164 : 158 : }
5165 : : /* Ditto lower-case */
5166 [ + - + + ]: 522 : if (rangelo <= 'z' && rangehi >= 'a')
5167 : : {
5168 [ + + ]: 432 : if (rangelo > 'a')
5169 : 1 : rangelo = 'a';
5170 [ + + ]: 432 : if (rangehi < 'z')
5171 : 430 : rangehi = 'z';
5172 : 432 : }
5173 : : /* Ditto digits */
5174 [ + + - + ]: 522 : if (rangelo <= '9' && rangehi >= '0')
5175 : : {
5176 [ + + ]: 43 : if (rangelo > '0')
5177 : 27 : rangelo = '0';
5178 [ + + ]: 43 : if (rangehi < '9')
5179 : 1 : rangehi = '9';
5180 : 43 : }
5181 : :
5182 : : /*
5183 : : * If range includes less than 10 chars, assume we have not got enough
5184 : : * data, and make it include regular ASCII set.
5185 : : */
5186 [ + - ]: 522 : if (rangehi - rangelo < 9)
5187 : : {
5188 : 0 : rangelo = ' ';
5189 : 0 : rangehi = 127;
5190 : 0 : }
5191 : :
5192 : : /*
5193 : : * Now strip any common prefix of the three strings.
5194 : : */
5195 [ - + ]: 857 : while (*lobound)
5196 : : {
5197 [ + + + - ]: 857 : if (*lobound != *hibound || *lobound != *value)
5198 : 522 : break;
5199 : 335 : lobound++, hibound++, value++;
5200 : : }
5201 : :
5202 : : /*
5203 : : * Now we can do the conversions.
5204 : : */
5205 : 522 : *scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
5206 : 522 : *scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
5207 : 522 : *scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
5208 : 522 : }
5209 : :
5210 : : static double
5211 : 1566 : convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
5212 : : {
5213 : 1566 : int slen = strlen(value);
5214 : 1566 : double num,
5215 : : denom,
5216 : : base;
5217 : :
5218 [ - + ]: 1566 : if (slen <= 0)
5219 : 0 : return 0.0; /* empty string has scalar value 0 */
5220 : :
5221 : : /*
5222 : : * There seems little point in considering more than a dozen bytes from
5223 : : * the string. Since base is at least 10, that will give us nominal
5224 : : * resolution of at least 12 decimal digits, which is surely far more
5225 : : * precision than this estimation technique has got anyway (especially in
5226 : : * non-C locales). Also, even with the maximum possible base of 256, this
5227 : : * ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
5228 : : * overflow on any known machine.
5229 : : */
5230 [ + + ]: 1566 : if (slen > 12)
5231 : 534 : slen = 12;
5232 : :
5233 : : /* Convert initial characters to fraction */
5234 : 1566 : base = rangehi - rangelo + 1;
5235 : 1566 : num = 0.0;
5236 : 1566 : denom = base;
5237 [ + + ]: 13731 : while (slen-- > 0)
5238 : : {
5239 : 12165 : int ch = (unsigned char) *value++;
5240 : :
5241 [ + + ]: 12165 : if (ch < rangelo)
5242 : 38 : ch = rangelo - 1;
5243 [ + - ]: 12127 : else if (ch > rangehi)
5244 : 0 : ch = rangehi + 1;
5245 : 12165 : num += ((double) (ch - rangelo)) / denom;
5246 : 12165 : denom *= base;
5247 : 12165 : }
5248 : :
5249 : 1566 : return num;
5250 : 1566 : }
5251 : :
5252 : : /*
5253 : : * Convert a string-type Datum into a palloc'd, null-terminated string.
5254 : : *
5255 : : * On failure (e.g., unsupported typid), set *failure to true;
5256 : : * otherwise, that variable is not changed. (We'll return NULL on failure.)
5257 : : *
5258 : : * When using a non-C locale, we must pass the string through pg_strxfrm()
5259 : : * before continuing, so as to generate correct locale-specific results.
5260 : : */
5261 : : static char *
5262 : 1566 : convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
5263 : : {
5264 : 1566 : char *val;
5265 : 1566 : pg_locale_t mylocale;
5266 : :
5267 [ - - + + ]: 1566 : switch (typid)
5268 : : {
5269 : : case CHAROID:
5270 : 0 : val = (char *) palloc(2);
5271 : 0 : val[0] = DatumGetChar(value);
5272 : 0 : val[1] = '\0';
5273 : 0 : break;
5274 : : case BPCHAROID:
5275 : : case VARCHAROID:
5276 : : case TEXTOID:
5277 : 440 : val = TextDatumGetCString(value);
5278 : 440 : break;
5279 : : case NAMEOID:
5280 : : {
5281 : 1126 : NameData *nm = (NameData *) DatumGetPointer(value);
5282 : :
5283 : 1126 : val = pstrdup(NameStr(*nm));
5284 : : break;
5285 : 1126 : }
5286 : : default:
5287 : 0 : *failure = true;
5288 : 0 : return NULL;
5289 : : }
5290 : :
5291 : 1566 : mylocale = pg_newlocale_from_collation(collid);
5292 : :
5293 [ + + ]: 1566 : if (!mylocale->collate_is_c)
5294 : : {
5295 : 24 : char *xfrmstr;
5296 : 24 : size_t xfrmlen;
5297 : 24 : size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
5298 : :
5299 : : /*
5300 : : * XXX: We could guess at a suitable output buffer size and only call
5301 : : * pg_strxfrm() twice if our guess is too small.
5302 : : *
5303 : : * XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
5304 : : * bogus data or set an error. This is not really a problem unless it
5305 : : * crashes since it will only give an estimation error and nothing
5306 : : * fatal.
5307 : : *
5308 : : * XXX: we do not check pg_strxfrm_enabled(). On some platforms and in
5309 : : * some cases, libc strxfrm() may return the wrong results, but that
5310 : : * will only lead to an estimation error.
5311 : : */
5312 : 24 : xfrmlen = pg_strxfrm(NULL, val, 0, mylocale);
5313 : : #ifdef WIN32
5314 : :
5315 : : /*
5316 : : * On Windows, strxfrm returns INT_MAX when an error occurs. Instead
5317 : : * of trying to allocate this much memory (and fail), just return the
5318 : : * original string unmodified as if we were in the C locale.
5319 : : */
5320 : : if (xfrmlen == INT_MAX)
5321 : : return val;
5322 : : #endif
5323 : 24 : xfrmstr = (char *) palloc(xfrmlen + 1);
5324 : 24 : xfrmlen2 = pg_strxfrm(xfrmstr, val, xfrmlen + 1, mylocale);
5325 : :
5326 : : /*
5327 : : * Some systems (e.g., glibc) can return a smaller value from the
5328 : : * second call than the first; thus the Assert must be <= not ==.
5329 : : */
5330 [ + - ]: 24 : Assert(xfrmlen2 <= xfrmlen);
5331 : 24 : pfree(val);
5332 : 24 : val = xfrmstr;
5333 : 24 : }
5334 : :
5335 : 1566 : return val;
5336 : 1566 : }
5337 : :
5338 : : /*
5339 : : * Do convert_to_scalar()'s work for any bytea data type.
5340 : : *
5341 : : * Very similar to convert_string_to_scalar except we can't assume
5342 : : * null-termination and therefore pass explicit lengths around.
5343 : : *
5344 : : * Also, assumptions about likely "normal" ranges of characters have been
5345 : : * removed - a data range of 0..255 is always used, for now. (Perhaps
5346 : : * someday we will add information about actual byte data range to
5347 : : * pg_statistic.)
5348 : : */
5349 : : static void
5350 : 0 : convert_bytea_to_scalar(Datum value,
5351 : : double *scaledvalue,
5352 : : Datum lobound,
5353 : : double *scaledlobound,
5354 : : Datum hibound,
5355 : : double *scaledhibound)
5356 : : {
5357 : 0 : bytea *valuep = DatumGetByteaPP(value);
5358 : 0 : bytea *loboundp = DatumGetByteaPP(lobound);
5359 : 0 : bytea *hiboundp = DatumGetByteaPP(hibound);
5360 : 0 : int rangelo,
5361 : : rangehi,
5362 : 0 : valuelen = VARSIZE_ANY_EXHDR(valuep),
5363 : 0 : loboundlen = VARSIZE_ANY_EXHDR(loboundp),
5364 : 0 : hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
5365 : : i,
5366 : : minlen;
5367 : 0 : unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
5368 : 0 : unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
5369 : 0 : unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
5370 : :
5371 : : /*
5372 : : * Assume bytea data is uniformly distributed across all byte values.
5373 : : */
5374 : 0 : rangelo = 0;
5375 : 0 : rangehi = 255;
5376 : :
5377 : : /*
5378 : : * Now strip any common prefix of the three strings.
5379 : : */
5380 [ # # # # : 0 : minlen = Min(Min(valuelen, loboundlen), hiboundlen);
# # ]
5381 [ # # ]: 0 : for (i = 0; i < minlen; i++)
5382 : : {
5383 [ # # # # ]: 0 : if (*lostr != *histr || *lostr != *valstr)
5384 : 0 : break;
5385 : 0 : lostr++, histr++, valstr++;
5386 : 0 : loboundlen--, hiboundlen--, valuelen--;
5387 : 0 : }
5388 : :
5389 : : /*
5390 : : * Now we can do the conversions.
5391 : : */
5392 : 0 : *scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
5393 : 0 : *scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
5394 : 0 : *scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
5395 : 0 : }
5396 : :
5397 : : static double
5398 : 0 : convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
5399 : : int rangelo, int rangehi)
5400 : : {
5401 : 0 : double num,
5402 : : denom,
5403 : : base;
5404 : :
5405 [ # # ]: 0 : if (valuelen <= 0)
5406 : 0 : return 0.0; /* empty string has scalar value 0 */
5407 : :
5408 : : /*
5409 : : * Since base is 256, need not consider more than about 10 chars (even
5410 : : * this many seems like overkill)
5411 : : */
5412 [ # # ]: 0 : if (valuelen > 10)
5413 : 0 : valuelen = 10;
5414 : :
5415 : : /* Convert initial characters to fraction */
5416 : 0 : base = rangehi - rangelo + 1;
5417 : 0 : num = 0.0;
5418 : 0 : denom = base;
5419 [ # # ]: 0 : while (valuelen-- > 0)
5420 : : {
5421 : 0 : int ch = *value++;
5422 : :
5423 [ # # ]: 0 : if (ch < rangelo)
5424 : 0 : ch = rangelo - 1;
5425 [ # # ]: 0 : else if (ch > rangehi)
5426 : 0 : ch = rangehi + 1;
5427 : 0 : num += ((double) (ch - rangelo)) / denom;
5428 : 0 : denom *= base;
5429 : 0 : }
5430 : :
5431 : 0 : return num;
5432 : 0 : }
5433 : :
5434 : : /*
5435 : : * Do convert_to_scalar()'s work for any timevalue data type.
5436 : : *
5437 : : * On failure (e.g., unsupported typid), set *failure to true;
5438 : : * otherwise, that variable is not changed.
5439 : : */
5440 : : static double
5441 : 0 : convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
5442 : : {
5443 [ # # # # : 0 : switch (typid)
# # # ]
5444 : : {
5445 : : case TIMESTAMPOID:
5446 : 0 : return DatumGetTimestamp(value);
5447 : : case TIMESTAMPTZOID:
5448 : 0 : return DatumGetTimestampTz(value);
5449 : : case DATEOID:
5450 : 0 : return date2timestamp_no_overflow(DatumGetDateADT(value));
5451 : : case INTERVALOID:
5452 : : {
5453 : 0 : Interval *interval = DatumGetIntervalP(value);
5454 : :
5455 : : /*
5456 : : * Convert the month part of Interval to days using assumed
5457 : : * average month length of 365.25/12.0 days. Not too
5458 : : * accurate, but plenty good enough for our purposes.
5459 : : *
5460 : : * This also works for infinite intervals, which just have all
5461 : : * fields set to INT_MIN/INT_MAX, and so will produce a result
5462 : : * smaller/larger than any finite interval.
5463 : : */
5464 : 0 : return interval->time + interval->day * (double) USECS_PER_DAY +
5465 : 0 : interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
5466 : 0 : }
5467 : : case TIMEOID:
5468 : 0 : return DatumGetTimeADT(value);
5469 : : case TIMETZOID:
5470 : : {
5471 : 0 : TimeTzADT *timetz = DatumGetTimeTzADTP(value);
5472 : :
5473 : : /* use GMT-equivalent time */
5474 : 0 : return (double) (timetz->time + (timetz->zone * 1000000.0));
5475 : 0 : }
5476 : : }
5477 : :
5478 : 0 : *failure = true;
5479 : 0 : return 0;
5480 : 0 : }
5481 : :
5482 : :
5483 : : /*
5484 : : * get_restriction_variable
5485 : : * Examine the args of a restriction clause to see if it's of the
5486 : : * form (variable op pseudoconstant) or (pseudoconstant op variable),
5487 : : * where "variable" could be either a Var or an expression in vars of a
5488 : : * single relation. If so, extract information about the variable,
5489 : : * and also indicate which side it was on and the other argument.
5490 : : *
5491 : : * Inputs:
5492 : : * root: the planner info
5493 : : * args: clause argument list
5494 : : * varRelid: see specs for restriction selectivity functions
5495 : : *
5496 : : * Outputs: (these are valid only if true is returned)
5497 : : * *vardata: gets information about variable (see examine_variable)
5498 : : * *other: gets other clause argument, aggressively reduced to a constant
5499 : : * *varonleft: set true if variable is on the left, false if on the right
5500 : : *
5501 : : * Returns true if a variable is identified, otherwise false.
5502 : : *
5503 : : * Note: if there are Vars on both sides of the clause, we must fail, because
5504 : : * callers are expecting that the other side will act like a pseudoconstant.
5505 : : */
5506 : : bool
5507 : 73549 : get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
5508 : : VariableStatData *vardata, Node **other,
5509 : : bool *varonleft)
5510 : : {
5511 : 73549 : Node *left,
5512 : : *right;
5513 : 73549 : VariableStatData rdata;
5514 : :
5515 : : /* Fail if not a binary opclause (probably shouldn't happen) */
5516 [ - + ]: 73549 : if (list_length(args) != 2)
5517 : 0 : return false;
5518 : :
5519 : 73549 : left = (Node *) linitial(args);
5520 : 73549 : right = (Node *) lsecond(args);
5521 : :
5522 : : /*
5523 : : * Examine both sides. Note that when varRelid is nonzero, Vars of other
5524 : : * relations will be treated as pseudoconstants.
5525 : : */
5526 : 73549 : examine_variable(root, left, varRelid, vardata);
5527 : 73549 : examine_variable(root, right, varRelid, &rdata);
5528 : :
5529 : : /*
5530 : : * If one side is a variable and the other not, we win.
5531 : : */
5532 [ + + + + ]: 73549 : if (vardata->rel && rdata.rel == NULL)
5533 : : {
5534 : 65235 : *varonleft = true;
5535 : 65235 : *other = estimate_expression_value(root, rdata.var);
5536 : : /* Assume we need no ReleaseVariableStats(rdata) here */
5537 : 65235 : return true;
5538 : : }
5539 : :
5540 [ + + + + ]: 8314 : if (vardata->rel == NULL && rdata.rel)
5541 : : {
5542 : 7910 : *varonleft = false;
5543 : 7910 : *other = estimate_expression_value(root, vardata->var);
5544 : : /* Assume we need no ReleaseVariableStats(*vardata) here */
5545 : 7910 : *vardata = rdata;
5546 : 7910 : return true;
5547 : : }
5548 : :
5549 : : /* Oops, clause has wrong structure (probably var op var) */
5550 [ + + ]: 404 : ReleaseVariableStats(*vardata);
5551 [ + + ]: 404 : ReleaseVariableStats(rdata);
5552 : :
5553 : 404 : return false;
5554 : 73549 : }
5555 : :
5556 : : /*
5557 : : * get_join_variables
5558 : : * Apply examine_variable() to each side of a join clause.
5559 : : * Also, attempt to identify whether the join clause has the same
5560 : : * or reversed sense compared to the SpecialJoinInfo.
5561 : : *
5562 : : * We consider the join clause "normal" if it is "lhs_var OP rhs_var",
5563 : : * or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
5564 : : * where we can't tell for sure, we default to assuming it's normal.
5565 : : */
5566 : : void
5567 : 25133 : get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
5568 : : VariableStatData *vardata1, VariableStatData *vardata2,
5569 : : bool *join_is_reversed)
5570 : : {
5571 : 25133 : Node *left,
5572 : : *right;
5573 : :
5574 [ + - ]: 25133 : if (list_length(args) != 2)
5575 [ # # # # ]: 0 : elog(ERROR, "join operator should take two arguments");
5576 : :
5577 : 25133 : left = (Node *) linitial(args);
5578 : 25133 : right = (Node *) lsecond(args);
5579 : :
5580 : 25133 : examine_variable(root, left, 0, vardata1);
5581 : 25133 : examine_variable(root, right, 0, vardata2);
5582 : :
5583 [ + + + + ]: 25133 : if (vardata1->rel &&
5584 : 25101 : bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
5585 : 7357 : *join_is_reversed = true; /* var1 is on RHS */
5586 [ + + + + ]: 17776 : else if (vardata2->rel &&
5587 : 17743 : bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
5588 : 48 : *join_is_reversed = true; /* var2 is on LHS */
5589 : : else
5590 : 17728 : *join_is_reversed = false;
5591 : 25133 : }
5592 : :
5593 : : /* statext_expressions_load copies the tuple, so just pfree it. */
5594 : : static void
5595 : 275 : ReleaseDummy(HeapTuple tuple)
5596 : : {
5597 : 275 : pfree(tuple);
5598 : 275 : }
5599 : :
5600 : : /*
5601 : : * examine_variable
5602 : : * Try to look up statistical data about an expression.
5603 : : * Fill in a VariableStatData struct to describe the expression.
5604 : : *
5605 : : * Inputs:
5606 : : * root: the planner info
5607 : : * node: the expression tree to examine
5608 : : * varRelid: see specs for restriction selectivity functions
5609 : : *
5610 : : * Outputs: *vardata is filled as follows:
5611 : : * var: the input expression (with any phvs or binary relabeling stripped,
5612 : : * if it is or contains a variable; but otherwise unchanged)
5613 : : * rel: RelOptInfo for relation containing variable; NULL if expression
5614 : : * contains no Vars (NOTE this could point to a RelOptInfo of a
5615 : : * subquery, not one in the current query).
5616 : : * statsTuple: the pg_statistic entry for the variable, if one exists;
5617 : : * otherwise NULL.
5618 : : * freefunc: pointer to a function to release statsTuple with.
5619 : : * vartype: exposed type of the expression; this should always match
5620 : : * the declared input type of the operator we are estimating for.
5621 : : * atttype, atttypmod: actual type/typmod of the "var" expression. This is
5622 : : * commonly the same as the exposed type of the variable argument,
5623 : : * but can be different in binary-compatible-type cases.
5624 : : * isunique: true if we were able to match the var to a unique index, a
5625 : : * single-column DISTINCT or GROUP-BY clause, implying its values are
5626 : : * unique for this query. (Caution: this should be trusted for
5627 : : * statistical purposes only, since we do not check indimmediate nor
5628 : : * verify that the exact same definition of equality applies.)
5629 : : * acl_ok: true if current user has permission to read all table rows from
5630 : : * the column(s) underlying the pg_statistic entry. This is consulted by
5631 : : * statistic_proc_security_check().
5632 : : *
5633 : : * Caller is responsible for doing ReleaseVariableStats() before exiting.
5634 : : */
5635 : : void
5636 : 299987 : examine_variable(PlannerInfo *root, Node *node, int varRelid,
5637 : : VariableStatData *vardata)
5638 : : {
5639 : 299987 : Node *basenode;
5640 : 299987 : Relids varnos;
5641 : 299987 : Relids basevarnos;
5642 : 299987 : RelOptInfo *onerel;
5643 : :
5644 : : /* Make sure we don't return dangling pointers in vardata */
5645 [ + - + - : 2099909 : MemSet(vardata, 0, sizeof(VariableStatData));
+ - - + +
+ ]
5646 : :
5647 : : /* Save the exposed type of the expression */
5648 : 299987 : vardata->vartype = exprType(node);
5649 : :
5650 : : /*
5651 : : * PlaceHolderVars are transparent for the purpose of statistics lookup;
5652 : : * they do not alter the value distribution of the underlying expression.
5653 : : * However, they can obscure the structure, preventing us from recognizing
5654 : : * matches to base columns, index expressions, or extended statistics. So
5655 : : * strip them out first.
5656 : : */
5657 : 299987 : basenode = strip_all_phvs_deep(root, node);
5658 : :
5659 : : /*
5660 : : * Look inside any binary-compatible relabeling. We need to handle nested
5661 : : * RelabelType nodes here, because the prior stripping of PlaceHolderVars
5662 : : * may have brought separate RelabelTypes into adjacency.
5663 : : */
5664 [ + + ]: 307505 : while (IsA(basenode, RelabelType))
5665 : 7518 : basenode = (Node *) ((RelabelType *) basenode)->arg;
5666 : :
5667 : : /* Fast path for a simple Var */
5668 [ + + + + ]: 370670 : if (IsA(basenode, Var) &&
5669 [ + + ]: 246065 : (varRelid == 0 || varRelid == ((Var *) basenode)->varno))
5670 : : {
5671 : 212579 : Var *var = (Var *) basenode;
5672 : :
5673 : : /* Set up result fields other than the stats tuple */
5674 : 212579 : vardata->var = basenode; /* return Var without phvs or relabeling */
5675 : 212579 : vardata->rel = find_base_rel(root, var->varno);
5676 : 212579 : vardata->atttype = var->vartype;
5677 : 212579 : vardata->atttypmod = var->vartypmod;
5678 : 212579 : vardata->isunique = has_unique_index(vardata->rel, var->varattno);
5679 : :
5680 : : /* Try to locate some stats */
5681 : 212579 : examine_simple_variable(root, var, vardata);
5682 : :
5683 : : return;
5684 : 212579 : }
5685 : :
5686 : : /*
5687 : : * Okay, it's a more complicated expression. Determine variable
5688 : : * membership. Note that when varRelid isn't zero, only vars of that
5689 : : * relation are considered "real" vars.
5690 : : */
5691 : 87408 : varnos = pull_varnos(root, basenode);
5692 : 87408 : basevarnos = bms_difference(varnos, root->outer_join_rels);
5693 : :
5694 : 87408 : onerel = NULL;
5695 : :
5696 [ + + ]: 87408 : if (bms_is_empty(basevarnos))
5697 : : {
5698 : : /* No Vars at all ... must be pseudo-constant clause */
5699 : 42126 : }
5700 : : else
5701 : : {
5702 : 45282 : int relid;
5703 : :
5704 : : /* Check if the expression is in vars of a single base relation */
5705 [ + + ]: 45282 : if (bms_get_singleton_member(basevarnos, &relid))
5706 : : {
5707 [ + + + + ]: 44696 : if (varRelid == 0 || varRelid == relid)
5708 : : {
5709 : 9218 : onerel = find_base_rel(root, relid);
5710 : 9218 : vardata->rel = onerel;
5711 : 9218 : node = basenode; /* strip any phvs or relabeling */
5712 : 9218 : }
5713 : : /* else treat it as a constant */
5714 : 44696 : }
5715 : : else
5716 : : {
5717 : : /* varnos has multiple relids */
5718 [ + + ]: 586 : if (varRelid == 0)
5719 : : {
5720 : : /* treat it as a variable of a join relation */
5721 : 444 : vardata->rel = find_join_rel(root, varnos);
5722 : 444 : node = basenode; /* strip any phvs or relabeling */
5723 : 444 : }
5724 [ + + ]: 142 : else if (bms_is_member(varRelid, varnos))
5725 : : {
5726 : : /* ignore the vars belonging to other relations */
5727 : 123 : vardata->rel = find_base_rel(root, varRelid);
5728 : 123 : node = basenode; /* strip any phvs or relabeling */
5729 : : /* note: no point in expressional-index search here */
5730 : 123 : }
5731 : : /* else treat it as a constant */
5732 : : }
5733 : 45282 : }
5734 : :
5735 : 87408 : bms_free(basevarnos);
5736 : :
5737 : 87408 : vardata->var = node;
5738 : 87408 : vardata->atttype = exprType(node);
5739 : 87408 : vardata->atttypmod = exprTypmod(node);
5740 : :
5741 [ + + ]: 87408 : if (onerel)
5742 : : {
5743 : : /*
5744 : : * We have an expression in vars of a single relation. Try to match
5745 : : * it to expressional index columns, in hopes of finding some
5746 : : * statistics.
5747 : : *
5748 : : * Note that we consider all index columns including INCLUDE columns,
5749 : : * since there could be stats for such columns. But the test for
5750 : : * uniqueness needs to be warier.
5751 : : *
5752 : : * XXX it's conceivable that there are multiple matches with different
5753 : : * index opfamilies; if so, we need to pick one that matches the
5754 : : * operator we are estimating for. FIXME later.
5755 : : */
5756 : 9218 : ListCell *ilist;
5757 : 9218 : ListCell *slist;
5758 : :
5759 : : /*
5760 : : * The nullingrels bits within the expression could prevent us from
5761 : : * matching it to expressional index columns or to the expressions in
5762 : : * extended statistics. So strip them out first.
5763 : : */
5764 [ + + ]: 9218 : if (bms_overlap(varnos, root->outer_join_rels))
5765 : 126 : node = remove_nulling_relids(node, root->outer_join_rels, NULL);
5766 : :
5767 [ + + + + : 19906 : foreach(ilist, onerel->indexlist)
+ + ]
5768 : : {
5769 : 10688 : IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
5770 : 10688 : ListCell *indexpr_item;
5771 : 10688 : int pos;
5772 : :
5773 : 10688 : indexpr_item = list_head(index->indexprs);
5774 [ + + ]: 10688 : if (indexpr_item == NULL)
5775 : 9876 : continue; /* no expressions here... */
5776 : :
5777 [ + + ]: 1139 : for (pos = 0; pos < index->ncolumns; pos++)
5778 : : {
5779 [ + + ]: 824 : if (index->indexkeys[pos] == 0)
5780 : : {
5781 : 812 : Node *indexkey;
5782 : :
5783 [ + - ]: 812 : if (indexpr_item == NULL)
5784 [ # # # # ]: 0 : elog(ERROR, "too few entries in indexprs list");
5785 : 812 : indexkey = (Node *) lfirst(indexpr_item);
5786 [ + - + - ]: 812 : if (indexkey && IsA(indexkey, RelabelType))
5787 : 0 : indexkey = (Node *) ((RelabelType *) indexkey)->arg;
5788 [ + + ]: 812 : if (equal(node, indexkey))
5789 : : {
5790 : : /*
5791 : : * Found a match ... is it a unique index? Tests here
5792 : : * should match has_unique_index().
5793 : : */
5794 [ + + ]: 603 : if (index->unique &&
5795 [ + - ]: 73 : index->nkeycolumns == 1 &&
5796 [ + - # # ]: 73 : pos == 0 &&
5797 [ - + ]: 73 : (index->indpred == NIL || index->predOK))
5798 : 73 : vardata->isunique = true;
5799 : :
5800 : : /*
5801 : : * Has it got stats? We only consider stats for
5802 : : * non-partial indexes, since partial indexes probably
5803 : : * don't reflect whole-relation statistics; the above
5804 : : * check for uniqueness is the only info we take from
5805 : : * a partial index.
5806 : : *
5807 : : * An index stats hook, however, must make its own
5808 : : * decisions about what to do with partial indexes.
5809 : : */
5810 [ - + # # ]: 603 : if (get_index_stats_hook &&
5811 : 0 : (*get_index_stats_hook) (root, index->indexoid,
5812 : 0 : pos + 1, vardata))
5813 : : {
5814 : : /*
5815 : : * The hook took control of acquiring a stats
5816 : : * tuple. If it did supply a tuple, it'd better
5817 : : * have supplied a freefunc.
5818 : : */
5819 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
5820 : 0 : !vardata->freefunc)
5821 [ # # # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
5822 : 0 : }
5823 [ - + ]: 603 : else if (index->indpred == NIL)
5824 : : {
5825 : 603 : vardata->statsTuple =
5826 : 603 : SearchSysCache3(STATRELATTINH,
5827 : 603 : ObjectIdGetDatum(index->indexoid),
5828 : 603 : Int16GetDatum(pos + 1),
5829 : 603 : BoolGetDatum(false));
5830 : 603 : vardata->freefunc = ReleaseSysCache;
5831 : :
5832 [ + + ]: 603 : if (HeapTupleIsValid(vardata->statsTuple))
5833 : : {
5834 : : /*
5835 : : * Test if user has permission to access all
5836 : : * rows from the index's table.
5837 : : *
5838 : : * For simplicity, we insist on the whole
5839 : : * table being selectable, rather than trying
5840 : : * to identify which column(s) the index
5841 : : * depends on.
5842 : : *
5843 : : * Note that for an inheritance child,
5844 : : * permissions are checked on the inheritance
5845 : : * root parent, and whole-table select
5846 : : * privilege on the parent doesn't quite
5847 : : * guarantee that the user could read all
5848 : : * columns of the child. But in practice it's
5849 : : * unlikely that any interesting security
5850 : : * violation could result from allowing access
5851 : : * to the expression index's stats, so we
5852 : : * allow it anyway. See similar code in
5853 : : * examine_simple_variable() for additional
5854 : : * comments.
5855 : : */
5856 : 497 : vardata->acl_ok =
5857 : 994 : all_rows_selectable(root,
5858 : 497 : index->rel->relid,
5859 : : NULL);
5860 : 497 : }
5861 : : else
5862 : : {
5863 : : /* suppress leakproofness checks later */
5864 : 106 : vardata->acl_ok = true;
5865 : : }
5866 : 603 : }
5867 [ + + ]: 603 : if (vardata->statsTuple)
5868 : 497 : break;
5869 : 106 : }
5870 : 315 : indexpr_item = lnext(index->indexprs, indexpr_item);
5871 [ + + ]: 812 : }
5872 : 327 : }
5873 [ + + ]: 812 : if (vardata->statsTuple)
5874 : 497 : break;
5875 [ + + + ]: 10688 : }
5876 : :
5877 : : /*
5878 : : * Search extended statistics for one with a matching expression.
5879 : : * There might be multiple ones, so just grab the first one. In the
5880 : : * future, we might consider the statistics target (and pick the most
5881 : : * accurate statistics) and maybe some other parameters.
5882 : : */
5883 [ + + + + : 9952 : foreach(slist, onerel->statlist)
+ + ]
5884 : : {
5885 : 734 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(slist);
5886 [ + - ]: 734 : RangeTblEntry *rte = planner_rt_fetch(onerel->relid, root);
5887 : 734 : ListCell *expr_item;
5888 : 734 : int pos;
5889 : :
5890 : : /*
5891 : : * Stop once we've found statistics for the expression (either
5892 : : * from extended stats, or for an index in the preceding loop).
5893 : : */
5894 [ + + ]: 734 : if (vardata->statsTuple)
5895 : 48 : break;
5896 : :
5897 : : /* skip stats without per-expression stats */
5898 [ + + ]: 686 : if (info->kind != STATS_EXT_EXPRESSIONS)
5899 : 351 : continue;
5900 : :
5901 : : /* skip stats with mismatching stxdinherit value */
5902 [ + + ]: 335 : if (info->inherit != rte->inh)
5903 : 1 : continue;
5904 : :
5905 : 334 : pos = 0;
5906 [ + - + + : 826 : foreach(expr_item, info->exprs)
+ + ]
5907 : : {
5908 : 492 : Node *expr = (Node *) lfirst(expr_item);
5909 : :
5910 [ + - ]: 492 : Assert(expr);
5911 : :
5912 : : /* strip RelabelType before comparing it */
5913 [ + - + - ]: 492 : if (expr && IsA(expr, RelabelType))
5914 : 0 : expr = (Node *) ((RelabelType *) expr)->arg;
5915 : :
5916 : : /* found a match, see if we can extract pg_statistic row */
5917 [ + + ]: 492 : if (equal(node, expr))
5918 : : {
5919 : : /*
5920 : : * XXX Not sure if we should cache the tuple somewhere.
5921 : : * Now we just create a new copy every time.
5922 : : */
5923 : 275 : vardata->statsTuple =
5924 : 275 : statext_expressions_load(info->statOid, rte->inh, pos);
5925 : :
5926 : 275 : vardata->freefunc = ReleaseDummy;
5927 : :
5928 : : /*
5929 : : * Test if user has permission to access all rows from the
5930 : : * table.
5931 : : *
5932 : : * For simplicity, we insist on the whole table being
5933 : : * selectable, rather than trying to identify which
5934 : : * column(s) the statistics object depends on.
5935 : : *
5936 : : * Note that for an inheritance child, permissions are
5937 : : * checked on the inheritance root parent, and whole-table
5938 : : * select privilege on the parent doesn't quite guarantee
5939 : : * that the user could read all columns of the child. But
5940 : : * in practice it's unlikely that any interesting security
5941 : : * violation could result from allowing access to the
5942 : : * expression stats, so we allow it anyway. See similar
5943 : : * code in examine_simple_variable() for additional
5944 : : * comments.
5945 : : */
5946 : 550 : vardata->acl_ok = all_rows_selectable(root,
5947 : 275 : onerel->relid,
5948 : : NULL);
5949 : :
5950 : 275 : break;
5951 : : }
5952 : :
5953 : 217 : pos++;
5954 [ + + ]: 492 : }
5955 [ + + + ]: 734 : }
5956 : 9218 : }
5957 : :
5958 : 87408 : bms_free(varnos);
5959 : 299987 : }
5960 : :
5961 : : /*
5962 : : * strip_all_phvs_deep
5963 : : * Deeply strip all PlaceHolderVars in an expression.
5964 : :
5965 : : * As a performance optimization, we first use a lightweight walker to check
5966 : : * for the presence of any PlaceHolderVars. The expensive mutator is invoked
5967 : : * only if a PlaceHolderVar is found, avoiding unnecessary memory allocation
5968 : : * and tree copying in the common case where no PlaceHolderVars are present.
5969 : : */
5970 : : static Node *
5971 : 299987 : strip_all_phvs_deep(PlannerInfo *root, Node *node)
5972 : : {
5973 : : /* If there are no PHVs anywhere, we needn't work hard */
5974 [ + + ]: 299987 : if (root->glob->lastPHId == 0)
5975 : 295253 : return node;
5976 : :
5977 [ + + ]: 4734 : if (!contain_placeholder_walker(node, NULL))
5978 : 4068 : return node;
5979 : 666 : return strip_all_phvs_mutator(node, NULL);
5980 : 299987 : }
5981 : :
5982 : : /*
5983 : : * contain_placeholder_walker
5984 : : * Lightweight walker to check if an expression contains any
5985 : : * PlaceHolderVars
5986 : : */
5987 : : static bool
5988 : 5294 : contain_placeholder_walker(Node *node, void *context)
5989 : : {
5990 [ + + ]: 5294 : if (node == NULL)
5991 : 2 : return false;
5992 [ + + ]: 5292 : if (IsA(node, PlaceHolderVar))
5993 : 666 : return true;
5994 : :
5995 : 4626 : return expression_tree_walker(node, contain_placeholder_walker, context);
5996 : 5294 : }
5997 : :
5998 : : /*
5999 : : * strip_all_phvs_mutator
6000 : : * Mutator to deeply strip all PlaceHolderVars
6001 : : */
6002 : : static Node *
6003 : 1835 : strip_all_phvs_mutator(Node *node, void *context)
6004 : : {
6005 [ + + ]: 1835 : if (node == NULL)
6006 : 8 : return NULL;
6007 [ + + ]: 1827 : if (IsA(node, PlaceHolderVar))
6008 : : {
6009 : : /* Strip it and recurse into its contained expression */
6010 : 691 : PlaceHolderVar *phv = (PlaceHolderVar *) node;
6011 : :
6012 : 691 : return strip_all_phvs_mutator((Node *) phv->phexpr, context);
6013 : 691 : }
6014 : :
6015 : 1136 : return expression_tree_mutator(node, strip_all_phvs_mutator, context);
6016 : 1835 : }
6017 : :
6018 : : /*
6019 : : * examine_simple_variable
6020 : : * Handle a simple Var for examine_variable
6021 : : *
6022 : : * This is split out as a subroutine so that we can recurse to deal with
6023 : : * Vars referencing subqueries (either sub-SELECT-in-FROM or CTE style).
6024 : : *
6025 : : * We already filled in all the fields of *vardata except for the stats tuple.
6026 : : */
6027 : : static void
6028 : 213080 : examine_simple_variable(PlannerInfo *root, Var *var,
6029 : : VariableStatData *vardata)
6030 : : {
6031 : 213080 : RangeTblEntry *rte = root->simple_rte_array[var->varno];
6032 : :
6033 [ + - ]: 213080 : Assert(IsA(rte, RangeTblEntry));
6034 : :
6035 [ - + # # ]: 213080 : if (get_relation_stats_hook &&
6036 : 0 : (*get_relation_stats_hook) (root, rte, var->varattno, vardata))
6037 : : {
6038 : : /*
6039 : : * The hook took control of acquiring a stats tuple. If it did supply
6040 : : * a tuple, it'd better have supplied a freefunc.
6041 : : */
6042 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6043 : 0 : !vardata->freefunc)
6044 [ # # # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6045 : 0 : }
6046 [ + + ]: 213080 : else if (rte->rtekind == RTE_RELATION)
6047 : : {
6048 : : /*
6049 : : * Plain table or parent of an inheritance appendrel, so look up the
6050 : : * column in pg_statistic
6051 : : */
6052 : 202123 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6053 : 202123 : ObjectIdGetDatum(rte->relid),
6054 : 202123 : Int16GetDatum(var->varattno),
6055 : 202123 : BoolGetDatum(rte->inh));
6056 : 202123 : vardata->freefunc = ReleaseSysCache;
6057 : :
6058 [ + + ]: 202123 : if (HeapTupleIsValid(vardata->statsTuple))
6059 : : {
6060 : : /*
6061 : : * Test if user has permission to read all rows from this column.
6062 : : *
6063 : : * This requires that the user has the appropriate SELECT
6064 : : * privileges and that there are no securityQuals from security
6065 : : * barrier views or RLS policies. If that's not the case, then we
6066 : : * only permit leakproof functions to be passed pg_statistic data
6067 : : * in vardata, otherwise the functions might reveal data that the
6068 : : * user doesn't have permission to see --- see
6069 : : * statistic_proc_security_check().
6070 : : */
6071 : 131795 : vardata->acl_ok =
6072 : 263590 : all_rows_selectable(root, var->varno,
6073 : 131795 : bms_make_singleton(var->varattno - FirstLowInvalidHeapAttributeNumber));
6074 : 131795 : }
6075 : : else
6076 : : {
6077 : : /* suppress any possible leakproofness checks later */
6078 : 70328 : vardata->acl_ok = true;
6079 : : }
6080 : 202123 : }
6081 [ + + + + ]: 11342 : else if ((rte->rtekind == RTE_SUBQUERY && !rte->inh) ||
6082 [ + + ]: 9681 : (rte->rtekind == RTE_CTE && !rte->self_reference))
6083 : : {
6084 : : /*
6085 : : * Plain subquery (not one that was converted to an appendrel) or
6086 : : * non-recursive CTE. In either case, we can try to find out what the
6087 : : * Var refers to within the subquery. We skip this for appendrel and
6088 : : * recursive-CTE cases because any column stats we did find would
6089 : : * likely not be very relevant.
6090 : : */
6091 : 1276 : PlannerInfo *subroot;
6092 : 1276 : Query *subquery;
6093 : 1276 : List *subtlist;
6094 : 1276 : TargetEntry *ste;
6095 : :
6096 : : /*
6097 : : * Punt if it's a whole-row var rather than a plain column reference.
6098 : : */
6099 [ + - ]: 1276 : if (var->varattno == InvalidAttrNumber)
6100 : 0 : return;
6101 : :
6102 : : /*
6103 : : * Otherwise, find the subquery's planner subroot.
6104 : : */
6105 [ + + ]: 1276 : if (rte->rtekind == RTE_SUBQUERY)
6106 : : {
6107 : 1047 : RelOptInfo *rel;
6108 : :
6109 : : /*
6110 : : * Fetch RelOptInfo for subquery. Note that we don't change the
6111 : : * rel returned in vardata, since caller expects it to be a rel of
6112 : : * the caller's query level. Because we might already be
6113 : : * recursing, we can't use that rel pointer either, but have to
6114 : : * look up the Var's rel afresh.
6115 : : */
6116 : 1047 : rel = find_base_rel(root, var->varno);
6117 : :
6118 : 1047 : subroot = rel->subroot;
6119 : 1047 : }
6120 : : else
6121 : : {
6122 : : /* CTE case is more difficult */
6123 : 229 : PlannerInfo *cteroot;
6124 : 229 : Index levelsup;
6125 : 229 : int ndx;
6126 : 229 : int plan_id;
6127 : 229 : ListCell *lc;
6128 : :
6129 : : /*
6130 : : * Find the referenced CTE, and locate the subroot previously made
6131 : : * for it.
6132 : : */
6133 : 229 : levelsup = rte->ctelevelsup;
6134 : 229 : cteroot = root;
6135 [ + + ]: 380 : while (levelsup-- > 0)
6136 : : {
6137 : 151 : cteroot = cteroot->parent_root;
6138 [ + - ]: 151 : if (!cteroot) /* shouldn't happen */
6139 [ # # # # ]: 0 : elog(ERROR, "bad levelsup for CTE \"%s\"", rte->ctename);
6140 : : }
6141 : :
6142 : : /*
6143 : : * Note: cte_plan_ids can be shorter than cteList, if we are still
6144 : : * working on planning the CTEs (ie, this is a side-reference from
6145 : : * another CTE). So we mustn't use forboth here.
6146 : : */
6147 : 229 : ndx = 0;
6148 [ + - - + : 490 : foreach(lc, cteroot->parse->cteList)
+ - ]
6149 : : {
6150 : 261 : CommonTableExpr *cte = (CommonTableExpr *) lfirst(lc);
6151 : :
6152 [ + + ]: 261 : if (strcmp(cte->ctename, rte->ctename) == 0)
6153 : 229 : break;
6154 : 32 : ndx++;
6155 [ + + ]: 261 : }
6156 [ + - ]: 229 : if (lc == NULL) /* shouldn't happen */
6157 [ # # # # ]: 0 : elog(ERROR, "could not find CTE \"%s\"", rte->ctename);
6158 [ + - ]: 229 : if (ndx >= list_length(cteroot->cte_plan_ids))
6159 [ # # # # ]: 0 : elog(ERROR, "could not find plan for CTE \"%s\"", rte->ctename);
6160 : 229 : plan_id = list_nth_int(cteroot->cte_plan_ids, ndx);
6161 [ + - ]: 229 : if (plan_id <= 0)
6162 [ # # # # ]: 0 : elog(ERROR, "no plan was made for CTE \"%s\"", rte->ctename);
6163 : 229 : subroot = list_nth(root->glob->subroots, plan_id - 1);
6164 : 229 : }
6165 : :
6166 : : /* If the subquery hasn't been planned yet, we have to punt */
6167 [ + - ]: 1276 : if (subroot == NULL)
6168 : 0 : return;
6169 [ + - ]: 1276 : Assert(IsA(subroot, PlannerInfo));
6170 : :
6171 : : /*
6172 : : * We must use the subquery parsetree as mangled by the planner, not
6173 : : * the raw version from the RTE, because we need a Var that will refer
6174 : : * to the subroot's live RelOptInfos. For instance, if any subquery
6175 : : * pullup happened during planning, Vars in the targetlist might have
6176 : : * gotten replaced, and we need to see the replacement expressions.
6177 : : */
6178 : 1276 : subquery = subroot->parse;
6179 [ + - ]: 1276 : Assert(IsA(subquery, Query));
6180 : :
6181 : : /*
6182 : : * Punt if subquery uses set operations or grouping sets, as these
6183 : : * will mash underlying columns' stats beyond recognition. (Set ops
6184 : : * are particularly nasty; if we forged ahead, we would return stats
6185 : : * relevant to only the leftmost subselect...) DISTINCT is also
6186 : : * problematic, but we check that later because there is a possibility
6187 : : * of learning something even with it.
6188 : : */
6189 [ + + + + ]: 1276 : if (subquery->setOperations ||
6190 : 1198 : subquery->groupingSets)
6191 : 94 : return;
6192 : :
6193 : : /* Get the subquery output expression referenced by the upper Var */
6194 [ + + ]: 1182 : if (subquery->returningList)
6195 : 29 : subtlist = subquery->returningList;
6196 : : else
6197 : 1153 : subtlist = subquery->targetList;
6198 : 1182 : ste = get_tle_by_resno(subtlist, var->varattno);
6199 [ + - ]: 1182 : if (ste == NULL || ste->resjunk)
6200 [ # # # # ]: 0 : elog(ERROR, "subquery %s does not have attribute %d",
6201 : : rte->eref->aliasname, var->varattno);
6202 : 1182 : var = (Var *) ste->expr;
6203 : :
6204 : : /*
6205 : : * If subquery uses DISTINCT, we can't make use of any stats for the
6206 : : * variable ... but, if it's the only DISTINCT column, we are entitled
6207 : : * to consider it unique. We do the test this way so that it works
6208 : : * for cases involving DISTINCT ON.
6209 : : */
6210 [ + + ]: 1182 : if (subquery->distinctClause)
6211 : : {
6212 [ + + - + ]: 71 : if (list_length(subquery->distinctClause) == 1 &&
6213 : 25 : targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
6214 : 25 : vardata->isunique = true;
6215 : : /* cannot go further */
6216 : 71 : return;
6217 : : }
6218 : :
6219 : : /* The same idea as with DISTINCT clause works for a GROUP-BY too */
6220 [ + + ]: 1111 : if (subquery->groupClause)
6221 : : {
6222 [ + + + + ]: 99 : if (list_length(subquery->groupClause) == 1 &&
6223 : 69 : targetIsInSortList(ste, InvalidOid, subquery->groupClause))
6224 : 57 : vardata->isunique = true;
6225 : : /* cannot go further */
6226 : 99 : return;
6227 : : }
6228 : :
6229 : : /*
6230 : : * If the sub-query originated from a view with the security_barrier
6231 : : * attribute, we must not look at the variable's statistics, though it
6232 : : * seems all right to notice the existence of a DISTINCT clause. So
6233 : : * stop here.
6234 : : *
6235 : : * This is probably a harsher restriction than necessary; it's
6236 : : * certainly OK for the selectivity estimator (which is a C function,
6237 : : * and therefore omnipotent anyway) to look at the statistics. But
6238 : : * many selectivity estimators will happily *invoke the operator
6239 : : * function* to try to work out a good estimate - and that's not OK.
6240 : : * So for now, don't dig down for stats.
6241 : : */
6242 [ + + ]: 1012 : if (rte->security_barrier)
6243 : 39 : return;
6244 : :
6245 : : /* Can only handle a simple Var of subquery's query level */
6246 [ + - + + : 973 : if (var && IsA(var, Var) &&
- + ]
6247 : 501 : var->varlevelsup == 0)
6248 : : {
6249 : : /*
6250 : : * OK, recurse into the subquery. Note that the original setting
6251 : : * of vardata->isunique (which will surely be false) is left
6252 : : * unchanged in this situation. That's what we want, since even
6253 : : * if the underlying column is unique, the subquery may have
6254 : : * joined to other tables in a way that creates duplicates.
6255 : : */
6256 : 501 : examine_simple_variable(subroot, var, vardata);
6257 : 501 : }
6258 [ + + ]: 1276 : }
6259 : : else
6260 : : {
6261 : : /*
6262 : : * Otherwise, the Var comes from a FUNCTION or VALUES RTE. (We won't
6263 : : * see RTE_JOIN here because join alias Vars have already been
6264 : : * flattened.) There's not much we can do with function outputs, but
6265 : : * maybe someday try to be smarter about VALUES.
6266 : : */
6267 : : }
6268 [ - + ]: 213080 : }
6269 : :
6270 : : /*
6271 : : * all_rows_selectable
6272 : : * Test whether the user has permission to select all rows from a given
6273 : : * relation.
6274 : : *
6275 : : * Inputs:
6276 : : * root: the planner info
6277 : : * varno: the index of the relation (assumed to be an RTE_RELATION)
6278 : : * varattnos: the attributes for which permission is required, or NULL if
6279 : : * whole-table access is required
6280 : : *
6281 : : * Returns true if the user has the required select permissions, and there are
6282 : : * no securityQuals from security barrier views or RLS policies.
6283 : : *
6284 : : * Note that if the relation is an inheritance child relation, securityQuals
6285 : : * and access permissions are checked against the inheritance root parent (the
6286 : : * relation actually mentioned in the query) --- see the comments in
6287 : : * expand_single_inheritance_child() for an explanation of why it has to be
6288 : : * done this way.
6289 : : *
6290 : : * If varattnos is non-NULL, its attribute numbers should be offset by
6291 : : * FirstLowInvalidHeapAttributeNumber so that system attributes can be
6292 : : * checked. If varattnos is NULL, only table-level SELECT privileges are
6293 : : * checked, not any column-level privileges.
6294 : : *
6295 : : * Note: if the relation is accessed via a view, this function actually tests
6296 : : * whether the view owner has permission to select from the relation. To
6297 : : * ensure that the current user has permission, it is also necessary to check
6298 : : * that the current user has permission to select from the view, which we do
6299 : : * at planner-startup --- see subquery_planner().
6300 : : *
6301 : : * This is exported so that other estimation functions can use it.
6302 : : */
6303 : : bool
6304 : 132609 : all_rows_selectable(PlannerInfo *root, Index varno, Bitmapset *varattnos)
6305 : : {
6306 : 132609 : RelOptInfo *rel = find_base_rel_noerr(root, varno);
6307 [ + - ]: 132609 : RangeTblEntry *rte = planner_rt_fetch(varno, root);
6308 : 132609 : Oid userid;
6309 : 132609 : int varattno;
6310 : :
6311 [ + - ]: 132609 : Assert(rte->rtekind == RTE_RELATION);
6312 : :
6313 : : /*
6314 : : * Determine the user ID to use for privilege checks (either the current
6315 : : * user or the view owner, if we're accessing the table via a view).
6316 : : *
6317 : : * Normally the relation will have an associated RelOptInfo from which we
6318 : : * can find the userid, but it might not if it's a RETURNING Var for an
6319 : : * INSERT target relation. In that case use the RTEPermissionInfo
6320 : : * associated with the RTE.
6321 : : *
6322 : : * If we navigate up to a parent relation, we keep using the same userid,
6323 : : * since it's the same in all relations of a given inheritance tree.
6324 : : */
6325 [ + + ]: 132609 : if (rel)
6326 : 132602 : userid = rel->userid;
6327 : : else
6328 : : {
6329 : 7 : RTEPermissionInfo *perminfo;
6330 : :
6331 : 7 : perminfo = getRTEPermissionInfo(root->parse->rteperminfos, rte);
6332 : 7 : userid = perminfo->checkAsUser;
6333 : 7 : }
6334 [ + + ]: 132609 : if (!OidIsValid(userid))
6335 : 118388 : userid = GetUserId();
6336 : :
6337 : : /*
6338 : : * Permissions and securityQuals must be checked on the table actually
6339 : : * mentioned in the query, so if this is an inheritance child, navigate up
6340 : : * to the inheritance root parent. If the user can read the whole table
6341 : : * or the required columns there, then they can read from the child table
6342 : : * too. For per-column checks, we must find out which of the root
6343 : : * parent's attributes the child relation's attributes correspond to.
6344 : : */
6345 [ + + ]: 132609 : if (root->append_rel_array != NULL)
6346 : : {
6347 : 37801 : AppendRelInfo *appinfo;
6348 : :
6349 : 37801 : appinfo = root->append_rel_array[varno];
6350 : :
6351 : : /*
6352 : : * Partitions are mapped to their immediate parent, not the root
6353 : : * parent, so must be ready to walk up multiple AppendRelInfos. But
6354 : : * stop if we hit a parent that is not RTE_RELATION --- that's a
6355 : : * flattened UNION ALL subquery, not an inheritance parent.
6356 : : */
6357 [ + + + + ]: 106281 : while (appinfo &&
6358 [ + - ]: 34271 : planner_rt_fetch(appinfo->parent_relid,
6359 : 34271 : root)->rtekind == RTE_RELATION)
6360 : : {
6361 : 34209 : Bitmapset *parent_varattnos = NULL;
6362 : :
6363 : : /*
6364 : : * For each child attribute, find the corresponding parent
6365 : : * attribute. In rare cases, the attribute may be local to the
6366 : : * child table, in which case, we've got to live with having no
6367 : : * access to this column.
6368 : : */
6369 : 34209 : varattno = -1;
6370 [ + + ]: 67943 : while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
6371 : : {
6372 : 33734 : AttrNumber attno;
6373 : 33734 : AttrNumber parent_attno;
6374 : :
6375 : 33734 : attno = varattno + FirstLowInvalidHeapAttributeNumber;
6376 : :
6377 [ + + ]: 33734 : if (attno == InvalidAttrNumber)
6378 : : {
6379 : : /*
6380 : : * Whole-row reference, so must map each column of the
6381 : : * child to the parent table.
6382 : : */
6383 [ + + ]: 6 : for (attno = 1; attno <= appinfo->num_child_cols; attno++)
6384 : : {
6385 : 4 : parent_attno = appinfo->parent_colnos[attno - 1];
6386 [ - + ]: 4 : if (parent_attno == 0)
6387 : 0 : return false; /* attr is local to child */
6388 : 4 : parent_varattnos =
6389 : 8 : bms_add_member(parent_varattnos,
6390 : 4 : parent_attno - FirstLowInvalidHeapAttributeNumber);
6391 : 4 : }
6392 : 2 : }
6393 : : else
6394 : : {
6395 [ - + ]: 33732 : if (attno < 0)
6396 : : {
6397 : : /* System attnos are the same in all tables */
6398 : 0 : parent_attno = attno;
6399 : 0 : }
6400 : : else
6401 : : {
6402 [ - + ]: 33732 : if (attno > appinfo->num_child_cols)
6403 : 0 : return false; /* safety check */
6404 : 33732 : parent_attno = appinfo->parent_colnos[attno - 1];
6405 [ + - ]: 33732 : if (parent_attno == 0)
6406 : 0 : return false; /* attr is local to child */
6407 : : }
6408 : 33732 : parent_varattnos =
6409 : 67464 : bms_add_member(parent_varattnos,
6410 : 33732 : parent_attno - FirstLowInvalidHeapAttributeNumber);
6411 : : }
6412 [ - + ]: 33734 : }
6413 : :
6414 : : /* If the parent is itself a child, continue up */
6415 : 34209 : varno = appinfo->parent_relid;
6416 : 34209 : varattnos = parent_varattnos;
6417 : 34209 : appinfo = root->append_rel_array[varno];
6418 [ - + ]: 34209 : }
6419 : :
6420 : : /* Perform the access check on this parent rel */
6421 [ + - ]: 37801 : rte = planner_rt_fetch(varno, root);
6422 [ + - ]: 37801 : Assert(rte->rtekind == RTE_RELATION);
6423 [ - + ]: 37801 : }
6424 : :
6425 : : /*
6426 : : * For all rows to be accessible, there must be no securityQuals from
6427 : : * security barrier views or RLS policies.
6428 : : */
6429 [ + + ]: 132609 : if (rte->securityQuals != NIL)
6430 : 138 : return false;
6431 : :
6432 : : /*
6433 : : * Test for table-level SELECT privilege.
6434 : : *
6435 : : * If varattnos is non-NULL, this is sufficient to give access to all
6436 : : * requested attributes, even for a child table, since we have verified
6437 : : * that all required child columns have matching parent columns.
6438 : : *
6439 : : * If varattnos is NULL (whole-table access requested), this doesn't
6440 : : * necessarily guarantee that the user can read all columns of a child
6441 : : * table, but we allow it anyway (see comments in examine_variable()) and
6442 : : * don't bother checking any column privileges.
6443 : : */
6444 [ + + ]: 132471 : if (pg_class_aclcheck(rte->relid, userid, ACL_SELECT) == ACLCHECK_OK)
6445 : 132407 : return true;
6446 : :
6447 [ + + ]: 64 : if (varattnos == NULL)
6448 : 2 : return false; /* whole-table access requested */
6449 : :
6450 : : /*
6451 : : * Don't have table-level SELECT privilege, so check per-column
6452 : : * privileges.
6453 : : */
6454 : 62 : varattno = -1;
6455 [ + + ]: 85 : while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
6456 : : {
6457 : 62 : AttrNumber attno = varattno + FirstLowInvalidHeapAttributeNumber;
6458 : :
6459 [ + + ]: 62 : if (attno == InvalidAttrNumber)
6460 : : {
6461 : : /* Whole-row reference, so must have access to all columns */
6462 : 1 : if (pg_attribute_aclcheck_all(rte->relid, userid, ACL_SELECT,
6463 [ + - ]: 1 : ACLMASK_ALL) != ACLCHECK_OK)
6464 : 1 : return false;
6465 : 0 : }
6466 : : else
6467 : : {
6468 : 61 : if (pg_attribute_aclcheck(rte->relid, attno, userid,
6469 [ + + ]: 61 : ACL_SELECT) != ACLCHECK_OK)
6470 : 38 : return false;
6471 : : }
6472 [ + + ]: 62 : }
6473 : :
6474 : : /* If we reach here, have all required column privileges */
6475 : 23 : return true;
6476 : 132609 : }
6477 : :
6478 : : /*
6479 : : * examine_indexcol_variable
6480 : : * Try to look up statistical data about an index column/expression.
6481 : : * Fill in a VariableStatData struct to describe the column.
6482 : : *
6483 : : * Inputs:
6484 : : * root: the planner info
6485 : : * index: the index whose column we're interested in
6486 : : * indexcol: 0-based index column number (subscripts index->indexkeys[])
6487 : : *
6488 : : * Outputs: *vardata is filled as follows:
6489 : : * var: the input expression (with any binary relabeling stripped, if
6490 : : * it is or contains a variable; but otherwise the type is preserved)
6491 : : * rel: RelOptInfo for table relation containing variable.
6492 : : * statsTuple: the pg_statistic entry for the variable, if one exists;
6493 : : * otherwise NULL.
6494 : : * freefunc: pointer to a function to release statsTuple with.
6495 : : *
6496 : : * Caller is responsible for doing ReleaseVariableStats() before exiting.
6497 : : */
6498 : : static void
6499 : 72841 : examine_indexcol_variable(PlannerInfo *root, IndexOptInfo *index,
6500 : : int indexcol, VariableStatData *vardata)
6501 : : {
6502 : 72841 : AttrNumber colnum;
6503 : 72841 : Oid relid;
6504 : :
6505 [ + + ]: 72841 : if (index->indexkeys[indexcol] != 0)
6506 : : {
6507 : : /* Simple variable --- look to stats for the underlying table */
6508 [ + - ]: 72474 : RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
6509 : :
6510 [ + - ]: 72474 : Assert(rte->rtekind == RTE_RELATION);
6511 : 72474 : relid = rte->relid;
6512 [ + - ]: 72474 : Assert(relid != InvalidOid);
6513 : 72474 : colnum = index->indexkeys[indexcol];
6514 : 72474 : vardata->rel = index->rel;
6515 : :
6516 [ - + # # ]: 72474 : if (get_relation_stats_hook &&
6517 : 0 : (*get_relation_stats_hook) (root, rte, colnum, vardata))
6518 : : {
6519 : : /*
6520 : : * The hook took control of acquiring a stats tuple. If it did
6521 : : * supply a tuple, it'd better have supplied a freefunc.
6522 : : */
6523 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6524 : 0 : !vardata->freefunc)
6525 [ # # # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6526 : 0 : }
6527 : : else
6528 : : {
6529 : 72474 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6530 : 72474 : ObjectIdGetDatum(relid),
6531 : 72474 : Int16GetDatum(colnum),
6532 : 72474 : BoolGetDatum(rte->inh));
6533 : 72474 : vardata->freefunc = ReleaseSysCache;
6534 : : }
6535 : 72474 : }
6536 : : else
6537 : : {
6538 : : /* Expression --- maybe there are stats for the index itself */
6539 : 367 : relid = index->indexoid;
6540 : 367 : colnum = indexcol + 1;
6541 : :
6542 [ - + # # ]: 367 : if (get_index_stats_hook &&
6543 : 0 : (*get_index_stats_hook) (root, relid, colnum, vardata))
6544 : : {
6545 : : /*
6546 : : * The hook took control of acquiring a stats tuple. If it did
6547 : : * supply a tuple, it'd better have supplied a freefunc.
6548 : : */
6549 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6550 : 0 : !vardata->freefunc)
6551 [ # # # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6552 : 0 : }
6553 : : else
6554 : : {
6555 : 367 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6556 : 367 : ObjectIdGetDatum(relid),
6557 : 367 : Int16GetDatum(colnum),
6558 : 367 : BoolGetDatum(false));
6559 : 367 : vardata->freefunc = ReleaseSysCache;
6560 : : }
6561 : : }
6562 : 72841 : }
6563 : :
6564 : : /*
6565 : : * Check whether it is permitted to call func_oid passing some of the
6566 : : * pg_statistic data in vardata. We allow this if either of the following
6567 : : * conditions is met: (1) the user has SELECT privileges on the table or
6568 : : * column underlying the pg_statistic data and there are no securityQuals from
6569 : : * security barrier views or RLS policies, or (2) the function is marked
6570 : : * leakproof.
6571 : : */
6572 : : bool
6573 : 107456 : statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
6574 : : {
6575 [ + + ]: 107456 : if (vardata->acl_ok)
6576 : 107163 : return true; /* have SELECT privs and no securityQuals */
6577 : :
6578 [ + - ]: 293 : if (!OidIsValid(func_oid))
6579 : 0 : return false;
6580 : :
6581 [ + + ]: 293 : if (get_func_leakproof(func_oid))
6582 : 145 : return true;
6583 : :
6584 [ - + - + ]: 148 : ereport(DEBUG2,
6585 : : (errmsg_internal("not using statistics because function \"%s\" is not leakproof",
6586 : : get_func_name(func_oid))));
6587 : 148 : return false;
6588 : 107456 : }
6589 : :
6590 : : /*
6591 : : * get_variable_numdistinct
6592 : : * Estimate the number of distinct values of a variable.
6593 : : *
6594 : : * vardata: results of examine_variable
6595 : : * *isdefault: set to true if the result is a default rather than based on
6596 : : * anything meaningful.
6597 : : *
6598 : : * NB: be careful to produce a positive integral result, since callers may
6599 : : * compare the result to exact integer counts, or might divide by it.
6600 : : */
6601 : : double
6602 : 147436 : get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
6603 : : {
6604 : 147436 : double stadistinct;
6605 : 147436 : double stanullfrac = 0.0;
6606 : 147436 : double ntuples;
6607 : :
6608 : 147436 : *isdefault = false;
6609 : :
6610 : : /*
6611 : : * Determine the stadistinct value to use. There are cases where we can
6612 : : * get an estimate even without a pg_statistic entry, or can get a better
6613 : : * value than is in pg_statistic. Grab stanullfrac too if we can find it
6614 : : * (otherwise, assume no nulls, for lack of any better idea).
6615 : : */
6616 [ + + ]: 147436 : if (HeapTupleIsValid(vardata->statsTuple))
6617 : : {
6618 : : /* Use the pg_statistic entry */
6619 : 91293 : Form_pg_statistic stats;
6620 : :
6621 : 91293 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
6622 : 91293 : stadistinct = stats->stadistinct;
6623 : 91293 : stanullfrac = stats->stanullfrac;
6624 : 91293 : }
6625 [ + + ]: 56143 : else if (vardata->vartype == BOOLOID)
6626 : : {
6627 : : /*
6628 : : * Special-case boolean columns: presumably, two distinct values.
6629 : : *
6630 : : * Are there any other datatypes we should wire in special estimates
6631 : : * for?
6632 : : */
6633 : 80 : stadistinct = 2.0;
6634 : 80 : }
6635 [ + + + + ]: 56063 : else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
6636 : : {
6637 : : /*
6638 : : * If the Var represents a column of a VALUES RTE, assume it's unique.
6639 : : * This could of course be very wrong, but it should tend to be true
6640 : : * in well-written queries. We could consider examining the VALUES'
6641 : : * contents to get some real statistics; but that only works if the
6642 : : * entries are all constants, and it would be pretty expensive anyway.
6643 : : */
6644 : 668 : stadistinct = -1.0; /* unique (and all non null) */
6645 : 668 : }
6646 : : else
6647 : : {
6648 : : /*
6649 : : * We don't keep statistics for system columns, but in some cases we
6650 : : * can infer distinctness anyway.
6651 : : */
6652 [ + + + + ]: 55395 : if (vardata->var && IsA(vardata->var, Var))
6653 : : {
6654 [ + + + ]: 50950 : switch (((Var *) vardata->var)->varattno)
6655 : : {
6656 : : case SelfItemPointerAttributeNumber:
6657 : 148 : stadistinct = -1.0; /* unique (and all non null) */
6658 : 148 : break;
6659 : : case TableOidAttributeNumber:
6660 : 369 : stadistinct = 1.0; /* only 1 value */
6661 : 369 : break;
6662 : : default:
6663 : 50433 : stadistinct = 0.0; /* means "unknown" */
6664 : 50433 : break;
6665 : : }
6666 : 50950 : }
6667 : : else
6668 : 4445 : stadistinct = 0.0; /* means "unknown" */
6669 : :
6670 : : /*
6671 : : * XXX consider using estimate_num_groups on expressions?
6672 : : */
6673 : : }
6674 : :
6675 : : /*
6676 : : * If there is a unique index, DISTINCT or GROUP-BY clause for the
6677 : : * variable, assume it is unique no matter what pg_statistic says; the
6678 : : * statistics could be out of date, or we might have found a partial
6679 : : * unique index that proves the var is unique for this query. However,
6680 : : * we'd better still believe the null-fraction statistic.
6681 : : */
6682 [ + + ]: 147436 : if (vardata->isunique)
6683 : 28898 : stadistinct = -1.0 * (1.0 - stanullfrac);
6684 : :
6685 : : /*
6686 : : * If we had an absolute estimate, use that.
6687 : : */
6688 [ + + ]: 147436 : if (stadistinct > 0.0)
6689 : 42840 : return clamp_row_est(stadistinct);
6690 : :
6691 : : /*
6692 : : * Otherwise we need to get the relation size; punt if not available.
6693 : : */
6694 [ + + ]: 104596 : if (vardata->rel == NULL)
6695 : : {
6696 : 96 : *isdefault = true;
6697 : 96 : return DEFAULT_NUM_DISTINCT;
6698 : : }
6699 : 104500 : ntuples = vardata->rel->tuples;
6700 [ + + ]: 104500 : if (ntuples <= 0.0)
6701 : : {
6702 : 6120 : *isdefault = true;
6703 : 6120 : return DEFAULT_NUM_DISTINCT;
6704 : : }
6705 : :
6706 : : /*
6707 : : * If we had a relative estimate, use that.
6708 : : */
6709 [ + + ]: 98380 : if (stadistinct < 0.0)
6710 : 55421 : return clamp_row_est(-stadistinct * ntuples);
6711 : :
6712 : : /*
6713 : : * With no data, estimate ndistinct = ntuples if the table is small, else
6714 : : * use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
6715 : : * that the behavior isn't discontinuous.
6716 : : */
6717 [ + + ]: 42959 : if (ntuples < DEFAULT_NUM_DISTINCT)
6718 : 19495 : return clamp_row_est(ntuples);
6719 : :
6720 : 23464 : *isdefault = true;
6721 : 23464 : return DEFAULT_NUM_DISTINCT;
6722 : 147436 : }
6723 : :
6724 : : /*
6725 : : * get_variable_range
6726 : : * Estimate the minimum and maximum value of the specified variable.
6727 : : * If successful, store values in *min and *max, and return true.
6728 : : * If no data available, return false.
6729 : : *
6730 : : * sortop is the "<" comparison operator to use. This should generally
6731 : : * be "<" not ">", as only the former is likely to be found in pg_statistic.
6732 : : * The collation must be specified too.
6733 : : */
6734 : : static bool
6735 : 24688 : get_variable_range(PlannerInfo *root, VariableStatData *vardata,
6736 : : Oid sortop, Oid collation,
6737 : : Datum *min, Datum *max)
6738 : : {
6739 : 24688 : Datum tmin = 0;
6740 : 24688 : Datum tmax = 0;
6741 : 24688 : bool have_data = false;
6742 : 24688 : int16 typLen;
6743 : 24688 : bool typByVal;
6744 : 24688 : Oid opfuncoid;
6745 : 24688 : FmgrInfo opproc;
6746 : 24688 : AttStatsSlot sslot;
6747 : :
6748 : : /*
6749 : : * XXX It's very tempting to try to use the actual column min and max, if
6750 : : * we can get them relatively-cheaply with an index probe. However, since
6751 : : * this function is called many times during join planning, that could
6752 : : * have unpleasant effects on planning speed. Need more investigation
6753 : : * before enabling this.
6754 : : */
6755 : : #ifdef NOT_USED
6756 : : if (get_actual_variable_range(root, vardata, sortop, collation, min, max))
6757 : : return true;
6758 : : #endif
6759 : :
6760 [ + + ]: 24688 : if (!HeapTupleIsValid(vardata->statsTuple))
6761 : : {
6762 : : /* no stats available, so default result */
6763 : 6054 : return false;
6764 : : }
6765 : :
6766 : : /*
6767 : : * If we can't apply the sortop to the stats data, just fail. In
6768 : : * principle, if there's a histogram and no MCVs, we could return the
6769 : : * histogram endpoints without ever applying the sortop ... but it's
6770 : : * probably not worth trying, because whatever the caller wants to do with
6771 : : * the endpoints would likely fail the security check too.
6772 : : */
6773 [ + - + - ]: 37268 : if (!statistic_proc_security_check(vardata,
6774 : 18634 : (opfuncoid = get_opcode(sortop))))
6775 : 0 : return false;
6776 : :
6777 : 18634 : opproc.fn_oid = InvalidOid; /* mark this as not looked up yet */
6778 : :
6779 : 18634 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
6780 : :
6781 : : /*
6782 : : * If there is a histogram with the ordering we want, grab the first and
6783 : : * last values.
6784 : : */
6785 [ + + + + ]: 37268 : if (get_attstatsslot(&sslot, vardata->statsTuple,
6786 : 18634 : STATISTIC_KIND_HISTOGRAM, sortop,
6787 : : ATTSTATSSLOT_VALUES))
6788 : : {
6789 [ + - - + ]: 9822 : if (sslot.stacoll == collation && sslot.nvalues > 0)
6790 : : {
6791 : 9822 : tmin = datumCopy(sslot.values[0], typByVal, typLen);
6792 : 9822 : tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
6793 : 9822 : have_data = true;
6794 : 9822 : }
6795 : 9822 : free_attstatsslot(&sslot);
6796 : 9822 : }
6797 : :
6798 : : /*
6799 : : * Otherwise, if there is a histogram with some other ordering, scan it
6800 : : * and get the min and max values according to the ordering we want. This
6801 : : * of course may not find values that are really extremal according to our
6802 : : * ordering, but it beats ignoring available data.
6803 : : */
6804 [ + + - + ]: 18634 : if (!have_data &&
6805 : 8812 : get_attstatsslot(&sslot, vardata->statsTuple,
6806 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
6807 : : ATTSTATSSLOT_VALUES))
6808 : : {
6809 : 0 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
6810 : 0 : collation, typLen, typByVal,
6811 : : &tmin, &tmax, &have_data);
6812 : 0 : free_attstatsslot(&sslot);
6813 : 0 : }
6814 : :
6815 : : /*
6816 : : * If we have most-common-values info, look for extreme MCVs. This is
6817 : : * needed even if we also have a histogram, since the histogram excludes
6818 : : * the MCVs. However, if we *only* have MCVs and no histogram, we should
6819 : : * be pretty wary of deciding that that is a full representation of the
6820 : : * data. Proceed only if the MCVs represent the whole table (to within
6821 : : * roundoff error).
6822 : : */
6823 [ + + + + ]: 37268 : if (get_attstatsslot(&sslot, vardata->statsTuple,
6824 : : STATISTIC_KIND_MCV, InvalidOid,
6825 : 18634 : have_data ? ATTSTATSSLOT_VALUES :
6826 : : (ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)))
6827 : : {
6828 : 11049 : bool use_mcvs = have_data;
6829 : :
6830 [ + + ]: 11049 : if (!have_data)
6831 : : {
6832 : 8761 : double sumcommon = 0.0;
6833 : 8761 : double nullfrac;
6834 : 8761 : int i;
6835 : :
6836 [ + + ]: 68916 : for (i = 0; i < sslot.nnumbers; i++)
6837 : 60155 : sumcommon += sslot.numbers[i];
6838 : 8761 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata->statsTuple))->stanullfrac;
6839 [ + + ]: 8761 : if (sumcommon + nullfrac > 0.99999)
6840 : 8730 : use_mcvs = true;
6841 : 8761 : }
6842 : :
6843 [ + + ]: 11049 : if (use_mcvs)
6844 : 22036 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
6845 : 11018 : collation, typLen, typByVal,
6846 : : &tmin, &tmax, &have_data);
6847 : 11049 : free_attstatsslot(&sslot);
6848 : 11049 : }
6849 : :
6850 : 18634 : *min = tmin;
6851 : 18634 : *max = tmax;
6852 : 18634 : return have_data;
6853 : 24688 : }
6854 : :
6855 : : /*
6856 : : * get_stats_slot_range: scan sslot for min/max values
6857 : : *
6858 : : * Subroutine for get_variable_range: update min/max/have_data according
6859 : : * to what we find in the statistics array.
6860 : : */
6861 : : static void
6862 : 11018 : get_stats_slot_range(AttStatsSlot *sslot, Oid opfuncoid, FmgrInfo *opproc,
6863 : : Oid collation, int16 typLen, bool typByVal,
6864 : : Datum *min, Datum *max, bool *p_have_data)
6865 : : {
6866 : 11018 : Datum tmin = *min;
6867 : 11018 : Datum tmax = *max;
6868 : 11018 : bool have_data = *p_have_data;
6869 : 11018 : bool found_tmin = false;
6870 : 11018 : bool found_tmax = false;
6871 : :
6872 : : /* Look up the comparison function, if we didn't already do so */
6873 [ - + ]: 11018 : if (opproc->fn_oid != opfuncoid)
6874 : 11018 : fmgr_info(opfuncoid, opproc);
6875 : :
6876 : : /* Scan all the slot's values */
6877 [ + + ]: 171263 : for (int i = 0; i < sslot->nvalues; i++)
6878 : : {
6879 [ + + ]: 160245 : if (!have_data)
6880 : : {
6881 : 8730 : tmin = tmax = sslot->values[i];
6882 : 8730 : found_tmin = found_tmax = true;
6883 : 8730 : *p_have_data = have_data = true;
6884 : 8730 : continue;
6885 : : }
6886 [ + + + + ]: 303030 : if (DatumGetBool(FunctionCall2Coll(opproc,
6887 : 151515 : collation,
6888 : 151515 : sslot->values[i], tmin)))
6889 : : {
6890 : 5874 : tmin = sslot->values[i];
6891 : 5874 : found_tmin = true;
6892 : 5874 : }
6893 [ + + + + ]: 303030 : if (DatumGetBool(FunctionCall2Coll(opproc,
6894 : 151515 : collation,
6895 : 151515 : tmax, sslot->values[i])))
6896 : : {
6897 : 37422 : tmax = sslot->values[i];
6898 : 37422 : found_tmax = true;
6899 : 37422 : }
6900 : 151515 : }
6901 : :
6902 : : /*
6903 : : * Copy the slot's values, if we found new extreme values.
6904 : : */
6905 [ + + ]: 11018 : if (found_tmin)
6906 : 10748 : *min = datumCopy(tmin, typByVal, typLen);
6907 [ + + ]: 11018 : if (found_tmax)
6908 : 8860 : *max = datumCopy(tmax, typByVal, typLen);
6909 : 11018 : }
6910 : :
6911 : :
6912 : : /*
6913 : : * get_actual_variable_range
6914 : : * Attempt to identify the current *actual* minimum and/or maximum
6915 : : * of the specified variable, by looking for a suitable btree index
6916 : : * and fetching its low and/or high values.
6917 : : * If successful, store values in *min and *max, and return true.
6918 : : * (Either pointer can be NULL if that endpoint isn't needed.)
6919 : : * If unsuccessful, return false.
6920 : : *
6921 : : * sortop is the "<" comparison operator to use.
6922 : : * collation is the required collation.
6923 : : */
6924 : : static bool
6925 : 13533 : get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
6926 : : Oid sortop, Oid collation,
6927 : : Datum *min, Datum *max)
6928 : : {
6929 : 13533 : bool have_data = false;
6930 : 13533 : RelOptInfo *rel = vardata->rel;
6931 : 13533 : RangeTblEntry *rte;
6932 : 13533 : ListCell *lc;
6933 : :
6934 : : /* No hope if no relation or it doesn't have indexes */
6935 [ + - + + ]: 13533 : if (rel == NULL || rel->indexlist == NIL)
6936 : 1859 : return false;
6937 : : /* If it has indexes it must be a plain relation */
6938 : 11674 : rte = root->simple_rte_array[rel->relid];
6939 [ + - ]: 11674 : Assert(rte->rtekind == RTE_RELATION);
6940 : :
6941 : : /* ignore partitioned tables. Any indexes here are not real indexes */
6942 [ + + ]: 11674 : if (rte->relkind == RELKIND_PARTITIONED_TABLE)
6943 : 178 : return false;
6944 : :
6945 : : /* Search through the indexes to see if any match our problem */
6946 [ + - + + : 31297 : foreach(lc, rel->indexlist)
+ + ]
6947 : : {
6948 : 19801 : IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
6949 : 19801 : ScanDirection indexscandir;
6950 : 19801 : StrategyNumber strategy;
6951 : :
6952 : : /* Ignore non-ordering indexes */
6953 [ + + ]: 19801 : if (index->sortopfamily == NULL)
6954 : 1 : continue;
6955 : :
6956 : : /*
6957 : : * Ignore partial indexes --- we only want stats that cover the entire
6958 : : * relation.
6959 : : */
6960 [ + + ]: 19800 : if (index->indpred != NIL)
6961 : 48 : continue;
6962 : :
6963 : : /*
6964 : : * The index list might include hypothetical indexes inserted by a
6965 : : * get_relation_info hook --- don't try to access them.
6966 : : */
6967 [ - + ]: 19752 : if (index->hypothetical)
6968 : 0 : continue;
6969 : :
6970 : : /*
6971 : : * get_actual_variable_endpoint uses the index-only-scan machinery, so
6972 : : * ignore indexes that can't use it on their first column.
6973 : : */
6974 [ + - ]: 19752 : if (!index->canreturn[0])
6975 : 0 : continue;
6976 : :
6977 : : /*
6978 : : * The first index column must match the desired variable, sortop, and
6979 : : * collation --- but we can use a descending-order index.
6980 : : */
6981 [ + + ]: 19752 : if (collation != index->indexcollations[0])
6982 : 2905 : continue; /* test first 'cause it's cheapest */
6983 [ + + ]: 16847 : if (!match_index_to_operand(vardata->var, 0, index))
6984 : 8174 : continue;
6985 : 8673 : strategy = get_op_opfamily_strategy(sortop, index->sortopfamily[0]);
6986 [ - + - ]: 8673 : switch (IndexAmTranslateStrategy(strategy, index->relam, index->sortopfamily[0], true))
6987 : : {
6988 : : case COMPARE_LT:
6989 [ - + ]: 8673 : if (index->reverse_sort[0])
6990 : 0 : indexscandir = BackwardScanDirection;
6991 : : else
6992 : 8673 : indexscandir = ForwardScanDirection;
6993 : 8673 : break;
6994 : : case COMPARE_GT:
6995 [ # # ]: 0 : if (index->reverse_sort[0])
6996 : 0 : indexscandir = ForwardScanDirection;
6997 : : else
6998 : 0 : indexscandir = BackwardScanDirection;
6999 : 0 : break;
7000 : : default:
7001 : : /* index doesn't match the sortop */
7002 : 0 : continue;
7003 : : }
7004 : :
7005 : : /*
7006 : : * Found a suitable index to extract data from. Set up some data that
7007 : : * can be used by both invocations of get_actual_variable_endpoint.
7008 : : */
7009 : : {
7010 : 8673 : MemoryContext tmpcontext;
7011 : 8673 : MemoryContext oldcontext;
7012 : 8673 : Relation heapRel;
7013 : 8673 : Relation indexRel;
7014 : 8673 : TupleTableSlot *slot;
7015 : 8673 : int16 typLen;
7016 : 8673 : bool typByVal;
7017 : 8673 : ScanKeyData scankeys[1];
7018 : :
7019 : : /* Make sure any cruft gets recycled when we're done */
7020 : 8673 : tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
7021 : : "get_actual_variable_range workspace",
7022 : : ALLOCSET_DEFAULT_SIZES);
7023 : 8673 : oldcontext = MemoryContextSwitchTo(tmpcontext);
7024 : :
7025 : : /*
7026 : : * Open the table and index so we can read from them. We should
7027 : : * already have some type of lock on each.
7028 : : */
7029 : 8673 : heapRel = table_open(rte->relid, NoLock);
7030 : 8673 : indexRel = index_open(index->indexoid, NoLock);
7031 : :
7032 : : /* build some stuff needed for indexscan execution */
7033 : 8673 : slot = table_slot_create(heapRel, NULL);
7034 : 8673 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
7035 : :
7036 : : /* set up an IS NOT NULL scan key so that we ignore nulls */
7037 : 8673 : ScanKeyEntryInitialize(&scankeys[0],
7038 : : SK_ISNULL | SK_SEARCHNOTNULL,
7039 : : 1, /* index col to scan */
7040 : : InvalidStrategy, /* no strategy */
7041 : : InvalidOid, /* no strategy subtype */
7042 : : InvalidOid, /* no collation */
7043 : : InvalidOid, /* no reg proc for this */
7044 : : (Datum) 0); /* constant */
7045 : :
7046 : : /* If min is requested ... */
7047 [ + + ]: 8673 : if (min)
7048 : : {
7049 : 10764 : have_data = get_actual_variable_endpoint(heapRel,
7050 : 5382 : indexRel,
7051 : 5382 : indexscandir,
7052 : 5382 : scankeys,
7053 : 5382 : typLen,
7054 : 5382 : typByVal,
7055 : 5382 : slot,
7056 : 5382 : oldcontext,
7057 : 5382 : min);
7058 : 5382 : }
7059 : : else
7060 : : {
7061 : : /* If min not requested, still want to fetch max */
7062 : 3291 : have_data = true;
7063 : : }
7064 : :
7065 : : /* If max is requested, and we didn't already fail ... */
7066 [ + + - + ]: 8673 : if (max && have_data)
7067 : : {
7068 : : /* scan in the opposite direction; all else is the same */
7069 : 6746 : have_data = get_actual_variable_endpoint(heapRel,
7070 : 3373 : indexRel,
7071 : 3373 : -indexscandir,
7072 : 3373 : scankeys,
7073 : 3373 : typLen,
7074 : 3373 : typByVal,
7075 : 3373 : slot,
7076 : 3373 : oldcontext,
7077 : 3373 : max);
7078 : 3373 : }
7079 : :
7080 : : /* Clean everything up */
7081 : 8673 : ExecDropSingleTupleTableSlot(slot);
7082 : :
7083 : 8673 : index_close(indexRel, NoLock);
7084 : 8673 : table_close(heapRel, NoLock);
7085 : :
7086 : 8673 : MemoryContextSwitchTo(oldcontext);
7087 : 8673 : MemoryContextDelete(tmpcontext);
7088 : :
7089 : : /* And we're done */
7090 : : break;
7091 : 8673 : }
7092 [ + + ]: 19801 : }
7093 : :
7094 : 11496 : return have_data;
7095 : 13533 : }
7096 : :
7097 : : /*
7098 : : * Get one endpoint datum (min or max depending on indexscandir) from the
7099 : : * specified index. Return true if successful, false if not.
7100 : : * On success, endpoint value is stored to *endpointDatum (and copied into
7101 : : * outercontext).
7102 : : *
7103 : : * scankeys is a 1-element scankey array set up to reject nulls.
7104 : : * typLen/typByVal describe the datatype of the index's first column.
7105 : : * tableslot is a slot suitable to hold table tuples, in case we need
7106 : : * to probe the heap.
7107 : : * (We could compute these values locally, but that would mean computing them
7108 : : * twice when get_actual_variable_range needs both the min and the max.)
7109 : : *
7110 : : * Failure occurs either when the index is empty, or we decide that it's
7111 : : * taking too long to find a suitable tuple.
7112 : : */
7113 : : static bool
7114 : 8755 : get_actual_variable_endpoint(Relation heapRel,
7115 : : Relation indexRel,
7116 : : ScanDirection indexscandir,
7117 : : ScanKey scankeys,
7118 : : int16 typLen,
7119 : : bool typByVal,
7120 : : TupleTableSlot *tableslot,
7121 : : MemoryContext outercontext,
7122 : : Datum *endpointDatum)
7123 : : {
7124 : 8755 : bool have_data = false;
7125 : 8755 : SnapshotData SnapshotNonVacuumable;
7126 : 8755 : IndexScanDesc index_scan;
7127 : 8755 : Buffer vmbuffer = InvalidBuffer;
7128 : 8755 : BlockNumber last_heap_block = InvalidBlockNumber;
7129 : 8755 : int n_visited_heap_pages = 0;
7130 : 8755 : ItemPointer tid;
7131 : 8755 : Datum values[INDEX_MAX_KEYS];
7132 : 8755 : bool isnull[INDEX_MAX_KEYS];
7133 : 8755 : MemoryContext oldcontext;
7134 : :
7135 : : /*
7136 : : * We use the index-only-scan machinery for this. With mostly-static
7137 : : * tables that's a win because it avoids a heap visit. It's also a win
7138 : : * for dynamic data, but the reason is less obvious; read on for details.
7139 : : *
7140 : : * In principle, we should scan the index with our current active
7141 : : * snapshot, which is the best approximation we've got to what the query
7142 : : * will see when executed. But that won't be exact if a new snap is taken
7143 : : * before running the query, and it can be very expensive if a lot of
7144 : : * recently-dead or uncommitted rows exist at the beginning or end of the
7145 : : * index (because we'll laboriously fetch each one and reject it).
7146 : : * Instead, we use SnapshotNonVacuumable. That will accept recently-dead
7147 : : * and uncommitted rows as well as normal visible rows. On the other
7148 : : * hand, it will reject known-dead rows, and thus not give a bogus answer
7149 : : * when the extreme value has been deleted (unless the deletion was quite
7150 : : * recent); that case motivates not using SnapshotAny here.
7151 : : *
7152 : : * A crucial point here is that SnapshotNonVacuumable, with
7153 : : * GlobalVisTestFor(heapRel) as horizon, yields the inverse of the
7154 : : * condition that the indexscan will use to decide that index entries are
7155 : : * killable (see heap_hot_search_buffer()). Therefore, if the snapshot
7156 : : * rejects a tuple (or more precisely, all tuples of a HOT chain) and we
7157 : : * have to continue scanning past it, we know that the indexscan will mark
7158 : : * that index entry killed. That means that the next
7159 : : * get_actual_variable_endpoint() call will not have to re-consider that
7160 : : * index entry. In this way we avoid repetitive work when this function
7161 : : * is used a lot during planning.
7162 : : *
7163 : : * But using SnapshotNonVacuumable creates a hazard of its own. In a
7164 : : * recently-created index, some index entries may point at "broken" HOT
7165 : : * chains in which not all the tuple versions contain data matching the
7166 : : * index entry. The live tuple version(s) certainly do match the index,
7167 : : * but SnapshotNonVacuumable can accept recently-dead tuple versions that
7168 : : * don't match. Hence, if we took data from the selected heap tuple, we
7169 : : * might get a bogus answer that's not close to the index extremal value,
7170 : : * or could even be NULL. We avoid this hazard because we take the data
7171 : : * from the index entry not the heap.
7172 : : *
7173 : : * Despite all this care, there are situations where we might find many
7174 : : * non-visible tuples near the end of the index. We don't want to expend
7175 : : * a huge amount of time here, so we give up once we've read too many heap
7176 : : * pages. When we fail for that reason, the caller will end up using
7177 : : * whatever extremal value is recorded in pg_statistic.
7178 : : */
7179 : 8755 : InitNonVacuumableSnapshot(SnapshotNonVacuumable,
7180 : : GlobalVisTestFor(heapRel));
7181 : :
7182 : 8755 : index_scan = index_beginscan(heapRel, indexRel,
7183 : : &SnapshotNonVacuumable, NULL,
7184 : : 1, 0);
7185 : : /* Set it up for index-only scan */
7186 : 8755 : index_scan->xs_want_itup = true;
7187 : 8755 : index_rescan(index_scan, scankeys, 1, NULL, 0);
7188 : :
7189 : : /* Fetch first/next tuple in specified direction */
7190 [ + + ]: 18976 : while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
7191 : : {
7192 : 10221 : BlockNumber block = ItemPointerGetBlockNumber(tid);
7193 : :
7194 [ + + ]: 10221 : if (!VM_ALL_VISIBLE(heapRel,
7195 : : block,
7196 : : &vmbuffer))
7197 : : {
7198 : : /* Rats, we have to visit the heap to check visibility */
7199 [ + + ]: 7454 : if (!index_fetch_heap(index_scan, tableslot))
7200 : : {
7201 : : /*
7202 : : * No visible tuple for this index entry, so we need to
7203 : : * advance to the next entry. Before doing so, count heap
7204 : : * page fetches and give up if we've done too many.
7205 : : *
7206 : : * We don't charge a page fetch if this is the same heap page
7207 : : * as the previous tuple. This is on the conservative side,
7208 : : * since other recently-accessed pages are probably still in
7209 : : * buffers too; but it's good enough for this heuristic.
7210 : : */
7211 : : #define VISITED_PAGES_LIMIT 100
7212 : :
7213 [ + + ]: 1466 : if (block != last_heap_block)
7214 : : {
7215 : 144 : last_heap_block = block;
7216 : 144 : n_visited_heap_pages++;
7217 [ - + ]: 144 : if (n_visited_heap_pages > VISITED_PAGES_LIMIT)
7218 : 0 : break;
7219 : 144 : }
7220 : :
7221 : 1466 : continue; /* no visible tuple, try next index entry */
7222 : : }
7223 : :
7224 : : /* We don't actually need the heap tuple for anything */
7225 : 5988 : ExecClearTuple(tableslot);
7226 : :
7227 : : /*
7228 : : * We don't care whether there's more than one visible tuple in
7229 : : * the HOT chain; if any are visible, that's good enough.
7230 : : */
7231 : 5988 : }
7232 : :
7233 : : /*
7234 : : * We expect that the index will return data in IndexTuple not
7235 : : * HeapTuple format.
7236 : : */
7237 [ - + ]: 8755 : if (!index_scan->xs_itup)
7238 [ # # # # ]: 0 : elog(ERROR, "no data returned for index-only scan");
7239 : :
7240 : : /*
7241 : : * We do not yet support recheck here.
7242 : : */
7243 [ - + ]: 8755 : if (index_scan->xs_recheck)
7244 : 0 : break;
7245 : :
7246 : : /* OK to deconstruct the index tuple */
7247 : 17510 : index_deform_tuple(index_scan->xs_itup,
7248 : 8755 : index_scan->xs_itupdesc,
7249 : 8755 : values, isnull);
7250 : :
7251 : : /* Shouldn't have got a null, but be careful */
7252 [ + - ]: 8755 : if (isnull[0])
7253 [ # # # # ]: 0 : elog(ERROR, "found unexpected null value in index \"%s\"",
7254 : : RelationGetRelationName(indexRel));
7255 : :
7256 : : /* Copy the index column value out to caller's context */
7257 : 8755 : oldcontext = MemoryContextSwitchTo(outercontext);
7258 : 8755 : *endpointDatum = datumCopy(values[0], typByVal, typLen);
7259 : 8755 : MemoryContextSwitchTo(oldcontext);
7260 : 8755 : have_data = true;
7261 : 8755 : break;
7262 [ - + ]: 10221 : }
7263 : :
7264 [ + + ]: 8755 : if (vmbuffer != InvalidBuffer)
7265 : 7075 : ReleaseBuffer(vmbuffer);
7266 : 8755 : index_endscan(index_scan);
7267 : :
7268 : 17510 : return have_data;
7269 : 8755 : }
7270 : :
7271 : : /*
7272 : : * find_join_input_rel
7273 : : * Look up the input relation for a join.
7274 : : *
7275 : : * We assume that the input relation's RelOptInfo must have been constructed
7276 : : * already.
7277 : : */
7278 : : static RelOptInfo *
7279 : 1380 : find_join_input_rel(PlannerInfo *root, Relids relids)
7280 : : {
7281 : 1380 : RelOptInfo *rel = NULL;
7282 : :
7283 [ - + ]: 1380 : if (!bms_is_empty(relids))
7284 : : {
7285 : 1380 : int relid;
7286 : :
7287 [ + + ]: 1380 : if (bms_get_singleton_member(relids, &relid))
7288 : 1334 : rel = find_base_rel(root, relid);
7289 : : else
7290 : 46 : rel = find_join_rel(root, relids);
7291 : 1380 : }
7292 : :
7293 [ + - ]: 1380 : if (rel == NULL)
7294 [ # # # # ]: 0 : elog(ERROR, "could not find RelOptInfo for given relids");
7295 : :
7296 : 2760 : return rel;
7297 : 1380 : }
7298 : :
7299 : :
7300 : : /*-------------------------------------------------------------------------
7301 : : *
7302 : : * Index cost estimation functions
7303 : : *
7304 : : *-------------------------------------------------------------------------
7305 : : */
7306 : :
7307 : : /*
7308 : : * Extract the actual indexquals (as RestrictInfos) from an IndexClause list
7309 : : */
7310 : : List *
7311 : 75698 : get_quals_from_indexclauses(List *indexclauses)
7312 : : {
7313 : 75698 : List *result = NIL;
7314 : 75698 : ListCell *lc;
7315 : :
7316 [ + + + + : 129708 : foreach(lc, indexclauses)
+ + ]
7317 : : {
7318 : 54010 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7319 : 54010 : ListCell *lc2;
7320 : :
7321 [ + - + + : 108450 : foreach(lc2, iclause->indexquals)
+ + ]
7322 : : {
7323 : 54440 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7324 : :
7325 : 54440 : result = lappend(result, rinfo);
7326 : 54440 : }
7327 : 54010 : }
7328 : 151396 : return result;
7329 : 75698 : }
7330 : :
7331 : : /*
7332 : : * Compute the total evaluation cost of the comparison operands in a list
7333 : : * of index qual expressions. Since we know these will be evaluated just
7334 : : * once per scan, there's no need to distinguish startup from per-row cost.
7335 : : *
7336 : : * This can be used either on the result of get_quals_from_indexclauses(),
7337 : : * or directly on an indexorderbys list. In both cases, we expect that the
7338 : : * index key expression is on the left side of binary clauses.
7339 : : */
7340 : : Cost
7341 : 149396 : index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
7342 : : {
7343 : 149396 : Cost qual_arg_cost = 0;
7344 : 149396 : ListCell *lc;
7345 : :
7346 [ + + + + : 203895 : foreach(lc, indexquals)
+ + ]
7347 : : {
7348 : 54499 : Expr *clause = (Expr *) lfirst(lc);
7349 : 54499 : Node *other_operand;
7350 : 54499 : QualCost index_qual_cost;
7351 : :
7352 : : /*
7353 : : * Index quals will have RestrictInfos, indexorderbys won't. Look
7354 : : * through RestrictInfo if present.
7355 : : */
7356 [ + + ]: 54499 : if (IsA(clause, RestrictInfo))
7357 : 54438 : clause = ((RestrictInfo *) clause)->clause;
7358 : :
7359 [ + + ]: 54499 : if (IsA(clause, OpExpr))
7360 : : {
7361 : 52499 : OpExpr *op = (OpExpr *) clause;
7362 : :
7363 : 52499 : other_operand = (Node *) lsecond(op->args);
7364 : 52499 : }
7365 [ + + ]: 2000 : else if (IsA(clause, RowCompareExpr))
7366 : : {
7367 : 66 : RowCompareExpr *rc = (RowCompareExpr *) clause;
7368 : :
7369 : 66 : other_operand = (Node *) rc->rargs;
7370 : 66 : }
7371 [ + + ]: 1934 : else if (IsA(clause, ScalarArrayOpExpr))
7372 : : {
7373 : 1462 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
7374 : :
7375 : 1462 : other_operand = (Node *) lsecond(saop->args);
7376 : 1462 : }
7377 [ + - ]: 472 : else if (IsA(clause, NullTest))
7378 : : {
7379 : 472 : other_operand = NULL;
7380 : 472 : }
7381 : : else
7382 : : {
7383 [ # # # # ]: 0 : elog(ERROR, "unsupported indexqual type: %d",
7384 : : (int) nodeTag(clause));
7385 : 0 : other_operand = NULL; /* keep compiler quiet */
7386 : : }
7387 : :
7388 : 54499 : cost_qual_eval_node(&index_qual_cost, other_operand, root);
7389 : 54499 : qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
7390 : 54499 : }
7391 : 298792 : return qual_arg_cost;
7392 : 149396 : }
7393 : :
7394 : : void
7395 : 73700 : genericcostestimate(PlannerInfo *root,
7396 : : IndexPath *path,
7397 : : double loop_count,
7398 : : GenericCosts *costs)
7399 : : {
7400 : 73700 : IndexOptInfo *index = path->indexinfo;
7401 : 73700 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
7402 : 73700 : List *indexOrderBys = path->indexorderbys;
7403 : 73700 : Cost indexStartupCost;
7404 : 73700 : Cost indexTotalCost;
7405 : 73700 : Selectivity indexSelectivity;
7406 : 73700 : double indexCorrelation;
7407 : 73700 : double numIndexPages;
7408 : 73700 : double numIndexTuples;
7409 : 73700 : double spc_random_page_cost;
7410 : 73700 : double num_sa_scans;
7411 : 73700 : double num_outer_scans;
7412 : 73700 : double num_scans;
7413 : 73700 : double qual_op_cost;
7414 : 73700 : double qual_arg_cost;
7415 : 73700 : List *selectivityQuals;
7416 : 73700 : ListCell *l;
7417 : :
7418 : : /*
7419 : : * If the index is partial, AND the index predicate with the explicitly
7420 : : * given indexquals to produce a more accurate idea of the index
7421 : : * selectivity.
7422 : : */
7423 : 73700 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
7424 : :
7425 : : /*
7426 : : * If caller didn't give us an estimate for ScalarArrayOpExpr index scans,
7427 : : * just assume that the number of index descents is the number of distinct
7428 : : * combinations of array elements from all of the scan's SAOP clauses.
7429 : : */
7430 : 73700 : num_sa_scans = costs->num_sa_scans;
7431 [ + + ]: 73700 : if (num_sa_scans < 1)
7432 : : {
7433 : 945 : num_sa_scans = 1;
7434 [ + + + + : 1981 : foreach(l, indexQuals)
+ + ]
7435 : : {
7436 : 1036 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
7437 : :
7438 [ + + ]: 1036 : if (IsA(rinfo->clause, ScalarArrayOpExpr))
7439 : : {
7440 : 4 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
7441 : 4 : double alength = estimate_array_length(root, lsecond(saop->args));
7442 : :
7443 [ - + ]: 4 : if (alength > 1)
7444 : 4 : num_sa_scans *= alength;
7445 : 4 : }
7446 : 1036 : }
7447 : 945 : }
7448 : :
7449 : : /* Estimate the fraction of main-table tuples that will be visited */
7450 : 147400 : indexSelectivity = clauselist_selectivity(root, selectivityQuals,
7451 : 73700 : index->rel->relid,
7452 : : JOIN_INNER,
7453 : : NULL);
7454 : :
7455 : : /*
7456 : : * If caller didn't give us an estimate, estimate the number of index
7457 : : * tuples that will be visited. We do it in this rather peculiar-looking
7458 : : * way in order to get the right answer for partial indexes.
7459 : : */
7460 : 73700 : numIndexTuples = costs->numIndexTuples;
7461 [ + + ]: 73700 : if (numIndexTuples <= 0.0)
7462 : : {
7463 : 7073 : numIndexTuples = indexSelectivity * index->rel->tuples;
7464 : :
7465 : : /*
7466 : : * The above calculation counts all the tuples visited across all
7467 : : * scans induced by ScalarArrayOpExpr nodes. We want to consider the
7468 : : * average per-indexscan number, so adjust. This is a handy place to
7469 : : * round to integer, too. (If caller supplied tuple estimate, it's
7470 : : * responsible for handling these considerations.)
7471 : : */
7472 : 7073 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
7473 : 7073 : }
7474 : :
7475 : : /*
7476 : : * We can bound the number of tuples by the index size in any case. Also,
7477 : : * always estimate at least one tuple is touched, even when
7478 : : * indexSelectivity estimate is tiny.
7479 : : */
7480 [ + + ]: 73700 : if (numIndexTuples > index->tuples)
7481 : 690 : numIndexTuples = index->tuples;
7482 [ + + ]: 73700 : if (numIndexTuples < 1.0)
7483 : 7000 : numIndexTuples = 1.0;
7484 : :
7485 : : /*
7486 : : * Estimate the number of index pages that will be retrieved.
7487 : : *
7488 : : * We use the simplistic method of taking a pro-rata fraction of the total
7489 : : * number of index pages. In effect, this counts only leaf pages and not
7490 : : * any overhead such as index metapage or upper tree levels.
7491 : : *
7492 : : * In practice access to upper index levels is often nearly free because
7493 : : * those tend to stay in cache under load; moreover, the cost involved is
7494 : : * highly dependent on index type. We therefore ignore such costs here
7495 : : * and leave it to the caller to add a suitable charge if needed.
7496 : : */
7497 [ + + + + ]: 73700 : if (index->pages > 1 && index->tuples > 1)
7498 : 68138 : numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
7499 : : else
7500 : 5562 : numIndexPages = 1.0;
7501 : :
7502 : : /* fetch estimated page cost for tablespace containing index */
7503 : 73700 : get_tablespace_page_costs(index->reltablespace,
7504 : : &spc_random_page_cost,
7505 : : NULL);
7506 : :
7507 : : /*
7508 : : * Now compute the disk access costs.
7509 : : *
7510 : : * The above calculations are all per-index-scan. However, if we are in a
7511 : : * nestloop inner scan, we can expect the scan to be repeated (with
7512 : : * different search keys) for each row of the outer relation. Likewise,
7513 : : * ScalarArrayOpExpr quals result in multiple index scans. This creates
7514 : : * the potential for cache effects to reduce the number of disk page
7515 : : * fetches needed. We want to estimate the average per-scan I/O cost in
7516 : : * the presence of caching.
7517 : : *
7518 : : * We use the Mackert-Lohman formula (see costsize.c for details) to
7519 : : * estimate the total number of page fetches that occur. While this
7520 : : * wasn't what it was designed for, it seems a reasonable model anyway.
7521 : : * Note that we are counting pages not tuples anymore, so we take N = T =
7522 : : * index size, as if there were one "tuple" per page.
7523 : : */
7524 : 73700 : num_outer_scans = loop_count;
7525 : 73700 : num_scans = num_sa_scans * num_outer_scans;
7526 : :
7527 [ + + ]: 73700 : if (num_scans > 1)
7528 : : {
7529 : 8411 : double pages_fetched;
7530 : :
7531 : : /* total page fetches ignoring cache effects */
7532 : 8411 : pages_fetched = numIndexPages * num_scans;
7533 : :
7534 : : /* use Mackert and Lohman formula to adjust for cache effects */
7535 : 16822 : pages_fetched = index_pages_fetched(pages_fetched,
7536 : 8411 : index->pages,
7537 : 8411 : (double) index->pages,
7538 : 8411 : root);
7539 : :
7540 : : /*
7541 : : * Now compute the total disk access cost, and then report a pro-rated
7542 : : * share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
7543 : : * since that's internal to the indexscan.)
7544 : : */
7545 : 16822 : indexTotalCost = (pages_fetched * spc_random_page_cost)
7546 : 8411 : / num_outer_scans;
7547 : 8411 : }
7548 : : else
7549 : : {
7550 : : /*
7551 : : * For a single index scan, we just charge spc_random_page_cost per
7552 : : * page touched.
7553 : : */
7554 : 65289 : indexTotalCost = numIndexPages * spc_random_page_cost;
7555 : : }
7556 : :
7557 : : /*
7558 : : * CPU cost: any complex expressions in the indexquals will need to be
7559 : : * evaluated once at the start of the scan to reduce them to runtime keys
7560 : : * to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
7561 : : * CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
7562 : : * indexqual operator. Because we have numIndexTuples as a per-scan
7563 : : * number, we have to multiply by num_sa_scans to get the correct result
7564 : : * for ScalarArrayOpExpr cases. Similarly add in costs for any index
7565 : : * ORDER BY expressions.
7566 : : *
7567 : : * Note: this neglects the possible costs of rechecking lossy operators.
7568 : : * Detecting that that might be needed seems more expensive than it's
7569 : : * worth, though, considering all the other inaccuracies here ...
7570 : : */
7571 : 147400 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
7572 : 73700 : index_other_operands_eval_cost(root, indexOrderBys);
7573 : 147400 : qual_op_cost = cpu_operator_cost *
7574 : 73700 : (list_length(indexQuals) + list_length(indexOrderBys));
7575 : :
7576 : 73700 : indexStartupCost = qual_arg_cost;
7577 : 73700 : indexTotalCost += qual_arg_cost;
7578 : 73700 : indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
7579 : :
7580 : : /*
7581 : : * Generic assumption about index correlation: there isn't any.
7582 : : */
7583 : 73700 : indexCorrelation = 0.0;
7584 : :
7585 : : /*
7586 : : * Return everything to caller.
7587 : : */
7588 : 73700 : costs->indexStartupCost = indexStartupCost;
7589 : 73700 : costs->indexTotalCost = indexTotalCost;
7590 : 73700 : costs->indexSelectivity = indexSelectivity;
7591 : 73700 : costs->indexCorrelation = indexCorrelation;
7592 : 73700 : costs->numIndexPages = numIndexPages;
7593 : 73700 : costs->numIndexTuples = numIndexTuples;
7594 : 73700 : costs->spc_random_page_cost = spc_random_page_cost;
7595 : 73700 : costs->num_sa_scans = num_sa_scans;
7596 : 73700 : }
7597 : :
7598 : : /*
7599 : : * If the index is partial, add its predicate to the given qual list.
7600 : : *
7601 : : * ANDing the index predicate with the explicitly given indexquals produces
7602 : : * a more accurate idea of the index's selectivity. However, we need to be
7603 : : * careful not to insert redundant clauses, because clauselist_selectivity()
7604 : : * is easily fooled into computing a too-low selectivity estimate. Our
7605 : : * approach is to add only the predicate clause(s) that cannot be proven to
7606 : : * be implied by the given indexquals. This successfully handles cases such
7607 : : * as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
7608 : : * There are many other cases where we won't detect redundancy, leading to a
7609 : : * too-low selectivity estimate, which will bias the system in favor of using
7610 : : * partial indexes where possible. That is not necessarily bad though.
7611 : : *
7612 : : * Note that indexQuals contains RestrictInfo nodes while the indpred
7613 : : * does not, so the output list will be mixed. This is OK for both
7614 : : * predicate_implied_by() and clauselist_selectivity(), but might be
7615 : : * problematic if the result were passed to other things.
7616 : : */
7617 : : List *
7618 : 128775 : add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
7619 : : {
7620 : 128775 : List *predExtraQuals = NIL;
7621 : 128775 : ListCell *lc;
7622 : :
7623 [ + + ]: 128775 : if (index->indpred == NIL)
7624 : 128521 : return indexQuals;
7625 : :
7626 [ + - + + : 510 : foreach(lc, index->indpred)
+ + ]
7627 : : {
7628 : 256 : Node *predQual = (Node *) lfirst(lc);
7629 : 256 : List *oneQual = list_make1(predQual);
7630 : :
7631 [ + + ]: 256 : if (!predicate_implied_by(oneQual, indexQuals, false))
7632 : 222 : predExtraQuals = list_concat(predExtraQuals, oneQual);
7633 : 256 : }
7634 : 254 : return list_concat(predExtraQuals, indexQuals);
7635 : 128775 : }
7636 : :
7637 : : /*
7638 : : * Estimate correlation of btree index's first column.
7639 : : *
7640 : : * If we can get an estimate of the first column's ordering correlation C
7641 : : * from pg_statistic, estimate the index correlation as C for a single-column
7642 : : * index, or C * 0.75 for multiple columns. The idea here is that multiple
7643 : : * columns dilute the importance of the first column's ordering, but don't
7644 : : * negate it entirely.
7645 : : *
7646 : : * We already filled in the stats tuple for *vardata when called.
7647 : : */
7648 : : static double
7649 : 46393 : btcost_correlation(IndexOptInfo *index, VariableStatData *vardata)
7650 : : {
7651 : 46393 : Oid sortop;
7652 : 46393 : AttStatsSlot sslot;
7653 : 46393 : double indexCorrelation = 0;
7654 : :
7655 [ + - ]: 46393 : Assert(HeapTupleIsValid(vardata->statsTuple));
7656 : :
7657 : 92786 : sortop = get_opfamily_member(index->opfamily[0],
7658 : 46393 : index->opcintype[0],
7659 : 46393 : index->opcintype[0],
7660 : : BTLessStrategyNumber);
7661 [ + - + + ]: 46393 : if (OidIsValid(sortop) &&
7662 : 92786 : get_attstatsslot(&sslot, vardata->statsTuple,
7663 : 46393 : STATISTIC_KIND_CORRELATION, sortop,
7664 : : ATTSTATSSLOT_NUMBERS))
7665 : : {
7666 : 46203 : double varCorrelation;
7667 : :
7668 [ + - ]: 46203 : Assert(sslot.nnumbers == 1);
7669 : 46203 : varCorrelation = sslot.numbers[0];
7670 : :
7671 [ + - ]: 46203 : if (index->reverse_sort[0])
7672 : 0 : varCorrelation = -varCorrelation;
7673 : :
7674 [ + + ]: 46203 : if (index->nkeycolumns > 1)
7675 : 15725 : indexCorrelation = varCorrelation * 0.75;
7676 : : else
7677 : 30478 : indexCorrelation = varCorrelation;
7678 : :
7679 : 46203 : free_attstatsslot(&sslot);
7680 : 46203 : }
7681 : :
7682 : 92786 : return indexCorrelation;
7683 : 46393 : }
7684 : :
7685 : : void
7686 : 72755 : btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7687 : : Cost *indexStartupCost, Cost *indexTotalCost,
7688 : : Selectivity *indexSelectivity, double *indexCorrelation,
7689 : : double *indexPages)
7690 : : {
7691 : 72755 : IndexOptInfo *index = path->indexinfo;
7692 : 72755 : GenericCosts costs = {0};
7693 : 72755 : VariableStatData vardata = {0};
7694 : 72755 : double numIndexTuples;
7695 : 72755 : Cost descentCost;
7696 : 72755 : List *indexBoundQuals;
7697 : 72755 : List *indexSkipQuals;
7698 : 72755 : int indexcol;
7699 : 72755 : bool eqQualHere;
7700 : 72755 : bool found_row_compare;
7701 : 72755 : bool found_array;
7702 : 72755 : bool found_is_null_op;
7703 : 72755 : bool have_correlation = false;
7704 : 72755 : double num_sa_scans;
7705 : 72755 : double correlation = 0.0;
7706 : 72755 : ListCell *lc;
7707 : :
7708 : : /*
7709 : : * For a btree scan, only leading '=' quals plus inequality quals for the
7710 : : * immediately next attribute contribute to index selectivity (these are
7711 : : * the "boundary quals" that determine the starting and stopping points of
7712 : : * the index scan). Additional quals can suppress visits to the heap, so
7713 : : * it's OK to count them in indexSelectivity, but they should not count
7714 : : * for estimating numIndexTuples. So we must examine the given indexquals
7715 : : * to find out which ones count as boundary quals. We rely on the
7716 : : * knowledge that they are given in index column order. Note that nbtree
7717 : : * preprocessing can add skip arrays that act as leading '=' quals in the
7718 : : * absence of ordinary input '=' quals, so in practice _most_ input quals
7719 : : * are able to act as index bound quals (which we take into account here).
7720 : : *
7721 : : * For a RowCompareExpr, we consider only the first column, just as
7722 : : * rowcomparesel() does.
7723 : : *
7724 : : * If there's a SAOP or skip array in the quals, we'll actually perform up
7725 : : * to N index descents (not just one), but the underlying array key's
7726 : : * operator can be considered to act the same as it normally does.
7727 : : */
7728 : 72755 : indexBoundQuals = NIL;
7729 : 72755 : indexSkipQuals = NIL;
7730 : 72755 : indexcol = 0;
7731 : 72755 : eqQualHere = false;
7732 : 72755 : found_row_compare = false;
7733 : 72755 : found_array = false;
7734 : 72755 : found_is_null_op = false;
7735 : 72755 : num_sa_scans = 1;
7736 [ + + + + : 123660 : foreach(lc, path->indexclauses)
+ + ]
7737 : : {
7738 : 50905 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7739 : 50905 : ListCell *lc2;
7740 : :
7741 [ + + ]: 50905 : if (indexcol < iclause->indexcol)
7742 : : {
7743 : 8935 : double num_sa_scans_prev_cols = num_sa_scans;
7744 : :
7745 : : /*
7746 : : * Beginning of a new column's quals.
7747 : : *
7748 : : * Skip scans use skip arrays, which are ScalarArrayOp style
7749 : : * arrays that generate their elements procedurally and on demand.
7750 : : * Given a multi-column index on "(a, b)", and an SQL WHERE clause
7751 : : * "WHERE b = 42", a skip scan will effectively use an indexqual
7752 : : * "WHERE a = ANY('{every col a value}') AND b = 42". (Obviously,
7753 : : * the array on "a" must also return "IS NULL" matches, since our
7754 : : * WHERE clause used no strict operator on "a").
7755 : : *
7756 : : * Here we consider how nbtree will backfill skip arrays for any
7757 : : * index columns that lacked an '=' qual. This maintains our
7758 : : * num_sa_scans estimate, and determines if this new column (the
7759 : : * "iclause->indexcol" column, not the prior "indexcol" column)
7760 : : * can have its RestrictInfos/quals added to indexBoundQuals.
7761 : : *
7762 : : * We'll need to handle columns that have inequality quals, where
7763 : : * the skip array generates values from a range constrained by the
7764 : : * quals (not every possible value). We've been maintaining
7765 : : * indexSkipQuals to help with this; it will now contain all of
7766 : : * the prior column's quals (that is, indexcol's quals) when they
7767 : : * might be used for this.
7768 : : */
7769 [ + + ]: 8935 : if (found_row_compare)
7770 : : {
7771 : : /*
7772 : : * Skip arrays can't be added after a RowCompare input qual
7773 : : * due to limitations in nbtree
7774 : : */
7775 : 4 : break;
7776 : : }
7777 [ + + ]: 8931 : if (eqQualHere)
7778 : : {
7779 : : /*
7780 : : * Don't need to add a skip array for an indexcol that already
7781 : : * has an '=' qual/equality constraint
7782 : : */
7783 : 6490 : indexcol++;
7784 : 6490 : indexSkipQuals = NIL;
7785 : 6490 : }
7786 : 8931 : eqQualHere = false;
7787 : :
7788 [ + + ]: 8994 : while (indexcol < iclause->indexcol)
7789 : : {
7790 : 2525 : double ndistinct;
7791 : 2525 : bool isdefault = true;
7792 : :
7793 : 2525 : found_array = true;
7794 : :
7795 : : /*
7796 : : * A skipped attribute's ndistinct forms the basis of our
7797 : : * estimate of the total number of "array elements" used by
7798 : : * its skip array at runtime. Look that up first.
7799 : : */
7800 : 2525 : examine_indexcol_variable(root, index, indexcol, &vardata);
7801 : 2525 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
7802 : :
7803 [ + + ]: 2525 : if (indexcol == 0)
7804 : : {
7805 : : /*
7806 : : * Get an estimate of the leading column's correlation in
7807 : : * passing (avoids rereading variable stats below)
7808 : : */
7809 [ + + ]: 2439 : if (HeapTupleIsValid(vardata.statsTuple))
7810 : 1425 : correlation = btcost_correlation(index, &vardata);
7811 : 2439 : have_correlation = true;
7812 : 2439 : }
7813 : :
7814 [ + + ]: 2525 : ReleaseVariableStats(vardata);
7815 : :
7816 : : /*
7817 : : * If ndistinct is a default estimate, conservatively assume
7818 : : * that no skipping will happen at runtime
7819 : : */
7820 [ + + ]: 2525 : if (isdefault)
7821 : : {
7822 : 841 : num_sa_scans = num_sa_scans_prev_cols;
7823 : 841 : break; /* done building indexBoundQuals */
7824 : : }
7825 : :
7826 : : /*
7827 : : * Apply indexcol's indexSkipQuals selectivity to ndistinct
7828 : : */
7829 [ + + ]: 1684 : if (indexSkipQuals != NIL)
7830 : : {
7831 : 110 : List *partialSkipQuals;
7832 : 110 : Selectivity ndistinctfrac;
7833 : :
7834 : : /*
7835 : : * If the index is partial, AND the index predicate with
7836 : : * the index-bound quals to produce a more accurate idea
7837 : : * of the number of distinct values for prior indexcol
7838 : : */
7839 : 220 : partialSkipQuals = add_predicate_to_index_quals(index,
7840 : 110 : indexSkipQuals);
7841 : :
7842 : 220 : ndistinctfrac = clauselist_selectivity(root, partialSkipQuals,
7843 : 110 : index->rel->relid,
7844 : : JOIN_INNER,
7845 : : NULL);
7846 : :
7847 : : /*
7848 : : * If ndistinctfrac is selective (on its own), the scan is
7849 : : * unlikely to benefit from repositioning itself using
7850 : : * later quals. Do not allow iclause->indexcol's quals to
7851 : : * be added to indexBoundQuals (it would increase descent
7852 : : * costs, without lowering numIndexTuples costs by much).
7853 : : */
7854 [ + + ]: 110 : if (ndistinctfrac < DEFAULT_RANGE_INEQ_SEL)
7855 : : {
7856 : 62 : num_sa_scans = num_sa_scans_prev_cols;
7857 : 62 : break; /* done building indexBoundQuals */
7858 : : }
7859 : :
7860 : : /* Adjust ndistinct downward */
7861 : 48 : ndistinct = rint(ndistinct * ndistinctfrac);
7862 [ + - ]: 48 : ndistinct = Max(ndistinct, 1);
7863 [ + + ]: 110 : }
7864 : :
7865 : : /*
7866 : : * When there's no inequality quals, account for the need to
7867 : : * find an initial value by counting -inf/+inf as a value.
7868 : : *
7869 : : * We don't charge anything extra for possible next/prior key
7870 : : * index probes, which are sometimes used to find the next
7871 : : * valid skip array element (ahead of using the located
7872 : : * element value to relocate the scan to the next position
7873 : : * that might contain matching tuples). It seems hard to do
7874 : : * better here. Use of the skip support infrastructure often
7875 : : * avoids most next/prior key probes. But even when it can't,
7876 : : * there's a decent chance that most individual next/prior key
7877 : : * probes will locate a leaf page whose key space overlaps all
7878 : : * of the scan's keys (even the lower-order keys) -- which
7879 : : * also avoids the need for a separate, extra index descent.
7880 : : * Note also that these probes are much cheaper than non-probe
7881 : : * primitive index scans: they're reliably very selective.
7882 : : */
7883 [ + + ]: 1622 : if (indexSkipQuals == NIL)
7884 : 1574 : ndistinct += 1;
7885 : :
7886 : : /*
7887 : : * Update num_sa_scans estimate by multiplying by ndistinct.
7888 : : *
7889 : : * We make the pessimistic assumption that there is no
7890 : : * naturally occurring cross-column correlation. This is
7891 : : * often wrong, but it seems best to err on the side of not
7892 : : * expecting skipping to be helpful...
7893 : : */
7894 : 1622 : num_sa_scans *= ndistinct;
7895 : :
7896 : : /*
7897 : : * ...but back out of adding this latest group of 1 or more
7898 : : * skip arrays when num_sa_scans exceeds the total number of
7899 : : * index pages (revert to num_sa_scans from before indexcol).
7900 : : * This causes a sharp discontinuity in cost (as a function of
7901 : : * the indexcol's ndistinct), but that is representative of
7902 : : * actual runtime costs.
7903 : : *
7904 : : * Note that skipping is helpful when each primitive index
7905 : : * scan only manages to skip over 1 or 2 irrelevant leaf pages
7906 : : * on average. Skip arrays bring savings in CPU costs due to
7907 : : * the scan not needing to evaluate indexquals against every
7908 : : * tuple, which can greatly exceed any savings in I/O costs.
7909 : : * This test is a test of whether num_sa_scans implies that
7910 : : * we're past the point where the ability to skip ceases to
7911 : : * lower the scan's costs (even qual evaluation CPU costs).
7912 : : */
7913 [ + + ]: 1622 : if (index->pages < num_sa_scans)
7914 : : {
7915 : 1559 : num_sa_scans = num_sa_scans_prev_cols;
7916 : 1559 : break; /* done building indexBoundQuals */
7917 : : }
7918 : :
7919 : 63 : indexcol++;
7920 : 63 : indexSkipQuals = NIL;
7921 [ - + + ]: 2525 : }
7922 : :
7923 : : /*
7924 : : * Finished considering the need to add skip arrays to bridge an
7925 : : * initial eqQualHere gap between the old and new index columns
7926 : : * (or there was no initial eqQualHere gap in the first place).
7927 : : *
7928 : : * If an initial gap could not be bridged, then new column's quals
7929 : : * (i.e. iclause->indexcol's quals) won't go into indexBoundQuals,
7930 : : * and so won't affect our final numIndexTuples estimate.
7931 : : */
7932 [ + + ]: 8931 : if (indexcol != iclause->indexcol)
7933 : 2462 : break; /* done building indexBoundQuals */
7934 [ + + ]: 8935 : }
7935 : :
7936 [ - + ]: 48439 : Assert(indexcol == iclause->indexcol);
7937 : :
7938 : : /* Examine each indexqual associated with this index clause */
7939 [ + - + + : 97292 : foreach(lc2, iclause->indexquals)
+ + ]
7940 : : {
7941 : 48853 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7942 : 48853 : Expr *clause = rinfo->clause;
7943 : 48853 : Oid clause_op = InvalidOid;
7944 : 48853 : int op_strategy;
7945 : :
7946 [ + + ]: 48853 : if (IsA(clause, OpExpr))
7947 : : {
7948 : 47029 : OpExpr *op = (OpExpr *) clause;
7949 : :
7950 : 47029 : clause_op = op->opno;
7951 : 47029 : }
7952 [ + + ]: 1824 : else if (IsA(clause, RowCompareExpr))
7953 : : {
7954 : 66 : RowCompareExpr *rc = (RowCompareExpr *) clause;
7955 : :
7956 : 66 : clause_op = linitial_oid(rc->opnos);
7957 : 66 : found_row_compare = true;
7958 : 66 : }
7959 [ + + ]: 1758 : else if (IsA(clause, ScalarArrayOpExpr))
7960 : : {
7961 : 1391 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
7962 : 1391 : Node *other_operand = (Node *) lsecond(saop->args);
7963 : 1391 : double alength = estimate_array_length(root, other_operand);
7964 : :
7965 : 1391 : clause_op = saop->opno;
7966 : 1391 : found_array = true;
7967 : : /* estimate SA descents by indexBoundQuals only */
7968 [ + + ]: 1391 : if (alength > 1)
7969 : 1347 : num_sa_scans *= alength;
7970 : 1391 : }
7971 [ + - ]: 367 : else if (IsA(clause, NullTest))
7972 : : {
7973 : 367 : NullTest *nt = (NullTest *) clause;
7974 : :
7975 [ + + ]: 367 : if (nt->nulltesttype == IS_NULL)
7976 : : {
7977 : 40 : found_is_null_op = true;
7978 : : /* IS NULL is like = for selectivity/skip scan purposes */
7979 : 40 : eqQualHere = true;
7980 : 40 : }
7981 : 367 : }
7982 : : else
7983 [ # # # # ]: 0 : elog(ERROR, "unsupported indexqual type: %d",
7984 : : (int) nodeTag(clause));
7985 : :
7986 : : /* check for equality operator */
7987 [ + + ]: 48853 : if (OidIsValid(clause_op))
7988 : : {
7989 : 96972 : op_strategy = get_op_opfamily_strategy(clause_op,
7990 : 48486 : index->opfamily[indexcol]);
7991 [ - + ]: 48486 : Assert(op_strategy != 0); /* not a member of opfamily?? */
7992 [ + + ]: 48486 : if (op_strategy == BTEqualStrategyNumber)
7993 : 45063 : eqQualHere = true;
7994 : 48486 : }
7995 : :
7996 : 48853 : indexBoundQuals = lappend(indexBoundQuals, rinfo);
7997 : :
7998 : : /*
7999 : : * We apply inequality selectivities to estimate index descent
8000 : : * costs with scans that use skip arrays. Save this indexcol's
8001 : : * RestrictInfos if it looks like they'll be needed for that.
8002 : : */
8003 [ + + + + : 48853 : if (!eqQualHere && !found_row_compare &&
+ + ]
8004 : 3576 : indexcol < index->nkeycolumns - 1)
8005 : 888 : indexSkipQuals = lappend(indexSkipQuals, rinfo);
8006 : 48853 : }
8007 [ + + ]: 50905 : }
8008 : :
8009 : : /*
8010 : : * If index is unique and we found an '=' clause for each column, we can
8011 : : * just assume numIndexTuples = 1 and skip the expensive
8012 : : * clauselist_selectivity calculations. However, an array or NullTest
8013 : : * always invalidates that theory (even when eqQualHere has been set).
8014 : : */
8015 [ + + ]: 72755 : if (index->unique &&
8016 [ + + ]: 55667 : indexcol == index->nkeycolumns - 1 &&
8017 [ + + ]: 38061 : eqQualHere &&
8018 [ + + + + ]: 18666 : !found_array &&
8019 : 18005 : !found_is_null_op)
8020 : 17997 : numIndexTuples = 1.0;
8021 : : else
8022 : : {
8023 : 54758 : List *selectivityQuals;
8024 : 54758 : Selectivity btreeSelectivity;
8025 : :
8026 : : /*
8027 : : * If the index is partial, AND the index predicate with the
8028 : : * index-bound quals to produce a more accurate idea of the number of
8029 : : * rows covered by the bound conditions.
8030 : : */
8031 : 54758 : selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
8032 : :
8033 : 109516 : btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
8034 : 54758 : index->rel->relid,
8035 : : JOIN_INNER,
8036 : : NULL);
8037 : 54758 : numIndexTuples = btreeSelectivity * index->rel->tuples;
8038 : :
8039 : : /*
8040 : : * btree automatically combines individual array element primitive
8041 : : * index scans whenever the tuples covered by the next set of array
8042 : : * keys are close to tuples covered by the current set. That puts a
8043 : : * natural ceiling on the worst case number of descents -- there
8044 : : * cannot possibly be more than one descent per leaf page scanned.
8045 : : *
8046 : : * Clamp the number of descents to at most 1/3 the number of index
8047 : : * pages. This avoids implausibly high estimates with low selectivity
8048 : : * paths, where scans usually require only one or two descents. This
8049 : : * is most likely to help when there are several SAOP clauses, where
8050 : : * naively accepting the total number of distinct combinations of
8051 : : * array elements as the number of descents would frequently lead to
8052 : : * wild overestimates.
8053 : : *
8054 : : * We somewhat arbitrarily don't just make the cutoff the total number
8055 : : * of leaf pages (we make it 1/3 the total number of pages instead) to
8056 : : * give the btree code credit for its ability to continue on the leaf
8057 : : * level with low selectivity scans.
8058 : : *
8059 : : * Note: num_sa_scans includes both ScalarArrayOp array elements and
8060 : : * skip array elements whose qual affects our numIndexTuples estimate.
8061 : : */
8062 [ + + ]: 54758 : num_sa_scans = Min(num_sa_scans, ceil(index->pages * 0.3333333));
8063 [ + + ]: 54758 : num_sa_scans = Max(num_sa_scans, 1);
8064 : :
8065 : : /*
8066 : : * As in genericcostestimate(), we have to adjust for any array quals
8067 : : * included in indexBoundQuals, and then round to integer.
8068 : : *
8069 : : * It is tempting to make genericcostestimate behave as if array
8070 : : * clauses work in almost the same way as scalar operators during
8071 : : * btree scans, making the top-level scan look like a continuous scan
8072 : : * (as opposed to num_sa_scans-many primitive index scans). After
8073 : : * all, btree scans mostly work like that at runtime. However, such a
8074 : : * scheme would badly bias genericcostestimate's simplistic approach
8075 : : * to calculating numIndexPages through prorating.
8076 : : *
8077 : : * Stick with the approach taken by non-native SAOP scans for now.
8078 : : * genericcostestimate will use the Mackert-Lohman formula to
8079 : : * compensate for repeat page fetches, even though that definitely
8080 : : * won't happen during btree scans (not for leaf pages, at least).
8081 : : * We're usually very pessimistic about the number of primitive index
8082 : : * scans that will be required, but it's not clear how to do better.
8083 : : */
8084 : 54758 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
8085 : 54758 : }
8086 : :
8087 : : /*
8088 : : * Now do generic index cost estimation.
8089 : : */
8090 : 72755 : costs.numIndexTuples = numIndexTuples;
8091 : 72755 : costs.num_sa_scans = num_sa_scans;
8092 : :
8093 : 72755 : genericcostestimate(root, path, loop_count, &costs);
8094 : :
8095 : : /*
8096 : : * Add a CPU-cost component to represent the costs of initial btree
8097 : : * descent. We don't charge any I/O cost for touching upper btree levels,
8098 : : * since they tend to stay in cache, but we still have to do about log2(N)
8099 : : * comparisons to descend a btree of N leaf tuples. We charge one
8100 : : * cpu_operator_cost per comparison.
8101 : : *
8102 : : * If there are SAOP or skip array keys, charge this once per estimated
8103 : : * index descent. The ones after the first one are not startup cost so
8104 : : * far as the overall plan goes, so just add them to "total" cost.
8105 : : */
8106 [ + + ]: 72755 : if (index->tuples > 1) /* avoid computing log(0) */
8107 : : {
8108 : 68517 : descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
8109 : 68517 : costs.indexStartupCost += descentCost;
8110 : 68517 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8111 : 68517 : }
8112 : :
8113 : : /*
8114 : : * Even though we're not charging I/O cost for touching upper btree pages,
8115 : : * it's still reasonable to charge some CPU cost per page descended
8116 : : * through. Moreover, if we had no such charge at all, bloated indexes
8117 : : * would appear to have the same search cost as unbloated ones, at least
8118 : : * in cases where only a single leaf page is expected to be visited. This
8119 : : * cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
8120 : : * touched. The number of such pages is btree tree height plus one (ie,
8121 : : * we charge for the leaf page too). As above, charge once per estimated
8122 : : * SAOP/skip array descent.
8123 : : */
8124 : 72755 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8125 : 72755 : costs.indexStartupCost += descentCost;
8126 : 72755 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8127 : :
8128 [ + + ]: 72755 : if (!have_correlation)
8129 : : {
8130 : 70316 : examine_indexcol_variable(root, index, 0, &vardata);
8131 [ + + ]: 70316 : if (HeapTupleIsValid(vardata.statsTuple))
8132 : 44968 : costs.indexCorrelation = btcost_correlation(index, &vardata);
8133 [ + + ]: 70316 : ReleaseVariableStats(vardata);
8134 : 70316 : }
8135 : : else
8136 : : {
8137 : : /* btcost_correlation already called earlier on */
8138 : 2439 : costs.indexCorrelation = correlation;
8139 : : }
8140 : :
8141 : 72755 : *indexStartupCost = costs.indexStartupCost;
8142 : 72755 : *indexTotalCost = costs.indexTotalCost;
8143 : 72755 : *indexSelectivity = costs.indexSelectivity;
8144 : 72755 : *indexCorrelation = costs.indexCorrelation;
8145 : 72755 : *indexPages = costs.numIndexPages;
8146 : 72755 : }
8147 : :
8148 : : void
8149 : 42 : hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8150 : : Cost *indexStartupCost, Cost *indexTotalCost,
8151 : : Selectivity *indexSelectivity, double *indexCorrelation,
8152 : : double *indexPages)
8153 : : {
8154 : 42 : GenericCosts costs = {0};
8155 : :
8156 : 42 : genericcostestimate(root, path, loop_count, &costs);
8157 : :
8158 : : /*
8159 : : * A hash index has no descent costs as such, since the index AM can go
8160 : : * directly to the target bucket after computing the hash value. There
8161 : : * are a couple of other hash-specific costs that we could conceivably add
8162 : : * here, though:
8163 : : *
8164 : : * Ideally we'd charge spc_random_page_cost for each page in the target
8165 : : * bucket, not just the numIndexPages pages that genericcostestimate
8166 : : * thought we'd visit. However in most cases we don't know which bucket
8167 : : * that will be. There's no point in considering the average bucket size
8168 : : * because the hash AM makes sure that's always one page.
8169 : : *
8170 : : * Likewise, we could consider charging some CPU for each index tuple in
8171 : : * the bucket, if we knew how many there were. But the per-tuple cost is
8172 : : * just a hash value comparison, not a general datatype-dependent
8173 : : * comparison, so any such charge ought to be quite a bit less than
8174 : : * cpu_operator_cost; which makes it probably not worth worrying about.
8175 : : *
8176 : : * A bigger issue is that chance hash-value collisions will result in
8177 : : * wasted probes into the heap. We don't currently attempt to model this
8178 : : * cost on the grounds that it's rare, but maybe it's not rare enough.
8179 : : * (Any fix for this ought to consider the generic lossy-operator problem,
8180 : : * though; it's not entirely hash-specific.)
8181 : : */
8182 : :
8183 : 42 : *indexStartupCost = costs.indexStartupCost;
8184 : 42 : *indexTotalCost = costs.indexTotalCost;
8185 : 42 : *indexSelectivity = costs.indexSelectivity;
8186 : 42 : *indexCorrelation = costs.indexCorrelation;
8187 : 42 : *indexPages = costs.numIndexPages;
8188 : 42 : }
8189 : :
8190 : : void
8191 : 607 : gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8192 : : Cost *indexStartupCost, Cost *indexTotalCost,
8193 : : Selectivity *indexSelectivity, double *indexCorrelation,
8194 : : double *indexPages)
8195 : : {
8196 : 607 : IndexOptInfo *index = path->indexinfo;
8197 : 607 : GenericCosts costs = {0};
8198 : 607 : Cost descentCost;
8199 : :
8200 : 607 : genericcostestimate(root, path, loop_count, &costs);
8201 : :
8202 : : /*
8203 : : * We model index descent costs similarly to those for btree, but to do
8204 : : * that we first need an idea of the tree height. We somewhat arbitrarily
8205 : : * assume that the fanout is 100, meaning the tree height is at most
8206 : : * log100(index->pages).
8207 : : *
8208 : : * Although this computation isn't really expensive enough to require
8209 : : * caching, we might as well use index->tree_height to cache it.
8210 : : */
8211 [ - + ]: 607 : if (index->tree_height < 0) /* unknown? */
8212 : : {
8213 [ + + ]: 607 : if (index->pages > 1) /* avoid computing log(0) */
8214 : 298 : index->tree_height = (int) (log(index->pages) / log(100.0));
8215 : : else
8216 : 309 : index->tree_height = 0;
8217 : 607 : }
8218 : :
8219 : : /*
8220 : : * Add a CPU-cost component to represent the costs of initial descent. We
8221 : : * just use log(N) here not log2(N) since the branching factor isn't
8222 : : * necessarily two anyway. As for btree, charge once per SA scan.
8223 : : */
8224 [ - + ]: 607 : if (index->tuples > 1) /* avoid computing log(0) */
8225 : : {
8226 : 607 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
8227 : 607 : costs.indexStartupCost += descentCost;
8228 : 607 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8229 : 607 : }
8230 : :
8231 : : /*
8232 : : * Likewise add a per-page charge, calculated the same as for btrees.
8233 : : */
8234 : 607 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8235 : 607 : costs.indexStartupCost += descentCost;
8236 : 607 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8237 : :
8238 : 607 : *indexStartupCost = costs.indexStartupCost;
8239 : 607 : *indexTotalCost = costs.indexTotalCost;
8240 : 607 : *indexSelectivity = costs.indexSelectivity;
8241 : 607 : *indexCorrelation = costs.indexCorrelation;
8242 : 607 : *indexPages = costs.numIndexPages;
8243 : 607 : }
8244 : :
8245 : : void
8246 : 296 : spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8247 : : Cost *indexStartupCost, Cost *indexTotalCost,
8248 : : Selectivity *indexSelectivity, double *indexCorrelation,
8249 : : double *indexPages)
8250 : : {
8251 : 296 : IndexOptInfo *index = path->indexinfo;
8252 : 296 : GenericCosts costs = {0};
8253 : 296 : Cost descentCost;
8254 : :
8255 : 296 : genericcostestimate(root, path, loop_count, &costs);
8256 : :
8257 : : /*
8258 : : * We model index descent costs similarly to those for btree, but to do
8259 : : * that we first need an idea of the tree height. We somewhat arbitrarily
8260 : : * assume that the fanout is 100, meaning the tree height is at most
8261 : : * log100(index->pages).
8262 : : *
8263 : : * Although this computation isn't really expensive enough to require
8264 : : * caching, we might as well use index->tree_height to cache it.
8265 : : */
8266 [ + + ]: 296 : if (index->tree_height < 0) /* unknown? */
8267 : : {
8268 [ + - ]: 295 : if (index->pages > 1) /* avoid computing log(0) */
8269 : 295 : index->tree_height = (int) (log(index->pages) / log(100.0));
8270 : : else
8271 : 0 : index->tree_height = 0;
8272 : 295 : }
8273 : :
8274 : : /*
8275 : : * Add a CPU-cost component to represent the costs of initial descent. We
8276 : : * just use log(N) here not log2(N) since the branching factor isn't
8277 : : * necessarily two anyway. As for btree, charge once per SA scan.
8278 : : */
8279 [ - + ]: 296 : if (index->tuples > 1) /* avoid computing log(0) */
8280 : : {
8281 : 296 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
8282 : 296 : costs.indexStartupCost += descentCost;
8283 : 296 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8284 : 296 : }
8285 : :
8286 : : /*
8287 : : * Likewise add a per-page charge, calculated the same as for btrees.
8288 : : */
8289 : 296 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8290 : 296 : costs.indexStartupCost += descentCost;
8291 : 296 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8292 : :
8293 : 296 : *indexStartupCost = costs.indexStartupCost;
8294 : 296 : *indexTotalCost = costs.indexTotalCost;
8295 : 296 : *indexSelectivity = costs.indexSelectivity;
8296 : 296 : *indexCorrelation = costs.indexCorrelation;
8297 : 296 : *indexPages = costs.numIndexPages;
8298 : 296 : }
8299 : :
8300 : :
8301 : : /*
8302 : : * Support routines for gincostestimate
8303 : : */
8304 : :
8305 : : typedef struct
8306 : : {
8307 : : bool attHasFullScan[INDEX_MAX_KEYS];
8308 : : bool attHasNormalScan[INDEX_MAX_KEYS];
8309 : : double partialEntries;
8310 : : double exactEntries;
8311 : : double searchEntries;
8312 : : double arrayScans;
8313 : : } GinQualCounts;
8314 : :
8315 : : /*
8316 : : * Estimate the number of index terms that need to be searched for while
8317 : : * testing the given GIN query, and increment the counts in *counts
8318 : : * appropriately. If the query is unsatisfiable, return false.
8319 : : */
8320 : : static bool
8321 : 237 : gincost_pattern(IndexOptInfo *index, int indexcol,
8322 : : Oid clause_op, Datum query,
8323 : : GinQualCounts *counts)
8324 : : {
8325 : 237 : FmgrInfo flinfo;
8326 : 237 : Oid extractProcOid;
8327 : 237 : Oid collation;
8328 : 237 : int strategy_op;
8329 : 237 : Oid lefttype,
8330 : : righttype;
8331 : 237 : int32 nentries = 0;
8332 : 237 : bool *partial_matches = NULL;
8333 : 237 : Pointer *extra_data = NULL;
8334 : 237 : bool *nullFlags = NULL;
8335 : 237 : int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
8336 : 237 : int32 i;
8337 : :
8338 [ + - ]: 237 : Assert(indexcol < index->nkeycolumns);
8339 : :
8340 : : /*
8341 : : * Get the operator's strategy number and declared input data types within
8342 : : * the index opfamily. (We don't need the latter, but we use
8343 : : * get_op_opfamily_properties because it will throw error if it fails to
8344 : : * find a matching pg_amop entry.)
8345 : : */
8346 : 237 : get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
8347 : : &strategy_op, &lefttype, &righttype);
8348 : :
8349 : : /*
8350 : : * GIN always uses the "default" support functions, which are those with
8351 : : * lefttype == righttype == the opclass' opcintype (see
8352 : : * IndexSupportInitialize in relcache.c).
8353 : : */
8354 : 474 : extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
8355 : 237 : index->opcintype[indexcol],
8356 : 237 : index->opcintype[indexcol],
8357 : : GIN_EXTRACTQUERY_PROC);
8358 : :
8359 [ + - ]: 237 : if (!OidIsValid(extractProcOid))
8360 : : {
8361 : : /* should not happen; throw same error as index_getprocinfo */
8362 [ # # # # ]: 0 : elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
8363 : : GIN_EXTRACTQUERY_PROC, indexcol + 1,
8364 : : get_rel_name(index->indexoid));
8365 : 0 : }
8366 : :
8367 : : /*
8368 : : * Choose collation to pass to extractProc (should match initGinState).
8369 : : */
8370 [ + + ]: 237 : if (OidIsValid(index->indexcollations[indexcol]))
8371 : 18 : collation = index->indexcollations[indexcol];
8372 : : else
8373 : 219 : collation = DEFAULT_COLLATION_OID;
8374 : :
8375 : 237 : fmgr_info(extractProcOid, &flinfo);
8376 : :
8377 : 237 : set_fn_opclass_options(&flinfo, index->opclassoptions[indexcol]);
8378 : :
8379 : 237 : FunctionCall7Coll(&flinfo,
8380 : 237 : collation,
8381 : 237 : query,
8382 : 237 : PointerGetDatum(&nentries),
8383 : 237 : UInt16GetDatum(strategy_op),
8384 : 237 : PointerGetDatum(&partial_matches),
8385 : 237 : PointerGetDatum(&extra_data),
8386 : 237 : PointerGetDatum(&nullFlags),
8387 : 237 : PointerGetDatum(&searchMode));
8388 : :
8389 [ + + + + ]: 237 : if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
8390 : : {
8391 : : /* No match is possible */
8392 : 2 : return false;
8393 : : }
8394 : :
8395 [ + + ]: 508 : for (i = 0; i < nentries; i++)
8396 : : {
8397 : : /*
8398 : : * For partial match we haven't any information to estimate number of
8399 : : * matched entries in index, so, we just estimate it as 100
8400 : : */
8401 [ + + + + ]: 273 : if (partial_matches && partial_matches[i])
8402 : 7 : counts->partialEntries += 100;
8403 : : else
8404 : 266 : counts->exactEntries++;
8405 : :
8406 : 273 : counts->searchEntries++;
8407 : 273 : }
8408 : :
8409 [ + + ]: 235 : if (searchMode == GIN_SEARCH_MODE_DEFAULT)
8410 : : {
8411 : 165 : counts->attHasNormalScan[indexcol] = true;
8412 : 165 : }
8413 [ + + ]: 70 : else if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
8414 : : {
8415 : : /* Treat "include empty" like an exact-match item */
8416 : 7 : counts->attHasNormalScan[indexcol] = true;
8417 : 7 : counts->exactEntries++;
8418 : 7 : counts->searchEntries++;
8419 : 7 : }
8420 : : else
8421 : : {
8422 : : /* It's GIN_SEARCH_MODE_ALL */
8423 : 63 : counts->attHasFullScan[indexcol] = true;
8424 : : }
8425 : :
8426 : 235 : return true;
8427 : 237 : }
8428 : :
8429 : : /*
8430 : : * Estimate the number of index terms that need to be searched for while
8431 : : * testing the given GIN index clause, and increment the counts in *counts
8432 : : * appropriately. If the query is unsatisfiable, return false.
8433 : : */
8434 : : static bool
8435 : 235 : gincost_opexpr(PlannerInfo *root,
8436 : : IndexOptInfo *index,
8437 : : int indexcol,
8438 : : OpExpr *clause,
8439 : : GinQualCounts *counts)
8440 : : {
8441 : 235 : Oid clause_op = clause->opno;
8442 : 235 : Node *operand = (Node *) lsecond(clause->args);
8443 : :
8444 : : /* aggressively reduce to a constant, and look through relabeling */
8445 : 235 : operand = estimate_expression_value(root, operand);
8446 : :
8447 [ + - ]: 235 : if (IsA(operand, RelabelType))
8448 : 0 : operand = (Node *) ((RelabelType *) operand)->arg;
8449 : :
8450 : : /*
8451 : : * It's impossible to call extractQuery method for unknown operand. So
8452 : : * unless operand is a Const we can't do much; just assume there will be
8453 : : * one ordinary search entry from the operand at runtime.
8454 : : */
8455 [ + - ]: 235 : if (!IsA(operand, Const))
8456 : : {
8457 : 0 : counts->exactEntries++;
8458 : 0 : counts->searchEntries++;
8459 : 0 : return true;
8460 : : }
8461 : :
8462 : : /* If Const is null, there can be no matches */
8463 [ - + ]: 235 : if (((Const *) operand)->constisnull)
8464 : 0 : return false;
8465 : :
8466 : : /* Otherwise, apply extractQuery and get the actual term counts */
8467 : 470 : return gincost_pattern(index, indexcol, clause_op,
8468 : 235 : ((Const *) operand)->constvalue,
8469 : 235 : counts);
8470 : 235 : }
8471 : :
8472 : : /*
8473 : : * Estimate the number of index terms that need to be searched for while
8474 : : * testing the given GIN index clause, and increment the counts in *counts
8475 : : * appropriately. If the query is unsatisfiable, return false.
8476 : : *
8477 : : * A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
8478 : : * each of which involves one value from the RHS array, plus all the
8479 : : * non-array quals (if any). To model this, we average the counts across
8480 : : * the RHS elements, and add the averages to the counts in *counts (which
8481 : : * correspond to per-indexscan costs). We also multiply counts->arrayScans
8482 : : * by N, causing gincostestimate to scale up its estimates accordingly.
8483 : : */
8484 : : static bool
8485 : 1 : gincost_scalararrayopexpr(PlannerInfo *root,
8486 : : IndexOptInfo *index,
8487 : : int indexcol,
8488 : : ScalarArrayOpExpr *clause,
8489 : : double numIndexEntries,
8490 : : GinQualCounts *counts)
8491 : : {
8492 : 1 : Oid clause_op = clause->opno;
8493 : 1 : Node *rightop = (Node *) lsecond(clause->args);
8494 : 1 : ArrayType *arrayval;
8495 : 1 : int16 elmlen;
8496 : 1 : bool elmbyval;
8497 : 1 : char elmalign;
8498 : 1 : int numElems;
8499 : 1 : Datum *elemValues;
8500 : 1 : bool *elemNulls;
8501 : 1 : GinQualCounts arraycounts;
8502 : 1 : int numPossible = 0;
8503 : 1 : int i;
8504 : :
8505 [ + - ]: 1 : Assert(clause->useOr);
8506 : :
8507 : : /* aggressively reduce to a constant, and look through relabeling */
8508 : 1 : rightop = estimate_expression_value(root, rightop);
8509 : :
8510 [ + - ]: 1 : if (IsA(rightop, RelabelType))
8511 : 0 : rightop = (Node *) ((RelabelType *) rightop)->arg;
8512 : :
8513 : : /*
8514 : : * It's impossible to call extractQuery method for unknown operand. So
8515 : : * unless operand is a Const we can't do much; just assume there will be
8516 : : * one ordinary search entry from each array entry at runtime, and fall
8517 : : * back on a probably-bad estimate of the number of array entries.
8518 : : */
8519 [ + - ]: 1 : if (!IsA(rightop, Const))
8520 : : {
8521 : 0 : counts->exactEntries++;
8522 : 0 : counts->searchEntries++;
8523 : 0 : counts->arrayScans *= estimate_array_length(root, rightop);
8524 : 0 : return true;
8525 : : }
8526 : :
8527 : : /* If Const is null, there can be no matches */
8528 [ - + ]: 1 : if (((Const *) rightop)->constisnull)
8529 : 0 : return false;
8530 : :
8531 : : /* Otherwise, extract the array elements and iterate over them */
8532 : 1 : arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
8533 : 1 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
8534 : : &elmlen, &elmbyval, &elmalign);
8535 : 2 : deconstruct_array(arrayval,
8536 : 1 : ARR_ELEMTYPE(arrayval),
8537 : 1 : elmlen, elmbyval, elmalign,
8538 : : &elemValues, &elemNulls, &numElems);
8539 : :
8540 : 1 : memset(&arraycounts, 0, sizeof(arraycounts));
8541 : :
8542 [ + + ]: 3 : for (i = 0; i < numElems; i++)
8543 : : {
8544 : 2 : GinQualCounts elemcounts;
8545 : :
8546 : : /* NULL can't match anything, so ignore, as the executor will */
8547 [ - + ]: 2 : if (elemNulls[i])
8548 : 0 : continue;
8549 : :
8550 : : /* Otherwise, apply extractQuery and get the actual term counts */
8551 : 2 : memset(&elemcounts, 0, sizeof(elemcounts));
8552 : :
8553 [ - + ]: 2 : if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
8554 : : &elemcounts))
8555 : : {
8556 : : /* We ignore array elements that are unsatisfiable patterns */
8557 : 2 : numPossible++;
8558 : :
8559 [ - + # # ]: 2 : if (elemcounts.attHasFullScan[indexcol] &&
8560 : 0 : !elemcounts.attHasNormalScan[indexcol])
8561 : : {
8562 : : /*
8563 : : * Full index scan will be required. We treat this as if
8564 : : * every key in the index had been listed in the query; is
8565 : : * that reasonable?
8566 : : */
8567 : 0 : elemcounts.partialEntries = 0;
8568 : 0 : elemcounts.exactEntries = numIndexEntries;
8569 : 0 : elemcounts.searchEntries = numIndexEntries;
8570 : 0 : }
8571 : 2 : arraycounts.partialEntries += elemcounts.partialEntries;
8572 : 2 : arraycounts.exactEntries += elemcounts.exactEntries;
8573 : 2 : arraycounts.searchEntries += elemcounts.searchEntries;
8574 : 2 : }
8575 [ - - + ]: 2 : }
8576 : :
8577 [ + - ]: 1 : if (numPossible == 0)
8578 : : {
8579 : : /* No satisfiable patterns in the array */
8580 : 0 : return false;
8581 : : }
8582 : :
8583 : : /*
8584 : : * Now add the averages to the global counts. This will give us an
8585 : : * estimate of the average number of terms searched for in each indexscan,
8586 : : * including contributions from both array and non-array quals.
8587 : : */
8588 : 1 : counts->partialEntries += arraycounts.partialEntries / numPossible;
8589 : 1 : counts->exactEntries += arraycounts.exactEntries / numPossible;
8590 : 1 : counts->searchEntries += arraycounts.searchEntries / numPossible;
8591 : :
8592 : 1 : counts->arrayScans *= numPossible;
8593 : :
8594 : 1 : return true;
8595 : 1 : }
8596 : :
8597 : : /*
8598 : : * GIN has search behavior completely different from other index types
8599 : : */
8600 : : void
8601 : 207 : gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8602 : : Cost *indexStartupCost, Cost *indexTotalCost,
8603 : : Selectivity *indexSelectivity, double *indexCorrelation,
8604 : : double *indexPages)
8605 : : {
8606 : 207 : IndexOptInfo *index = path->indexinfo;
8607 : 207 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
8608 : 207 : List *selectivityQuals;
8609 : 207 : double numPages = index->pages,
8610 : 207 : numTuples = index->tuples;
8611 : 207 : double numEntryPages,
8612 : : numDataPages,
8613 : : numPendingPages,
8614 : : numEntries;
8615 : 207 : GinQualCounts counts;
8616 : 207 : bool matchPossible;
8617 : 207 : bool fullIndexScan;
8618 : 207 : double partialScale;
8619 : 207 : double entryPagesFetched,
8620 : : dataPagesFetched,
8621 : : dataPagesFetchedBySel;
8622 : 207 : double qual_op_cost,
8623 : : qual_arg_cost,
8624 : : spc_random_page_cost,
8625 : : outer_scans;
8626 : 207 : Cost descentCost;
8627 : 207 : Relation indexRel;
8628 : 207 : GinStatsData ginStats;
8629 : 207 : ListCell *lc;
8630 : 207 : int i;
8631 : :
8632 : : /*
8633 : : * Obtain statistical information from the meta page, if possible. Else
8634 : : * set ginStats to zeroes, and we'll cope below.
8635 : : */
8636 [ - + ]: 207 : if (!index->hypothetical)
8637 : : {
8638 : : /* Lock should have already been obtained in plancat.c */
8639 : 207 : indexRel = index_open(index->indexoid, NoLock);
8640 : 207 : ginGetStats(indexRel, &ginStats);
8641 : 207 : index_close(indexRel, NoLock);
8642 : 207 : }
8643 : : else
8644 : : {
8645 : 0 : memset(&ginStats, 0, sizeof(ginStats));
8646 : : }
8647 : :
8648 : : /*
8649 : : * Assuming we got valid (nonzero) stats at all, nPendingPages can be
8650 : : * trusted, but the other fields are data as of the last VACUUM. We can
8651 : : * scale them up to account for growth since then, but that method only
8652 : : * goes so far; in the worst case, the stats might be for a completely
8653 : : * empty index, and scaling them will produce pretty bogus numbers.
8654 : : * Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
8655 : : * it's grown more than that, fall back to estimating things only from the
8656 : : * assumed-accurate index size. But we'll trust nPendingPages in any case
8657 : : * so long as it's not clearly insane, ie, more than the index size.
8658 : : */
8659 [ + - ]: 207 : if (ginStats.nPendingPages < numPages)
8660 : 207 : numPendingPages = ginStats.nPendingPages;
8661 : : else
8662 : 0 : numPendingPages = 0;
8663 : :
8664 [ + - + - ]: 207 : if (numPages > 0 && ginStats.nTotalPages <= numPages &&
8665 [ + + ]: 207 : ginStats.nTotalPages > numPages / 4 &&
8666 [ + - + + ]: 200 : ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
8667 : : {
8668 : : /*
8669 : : * OK, the stats seem close enough to sane to be trusted. But we
8670 : : * still need to scale them by the ratio numPages / nTotalPages to
8671 : : * account for growth since the last VACUUM.
8672 : : */
8673 : 159 : double scale = numPages / ginStats.nTotalPages;
8674 : :
8675 : 159 : numEntryPages = ceil(ginStats.nEntryPages * scale);
8676 : 159 : numDataPages = ceil(ginStats.nDataPages * scale);
8677 : 159 : numEntries = ceil(ginStats.nEntries * scale);
8678 : : /* ensure we didn't round up too much */
8679 [ + + ]: 159 : numEntryPages = Min(numEntryPages, numPages - numPendingPages);
8680 [ + + ]: 159 : numDataPages = Min(numDataPages,
8681 : : numPages - numPendingPages - numEntryPages);
8682 : 159 : }
8683 : : else
8684 : : {
8685 : : /*
8686 : : * We might get here because it's a hypothetical index, or an index
8687 : : * created pre-9.1 and never vacuumed since upgrading (in which case
8688 : : * its stats would read as zeroes), or just because it's grown too
8689 : : * much since the last VACUUM for us to put our faith in scaling.
8690 : : *
8691 : : * Invent some plausible internal statistics based on the index page
8692 : : * count (and clamp that to at least 10 pages, just in case). We
8693 : : * estimate that 90% of the index is entry pages, and the rest is data
8694 : : * pages. Estimate 100 entries per entry page; this is rather bogus
8695 : : * since it'll depend on the size of the keys, but it's more robust
8696 : : * than trying to predict the number of entries per heap tuple.
8697 : : */
8698 [ + + ]: 48 : numPages = Max(numPages, 10);
8699 : 48 : numEntryPages = floor((numPages - numPendingPages) * 0.90);
8700 : 48 : numDataPages = numPages - numPendingPages - numEntryPages;
8701 : 48 : numEntries = floor(numEntryPages * 100);
8702 : : }
8703 : :
8704 : : /* In an empty index, numEntries could be zero. Avoid divide-by-zero */
8705 [ + - ]: 207 : if (numEntries < 1)
8706 : 0 : numEntries = 1;
8707 : :
8708 : : /*
8709 : : * If the index is partial, AND the index predicate with the index-bound
8710 : : * quals to produce a more accurate idea of the number of rows covered by
8711 : : * the bound conditions.
8712 : : */
8713 : 207 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
8714 : :
8715 : : /* Estimate the fraction of main-table tuples that will be visited */
8716 : 414 : *indexSelectivity = clauselist_selectivity(root, selectivityQuals,
8717 : 207 : index->rel->relid,
8718 : : JOIN_INNER,
8719 : : NULL);
8720 : :
8721 : : /* fetch estimated page cost for tablespace containing index */
8722 : 207 : get_tablespace_page_costs(index->reltablespace,
8723 : : &spc_random_page_cost,
8724 : : NULL);
8725 : :
8726 : : /*
8727 : : * Generic assumption about index correlation: there isn't any.
8728 : : */
8729 : 207 : *indexCorrelation = 0.0;
8730 : :
8731 : : /*
8732 : : * Examine quals to estimate number of search entries & partial matches
8733 : : */
8734 : 207 : memset(&counts, 0, sizeof(counts));
8735 : 207 : counts.arrayScans = 1;
8736 : 207 : matchPossible = true;
8737 : :
8738 [ + - + + : 443 : foreach(lc, path->indexclauses)
+ + ]
8739 : : {
8740 : 236 : IndexClause *iclause = lfirst_node(IndexClause, lc);
8741 : 236 : ListCell *lc2;
8742 : :
8743 [ + - + + : 472 : foreach(lc2, iclause->indexquals)
+ + ]
8744 : : {
8745 : 236 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
8746 : 236 : Expr *clause = rinfo->clause;
8747 : :
8748 [ + + ]: 236 : if (IsA(clause, OpExpr))
8749 : : {
8750 : 470 : matchPossible = gincost_opexpr(root,
8751 : 235 : index,
8752 : 235 : iclause->indexcol,
8753 : 235 : (OpExpr *) clause,
8754 : : &counts);
8755 [ + + ]: 235 : if (!matchPossible)
8756 : 2 : break;
8757 : 233 : }
8758 [ - + ]: 1 : else if (IsA(clause, ScalarArrayOpExpr))
8759 : : {
8760 : 2 : matchPossible = gincost_scalararrayopexpr(root,
8761 : 1 : index,
8762 : 1 : iclause->indexcol,
8763 : 1 : (ScalarArrayOpExpr *) clause,
8764 : 1 : numEntries,
8765 : : &counts);
8766 [ + - ]: 1 : if (!matchPossible)
8767 : 0 : break;
8768 : 1 : }
8769 : : else
8770 : : {
8771 : : /* shouldn't be anything else for a GIN index */
8772 [ # # # # ]: 0 : elog(ERROR, "unsupported GIN indexqual type: %d",
8773 : : (int) nodeTag(clause));
8774 : : }
8775 [ + + ]: 236 : }
8776 : 236 : }
8777 : :
8778 : : /* Fall out if there were any provably-unsatisfiable quals */
8779 [ + + ]: 207 : if (!matchPossible)
8780 : : {
8781 : 2 : *indexStartupCost = 0;
8782 : 2 : *indexTotalCost = 0;
8783 : 2 : *indexSelectivity = 0;
8784 : 2 : return;
8785 : : }
8786 : :
8787 : : /*
8788 : : * If attribute has a full scan and at the same time doesn't have normal
8789 : : * scan, then we'll have to scan all non-null entries of that attribute.
8790 : : * Currently, we don't have per-attribute statistics for GIN. Thus, we
8791 : : * must assume the whole GIN index has to be scanned in this case.
8792 : : */
8793 : 205 : fullIndexScan = false;
8794 [ + + ]: 396 : for (i = 0; i < index->nkeycolumns; i++)
8795 : : {
8796 [ + + + + ]: 243 : if (counts.attHasFullScan[i] && !counts.attHasNormalScan[i])
8797 : : {
8798 : 52 : fullIndexScan = true;
8799 : 52 : break;
8800 : : }
8801 : 191 : }
8802 : :
8803 [ + + + - ]: 205 : if (fullIndexScan || indexQuals == NIL)
8804 : : {
8805 : : /*
8806 : : * Full index scan will be required. We treat this as if every key in
8807 : : * the index had been listed in the query; is that reasonable?
8808 : : */
8809 : 52 : counts.partialEntries = 0;
8810 : 52 : counts.exactEntries = numEntries;
8811 : 52 : counts.searchEntries = numEntries;
8812 : 52 : }
8813 : :
8814 : : /* Will we have more than one iteration of a nestloop scan? */
8815 : 205 : outer_scans = loop_count;
8816 : :
8817 : : /*
8818 : : * Compute cost to begin scan, first of all, pay attention to pending
8819 : : * list.
8820 : : */
8821 : 205 : entryPagesFetched = numPendingPages;
8822 : :
8823 : : /*
8824 : : * Estimate number of entry pages read. We need to do
8825 : : * counts.searchEntries searches. Use a power function as it should be,
8826 : : * but tuples on leaf pages usually is much greater. Here we include all
8827 : : * searches in entry tree, including search of first entry in partial
8828 : : * match algorithm
8829 : : */
8830 : 205 : entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
8831 : :
8832 : : /*
8833 : : * Add an estimate of entry pages read by partial match algorithm. It's a
8834 : : * scan over leaf pages in entry tree. We haven't any useful stats here,
8835 : : * so estimate it as proportion. Because counts.partialEntries is really
8836 : : * pretty bogus (see code above), it's possible that it is more than
8837 : : * numEntries; clamp the proportion to ensure sanity.
8838 : : */
8839 : 205 : partialScale = counts.partialEntries / numEntries;
8840 [ + - ]: 205 : partialScale = Min(partialScale, 1.0);
8841 : :
8842 : 205 : entryPagesFetched += ceil(numEntryPages * partialScale);
8843 : :
8844 : : /*
8845 : : * Partial match algorithm reads all data pages before doing actual scan,
8846 : : * so it's a startup cost. Again, we haven't any useful stats here, so
8847 : : * estimate it as proportion.
8848 : : */
8849 : 205 : dataPagesFetched = ceil(numDataPages * partialScale);
8850 : :
8851 : 205 : *indexStartupCost = 0;
8852 : 205 : *indexTotalCost = 0;
8853 : :
8854 : : /*
8855 : : * Add a CPU-cost component to represent the costs of initial entry btree
8856 : : * descent. We don't charge any I/O cost for touching upper btree levels,
8857 : : * since they tend to stay in cache, but we still have to do about log2(N)
8858 : : * comparisons to descend a btree of N leaf tuples. We charge one
8859 : : * cpu_operator_cost per comparison.
8860 : : *
8861 : : * If there are ScalarArrayOpExprs, charge this once per SA scan. The
8862 : : * ones after the first one are not startup cost so far as the overall
8863 : : * plan is concerned, so add them only to "total" cost.
8864 : : */
8865 [ - + ]: 205 : if (numEntries > 1) /* avoid computing log(0) */
8866 : : {
8867 : 205 : descentCost = ceil(log(numEntries) / log(2.0)) * cpu_operator_cost;
8868 : 205 : *indexStartupCost += descentCost * counts.searchEntries;
8869 : 205 : *indexTotalCost += counts.arrayScans * descentCost * counts.searchEntries;
8870 : 205 : }
8871 : :
8872 : : /*
8873 : : * Add a cpu cost per entry-page fetched. This is not amortized over a
8874 : : * loop.
8875 : : */
8876 : 205 : *indexStartupCost += entryPagesFetched * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8877 : 205 : *indexTotalCost += entryPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8878 : :
8879 : : /*
8880 : : * Add a cpu cost per data-page fetched. This is also not amortized over a
8881 : : * loop. Since those are the data pages from the partial match algorithm,
8882 : : * charge them as startup cost.
8883 : : */
8884 : 205 : *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * dataPagesFetched;
8885 : :
8886 : : /*
8887 : : * Since we add the startup cost to the total cost later on, remove the
8888 : : * initial arrayscan from the total.
8889 : : */
8890 : 205 : *indexTotalCost += dataPagesFetched * (counts.arrayScans - 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8891 : :
8892 : : /*
8893 : : * Calculate cache effects if more than one scan due to nestloops or array
8894 : : * quals. The result is pro-rated per nestloop scan, but the array qual
8895 : : * factor shouldn't be pro-rated (compare genericcostestimate).
8896 : : */
8897 [ + - + + ]: 205 : if (outer_scans > 1 || counts.arrayScans > 1)
8898 : : {
8899 : 1 : entryPagesFetched *= outer_scans * counts.arrayScans;
8900 : 2 : entryPagesFetched = index_pages_fetched(entryPagesFetched,
8901 : 1 : (BlockNumber) numEntryPages,
8902 : 1 : numEntryPages, root);
8903 : 1 : entryPagesFetched /= outer_scans;
8904 : 1 : dataPagesFetched *= outer_scans * counts.arrayScans;
8905 : 2 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
8906 : 1 : (BlockNumber) numDataPages,
8907 : 1 : numDataPages, root);
8908 : 1 : dataPagesFetched /= outer_scans;
8909 : 1 : }
8910 : :
8911 : : /*
8912 : : * Here we use random page cost because logically-close pages could be far
8913 : : * apart on disk.
8914 : : */
8915 : 205 : *indexStartupCost += (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
8916 : :
8917 : : /*
8918 : : * Now compute the number of data pages fetched during the scan.
8919 : : *
8920 : : * We assume every entry to have the same number of items, and that there
8921 : : * is no overlap between them. (XXX: tsvector and array opclasses collect
8922 : : * statistics on the frequency of individual keys; it would be nice to use
8923 : : * those here.)
8924 : : */
8925 : 205 : dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
8926 : :
8927 : : /*
8928 : : * If there is a lot of overlap among the entries, in particular if one of
8929 : : * the entries is very frequent, the above calculation can grossly
8930 : : * under-estimate. As a simple cross-check, calculate a lower bound based
8931 : : * on the overall selectivity of the quals. At a minimum, we must read
8932 : : * one item pointer for each matching entry.
8933 : : *
8934 : : * The width of each item pointer varies, based on the level of
8935 : : * compression. We don't have statistics on that, but an average of
8936 : : * around 3 bytes per item is fairly typical.
8937 : : */
8938 : 410 : dataPagesFetchedBySel = ceil(*indexSelectivity *
8939 : 205 : (numTuples / (BLCKSZ / 3)));
8940 [ + + ]: 205 : if (dataPagesFetchedBySel > dataPagesFetched)
8941 : 155 : dataPagesFetched = dataPagesFetchedBySel;
8942 : :
8943 : : /* Add one page cpu-cost to the startup cost */
8944 : 205 : *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * counts.searchEntries;
8945 : :
8946 : : /*
8947 : : * Add once again a CPU-cost for those data pages, before amortizing for
8948 : : * cache.
8949 : : */
8950 : 205 : *indexTotalCost += dataPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8951 : :
8952 : : /* Account for cache effects, the same as above */
8953 [ + - + + ]: 205 : if (outer_scans > 1 || counts.arrayScans > 1)
8954 : : {
8955 : 1 : dataPagesFetched *= outer_scans * counts.arrayScans;
8956 : 2 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
8957 : 1 : (BlockNumber) numDataPages,
8958 : 1 : numDataPages, root);
8959 : 1 : dataPagesFetched /= outer_scans;
8960 : 1 : }
8961 : :
8962 : : /* And apply random_page_cost as the cost per page */
8963 : 410 : *indexTotalCost += *indexStartupCost +
8964 : 205 : dataPagesFetched * spc_random_page_cost;
8965 : :
8966 : : /*
8967 : : * Add on index qual eval costs, much as in genericcostestimate. We charge
8968 : : * cpu but we can disregard indexorderbys, since GIN doesn't support
8969 : : * those.
8970 : : */
8971 : 205 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
8972 : 205 : qual_op_cost = cpu_operator_cost * list_length(indexQuals);
8973 : :
8974 : 205 : *indexStartupCost += qual_arg_cost;
8975 : 205 : *indexTotalCost += qual_arg_cost;
8976 : :
8977 : : /*
8978 : : * Add a cpu cost per search entry, corresponding to the actual visited
8979 : : * entries.
8980 : : */
8981 : 205 : *indexTotalCost += (counts.searchEntries * counts.arrayScans) * (qual_op_cost);
8982 : : /* Now add a cpu cost per tuple in the posting lists / trees */
8983 : 205 : *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost);
8984 : 205 : *indexPages = dataPagesFetched;
8985 [ - + ]: 207 : }
8986 : :
8987 : : /*
8988 : : * BRIN has search behavior completely different from other index types
8989 : : */
8990 : : void
8991 : 1791 : brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8992 : : Cost *indexStartupCost, Cost *indexTotalCost,
8993 : : Selectivity *indexSelectivity, double *indexCorrelation,
8994 : : double *indexPages)
8995 : : {
8996 : 1791 : IndexOptInfo *index = path->indexinfo;
8997 : 1791 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
8998 : 1791 : double numPages = index->pages;
8999 : 1791 : RelOptInfo *baserel = index->rel;
9000 [ + - ]: 1791 : RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
9001 : 1791 : Cost spc_seq_page_cost;
9002 : 1791 : Cost spc_random_page_cost;
9003 : 1791 : double qual_arg_cost;
9004 : 1791 : double qualSelectivity;
9005 : 1791 : BrinStatsData statsData;
9006 : 1791 : double indexRanges;
9007 : 1791 : double minimalRanges;
9008 : 1791 : double estimatedRanges;
9009 : 1791 : double selec;
9010 : 1791 : Relation indexRel;
9011 : 1791 : ListCell *l;
9012 : 1791 : VariableStatData vardata;
9013 : :
9014 [ + - ]: 1791 : Assert(rte->rtekind == RTE_RELATION);
9015 : :
9016 : : /* fetch estimated page cost for the tablespace containing the index */
9017 : 1791 : get_tablespace_page_costs(index->reltablespace,
9018 : : &spc_random_page_cost,
9019 : : &spc_seq_page_cost);
9020 : :
9021 : : /*
9022 : : * Obtain some data from the index itself, if possible. Otherwise invent
9023 : : * some plausible internal statistics based on the relation page count.
9024 : : */
9025 [ - + ]: 1791 : if (!index->hypothetical)
9026 : : {
9027 : : /*
9028 : : * A lock should have already been obtained on the index in plancat.c.
9029 : : */
9030 : 1791 : indexRel = index_open(index->indexoid, NoLock);
9031 : 1791 : brinGetStats(indexRel, &statsData);
9032 : 1791 : index_close(indexRel, NoLock);
9033 : :
9034 : : /* work out the actual number of ranges in the index */
9035 [ + + ]: 1791 : indexRanges = Max(ceil((double) baserel->pages /
9036 : : statsData.pagesPerRange), 1.0);
9037 : 1791 : }
9038 : : else
9039 : : {
9040 : : /*
9041 : : * Assume default number of pages per range, and estimate the number
9042 : : * of ranges based on that.
9043 : : */
9044 [ # # ]: 0 : indexRanges = Max(ceil((double) baserel->pages /
9045 : : BRIN_DEFAULT_PAGES_PER_RANGE), 1.0);
9046 : :
9047 : 0 : statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE;
9048 : 0 : statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1;
9049 : : }
9050 : :
9051 : : /*
9052 : : * Compute index correlation
9053 : : *
9054 : : * Because we can use all index quals equally when scanning, we can use
9055 : : * the largest correlation (in absolute value) among columns used by the
9056 : : * query. Start at zero, the worst possible case. If we cannot find any
9057 : : * correlation statistics, we will keep it as 0.
9058 : : */
9059 : 1791 : *indexCorrelation = 0;
9060 : :
9061 [ + - + + : 3582 : foreach(l, path->indexclauses)
+ + ]
9062 : : {
9063 : 1791 : IndexClause *iclause = lfirst_node(IndexClause, l);
9064 : 1791 : AttrNumber attnum = index->indexkeys[iclause->indexcol];
9065 : :
9066 : : /* attempt to lookup stats in relation for this index column */
9067 [ + - ]: 1791 : if (attnum != 0)
9068 : : {
9069 : : /* Simple variable -- look to stats for the underlying table */
9070 [ - + # # ]: 1791 : if (get_relation_stats_hook &&
9071 : 0 : (*get_relation_stats_hook) (root, rte, attnum, &vardata))
9072 : : {
9073 : : /*
9074 : : * The hook took control of acquiring a stats tuple. If it
9075 : : * did supply a tuple, it'd better have supplied a freefunc.
9076 : : */
9077 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
9078 [ # # # # ]: 0 : elog(ERROR,
9079 : : "no function provided to release variable stats with");
9080 : 0 : }
9081 : : else
9082 : : {
9083 : 1791 : vardata.statsTuple =
9084 : 1791 : SearchSysCache3(STATRELATTINH,
9085 : 1791 : ObjectIdGetDatum(rte->relid),
9086 : 1791 : Int16GetDatum(attnum),
9087 : 1791 : BoolGetDatum(false));
9088 : 1791 : vardata.freefunc = ReleaseSysCache;
9089 : : }
9090 : 1791 : }
9091 : : else
9092 : : {
9093 : : /*
9094 : : * Looks like we've found an expression column in the index. Let's
9095 : : * see if there's any stats for it.
9096 : : */
9097 : :
9098 : : /* get the attnum from the 0-based index. */
9099 : 0 : attnum = iclause->indexcol + 1;
9100 : :
9101 [ # # # # ]: 0 : if (get_index_stats_hook &&
9102 : 0 : (*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
9103 : : {
9104 : : /*
9105 : : * The hook took control of acquiring a stats tuple. If it
9106 : : * did supply a tuple, it'd better have supplied a freefunc.
9107 : : */
9108 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
9109 : 0 : !vardata.freefunc)
9110 [ # # # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
9111 : 0 : }
9112 : : else
9113 : : {
9114 : 0 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
9115 : 0 : ObjectIdGetDatum(index->indexoid),
9116 : 0 : Int16GetDatum(attnum),
9117 : 0 : BoolGetDatum(false));
9118 : 0 : vardata.freefunc = ReleaseSysCache;
9119 : : }
9120 : : }
9121 : :
9122 [ + + ]: 1791 : if (HeapTupleIsValid(vardata.statsTuple))
9123 : : {
9124 : 9 : AttStatsSlot sslot;
9125 : :
9126 [ - + ]: 9 : if (get_attstatsslot(&sslot, vardata.statsTuple,
9127 : : STATISTIC_KIND_CORRELATION, InvalidOid,
9128 : : ATTSTATSSLOT_NUMBERS))
9129 : : {
9130 : 9 : double varCorrelation = 0.0;
9131 : :
9132 [ - + ]: 9 : if (sslot.nnumbers > 0)
9133 : 9 : varCorrelation = fabs(sslot.numbers[0]);
9134 : :
9135 [ - + ]: 9 : if (varCorrelation > *indexCorrelation)
9136 : 9 : *indexCorrelation = varCorrelation;
9137 : :
9138 : 9 : free_attstatsslot(&sslot);
9139 : 9 : }
9140 : 9 : }
9141 : :
9142 [ + + ]: 1791 : ReleaseVariableStats(vardata);
9143 : 1791 : }
9144 : :
9145 : 3582 : qualSelectivity = clauselist_selectivity(root, indexQuals,
9146 : 1791 : baserel->relid,
9147 : : JOIN_INNER, NULL);
9148 : :
9149 : : /*
9150 : : * Now calculate the minimum possible ranges we could match with if all of
9151 : : * the rows were in the perfect order in the table's heap.
9152 : : */
9153 : 1791 : minimalRanges = ceil(indexRanges * qualSelectivity);
9154 : :
9155 : : /*
9156 : : * Now estimate the number of ranges that we'll touch by using the
9157 : : * indexCorrelation from the stats. Careful not to divide by zero (note
9158 : : * we're using the absolute value of the correlation).
9159 : : */
9160 [ + + ]: 1791 : if (*indexCorrelation < 1.0e-10)
9161 : 1782 : estimatedRanges = indexRanges;
9162 : : else
9163 [ + + ]: 9 : estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
9164 : :
9165 : : /* we expect to visit this portion of the table */
9166 : 1791 : selec = estimatedRanges / indexRanges;
9167 : :
9168 [ - + + - ]: 3582 : CLAMP_PROBABILITY(selec);
9169 : :
9170 : 1791 : *indexSelectivity = selec;
9171 : :
9172 : : /*
9173 : : * Compute the index qual costs, much as in genericcostestimate, to add to
9174 : : * the index costs. We can disregard indexorderbys, since BRIN doesn't
9175 : : * support those.
9176 : : */
9177 : 1791 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
9178 : :
9179 : : /*
9180 : : * Compute the startup cost as the cost to read the whole revmap
9181 : : * sequentially, including the cost to execute the index quals.
9182 : : */
9183 : 1791 : *indexStartupCost =
9184 : 1791 : spc_seq_page_cost * statsData.revmapNumPages * loop_count;
9185 : 1791 : *indexStartupCost += qual_arg_cost;
9186 : :
9187 : : /*
9188 : : * To read a BRIN index there might be a bit of back and forth over
9189 : : * regular pages, as revmap might point to them out of sequential order;
9190 : : * calculate the total cost as reading the whole index in random order.
9191 : : */
9192 : 3582 : *indexTotalCost = *indexStartupCost +
9193 : 1791 : spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
9194 : :
9195 : : /*
9196 : : * Charge a small amount per range tuple which we expect to match to. This
9197 : : * is meant to reflect the costs of manipulating the bitmap. The BRIN scan
9198 : : * will set a bit for each page in the range when we find a matching
9199 : : * range, so we must multiply the charge by the number of pages in the
9200 : : * range.
9201 : : */
9202 : 3582 : *indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
9203 : 1791 : statsData.pagesPerRange;
9204 : :
9205 : 1791 : *indexPages = index->pages;
9206 : 1791 : }
|
