Age Owner Branch data TLA Line data Source code
1 : : /*-------------------------------------------------------------------------
2 : : *
3 : : * selfuncs.c
4 : : * Selectivity functions and index cost estimation functions for
5 : : * standard operators and index access methods.
6 : : *
7 : : * Selectivity routines are registered in the pg_operator catalog
8 : : * in the "oprrest" and "oprjoin" attributes.
9 : : *
10 : : * Index cost functions are located via the index AM's API struct,
11 : : * which is obtained from the handler function registered in pg_am.
12 : : *
13 : : * Portions Copyright (c) 1996-2025, 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 void examine_simple_variable(PlannerInfo *root, Var *var,
246 : : VariableStatData *vardata);
247 : : static void examine_indexcol_variable(PlannerInfo *root, IndexOptInfo *index,
248 : : int indexcol, VariableStatData *vardata);
249 : : static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
250 : : Oid sortop, Oid collation,
251 : : Datum *min, Datum *max);
252 : : static void get_stats_slot_range(AttStatsSlot *sslot,
253 : : Oid opfuncoid, FmgrInfo *opproc,
254 : : Oid collation, int16 typLen, bool typByVal,
255 : : Datum *min, Datum *max, bool *p_have_data);
256 : : static bool get_actual_variable_range(PlannerInfo *root,
257 : : VariableStatData *vardata,
258 : : Oid sortop, Oid collation,
259 : : Datum *min, Datum *max);
260 : : static bool get_actual_variable_endpoint(Relation heapRel,
261 : : Relation indexRel,
262 : : ScanDirection indexscandir,
263 : : ScanKey scankeys,
264 : : int16 typLen,
265 : : bool typByVal,
266 : : TupleTableSlot *tableslot,
267 : : MemoryContext outercontext,
268 : : Datum *endpointDatum);
269 : : static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
270 : : static double btcost_correlation(IndexOptInfo *index,
271 : : VariableStatData *vardata);
272 : :
273 : : /* Define support routines for MCV hash tables */
274 : : #define SH_PREFIX MCVHashTable
275 : : #define SH_ELEMENT_TYPE MCVHashEntry
276 : : #define SH_KEY_TYPE Datum
277 : : #define SH_KEY value
278 : : #define SH_HASH_KEY(tab,key) hash_mcv(tab, key)
279 : : #define SH_EQUAL(tab,key0,key1) mcvs_equal(tab, key0, key1)
280 : : #define SH_SCOPE static inline
281 : : #define SH_STORE_HASH
282 : : #define SH_GET_HASH(tab,ent) (ent)->hash
283 : : #define SH_DEFINE
284 : : #define SH_DECLARE
285 : : #include "lib/simplehash.h"
286 : :
287 : :
288 : : /*
289 : : * eqsel - Selectivity of "=" for any data types.
290 : : *
291 : : * Note: this routine is also used to estimate selectivity for some
292 : : * operators that are not "=" but have comparable selectivity behavior,
293 : : * such as "~=" (geometric approximate-match). Even for "=", we must
294 : : * keep in mind that the left and right datatypes may differ.
295 : : */
296 : : Datum
9326 tgl@sss.pgh.pa.us 297 :CBC 353750 : eqsel(PG_FUNCTION_ARGS)
298 : : {
3119 299 : 353750 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
300 : : }
301 : :
302 : : /*
303 : : * Common code for eqsel() and neqsel()
304 : : */
305 : : static double
306 : 377259 : eqsel_internal(PG_FUNCTION_ARGS, bool negate)
307 : : {
7500 308 : 377259 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
8977 309 : 377259 : Oid operator = PG_GETARG_OID(1);
310 : 377259 : List *args = (List *) PG_GETARG_POINTER(2);
311 : 377259 : int varRelid = PG_GETARG_INT32(3);
2021 312 : 377259 : Oid collation = PG_GET_COLLATION();
313 : : VariableStatData vardata;
314 : : Node *other;
315 : : bool varonleft;
316 : : double selec;
317 : :
318 : : /*
319 : : * When asked about <>, we do the estimation using the corresponding =
320 : : * operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
321 : : */
3119 322 [ + + ]: 377259 : if (negate)
323 : : {
324 : 23509 : operator = get_negator(operator);
325 [ - + ]: 23509 : if (!OidIsValid(operator))
326 : : {
327 : : /* Use default selectivity (should we raise an error instead?) */
3119 tgl@sss.pgh.pa.us 328 :UBC 0 : return 1.0 - DEFAULT_EQ_SEL;
329 : : }
330 : : }
331 : :
332 : : /*
333 : : * If expression is not variable = something or something = variable, then
334 : : * punt and return a default estimate.
335 : : */
7974 tgl@sss.pgh.pa.us 336 [ + + ]:CBC 377259 : if (!get_restriction_variable(root, args, varRelid,
337 : : &vardata, &other, &varonleft))
3119 338 [ + + ]: 2757 : return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
339 : :
340 : : /*
341 : : * We can do a lot better if the something is a constant. (Note: the
342 : : * Const might result from estimation rather than being a simple constant
343 : : * in the query.)
344 : : */
6493 345 [ + + ]: 374499 : if (IsA(other, Const))
2021 346 : 155497 : selec = var_eq_const(&vardata, operator, collation,
6493 347 : 155497 : ((Const *) other)->constvalue,
348 : 155497 : ((Const *) other)->constisnull,
349 : : varonleft, negate);
350 : : else
2021 351 : 219002 : selec = var_eq_non_const(&vardata, operator, collation, other,
352 : : varonleft, negate);
353 : :
6493 354 [ + + ]: 374499 : ReleaseVariableStats(vardata);
355 : :
3119 356 : 374499 : return selec;
357 : : }
358 : :
359 : : /*
360 : : * var_eq_const --- eqsel for var = const case
361 : : *
362 : : * This is exported so that some other estimation functions can use it.
363 : : */
364 : : double
1184 pg@bowt.ie 365 : 178061 : var_eq_const(VariableStatData *vardata, Oid oproid, Oid collation,
366 : : Datum constval, bool constisnull,
367 : : bool varonleft, bool negate)
368 : : {
369 : : double selec;
3119 tgl@sss.pgh.pa.us 370 : 178061 : double nullfrac = 0.0;
371 : : bool isdefault;
372 : : Oid opfuncoid;
373 : :
374 : : /*
375 : : * If the constant is NULL, assume operator is strict and return zero, ie,
376 : : * operator will never return TRUE. (It's zero even for a negator op.)
377 : : */
6493 378 [ + + ]: 178061 : if (constisnull)
379 : 204 : return 0.0;
380 : :
381 : : /*
382 : : * Grab the nullfrac for use below. Note we allow use of nullfrac
383 : : * regardless of security check.
384 : : */
3119 385 [ + + ]: 177857 : if (HeapTupleIsValid(vardata->statsTuple))
386 : : {
387 : : Form_pg_statistic stats;
388 : :
389 : 135423 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
390 : 135423 : nullfrac = stats->stanullfrac;
391 : : }
392 : :
393 : : /*
394 : : * If we matched the var to a unique index, DISTINCT or GROUP-BY clause,
395 : : * assume there is exactly one match regardless of anything else. (This
396 : : * is slightly bogus, since the index or clause's equality operator might
397 : : * be different from ours, but it's much more likely to be right than
398 : : * ignoring the information.)
399 : : */
6149 400 [ + + + - : 177857 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
+ + ]
401 : : {
3119 402 : 44316 : selec = 1.0 / vardata->rel->tuples;
403 : : }
404 [ + + + - ]: 233671 : else if (HeapTupleIsValid(vardata->statsTuple) &&
405 : 100130 : statistic_proc_security_check(vardata,
1184 pg@bowt.ie 406 : 100130 : (opfuncoid = get_opcode(oproid))))
9635 tgl@sss.pgh.pa.us 407 : 100130 : {
408 : : AttStatsSlot sslot;
6493 409 : 100130 : bool match = false;
410 : : int i;
411 : :
412 : : /*
413 : : * Is the constant "=" to any of the column's most common values?
414 : : * (Although the given operator may not really be "=", we will assume
415 : : * that seeing whether it returns TRUE is an appropriate test. If you
416 : : * don't like this, maybe you shouldn't be using eqsel for your
417 : : * operator...)
418 : : */
3140 419 [ + + ]: 100130 : if (get_attstatsslot(&sslot, vardata->statsTuple,
420 : : STATISTIC_KIND_MCV, InvalidOid,
421 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
422 : : {
2066 423 : 91062 : LOCAL_FCINFO(fcinfo, 2);
424 : : FmgrInfo eqproc;
425 : :
3148 peter_e@gmx.net 426 : 91062 : fmgr_info(opfuncoid, &eqproc);
427 : :
428 : : /*
429 : : * Save a few cycles by setting up the fcinfo struct just once.
430 : : * Using FunctionCallInvoke directly also avoids failure if the
431 : : * eqproc returns NULL, though really equality functions should
432 : : * never do that.
433 : : */
2021 tgl@sss.pgh.pa.us 434 : 91062 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
435 : : NULL, NULL);
2066 436 : 91062 : fcinfo->args[0].isnull = false;
437 : 91062 : fcinfo->args[1].isnull = false;
438 : : /* be careful to apply operator right way 'round */
439 [ + + ]: 91062 : if (varonleft)
440 : 91046 : fcinfo->args[1].value = constval;
441 : : else
442 : 16 : fcinfo->args[0].value = constval;
443 : :
3140 444 [ + + ]: 1587536 : for (i = 0; i < sslot.nvalues; i++)
445 : : {
446 : : Datum fresult;
447 : :
6493 448 [ + + ]: 1545316 : if (varonleft)
2066 449 : 1545284 : fcinfo->args[0].value = sslot.values[i];
450 : : else
451 : 32 : fcinfo->args[1].value = sslot.values[i];
452 : 1545316 : fcinfo->isnull = false;
453 : 1545316 : fresult = FunctionCallInvoke(fcinfo);
454 [ + - + + ]: 1545316 : if (!fcinfo->isnull && DatumGetBool(fresult))
455 : : {
456 : 48842 : match = true;
6493 457 : 48842 : break;
458 : : }
459 : : }
460 : : }
461 : : else
462 : : {
463 : : /* no most-common-value info available */
3140 464 : 9068 : i = 0; /* keep compiler quiet */
465 : : }
466 : :
6493 467 [ + + ]: 100130 : if (match)
468 : : {
469 : : /*
470 : : * Constant is "=" to this common value. We know selectivity
471 : : * exactly (or as exactly as ANALYZE could calculate it, anyway).
472 : : */
3140 473 : 48842 : selec = sslot.numbers[i];
474 : : }
475 : : else
476 : : {
477 : : /*
478 : : * Comparison is against a constant that is neither NULL nor any
479 : : * of the common values. Its selectivity cannot be more than
480 : : * this:
481 : : */
6493 482 : 51288 : double sumcommon = 0.0;
483 : : double otherdistinct;
484 : :
3140 485 [ + + ]: 1352544 : for (i = 0; i < sslot.nnumbers; i++)
486 : 1301256 : sumcommon += sslot.numbers[i];
3119 487 : 51288 : selec = 1.0 - sumcommon - nullfrac;
6493 488 [ + + - + ]: 51288 : CLAMP_PROBABILITY(selec);
489 : :
490 : : /*
491 : : * and in fact it's probably a good deal less. We approximate that
492 : : * all the not-common values share this remaining fraction
493 : : * equally, so we divide by the number of other distinct values.
494 : : */
3140 495 : 51288 : otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
496 : 51288 : sslot.nnumbers;
6493 497 [ + + ]: 51288 : if (otherdistinct > 1)
498 : 26581 : selec /= otherdistinct;
499 : :
500 : : /*
501 : : * Another cross-check: selectivity shouldn't be estimated as more
502 : : * than the least common "most common value".
503 : : */
3140 504 [ + + - + ]: 51288 : if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
3140 tgl@sss.pgh.pa.us 505 :UBC 0 : selec = sslot.numbers[sslot.nnumbers - 1];
506 : : }
507 : :
3140 tgl@sss.pgh.pa.us 508 :CBC 100130 : free_attstatsslot(&sslot);
509 : : }
510 : : else
511 : : {
512 : : /*
513 : : * No ANALYZE stats available, so make a guess using estimated number
514 : : * of distinct values and assuming they are equally common. (The guess
515 : : * is unlikely to be very good, but we do know a few special cases.)
516 : : */
5218 517 : 33411 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
518 : : }
519 : :
520 : : /* now adjust if we wanted <> rather than = */
3119 521 [ + + ]: 177857 : if (negate)
522 : 18996 : selec = 1.0 - selec - nullfrac;
523 : :
524 : : /* result should be in range, but make sure... */
6493 525 [ - + - + ]: 177857 : CLAMP_PROBABILITY(selec);
526 : :
527 : 177857 : return selec;
528 : : }
529 : :
530 : : /*
531 : : * var_eq_non_const --- eqsel for var = something-other-than-const case
532 : : *
533 : : * This is exported so that some other estimation functions can use it.
534 : : */
535 : : double
1184 pg@bowt.ie 536 : 219002 : var_eq_non_const(VariableStatData *vardata, Oid oproid, Oid collation,
537 : : Node *other,
538 : : bool varonleft, bool negate)
539 : : {
540 : : double selec;
3119 tgl@sss.pgh.pa.us 541 : 219002 : double nullfrac = 0.0;
542 : : bool isdefault;
543 : :
544 : : /*
545 : : * Grab the nullfrac for use below.
546 : : */
547 [ + + ]: 219002 : if (HeapTupleIsValid(vardata->statsTuple))
548 : : {
549 : : Form_pg_statistic stats;
550 : :
551 : 150514 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
552 : 150514 : nullfrac = stats->stanullfrac;
553 : : }
554 : :
555 : : /*
556 : : * If we matched the var to a unique index, DISTINCT or GROUP-BY clause,
557 : : * assume there is exactly one match regardless of anything else. (This
558 : : * is slightly bogus, since the index or clause's equality operator might
559 : : * be different from ours, but it's much more likely to be right than
560 : : * ignoring the information.)
561 : : */
6149 562 [ + + + - : 219002 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
+ + ]
563 : : {
3119 564 : 82266 : selec = 1.0 / vardata->rel->tuples;
565 : : }
566 [ + + ]: 136736 : else if (HeapTupleIsValid(vardata->statsTuple))
567 : : {
568 : : double ndistinct;
569 : : AttStatsSlot sslot;
570 : :
571 : : /*
572 : : * Search is for a value that we do not know a priori, but we will
573 : : * assume it is not NULL. Estimate the selectivity as non-null
574 : : * fraction divided by number of distinct values, so that we get a
575 : : * result averaged over all possible values whether common or
576 : : * uncommon. (Essentially, we are assuming that the not-yet-known
577 : : * comparison value is equally likely to be any of the possible
578 : : * values, regardless of their frequency in the table. Is that a good
579 : : * idea?)
580 : : */
581 : 76852 : selec = 1.0 - nullfrac;
5218 582 : 76852 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
6493 583 [ + + ]: 76852 : if (ndistinct > 1)
584 : 74975 : selec /= ndistinct;
585 : :
586 : : /*
587 : : * Cross-check: selectivity should never be estimated as more than the
588 : : * most common value's.
589 : : */
3140 590 [ + + ]: 76852 : if (get_attstatsslot(&sslot, vardata->statsTuple,
591 : : STATISTIC_KIND_MCV, InvalidOid,
592 : : ATTSTATSSLOT_NUMBERS))
593 : : {
594 [ + - + + ]: 67268 : if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
595 : 279 : selec = sslot.numbers[0];
596 : 67268 : free_attstatsslot(&sslot);
597 : : }
598 : : }
599 : : else
600 : : {
601 : : /*
602 : : * No ANALYZE stats available, so make a guess using estimated number
603 : : * of distinct values and assuming they are equally common. (The guess
604 : : * is unlikely to be very good, but we do know a few special cases.)
605 : : */
5218 606 : 59884 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
607 : : }
608 : :
609 : : /* now adjust if we wanted <> rather than = */
3119 610 [ + + ]: 219002 : if (negate)
611 : 3313 : selec = 1.0 - selec - nullfrac;
612 : :
613 : : /* result should be in range, but make sure... */
8749 614 [ - + - + ]: 219002 : CLAMP_PROBABILITY(selec);
615 : :
6493 616 : 219002 : return selec;
617 : : }
618 : :
619 : : /*
620 : : * neqsel - Selectivity of "!=" for any data types.
621 : : *
622 : : * This routine is also used for some operators that are not "!="
623 : : * but have comparable selectivity behavior. See above comments
624 : : * for eqsel().
625 : : */
626 : : Datum
9326 627 : 23509 : neqsel(PG_FUNCTION_ARGS)
628 : : {
3119 629 : 23509 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
630 : : }
631 : :
632 : : /*
633 : : * scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
634 : : *
635 : : * This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
636 : : * The isgt and iseq flags distinguish which of the four cases apply.
637 : : *
638 : : * The caller has commuted the clause, if necessary, so that we can treat
639 : : * the variable as being on the left. The caller must also make sure that
640 : : * the other side of the clause is a non-null Const, and dissect that into
641 : : * a value and datatype. (This definition simplifies some callers that
642 : : * want to estimate against a computed value instead of a Const node.)
643 : : *
644 : : * This routine works for any datatype (or pair of datatypes) known to
645 : : * convert_to_scalar(). If it is applied to some other datatype,
646 : : * it will return an approximate estimate based on assuming that the constant
647 : : * value falls in the middle of the bin identified by binary search.
648 : : */
649 : : static double
3017 650 : 188936 : scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
651 : : Oid collation,
652 : : VariableStatData *vardata, Datum constval, Oid consttype)
653 : : {
654 : : Form_pg_statistic stats;
655 : : FmgrInfo opproc;
656 : : double mcv_selec,
657 : : hist_selec,
658 : : sumcommon;
659 : : double selec;
660 : :
7974 661 [ + + ]: 188936 : if (!HeapTupleIsValid(vardata->statsTuple))
662 : : {
663 : : /*
664 : : * No stats are available. Typically this means we have to fall back
665 : : * on the default estimate; but if the variable is CTID then we can
666 : : * make an estimate based on comparing the constant to the table size.
667 : : */
2459 668 [ + - + + ]: 14185 : if (vardata->var && IsA(vardata->var, Var) &&
669 [ + + ]: 11719 : ((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
670 : : {
671 : : ItemPointer itemptr;
672 : : double block;
673 : : double density;
674 : :
675 : : /*
676 : : * If the relation's empty, we're going to include all of it.
677 : : * (This is mostly to avoid divide-by-zero below.)
678 : : */
679 [ - + ]: 1008 : if (vardata->rel->pages == 0)
2459 tgl@sss.pgh.pa.us 680 :UBC 0 : return 1.0;
681 : :
2459 tgl@sss.pgh.pa.us 682 :CBC 1008 : itemptr = (ItemPointer) DatumGetPointer(constval);
683 : 1008 : block = ItemPointerGetBlockNumberNoCheck(itemptr);
684 : :
685 : : /*
686 : : * Determine the average number of tuples per page (density).
687 : : *
688 : : * Since the last page will, on average, be only half full, we can
689 : : * estimate it to have half as many tuples as earlier pages. So
690 : : * give it half the weight of a regular page.
691 : : */
692 : 1008 : density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
693 : :
694 : : /* If target is the last page, use half the density. */
695 [ + + ]: 1008 : if (block >= vardata->rel->pages - 1)
696 : 15 : density *= 0.5;
697 : :
698 : : /*
699 : : * Using the average tuples per page, calculate how far into the
700 : : * page the itemptr is likely to be and adjust block accordingly,
701 : : * by adding that fraction of a whole block (but never more than a
702 : : * whole block, no matter how high the itemptr's offset is). Here
703 : : * we are ignoring the possibility of dead-tuple line pointers,
704 : : * which is fairly bogus, but we lack the info to do better.
705 : : */
706 [ + - ]: 1008 : if (density > 0.0)
707 : : {
708 : 1008 : OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
709 : :
710 [ + + ]: 1008 : block += Min(offset / density, 1.0);
711 : : }
712 : :
713 : : /*
714 : : * Convert relative block number to selectivity. Again, the last
715 : : * page has only half weight.
716 : : */
717 : 1008 : selec = block / (vardata->rel->pages - 0.5);
718 : :
719 : : /*
720 : : * The calculation so far gave us a selectivity for the "<=" case.
721 : : * We'll have one fewer tuple for "<" and one additional tuple for
722 : : * ">=", the latter of which we'll reverse the selectivity for
723 : : * below, so we can simply subtract one tuple for both cases. The
724 : : * cases that need this adjustment can be identified by iseq being
725 : : * equal to isgt.
726 : : */
727 [ + + + - ]: 1008 : if (iseq == isgt && vardata->rel->tuples >= 1.0)
728 : 938 : selec -= (1.0 / vardata->rel->tuples);
729 : :
730 : : /* Finally, reverse the selectivity for the ">", ">=" cases. */
731 [ + + ]: 1008 : if (isgt)
732 : 931 : selec = 1.0 - selec;
733 : :
734 [ + + - + ]: 1008 : CLAMP_PROBABILITY(selec);
735 : 1008 : return selec;
736 : : }
737 : :
738 : : /* no stats available, so default result */
8977 739 : 13177 : return DEFAULT_INEQ_SEL;
740 : : }
7974 741 : 174751 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
742 : :
8977 743 : 174751 : fmgr_info(get_opcode(operator), &opproc);
744 : :
745 : : /*
746 : : * If we have most-common-values info, add up the fractions of the MCV
747 : : * entries that satisfy MCV OP CONST. These fractions contribute directly
748 : : * to the result selectivity. Also add up the total fraction represented
749 : : * by MCV entries.
750 : : */
2021 751 : 174751 : mcv_selec = mcv_selectivity(vardata, &opproc, collation, constval, true,
752 : : &sumcommon);
753 : :
754 : : /*
755 : : * If there is a histogram, determine which bin the constant falls in, and
756 : : * compute the resulting contribution to selectivity.
757 : : */
3017 758 : 174751 : hist_selec = ineq_histogram_selectivity(root, vardata,
759 : : operator, &opproc, isgt, iseq,
760 : : collation,
761 : : constval, consttype);
762 : :
763 : : /*
764 : : * Now merge the results from the MCV and histogram calculations,
765 : : * realizing that the histogram covers only the non-null values that are
766 : : * not listed in MCV.
767 : : */
7281 768 : 174751 : selec = 1.0 - stats->stanullfrac - sumcommon;
769 : :
5826 770 [ + + ]: 174751 : if (hist_selec >= 0.0)
7281 771 : 110117 : selec *= hist_selec;
772 : : else
773 : : {
774 : : /*
775 : : * If no histogram but there are values not accounted for by MCV,
776 : : * arbitrarily assume half of them will match.
777 : : */
778 : 64634 : selec *= 0.5;
779 : : }
780 : :
781 : 174751 : selec += mcv_selec;
782 : :
783 : : /* result should be in range, but make sure... */
784 [ + + + + ]: 174751 : CLAMP_PROBABILITY(selec);
785 : :
786 : 174751 : return selec;
787 : : }
788 : :
789 : : /*
790 : : * mcv_selectivity - Examine the MCV list for selectivity estimates
791 : : *
792 : : * Determine the fraction of the variable's MCV population that satisfies
793 : : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
794 : : * compute the fraction of the total column population represented by the MCV
795 : : * list. This code will work for any boolean-returning predicate operator.
796 : : *
797 : : * The function result is the MCV selectivity, and the fraction of the
798 : : * total population is returned into *sumcommonp. Zeroes are returned
799 : : * if there is no MCV list.
800 : : */
801 : : double
2021 802 : 177892 : mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc, Oid collation,
803 : : Datum constval, bool varonleft,
804 : : double *sumcommonp)
805 : : {
806 : : double mcv_selec,
807 : : sumcommon;
808 : : AttStatsSlot sslot;
809 : : int i;
810 : :
8990 811 : 177892 : mcv_selec = 0.0;
812 : 177892 : sumcommon = 0.0;
813 : :
7281 814 [ + + + + ]: 354554 : if (HeapTupleIsValid(vardata->statsTuple) &&
3148 peter_e@gmx.net 815 [ + + ]: 353159 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3140 tgl@sss.pgh.pa.us 816 : 176497 : get_attstatsslot(&sslot, vardata->statsTuple,
817 : : STATISTIC_KIND_MCV, InvalidOid,
818 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
819 : : {
2066 820 : 97745 : LOCAL_FCINFO(fcinfo, 2);
821 : :
822 : : /*
823 : : * We invoke the opproc "by hand" so that we won't fail on NULL
824 : : * results. Such cases won't arise for normal comparison functions,
825 : : * but generic_restriction_selectivity could perhaps be used with
826 : : * operators that can return NULL. A small side benefit is to not
827 : : * need to re-initialize the fcinfo struct from scratch each time.
828 : : */
2021 829 : 97745 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
830 : : NULL, NULL);
2066 831 : 97745 : fcinfo->args[0].isnull = false;
832 : 97745 : fcinfo->args[1].isnull = false;
833 : : /* be careful to apply operator right way 'round */
834 [ + - ]: 97745 : if (varonleft)
835 : 97745 : fcinfo->args[1].value = constval;
836 : : else
2066 tgl@sss.pgh.pa.us 837 :UBC 0 : fcinfo->args[0].value = constval;
838 : :
3140 tgl@sss.pgh.pa.us 839 [ + + ]:CBC 2367535 : for (i = 0; i < sslot.nvalues; i++)
840 : : {
841 : : Datum fresult;
842 : :
2066 843 [ + - ]: 2269790 : if (varonleft)
844 : 2269790 : fcinfo->args[0].value = sslot.values[i];
845 : : else
2066 tgl@sss.pgh.pa.us 846 :UBC 0 : fcinfo->args[1].value = sslot.values[i];
2066 tgl@sss.pgh.pa.us 847 :CBC 2269790 : fcinfo->isnull = false;
848 : 2269790 : fresult = FunctionCallInvoke(fcinfo);
849 [ + - + + ]: 2269790 : if (!fcinfo->isnull && DatumGetBool(fresult))
3140 850 : 885182 : mcv_selec += sslot.numbers[i];
851 : 2269790 : sumcommon += sslot.numbers[i];
852 : : }
853 : 97745 : free_attstatsslot(&sslot);
854 : : }
855 : :
7281 856 : 177892 : *sumcommonp = sumcommon;
857 : 177892 : return mcv_selec;
858 : : }
859 : :
860 : : /*
861 : : * histogram_selectivity - Examine the histogram for selectivity estimates
862 : : *
863 : : * Determine the fraction of the variable's histogram entries that satisfy
864 : : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
865 : : *
866 : : * This code will work for any boolean-returning predicate operator, whether
867 : : * or not it has anything to do with the histogram sort operator. We are
868 : : * essentially using the histogram just as a representative sample. However,
869 : : * small histograms are unlikely to be all that representative, so the caller
870 : : * should be prepared to fall back on some other estimation approach when the
871 : : * histogram is missing or very small. It may also be prudent to combine this
872 : : * approach with another one when the histogram is small.
873 : : *
874 : : * If the actual histogram size is not at least min_hist_size, we won't bother
875 : : * to do the calculation at all. Also, if the n_skip parameter is > 0, we
876 : : * ignore the first and last n_skip histogram elements, on the grounds that
877 : : * they are outliers and hence not very representative. Typical values for
878 : : * these parameters are 10 and 1.
879 : : *
880 : : * The function result is the selectivity, or -1 if there is no histogram
881 : : * or it's smaller than min_hist_size.
882 : : *
883 : : * The output parameter *hist_size receives the actual histogram size,
884 : : * or zero if no histogram. Callers may use this number to decide how
885 : : * much faith to put in the function result.
886 : : *
887 : : * Note that the result disregards both the most-common-values (if any) and
888 : : * null entries. The caller is expected to combine this result with
889 : : * statistics for those portions of the column population. It may also be
890 : : * prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
891 : : */
892 : : double
2021 893 : 3141 : histogram_selectivity(VariableStatData *vardata,
894 : : FmgrInfo *opproc, Oid collation,
895 : : Datum constval, bool varonleft,
896 : : int min_hist_size, int n_skip,
897 : : int *hist_size)
898 : : {
899 : : double result;
900 : : AttStatsSlot sslot;
901 : :
902 : : /* check sanity of parameters */
7028 903 [ - + ]: 3141 : Assert(n_skip >= 0);
904 [ - + ]: 3141 : Assert(min_hist_size > 2 * n_skip);
905 : :
906 [ + + + + ]: 5052 : if (HeapTupleIsValid(vardata->statsTuple) &&
3148 peter_e@gmx.net 907 [ + + ]: 3819 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3140 tgl@sss.pgh.pa.us 908 : 1908 : get_attstatsslot(&sslot, vardata->statsTuple,
909 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
910 : : ATTSTATSSLOT_VALUES))
911 : : {
912 : 1861 : *hist_size = sslot.nvalues;
913 [ + + ]: 1861 : if (sslot.nvalues >= min_hist_size)
914 : : {
2066 915 : 900 : LOCAL_FCINFO(fcinfo, 2);
7028 916 : 900 : int nmatch = 0;
917 : : int i;
918 : :
919 : : /*
920 : : * We invoke the opproc "by hand" so that we won't fail on NULL
921 : : * results. Such cases won't arise for normal comparison
922 : : * functions, but generic_restriction_selectivity could perhaps be
923 : : * used with operators that can return NULL. A small side benefit
924 : : * is to not need to re-initialize the fcinfo struct from scratch
925 : : * each time.
926 : : */
2021 927 : 900 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
928 : : NULL, NULL);
2066 929 : 900 : fcinfo->args[0].isnull = false;
930 : 900 : fcinfo->args[1].isnull = false;
931 : : /* be careful to apply operator right way 'round */
932 [ + - ]: 900 : if (varonleft)
933 : 900 : fcinfo->args[1].value = constval;
934 : : else
2066 tgl@sss.pgh.pa.us 935 :UBC 0 : fcinfo->args[0].value = constval;
936 : :
3140 tgl@sss.pgh.pa.us 937 [ + + ]:CBC 73610 : for (i = n_skip; i < sslot.nvalues - n_skip; i++)
938 : : {
939 : : Datum fresult;
940 : :
2066 941 [ + - ]: 72710 : if (varonleft)
942 : 72710 : fcinfo->args[0].value = sslot.values[i];
943 : : else
2066 tgl@sss.pgh.pa.us 944 :UBC 0 : fcinfo->args[1].value = sslot.values[i];
2066 tgl@sss.pgh.pa.us 945 :CBC 72710 : fcinfo->isnull = false;
946 : 72710 : fresult = FunctionCallInvoke(fcinfo);
947 [ + - + + ]: 72710 : if (!fcinfo->isnull && DatumGetBool(fresult))
7028 948 : 5030 : nmatch++;
949 : : }
3140 950 : 900 : result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
951 : : }
952 : : else
7028 953 : 961 : result = -1;
3140 954 : 1861 : free_attstatsslot(&sslot);
955 : : }
956 : : else
957 : : {
6492 958 : 1280 : *hist_size = 0;
7028 959 : 1280 : result = -1;
960 : : }
961 : :
962 : 3141 : return result;
963 : : }
964 : :
965 : : /*
966 : : * generic_restriction_selectivity - Selectivity for almost anything
967 : : *
968 : : * This function estimates selectivity for operators that we don't have any
969 : : * special knowledge about, but are on data types that we collect standard
970 : : * MCV and/or histogram statistics for. (Additional assumptions are that
971 : : * the operator is strict and immutable, or at least stable.)
972 : : *
973 : : * If we have "VAR OP CONST" or "CONST OP VAR", selectivity is estimated by
974 : : * applying the operator to each element of the column's MCV and/or histogram
975 : : * stats, and merging the results using the assumption that the histogram is
976 : : * a reasonable random sample of the column's non-MCV population. Note that
977 : : * if the operator's semantics are related to the histogram ordering, this
978 : : * might not be such a great assumption; other functions such as
979 : : * scalarineqsel() are probably a better match in such cases.
980 : : *
981 : : * Otherwise, fall back to the default selectivity provided by the caller.
982 : : */
983 : : double
2021 984 : 565 : generic_restriction_selectivity(PlannerInfo *root, Oid oproid, Oid collation,
985 : : List *args, int varRelid,
986 : : double default_selectivity)
987 : : {
988 : : double selec;
989 : : VariableStatData vardata;
990 : : Node *other;
991 : : bool varonleft;
992 : :
993 : : /*
994 : : * If expression is not variable OP something or something OP variable,
995 : : * then punt and return the default estimate.
996 : : */
2086 997 [ - + ]: 565 : if (!get_restriction_variable(root, args, varRelid,
998 : : &vardata, &other, &varonleft))
2086 tgl@sss.pgh.pa.us 999 :UBC 0 : return default_selectivity;
1000 : :
1001 : : /*
1002 : : * If the something is a NULL constant, assume operator is strict and
1003 : : * return zero, ie, operator will never return TRUE.
1004 : : */
2086 tgl@sss.pgh.pa.us 1005 [ + - ]:CBC 565 : if (IsA(other, Const) &&
1006 [ - + ]: 565 : ((Const *) other)->constisnull)
1007 : : {
2086 tgl@sss.pgh.pa.us 1008 [ # # ]:UBC 0 : ReleaseVariableStats(vardata);
1009 : 0 : return 0.0;
1010 : : }
1011 : :
2086 tgl@sss.pgh.pa.us 1012 [ + - ]:CBC 565 : if (IsA(other, Const))
1013 : : {
1014 : : /* Variable is being compared to a known non-null constant */
1015 : 565 : Datum constval = ((Const *) other)->constvalue;
1016 : : FmgrInfo opproc;
1017 : : double mcvsum;
1018 : : double mcvsel;
1019 : : double nullfrac;
1020 : : int hist_size;
1021 : :
2066 1022 : 565 : fmgr_info(get_opcode(oproid), &opproc);
1023 : :
1024 : : /*
1025 : : * Calculate the selectivity for the column's most common values.
1026 : : */
2021 1027 : 565 : mcvsel = mcv_selectivity(&vardata, &opproc, collation,
1028 : : constval, varonleft,
1029 : : &mcvsum);
1030 : :
1031 : : /*
1032 : : * If the histogram is large enough, see what fraction of it matches
1033 : : * the query, and assume that's representative of the non-MCV
1034 : : * population. Otherwise use the default selectivity for the non-MCV
1035 : : * population.
1036 : : */
1037 : 565 : selec = histogram_selectivity(&vardata, &opproc, collation,
1038 : : constval, varonleft,
1039 : : 10, 1, &hist_size);
2086 1040 [ + - ]: 565 : if (selec < 0)
1041 : : {
1042 : : /* Nope, fall back on default */
1043 : 565 : selec = default_selectivity;
1044 : : }
2086 tgl@sss.pgh.pa.us 1045 [ # # ]:UBC 0 : else if (hist_size < 100)
1046 : : {
1047 : : /*
1048 : : * For histogram sizes from 10 to 100, we combine the histogram
1049 : : * and default selectivities, putting increasingly more trust in
1050 : : * the histogram for larger sizes.
1051 : : */
1052 : 0 : double hist_weight = hist_size / 100.0;
1053 : :
1054 : 0 : selec = selec * hist_weight +
1055 : 0 : default_selectivity * (1.0 - hist_weight);
1056 : : }
1057 : :
1058 : : /* In any case, don't believe extremely small or large estimates. */
2086 tgl@sss.pgh.pa.us 1059 [ - + ]:CBC 565 : if (selec < 0.0001)
2086 tgl@sss.pgh.pa.us 1060 :UBC 0 : selec = 0.0001;
2086 tgl@sss.pgh.pa.us 1061 [ - + ]:CBC 565 : else if (selec > 0.9999)
2086 tgl@sss.pgh.pa.us 1062 :UBC 0 : selec = 0.9999;
1063 : :
1064 : : /* Don't forget to account for nulls. */
2086 tgl@sss.pgh.pa.us 1065 [ + + ]:CBC 565 : if (HeapTupleIsValid(vardata.statsTuple))
1066 : 42 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
1067 : : else
1068 : 523 : nullfrac = 0.0;
1069 : :
1070 : : /*
1071 : : * Now merge the results from the MCV and histogram calculations,
1072 : : * realizing that the histogram covers only the non-null values that
1073 : : * are not listed in MCV.
1074 : : */
1075 : 565 : selec *= 1.0 - nullfrac - mcvsum;
1076 : 565 : selec += mcvsel;
1077 : : }
1078 : : else
1079 : : {
1080 : : /* Comparison value is not constant, so we can't do anything */
2086 tgl@sss.pgh.pa.us 1081 :UBC 0 : selec = default_selectivity;
1082 : : }
1083 : :
2086 tgl@sss.pgh.pa.us 1084 [ + + ]:CBC 565 : ReleaseVariableStats(vardata);
1085 : :
1086 : : /* result should be in range, but make sure... */
1087 [ - + - + ]: 565 : CLAMP_PROBABILITY(selec);
1088 : :
1089 : 565 : return selec;
1090 : : }
1091 : :
1092 : : /*
1093 : : * ineq_histogram_selectivity - Examine the histogram for scalarineqsel
1094 : : *
1095 : : * Determine the fraction of the variable's histogram population that
1096 : : * satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
1097 : : * The isgt and iseq flags distinguish which of the four cases apply.
1098 : : *
1099 : : * While opproc could be looked up from the operator OID, common callers
1100 : : * also need to call it separately, so we make the caller pass both.
1101 : : *
1102 : : * Returns -1 if there is no histogram (valid results will always be >= 0).
1103 : : *
1104 : : * Note that the result disregards both the most-common-values (if any) and
1105 : : * null entries. The caller is expected to combine this result with
1106 : : * statistics for those portions of the column population.
1107 : : *
1108 : : * This is exported so that some other estimation functions can use it.
1109 : : */
1110 : : double
5826 1111 : 177455 : ineq_histogram_selectivity(PlannerInfo *root,
1112 : : VariableStatData *vardata,
1113 : : Oid opoid, FmgrInfo *opproc, bool isgt, bool iseq,
1114 : : Oid collation,
1115 : : Datum constval, Oid consttype)
1116 : : {
1117 : : double hist_selec;
1118 : : AttStatsSlot sslot;
1119 : :
1120 : 177455 : hist_selec = -1.0;
1121 : :
1122 : : /*
1123 : : * Someday, ANALYZE might store more than one histogram per rel/att,
1124 : : * corresponding to more than one possible sort ordering defined for the
1125 : : * column type. Right now, we know there is only one, so just grab it and
1126 : : * see if it matches the query.
1127 : : *
1128 : : * Note that we can't use opoid as search argument; the staop appearing in
1129 : : * pg_statistic will be for the relevant '<' operator, but what we have
1130 : : * might be some other inequality operator such as '>='. (Even if opoid
1131 : : * is a '<' operator, it could be cross-type.) Hence we must use
1132 : : * comparison_ops_are_compatible() to see if the operators match.
1133 : : */
7281 1134 [ + + + + ]: 354561 : if (HeapTupleIsValid(vardata->statsTuple) &&
3148 peter_e@gmx.net 1135 [ + + ]: 354050 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3140 tgl@sss.pgh.pa.us 1136 : 176944 : get_attstatsslot(&sslot, vardata->statsTuple,
1137 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
1138 : : ATTSTATSSLOT_VALUES))
1139 : : {
2021 1140 [ + - ]: 112471 : if (sslot.nvalues > 1 &&
1141 [ + + + + ]: 224904 : sslot.stacoll == collation &&
1142 : 112433 : comparison_ops_are_compatible(sslot.staop, opoid))
9634 1143 : 112379 : {
1144 : : /*
1145 : : * Use binary search to find the desired location, namely the
1146 : : * right end of the histogram bin containing the comparison value,
1147 : : * which is the leftmost entry for which the comparison operator
1148 : : * succeeds (if isgt) or fails (if !isgt).
1149 : : *
1150 : : * In this loop, we pay no attention to whether the operator iseq
1151 : : * or not; that detail will be mopped up below. (We cannot tell,
1152 : : * anyway, whether the operator thinks the values are equal.)
1153 : : *
1154 : : * If the binary search accesses the first or last histogram
1155 : : * entry, we try to replace that endpoint with the true column min
1156 : : * or max as found by get_actual_variable_range(). This
1157 : : * ameliorates misestimates when the min or max is moving as a
1158 : : * result of changes since the last ANALYZE. Note that this could
1159 : : * result in effectively including MCVs into the histogram that
1160 : : * weren't there before, but we don't try to correct for that.
1161 : : */
1162 : : double histfrac;
7014 bruce@momjian.us 1163 : 112379 : int lobound = 0; /* first possible slot to search */
3101 tgl@sss.pgh.pa.us 1164 : 112379 : int hibound = sslot.nvalues; /* last+1 slot to search */
5826 1165 : 112379 : bool have_end = false;
1166 : :
1167 : : /*
1168 : : * If there are only two histogram entries, we'll want up-to-date
1169 : : * values for both. (If there are more than two, we need at most
1170 : : * one of them to be updated, so we deal with that within the
1171 : : * loop.)
1172 : : */
3140 1173 [ + + ]: 112379 : if (sslot.nvalues == 2)
5826 1174 : 1619 : have_end = get_actual_variable_range(root,
1175 : : vardata,
1176 : : sslot.staop,
1177 : : collation,
1178 : : &sslot.values[0],
3140 1179 : 1619 : &sslot.values[1]);
1180 : :
7028 1181 [ + + ]: 748763 : while (lobound < hibound)
1182 : : {
7014 bruce@momjian.us 1183 : 636384 : int probe = (lobound + hibound) / 2;
1184 : : bool ltcmp;
1185 : :
1186 : : /*
1187 : : * If we find ourselves about to compare to the first or last
1188 : : * histogram entry, first try to replace it with the actual
1189 : : * current min or max (unless we already did so above).
1190 : : */
3140 tgl@sss.pgh.pa.us 1191 [ + + + + ]: 636384 : if (probe == 0 && sslot.nvalues > 2)
5826 1192 : 54866 : have_end = get_actual_variable_range(root,
1193 : : vardata,
1194 : : sslot.staop,
1195 : : collation,
1196 : : &sslot.values[0],
1197 : : NULL);
3140 1198 [ + + + + ]: 581518 : else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
5826 1199 : 38560 : have_end = get_actual_variable_range(root,
1200 : : vardata,
1201 : : sslot.staop,
1202 : : collation,
1203 : : NULL,
3101 1204 : 38560 : &sslot.values[probe]);
1205 : :
5363 1206 : 636384 : ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
1207 : : collation,
3140 1208 : 636384 : sslot.values[probe],
1209 : : constval));
7028 1210 [ + + ]: 636384 : if (isgt)
1211 : 34906 : ltcmp = !ltcmp;
1212 [ + + ]: 636384 : if (ltcmp)
1213 : 242375 : lobound = probe + 1;
1214 : : else
1215 : 394009 : hibound = probe;
1216 : : }
1217 : :
1218 [ + + ]: 112379 : if (lobound <= 0)
1219 : : {
1220 : : /*
1221 : : * Constant is below lower histogram boundary. More
1222 : : * precisely, we have found that no entry in the histogram
1223 : : * satisfies the inequality clause (if !isgt) or they all do
1224 : : * (if isgt). We estimate that that's true of the entire
1225 : : * table, so set histfrac to 0.0 (which we'll flip to 1.0
1226 : : * below, if isgt).
1227 : : */
8990 1228 : 47649 : histfrac = 0.0;
1229 : : }
3140 1230 [ + + ]: 64730 : else if (lobound >= sslot.nvalues)
1231 : : {
1232 : : /*
1233 : : * Inverse case: constant is above upper histogram boundary.
1234 : : */
7028 1235 : 17966 : histfrac = 1.0;
1236 : : }
1237 : : else
1238 : : {
1239 : : /* We have values[i-1] <= constant <= values[i]. */
1240 : 46764 : int i = lobound;
3017 1241 : 46764 : double eq_selec = 0;
1242 : : double val,
1243 : : high,
1244 : : low;
1245 : : double binfrac;
1246 : :
1247 : : /*
1248 : : * In the cases where we'll need it below, obtain an estimate
1249 : : * of the selectivity of "x = constval". We use a calculation
1250 : : * similar to what var_eq_const() does for a non-MCV constant,
1251 : : * ie, estimate that all distinct non-MCV values occur equally
1252 : : * often. But multiplication by "1.0 - sumcommon - nullfrac"
1253 : : * will be done by our caller, so we shouldn't do that here.
1254 : : * Therefore we can't try to clamp the estimate by reference
1255 : : * to the least common MCV; the result would be too small.
1256 : : *
1257 : : * Note: since this is effectively assuming that constval
1258 : : * isn't an MCV, it's logically dubious if constval in fact is
1259 : : * one. But we have to apply *some* correction for equality,
1260 : : * and anyway we cannot tell if constval is an MCV, since we
1261 : : * don't have a suitable equality operator at hand.
1262 : : */
1263 [ + + + + ]: 46764 : if (i == 1 || isgt == iseq)
1264 : : {
1265 : : double otherdistinct;
1266 : : bool isdefault;
1267 : : AttStatsSlot mcvslot;
1268 : :
1269 : : /* Get estimated number of distinct values */
1270 : 19918 : otherdistinct = get_variable_numdistinct(vardata,
1271 : : &isdefault);
1272 : :
1273 : : /* Subtract off the number of known MCVs */
1274 [ + + ]: 19918 : if (get_attstatsslot(&mcvslot, vardata->statsTuple,
1275 : : STATISTIC_KIND_MCV, InvalidOid,
1276 : : ATTSTATSSLOT_NUMBERS))
1277 : : {
1278 : 2183 : otherdistinct -= mcvslot.nnumbers;
1279 : 2183 : free_attstatsslot(&mcvslot);
1280 : : }
1281 : :
1282 : : /* If result doesn't seem sane, leave eq_selec at 0 */
1283 [ + + ]: 19918 : if (otherdistinct > 1)
1284 : 19895 : eq_selec = 1.0 / otherdistinct;
1285 : : }
1286 : :
1287 : : /*
1288 : : * Convert the constant and the two nearest bin boundary
1289 : : * values to a uniform comparison scale, and do a linear
1290 : : * interpolation within this bin.
1291 : : */
2021 1292 [ + - ]: 46764 : if (convert_to_scalar(constval, consttype, collation,
1293 : : &val,
3140 1294 : 46764 : sslot.values[i - 1], sslot.values[i],
1295 : : vardata->vartype,
1296 : : &low, &high))
1297 : : {
7028 1298 [ - + ]: 46764 : if (high <= low)
1299 : : {
1300 : : /* cope if bin boundaries appear identical */
7028 tgl@sss.pgh.pa.us 1301 :UBC 0 : binfrac = 0.5;
1302 : : }
7028 tgl@sss.pgh.pa.us 1303 [ + + ]:CBC 46764 : else if (val <= low)
1304 : 9356 : binfrac = 0.0;
1305 [ + + ]: 37408 : else if (val >= high)
1306 : 1598 : binfrac = 1.0;
1307 : : else
1308 : : {
1309 : 35810 : binfrac = (val - low) / (high - low);
1310 : :
1311 : : /*
1312 : : * Watch out for the possibility that we got a NaN or
1313 : : * Infinity from the division. This can happen
1314 : : * despite the previous checks, if for example "low"
1315 : : * is -Infinity.
1316 : : */
1317 [ + - + - ]: 35810 : if (isnan(binfrac) ||
1318 [ - + ]: 35810 : binfrac < 0.0 || binfrac > 1.0)
7028 tgl@sss.pgh.pa.us 1319 :UBC 0 : binfrac = 0.5;
1320 : : }
1321 : : }
1322 : : else
1323 : : {
1324 : : /*
1325 : : * Ideally we'd produce an error here, on the grounds that
1326 : : * the given operator shouldn't have scalarXXsel
1327 : : * registered as its selectivity func unless we can deal
1328 : : * with its operand types. But currently, all manner of
1329 : : * stuff is invoking scalarXXsel, so give a default
1330 : : * estimate until that can be fixed.
1331 : : */
1332 : 0 : binfrac = 0.5;
1333 : : }
1334 : :
1335 : : /*
1336 : : * Now, compute the overall selectivity across the values
1337 : : * represented by the histogram. We have i-1 full bins and
1338 : : * binfrac partial bin below the constant.
1339 : : */
7028 tgl@sss.pgh.pa.us 1340 :CBC 46764 : histfrac = (double) (i - 1) + binfrac;
3140 1341 : 46764 : histfrac /= (double) (sslot.nvalues - 1);
1342 : :
1343 : : /*
1344 : : * At this point, histfrac is an estimate of the fraction of
1345 : : * the population represented by the histogram that satisfies
1346 : : * "x <= constval". Somewhat remarkably, this statement is
1347 : : * true regardless of which operator we were doing the probes
1348 : : * with, so long as convert_to_scalar() delivers reasonable
1349 : : * results. If the probe constant is equal to some histogram
1350 : : * entry, we would have considered the bin to the left of that
1351 : : * entry if probing with "<" or ">=", or the bin to the right
1352 : : * if probing with "<=" or ">"; but binfrac would have come
1353 : : * out as 1.0 in the first case and 0.0 in the second, leading
1354 : : * to the same histfrac in either case. For probe constants
1355 : : * between histogram entries, we find the same bin and get the
1356 : : * same estimate with any operator.
1357 : : *
1358 : : * The fact that the estimate corresponds to "x <= constval"
1359 : : * and not "x < constval" is because of the way that ANALYZE
1360 : : * constructs the histogram: each entry is, effectively, the
1361 : : * rightmost value in its sample bucket. So selectivity
1362 : : * values that are exact multiples of 1/(histogram_size-1)
1363 : : * should be understood as estimates including a histogram
1364 : : * entry plus everything to its left.
1365 : : *
1366 : : * However, that breaks down for the first histogram entry,
1367 : : * which necessarily is the leftmost value in its sample
1368 : : * bucket. That means the first histogram bin is slightly
1369 : : * narrower than the rest, by an amount equal to eq_selec.
1370 : : * Another way to say that is that we want "x <= leftmost" to
1371 : : * be estimated as eq_selec not zero. So, if we're dealing
1372 : : * with the first bin (i==1), rescale to make that true while
1373 : : * adjusting the rest of that bin linearly.
1374 : : */
3017 1375 [ + + ]: 46764 : if (i == 1)
1376 : 8323 : histfrac += eq_selec * (1.0 - binfrac);
1377 : :
1378 : : /*
1379 : : * "x <= constval" is good if we want an estimate for "<=" or
1380 : : * ">", but if we are estimating for "<" or ">=", we now need
1381 : : * to decrease the estimate by eq_selec.
1382 : : */
1383 [ + + ]: 46764 : if (isgt == iseq)
1384 : 15212 : histfrac -= eq_selec;
1385 : : }
1386 : :
1387 : : /*
1388 : : * Now the estimate is finished for "<" and "<=" cases. If we are
1389 : : * estimating for ">" or ">=", flip it.
1390 : : */
8990 1391 [ + + ]: 112379 : hist_selec = isgt ? (1.0 - histfrac) : histfrac;
1392 : :
1393 : : /*
1394 : : * The histogram boundaries are only approximate to begin with,
1395 : : * and may well be out of date anyway. Therefore, don't believe
1396 : : * extremely small or large selectivity estimates --- unless we
1397 : : * got actual current endpoint values from the table, in which
1398 : : * case just do the usual sanity clamp. Somewhat arbitrarily, we
1399 : : * set the cutoff for other cases at a hundredth of the histogram
1400 : : * resolution.
1401 : : */
5826 1402 [ + + ]: 112379 : if (have_end)
1403 [ - + - + ]: 64353 : CLAMP_PROBABILITY(hist_selec);
1404 : : else
1405 : : {
3017 1406 : 48026 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1407 : :
1408 [ + + ]: 48026 : if (hist_selec < cutoff)
1409 : 16843 : hist_selec = cutoff;
1410 [ + + ]: 31183 : else if (hist_selec > 1.0 - cutoff)
1411 : 10989 : hist_selec = 1.0 - cutoff;
1412 : : }
1413 : : }
2021 1414 [ + - ]: 92 : else if (sslot.nvalues > 1)
1415 : : {
1416 : : /*
1417 : : * If we get here, we have a histogram but it's not sorted the way
1418 : : * we want. Do a brute-force search to see how many of the
1419 : : * entries satisfy the comparison condition, and take that
1420 : : * fraction as our estimate. (This is identical to the inner loop
1421 : : * of histogram_selectivity; maybe share code?)
1422 : : */
1423 : 92 : LOCAL_FCINFO(fcinfo, 2);
1424 : 92 : int nmatch = 0;
1425 : :
1426 : 92 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
1427 : : NULL, NULL);
1428 : 92 : fcinfo->args[0].isnull = false;
1429 : 92 : fcinfo->args[1].isnull = false;
1430 : 92 : fcinfo->args[1].value = constval;
1431 [ + + ]: 481274 : for (int i = 0; i < sslot.nvalues; i++)
1432 : : {
1433 : : Datum fresult;
1434 : :
1435 : 481182 : fcinfo->args[0].value = sslot.values[i];
1436 : 481182 : fcinfo->isnull = false;
1437 : 481182 : fresult = FunctionCallInvoke(fcinfo);
1438 [ + - + + ]: 481182 : if (!fcinfo->isnull && DatumGetBool(fresult))
1439 : 1124 : nmatch++;
1440 : : }
1441 : 92 : hist_selec = ((double) nmatch) / ((double) sslot.nvalues);
1442 : :
1443 : : /*
1444 : : * As above, clamp to a hundredth of the histogram resolution.
1445 : : * This case is surely even less trustworthy than the normal one,
1446 : : * so we shouldn't believe exact 0 or 1 selectivity. (Maybe the
1447 : : * clamp should be more restrictive in this case?)
1448 : : */
1449 : : {
1450 : 92 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1451 : :
1452 [ - + ]: 92 : if (hist_selec < cutoff)
2021 tgl@sss.pgh.pa.us 1453 :UBC 0 : hist_selec = cutoff;
2021 tgl@sss.pgh.pa.us 1454 [ - + ]:CBC 92 : else if (hist_selec > 1.0 - cutoff)
2021 tgl@sss.pgh.pa.us 1455 :UBC 0 : hist_selec = 1.0 - cutoff;
1456 : : }
1457 : : }
1458 : :
3140 tgl@sss.pgh.pa.us 1459 :CBC 112471 : free_attstatsslot(&sslot);
1460 : : }
1461 : :
7281 1462 : 177455 : return hist_selec;
1463 : : }
1464 : :
1465 : : /*
1466 : : * Common wrapper function for the selectivity estimators that simply
1467 : : * invoke scalarineqsel().
1468 : : */
1469 : : static Datum
3017 1470 : 25491 : scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
1471 : : {
7500 1472 : 25491 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
8977 1473 : 25491 : Oid operator = PG_GETARG_OID(1);
1474 : 25491 : List *args = (List *) PG_GETARG_POINTER(2);
1475 : 25491 : int varRelid = PG_GETARG_INT32(3);
2021 1476 : 25491 : Oid collation = PG_GET_COLLATION();
1477 : : VariableStatData vardata;
1478 : : Node *other;
1479 : : bool varonleft;
1480 : : Datum constval;
1481 : : Oid consttype;
1482 : : double selec;
1483 : :
1484 : : /*
1485 : : * If expression is not variable op something or something op variable,
1486 : : * then punt and return a default estimate.
1487 : : */
7974 1488 [ + + ]: 25491 : if (!get_restriction_variable(root, args, varRelid,
1489 : : &vardata, &other, &varonleft))
8977 1490 : 331 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1491 : :
1492 : : /*
1493 : : * Can't do anything useful if the something is not a constant, either.
1494 : : */
8692 1495 [ + + ]: 25160 : if (!IsA(other, Const))
1496 : : {
7974 1497 [ + + ]: 1415 : ReleaseVariableStats(vardata);
8692 1498 : 1415 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1499 : : }
1500 : :
1501 : : /*
1502 : : * If the constant is NULL, assume operator is strict and return zero, ie,
1503 : : * operator will never return TRUE.
1504 : : */
1505 [ + + ]: 23745 : if (((Const *) other)->constisnull)
1506 : : {
7974 1507 [ + + ]: 33 : ReleaseVariableStats(vardata);
8692 1508 : 33 : PG_RETURN_FLOAT8(0.0);
1509 : : }
1510 : 23712 : constval = ((Const *) other)->constvalue;
1511 : 23712 : consttype = ((Const *) other)->consttype;
1512 : :
1513 : : /*
1514 : : * Force the var to be on the left to simplify logic in scalarineqsel.
1515 : : */
3017 1516 [ + + ]: 23712 : if (!varonleft)
1517 : : {
8977 1518 : 192 : operator = get_commutator(operator);
1519 [ - + ]: 192 : if (!operator)
1520 : : {
1521 : : /* Use default selectivity (should we raise an error instead?) */
7974 tgl@sss.pgh.pa.us 1522 [ # # ]:UBC 0 : ReleaseVariableStats(vardata);
8977 1523 : 0 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1524 : : }
3017 tgl@sss.pgh.pa.us 1525 :CBC 192 : isgt = !isgt;
1526 : : }
1527 : :
1528 : : /* The rest of the work is done by scalarineqsel(). */
2021 1529 : 23712 : selec = scalarineqsel(root, operator, isgt, iseq, collation,
1530 : : &vardata, constval, consttype);
1531 : :
7974 1532 [ + + ]: 23712 : ReleaseVariableStats(vardata);
1533 : :
8990 1534 : 23712 : PG_RETURN_FLOAT8((float8) selec);
1535 : : }
1536 : :
1537 : : /*
1538 : : * scalarltsel - Selectivity of "<" for scalars.
1539 : : */
1540 : : Datum
3017 1541 : 7581 : scalarltsel(PG_FUNCTION_ARGS)
1542 : : {
1543 : 7581 : return scalarineqsel_wrapper(fcinfo, false, false);
1544 : : }
1545 : :
1546 : : /*
1547 : : * scalarlesel - Selectivity of "<=" for scalars.
1548 : : */
1549 : : Datum
1550 : 2302 : scalarlesel(PG_FUNCTION_ARGS)
1551 : : {
1552 : 2302 : return scalarineqsel_wrapper(fcinfo, false, true);
1553 : : }
1554 : :
1555 : : /*
1556 : : * scalargtsel - Selectivity of ">" for scalars.
1557 : : */
1558 : : Datum
1559 : 7810 : scalargtsel(PG_FUNCTION_ARGS)
1560 : : {
1561 : 7810 : return scalarineqsel_wrapper(fcinfo, true, false);
1562 : : }
1563 : :
1564 : : /*
1565 : : * scalargesel - Selectivity of ">=" for scalars.
1566 : : */
1567 : : Datum
1568 : 7798 : scalargesel(PG_FUNCTION_ARGS)
1569 : : {
1570 : 7798 : return scalarineqsel_wrapper(fcinfo, true, true);
1571 : : }
1572 : :
1573 : : /*
1574 : : * boolvarsel - Selectivity of Boolean variable.
1575 : : *
1576 : : * This can actually be called on any boolean-valued expression. If it
1577 : : * involves only Vars of the specified relation, and if there are statistics
1578 : : * about the Var or expression (the latter is possible if it's indexed) then
1579 : : * we'll produce a real estimate; otherwise it's just a default.
1580 : : */
1581 : : Selectivity
3737 1582 : 28595 : boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
1583 : : {
1584 : : VariableStatData vardata;
1585 : : double selec;
1586 : :
1587 : 28595 : examine_variable(root, arg, varRelid, &vardata);
1588 [ + + ]: 28595 : if (HeapTupleIsValid(vardata.statsTuple))
1589 : : {
1590 : : /*
1591 : : * A boolean variable V is equivalent to the clause V = 't', so we
1592 : : * compute the selectivity as if that is what we have.
1593 : : */
2021 1594 : 18055 : selec = var_eq_const(&vardata, BooleanEqualOperator, InvalidOid,
1595 : : BoolGetDatum(true), false, true, false);
1596 : : }
88 tgl@sss.pgh.pa.us 1597 [ + + ]:GNC 10540 : else if (is_funcclause(arg))
1598 : : {
1599 : : /*
1600 : : * If we have no stats and it's a function call, estimate 0.3333333.
1601 : : * This seems a pretty unprincipled choice, but Postgres has been
1602 : : * using that estimate for function calls since 1992. The hoariness
1603 : : * of this behavior suggests that we should not be in too much hurry
1604 : : * to use another value.
1605 : : */
1606 : 6241 : selec = 0.3333333;
1607 : : }
1608 : : else
1609 : : {
1610 : : /* Otherwise, the default estimate is 0.5 */
3737 tgl@sss.pgh.pa.us 1611 :CBC 4299 : selec = 0.5;
1612 : : }
1613 [ + + ]: 28595 : ReleaseVariableStats(vardata);
1614 : 28595 : return selec;
1615 : : }
1616 : :
1617 : : /*
1618 : : * booltestsel - Selectivity of BooleanTest Node.
1619 : : */
1620 : : Selectivity
7500 1621 : 451 : booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
1622 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1623 : : {
1624 : : VariableStatData vardata;
1625 : : double selec;
1626 : :
7974 1627 : 451 : examine_variable(root, arg, varRelid, &vardata);
1628 : :
1629 [ + + ]: 451 : if (HeapTupleIsValid(vardata.statsTuple))
1630 : : {
1631 : : Form_pg_statistic stats;
1632 : : double freq_null;
1633 : : AttStatsSlot sslot;
1634 : :
7974 tgl@sss.pgh.pa.us 1635 :GBC 6 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
8941 1636 : 6 : freq_null = stats->stanullfrac;
1637 : :
3140 1638 [ + - ]: 6 : if (get_attstatsslot(&sslot, vardata.statsTuple,
1639 : : STATISTIC_KIND_MCV, InvalidOid,
1640 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
1641 [ + - ]: 6 : && sslot.nnumbers > 0)
8941 1642 : 6 : {
1643 : : double freq_true;
1644 : : double freq_false;
1645 : :
1646 : : /*
1647 : : * Get first MCV frequency and derive frequency for true.
1648 : : */
3140 1649 [ - + ]: 6 : if (DatumGetBool(sslot.values[0]))
3140 tgl@sss.pgh.pa.us 1650 :UBC 0 : freq_true = sslot.numbers[0];
1651 : : else
3140 tgl@sss.pgh.pa.us 1652 :GBC 6 : freq_true = 1.0 - sslot.numbers[0] - freq_null;
1653 : :
1654 : : /*
1655 : : * Next derive frequency for false. Then use these as appropriate
1656 : : * to derive frequency for each case.
1657 : : */
8941 1658 : 6 : freq_false = 1.0 - freq_true - freq_null;
1659 : :
8460 1660 [ - - + - : 6 : switch (booltesttype)
- - - ]
1661 : : {
8819 bruce@momjian.us 1662 :UBC 0 : case IS_UNKNOWN:
1663 : : /* select only NULL values */
8941 tgl@sss.pgh.pa.us 1664 : 0 : selec = freq_null;
1665 : 0 : break;
8819 bruce@momjian.us 1666 : 0 : case IS_NOT_UNKNOWN:
1667 : : /* select non-NULL values */
8941 tgl@sss.pgh.pa.us 1668 : 0 : selec = 1.0 - freq_null;
1669 : 0 : break;
8819 bruce@momjian.us 1670 :GBC 6 : case IS_TRUE:
1671 : : /* select only TRUE values */
8941 tgl@sss.pgh.pa.us 1672 : 6 : selec = freq_true;
1673 : 6 : break;
8819 bruce@momjian.us 1674 :UBC 0 : case IS_NOT_TRUE:
1675 : : /* select non-TRUE values */
8941 tgl@sss.pgh.pa.us 1676 : 0 : selec = 1.0 - freq_true;
1677 : 0 : break;
8819 bruce@momjian.us 1678 : 0 : case IS_FALSE:
1679 : : /* select only FALSE values */
8941 tgl@sss.pgh.pa.us 1680 : 0 : selec = freq_false;
1681 : 0 : break;
8819 bruce@momjian.us 1682 : 0 : case IS_NOT_FALSE:
1683 : : /* select non-FALSE values */
8941 tgl@sss.pgh.pa.us 1684 : 0 : selec = 1.0 - freq_false;
1685 : 0 : break;
8819 bruce@momjian.us 1686 : 0 : default:
8179 tgl@sss.pgh.pa.us 1687 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1688 : : (int) booltesttype);
1689 : : selec = 0.0; /* Keep compiler quiet */
1690 : : break;
1691 : : }
1692 : :
3140 tgl@sss.pgh.pa.us 1693 :GBC 6 : free_attstatsslot(&sslot);
1694 : : }
1695 : : else
1696 : : {
1697 : : /*
1698 : : * No most-common-value info available. Still have null fraction
1699 : : * information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
1700 : : * for null fraction and assume a 50-50 split of TRUE and FALSE.
1701 : : */
8460 tgl@sss.pgh.pa.us 1702 [ # # # # :UBC 0 : switch (booltesttype)
# ]
1703 : : {
8819 bruce@momjian.us 1704 : 0 : case IS_UNKNOWN:
1705 : : /* select only NULL values */
8941 tgl@sss.pgh.pa.us 1706 : 0 : selec = freq_null;
1707 : 0 : break;
8819 bruce@momjian.us 1708 : 0 : case IS_NOT_UNKNOWN:
1709 : : /* select non-NULL values */
8941 tgl@sss.pgh.pa.us 1710 : 0 : selec = 1.0 - freq_null;
1711 : 0 : break;
8819 bruce@momjian.us 1712 : 0 : case IS_TRUE:
1713 : : case IS_FALSE:
1714 : : /* Assume we select half of the non-NULL values */
8941 tgl@sss.pgh.pa.us 1715 : 0 : selec = (1.0 - freq_null) / 2.0;
1716 : 0 : break;
4529 1717 : 0 : case IS_NOT_TRUE:
1718 : : case IS_NOT_FALSE:
1719 : : /* Assume we select NULLs plus half of the non-NULLs */
1720 : : /* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
1721 : 0 : selec = (freq_null + 1.0) / 2.0;
1722 : 0 : break;
8819 bruce@momjian.us 1723 : 0 : default:
8179 tgl@sss.pgh.pa.us 1724 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1725 : : (int) booltesttype);
1726 : : selec = 0.0; /* Keep compiler quiet */
1727 : : break;
1728 : : }
1729 : : }
1730 : : }
1731 : : else
1732 : : {
1733 : : /*
1734 : : * If we can't get variable statistics for the argument, perhaps
1735 : : * clause_selectivity can do something with it. We ignore the
1736 : : * possibility of a NULL value when using clause_selectivity, and just
1737 : : * assume the value is either TRUE or FALSE.
1738 : : */
8460 tgl@sss.pgh.pa.us 1739 [ + + + + :CBC 445 : switch (booltesttype)
- ]
1740 : : {
8941 1741 : 24 : case IS_UNKNOWN:
1742 : 24 : selec = DEFAULT_UNK_SEL;
1743 : 24 : break;
1744 : 54 : case IS_NOT_UNKNOWN:
1745 : 54 : selec = DEFAULT_NOT_UNK_SEL;
1746 : 54 : break;
1747 : 126 : case IS_TRUE:
1748 : : case IS_NOT_FALSE:
7974 1749 : 126 : selec = (double) clause_selectivity(root, arg,
1750 : : varRelid,
1751 : : jointype, sjinfo);
1752 : 126 : break;
1753 : 241 : case IS_FALSE:
1754 : : case IS_NOT_TRUE:
1755 : 241 : selec = 1.0 - (double) clause_selectivity(root, arg,
1756 : : varRelid,
1757 : : jointype, sjinfo);
8941 1758 : 241 : break;
8941 tgl@sss.pgh.pa.us 1759 :UBC 0 : default:
8179 1760 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1761 : : (int) booltesttype);
1762 : : selec = 0.0; /* Keep compiler quiet */
1763 : : break;
1764 : : }
1765 : : }
1766 : :
7974 tgl@sss.pgh.pa.us 1767 [ + + ]:CBC 451 : ReleaseVariableStats(vardata);
1768 : :
1769 : : /* result should be in range, but make sure... */
8749 1770 [ - + - + ]: 451 : CLAMP_PROBABILITY(selec);
1771 : :
8941 1772 : 451 : return (Selectivity) selec;
1773 : : }
1774 : :
1775 : : /*
1776 : : * nulltestsel - Selectivity of NullTest Node.
1777 : : */
1778 : : Selectivity
6334 1779 : 8906 : nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
1780 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1781 : : {
1782 : : VariableStatData vardata;
1783 : : double selec;
1784 : :
7974 1785 : 8906 : examine_variable(root, arg, varRelid, &vardata);
1786 : :
1787 [ + + ]: 8906 : if (HeapTupleIsValid(vardata.statsTuple))
1788 : : {
1789 : : Form_pg_statistic stats;
1790 : : double freq_null;
1791 : :
1792 : 4882 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
8941 1793 : 4882 : freq_null = stats->stanullfrac;
1794 : :
8460 1795 [ + + - ]: 4882 : switch (nulltesttype)
1796 : : {
8819 bruce@momjian.us 1797 : 3612 : case IS_NULL:
1798 : :
1799 : : /*
1800 : : * Use freq_null directly.
1801 : : */
8941 tgl@sss.pgh.pa.us 1802 : 3612 : selec = freq_null;
1803 : 3612 : break;
8819 bruce@momjian.us 1804 : 1270 : case IS_NOT_NULL:
1805 : :
1806 : : /*
1807 : : * Select not unknown (not null) values. Calculate from
1808 : : * freq_null.
1809 : : */
8941 tgl@sss.pgh.pa.us 1810 : 1270 : selec = 1.0 - freq_null;
1811 : 1270 : break;
8819 bruce@momjian.us 1812 :UBC 0 : default:
8179 tgl@sss.pgh.pa.us 1813 [ # # ]: 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1814 : : (int) nulltesttype);
1815 : : return (Selectivity) 0; /* keep compiler quiet */
1816 : : }
1817 : : }
2518 tgl@sss.pgh.pa.us 1818 [ + - + + ]:CBC 4024 : else if (vardata.var && IsA(vardata.var, Var) &&
1819 [ + + ]: 3642 : ((Var *) vardata.var)->varattno < 0)
1820 : : {
1821 : : /*
1822 : : * There are no stats for system columns, but we know they are never
1823 : : * NULL.
1824 : : */
1825 [ + - ]: 52 : selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
1826 : : }
1827 : : else
1828 : : {
1829 : : /*
1830 : : * No ANALYZE stats available, so make a guess
1831 : : */
7974 1832 [ + + - ]: 3972 : switch (nulltesttype)
1833 : : {
1834 : 1054 : case IS_NULL:
1835 : 1054 : selec = DEFAULT_UNK_SEL;
1836 : 1054 : break;
1837 : 2918 : case IS_NOT_NULL:
1838 : 2918 : selec = DEFAULT_NOT_UNK_SEL;
1839 : 2918 : break;
7974 tgl@sss.pgh.pa.us 1840 :UBC 0 : default:
1841 [ # # ]: 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1842 : : (int) nulltesttype);
1843 : : return (Selectivity) 0; /* keep compiler quiet */
1844 : : }
1845 : : }
1846 : :
7974 tgl@sss.pgh.pa.us 1847 [ + + ]:CBC 8906 : ReleaseVariableStats(vardata);
1848 : :
1849 : : /* result should be in range, but make sure... */
8749 1850 [ - + - + ]: 8906 : CLAMP_PROBABILITY(selec);
1851 : :
8941 1852 : 8906 : return (Selectivity) selec;
1853 : : }
1854 : :
1855 : : /*
1856 : : * strip_array_coercion - strip binary-compatible relabeling from an array expr
1857 : : *
1858 : : * For array values, the parser normally generates ArrayCoerceExpr conversions,
1859 : : * but it seems possible that RelabelType might show up. Also, the planner
1860 : : * is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
1861 : : * so we need to be ready to deal with more than one level.
1862 : : */
1863 : : static Node *
6898 1864 : 65274 : strip_array_coercion(Node *node)
1865 : : {
1866 : : for (;;)
1867 : : {
3000 1868 [ + - + + ]: 65330 : if (node && IsA(node, ArrayCoerceExpr))
6898 1869 : 56 : {
3000 1870 : 1516 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
1871 : :
1872 : : /*
1873 : : * If the per-element expression is just a RelabelType on top of
1874 : : * CaseTestExpr, then we know it's a binary-compatible relabeling.
1875 : : */
1876 [ + + ]: 1516 : if (IsA(acoerce->elemexpr, RelabelType) &&
1877 [ + - ]: 56 : IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
1878 : 56 : node = (Node *) acoerce->arg;
1879 : : else
1880 : : break;
1881 : : }
6840 1882 [ + - - + ]: 63814 : else if (node && IsA(node, RelabelType))
1883 : : {
1884 : : /* We don't really expect this case, but may as well cope */
6840 tgl@sss.pgh.pa.us 1885 :UBC 0 : node = (Node *) ((RelabelType *) node)->arg;
1886 : : }
1887 : : else
1888 : : break;
1889 : : }
6898 tgl@sss.pgh.pa.us 1890 :CBC 65274 : return node;
1891 : : }
1892 : :
1893 : : /*
1894 : : * scalararraysel - Selectivity of ScalarArrayOpExpr Node.
1895 : : */
1896 : : Selectivity
7327 1897 : 11405 : scalararraysel(PlannerInfo *root,
1898 : : ScalarArrayOpExpr *clause,
1899 : : bool is_join_clause,
1900 : : int varRelid,
1901 : : JoinType jointype,
1902 : : SpecialJoinInfo *sjinfo)
1903 : : {
1904 : 11405 : Oid operator = clause->opno;
1905 : 11405 : bool useOr = clause->useOr;
5037 1906 : 11405 : bool isEquality = false;
1907 : 11405 : bool isInequality = false;
1908 : : Node *leftop;
1909 : : Node *rightop;
1910 : : Oid nominal_element_type;
1911 : : Oid nominal_element_collation;
1912 : : TypeCacheEntry *typentry;
1913 : : RegProcedure oprsel;
1914 : : FmgrInfo oprselproc;
1915 : : Selectivity s1;
1916 : : Selectivity s1disjoint;
1917 : :
1918 : : /* First, deconstruct the expression */
6898 1919 [ - + ]: 11405 : Assert(list_length(clause->args) == 2);
1920 : 11405 : leftop = (Node *) linitial(clause->args);
1921 : 11405 : rightop = (Node *) lsecond(clause->args);
1922 : :
1923 : : /* aggressively reduce both sides to constants */
4317 1924 : 11405 : leftop = estimate_expression_value(root, leftop);
1925 : 11405 : rightop = estimate_expression_value(root, rightop);
1926 : :
1927 : : /* get nominal (after relabeling) element type of rightop */
5536 1928 : 11405 : nominal_element_type = get_base_element_type(exprType(rightop));
6898 1929 [ - + ]: 11405 : if (!OidIsValid(nominal_element_type))
3101 tgl@sss.pgh.pa.us 1930 :UBC 0 : return (Selectivity) 0.5; /* probably shouldn't happen */
1931 : : /* get nominal collation, too, for generating constants */
5381 tgl@sss.pgh.pa.us 1932 :CBC 11405 : nominal_element_collation = exprCollation(rightop);
1933 : :
1934 : : /* look through any binary-compatible relabeling of rightop */
6898 1935 : 11405 : rightop = strip_array_coercion(rightop);
1936 : :
1937 : : /*
1938 : : * Detect whether the operator is the default equality or inequality
1939 : : * operator of the array element type.
1940 : : */
5037 1941 : 11405 : typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
1942 [ + + ]: 11405 : if (OidIsValid(typentry->eq_opr))
1943 : : {
1944 [ + + ]: 11403 : if (operator == typentry->eq_opr)
1945 : 9722 : isEquality = true;
1946 [ + + ]: 1681 : else if (get_negator(operator) == typentry->eq_opr)
1947 : 1398 : isInequality = true;
1948 : : }
1949 : :
1950 : : /*
1951 : : * If it is equality or inequality, we might be able to estimate this as a
1952 : : * form of array containment; for instance "const = ANY(column)" can be
1953 : : * treated as "ARRAY[const] <@ column". scalararraysel_containment tries
1954 : : * that, and returns the selectivity estimate if successful, or -1 if not.
1955 : : */
1956 [ + + + + : 11405 : if ((isEquality || isInequality) && !is_join_clause)
+ - ]
1957 : : {
1958 : 11120 : s1 = scalararraysel_containment(root, leftop, rightop,
1959 : : nominal_element_type,
1960 : : isEquality, useOr, varRelid);
1961 [ + + ]: 11120 : if (s1 >= 0.0)
1962 : 59 : return s1;
1963 : : }
1964 : :
1965 : : /*
1966 : : * Look up the underlying operator's selectivity estimator. Punt if it
1967 : : * hasn't got one.
1968 : : */
1969 [ - + ]: 11346 : if (is_join_clause)
5037 tgl@sss.pgh.pa.us 1970 :UBC 0 : oprsel = get_oprjoin(operator);
1971 : : else
5037 tgl@sss.pgh.pa.us 1972 :CBC 11346 : oprsel = get_oprrest(operator);
1973 [ + + ]: 11346 : if (!oprsel)
1974 : 2 : return (Selectivity) 0.5;
1975 : 11344 : fmgr_info(oprsel, &oprselproc);
1976 : :
1977 : : /*
1978 : : * In the array-containment check above, we must only believe that an
1979 : : * operator is equality or inequality if it is the default btree equality
1980 : : * operator (or its negator) for the element type, since those are the
1981 : : * operators that array containment will use. But in what follows, we can
1982 : : * be a little laxer, and also believe that any operators using eqsel() or
1983 : : * neqsel() as selectivity estimator act like equality or inequality.
1984 : : */
5033 1985 [ + + - + ]: 11344 : if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
1986 : 9761 : isEquality = true;
1987 [ + + - + ]: 1583 : else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
1988 : 1343 : isInequality = true;
1989 : :
1990 : : /*
1991 : : * We consider three cases:
1992 : : *
1993 : : * 1. rightop is an Array constant: deconstruct the array, apply the
1994 : : * operator's selectivity function for each array element, and merge the
1995 : : * results in the same way that clausesel.c does for AND/OR combinations.
1996 : : *
1997 : : * 2. rightop is an ARRAY[] construct: apply the operator's selectivity
1998 : : * function for each element of the ARRAY[] construct, and merge.
1999 : : *
2000 : : * 3. otherwise, make a guess ...
2001 : : */
7327 2002 [ + - + + ]: 11344 : if (rightop && IsA(rightop, Const))
2003 : 9176 : {
2004 : 9191 : Datum arraydatum = ((Const *) rightop)->constvalue;
2005 : 9191 : bool arrayisnull = ((Const *) rightop)->constisnull;
2006 : : ArrayType *arrayval;
2007 : : int16 elmlen;
2008 : : bool elmbyval;
2009 : : char elmalign;
2010 : : int num_elems;
2011 : : Datum *elem_values;
2012 : : bool *elem_nulls;
2013 : : int i;
2014 : :
2015 [ + + ]: 9191 : if (arrayisnull) /* qual can't succeed if null array */
2016 : 15 : return (Selectivity) 0.0;
2017 : 9176 : arrayval = DatumGetArrayTypeP(arraydatum);
2018 : 9176 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
2019 : : &elmlen, &elmbyval, &elmalign);
2020 : 9176 : deconstruct_array(arrayval,
2021 : : ARR_ELEMTYPE(arrayval),
2022 : : elmlen, elmbyval, elmalign,
2023 : : &elem_values, &elem_nulls, &num_elems);
2024 : :
2025 : : /*
2026 : : * For generic operators, we assume the probability of success is
2027 : : * independent for each array element. But for "= ANY" or "<> ALL",
2028 : : * if the array elements are distinct (which'd typically be the case)
2029 : : * then the probabilities are disjoint, and we should just sum them.
2030 : : *
2031 : : * If we were being really tense we would try to confirm that the
2032 : : * elements are all distinct, but that would be expensive and it
2033 : : * doesn't seem to be worth the cycles; it would amount to penalizing
2034 : : * well-written queries in favor of poorly-written ones. However, we
2035 : : * do protect ourselves a little bit by checking whether the
2036 : : * disjointness assumption leads to an impossible (out of range)
2037 : : * probability; if so, we fall back to the normal calculation.
2038 : : */
5033 2039 [ + + ]: 9176 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
2040 : :
7327 2041 [ + + ]: 39160 : for (i = 0; i < num_elems; i++)
2042 : : {
2043 : : List *args;
2044 : : Selectivity s2;
2045 : :
2046 : 29984 : args = list_make2(leftop,
2047 : : makeConst(nominal_element_type,
2048 : : -1,
2049 : : nominal_element_collation,
2050 : : elmlen,
2051 : : elem_values[i],
2052 : : elem_nulls[i],
2053 : : elmbyval));
6332 2054 [ - + ]: 29984 : if (is_join_clause)
4910 tgl@sss.pgh.pa.us 2055 :UBC 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2056 : : clause->inputcollid,
2057 : : PointerGetDatum(root),
2058 : : ObjectIdGetDatum(operator),
2059 : : PointerGetDatum(args),
2060 : : Int16GetDatum(jointype),
2061 : : PointerGetDatum(sjinfo)));
2062 : : else
4910 tgl@sss.pgh.pa.us 2063 :CBC 29984 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2064 : : clause->inputcollid,
2065 : : PointerGetDatum(root),
2066 : : ObjectIdGetDatum(operator),
2067 : : PointerGetDatum(args),
2068 : : Int32GetDatum(varRelid)));
2069 : :
7327 2070 [ + + ]: 29984 : if (useOr)
2071 : : {
2072 : 25641 : s1 = s1 + s2 - s1 * s2;
5033 2073 [ + + ]: 25641 : if (isEquality)
2074 : 25119 : s1disjoint += s2;
2075 : : }
2076 : : else
2077 : : {
7327 2078 : 4343 : s1 = s1 * s2;
5033 2079 [ + + ]: 4343 : if (isInequality)
2080 : 4187 : s1disjoint += s2 - 1.0;
2081 : : }
2082 : : }
2083 : :
2084 : : /* accept disjoint-probability estimate if in range */
2085 [ + + + + : 9176 : if ((useOr ? isEquality : isInequality) &&
+ + ]
2086 [ + + ]: 8849 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2087 : 8834 : s1 = s1disjoint;
2088 : : }
7327 2089 [ + - + + ]: 2153 : else if (rightop && IsA(rightop, ArrayExpr) &&
2090 [ + - ]: 183 : !((ArrayExpr *) rightop)->multidims)
2091 : 183 : {
2092 : 183 : ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
2093 : : int16 elmlen;
2094 : : bool elmbyval;
2095 : : ListCell *l;
2096 : :
2097 : 183 : get_typlenbyval(arrayexpr->element_typeid,
2098 : : &elmlen, &elmbyval);
2099 : :
2100 : : /*
2101 : : * We use the assumption of disjoint probabilities here too, although
2102 : : * the odds of equal array elements are rather higher if the elements
2103 : : * are not all constants (which they won't be, else constant folding
2104 : : * would have reduced the ArrayExpr to a Const). In this path it's
2105 : : * critical to have the sanity check on the s1disjoint estimate.
2106 : : */
5033 2107 [ + - ]: 183 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
2108 : :
7327 2109 [ + - + + : 674 : foreach(l, arrayexpr->elements)
+ + ]
2110 : : {
6898 2111 : 491 : Node *elem = (Node *) lfirst(l);
2112 : : List *args;
2113 : : Selectivity s2;
2114 : :
2115 : : /*
2116 : : * Theoretically, if elem isn't of nominal_element_type we should
2117 : : * insert a RelabelType, but it seems unlikely that any operator
2118 : : * estimation function would really care ...
2119 : : */
2120 : 491 : args = list_make2(leftop, elem);
6332 2121 [ - + ]: 491 : if (is_join_clause)
4910 tgl@sss.pgh.pa.us 2122 :UBC 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2123 : : clause->inputcollid,
2124 : : PointerGetDatum(root),
2125 : : ObjectIdGetDatum(operator),
2126 : : PointerGetDatum(args),
2127 : : Int16GetDatum(jointype),
2128 : : PointerGetDatum(sjinfo)));
2129 : : else
4910 tgl@sss.pgh.pa.us 2130 :CBC 491 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2131 : : clause->inputcollid,
2132 : : PointerGetDatum(root),
2133 : : ObjectIdGetDatum(operator),
2134 : : PointerGetDatum(args),
2135 : : Int32GetDatum(varRelid)));
2136 : :
7327 2137 [ + - ]: 491 : if (useOr)
2138 : : {
2139 : 491 : s1 = s1 + s2 - s1 * s2;
5033 2140 [ + - ]: 491 : if (isEquality)
2141 : 491 : s1disjoint += s2;
2142 : : }
2143 : : else
2144 : : {
7327 tgl@sss.pgh.pa.us 2145 :UBC 0 : s1 = s1 * s2;
5033 2146 [ # # ]: 0 : if (isInequality)
2147 : 0 : s1disjoint += s2 - 1.0;
2148 : : }
2149 : : }
2150 : :
2151 : : /* accept disjoint-probability estimate if in range */
5033 tgl@sss.pgh.pa.us 2152 [ + - + - :CBC 183 : if ((useOr ? isEquality : isInequality) &&
+ - ]
2153 [ + - ]: 183 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2154 : 183 : s1 = s1disjoint;
2155 : : }
2156 : : else
2157 : : {
2158 : : CaseTestExpr *dummyexpr;
2159 : : List *args;
2160 : : Selectivity s2;
2161 : : int i;
2162 : :
2163 : : /*
2164 : : * We need a dummy rightop to pass to the operator selectivity
2165 : : * routine. It can be pretty much anything that doesn't look like a
2166 : : * constant; CaseTestExpr is a convenient choice.
2167 : : */
7327 2168 : 1970 : dummyexpr = makeNode(CaseTestExpr);
6898 2169 : 1970 : dummyexpr->typeId = nominal_element_type;
7327 2170 : 1970 : dummyexpr->typeMod = -1;
5387 2171 : 1970 : dummyexpr->collation = clause->inputcollid;
7327 2172 : 1970 : args = list_make2(leftop, dummyexpr);
6332 2173 [ - + ]: 1970 : if (is_join_clause)
4910 tgl@sss.pgh.pa.us 2174 :UBC 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2175 : : clause->inputcollid,
2176 : : PointerGetDatum(root),
2177 : : ObjectIdGetDatum(operator),
2178 : : PointerGetDatum(args),
2179 : : Int16GetDatum(jointype),
2180 : : PointerGetDatum(sjinfo)));
2181 : : else
4910 tgl@sss.pgh.pa.us 2182 :CBC 1970 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2183 : : clause->inputcollid,
2184 : : PointerGetDatum(root),
2185 : : ObjectIdGetDatum(operator),
2186 : : PointerGetDatum(args),
2187 : : Int32GetDatum(varRelid)));
7327 2188 [ + - ]: 1970 : s1 = useOr ? 0.0 : 1.0;
2189 : :
2190 : : /*
2191 : : * Arbitrarily assume 10 elements in the eventual array value (see
2192 : : * also estimate_array_length). We don't risk an assumption of
2193 : : * disjoint probabilities here.
2194 : : */
2195 [ + + ]: 21670 : for (i = 0; i < 10; i++)
2196 : : {
2197 [ + - ]: 19700 : if (useOr)
2198 : 19700 : s1 = s1 + s2 - s1 * s2;
2199 : : else
7327 tgl@sss.pgh.pa.us 2200 :UBC 0 : s1 = s1 * s2;
2201 : : }
2202 : : }
2203 : :
2204 : : /* result should be in range, but make sure... */
7327 tgl@sss.pgh.pa.us 2205 [ - + - + ]:CBC 11329 : CLAMP_PROBABILITY(s1);
2206 : :
2207 : 11329 : return s1;
2208 : : }
2209 : :
2210 : : /*
2211 : : * Estimate number of elements in the array yielded by an expression.
2212 : : *
2213 : : * Note: the result is integral, but we use "double" to avoid overflow
2214 : : * concerns. Most callers will use it in double-type expressions anyway.
2215 : : *
2216 : : * Note: in some code paths root can be passed as NULL, resulting in
2217 : : * slightly worse estimates.
2218 : : */
2219 : : double
713 2220 : 53869 : estimate_array_length(PlannerInfo *root, Node *arrayexpr)
2221 : : {
2222 : : /* look through any binary-compatible relabeling of arrayexpr */
6898 2223 : 53869 : arrayexpr = strip_array_coercion(arrayexpr);
2224 : :
7109 2225 [ + - + + ]: 53869 : if (arrayexpr && IsA(arrayexpr, Const))
2226 : : {
2227 : 24164 : Datum arraydatum = ((Const *) arrayexpr)->constvalue;
2228 : 24164 : bool arrayisnull = ((Const *) arrayexpr)->constisnull;
2229 : : ArrayType *arrayval;
2230 : :
2231 [ + + ]: 24164 : if (arrayisnull)
2232 : 45 : return 0;
2233 : 24119 : arrayval = DatumGetArrayTypeP(arraydatum);
2234 : 24119 : return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
2235 : : }
2236 [ + - + + ]: 29705 : else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
2237 [ + - ]: 321 : !((ArrayExpr *) arrayexpr)->multidims)
2238 : : {
2239 : 321 : return list_length(((ArrayExpr *) arrayexpr)->elements);
2240 : : }
434 2241 [ + - + + ]: 29384 : else if (arrayexpr && root)
2242 : : {
2243 : : /* See if we can find any statistics about it */
2244 : : VariableStatData vardata;
2245 : : AttStatsSlot sslot;
713 2246 : 29372 : double nelem = 0;
2247 : :
2248 : 29372 : examine_variable(root, arrayexpr, 0, &vardata);
2249 [ + + ]: 29372 : if (HeapTupleIsValid(vardata.statsTuple))
2250 : : {
2251 : : /*
2252 : : * Found stats, so use the average element count, which is stored
2253 : : * in the last stanumbers element of the DECHIST statistics.
2254 : : * Actually that is the average count of *distinct* elements;
2255 : : * perhaps we should scale it up somewhat?
2256 : : */
2257 [ + + ]: 6807 : if (get_attstatsslot(&sslot, vardata.statsTuple,
2258 : : STATISTIC_KIND_DECHIST, InvalidOid,
2259 : : ATTSTATSSLOT_NUMBERS))
2260 : : {
2261 [ + - ]: 6751 : if (sslot.nnumbers > 0)
2262 : 6751 : nelem = clamp_row_est(sslot.numbers[sslot.nnumbers - 1]);
2263 : 6751 : free_attstatsslot(&sslot);
2264 : : }
2265 : : }
2266 [ + + ]: 29372 : ReleaseVariableStats(vardata);
2267 : :
2268 [ + + ]: 29372 : if (nelem > 0)
2269 : 6751 : return nelem;
2270 : : }
2271 : :
2272 : : /* Else use a default guess --- this should match scalararraysel */
2273 : 22633 : return 10;
2274 : : }
2275 : :
2276 : : /*
2277 : : * rowcomparesel - Selectivity of RowCompareExpr Node.
2278 : : *
2279 : : * We estimate RowCompare selectivity by considering just the first (high
2280 : : * order) columns, which makes it equivalent to an ordinary OpExpr. While
2281 : : * this estimate could be refined by considering additional columns, it
2282 : : * seems unlikely that we could do a lot better without multi-column
2283 : : * statistics.
2284 : : */
2285 : : Selectivity
7277 2286 : 126 : rowcomparesel(PlannerInfo *root,
2287 : : RowCompareExpr *clause,
2288 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
2289 : : {
2290 : : Selectivity s1;
2291 : 126 : Oid opno = linitial_oid(clause->opnos);
4910 2292 : 126 : Oid inputcollid = linitial_oid(clause->inputcollids);
2293 : : List *opargs;
2294 : : bool is_join_clause;
2295 : :
2296 : : /* Build equivalent arg list for single operator */
7277 2297 : 126 : opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
2298 : :
2299 : : /*
2300 : : * Decide if it's a join clause. This should match clausesel.c's
2301 : : * treat_as_join_clause(), except that we intentionally consider only the
2302 : : * leading columns and not the rest of the clause.
2303 : : */
2304 [ + + ]: 126 : if (varRelid != 0)
2305 : : {
2306 : : /*
2307 : : * Caller is forcing restriction mode (eg, because we are examining an
2308 : : * inner indexscan qual).
2309 : : */
6332 2310 : 27 : is_join_clause = false;
2311 : : }
2312 [ + + ]: 99 : else if (sjinfo == NULL)
2313 : : {
2314 : : /*
2315 : : * It must be a restriction clause, since it's being evaluated at a
2316 : : * scan node.
2317 : : */
7277 2318 : 93 : is_join_clause = false;
2319 : : }
2320 : : else
2321 : : {
2322 : : /*
2323 : : * Otherwise, it's a join if there's more than one base relation used.
2324 : : */
1791 2325 : 6 : is_join_clause = (NumRelids(root, (Node *) opargs) > 1);
2326 : : }
2327 : :
7277 2328 [ + + ]: 126 : if (is_join_clause)
2329 : : {
2330 : : /* Estimate selectivity for a join clause. */
2331 : 6 : s1 = join_selectivity(root, opno,
2332 : : opargs,
2333 : : inputcollid,
2334 : : jointype,
2335 : : sjinfo);
2336 : : }
2337 : : else
2338 : : {
2339 : : /* Estimate selectivity for a restriction clause. */
2340 : 120 : s1 = restriction_selectivity(root, opno,
2341 : : opargs,
2342 : : inputcollid,
2343 : : varRelid);
2344 : : }
2345 : :
2346 : 126 : return s1;
2347 : : }
2348 : :
2349 : : /*
2350 : : * eqjoinsel - Join selectivity of "="
2351 : : */
2352 : : Datum
9326 2353 : 134378 : eqjoinsel(PG_FUNCTION_ARGS)
2354 : : {
7500 2355 : 134378 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
8977 2356 : 134378 : Oid operator = PG_GETARG_OID(1);
2357 : 134378 : List *args = (List *) PG_GETARG_POINTER(2);
2358 : :
2359 : : #ifdef NOT_USED
2360 : : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
2361 : : #endif
6332 2362 : 134378 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
2021 2363 : 134378 : Oid collation = PG_GET_COLLATION();
2364 : : double selec;
2365 : : double selec_inner;
2366 : : VariableStatData vardata1;
2367 : : VariableStatData vardata2;
2368 : : double nd1;
2369 : : double nd2;
2370 : : bool isdefault1;
2371 : : bool isdefault2;
2372 : : Oid opfuncoid;
2373 : : FmgrInfo eqproc;
28 tgl@sss.pgh.pa.us 2374 :GNC 134378 : Oid hashLeft = InvalidOid;
2375 : 134378 : Oid hashRight = InvalidOid;
2376 : : AttStatsSlot sslot1;
2377 : : AttStatsSlot sslot2;
2581 tgl@sss.pgh.pa.us 2378 :CBC 134378 : Form_pg_statistic stats1 = NULL;
2379 : 134378 : Form_pg_statistic stats2 = NULL;
2380 : 134378 : bool have_mcvs1 = false;
2381 : 134378 : bool have_mcvs2 = false;
28 tgl@sss.pgh.pa.us 2382 :GNC 134378 : bool *hasmatch1 = NULL;
2383 : 134378 : bool *hasmatch2 = NULL;
2384 : 134378 : int nmatches = 0;
2385 : : bool get_mcv_stats;
2386 : : bool join_is_reversed;
2387 : : RelOptInfo *inner_rel;
2388 : :
6332 tgl@sss.pgh.pa.us 2389 :CBC 134378 : get_join_variables(root, args, sjinfo,
2390 : : &vardata1, &vardata2, &join_is_reversed);
2391 : :
2581 2392 : 134378 : nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
2393 : 134378 : nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
2394 : :
2395 : 134378 : opfuncoid = get_opcode(operator);
2396 : :
2397 : 134378 : memset(&sslot1, 0, sizeof(sslot1));
2398 : 134378 : memset(&sslot2, 0, sizeof(sslot2));
2399 : :
2400 : : /*
2401 : : * There is no use in fetching one side's MCVs if we lack MCVs for the
2402 : : * other side, so do a quick check to verify that both stats exist.
2403 : : */
1125 2404 : 370121 : get_mcv_stats = (HeapTupleIsValid(vardata1.statsTuple) &&
2405 [ + + + + ]: 181495 : HeapTupleIsValid(vardata2.statsTuple) &&
2406 : 80130 : get_attstatsslot(&sslot1, vardata1.statsTuple,
2407 : : STATISTIC_KIND_MCV, InvalidOid,
2408 [ + + + + ]: 235743 : 0) &&
2409 : 36335 : get_attstatsslot(&sslot2, vardata2.statsTuple,
2410 : : STATISTIC_KIND_MCV, InvalidOid,
2411 : : 0));
2412 : :
2581 2413 [ + + ]: 134378 : if (HeapTupleIsValid(vardata1.statsTuple))
2414 : : {
2415 : : /* note we allow use of nullfrac regardless of security check */
2416 : 101365 : stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
1125 2417 [ + + + - ]: 115457 : if (get_mcv_stats &&
2418 : 14092 : statistic_proc_security_check(&vardata1, opfuncoid))
2581 2419 : 14092 : have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
2420 : : STATISTIC_KIND_MCV, InvalidOid,
2421 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2422 : : }
2423 : :
2424 [ + + ]: 134378 : if (HeapTupleIsValid(vardata2.statsTuple))
2425 : : {
2426 : : /* note we allow use of nullfrac regardless of security check */
2427 : 90697 : stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
1125 2428 [ + + + - ]: 104789 : if (get_mcv_stats &&
2429 : 14092 : statistic_proc_security_check(&vardata2, opfuncoid))
2581 2430 : 14092 : have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
2431 : : STATISTIC_KIND_MCV, InvalidOid,
2432 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2433 : : }
2434 : :
2435 : : /* Prepare info usable by both eqjoinsel_inner and eqjoinsel_semi */
28 tgl@sss.pgh.pa.us 2436 [ + + + - ]:GNC 134378 : if (have_mcvs1 && have_mcvs2)
2437 : : {
2438 : 14092 : fmgr_info(opfuncoid, &eqproc);
2439 : 14092 : hasmatch1 = (bool *) palloc0(sslot1.nvalues * sizeof(bool));
2440 : 14092 : hasmatch2 = (bool *) palloc0(sslot2.nvalues * sizeof(bool));
2441 : :
2442 : : /*
2443 : : * If the MCV lists are long enough to justify hashing, try to look up
2444 : : * hash functions for the join operator.
2445 : : */
2446 [ + + ]: 14092 : if ((sslot1.nvalues + sslot2.nvalues) >= EQJOINSEL_MCV_HASH_THRESHOLD)
2447 : 6362 : (void) get_op_hash_functions(operator, &hashLeft, &hashRight);
2448 : : }
2449 : : else
2450 : 120286 : memset(&eqproc, 0, sizeof(eqproc)); /* silence uninit-var warnings */
2451 : :
2452 : : /* We need to compute the inner-join selectivity in all cases */
2453 : 134378 : selec_inner = eqjoinsel_inner(&eqproc, collation,
2454 : : hashLeft, hashRight,
2455 : : &vardata1, &vardata2,
2456 : : nd1, nd2,
2457 : : isdefault1, isdefault2,
2458 : : &sslot1, &sslot2,
2459 : : stats1, stats2,
2460 : : have_mcvs1, have_mcvs2,
2461 : : hasmatch1, hasmatch2,
2462 : : &nmatches);
2463 : :
6332 tgl@sss.pgh.pa.us 2464 [ + + - ]:CBC 134378 : switch (sjinfo->jointype)
2465 : : {
2466 : 128927 : case JOIN_INNER:
2467 : : case JOIN_LEFT:
2468 : : case JOIN_FULL:
2581 2469 : 128927 : selec = selec_inner;
6332 2470 : 128927 : break;
2471 : 5451 : case JOIN_SEMI:
2472 : : case JOIN_ANTI:
2473 : :
2474 : : /*
2475 : : * Look up the join's inner relation. min_righthand is sufficient
2476 : : * information because neither SEMI nor ANTI joins permit any
2477 : : * reassociation into or out of their RHS, so the righthand will
2478 : : * always be exactly that set of rels.
2479 : : */
5221 2480 : 5451 : inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
2481 : :
6332 2482 [ + + ]: 5451 : if (!join_is_reversed)
28 tgl@sss.pgh.pa.us 2483 :GNC 3373 : selec = eqjoinsel_semi(&eqproc, collation,
2484 : : hashLeft, hashRight,
2485 : : false,
2486 : : &vardata1, &vardata2,
2487 : : nd1, nd2,
2488 : : isdefault1, isdefault2,
2489 : : &sslot1, &sslot2,
2490 : : stats1, stats2,
2491 : : have_mcvs1, have_mcvs2,
2492 : : hasmatch1, hasmatch2,
2493 : : &nmatches,
2494 : : inner_rel);
2495 : : else
2496 : 2078 : selec = eqjoinsel_semi(&eqproc, collation,
2497 : : hashLeft, hashRight,
2498 : : true,
2499 : : &vardata2, &vardata1,
2500 : : nd2, nd1,
2501 : : isdefault2, isdefault1,
2502 : : &sslot2, &sslot1,
2503 : : stats2, stats1,
2504 : : have_mcvs2, have_mcvs1,
2505 : : hasmatch2, hasmatch1,
2506 : : &nmatches,
2507 : : inner_rel);
2508 : :
2509 : : /*
2510 : : * We should never estimate the output of a semijoin to be more
2511 : : * rows than we estimate for an inner join with the same input
2512 : : * rels and join condition; it's obviously impossible for that to
2513 : : * happen. The former estimate is N1 * Ssemi while the latter is
2514 : : * N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
2515 : : * this is worthwhile because of the shakier estimation rules we
2516 : : * use in eqjoinsel_semi, particularly in cases where it has to
2517 : : * punt entirely.
2518 : : */
2581 tgl@sss.pgh.pa.us 2519 [ + + ]:CBC 5451 : selec = Min(selec, inner_rel->rows * selec_inner);
6332 2520 : 5451 : break;
6332 tgl@sss.pgh.pa.us 2521 :UBC 0 : default:
2522 : : /* other values not expected here */
2523 [ # # ]: 0 : elog(ERROR, "unrecognized join type: %d",
2524 : : (int) sjinfo->jointype);
2525 : : selec = 0; /* keep compiler quiet */
2526 : : break;
2527 : : }
2528 : :
2581 tgl@sss.pgh.pa.us 2529 :CBC 134378 : free_attstatsslot(&sslot1);
2530 : 134378 : free_attstatsslot(&sslot2);
2531 : :
6332 2532 [ + + ]: 134378 : ReleaseVariableStats(vardata1);
2533 [ + + ]: 134378 : ReleaseVariableStats(vardata2);
2534 : :
28 tgl@sss.pgh.pa.us 2535 [ + + ]:GNC 134378 : if (hasmatch1)
2536 : 14092 : pfree(hasmatch1);
2537 [ + + ]: 134378 : if (hasmatch2)
2538 : 14092 : pfree(hasmatch2);
2539 : :
6332 tgl@sss.pgh.pa.us 2540 [ - + - + ]:CBC 134378 : CLAMP_PROBABILITY(selec);
2541 : :
2542 : 134378 : PG_RETURN_FLOAT8((float8) selec);
2543 : : }
2544 : :
2545 : : /*
2546 : : * eqjoinsel_inner --- eqjoinsel for normal inner join
2547 : : *
2548 : : * In addition to computing the selectivity estimate, this will fill
2549 : : * hasmatch1[], hasmatch2[], and *p_nmatches (if have_mcvs1 && have_mcvs2).
2550 : : * We may be able to re-use that data in eqjoinsel_semi.
2551 : : *
2552 : : * We also use this for LEFT/FULL outer joins; it's not presently clear
2553 : : * that it's worth trying to distinguish them here.
2554 : : */
2555 : : static double
28 tgl@sss.pgh.pa.us 2556 :GNC 134378 : eqjoinsel_inner(FmgrInfo *eqproc, Oid collation,
2557 : : Oid hashLeft, Oid hashRight,
2558 : : VariableStatData *vardata1, VariableStatData *vardata2,
2559 : : double nd1, double nd2,
2560 : : bool isdefault1, bool isdefault2,
2561 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2562 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
2563 : : bool have_mcvs1, bool have_mcvs2,
2564 : : bool *hasmatch1, bool *hasmatch2,
2565 : : int *p_nmatches)
2566 : : {
2567 : : double selec;
2568 : :
7974 tgl@sss.pgh.pa.us 2569 [ + + + - ]:CBC 134378 : if (have_mcvs1 && have_mcvs2)
10328 bruce@momjian.us 2570 : 14092 : {
2571 : : /*
2572 : : * We have most-common-value lists for both relations. Run through
2573 : : * the lists to see which MCVs actually join to each other with the
2574 : : * given operator. This allows us to determine the exact join
2575 : : * selectivity for the portion of the relations represented by the MCV
2576 : : * lists. We still have to estimate for the remaining population, but
2577 : : * in a skewed distribution this gives us a big leg up in accuracy.
2578 : : * For motivation see the analysis in Y. Ioannidis and S.
2579 : : * Christodoulakis, "On the propagation of errors in the size of join
2580 : : * results", Technical Report 1018, Computer Science Dept., University
2581 : : * of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
2582 : : */
7974 tgl@sss.pgh.pa.us 2583 : 14092 : double nullfrac1 = stats1->stanullfrac;
2584 : 14092 : double nullfrac2 = stats2->stanullfrac;
2585 : : double matchprodfreq,
2586 : : matchfreq1,
2587 : : matchfreq2,
2588 : : unmatchfreq1,
2589 : : unmatchfreq2,
2590 : : otherfreq1,
2591 : : otherfreq2,
2592 : : totalsel1,
2593 : : totalsel2;
2594 : : int i,
2595 : : nmatches;
2596 : :
2597 : : /* Fill the match arrays */
28 tgl@sss.pgh.pa.us 2598 :GNC 14092 : eqjoinsel_find_matches(eqproc, collation,
2599 : : hashLeft, hashRight,
2600 : : false,
2601 : : sslot1, sslot2,
2602 : : sslot1->nvalues, sslot2->nvalues,
2603 : : hasmatch1, hasmatch2,
2604 : : p_nmatches, &matchprodfreq);
2605 : 14092 : nmatches = *p_nmatches;
7974 tgl@sss.pgh.pa.us 2606 [ - + - + ]:CBC 14092 : CLAMP_PROBABILITY(matchprodfreq);
2607 : :
2608 : : /* Sum up frequencies of matched and unmatched MCVs */
2609 : 14092 : matchfreq1 = unmatchfreq1 = 0.0;
2581 2610 [ + + ]: 356228 : for (i = 0; i < sslot1->nvalues; i++)
2611 : : {
7974 2612 [ + + ]: 342136 : if (hasmatch1[i])
2581 2613 : 147145 : matchfreq1 += sslot1->numbers[i];
2614 : : else
2615 : 194991 : unmatchfreq1 += sslot1->numbers[i];
2616 : : }
7974 2617 [ - + + + ]: 14092 : CLAMP_PROBABILITY(matchfreq1);
2618 [ - + - + ]: 14092 : CLAMP_PROBABILITY(unmatchfreq1);
2619 : 14092 : matchfreq2 = unmatchfreq2 = 0.0;
2581 2620 [ + + ]: 265909 : for (i = 0; i < sslot2->nvalues; i++)
2621 : : {
7974 2622 [ + + ]: 251817 : if (hasmatch2[i])
2581 2623 : 147145 : matchfreq2 += sslot2->numbers[i];
2624 : : else
2625 : 104672 : unmatchfreq2 += sslot2->numbers[i];
2626 : : }
7974 2627 [ - + + + ]: 14092 : CLAMP_PROBABILITY(matchfreq2);
2628 [ - + - + ]: 14092 : CLAMP_PROBABILITY(unmatchfreq2);
2629 : :
2630 : : /*
2631 : : * Compute total frequency of non-null values that are not in the MCV
2632 : : * lists.
2633 : : */
2634 : 14092 : otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
2635 : 14092 : otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
2636 [ + + - + ]: 14092 : CLAMP_PROBABILITY(otherfreq1);
2637 [ + + - + ]: 14092 : CLAMP_PROBABILITY(otherfreq2);
2638 : :
2639 : : /*
2640 : : * We can estimate the total selectivity from the point of view of
2641 : : * relation 1 as: the known selectivity for matched MCVs, plus
2642 : : * unmatched MCVs that are assumed to match against random members of
2643 : : * relation 2's non-MCV population, plus non-MCV values that are
2644 : : * assumed to match against random members of relation 2's unmatched
2645 : : * MCVs plus non-MCV values.
2646 : : */
2647 : 14092 : totalsel1 = matchprodfreq;
2581 2648 [ + + ]: 14092 : if (nd2 > sslot2->nvalues)
2649 : 2918 : totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
7974 2650 [ + + ]: 14092 : if (nd2 > nmatches)
2651 : 5408 : totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
2652 : 5408 : (nd2 - nmatches);
2653 : : /* Same estimate from the point of view of relation 2. */
2654 : 14092 : totalsel2 = matchprodfreq;
2581 2655 [ + + ]: 14092 : if (nd1 > sslot1->nvalues)
2656 : 3558 : totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
7974 2657 [ + + ]: 14092 : if (nd1 > nmatches)
2658 : 4764 : totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
2659 : 4764 : (nd1 - nmatches);
2660 : :
2661 : : /*
2662 : : * Use the smaller of the two estimates. This can be justified in
2663 : : * essentially the same terms as given below for the no-stats case: to
2664 : : * a first approximation, we are estimating from the point of view of
2665 : : * the relation with smaller nd.
2666 : : */
2667 [ + + ]: 14092 : selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
2668 : : }
2669 : : else
2670 : : {
2671 : : /*
2672 : : * We do not have MCV lists for both sides. Estimate the join
2673 : : * selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
2674 : : * is plausible if we assume that the join operator is strict and the
2675 : : * non-null values are about equally distributed: a given non-null
2676 : : * tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
2677 : : * of rel2, so total join rows are at most
2678 : : * N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
2679 : : * not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
2680 : : * is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
2681 : : * with MIN() is an upper bound. Using the MIN() means we estimate
2682 : : * from the point of view of the relation with smaller nd (since the
2683 : : * larger nd is determining the MIN). It is reasonable to assume that
2684 : : * most tuples in this rel will have join partners, so the bound is
2685 : : * probably reasonably tight and should be taken as-is.
2686 : : *
2687 : : * XXX Can we be smarter if we have an MCV list for just one side? It
2688 : : * seems that if we assume equal distribution for the other side, we
2689 : : * end up with the same answer anyway.
2690 : : */
2691 [ + + ]: 120286 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2692 [ + + ]: 120286 : double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
2693 : :
2694 : 120286 : selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
2695 [ + + ]: 120286 : if (nd1 > nd2)
2696 : 64272 : selec /= nd1;
2697 : : else
2698 : 56014 : selec /= nd2;
2699 : : }
2700 : :
6332 2701 : 134378 : return selec;
2702 : : }
2703 : :
2704 : : /*
2705 : : * eqjoinsel_semi --- eqjoinsel for semi join
2706 : : *
2707 : : * (Also used for anti join, which we are supposed to estimate the same way.)
2708 : : * Caller has ensured that vardata1 is the LHS variable; however, eqproc
2709 : : * is for the original join operator, which might now need to have the inputs
2710 : : * swapped in order to apply correctly. Also, if have_mcvs1 && have_mcvs2
2711 : : * then hasmatch1[], hasmatch2[], and *p_nmatches were filled by
2712 : : * eqjoinsel_inner.
2713 : : */
2714 : : static double
28 tgl@sss.pgh.pa.us 2715 :GNC 5451 : eqjoinsel_semi(FmgrInfo *eqproc, Oid collation,
2716 : : Oid hashLeft, Oid hashRight,
2717 : : bool op_is_reversed,
2718 : : VariableStatData *vardata1, VariableStatData *vardata2,
2719 : : double nd1, double nd2,
2720 : : bool isdefault1, bool isdefault2,
2721 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2722 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
2723 : : bool have_mcvs1, bool have_mcvs2,
2724 : : bool *hasmatch1, bool *hasmatch2,
2725 : : int *p_nmatches,
2726 : : RelOptInfo *inner_rel)
2727 : : {
2728 : : double selec;
2729 : :
2730 : : /*
2731 : : * We clamp nd2 to be not more than what we estimate the inner relation's
2732 : : * size to be. This is intuitively somewhat reasonable since obviously
2733 : : * there can't be more than that many distinct values coming from the
2734 : : * inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
2735 : : * likewise) is that this is the only pathway by which restriction clauses
2736 : : * applied to the inner rel will affect the join result size estimate,
2737 : : * since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
2738 : : * only the outer rel's size. If we clamped nd1 we'd be double-counting
2739 : : * the selectivity of outer-rel restrictions.
2740 : : *
2741 : : * We can apply this clamping both with respect to the base relation from
2742 : : * which the join variable comes (if there is just one), and to the
2743 : : * immediate inner input relation of the current join.
2744 : : *
2745 : : * If we clamp, we can treat nd2 as being a non-default estimate; it's not
2746 : : * great, maybe, but it didn't come out of nowhere either. This is most
2747 : : * helpful when the inner relation is empty and consequently has no stats.
2748 : : */
5221 tgl@sss.pgh.pa.us 2749 [ + + ]:CBC 5451 : if (vardata2->rel)
2750 : : {
3305 2751 [ + + ]: 5448 : if (nd2 >= vardata2->rel->rows)
2752 : : {
2753 : 4376 : nd2 = vardata2->rel->rows;
2754 : 4376 : isdefault2 = false;
2755 : : }
2756 : : }
2757 [ + + ]: 5451 : if (nd2 >= inner_rel->rows)
2758 : : {
2759 : 4349 : nd2 = inner_rel->rows;
2760 : 4349 : isdefault2 = false;
2761 : : }
2762 : :
28 tgl@sss.pgh.pa.us 2763 [ + + + - ]:GNC 5451 : if (have_mcvs1 && have_mcvs2)
6332 tgl@sss.pgh.pa.us 2764 :CBC 315 : {
2765 : : /*
2766 : : * We have most-common-value lists for both relations. Run through
2767 : : * the lists to see which MCVs actually join to each other with the
2768 : : * given operator. This allows us to determine the exact join
2769 : : * selectivity for the portion of the relations represented by the MCV
2770 : : * lists. We still have to estimate for the remaining population, but
2771 : : * in a skewed distribution this gives us a big leg up in accuracy.
2772 : : */
2773 : 315 : double nullfrac1 = stats1->stanullfrac;
2774 : : double matchprodfreq,
2775 : : matchfreq1,
2776 : : uncertainfrac,
2777 : : uncertain;
2778 : : int i,
2779 : : nmatches,
2780 : : clamped_nvalues2;
2781 : :
2782 : : /*
2783 : : * The clamping above could have resulted in nd2 being less than
2784 : : * sslot2->nvalues; in which case, we assume that precisely the nd2
2785 : : * most common values in the relation will appear in the join input,
2786 : : * and so compare to only the first nd2 members of the MCV list. Of
2787 : : * course this is frequently wrong, but it's the best bet we can make.
2788 : : */
2581 2789 [ + + ]: 315 : clamped_nvalues2 = Min(sslot2->nvalues, nd2);
2790 : :
2791 : : /*
2792 : : * If we did not set clamped_nvalues2 to less than sslot2->nvalues,
2793 : : * then the hasmatch1[] and hasmatch2[] match flags computed by
2794 : : * eqjoinsel_inner are still perfectly applicable, so we need not
2795 : : * re-do the matching work. Note that it does not matter if
2796 : : * op_is_reversed: we'd get the same answers.
2797 : : *
2798 : : * If we did clamp, then a different set of sslot2 values is to be
2799 : : * compared, so we have to re-do the matching.
2800 : : */
28 tgl@sss.pgh.pa.us 2801 [ - + ]:GNC 315 : if (clamped_nvalues2 != sslot2->nvalues)
2802 : : {
2803 : : /* Must re-zero the arrays */
28 tgl@sss.pgh.pa.us 2804 :UNC 0 : memset(hasmatch1, 0, sslot1->nvalues * sizeof(bool));
2805 : 0 : memset(hasmatch2, 0, clamped_nvalues2 * sizeof(bool));
2806 : : /* Re-fill the match arrays */
2807 : 0 : eqjoinsel_find_matches(eqproc, collation,
2808 : : hashLeft, hashRight,
2809 : : op_is_reversed,
2810 : : sslot1, sslot2,
2811 : : sslot1->nvalues, clamped_nvalues2,
2812 : : hasmatch1, hasmatch2,
2813 : : p_nmatches, &matchprodfreq);
2814 : : }
28 tgl@sss.pgh.pa.us 2815 :GNC 315 : nmatches = *p_nmatches;
2816 : :
2817 : : /* Sum up frequencies of matched MCVs */
6332 tgl@sss.pgh.pa.us 2818 :CBC 315 : matchfreq1 = 0.0;
2581 2819 [ + + ]: 6945 : for (i = 0; i < sslot1->nvalues; i++)
2820 : : {
6332 2821 [ + + ]: 6630 : if (hasmatch1[i])
2581 2822 : 5704 : matchfreq1 += sslot1->numbers[i];
2823 : : }
6332 2824 [ - + + + ]: 315 : CLAMP_PROBABILITY(matchfreq1);
2825 : :
2826 : : /*
2827 : : * Now we need to estimate the fraction of relation 1 that has at
2828 : : * least one join partner. We know for certain that the matched MCVs
2829 : : * do, so that gives us a lower bound, but we're really in the dark
2830 : : * about everything else. Our crude approach is: if nd1 <= nd2 then
2831 : : * assume all non-null rel1 rows have join partners, else assume for
2832 : : * the uncertain rows that a fraction nd2/nd1 have join partners. We
2833 : : * can discount the known-matched MCVs from the distinct-values counts
2834 : : * before doing the division.
2835 : : *
2836 : : * Crude as the above is, it's completely useless if we don't have
2837 : : * reliable ndistinct values for both sides. Hence, if either nd1 or
2838 : : * nd2 is default, punt and assume half of the uncertain rows have
2839 : : * join partners.
2840 : : */
5218 2841 [ + - + - ]: 315 : if (!isdefault1 && !isdefault2)
2842 : : {
5363 2843 : 315 : nd1 -= nmatches;
2844 : 315 : nd2 -= nmatches;
5221 2845 [ + + - + ]: 315 : if (nd1 <= nd2 || nd2 < 0)
5363 2846 : 300 : uncertainfrac = 1.0;
2847 : : else
2848 : 15 : uncertainfrac = nd2 / nd1;
2849 : : }
2850 : : else
5363 tgl@sss.pgh.pa.us 2851 :UBC 0 : uncertainfrac = 0.5;
5363 tgl@sss.pgh.pa.us 2852 :CBC 315 : uncertain = 1.0 - matchfreq1 - nullfrac1;
2853 [ - + - + ]: 315 : CLAMP_PROBABILITY(uncertain);
2854 : 315 : selec = matchfreq1 + uncertainfrac * uncertain;
2855 : : }
2856 : : else
2857 : : {
2858 : : /*
2859 : : * Without MCV lists for both sides, we can only use the heuristic
2860 : : * about nd1 vs nd2.
2861 : : */
6332 2862 [ + + ]: 5136 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2863 : :
5218 2864 [ + + + + ]: 5136 : if (!isdefault1 && !isdefault2)
2865 : : {
5221 2866 [ + + - + ]: 3941 : if (nd1 <= nd2 || nd2 < 0)
5363 2867 : 2569 : selec = 1.0 - nullfrac1;
2868 : : else
2869 : 1372 : selec = (nd2 / nd1) * (1.0 - nullfrac1);
2870 : : }
2871 : : else
2872 : 1195 : selec = 0.5 * (1.0 - nullfrac1);
2873 : : }
2874 : :
6332 2875 : 5451 : return selec;
2876 : : }
2877 : :
2878 : : /*
2879 : : * Identify matching MCVs for eqjoinsel_inner or eqjoinsel_semi.
2880 : : *
2881 : : * Inputs:
2882 : : * eqproc: FmgrInfo for equality function to use (might be reversed)
2883 : : * collation: OID of collation to use
2884 : : * hashLeft, hashRight: OIDs of hash functions associated with equality op,
2885 : : * or InvalidOid if we're not to use hashing
2886 : : * op_is_reversed: indicates that eqproc compares right type to left type
2887 : : * sslot1, sslot2: MCV values for the lefthand and righthand inputs
2888 : : * nvalues1, nvalues2: number of values to be considered (can be less than
2889 : : * sslotN->nvalues, but not more)
2890 : : * Outputs:
2891 : : * hasmatch1[], hasmatch2[]: pre-zeroed arrays of lengths nvalues1, nvalues2;
2892 : : * entries are set to true if that MCV has a match on the other side
2893 : : * *p_nmatches: receives number of MCV pairs that match
2894 : : * *p_matchprodfreq: receives sum(sslot1->numbers[i] * sslot2->numbers[j])
2895 : : * for matching MCVs
2896 : : *
2897 : : * Note that hashLeft is for the eqproc's left-hand input type, hashRight
2898 : : * for its right, regardless of op_is_reversed.
2899 : : *
2900 : : * Note we assume that each MCV will match at most one member of the other
2901 : : * MCV list. If the operator isn't really equality, there could be multiple
2902 : : * matches --- but we don't look for them, both for speed and because the
2903 : : * math wouldn't add up...
2904 : : */
2905 : : static void
28 tgl@sss.pgh.pa.us 2906 :GNC 14092 : eqjoinsel_find_matches(FmgrInfo *eqproc, Oid collation,
2907 : : Oid hashLeft, Oid hashRight,
2908 : : bool op_is_reversed,
2909 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2910 : : int nvalues1, int nvalues2,
2911 : : bool *hasmatch1, bool *hasmatch2,
2912 : : int *p_nmatches, double *p_matchprodfreq)
2913 : : {
2914 : 14092 : LOCAL_FCINFO(fcinfo, 2);
2915 : 14092 : double matchprodfreq = 0.0;
2916 : 14092 : int nmatches = 0;
2917 : :
2918 : : /*
2919 : : * Save a few cycles by setting up the fcinfo struct just once. Using
2920 : : * FunctionCallInvoke directly also avoids failure if the eqproc returns
2921 : : * NULL, though really equality functions should never do that.
2922 : : */
2923 : 14092 : InitFunctionCallInfoData(*fcinfo, eqproc, 2, collation,
2924 : : NULL, NULL);
2925 : 14092 : fcinfo->args[0].isnull = false;
2926 : 14092 : fcinfo->args[1].isnull = false;
2927 : :
2928 [ + + + - ]: 14092 : if (OidIsValid(hashLeft) && OidIsValid(hashRight))
2929 : 6362 : {
2930 : : /* Use a hash table to speed up the matching */
2931 : 6362 : LOCAL_FCINFO(hash_fcinfo, 1);
2932 : : FmgrInfo hash_proc;
2933 : : MCVHashContext hashContext;
2934 : : MCVHashTable_hash *hashTable;
2935 : : AttStatsSlot *statsProbe;
2936 : : AttStatsSlot *statsHash;
2937 : : bool *hasMatchProbe;
2938 : : bool *hasMatchHash;
2939 : : int nvaluesProbe;
2940 : : int nvaluesHash;
2941 : :
2942 : : /* Make sure we build the hash table on the smaller array. */
2943 [ + + ]: 6362 : if (sslot1->nvalues >= sslot2->nvalues)
2944 : : {
2945 : 5311 : statsProbe = sslot1;
2946 : 5311 : statsHash = sslot2;
2947 : 5311 : hasMatchProbe = hasmatch1;
2948 : 5311 : hasMatchHash = hasmatch2;
2949 : 5311 : nvaluesProbe = nvalues1;
2950 : 5311 : nvaluesHash = nvalues2;
2951 : : }
2952 : : else
2953 : : {
2954 : : /* We'll have to reverse the direction of use of the operator. */
2955 : 1051 : op_is_reversed = !op_is_reversed;
2956 : 1051 : statsProbe = sslot2;
2957 : 1051 : statsHash = sslot1;
2958 : 1051 : hasMatchProbe = hasmatch2;
2959 : 1051 : hasMatchHash = hasmatch1;
2960 : 1051 : nvaluesProbe = nvalues2;
2961 : 1051 : nvaluesHash = nvalues1;
2962 : : }
2963 : :
2964 : : /*
2965 : : * Build the hash table on the smaller array, using the appropriate
2966 : : * hash function for its data type.
2967 : : */
2968 [ + + ]: 6362 : fmgr_info(op_is_reversed ? hashLeft : hashRight, &hash_proc);
2969 : 6362 : InitFunctionCallInfoData(*hash_fcinfo, &hash_proc, 1, collation,
2970 : : NULL, NULL);
2971 : 6362 : hash_fcinfo->args[0].isnull = false;
2972 : :
2973 : 6362 : hashContext.equal_fcinfo = fcinfo;
2974 : 6362 : hashContext.hash_fcinfo = hash_fcinfo;
2975 : 6362 : hashContext.op_is_reversed = op_is_reversed;
2976 : 6362 : hashContext.insert_mode = true;
2977 : 6362 : get_typlenbyval(statsHash->valuetype,
2978 : : &hashContext.hash_typlen,
2979 : : &hashContext.hash_typbyval);
2980 : :
2981 : 6362 : hashTable = MCVHashTable_create(CurrentMemoryContext,
2982 : : nvaluesHash,
2983 : : &hashContext);
2984 : :
2985 [ + + ]: 201517 : for (int i = 0; i < nvaluesHash; i++)
2986 : : {
2987 : 195155 : bool found = false;
2988 : 195155 : MCVHashEntry *entry = MCVHashTable_insert(hashTable,
2989 : 195155 : statsHash->values[i],
2990 : : &found);
2991 : :
2992 : : /*
2993 : : * MCVHashTable_insert will only report "found" if the new value
2994 : : * is equal to some previous one per datum_image_eq(). That
2995 : : * probably shouldn't happen, since we're not expecting duplicates
2996 : : * in the MCV list. If we do find a dup, just ignore it, leaving
2997 : : * the hash entry's index pointing at the first occurrence. That
2998 : : * matches the behavior that the non-hashed code path would have.
2999 : : */
3000 [ + - ]: 195155 : if (likely(!found))
3001 : 195155 : entry->index = i;
3002 : : }
3003 : :
3004 : : /*
3005 : : * Prepare to probe the hash table. If the probe values are of a
3006 : : * different data type, then we need to change hash functions. (This
3007 : : * code relies on the assumption that since we defined SH_STORE_HASH,
3008 : : * simplehash.h will never need to compute hash values for existing
3009 : : * hash table entries.)
3010 : : */
3011 : 6362 : hashContext.insert_mode = false;
3012 [ + + ]: 6362 : if (hashLeft != hashRight)
3013 : : {
3014 [ + + ]: 914 : fmgr_info(op_is_reversed ? hashRight : hashLeft, &hash_proc);
3015 : : /* Resetting hash_fcinfo is probably unnecessary, but be safe */
3016 : 914 : InitFunctionCallInfoData(*hash_fcinfo, &hash_proc, 1, collation,
3017 : : NULL, NULL);
3018 : 914 : hash_fcinfo->args[0].isnull = false;
3019 : : }
3020 : :
3021 : : /* Look up each probe value in turn. */
3022 [ + + ]: 336689 : for (int i = 0; i < nvaluesProbe; i++)
3023 : : {
3024 : 330327 : MCVHashEntry *entry = MCVHashTable_lookup(hashTable,
3025 : 330327 : statsProbe->values[i]);
3026 : :
3027 : : /* As in the other code path, skip already-matched hash entries */
3028 [ + + + - ]: 330327 : if (entry != NULL && !hasMatchHash[entry->index])
3029 : : {
3030 : 116194 : hasMatchHash[entry->index] = hasMatchProbe[i] = true;
3031 : 116194 : nmatches++;
3032 : 116194 : matchprodfreq += statsHash->numbers[entry->index] * statsProbe->numbers[i];
3033 : : }
3034 : : }
3035 : :
3036 : 6362 : MCVHashTable_destroy(hashTable);
3037 : : }
3038 : : else
3039 : : {
3040 : : /* We're not to use hashing, so do it the O(N^2) way */
3041 : : int index1,
3042 : : index2;
3043 : :
3044 : : /* Set up to supply the values in the order the operator expects */
3045 [ - + ]: 7730 : if (op_is_reversed)
3046 : : {
28 tgl@sss.pgh.pa.us 3047 :UNC 0 : index1 = 1;
3048 : 0 : index2 = 0;
3049 : : }
3050 : : else
3051 : : {
28 tgl@sss.pgh.pa.us 3052 :GNC 7730 : index1 = 0;
3053 : 7730 : index2 = 1;
3054 : : }
3055 : :
3056 [ + + ]: 40574 : for (int i = 0; i < nvalues1; i++)
3057 : : {
3058 : 32844 : fcinfo->args[index1].value = sslot1->values[i];
3059 : :
3060 [ + + ]: 101017 : for (int j = 0; j < nvalues2; j++)
3061 : : {
3062 : : Datum fresult;
3063 : :
3064 [ + + ]: 99124 : if (hasmatch2[j])
3065 : 56889 : continue;
3066 : 42235 : fcinfo->args[index2].value = sslot2->values[j];
3067 : 42235 : fcinfo->isnull = false;
3068 : 42235 : fresult = FunctionCallInvoke(fcinfo);
3069 [ + - + + ]: 42235 : if (!fcinfo->isnull && DatumGetBool(fresult))
3070 : : {
3071 : 30951 : hasmatch1[i] = hasmatch2[j] = true;
3072 : 30951 : matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
3073 : 30951 : nmatches++;
3074 : 30951 : break;
3075 : : }
3076 : : }
3077 : : }
3078 : : }
3079 : :
3080 : 14092 : *p_nmatches = nmatches;
3081 : 14092 : *p_matchprodfreq = matchprodfreq;
3082 : 14092 : }
3083 : :
3084 : : /*
3085 : : * Support functions for the hash tables used by eqjoinsel_find_matches
3086 : : */
3087 : : static uint32
3088 : 525482 : hash_mcv(MCVHashTable_hash *tab, Datum key)
3089 : : {
3090 : 525482 : MCVHashContext *context = (MCVHashContext *) tab->private_data;
3091 : 525482 : FunctionCallInfo fcinfo = context->hash_fcinfo;
3092 : : Datum fresult;
3093 : :
3094 : 525482 : fcinfo->args[0].value = key;
3095 : 525482 : fcinfo->isnull = false;
3096 : 525482 : fresult = FunctionCallInvoke(fcinfo);
3097 [ - + ]: 525482 : Assert(!fcinfo->isnull);
3098 : 525482 : return DatumGetUInt32(fresult);
3099 : : }
3100 : :
3101 : : static bool
3102 : 116194 : mcvs_equal(MCVHashTable_hash *tab, Datum key0, Datum key1)
3103 : : {
3104 : 116194 : MCVHashContext *context = (MCVHashContext *) tab->private_data;
3105 : :
3106 [ - + ]: 116194 : if (context->insert_mode)
3107 : : {
3108 : : /*
3109 : : * During the insertion step, any comparisons will be between two
3110 : : * Datums of the hash table's data type, so if the given operator is
3111 : : * cross-type it will be the wrong thing to use. Fortunately, we can
3112 : : * use datum_image_eq instead. The MCV values should all be distinct
3113 : : * anyway, so it's mostly pro-forma to compare them at all.
3114 : : */
28 tgl@sss.pgh.pa.us 3115 :UNC 0 : return datum_image_eq(key0, key1,
3116 : 0 : context->hash_typbyval, context->hash_typlen);
3117 : : }
3118 : : else
3119 : : {
28 tgl@sss.pgh.pa.us 3120 :GNC 116194 : FunctionCallInfo fcinfo = context->equal_fcinfo;
3121 : : Datum fresult;
3122 : :
3123 : : /*
3124 : : * Apply the operator the correct way around. Although simplehash.h
3125 : : * doesn't document this explicitly, during lookups key0 is from the
3126 : : * hash table while key1 is the probe value, so we should compare them
3127 : : * in that order only if op_is_reversed.
3128 : : */
3129 [ + + ]: 116194 : if (context->op_is_reversed)
3130 : : {
3131 : 29459 : fcinfo->args[0].value = key0;
3132 : 29459 : fcinfo->args[1].value = key1;
3133 : : }
3134 : : else
3135 : : {
3136 : 86735 : fcinfo->args[0].value = key1;
3137 : 86735 : fcinfo->args[1].value = key0;
3138 : : }
3139 : 116194 : fcinfo->isnull = false;
3140 : 116194 : fresult = FunctionCallInvoke(fcinfo);
3141 [ + - + - ]: 116194 : return (!fcinfo->isnull && DatumGetBool(fresult));
3142 : : }
3143 : : }
3144 : :
3145 : : /*
3146 : : * neqjoinsel - Join selectivity of "!="
3147 : : */
3148 : : Datum
9326 tgl@sss.pgh.pa.us 3149 :CBC 1906 : neqjoinsel(PG_FUNCTION_ARGS)
3150 : : {
7500 3151 : 1906 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
8970 3152 : 1906 : Oid operator = PG_GETARG_OID(1);
3153 : 1906 : List *args = (List *) PG_GETARG_POINTER(2);
8359 3154 : 1906 : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
6332 3155 : 1906 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
1975 3156 : 1906 : Oid collation = PG_GET_COLLATION();
3157 : : float8 result;
3158 : :
2940 3159 [ + + - + ]: 1906 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
8970 3160 : 645 : {
3161 : : /*
3162 : : * For semi-joins, if there is more than one distinct value in the RHS
3163 : : * relation then every non-null LHS row must find a row to join since
3164 : : * it can only be equal to one of them. We'll assume that there is
3165 : : * always more than one distinct RHS value for the sake of stability,
3166 : : * though in theory we could have special cases for empty RHS
3167 : : * (selectivity = 0) and single-distinct-value RHS (selectivity =
3168 : : * fraction of LHS that has the same value as the single RHS value).
3169 : : *
3170 : : * For anti-joins, if we use the same assumption that there is more
3171 : : * than one distinct key in the RHS relation, then every non-null LHS
3172 : : * row must be suppressed by the anti-join.
3173 : : *
3174 : : * So either way, the selectivity estimate should be 1 - nullfrac.
3175 : : */
3176 : : VariableStatData leftvar;
3177 : : VariableStatData rightvar;
3178 : : bool reversed;
3179 : : HeapTuple statsTuple;
3180 : : double nullfrac;
3181 : :
2940 3182 : 645 : get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
3183 [ + + ]: 645 : statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
3184 [ + + ]: 645 : if (HeapTupleIsValid(statsTuple))
3185 : 527 : nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
3186 : : else
3187 : 118 : nullfrac = 0.0;
3188 [ + + ]: 645 : ReleaseVariableStats(leftvar);
3189 [ + + ]: 645 : ReleaseVariableStats(rightvar);
3190 : :
3191 : 645 : result = 1.0 - nullfrac;
3192 : : }
3193 : : else
3194 : : {
3195 : : /*
3196 : : * We want 1 - eqjoinsel() where the equality operator is the one
3197 : : * associated with this != operator, that is, its negator.
3198 : : */
3199 : 1261 : Oid eqop = get_negator(operator);
3200 : :
3201 [ + - ]: 1261 : if (eqop)
3202 : : {
3203 : : result =
1975 3204 : 1261 : DatumGetFloat8(DirectFunctionCall5Coll(eqjoinsel,
3205 : : collation,
3206 : : PointerGetDatum(root),
3207 : : ObjectIdGetDatum(eqop),
3208 : : PointerGetDatum(args),
3209 : : Int16GetDatum(jointype),
3210 : : PointerGetDatum(sjinfo)));
3211 : : }
3212 : : else
3213 : : {
3214 : : /* Use default selectivity (should we raise an error instead?) */
2940 tgl@sss.pgh.pa.us 3215 :UBC 0 : result = DEFAULT_EQ_SEL;
3216 : : }
2940 tgl@sss.pgh.pa.us 3217 :CBC 1261 : result = 1.0 - result;
3218 : : }
3219 : :
9326 3220 : 1906 : PG_RETURN_FLOAT8(result);
3221 : : }
3222 : :
3223 : : /*
3224 : : * scalarltjoinsel - Join selectivity of "<" for scalars
3225 : : */
3226 : : Datum
3227 : 162 : scalarltjoinsel(PG_FUNCTION_ARGS)
3228 : : {
3229 : 162 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3230 : : }
3231 : :
3232 : : /*
3233 : : * scalarlejoinsel - Join selectivity of "<=" for scalars
3234 : : */
3235 : : Datum
3017 3236 : 138 : scalarlejoinsel(PG_FUNCTION_ARGS)
3237 : : {
3238 : 138 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3239 : : }
3240 : :
3241 : : /*
3242 : : * scalargtjoinsel - Join selectivity of ">" for scalars
3243 : : */
3244 : : Datum
9326 3245 : 138 : scalargtjoinsel(PG_FUNCTION_ARGS)
3246 : : {
3247 : 138 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3248 : : }
3249 : :
3250 : : /*
3251 : : * scalargejoinsel - Join selectivity of ">=" for scalars
3252 : : */
3253 : : Datum
3017 3254 : 92 : scalargejoinsel(PG_FUNCTION_ARGS)
3255 : : {
3256 : 92 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3257 : : }
3258 : :
3259 : :
3260 : : /*
3261 : : * mergejoinscansel - Scan selectivity of merge join.
3262 : : *
3263 : : * A merge join will stop as soon as it exhausts either input stream.
3264 : : * Therefore, if we can estimate the ranges of both input variables,
3265 : : * we can estimate how much of the input will actually be read. This
3266 : : * can have a considerable impact on the cost when using indexscans.
3267 : : *
3268 : : * Also, we can estimate how much of each input has to be read before the
3269 : : * first join pair is found, which will affect the join's startup time.
3270 : : *
3271 : : * clause should be a clause already known to be mergejoinable. opfamily,
3272 : : * cmptype, and nulls_first specify the sort ordering being used.
3273 : : *
3274 : : * The outputs are:
3275 : : * *leftstart is set to the fraction of the left-hand variable expected
3276 : : * to be scanned before the first join pair is found (0 to 1).
3277 : : * *leftend is set to the fraction of the left-hand variable expected
3278 : : * to be scanned before the join terminates (0 to 1).
3279 : : * *rightstart, *rightend similarly for the right-hand variable.
3280 : : */
3281 : : void
2498 3282 : 71646 : mergejoinscansel(PlannerInfo *root, Node *clause,
3283 : : Oid opfamily, CompareType cmptype, bool nulls_first,
3284 : : Selectivity *leftstart, Selectivity *leftend,
3285 : : Selectivity *rightstart, Selectivity *rightend)
3286 : : {
3287 : : Node *left,
3288 : : *right;
3289 : : VariableStatData leftvar,
3290 : : rightvar;
3291 : : Oid opmethod;
3292 : : int op_strategy;
3293 : : Oid op_lefttype;
3294 : : Oid op_righttype;
3295 : : Oid opno,
3296 : : collation,
3297 : : lsortop,
3298 : : rsortop,
3299 : : lstatop,
3300 : : rstatop,
3301 : : ltop,
3302 : : leop,
3303 : : revltop,
3304 : : revleop;
3305 : : StrategyNumber ltstrat,
3306 : : lestrat,
3307 : : gtstrat,
3308 : : gestrat;
3309 : : bool isgt;
3310 : : Datum leftmin,
3311 : : leftmax,
3312 : : rightmin,
3313 : : rightmax;
3314 : : double selec;
3315 : :
3316 : : /* Set default results if we can't figure anything out. */
3317 : : /* XXX should default "start" fraction be a bit more than 0? */
6584 3318 : 71646 : *leftstart = *rightstart = 0.0;
3319 : 71646 : *leftend = *rightend = 1.0;
3320 : :
3321 : : /* Deconstruct the merge clause */
8692 3322 [ - + ]: 71646 : if (!is_opclause(clause))
8692 tgl@sss.pgh.pa.us 3323 :UBC 0 : return; /* shouldn't happen */
8406 tgl@sss.pgh.pa.us 3324 :CBC 71646 : opno = ((OpExpr *) clause)->opno;
2021 3325 : 71646 : collation = ((OpExpr *) clause)->inputcollid;
7974 3326 : 71646 : left = get_leftop((Expr *) clause);
3327 : 71646 : right = get_rightop((Expr *) clause);
8692 3328 [ - + ]: 71646 : if (!right)
8692 tgl@sss.pgh.pa.us 3329 :UBC 0 : return; /* shouldn't happen */
3330 : :
3331 : : /* Look for stats for the inputs */
7974 tgl@sss.pgh.pa.us 3332 :CBC 71646 : examine_variable(root, left, 0, &leftvar);
3333 : 71646 : examine_variable(root, right, 0, &rightvar);
3334 : :
255 peter@eisentraut.org 3335 : 71646 : opmethod = get_opfamily_method(opfamily);
3336 : :
3337 : : /* Extract the operator's declared left/right datatypes */
5494 tgl@sss.pgh.pa.us 3338 : 71646 : get_op_opfamily_properties(opno, opfamily, false,
3339 : : &op_strategy,
3340 : : &op_lefttype,
3341 : : &op_righttype);
255 peter@eisentraut.org 3342 [ - + ]: 71646 : Assert(IndexAmTranslateStrategy(op_strategy, opmethod, opfamily, true) == COMPARE_EQ);
3343 : :
3344 : : /*
3345 : : * Look up the various operators we need. If we don't find them all, it
3346 : : * probably means the opfamily is broken, but we just fail silently.
3347 : : *
3348 : : * Note: we expect that pg_statistic histograms will be sorted by the '<'
3349 : : * operator, regardless of which sort direction we are considering.
3350 : : */
3351 [ + + - ]: 71646 : switch (cmptype)
3352 : : {
3353 : 71628 : case COMPARE_LT:
6584 tgl@sss.pgh.pa.us 3354 : 71628 : isgt = false;
255 peter@eisentraut.org 3355 : 71628 : ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
3356 : 71628 : lestrat = IndexAmTranslateCompareType(COMPARE_LE, opmethod, opfamily, true);
6584 tgl@sss.pgh.pa.us 3357 [ + + ]: 71628 : if (op_lefttype == op_righttype)
3358 : : {
3359 : : /* easy case */
3360 : 70735 : ltop = get_opfamily_member(opfamily,
3361 : : op_lefttype, op_righttype,
3362 : : ltstrat);
3363 : 70735 : leop = get_opfamily_member(opfamily,
3364 : : op_lefttype, op_righttype,
3365 : : lestrat);
3366 : 70735 : lsortop = ltop;
3367 : 70735 : rsortop = ltop;
3368 : 70735 : lstatop = lsortop;
3369 : 70735 : rstatop = rsortop;
3370 : 70735 : revltop = ltop;
3371 : 70735 : revleop = leop;
3372 : : }
3373 : : else
3374 : : {
3375 : 893 : ltop = get_opfamily_member(opfamily,
3376 : : op_lefttype, op_righttype,
3377 : : ltstrat);
3378 : 893 : leop = get_opfamily_member(opfamily,
3379 : : op_lefttype, op_righttype,
3380 : : lestrat);
3381 : 893 : lsortop = get_opfamily_member(opfamily,
3382 : : op_lefttype, op_lefttype,
3383 : : ltstrat);
3384 : 893 : rsortop = get_opfamily_member(opfamily,
3385 : : op_righttype, op_righttype,
3386 : : ltstrat);
3387 : 893 : lstatop = lsortop;
3388 : 893 : rstatop = rsortop;
3389 : 893 : revltop = get_opfamily_member(opfamily,
3390 : : op_righttype, op_lefttype,
3391 : : ltstrat);
3392 : 893 : revleop = get_opfamily_member(opfamily,
3393 : : op_righttype, op_lefttype,
3394 : : lestrat);
3395 : : }
6934 3396 : 71628 : break;
255 peter@eisentraut.org 3397 : 18 : case COMPARE_GT:
3398 : : /* descending-order case */
6584 tgl@sss.pgh.pa.us 3399 : 18 : isgt = true;
255 peter@eisentraut.org 3400 : 18 : ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
3401 : 18 : gtstrat = IndexAmTranslateCompareType(COMPARE_GT, opmethod, opfamily, true);
3402 : 18 : gestrat = IndexAmTranslateCompareType(COMPARE_GE, opmethod, opfamily, true);
6584 tgl@sss.pgh.pa.us 3403 [ + - ]: 18 : if (op_lefttype == op_righttype)
3404 : : {
3405 : : /* easy case */
3406 : 18 : ltop = get_opfamily_member(opfamily,
3407 : : op_lefttype, op_righttype,
3408 : : gtstrat);
3409 : 18 : leop = get_opfamily_member(opfamily,
3410 : : op_lefttype, op_righttype,
3411 : : gestrat);
3412 : 18 : lsortop = ltop;
3413 : 18 : rsortop = ltop;
3414 : 18 : lstatop = get_opfamily_member(opfamily,
3415 : : op_lefttype, op_lefttype,
3416 : : ltstrat);
3417 : 18 : rstatop = lstatop;
3418 : 18 : revltop = ltop;
3419 : 18 : revleop = leop;
3420 : : }
3421 : : else
3422 : : {
6584 tgl@sss.pgh.pa.us 3423 :UBC 0 : ltop = get_opfamily_member(opfamily,
3424 : : op_lefttype, op_righttype,
3425 : : gtstrat);
3426 : 0 : leop = get_opfamily_member(opfamily,
3427 : : op_lefttype, op_righttype,
3428 : : gestrat);
3429 : 0 : lsortop = get_opfamily_member(opfamily,
3430 : : op_lefttype, op_lefttype,
3431 : : gtstrat);
3432 : 0 : rsortop = get_opfamily_member(opfamily,
3433 : : op_righttype, op_righttype,
3434 : : gtstrat);
3435 : 0 : lstatop = get_opfamily_member(opfamily,
3436 : : op_lefttype, op_lefttype,
3437 : : ltstrat);
3438 : 0 : rstatop = get_opfamily_member(opfamily,
3439 : : op_righttype, op_righttype,
3440 : : ltstrat);
3441 : 0 : revltop = get_opfamily_member(opfamily,
3442 : : op_righttype, op_lefttype,
3443 : : gtstrat);
3444 : 0 : revleop = get_opfamily_member(opfamily,
3445 : : op_righttype, op_lefttype,
3446 : : gestrat);
3447 : : }
6934 tgl@sss.pgh.pa.us 3448 :CBC 18 : break;
6934 tgl@sss.pgh.pa.us 3449 :UBC 0 : default:
3450 : 0 : goto fail; /* shouldn't get here */
3451 : : }
3452 : :
6934 tgl@sss.pgh.pa.us 3453 [ + - + - ]:CBC 71646 : if (!OidIsValid(lsortop) ||
3454 [ + - ]: 71646 : !OidIsValid(rsortop) ||
6584 3455 [ + - ]: 71646 : !OidIsValid(lstatop) ||
3456 [ + + ]: 71646 : !OidIsValid(rstatop) ||
3457 [ + - ]: 71640 : !OidIsValid(ltop) ||
6934 3458 [ + - ]: 71640 : !OidIsValid(leop) ||
6584 3459 [ - + ]: 71640 : !OidIsValid(revltop) ||
3460 : : !OidIsValid(revleop))
6934 3461 : 6 : goto fail; /* insufficient info in catalogs */
3462 : :
3463 : : /* Try to get ranges of both inputs */
6584 3464 [ + + ]: 71640 : if (!isgt)
3465 : : {
2021 3466 [ + + ]: 71622 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3467 : : &leftmin, &leftmax))
6584 3468 : 18071 : goto fail; /* no range available from stats */
2021 3469 [ + + ]: 53551 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3470 : : &rightmin, &rightmax))
6584 3471 : 12248 : goto fail; /* no range available from stats */
3472 : : }
3473 : : else
3474 : : {
3475 : : /* need to swap the max and min */
2021 3476 [ + + ]: 18 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3477 : : &leftmax, &leftmin))
6584 3478 : 15 : goto fail; /* no range available from stats */
2021 3479 [ - + ]: 3 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3480 : : &rightmax, &rightmin))
6584 tgl@sss.pgh.pa.us 3481 :UBC 0 : goto fail; /* no range available from stats */
3482 : : }
3483 : :
3484 : : /*
3485 : : * Now, the fraction of the left variable that will be scanned is the
3486 : : * fraction that's <= the right-side maximum value. But only believe
3487 : : * non-default estimates, else stick with our 1.0.
3488 : : */
2021 tgl@sss.pgh.pa.us 3489 :CBC 41306 : selec = scalarineqsel(root, leop, isgt, true, collation, &leftvar,
3490 : : rightmax, op_righttype);
8692 3491 [ + + ]: 41306 : if (selec != DEFAULT_INEQ_SEL)
6584 3492 : 41303 : *leftend = selec;
3493 : :
3494 : : /* And similarly for the right variable. */
2021 3495 : 41306 : selec = scalarineqsel(root, revleop, isgt, true, collation, &rightvar,
3496 : : leftmax, op_lefttype);
8692 3497 [ + - ]: 41306 : if (selec != DEFAULT_INEQ_SEL)
6584 3498 : 41306 : *rightend = selec;
3499 : :
3500 : : /*
3501 : : * Only one of the two "end" fractions can really be less than 1.0;
3502 : : * believe the smaller estimate and reset the other one to exactly 1.0. If
3503 : : * we get exactly equal estimates (as can easily happen with self-joins),
3504 : : * believe neither.
3505 : : */
3506 [ + + ]: 41306 : if (*leftend > *rightend)
3507 : 11826 : *leftend = 1.0;
3508 [ + + ]: 29480 : else if (*leftend < *rightend)
3509 : 16853 : *rightend = 1.0;
3510 : : else
3511 : 12627 : *leftend = *rightend = 1.0;
3512 : :
3513 : : /*
3514 : : * Also, the fraction of the left variable that will be scanned before the
3515 : : * first join pair is found is the fraction that's < the right-side
3516 : : * minimum value. But only believe non-default estimates, else stick with
3517 : : * our own default.
3518 : : */
2021 3519 : 41306 : selec = scalarineqsel(root, ltop, isgt, false, collation, &leftvar,
3520 : : rightmin, op_righttype);
6584 3521 [ + - ]: 41306 : if (selec != DEFAULT_INEQ_SEL)
3522 : 41306 : *leftstart = selec;
3523 : :
3524 : : /* And similarly for the right variable. */
2021 3525 : 41306 : selec = scalarineqsel(root, revltop, isgt, false, collation, &rightvar,
3526 : : leftmin, op_lefttype);
6584 3527 [ + - ]: 41306 : if (selec != DEFAULT_INEQ_SEL)
3528 : 41306 : *rightstart = selec;
3529 : :
3530 : : /*
3531 : : * Only one of the two "start" fractions can really be more than zero;
3532 : : * believe the larger estimate and reset the other one to exactly 0.0. If
3533 : : * we get exactly equal estimates (as can easily happen with self-joins),
3534 : : * believe neither.
3535 : : */
3536 [ + + ]: 41306 : if (*leftstart < *rightstart)
3537 : 8124 : *leftstart = 0.0;
3538 [ + + ]: 33182 : else if (*leftstart > *rightstart)
3539 : 12543 : *rightstart = 0.0;
3540 : : else
3541 : 20639 : *leftstart = *rightstart = 0.0;
3542 : :
3543 : : /*
3544 : : * If the sort order is nulls-first, we're going to have to skip over any
3545 : : * nulls too. These would not have been counted by scalarineqsel, and we
3546 : : * can safely add in this fraction regardless of whether we believe
3547 : : * scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
3548 : : */
3549 [ + + ]: 41306 : if (nulls_first)
3550 : : {
3551 : : Form_pg_statistic stats;
3552 : :
3553 [ + - ]: 3 : if (HeapTupleIsValid(leftvar.statsTuple))
3554 : : {
3555 : 3 : stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
3556 : 3 : *leftstart += stats->stanullfrac;
3557 [ - + - + ]: 3 : CLAMP_PROBABILITY(*leftstart);
3558 : 3 : *leftend += stats->stanullfrac;
3559 [ - + - + ]: 3 : CLAMP_PROBABILITY(*leftend);
3560 : : }
3561 [ + - ]: 3 : if (HeapTupleIsValid(rightvar.statsTuple))
3562 : : {
6904 3563 : 3 : stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple);
6584 3564 : 3 : *rightstart += stats->stanullfrac;
3565 [ - + - + ]: 3 : CLAMP_PROBABILITY(*rightstart);
3566 : 3 : *rightend += stats->stanullfrac;
3567 [ - + - + ]: 3 : CLAMP_PROBABILITY(*rightend);
3568 : : }
3569 : : }
3570 : :
3571 : : /* Disbelieve start >= end, just in case that can happen */
3572 [ + + ]: 41306 : if (*leftstart >= *leftend)
3573 : : {
3574 : 73 : *leftstart = 0.0;
3575 : 73 : *leftend = 1.0;
3576 : : }
3577 [ + + ]: 41306 : if (*rightstart >= *rightend)
3578 : : {
3579 : 577 : *rightstart = 0.0;
3580 : 577 : *rightend = 1.0;
3581 : : }
3582 : :
7974 3583 : 40729 : fail:
3584 [ + + ]: 71646 : ReleaseVariableStats(leftvar);
3585 [ + + ]: 71646 : ReleaseVariableStats(rightvar);
3586 : : }
3587 : :
3588 : :
3589 : : /*
3590 : : * matchingsel -- generic matching-operator selectivity support
3591 : : *
3592 : : * Use these for any operators that (a) are on data types for which we collect
3593 : : * standard statistics, and (b) have behavior for which the default estimate
3594 : : * (twice DEFAULT_EQ_SEL) is sane. Typically that is good for match-like
3595 : : * operators.
3596 : : */
3597 : :
3598 : : Datum
2086 3599 : 565 : matchingsel(PG_FUNCTION_ARGS)
3600 : : {
3601 : 565 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
3602 : 565 : Oid operator = PG_GETARG_OID(1);
3603 : 565 : List *args = (List *) PG_GETARG_POINTER(2);
3604 : 565 : int varRelid = PG_GETARG_INT32(3);
2021 3605 : 565 : Oid collation = PG_GET_COLLATION();
3606 : : double selec;
3607 : :
3608 : : /* Use generic restriction selectivity logic. */
3609 : 565 : selec = generic_restriction_selectivity(root, operator, collation,
3610 : : args, varRelid,
3611 : : DEFAULT_MATCHING_SEL);
3612 : :
2086 3613 : 565 : PG_RETURN_FLOAT8((float8) selec);
3614 : : }
3615 : :
3616 : : Datum
3617 : 3 : matchingjoinsel(PG_FUNCTION_ARGS)
3618 : : {
3619 : : /* Just punt, for the moment. */
3620 : 3 : PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
3621 : : }
3622 : :
3623 : :
3624 : : /*
3625 : : * Helper routine for estimate_num_groups: add an item to a list of
3626 : : * GroupVarInfos, but only if it's not known equal to any of the existing
3627 : : * entries.
3628 : : */
3629 : : typedef struct
3630 : : {
3631 : : Node *var; /* might be an expression, not just a Var */
3632 : : RelOptInfo *rel; /* relation it belongs to */
3633 : : double ndistinct; /* # distinct values */
3634 : : bool isdefault; /* true if DEFAULT_NUM_DISTINCT was used */
3635 : : } GroupVarInfo;
3636 : :
3637 : : static List *
7500 3638 : 199660 : add_unique_group_var(PlannerInfo *root, List *varinfos,
3639 : : Node *var, VariableStatData *vardata)
3640 : : {
3641 : : GroupVarInfo *varinfo;
3642 : : double ndistinct;
3643 : : bool isdefault;
3644 : : ListCell *lc;
3645 : :
5218 3646 : 199660 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
3647 : :
3648 : : /*
3649 : : * The nullingrels bits within the var could cause the same var to be
3650 : : * counted multiple times if it's marked with different nullingrels. They
3651 : : * could also prevent us from matching the var to the expressions in
3652 : : * extended statistics (see estimate_multivariate_ndistinct). So strip
3653 : : * them out first.
3654 : : */
349 rguo@postgresql.org 3655 : 199660 : var = remove_nulling_relids(var, root->outer_join_rels, NULL);
3656 : :
2347 tgl@sss.pgh.pa.us 3657 [ + + + + : 241317 : foreach(lc, varinfos)
+ + ]
3658 : : {
7760 3659 : 42216 : varinfo = (GroupVarInfo *) lfirst(lc);
3660 : :
3661 : : /* Drop exact duplicates */
3662 [ + + ]: 42216 : if (equal(var, varinfo->var))
3663 : 559 : return varinfos;
3664 : :
3665 : : /*
3666 : : * Drop known-equal vars, but only if they belong to different
3667 : : * relations (see comments for estimate_num_groups). We aren't too
3668 : : * fussy about the semantics of "equal" here.
3669 : : */
3670 [ + + + + ]: 45216 : if (vardata->rel != varinfo->rel &&
505 rguo@postgresql.org 3671 : 3421 : exprs_known_equal(root, var, varinfo->var, InvalidOid))
3672 : : {
7760 tgl@sss.pgh.pa.us 3673 [ + + ]: 150 : if (varinfo->ndistinct <= ndistinct)
3674 : : {
3675 : : /* Keep older item, forget new one */
3676 : 138 : return varinfos;
3677 : : }
3678 : : else
3679 : : {
3680 : : /* Delete the older item */
2347 3681 : 12 : varinfos = foreach_delete_current(varinfos, lc);
3682 : : }
3683 : : }
3684 : : }
3685 : :
7 michael@paquier.xyz 3686 :GNC 199101 : varinfo = palloc_object(GroupVarInfo);
3687 : :
7760 tgl@sss.pgh.pa.us 3688 :CBC 199101 : varinfo->var = var;
3689 : 199101 : varinfo->rel = vardata->rel;
3690 : 199101 : varinfo->ndistinct = ndistinct;
1723 drowley@postgresql.o 3691 : 199101 : varinfo->isdefault = isdefault;
7760 tgl@sss.pgh.pa.us 3692 : 199101 : varinfos = lappend(varinfos, varinfo);
3693 : 199101 : return varinfos;
3694 : : }
3695 : :
3696 : : /*
3697 : : * estimate_num_groups - Estimate number of groups in a grouped query
3698 : : *
3699 : : * Given a query having a GROUP BY clause, estimate how many groups there
3700 : : * will be --- ie, the number of distinct combinations of the GROUP BY
3701 : : * expressions.
3702 : : *
3703 : : * This routine is also used to estimate the number of rows emitted by
3704 : : * a DISTINCT filtering step; that is an isomorphic problem. (Note:
3705 : : * actually, we only use it for DISTINCT when there's no grouping or
3706 : : * aggregation ahead of the DISTINCT.)
3707 : : *
3708 : : * Inputs:
3709 : : * root - the query
3710 : : * groupExprs - list of expressions being grouped by
3711 : : * input_rows - number of rows estimated to arrive at the group/unique
3712 : : * filter step
3713 : : * pgset - NULL, or a List** pointing to a grouping set to filter the
3714 : : * groupExprs against
3715 : : *
3716 : : * Outputs:
3717 : : * estinfo - When passed as non-NULL, the function will set bits in the
3718 : : * "flags" field in order to provide callers with additional information
3719 : : * about the estimation. Currently, we only set the SELFLAG_USED_DEFAULT
3720 : : * bit if we used any default values in the estimation.
3721 : : *
3722 : : * Given the lack of any cross-correlation statistics in the system, it's
3723 : : * impossible to do anything really trustworthy with GROUP BY conditions
3724 : : * involving multiple Vars. We should however avoid assuming the worst
3725 : : * case (all possible cross-product terms actually appear as groups) since
3726 : : * very often the grouped-by Vars are highly correlated. Our current approach
3727 : : * is as follows:
3728 : : * 1. Expressions yielding boolean are assumed to contribute two groups,
3729 : : * independently of their content, and are ignored in the subsequent
3730 : : * steps. This is mainly because tests like "col IS NULL" break the
3731 : : * heuristic used in step 2 especially badly.
3732 : : * 2. Reduce the given expressions to a list of unique Vars used. For
3733 : : * example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
3734 : : * It is clearly correct not to count the same Var more than once.
3735 : : * It is also reasonable to treat f(x) the same as x: f() cannot
3736 : : * increase the number of distinct values (unless it is volatile,
3737 : : * which we consider unlikely for grouping), but it probably won't
3738 : : * reduce the number of distinct values much either.
3739 : : * As a special case, if a GROUP BY expression can be matched to an
3740 : : * expressional index for which we have statistics, then we treat the
3741 : : * whole expression as though it were just a Var.
3742 : : * 3. If the list contains Vars of different relations that are known equal
3743 : : * due to equivalence classes, then drop all but one of the Vars from each
3744 : : * known-equal set, keeping the one with smallest estimated # of values
3745 : : * (since the extra values of the others can't appear in joined rows).
3746 : : * Note the reason we only consider Vars of different relations is that
3747 : : * if we considered ones of the same rel, we'd be double-counting the
3748 : : * restriction selectivity of the equality in the next step.
3749 : : * 4. For Vars within a single source rel, we multiply together the numbers
3750 : : * of values, clamp to the number of rows in the rel (divided by 10 if
3751 : : * more than one Var), and then multiply by a factor based on the
3752 : : * selectivity of the restriction clauses for that rel. When there's
3753 : : * more than one Var, the initial product is probably too high (it's the
3754 : : * worst case) but clamping to a fraction of the rel's rows seems to be a
3755 : : * helpful heuristic for not letting the estimate get out of hand. (The
3756 : : * factor of 10 is derived from pre-Postgres-7.4 practice.) The factor
3757 : : * we multiply by to adjust for the restriction selectivity assumes that
3758 : : * the restriction clauses are independent of the grouping, which may not
3759 : : * be a valid assumption, but it's hard to do better.
3760 : : * 5. If there are Vars from multiple rels, we repeat step 4 for each such
3761 : : * rel, and multiply the results together.
3762 : : * Note that rels not containing grouped Vars are ignored completely, as are
3763 : : * join clauses. Such rels cannot increase the number of groups, and we
3764 : : * assume such clauses do not reduce the number either (somewhat bogus,
3765 : : * but we don't have the info to do better).
3766 : : */
3767 : : double
3868 andres@anarazel.de 3768 : 173616 : estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
3769 : : List **pgset, EstimationInfo *estinfo)
3770 : : {
1171 tgl@sss.pgh.pa.us 3771 : 173616 : List *varinfos = NIL;
2944 3772 : 173616 : double srf_multiplier = 1.0;
3773 : : double numdistinct;
3774 : : ListCell *l;
3775 : : int i;
3776 : :
3777 : : /* Zero the estinfo output parameter, if non-NULL */
1723 drowley@postgresql.o 3778 [ + + ]: 173616 : if (estinfo != NULL)
3779 : 148702 : memset(estinfo, 0, sizeof(EstimationInfo));
3780 : :
3781 : : /*
3782 : : * We don't ever want to return an estimate of zero groups, as that tends
3783 : : * to lead to division-by-zero and other unpleasantness. The input_rows
3784 : : * estimate is usually already at least 1, but clamp it just in case it
3785 : : * isn't.
3786 : : */
4604 tgl@sss.pgh.pa.us 3787 : 173616 : input_rows = clamp_row_est(input_rows);
3788 : :
3789 : : /*
3790 : : * If no grouping columns, there's exactly one group. (This can't happen
3791 : : * for normal cases with GROUP BY or DISTINCT, but it is possible for
3792 : : * corner cases with set operations.)
3793 : : */
1218 3794 [ + + + + : 173616 : if (groupExprs == NIL || (pgset && *pgset == NIL))
+ + ]
5000 3795 : 555 : return 1.0;
3796 : :
3797 : : /*
3798 : : * Count groups derived from boolean grouping expressions. For other
3799 : : * expressions, find the unique Vars used, treating an expression as a Var
3800 : : * if we can find stats for it. For each one, record the statistical
3801 : : * estimate of number of distinct values (total in its table, without
3802 : : * regard for filtering).
3803 : : */
6372 3804 : 173061 : numdistinct = 1.0;
3805 : :
1171 3806 : 173061 : i = 0;
8367 3807 [ + - + + : 371832 : foreach(l, groupExprs)
+ + ]
3808 : : {
3809 : 198795 : Node *groupexpr = (Node *) lfirst(l);
3810 : : double this_srf_multiplier;
3811 : : VariableStatData vardata;
3812 : : List *varshere;
3813 : : ListCell *l2;
3814 : :
3815 : : /* is expression in this grouping set? */
3868 andres@anarazel.de 3816 [ + + + + ]: 198795 : if (pgset && !list_member_int(*pgset, i++))
3817 : 164251 : continue;
3818 : :
3819 : : /*
3820 : : * Set-returning functions in grouping columns are a bit problematic.
3821 : : * The code below will effectively ignore their SRF nature and come up
3822 : : * with a numdistinct estimate as though they were scalar functions.
3823 : : * We compensate by scaling up the end result by the largest SRF
3824 : : * rowcount estimate. (This will be an overestimate if the SRF
3825 : : * produces multiple copies of any output value, but it seems best to
3826 : : * assume the SRF's outputs are distinct. In any case, it's probably
3827 : : * pointless to worry too much about this without much better
3828 : : * estimates for SRF output rowcounts than we have today.)
3829 : : */
2503 tgl@sss.pgh.pa.us 3830 : 198395 : this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
2944 3831 [ + + ]: 198395 : if (srf_multiplier < this_srf_multiplier)
3832 : 66 : srf_multiplier = this_srf_multiplier;
3833 : :
3834 : : /* Short-circuit for expressions returning boolean */
6372 3835 [ + + ]: 198395 : if (exprType(groupexpr) == BOOLOID)
3836 : : {
3837 : 102 : numdistinct *= 2.0;
3838 : 102 : continue;
3839 : : }
3840 : :
3841 : : /*
3842 : : * If examine_variable is able to deduce anything about the GROUP BY
3843 : : * expression, treat it as a single variable even if it's really more
3844 : : * complicated.
3845 : : *
3846 : : * XXX This has the consequence that if there's a statistics object on
3847 : : * the expression, we don't split it into individual Vars. This
3848 : : * affects our selection of statistics in
3849 : : * estimate_multivariate_ndistinct, because it's probably better to
3850 : : * use more accurate estimate for each expression and treat them as
3851 : : * independent, than to combine estimates for the extracted variables
3852 : : * when we don't know how that relates to the expressions.
3853 : : */
7760 3854 : 198293 : examine_variable(root, groupexpr, 0, &vardata);
6289 3855 [ + + + + ]: 198293 : if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
3856 : : {
7760 3857 : 163407 : varinfos = add_unique_group_var(root, varinfos,
3858 : : groupexpr, &vardata);
3859 [ + + ]: 163407 : ReleaseVariableStats(vardata);
3860 : 163407 : continue;
3861 : : }
3862 [ - + ]: 34886 : ReleaseVariableStats(vardata);
3863 : :
3864 : : /*
3865 : : * Else pull out the component Vars. Handle PlaceHolderVars by
3866 : : * recursing into their arguments (effectively assuming that the
3867 : : * PlaceHolderVar doesn't change the number of groups, which boils
3868 : : * down to ignoring the possible addition of nulls to the result set).
3869 : : */
5272 3870 : 34886 : varshere = pull_var_clause(groupexpr,
3871 : : PVC_RECURSE_AGGREGATES |
3872 : : PVC_RECURSE_WINDOWFUNCS |
3873 : : PVC_RECURSE_PLACEHOLDERS);
3874 : :
3875 : : /*
3876 : : * If we find any variable-free GROUP BY item, then either it is a
3877 : : * constant (and we can ignore it) or it contains a volatile function;
3878 : : * in the latter case we punt and assume that each input row will
3879 : : * yield a distinct group.
3880 : : */
8429 3881 [ + + ]: 34886 : if (varshere == NIL)
3882 : : {
3883 [ + + ]: 366 : if (contain_volatile_functions(groupexpr))
3884 : 24 : return input_rows;
3885 : 342 : continue;
3886 : : }
3887 : :
3888 : : /*
3889 : : * Else add variables to varinfos list
3890 : : */
7760 3891 [ + - + + : 70773 : foreach(l2, varshere)
+ + ]
3892 : : {
3893 : 36253 : Node *var = (Node *) lfirst(l2);
3894 : :
3895 : 36253 : examine_variable(root, var, 0, &vardata);
3896 : 36253 : varinfos = add_unique_group_var(root, varinfos, var, &vardata);
3897 [ + + ]: 36253 : ReleaseVariableStats(vardata);
3898 : : }
3899 : : }
3900 : :
3901 : : /*
3902 : : * If now no Vars, we must have an all-constant or all-boolean GROUP BY
3903 : : * list.
3904 : : */
3905 [ + + ]: 173037 : if (varinfos == NIL)
3906 : : {
3907 : : /* Apply SRF multiplier as we would do in the long path */
2944 3908 : 200 : numdistinct *= srf_multiplier;
3909 : : /* Round off */
3910 : 200 : numdistinct = ceil(numdistinct);
3911 : : /* Guard against out-of-range answers */
6372 3912 [ + + ]: 200 : if (numdistinct > input_rows)
3913 : 22 : numdistinct = input_rows;
2944 3914 [ - + ]: 200 : if (numdistinct < 1.0)
2944 tgl@sss.pgh.pa.us 3915 :UBC 0 : numdistinct = 1.0;
6372 tgl@sss.pgh.pa.us 3916 :CBC 200 : return numdistinct;
3917 : : }
3918 : :
3919 : : /*
3920 : : * Group Vars by relation and estimate total numdistinct.
3921 : : *
3922 : : * For each iteration of the outer loop, we process the frontmost Var in
3923 : : * varinfos, plus all other Vars in the same relation. We remove these
3924 : : * Vars from the newvarinfos list for the next iteration. This is the
3925 : : * easiest way to group Vars of same rel together.
3926 : : */
3927 : : do
3928 : : {
7760 3929 : 174293 : GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
3930 : 174293 : RelOptInfo *rel = varinfo1->rel;
3190 alvherre@alvh.no-ip. 3931 : 174293 : double reldistinct = 1;
7624 tgl@sss.pgh.pa.us 3932 : 174293 : double relmaxndistinct = reldistinct;
3187 alvherre@alvh.no-ip. 3933 : 174293 : int relvarcount = 0;
8171 bruce@momjian.us 3934 : 174293 : List *newvarinfos = NIL;
3190 alvherre@alvh.no-ip. 3935 : 174293 : List *relvarinfos = NIL;
3936 : :
3937 : : /*
3938 : : * Split the list of varinfos in two - one for the current rel, one
3939 : : * for remaining Vars on other rels.
3940 : : */
2345 tgl@sss.pgh.pa.us 3941 : 174293 : relvarinfos = lappend(relvarinfos, varinfo1);
1906 3942 [ + - + + : 201859 : for_each_from(l, varinfos, 1)
+ + ]
3943 : : {
7760 3944 : 27566 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3945 : :
3946 [ + + ]: 27566 : if (varinfo2->rel == varinfo1->rel)
3947 : : {
3948 : : /* varinfos on current rel */
2345 3949 : 24796 : relvarinfos = lappend(relvarinfos, varinfo2);
3950 : : }
3951 : : else
3952 : : {
3953 : : /* not time to process varinfo2 yet */
3954 : 2770 : newvarinfos = lappend(newvarinfos, varinfo2);
3955 : : }
3956 : : }
3957 : :
3958 : : /*
3959 : : * Get the numdistinct estimate for the Vars of this rel. We
3960 : : * iteratively search for multivariate n-distinct with maximum number
3961 : : * of vars; assuming that each var group is independent of the others,
3962 : : * we multiply them together. Any remaining relvarinfos after no more
3963 : : * multivariate matches are found are assumed independent too, so
3964 : : * their individual ndistinct estimates are multiplied also.
3965 : : *
3966 : : * While iterating, count how many separate numdistinct values we
3967 : : * apply. We apply a fudge factor below, but only if we multiplied
3968 : : * more than one such values.
3969 : : */
3190 alvherre@alvh.no-ip. 3970 [ + + ]: 348649 : while (relvarinfos)
3971 : : {
3972 : : double mvndistinct;
3973 : :
3974 [ + + ]: 174356 : if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
3975 : : &mvndistinct))
3976 : : {
3977 : 207 : reldistinct *= mvndistinct;
3978 [ + + ]: 207 : if (relmaxndistinct < mvndistinct)
3979 : 201 : relmaxndistinct = mvndistinct;
3187 3980 : 207 : relvarcount++;
3981 : : }
3982 : : else
3983 : : {
3136 bruce@momjian.us 3984 [ + - + + : 372800 : foreach(l, relvarinfos)
+ + ]
3985 : : {
3190 alvherre@alvh.no-ip. 3986 : 198651 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3987 : :
3988 : 198651 : reldistinct *= varinfo2->ndistinct;
3989 [ + + ]: 198651 : if (relmaxndistinct < varinfo2->ndistinct)
3990 : 175126 : relmaxndistinct = varinfo2->ndistinct;
3991 : 198651 : relvarcount++;
3992 : :
3993 : : /*
3994 : : * When varinfo2's isdefault is set then we'd better set
3995 : : * the SELFLAG_USED_DEFAULT bit in the EstimationInfo.
3996 : : */
1723 drowley@postgresql.o 3997 [ + + + + ]: 198651 : if (estinfo != NULL && varinfo2->isdefault)
3998 : 9692 : estinfo->flags |= SELFLAG_USED_DEFAULT;
3999 : : }
4000 : :
4001 : : /* we're done with this relation */
3190 alvherre@alvh.no-ip. 4002 : 174149 : relvarinfos = NIL;
4003 : : }
4004 : : }
4005 : :
4006 : : /*
4007 : : * Sanity check --- don't divide by zero if empty relation.
4008 : : */
3180 rhaas@postgresql.org 4009 [ + + - + ]: 174293 : Assert(IS_SIMPLE_REL(rel));
8098 tgl@sss.pgh.pa.us 4010 [ + + ]: 174293 : if (rel->tuples > 0)
4011 : : {
4012 : : /*
4013 : : * Clamp to size of rel, or size of rel / 10 if multiple Vars. The
4014 : : * fudge factor is because the Vars are probably correlated but we
4015 : : * don't know by how much. We should never clamp to less than the
4016 : : * largest ndistinct value for any of the Vars, though, since
4017 : : * there will surely be at least that many groups.
4018 : : */
7628 4019 : 173769 : double clamp = rel->tuples;
4020 : :
4021 [ + + ]: 173769 : if (relvarcount > 1)
4022 : : {
4023 : 22410 : clamp *= 0.1;
7624 4024 [ + + ]: 22410 : if (clamp < relmaxndistinct)
4025 : : {
4026 : 21071 : clamp = relmaxndistinct;
4027 : : /* for sanity in case some ndistinct is too large: */
4028 [ + + ]: 21071 : if (clamp > rel->tuples)
4029 : 39 : clamp = rel->tuples;
4030 : : }
4031 : : }
7628 4032 [ + + ]: 173769 : if (reldistinct > clamp)
4033 : 18323 : reldistinct = clamp;
4034 : :
4035 : : /*
4036 : : * Update the estimate based on the restriction selectivity,
4037 : : * guarding against division by zero when reldistinct is zero.
4038 : : * Also skip this if we know that we are returning all rows.
4039 : : */
3544 dean.a.rasheed@gmail 4040 [ + - + + ]: 173769 : if (reldistinct > 0 && rel->rows < rel->tuples)
4041 : : {
4042 : : /*
4043 : : * Given a table containing N rows with n distinct values in a
4044 : : * uniform distribution, if we select p rows at random then
4045 : : * the expected number of distinct values selected is
4046 : : *
4047 : : * n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
4048 : : *
4049 : : * = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
4050 : : *
4051 : : * See "Approximating block accesses in database
4052 : : * organizations", S. B. Yao, Communications of the ACM,
4053 : : * Volume 20 Issue 4, April 1977 Pages 260-261.
4054 : : *
4055 : : * Alternatively, re-arranging the terms from the factorials,
4056 : : * this may be written as
4057 : : *
4058 : : * n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
4059 : : *
4060 : : * This form of the formula is more efficient to compute in
4061 : : * the common case where p is larger than N/n. Additionally,
4062 : : * as pointed out by Dell'Era, if i << N for all terms in the
4063 : : * product, it can be approximated by
4064 : : *
4065 : : * n * (1 - ((N-p)/N)^(N/n))
4066 : : *
4067 : : * See "Expected distinct values when selecting from a bag
4068 : : * without replacement", Alberto Dell'Era,
4069 : : * http://www.adellera.it/investigations/distinct_balls/.
4070 : : *
4071 : : * The condition i << N is equivalent to n >> 1, so this is a
4072 : : * good approximation when the number of distinct values in
4073 : : * the table is large. It turns out that this formula also
4074 : : * works well even when n is small.
4075 : : */
4076 : 55049 : reldistinct *=
4077 : 55049 : (1 - pow((rel->tuples - rel->rows) / rel->tuples,
4078 : 55049 : rel->tuples / reldistinct));
4079 : : }
4080 : 173769 : reldistinct = clamp_row_est(reldistinct);
4081 : :
4082 : : /*
4083 : : * Update estimate of total distinct groups.
4084 : : */
8098 tgl@sss.pgh.pa.us 4085 : 173769 : numdistinct *= reldistinct;
4086 : : }
4087 : :
8429 4088 : 174293 : varinfos = newvarinfos;
4089 [ + + ]: 174293 : } while (varinfos != NIL);
4090 : :
4091 : : /* Now we can account for the effects of any SRFs */
2944 4092 : 172837 : numdistinct *= srf_multiplier;
4093 : :
4094 : : /* Round off */
8360 4095 : 172837 : numdistinct = ceil(numdistinct);
4096 : :
4097 : : /* Guard against out-of-range answers */
8429 4098 [ + + ]: 172837 : if (numdistinct > input_rows)
4099 : 35705 : numdistinct = input_rows;
4100 [ - + ]: 172837 : if (numdistinct < 1.0)
8429 tgl@sss.pgh.pa.us 4101 :UBC 0 : numdistinct = 1.0;
4102 : :
8429 tgl@sss.pgh.pa.us 4103 :CBC 172837 : return numdistinct;
4104 : : }
4105 : :
4106 : : /*
4107 : : * Try to estimate the bucket size of the hash join inner side when the join
4108 : : * condition contains two or more clauses by employing extended statistics.
4109 : : *
4110 : : * The main idea of this approach is that the distinct value generated by
4111 : : * multivariate estimation on two or more columns would provide less bucket size
4112 : : * than estimation on one separate column.
4113 : : *
4114 : : * IMPORTANT: It is crucial to synchronize the approach of combining different
4115 : : * estimations with the caller's method.
4116 : : *
4117 : : * Return a list of clauses that didn't fetch any extended statistics.
4118 : : */
4119 : : List *
282 akorotkov@postgresql 4120 : 226038 : estimate_multivariate_bucketsize(PlannerInfo *root, RelOptInfo *inner,
4121 : : List *hashclauses,
4122 : : Selectivity *innerbucketsize)
4123 : : {
4124 : : List *clauses;
4125 : : List *otherclauses;
4126 : : double ndistinct;
4127 : :
4128 [ + + ]: 226038 : if (list_length(hashclauses) <= 1)
4129 : : {
4130 : : /*
4131 : : * Nothing to do for a single clause. Could we employ univariate
4132 : : * extended stat here?
4133 : : */
4134 : 207772 : return hashclauses;
4135 : : }
4136 : :
4137 : : /* "clauses" is the list of hashclauses we've not dealt with yet */
151 tgl@sss.pgh.pa.us 4138 :GNC 18266 : clauses = list_copy(hashclauses);
4139 : : /* "otherclauses" holds clauses we are going to return to caller */
4140 : 18266 : otherclauses = NIL;
4141 : : /* current estimate of ndistinct */
4142 : 18266 : ndistinct = 1.0;
282 akorotkov@postgresql 4143 [ + + ]:CBC 36538 : while (clauses != NIL)
4144 : : {
4145 : : ListCell *lc;
4146 : 18272 : int relid = -1;
4147 : 18272 : List *varinfos = NIL;
4148 : 18272 : List *origin_rinfos = NIL;
4149 : : double mvndistinct;
4150 : : List *origin_varinfos;
4151 : 18272 : int group_relid = -1;
4152 : 18272 : RelOptInfo *group_rel = NULL;
4153 : : ListCell *lc1,
4154 : : *lc2;
4155 : :
4156 : : /*
4157 : : * Find clauses, referencing the same single base relation and try to
4158 : : * estimate such a group with extended statistics. Create varinfo for
4159 : : * an approved clause, push it to otherclauses, if it can't be
4160 : : * estimated here or ignore to process at the next iteration.
4161 : : */
4162 [ + + + + : 55122 : foreach(lc, clauses)
+ + ]
4163 : : {
4164 : 36850 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
4165 : : Node *expr;
4166 : : Relids relids;
4167 : : GroupVarInfo *varinfo;
4168 : :
4169 : : /*
4170 : : * Find the inner side of the join, which we need to estimate the
4171 : : * number of buckets. Use outer_is_left because the
4172 : : * clause_sides_match_join routine has called on hash clauses.
4173 : : */
4174 : 73700 : relids = rinfo->outer_is_left ?
4175 [ + + ]: 36850 : rinfo->right_relids : rinfo->left_relids;
4176 : 73700 : expr = rinfo->outer_is_left ?
4177 [ + + ]: 36850 : get_rightop(rinfo->clause) : get_leftop(rinfo->clause);
4178 : :
4179 [ + + ]: 36850 : if (bms_get_singleton_member(relids, &relid) &&
4180 [ + + ]: 35531 : root->simple_rel_array[relid]->statlist != NIL)
4181 : 24 : {
238 4182 : 30 : bool is_duplicate = false;
4183 : :
4184 : : /*
4185 : : * This inner-side expression references only one relation.
4186 : : * Extended statistics on this clause can exist.
4187 : : */
282 4188 [ + + ]: 30 : if (group_relid < 0)
4189 : : {
4190 : 15 : RangeTblEntry *rte = root->simple_rte_array[relid];
4191 : :
4192 [ + - - + ]: 15 : if (!rte || (rte->relkind != RELKIND_RELATION &&
282 akorotkov@postgresql 4193 [ # # ]:UBC 0 : rte->relkind != RELKIND_MATVIEW &&
4194 [ # # ]: 0 : rte->relkind != RELKIND_FOREIGN_TABLE &&
4195 [ # # ]: 0 : rte->relkind != RELKIND_PARTITIONED_TABLE))
4196 : : {
4197 : : /* Extended statistics can't exist in principle */
4198 : 0 : otherclauses = lappend(otherclauses, rinfo);
4199 : 0 : clauses = foreach_delete_current(clauses, lc);
4200 : 0 : continue;
4201 : : }
4202 : :
282 akorotkov@postgresql 4203 :CBC 15 : group_relid = relid;
4204 : 15 : group_rel = root->simple_rel_array[relid];
4205 : : }
4206 [ - + ]: 15 : else if (group_relid != relid)
4207 : : {
4208 : : /*
4209 : : * Being in the group forming state we don't need other
4210 : : * clauses.
4211 : : */
282 akorotkov@postgresql 4212 :UBC 0 : continue;
4213 : : }
4214 : :
4215 : : /*
4216 : : * We're going to add the new clause to the varinfos list. We
4217 : : * might re-use add_unique_group_var(), but we don't do so for
4218 : : * two reasons.
4219 : : *
4220 : : * 1) We must keep the origin_rinfos list ordered exactly the
4221 : : * same way as varinfos.
4222 : : *
4223 : : * 2) add_unique_group_var() is designed for
4224 : : * estimate_num_groups(), where a larger number of groups is
4225 : : * worse. While estimating the number of hash buckets, we
4226 : : * have the opposite: a lesser number of groups is worse.
4227 : : * Therefore, we don't have to remove "known equal" vars: the
4228 : : * removed var may valuably contribute to the multivariate
4229 : : * statistics to grow the number of groups.
4230 : : */
4231 : :
4232 : : /*
4233 : : * Clear nullingrels to correctly match hash keys. See
4234 : : * add_unique_group_var()'s comment for details.
4235 : : */
238 akorotkov@postgresql 4236 :CBC 30 : expr = remove_nulling_relids(expr, root->outer_join_rels, NULL);
4237 : :
4238 : : /*
4239 : : * Detect and exclude exact duplicates from the list of hash
4240 : : * keys (like add_unique_group_var does).
4241 : : */
4242 [ + + + + : 42 : foreach(lc1, varinfos)
+ + ]
4243 : : {
4244 : 18 : varinfo = (GroupVarInfo *) lfirst(lc1);
4245 : :
4246 [ + + ]: 18 : if (!equal(expr, varinfo->var))
4247 : 12 : continue;
4248 : :
4249 : 6 : is_duplicate = true;
4250 : 6 : break;
4251 : : }
4252 : :
4253 [ + + ]: 30 : if (is_duplicate)
4254 : : {
4255 : : /*
4256 : : * Skip exact duplicates. Adding them to the otherclauses
4257 : : * list also doesn't make sense.
4258 : : */
4259 : 6 : continue;
4260 : : }
4261 : :
4262 : : /*
4263 : : * Initialize GroupVarInfo. We only use it to call
4264 : : * estimate_multivariate_ndistinct(), which doesn't care about
4265 : : * ndistinct and isdefault fields. Thus, skip these fields.
4266 : : */
7 michael@paquier.xyz 4267 :GNC 24 : varinfo = palloc0_object(GroupVarInfo);
282 akorotkov@postgresql 4268 :CBC 24 : varinfo->var = expr;
4269 : 24 : varinfo->rel = root->simple_rel_array[relid];
4270 : 24 : varinfos = lappend(varinfos, varinfo);
4271 : :
4272 : : /*
4273 : : * Remember the link to RestrictInfo for the case the clause
4274 : : * is failed to be estimated.
4275 : : */
4276 : 24 : origin_rinfos = lappend(origin_rinfos, rinfo);
4277 : : }
4278 : : else
4279 : : {
4280 : : /* This clause can't be estimated with extended statistics */
4281 : 36820 : otherclauses = lappend(otherclauses, rinfo);
4282 : : }
4283 : :
4284 : 36844 : clauses = foreach_delete_current(clauses, lc);
4285 : : }
4286 : :
4287 [ + + ]: 18272 : if (list_length(varinfos) < 2)
4288 : : {
4289 : : /*
4290 : : * Multivariate statistics doesn't apply to single columns except
4291 : : * for expressions, but it has not been implemented yet.
4292 : : */
4293 : 18266 : otherclauses = list_concat(otherclauses, origin_rinfos);
4294 : 18266 : list_free_deep(varinfos);
4295 : 18266 : list_free(origin_rinfos);
4296 : 18266 : continue;
4297 : : }
4298 : :
4299 [ - + ]: 6 : Assert(group_rel != NULL);
4300 : :
4301 : : /* Employ the extended statistics. */
4302 : 6 : origin_varinfos = varinfos;
4303 : : for (;;)
4304 : 6 : {
4305 : 12 : bool estimated = estimate_multivariate_ndistinct(root,
4306 : : group_rel,
4307 : : &varinfos,
4308 : : &mvndistinct);
4309 : :
4310 [ + + ]: 12 : if (!estimated)
4311 : 6 : break;
4312 : :
4313 : : /*
4314 : : * We've got an estimation. Use ndistinct value in a consistent
4315 : : * way - according to the caller's logic (see
4316 : : * final_cost_hashjoin).
4317 : : */
4318 [ + - ]: 6 : if (ndistinct < mvndistinct)
4319 : 6 : ndistinct = mvndistinct;
4320 [ - + ]: 6 : Assert(ndistinct >= 1.0);
4321 : : }
4322 : :
4323 [ - + ]: 6 : Assert(list_length(origin_varinfos) == list_length(origin_rinfos));
4324 : :
4325 : : /* Collect unmatched clauses as otherclauses. */
4326 [ + - + + : 21 : forboth(lc1, origin_varinfos, lc2, origin_rinfos)
+ - + + +
+ + - +
+ ]
4327 : : {
4328 : 15 : GroupVarInfo *vinfo = lfirst(lc1);
4329 : :
4330 [ + - ]: 15 : if (!list_member_ptr(varinfos, vinfo))
4331 : : /* Already estimated */
4332 : 15 : continue;
4333 : :
4334 : : /* Can't be estimated here - push to the returning list */
282 akorotkov@postgresql 4335 :UBC 0 : otherclauses = lappend(otherclauses, lfirst(lc2));
4336 : : }
4337 : : }
4338 : :
282 akorotkov@postgresql 4339 :CBC 18266 : *innerbucketsize = 1.0 / ndistinct;
4340 : 18266 : return otherclauses;
4341 : : }
4342 : :
4343 : : /*
4344 : : * Estimate hash bucket statistics when the specified expression is used
4345 : : * as a hash key for the given number of buckets.
4346 : : *
4347 : : * This attempts to determine two values:
4348 : : *
4349 : : * 1. The frequency of the most common value of the expression (returns
4350 : : * zero into *mcv_freq if we can't get that).
4351 : : *
4352 : : * 2. The "bucketsize fraction", ie, average number of entries in a bucket
4353 : : * divided by total tuples in relation.
4354 : : *
4355 : : * XXX This is really pretty bogus since we're effectively assuming that the
4356 : : * distribution of hash keys will be the same after applying restriction
4357 : : * clauses as it was in the underlying relation. However, we are not nearly
4358 : : * smart enough to figure out how the restrict clauses might change the
4359 : : * distribution, so this will have to do for now.
4360 : : *
4361 : : * We are passed the number of buckets the executor will use for the given
4362 : : * input relation. If the data were perfectly distributed, with the same
4363 : : * number of tuples going into each available bucket, then the bucketsize
4364 : : * fraction would be 1/nbuckets. But this happy state of affairs will occur
4365 : : * only if (a) there are at least nbuckets distinct data values, and (b)
4366 : : * we have a not-too-skewed data distribution. Otherwise the buckets will
4367 : : * be nonuniformly occupied. If the other relation in the join has a key
4368 : : * distribution similar to this one's, then the most-loaded buckets are
4369 : : * exactly those that will be probed most often. Therefore, the "average"
4370 : : * bucket size for costing purposes should really be taken as something close
4371 : : * to the "worst case" bucket size. We try to estimate this by adjusting the
4372 : : * fraction if there are too few distinct data values, and then scaling up
4373 : : * by the ratio of the most common value's frequency to the average frequency.
4374 : : *
4375 : : * If no statistics are available, use a default estimate of 0.1. This will
4376 : : * discourage use of a hash rather strongly if the inner relation is large,
4377 : : * which is what we want. We do not want to hash unless we know that the
4378 : : * inner rel is well-dispersed (or the alternatives seem much worse).
4379 : : *
4380 : : * The caller should also check that the mcv_freq is not so large that the
4381 : : * most common value would by itself require an impractically large bucket.
4382 : : * In a hash join, the executor can split buckets if they get too big, but
4383 : : * obviously that doesn't help for a bucket that contains many duplicates of
4384 : : * the same value.
4385 : : */
4386 : : void
3046 tgl@sss.pgh.pa.us 4387 : 102224 : estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
4388 : : Selectivity *mcv_freq,
4389 : : Selectivity *bucketsize_frac)
4390 : : {
4391 : : VariableStatData vardata;
4392 : : double estfract,
4393 : : ndistinct,
4394 : : stanullfrac,
4395 : : avgfreq;
4396 : : bool isdefault;
4397 : : AttStatsSlot sslot;
4398 : :
7974 4399 : 102224 : examine_variable(root, hashkey, 0, &vardata);
4400 : :
4401 : : /* Look up the frequency of the most common value, if available */
3046 4402 : 102224 : *mcv_freq = 0.0;
4403 : :
4404 [ + + ]: 102224 : if (HeapTupleIsValid(vardata.statsTuple))
4405 : : {
4406 [ + + ]: 73734 : if (get_attstatsslot(&sslot, vardata.statsTuple,
4407 : : STATISTIC_KIND_MCV, InvalidOid,
4408 : : ATTSTATSSLOT_NUMBERS))
4409 : : {
4410 : : /*
4411 : : * The first MCV stat is for the most common value.
4412 : : */
4413 [ + - ]: 43566 : if (sslot.nnumbers > 0)
4414 : 43566 : *mcv_freq = sslot.numbers[0];
4415 : 43566 : free_attstatsslot(&sslot);
4416 : : }
4417 : : }
4418 : :
4419 : : /* Get number of distinct values */
5218 4420 : 102224 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
4421 : :
4422 : : /*
4423 : : * If ndistinct isn't real, punt. We normally return 0.1, but if the
4424 : : * mcv_freq is known to be even higher than that, use it instead.
4425 : : */
4426 [ + + ]: 102224 : if (isdefault)
4427 : : {
3046 4428 [ + - ]: 13051 : *bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
5218 4429 [ + + ]: 13051 : ReleaseVariableStats(vardata);
3046 4430 : 13051 : return;
4431 : : }
4432 : :
4433 : : /* Get fraction that are null */
7974 4434 [ + + ]: 89173 : if (HeapTupleIsValid(vardata.statsTuple))
4435 : : {
4436 : : Form_pg_statistic stats;
4437 : :
4438 : 73725 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
4439 : 73725 : stanullfrac = stats->stanullfrac;
4440 : : }
4441 : : else
4442 : 15448 : stanullfrac = 0.0;
4443 : :
4444 : : /* Compute avg freq of all distinct data values in raw relation */
4445 : 89173 : avgfreq = (1.0 - stanullfrac) / ndistinct;
4446 : :
4447 : : /*
4448 : : * Adjust ndistinct to account for restriction clauses. Observe we are
4449 : : * assuming that the data distribution is affected uniformly by the
4450 : : * restriction clauses!
4451 : : *
4452 : : * XXX Possibly better way, but much more expensive: multiply by
4453 : : * selectivity of rel's restriction clauses that mention the target Var.
4454 : : */
3552 4455 [ + - + + ]: 89173 : if (vardata.rel && vardata.rel->tuples > 0)
4456 : : {
7974 4457 : 89144 : ndistinct *= vardata.rel->rows / vardata.rel->tuples;
3552 4458 : 89144 : ndistinct = clamp_row_est(ndistinct);
4459 : : }
4460 : :
4461 : : /*
4462 : : * Initial estimate of bucketsize fraction is 1/nbuckets as long as the
4463 : : * number of buckets is less than the expected number of distinct values;
4464 : : * otherwise it is 1/ndistinct.
4465 : : */
7591 4466 [ + + ]: 89173 : if (ndistinct > nbuckets)
4467 : 44 : estfract = 1.0 / nbuckets;
4468 : : else
7974 4469 : 89129 : estfract = 1.0 / ndistinct;
4470 : :
4471 : : /*
4472 : : * Adjust estimated bucketsize upward to account for skewed distribution.
4473 : : */
3046 4474 [ + + + + ]: 89173 : if (avgfreq > 0.0 && *mcv_freq > avgfreq)
4475 : 40311 : estfract *= *mcv_freq / avgfreq;
4476 : :
4477 : : /*
4478 : : * Clamp bucketsize to sane range (the above adjustment could easily
4479 : : * produce an out-of-range result). We set the lower bound a little above
4480 : : * zero, since zero isn't a very sane result.
4481 : : */
7974 4482 [ - + ]: 89173 : if (estfract < 1.0e-6)
7974 tgl@sss.pgh.pa.us 4483 :UBC 0 : estfract = 1.0e-6;
7974 tgl@sss.pgh.pa.us 4484 [ + + ]:CBC 89173 : else if (estfract > 1.0)
4485 : 18775 : estfract = 1.0;
4486 : :
3046 4487 : 89173 : *bucketsize_frac = (Selectivity) estfract;
4488 : :
4489 [ + + ]: 89173 : ReleaseVariableStats(vardata);
4490 : : }
4491 : :
4492 : : /*
4493 : : * estimate_hashagg_tablesize
4494 : : * estimate the number of bytes that a hash aggregate hashtable will
4495 : : * require based on the agg_costs, path width and number of groups.
4496 : : *
4497 : : * We return the result as "double" to forestall any possible overflow
4498 : : * problem in the multiplication by dNumGroups.
4499 : : *
4500 : : * XXX this may be over-estimating the size now that hashagg knows to omit
4501 : : * unneeded columns from the hashtable. Also for mixed-mode grouping sets,
4502 : : * grouping columns not in the hashed set are counted here even though hashagg
4503 : : * won't store them. Is this a problem?
4504 : : */
4505 : : double
1849 heikki.linnakangas@i 4506 : 1223 : estimate_hashagg_tablesize(PlannerInfo *root, Path *path,
4507 : : const AggClauseCosts *agg_costs, double dNumGroups)
4508 : : {
4509 : : Size hashentrysize;
4510 : :
4511 : 1223 : hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
4512 : 1223 : path->pathtarget->width,
4513 : 1223 : agg_costs->transitionSpace);
4514 : :
4515 : : /*
4516 : : * Note that this disregards the effect of fill-factor and growth policy
4517 : : * of the hash table. That's probably ok, given that the default
4518 : : * fill-factor is relatively high. It'd be hard to meaningfully factor in
4519 : : * "double-in-size" growth policies here.
4520 : : */
2491 tgl@sss.pgh.pa.us 4521 : 1223 : return hashentrysize * dNumGroups;
4522 : : }
4523 : :
4524 : :
4525 : : /*-------------------------------------------------------------------------
4526 : : *
4527 : : * Support routines
4528 : : *
4529 : : *-------------------------------------------------------------------------
4530 : : */
4531 : :
4532 : : /*
4533 : : * Find the best matching ndistinct extended statistics for the given list of
4534 : : * GroupVarInfos.
4535 : : *
4536 : : * Callers must ensure that the given GroupVarInfos all belong to 'rel' and
4537 : : * the GroupVarInfos list does not contain any duplicate Vars or expressions.
4538 : : *
4539 : : * When statistics are found that match > 1 of the given GroupVarInfo, the
4540 : : * *ndistinct parameter is set according to the ndistinct estimate and a new
4541 : : * list is built with the matching GroupVarInfos removed, which is output via
4542 : : * the *varinfos parameter before returning true. When no matching stats are
4543 : : * found, false is returned and the *varinfos and *ndistinct parameters are
4544 : : * left untouched.
4545 : : */
4546 : : static bool
3190 alvherre@alvh.no-ip. 4547 : 174368 : estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
4548 : : List **varinfos, double *ndistinct)
4549 : : {
4550 : : ListCell *lc;
4551 : : int nmatches_vars;
4552 : : int nmatches_exprs;
4553 : 174368 : Oid statOid = InvalidOid;
4554 : : MVNDistinct *stats;
1727 tomas.vondra@postgre 4555 : 174368 : StatisticExtInfo *matched_info = NULL;
1142 tgl@sss.pgh.pa.us 4556 [ + - ]: 174368 : RangeTblEntry *rte = planner_rt_fetch(rel->relid, root);
4557 : :
4558 : : /* bail out immediately if the table has no extended statistics */
3190 alvherre@alvh.no-ip. 4559 [ + + ]: 174368 : if (!rel->statlist)
4560 : 174086 : return false;
4561 : :
4562 : : /* look for the ndistinct statistics object matching the most vars */
1727 tomas.vondra@postgre 4563 : 282 : nmatches_vars = 0; /* we require at least two matches */
4564 : 282 : nmatches_exprs = 0;
3190 alvherre@alvh.no-ip. 4565 [ + - + + : 1122 : foreach(lc, rel->statlist)
+ + ]
4566 : : {
4567 : : ListCell *lc2;
4568 : 840 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
1727 tomas.vondra@postgre 4569 : 840 : int nshared_vars = 0;
4570 : 840 : int nshared_exprs = 0;
4571 : :
4572 : : /* skip statistics of other kinds */
3190 alvherre@alvh.no-ip. 4573 [ + + ]: 840 : if (info->kind != STATS_EXT_NDISTINCT)
4574 : 396 : continue;
4575 : :
4576 : : /* skip statistics with mismatching stxdinherit value */
1142 tgl@sss.pgh.pa.us 4577 [ + + ]: 444 : if (info->inherit != rte->inh)
4578 : 15 : continue;
4579 : :
4580 : : /*
4581 : : * Determine how many expressions (and variables in non-matched
4582 : : * expressions) match. We'll then use these numbers to pick the
4583 : : * statistics object that best matches the clauses.
4584 : : */
1727 tomas.vondra@postgre 4585 [ + + + + : 1359 : foreach(lc2, *varinfos)
+ + ]
4586 : : {
4587 : : ListCell *lc3;
4588 : 930 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
4589 : : AttrNumber attnum;
4590 : :
4591 [ - + ]: 930 : Assert(varinfo->rel == rel);
4592 : :
4593 : : /* simple Var, search in statistics keys directly */
4594 [ + + ]: 930 : if (IsA(varinfo->var, Var))
4595 : : {
4596 : 747 : attnum = ((Var *) varinfo->var)->varattno;
4597 : :
4598 : : /*
4599 : : * Ignore system attributes - we don't support statistics on
4600 : : * them, so can't match them (and it'd fail as the values are
4601 : : * negative).
4602 : : */
4603 [ + + ]: 747 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4604 : 6 : continue;
4605 : :
4606 [ + + ]: 741 : if (bms_is_member(attnum, info->keys))
4607 : 438 : nshared_vars++;
4608 : :
4609 : 741 : continue;
4610 : : }
4611 : :
4612 : : /* expression - see if it's in the statistics object */
4613 [ + + + + : 330 : foreach(lc3, info->exprs)
+ + ]
4614 : : {
4615 : 264 : Node *expr = (Node *) lfirst(lc3);
4616 : :
4617 [ + + ]: 264 : if (equal(varinfo->var, expr))
4618 : : {
4619 : 117 : nshared_exprs++;
4620 : 117 : break;
4621 : : }
4622 : : }
4623 : : }
4624 : :
4625 : : /*
4626 : : * The ndistinct extended statistics contain estimates for a minimum
4627 : : * of pairs of columns which the statistics are defined on and
4628 : : * certainly not single columns. Here we skip unless we managed to
4629 : : * match to at least two columns.
4630 : : */
4631 [ + + ]: 429 : if (nshared_vars + nshared_exprs < 2)
4632 : 198 : continue;
4633 : :
4634 : : /*
4635 : : * Check if these statistics are a better match than the previous best
4636 : : * match and if so, take note of the StatisticExtInfo.
4637 : : *
4638 : : * The statslist is sorted by statOid, so the StatisticExtInfo we
4639 : : * select as the best match is deterministic even when multiple sets
4640 : : * of statistics match equally as well.
4641 : : */
4642 [ + + + - ]: 231 : if ((nshared_exprs > nmatches_exprs) ||
4643 [ + + ]: 177 : (((nshared_exprs == nmatches_exprs)) && (nshared_vars > nmatches_vars)))
4644 : : {
3190 alvherre@alvh.no-ip. 4645 : 219 : statOid = info->statOid;
1727 tomas.vondra@postgre 4646 : 219 : nmatches_vars = nshared_vars;
4647 : 219 : nmatches_exprs = nshared_exprs;
4648 : 219 : matched_info = info;
4649 : : }
4650 : : }
4651 : :
4652 : : /* No match? */
3190 alvherre@alvh.no-ip. 4653 [ + + ]: 282 : if (statOid == InvalidOid)
4654 : 69 : return false;
4655 : :
1727 tomas.vondra@postgre 4656 [ - + ]: 213 : Assert(nmatches_vars + nmatches_exprs > 1);
4657 : :
1431 4658 : 213 : stats = statext_ndistinct_load(statOid, rte->inh);
4659 : :
4660 : : /*
4661 : : * If we have a match, search it for the specific item that matches (there
4662 : : * must be one), and construct the output values.
4663 : : */
3190 alvherre@alvh.no-ip. 4664 [ + - ]: 213 : if (stats)
4665 : : {
4666 : : int i;
3136 bruce@momjian.us 4667 : 213 : List *newlist = NIL;
3190 alvherre@alvh.no-ip. 4668 : 213 : MVNDistinctItem *item = NULL;
4669 : : ListCell *lc2;
1727 tomas.vondra@postgre 4670 : 213 : Bitmapset *matched = NULL;
4671 : : AttrNumber attnum_offset;
4672 : :
4673 : : /*
4674 : : * How much we need to offset the attnums? If there are no
4675 : : * expressions, no offset is needed. Otherwise offset enough to move
4676 : : * the lowest one (which is equal to number of expressions) to 1.
4677 : : */
4678 [ + + ]: 213 : if (matched_info->exprs)
4679 : 75 : attnum_offset = (list_length(matched_info->exprs) + 1);
4680 : : else
4681 : 138 : attnum_offset = 0;
4682 : :
4683 : : /* see what actually matched */
4684 [ + - + + : 744 : foreach(lc2, *varinfos)
+ + ]
4685 : : {
4686 : : ListCell *lc3;
4687 : : int idx;
4688 : 531 : bool found = false;
4689 : :
4690 : 531 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
4691 : :
4692 : : /*
4693 : : * Process a simple Var expression, by matching it to keys
4694 : : * directly. If there's a matching expression, we'll try matching
4695 : : * it later.
4696 : : */
4697 [ + + ]: 531 : if (IsA(varinfo->var, Var))
4698 : : {
4699 : 438 : AttrNumber attnum = ((Var *) varinfo->var)->varattno;
4700 : :
4701 : : /*
4702 : : * Ignore expressions on system attributes. Can't rely on the
4703 : : * bms check for negative values.
4704 : : */
4705 [ + + ]: 438 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4706 : 3 : continue;
4707 : :
4708 : : /* Is the variable covered by the statistics object? */
4709 [ + + ]: 435 : if (!bms_is_member(attnum, matched_info->keys))
4710 : 60 : continue;
4711 : :
4712 : 375 : attnum = attnum + attnum_offset;
4713 : :
4714 : : /* ensure sufficient offset */
4715 [ - + ]: 375 : Assert(AttrNumberIsForUserDefinedAttr(attnum));
4716 : :
4717 : 375 : matched = bms_add_member(matched, attnum);
4718 : :
4719 : 375 : found = true;
4720 : : }
4721 : :
4722 : : /*
4723 : : * XXX Maybe we should allow searching the expressions even if we
4724 : : * found an attribute matching the expression? That would handle
4725 : : * trivial expressions like "(a)" but it seems fairly useless.
4726 : : */
4727 [ + + ]: 468 : if (found)
4728 : 375 : continue;
4729 : :
4730 : : /* expression - see if it's in the statistics object */
4731 : 93 : idx = 0;
4732 [ + + + + : 153 : foreach(lc3, matched_info->exprs)
+ + ]
4733 : : {
4734 : 138 : Node *expr = (Node *) lfirst(lc3);
4735 : :
4736 [ + + ]: 138 : if (equal(varinfo->var, expr))
4737 : : {
4738 : 78 : AttrNumber attnum = -(idx + 1);
4739 : :
4740 : 78 : attnum = attnum + attnum_offset;
4741 : :
4742 : : /* ensure sufficient offset */
4743 [ - + ]: 78 : Assert(AttrNumberIsForUserDefinedAttr(attnum));
4744 : :
4745 : 78 : matched = bms_add_member(matched, attnum);
4746 : :
4747 : : /* there should be just one matching expression */
4748 : 78 : break;
4749 : : }
4750 : :
4751 : 60 : idx++;
4752 : : }
4753 : : }
4754 : :
4755 : : /* Find the specific item that exactly matches the combination */
3190 alvherre@alvh.no-ip. 4756 [ + - ]: 432 : for (i = 0; i < stats->nitems; i++)
4757 : : {
4758 : : int j;
4759 : 432 : MVNDistinctItem *tmpitem = &stats->items[i];
4760 : :
1727 tomas.vondra@postgre 4761 [ + + ]: 432 : if (tmpitem->nattributes != bms_num_members(matched))
4762 : 81 : continue;
4763 : :
4764 : : /* assume it's the right item */
4765 : 351 : item = tmpitem;
4766 : :
4767 : : /* check that all item attributes/expressions fit the match */
4768 [ + + ]: 846 : for (j = 0; j < tmpitem->nattributes; j++)
4769 : : {
4770 : 633 : AttrNumber attnum = tmpitem->attributes[j];
4771 : :
4772 : : /*
4773 : : * Thanks to how we constructed the matched bitmap above, we
4774 : : * can just offset all attnums the same way.
4775 : : */
4776 : 633 : attnum = attnum + attnum_offset;
4777 : :
4778 [ + + ]: 633 : if (!bms_is_member(attnum, matched))
4779 : : {
4780 : : /* nah, it's not this item */
4781 : 138 : item = NULL;
4782 : 138 : break;
4783 : : }
4784 : : }
4785 : :
4786 : : /*
4787 : : * If the item has all the matched attributes, we know it's the
4788 : : * right one - there can't be a better one. matching more.
4789 : : */
4790 [ + + ]: 351 : if (item)
4791 : 213 : break;
4792 : : }
4793 : :
4794 : : /*
4795 : : * Make sure we found an item. There has to be one, because ndistinct
4796 : : * statistics includes all combinations of attributes.
4797 : : */
3190 alvherre@alvh.no-ip. 4798 [ - + ]: 213 : if (!item)
3190 alvherre@alvh.no-ip. 4799 [ # # ]:UBC 0 : elog(ERROR, "corrupt MVNDistinct entry");
4800 : :
4801 : : /* Form the output varinfo list, keeping only unmatched ones */
3190 alvherre@alvh.no-ip. 4802 [ + - + + :CBC 744 : foreach(lc, *varinfos)
+ + ]
4803 : : {
4804 : 531 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
4805 : : ListCell *lc3;
1727 tomas.vondra@postgre 4806 : 531 : bool found = false;
4807 : :
4808 : : /*
4809 : : * Let's look at plain variables first, because it's the most
4810 : : * common case and the check is quite cheap. We can simply get the
4811 : : * attnum and check (with an offset) matched bitmap.
4812 : : */
4813 [ + + ]: 531 : if (IsA(varinfo->var, Var))
3190 alvherre@alvh.no-ip. 4814 : 435 : {
1727 tomas.vondra@postgre 4815 : 438 : AttrNumber attnum = ((Var *) varinfo->var)->varattno;
4816 : :
4817 : : /*
4818 : : * If it's a system attribute, we're done. We don't support
4819 : : * extended statistics on system attributes, so it's clearly
4820 : : * not matched. Just keep the expression and continue.
4821 : : */
4822 [ + + ]: 438 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4823 : : {
4824 : 3 : newlist = lappend(newlist, varinfo);
4825 : 3 : continue;
4826 : : }
4827 : :
4828 : : /* apply the same offset as above */
4829 : 435 : attnum += attnum_offset;
4830 : :
4831 : : /* if it's not matched, keep the varinfo */
4832 [ + + ]: 435 : if (!bms_is_member(attnum, matched))
4833 : 60 : newlist = lappend(newlist, varinfo);
4834 : :
4835 : : /* The rest of the loop deals with complex expressions. */
3190 alvherre@alvh.no-ip. 4836 : 435 : continue;
4837 : : }
4838 : :
4839 : : /*
4840 : : * Process complex expressions, not just simple Vars.
4841 : : *
4842 : : * First, we search for an exact match of an expression. If we
4843 : : * find one, we can just discard the whole GroupVarInfo, with all
4844 : : * the variables we extracted from it.
4845 : : *
4846 : : * Otherwise we inspect the individual vars, and try matching it
4847 : : * to variables in the item.
4848 : : */
1727 tomas.vondra@postgre 4849 [ + + + + : 153 : foreach(lc3, matched_info->exprs)
+ + ]
4850 : : {
4851 : 138 : Node *expr = (Node *) lfirst(lc3);
4852 : :
4853 [ + + ]: 138 : if (equal(varinfo->var, expr))
4854 : : {
4855 : 78 : found = true;
4856 : 78 : break;
4857 : : }
4858 : : }
4859 : :
4860 : : /* found exact match, skip */
4861 [ + + ]: 93 : if (found)
2223 4862 : 78 : continue;
4863 : :
1727 4864 : 15 : newlist = lappend(newlist, varinfo);
4865 : : }
4866 : :
3190 alvherre@alvh.no-ip. 4867 : 213 : *varinfos = newlist;
4868 : 213 : *ndistinct = item->ndistinct;
4869 : 213 : return true;
4870 : : }
4871 : :
3190 alvherre@alvh.no-ip. 4872 :UBC 0 : return false;
4873 : : }
4874 : :
4875 : : /*
4876 : : * convert_to_scalar
4877 : : * Convert non-NULL values of the indicated types to the comparison
4878 : : * scale needed by scalarineqsel().
4879 : : * Returns "true" if successful.
4880 : : *
4881 : : * XXX this routine is a hack: ideally we should look up the conversion
4882 : : * subroutines in pg_type.
4883 : : *
4884 : : * All numeric datatypes are simply converted to their equivalent
4885 : : * "double" values. (NUMERIC values that are outside the range of "double"
4886 : : * are clamped to +/- HUGE_VAL.)
4887 : : *
4888 : : * String datatypes are converted by convert_string_to_scalar(),
4889 : : * which is explained below. The reason why this routine deals with
4890 : : * three values at a time, not just one, is that we need it for strings.
4891 : : *
4892 : : * The bytea datatype is just enough different from strings that it has
4893 : : * to be treated separately.
4894 : : *
4895 : : * The several datatypes representing absolute times are all converted
4896 : : * to Timestamp, which is actually an int64, and then we promote that to
4897 : : * a double. Note this will give correct results even for the "special"
4898 : : * values of Timestamp, since those are chosen to compare correctly;
4899 : : * see timestamp_cmp.
4900 : : *
4901 : : * The several datatypes representing relative times (intervals) are all
4902 : : * converted to measurements expressed in seconds.
4903 : : */
4904 : : static bool
2560 tgl@sss.pgh.pa.us 4905 :CBC 46764 : convert_to_scalar(Datum value, Oid valuetypid, Oid collid, double *scaledvalue,
4906 : : Datum lobound, Datum hibound, Oid boundstypid,
4907 : : double *scaledlobound, double *scaledhibound)
4908 : : {
2846 4909 : 46764 : bool failure = false;
4910 : :
4911 : : /*
4912 : : * Both the valuetypid and the boundstypid should exactly match the
4913 : : * declared input type(s) of the operator we are invoked for. However,
4914 : : * extensions might try to use scalarineqsel as estimator for operators
4915 : : * with input type(s) we don't handle here; in such cases, we want to
4916 : : * return false, not fail. In any case, we mustn't assume that valuetypid
4917 : : * and boundstypid are identical.
4918 : : *
4919 : : * XXX The histogram we are interpolating between points of could belong
4920 : : * to a column that's only binary-compatible with the declared type. In
4921 : : * essence we are assuming that the semantics of binary-compatible types
4922 : : * are enough alike that we can use a histogram generated with one type's
4923 : : * operators to estimate selectivity for the other's. This is outright
4924 : : * wrong in some cases --- in particular signed versus unsigned
4925 : : * interpretation could trip us up. But it's useful enough in the
4926 : : * majority of cases that we do it anyway. Should think about more
4927 : : * rigorous ways to do it.
4928 : : */
9376 4929 [ + + - - : 46764 : switch (valuetypid)
- - ]
4930 : : {
4931 : : /*
4932 : : * Built-in numeric types
4933 : : */
8977 4934 : 43177 : case BOOLOID:
4935 : : case INT2OID:
4936 : : case INT4OID:
4937 : : case INT8OID:
4938 : : case FLOAT4OID:
4939 : : case FLOAT8OID:
4940 : : case NUMERICOID:
4941 : : case OIDOID:
4942 : : case REGPROCOID:
4943 : : case REGPROCEDUREOID:
4944 : : case REGOPEROID:
4945 : : case REGOPERATOROID:
4946 : : case REGCLASSOID:
4947 : : case REGTYPEOID:
4948 : : case REGCOLLATIONOID:
4949 : : case REGCONFIGOID:
4950 : : case REGDICTIONARYOID:
4951 : : case REGROLEOID:
4952 : : case REGNAMESPACEOID:
4953 : : case REGDATABASEOID:
2846 4954 : 43177 : *scaledvalue = convert_numeric_to_scalar(value, valuetypid,
4955 : : &failure);
4956 : 43177 : *scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
4957 : : &failure);
4958 : 43177 : *scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
4959 : : &failure);
4960 : 43177 : return !failure;
4961 : :
4962 : : /*
4963 : : * Built-in string types
4964 : : */
9459 4965 : 3587 : case CHAROID:
4966 : : case BPCHAROID:
4967 : : case VARCHAROID:
4968 : : case TEXTOID:
4969 : : case NAMEOID:
4970 : : {
2846 4971 : 3587 : char *valstr = convert_string_datum(value, valuetypid,
4972 : : collid, &failure);
4973 : 3587 : char *lostr = convert_string_datum(lobound, boundstypid,
4974 : : collid, &failure);
4975 : 3587 : char *histr = convert_string_datum(hibound, boundstypid,
4976 : : collid, &failure);
4977 : :
4978 : : /*
4979 : : * Bail out if any of the values is not of string type. We
4980 : : * might leak converted strings for the other value(s), but
4981 : : * that's not worth troubling over.
4982 : : */
4983 [ - + ]: 3587 : if (failure)
2846 tgl@sss.pgh.pa.us 4984 :UBC 0 : return false;
4985 : :
9036 bruce@momjian.us 4986 :CBC 3587 : convert_string_to_scalar(valstr, scaledvalue,
4987 : : lostr, scaledlobound,
4988 : : histr, scaledhibound);
4989 : 3587 : pfree(valstr);
4990 : 3587 : pfree(lostr);
4991 : 3587 : pfree(histr);
4992 : 3587 : return true;
4993 : : }
4994 : :
4995 : : /*
4996 : : * Built-in bytea type
4997 : : */
8892 tgl@sss.pgh.pa.us 4998 :UBC 0 : case BYTEAOID:
4999 : : {
5000 : : /* We only support bytea vs bytea comparison */
2846 5001 [ # # ]: 0 : if (boundstypid != BYTEAOID)
5002 : 0 : return false;
8892 5003 : 0 : convert_bytea_to_scalar(value, scaledvalue,
5004 : : lobound, scaledlobound,
5005 : : hibound, scaledhibound);
5006 : 0 : return true;
5007 : : }
5008 : :
5009 : : /*
5010 : : * Built-in time types
5011 : : */
9426 5012 : 0 : case TIMESTAMPOID:
5013 : : case TIMESTAMPTZOID:
5014 : : case DATEOID:
5015 : : case INTERVALOID:
5016 : : case TIMEOID:
5017 : : case TIMETZOID:
2846 5018 : 0 : *scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
5019 : : &failure);
5020 : 0 : *scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
5021 : : &failure);
5022 : 0 : *scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
5023 : : &failure);
5024 : 0 : return !failure;
5025 : :
5026 : : /*
5027 : : * Built-in network types
5028 : : */
8957 5029 : 0 : case INETOID:
5030 : : case CIDROID:
5031 : : case MACADDROID:
5032 : : case MACADDR8OID:
2846 5033 : 0 : *scaledvalue = convert_network_to_scalar(value, valuetypid,
5034 : : &failure);
5035 : 0 : *scaledlobound = convert_network_to_scalar(lobound, boundstypid,
5036 : : &failure);
5037 : 0 : *scaledhibound = convert_network_to_scalar(hibound, boundstypid,
5038 : : &failure);
5039 : 0 : return !failure;
5040 : : }
5041 : : /* Don't know how to convert */
7389 5042 : 0 : *scaledvalue = *scaledlobound = *scaledhibound = 0;
9634 5043 : 0 : return false;
5044 : : }
5045 : :
5046 : : /*
5047 : : * Do convert_to_scalar()'s work for any numeric data type.
5048 : : *
5049 : : * On failure (e.g., unsupported typid), set *failure to true;
5050 : : * otherwise, that variable is not changed.
5051 : : */
5052 : : static double
2846 tgl@sss.pgh.pa.us 5053 :CBC 129531 : convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
5054 : : {
9376 5055 [ - + + - : 129531 : switch (typid)
- + - +
- ]
5056 : : {
8990 tgl@sss.pgh.pa.us 5057 :UBC 0 : case BOOLOID:
9334 5058 : 0 : return (double) DatumGetBool(value);
9376 tgl@sss.pgh.pa.us 5059 :CBC 6 : case INT2OID:
5060 : 6 : return (double) DatumGetInt16(value);
5061 : 15903 : case INT4OID:
5062 : 15903 : return (double) DatumGetInt32(value);
9376 tgl@sss.pgh.pa.us 5063 :UBC 0 : case INT8OID:
9334 5064 : 0 : return (double) DatumGetInt64(value);
9376 5065 : 0 : case FLOAT4OID:
9334 5066 : 0 : return (double) DatumGetFloat4(value);
9376 tgl@sss.pgh.pa.us 5067 :CBC 27 : case FLOAT8OID:
9334 5068 : 27 : return (double) DatumGetFloat8(value);
9376 tgl@sss.pgh.pa.us 5069 :UBC 0 : case NUMERICOID:
5070 : : /* Note: out-of-range values will be clamped to +-HUGE_VAL */
8831 5071 : 0 : return (double)
5072 : 0 : DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
5073 : : value));
9376 tgl@sss.pgh.pa.us 5074 :CBC 113595 : case OIDOID:
5075 : : case REGPROCOID:
5076 : : case REGPROCEDUREOID:
5077 : : case REGOPEROID:
5078 : : case REGOPERATOROID:
5079 : : case REGCLASSOID:
5080 : : case REGTYPEOID:
5081 : : case REGCOLLATIONOID:
5082 : : case REGCONFIGOID:
5083 : : case REGDICTIONARYOID:
5084 : : case REGROLEOID:
5085 : : case REGNAMESPACEOID:
5086 : : case REGDATABASEOID:
5087 : : /* we can treat OIDs as integers... */
5088 : 113595 : return (double) DatumGetObjectId(value);
5089 : : }
5090 : :
2846 tgl@sss.pgh.pa.us 5091 :UBC 0 : *failure = true;
9376 5092 : 0 : return 0;
5093 : : }
5094 : :
5095 : : /*
5096 : : * Do convert_to_scalar()'s work for any character-string data type.
5097 : : *
5098 : : * String datatypes are converted to a scale that ranges from 0 to 1,
5099 : : * where we visualize the bytes of the string as fractional digits.
5100 : : *
5101 : : * We do not want the base to be 256, however, since that tends to
5102 : : * generate inflated selectivity estimates; few databases will have
5103 : : * occurrences of all 256 possible byte values at each position.
5104 : : * Instead, use the smallest and largest byte values seen in the bounds
5105 : : * as the estimated range for each byte, after some fudging to deal with
5106 : : * the fact that we probably aren't going to see the full range that way.
5107 : : *
5108 : : * An additional refinement is that we discard any common prefix of the
5109 : : * three strings before computing the scaled values. This allows us to
5110 : : * "zoom in" when we encounter a narrow data range. An example is a phone
5111 : : * number database where all the values begin with the same area code.
5112 : : * (Actually, the bounds will be adjacent histogram-bin-boundary values,
5113 : : * so this is more likely to happen than you might think.)
5114 : : */
5115 : : static void
7389 tgl@sss.pgh.pa.us 5116 :CBC 3587 : convert_string_to_scalar(char *value,
5117 : : double *scaledvalue,
5118 : : char *lobound,
5119 : : double *scaledlobound,
5120 : : char *hibound,
5121 : : double *scaledhibound)
5122 : : {
5123 : : int rangelo,
5124 : : rangehi;
5125 : : char *sptr;
5126 : :
5127 : 3587 : rangelo = rangehi = (unsigned char) hibound[0];
9376 5128 [ + + ]: 46015 : for (sptr = lobound; *sptr; sptr++)
5129 : : {
7389 5130 [ + + ]: 42428 : if (rangelo > (unsigned char) *sptr)
5131 : 8470 : rangelo = (unsigned char) *sptr;
5132 [ + + ]: 42428 : if (rangehi < (unsigned char) *sptr)
5133 : 4458 : rangehi = (unsigned char) *sptr;
5134 : : }
9376 5135 [ + + ]: 37999 : for (sptr = hibound; *sptr; sptr++)
5136 : : {
7389 5137 [ + + ]: 34412 : if (rangelo > (unsigned char) *sptr)
5138 : 471 : rangelo = (unsigned char) *sptr;
5139 [ + + ]: 34412 : if (rangehi < (unsigned char) *sptr)
5140 : 1696 : rangehi = (unsigned char) *sptr;
5141 : : }
5142 : : /* If range includes any upper-case ASCII chars, make it include all */
9376 5143 [ + + + + ]: 3587 : if (rangelo <= 'Z' && rangehi >= 'A')
5144 : : {
5145 [ + + ]: 748 : if (rangelo > 'A')
5146 : 111 : rangelo = 'A';
5147 [ + + ]: 748 : if (rangehi < 'Z')
5148 : 240 : rangehi = 'Z';
5149 : : }
5150 : : /* Ditto lower-case */
5151 [ + - + + ]: 3587 : if (rangelo <= 'z' && rangehi >= 'a')
5152 : : {
5153 [ + + ]: 3324 : if (rangelo > 'a')
5154 : 6 : rangelo = 'a';
5155 [ + + ]: 3324 : if (rangehi < 'z')
5156 : 3281 : rangehi = 'z';
5157 : : }
5158 : : /* Ditto digits */
5159 [ + + + - ]: 3587 : if (rangelo <= '9' && rangehi >= '0')
5160 : : {
5161 [ + + ]: 420 : if (rangelo > '0')
5162 : 350 : rangelo = '0';
5163 [ + + ]: 420 : if (rangehi < '9')
5164 : 17 : rangehi = '9';
5165 : : }
5166 : :
5167 : : /*
5168 : : * If range includes less than 10 chars, assume we have not got enough
5169 : : * data, and make it include regular ASCII set.
5170 : : */
5171 [ - + ]: 3587 : if (rangehi - rangelo < 9)
5172 : : {
9376 tgl@sss.pgh.pa.us 5173 :UBC 0 : rangelo = ' ';
5174 : 0 : rangehi = 127;
5175 : : }
5176 : :
5177 : : /*
5178 : : * Now strip any common prefix of the three strings.
5179 : : */
9376 tgl@sss.pgh.pa.us 5180 [ + + ]:CBC 7848 : while (*lobound)
5181 : : {
5182 [ + + + - ]: 7838 : if (*lobound != *hibound || *lobound != *value)
5183 : : break;
5184 : 4261 : lobound++, hibound++, value++;
5185 : : }
5186 : :
5187 : : /*
5188 : : * Now we can do the conversions.
5189 : : */
5190 : 3587 : *scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
5191 : 3587 : *scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
5192 : 3587 : *scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
5193 : 3587 : }
5194 : :
5195 : : static double
7389 5196 : 10761 : convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
5197 : : {
5198 : 10761 : int slen = strlen(value);
5199 : : double num,
5200 : : denom,
5201 : : base;
5202 : :
9376 5203 [ + + ]: 10761 : if (slen <= 0)
5204 : 10 : return 0.0; /* empty string has scalar value 0 */
5205 : :
5206 : : /*
5207 : : * There seems little point in considering more than a dozen bytes from
5208 : : * the string. Since base is at least 10, that will give us nominal
5209 : : * resolution of at least 12 decimal digits, which is surely far more
5210 : : * precision than this estimation technique has got anyway (especially in
5211 : : * non-C locales). Also, even with the maximum possible base of 256, this
5212 : : * ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
5213 : : * overflow on any known machine.
5214 : : */
3769 5215 [ + + ]: 10751 : if (slen > 12)
5216 : 2525 : slen = 12;
5217 : :
5218 : : /* Convert initial characters to fraction */
9376 5219 : 10751 : base = rangehi - rangelo + 1;
5220 : 10751 : num = 0.0;
5221 : 10751 : denom = base;
5222 [ + + ]: 87327 : while (slen-- > 0)
5223 : : {
7389 5224 : 76576 : int ch = (unsigned char) *value++;
5225 : :
9376 5226 [ + + ]: 76576 : if (ch < rangelo)
9036 bruce@momjian.us 5227 : 82 : ch = rangelo - 1;
9376 tgl@sss.pgh.pa.us 5228 [ - + ]: 76494 : else if (ch > rangehi)
9036 bruce@momjian.us 5229 :UBC 0 : ch = rangehi + 1;
9376 tgl@sss.pgh.pa.us 5230 :CBC 76576 : num += ((double) (ch - rangelo)) / denom;
5231 : 76576 : denom *= base;
5232 : : }
5233 : :
5234 : 10751 : return num;
5235 : : }
5236 : :
5237 : : /*
5238 : : * Convert a string-type Datum into a palloc'd, null-terminated string.
5239 : : *
5240 : : * On failure (e.g., unsupported typid), set *failure to true;
5241 : : * otherwise, that variable is not changed. (We'll return NULL on failure.)
5242 : : *
5243 : : * When using a non-C locale, we must pass the string through pg_strxfrm()
5244 : : * before continuing, so as to generate correct locale-specific results.
5245 : : */
5246 : : static char *
2560 5247 : 10761 : convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
5248 : : {
5249 : : char *val;
5250 : : pg_locale_t mylocale;
5251 : :
9376 5252 [ - + + - ]: 10761 : switch (typid)
5253 : : {
9376 tgl@sss.pgh.pa.us 5254 :UBC 0 : case CHAROID:
5255 : 0 : val = (char *) palloc(2);
5256 : 0 : val[0] = DatumGetChar(value);
5257 : 0 : val[1] = '\0';
5258 : 0 : break;
9376 tgl@sss.pgh.pa.us 5259 :CBC 3319 : case BPCHAROID:
5260 : : case VARCHAROID:
5261 : : case TEXTOID:
6476 5262 : 3319 : val = TextDatumGetCString(value);
5263 : 3319 : break;
9376 5264 : 7442 : case NAMEOID:
5265 : : {
9036 bruce@momjian.us 5266 : 7442 : NameData *nm = (NameData *) DatumGetPointer(value);
5267 : :
5268 : 7442 : val = pstrdup(NameStr(*nm));
5269 : 7442 : break;
5270 : : }
9376 tgl@sss.pgh.pa.us 5271 :UBC 0 : default:
2846 5272 : 0 : *failure = true;
9376 5273 : 0 : return NULL;
5274 : : }
5275 : :
469 jdavis@postgresql.or 5276 :CBC 10761 : mylocale = pg_newlocale_from_collation(collid);
5277 : :
5278 [ + + ]: 10761 : if (!mylocale->collate_is_c)
5279 : : {
5280 : : char *xfrmstr;
5281 : : size_t xfrmlen;
5282 : : size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
5283 : :
5284 : : /*
5285 : : * XXX: We could guess at a suitable output buffer size and only call
5286 : : * pg_strxfrm() twice if our guess is too small.
5287 : : *
5288 : : * XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
5289 : : * bogus data or set an error. This is not really a problem unless it
5290 : : * crashes since it will only give an estimation error and nothing
5291 : : * fatal.
5292 : : *
5293 : : * XXX: we do not check pg_strxfrm_enabled(). On some platforms and in
5294 : : * some cases, libc strxfrm() may return the wrong results, but that
5295 : : * will only lead to an estimation error.
5296 : : */
498 5297 : 36 : xfrmlen = pg_strxfrm(NULL, val, 0, mylocale);
5298 : : #ifdef WIN32
5299 : :
5300 : : /*
5301 : : * On Windows, strxfrm returns INT_MAX when an error occurs. Instead
5302 : : * of trying to allocate this much memory (and fail), just return the
5303 : : * original string unmodified as if we were in the C locale.
5304 : : */
5305 : : if (xfrmlen == INT_MAX)
5306 : : return val;
5307 : : #endif
8189 tgl@sss.pgh.pa.us 5308 : 36 : xfrmstr = (char *) palloc(xfrmlen + 1);
498 jdavis@postgresql.or 5309 : 36 : xfrmlen2 = pg_strxfrm(xfrmstr, val, xfrmlen + 1, mylocale);
5310 : :
5311 : : /*
5312 : : * Some systems (e.g., glibc) can return a smaller value from the
5313 : : * second call than the first; thus the Assert must be <= not ==.
5314 : : */
8189 tgl@sss.pgh.pa.us 5315 [ - + ]: 36 : Assert(xfrmlen2 <= xfrmlen);
8659 peter_e@gmx.net 5316 : 36 : pfree(val);
5317 : 36 : val = xfrmstr;
5318 : : }
5319 : :
7389 tgl@sss.pgh.pa.us 5320 : 10761 : return val;
5321 : : }
5322 : :
5323 : : /*
5324 : : * Do convert_to_scalar()'s work for any bytea data type.
5325 : : *
5326 : : * Very similar to convert_string_to_scalar except we can't assume
5327 : : * null-termination and therefore pass explicit lengths around.
5328 : : *
5329 : : * Also, assumptions about likely "normal" ranges of characters have been
5330 : : * removed - a data range of 0..255 is always used, for now. (Perhaps
5331 : : * someday we will add information about actual byte data range to
5332 : : * pg_statistic.)
5333 : : */
5334 : : static void
8892 tgl@sss.pgh.pa.us 5335 :UBC 0 : convert_bytea_to_scalar(Datum value,
5336 : : double *scaledvalue,
5337 : : Datum lobound,
5338 : : double *scaledlobound,
5339 : : Datum hibound,
5340 : : double *scaledhibound)
5341 : : {
2846 5342 : 0 : bytea *valuep = DatumGetByteaPP(value);
5343 : 0 : bytea *loboundp = DatumGetByteaPP(lobound);
5344 : 0 : bytea *hiboundp = DatumGetByteaPP(hibound);
5345 : : int rangelo,
5346 : : rangehi,
5347 [ # # # # : 0 : valuelen = VARSIZE_ANY_EXHDR(valuep),
# # # # #
# ]
5348 [ # # # # : 0 : loboundlen = VARSIZE_ANY_EXHDR(loboundp),
# # # # #
# ]
5349 [ # # # # : 0 : hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
# # # # #
# ]
5350 : : i,
5351 : : minlen;
5352 [ # # ]: 0 : unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
5353 [ # # ]: 0 : unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
5354 [ # # ]: 0 : unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
5355 : :
5356 : : /*
5357 : : * Assume bytea data is uniformly distributed across all byte values.
5358 : : */
8892 5359 : 0 : rangelo = 0;
5360 : 0 : rangehi = 255;
5361 : :
5362 : : /*
5363 : : * Now strip any common prefix of the three strings.
5364 : : */
5365 : 0 : minlen = Min(Min(valuelen, loboundlen), hiboundlen);
5366 [ # # ]: 0 : for (i = 0; i < minlen; i++)
5367 : : {
5368 [ # # # # ]: 0 : if (*lostr != *histr || *lostr != *valstr)
5369 : : break;
5370 : 0 : lostr++, histr++, valstr++;
5371 : 0 : loboundlen--, hiboundlen--, valuelen--;
5372 : : }
5373 : :
5374 : : /*
5375 : : * Now we can do the conversions.
5376 : : */
5377 : 0 : *scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
5378 : 0 : *scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
5379 : 0 : *scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
5380 : 0 : }
5381 : :
5382 : : static double
5383 : 0 : convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
5384 : : int rangelo, int rangehi)
5385 : : {
5386 : : double num,
5387 : : denom,
5388 : : base;
5389 : :
5390 [ # # ]: 0 : if (valuelen <= 0)
5391 : 0 : return 0.0; /* empty string has scalar value 0 */
5392 : :
5393 : : /*
5394 : : * Since base is 256, need not consider more than about 10 chars (even
5395 : : * this many seems like overkill)
5396 : : */
5397 [ # # ]: 0 : if (valuelen > 10)
5398 : 0 : valuelen = 10;
5399 : :
5400 : : /* Convert initial characters to fraction */
5401 : 0 : base = rangehi - rangelo + 1;
5402 : 0 : num = 0.0;
5403 : 0 : denom = base;
5404 [ # # ]: 0 : while (valuelen-- > 0)
5405 : : {
5406 : 0 : int ch = *value++;
5407 : :
5408 [ # # ]: 0 : if (ch < rangelo)
5409 : 0 : ch = rangelo - 1;
5410 [ # # ]: 0 : else if (ch > rangehi)
5411 : 0 : ch = rangehi + 1;
5412 : 0 : num += ((double) (ch - rangelo)) / denom;
5413 : 0 : denom *= base;
5414 : : }
5415 : :
5416 : 0 : return num;
5417 : : }
5418 : :
5419 : : /*
5420 : : * Do convert_to_scalar()'s work for any timevalue data type.
5421 : : *
5422 : : * On failure (e.g., unsupported typid), set *failure to true;
5423 : : * otherwise, that variable is not changed.
5424 : : */
5425 : : static double
2846 5426 : 0 : convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
5427 : : {
9376 5428 [ # # # # : 0 : switch (typid)
# # # ]
5429 : : {
8977 5430 : 0 : case TIMESTAMPOID:
9322 5431 : 0 : return DatumGetTimestamp(value);
8841 5432 : 0 : case TIMESTAMPTZOID:
5433 : 0 : return DatumGetTimestampTz(value);
9376 5434 : 0 : case DATEOID:
5468 5435 : 0 : return date2timestamp_no_overflow(DatumGetDateADT(value));
9376 5436 : 0 : case INTERVALOID:
5437 : : {
9036 bruce@momjian.us 5438 : 0 : Interval *interval = DatumGetIntervalP(value);
5439 : :
5440 : : /*
5441 : : * Convert the month part of Interval to days using assumed
5442 : : * average month length of 365.25/12.0 days. Not too
5443 : : * accurate, but plenty good enough for our purposes.
5444 : : *
5445 : : * This also works for infinite intervals, which just have all
5446 : : * fields set to INT_MIN/INT_MAX, and so will produce a result
5447 : : * smaller/larger than any finite interval.
5448 : : */
7368 5449 : 0 : return interval->time + interval->day * (double) USECS_PER_DAY +
5450 : 0 : interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
5451 : : }
9376 tgl@sss.pgh.pa.us 5452 : 0 : case TIMEOID:
9322 5453 : 0 : return DatumGetTimeADT(value);
8957 5454 : 0 : case TIMETZOID:
5455 : : {
5456 : 0 : TimeTzADT *timetz = DatumGetTimeTzADTP(value);
5457 : :
5458 : : /* use GMT-equivalent time */
8641 lockhart@fourpalms.o 5459 : 0 : return (double) (timetz->time + (timetz->zone * 1000000.0));
5460 : : }
5461 : : }
5462 : :
2846 tgl@sss.pgh.pa.us 5463 : 0 : *failure = true;
9376 5464 : 0 : return 0;
5465 : : }
5466 : :
5467 : :
5468 : : /*
5469 : : * get_restriction_variable
5470 : : * Examine the args of a restriction clause to see if it's of the
5471 : : * form (variable op pseudoconstant) or (pseudoconstant op variable),
5472 : : * where "variable" could be either a Var or an expression in vars of a
5473 : : * single relation. If so, extract information about the variable,
5474 : : * and also indicate which side it was on and the other argument.
5475 : : *
5476 : : * Inputs:
5477 : : * root: the planner info
5478 : : * args: clause argument list
5479 : : * varRelid: see specs for restriction selectivity functions
5480 : : *
5481 : : * Outputs: (these are valid only if true is returned)
5482 : : * *vardata: gets information about variable (see examine_variable)
5483 : : * *other: gets other clause argument, aggressively reduced to a constant
5484 : : * *varonleft: set true if variable is on the left, false if on the right
5485 : : *
5486 : : * Returns true if a variable is identified, otherwise false.
5487 : : *
5488 : : * Note: if there are Vars on both sides of the clause, we must fail, because
5489 : : * callers are expecting that the other side will act like a pseudoconstant.
5490 : : */
5491 : : bool
7500 tgl@sss.pgh.pa.us 5492 :CBC 412450 : get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
5493 : : VariableStatData *vardata, Node **other,
5494 : : bool *varonleft)
5495 : : {
5496 : : Node *left,
5497 : : *right;
5498 : : VariableStatData rdata;
5499 : :
5500 : : /* Fail if not a binary opclause (probably shouldn't happen) */
7871 neilc@samurai.com 5501 [ - + ]: 412450 : if (list_length(args) != 2)
7974 tgl@sss.pgh.pa.us 5502 :UBC 0 : return false;
5503 : :
7875 neilc@samurai.com 5504 :CBC 412450 : left = (Node *) linitial(args);
7974 tgl@sss.pgh.pa.us 5505 : 412450 : right = (Node *) lsecond(args);
5506 : :
5507 : : /*
5508 : : * Examine both sides. Note that when varRelid is nonzero, Vars of other
5509 : : * relations will be treated as pseudoconstants.
5510 : : */
5511 : 412450 : examine_variable(root, left, varRelid, vardata);
5512 : 412450 : examine_variable(root, right, varRelid, &rdata);
5513 : :
5514 : : /*
5515 : : * If one side is a variable and the other not, we win.
5516 : : */
5517 [ + + + + ]: 412450 : if (vardata->rel && rdata.rel == NULL)
5518 : : {
5519 : 367986 : *varonleft = true;
6876 5520 : 367986 : *other = estimate_expression_value(root, rdata.var);
5521 : : /* Assume we need no ReleaseVariableStats(rdata) here */
7974 5522 : 367983 : return true;
5523 : : }
5524 : :
5525 [ + + + + ]: 44464 : if (vardata->rel == NULL && rdata.rel)
5526 : : {
5527 : 41154 : *varonleft = false;
6876 5528 : 41154 : *other = estimate_expression_value(root, vardata->var);
5529 : : /* Assume we need no ReleaseVariableStats(*vardata) here */
7974 5530 : 41154 : *vardata = rdata;
5531 : 41154 : return true;
5532 : : }
5533 : :
5534 : : /* Oops, clause has wrong structure (probably var op var) */
5535 [ + + ]: 3310 : ReleaseVariableStats(*vardata);
5536 [ + + ]: 3310 : ReleaseVariableStats(rdata);
5537 : :
5538 : 3310 : return false;
5539 : : }
5540 : :
5541 : : /*
5542 : : * get_join_variables
5543 : : * Apply examine_variable() to each side of a join clause.
5544 : : * Also, attempt to identify whether the join clause has the same
5545 : : * or reversed sense compared to the SpecialJoinInfo.
5546 : : *
5547 : : * We consider the join clause "normal" if it is "lhs_var OP rhs_var",
5548 : : * or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
5549 : : * where we can't tell for sure, we default to assuming it's normal.
5550 : : */
5551 : : void
6332 5552 : 135023 : get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
5553 : : VariableStatData *vardata1, VariableStatData *vardata2,
5554 : : bool *join_is_reversed)
5555 : : {
5556 : : Node *left,
5557 : : *right;
5558 : :
7871 neilc@samurai.com 5559 [ - + ]: 135023 : if (list_length(args) != 2)
7974 tgl@sss.pgh.pa.us 5560 [ # # ]:UBC 0 : elog(ERROR, "join operator should take two arguments");
5561 : :
7875 neilc@samurai.com 5562 :CBC 135023 : left = (Node *) linitial(args);
8977 tgl@sss.pgh.pa.us 5563 : 135023 : right = (Node *) lsecond(args);
5564 : :
7974 5565 : 135023 : examine_variable(root, left, 0, vardata1);
5566 : 135023 : examine_variable(root, right, 0, vardata2);
5567 : :
6332 5568 [ + + + + ]: 269955 : if (vardata1->rel &&
5569 : 134932 : bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
3101 5570 : 45575 : *join_is_reversed = true; /* var1 is on RHS */
6332 5571 [ + + + + ]: 178821 : else if (vardata2->rel &&
5572 : 89373 : bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
3101 5573 : 73 : *join_is_reversed = true; /* var2 is on LHS */
5574 : : else
6332 5575 : 89375 : *join_is_reversed = false;
7974 5576 : 135023 : }
5577 : :
5578 : : /* statext_expressions_load copies the tuple, so just pfree it. */
5579 : : static void
1727 tomas.vondra@postgre 5580 : 825 : ReleaseDummy(HeapTuple tuple)
5581 : : {
5582 : 825 : pfree(tuple);
5583 : 825 : }
5584 : :
5585 : : /*
5586 : : * examine_variable
5587 : : * Try to look up statistical data about an expression.
5588 : : * Fill in a VariableStatData struct to describe the expression.
5589 : : *
5590 : : * Inputs:
5591 : : * root: the planner info
5592 : : * node: the expression tree to examine
5593 : : * varRelid: see specs for restriction selectivity functions
5594 : : *
5595 : : * Outputs: *vardata is filled as follows:
5596 : : * var: the input expression (with any binary relabeling stripped, if
5597 : : * it is or contains a variable; but otherwise the type is preserved)
5598 : : * rel: RelOptInfo for relation containing variable; NULL if expression
5599 : : * contains no Vars (NOTE this could point to a RelOptInfo of a
5600 : : * subquery, not one in the current query).
5601 : : * statsTuple: the pg_statistic entry for the variable, if one exists;
5602 : : * otherwise NULL.
5603 : : * freefunc: pointer to a function to release statsTuple with.
5604 : : * vartype: exposed type of the expression; this should always match
5605 : : * the declared input type of the operator we are estimating for.
5606 : : * atttype, atttypmod: actual type/typmod of the "var" expression. This is
5607 : : * commonly the same as the exposed type of the variable argument,
5608 : : * but can be different in binary-compatible-type cases.
5609 : : * isunique: true if we were able to match the var to a unique index, a
5610 : : * single-column DISTINCT or GROUP-BY clause, implying its values are
5611 : : * unique for this query. (Caution: this should be trusted for
5612 : : * statistical purposes only, since we do not check indimmediate nor
5613 : : * verify that the exact same definition of equality applies.)
5614 : : * acl_ok: true if current user has permission to read all table rows from
5615 : : * the column(s) underlying the pg_statistic entry. This is consulted by
5616 : : * statistic_proc_security_check().
5617 : : *
5618 : : * Caller is responsible for doing ReleaseVariableStats() before exiting.
5619 : : */
5620 : : void
7500 tgl@sss.pgh.pa.us 5621 : 1653452 : examine_variable(PlannerInfo *root, Node *node, int varRelid,
5622 : : VariableStatData *vardata)
5623 : : {
5624 : : Node *basenode;
5625 : : Relids varnos;
5626 : : Relids basevarnos;
5627 : : RelOptInfo *onerel;
5628 : :
5629 : : /* Make sure we don't return dangling pointers in vardata */
7974 5630 [ + - + - : 11574164 : MemSet(vardata, 0, sizeof(VariableStatData));
+ - + - +
+ ]
5631 : :
5632 : : /* Save the exposed type of the expression */
7565 5633 : 1653452 : vardata->vartype = exprType(node);
5634 : :
5635 : : /* Look inside any binary-compatible relabeling */
5636 : :
7974 5637 [ + + ]: 1653452 : if (IsA(node, RelabelType))
7571 5638 : 24136 : basenode = (Node *) ((RelabelType *) node)->arg;
5639 : : else
5640 : 1629316 : basenode = node;
5641 : :
5642 : : /* Fast path for a simple Var */
5643 : :
5644 [ + + + + ]: 1653452 : if (IsA(basenode, Var) &&
5645 [ + + ]: 398923 : (varRelid == 0 || varRelid == ((Var *) basenode)->varno))
5646 : : {
5647 : 1183415 : Var *var = (Var *) basenode;
5648 : :
5649 : : /* Set up result fields other than the stats tuple */
5650 : 1183415 : vardata->var = basenode; /* return Var without relabeling */
7974 5651 : 1183415 : vardata->rel = find_base_rel(root, var->varno);
5652 : 1183415 : vardata->atttype = var->vartype;
5653 : 1183415 : vardata->atttypmod = var->vartypmod;
6149 5654 : 1183415 : vardata->isunique = has_unique_index(vardata->rel, var->varattno);
5655 : :
5656 : : /* Try to locate some stats */
5218 5657 : 1183415 : examine_simple_variable(root, var, vardata);
5658 : :
7974 5659 : 1183415 : return;
5660 : : }
5661 : :
5662 : : /*
5663 : : * Okay, it's a more complicated expression. Determine variable
5664 : : * membership. Note that when varRelid isn't zero, only vars of that
5665 : : * relation are considered "real" vars.
5666 : : */
1791 5667 : 470037 : varnos = pull_varnos(root, basenode);
349 rguo@postgresql.org 5668 : 470037 : basevarnos = bms_difference(varnos, root->outer_join_rels);
5669 : :
7974 tgl@sss.pgh.pa.us 5670 : 470037 : onerel = NULL;
5671 : :
349 rguo@postgresql.org 5672 [ + + ]: 470037 : if (bms_is_empty(basevarnos))
5673 : : {
5674 : : /* No Vars at all ... must be pseudo-constant clause */
5675 : : }
5676 : : else
5677 : : {
5678 : : int relid;
5679 : :
5680 : : /* Check if the expression is in vars of a single base relation */
5681 [ + + ]: 238965 : if (bms_get_singleton_member(basevarnos, &relid))
5682 : : {
750 drowley@postgresql.o 5683 [ + + + + ]: 235501 : if (varRelid == 0 || varRelid == relid)
5684 : : {
5685 : 35176 : onerel = find_base_rel(root, relid);
7974 tgl@sss.pgh.pa.us 5686 : 35176 : vardata->rel = onerel;
7368 bruce@momjian.us 5687 : 35176 : node = basenode; /* strip any relabeling */
5688 : : }
5689 : : /* else treat it as a constant */
5690 : : }
5691 : : else
5692 : : {
5693 : : /* varnos has multiple relids */
7974 tgl@sss.pgh.pa.us 5694 [ + + ]: 3464 : if (varRelid == 0)
5695 : : {
5696 : : /* treat it as a variable of a join relation */
5697 : 2726 : vardata->rel = find_join_rel(root, varnos);
7368 bruce@momjian.us 5698 : 2726 : node = basenode; /* strip any relabeling */
5699 : : }
7974 tgl@sss.pgh.pa.us 5700 [ + + ]: 738 : else if (bms_is_member(varRelid, varnos))
5701 : : {
5702 : : /* ignore the vars belonging to other relations */
5703 : 651 : vardata->rel = find_base_rel(root, varRelid);
7368 bruce@momjian.us 5704 : 651 : node = basenode; /* strip any relabeling */
5705 : : /* note: no point in expressional-index search here */
5706 : : }
5707 : : /* else treat it as a constant */
5708 : : }
5709 : : }
5710 : :
349 rguo@postgresql.org 5711 : 470037 : bms_free(basevarnos);
5712 : :
7571 tgl@sss.pgh.pa.us 5713 : 470037 : vardata->var = node;
7974 5714 : 470037 : vardata->atttype = exprType(node);
5715 : 470037 : vardata->atttypmod = exprTypmod(node);
5716 : :
5717 [ + + ]: 470037 : if (onerel)
5718 : : {
5719 : : /*
5720 : : * We have an expression in vars of a single relation. Try to match
5721 : : * it to expressional index columns, in hopes of finding some
5722 : : * statistics.
5723 : : *
5724 : : * Note that we consider all index columns including INCLUDE columns,
5725 : : * since there could be stats for such columns. But the test for
5726 : : * uniqueness needs to be warier.
5727 : : *
5728 : : * XXX it's conceivable that there are multiple matches with different
5729 : : * index opfamilies; if so, we need to pick one that matches the
5730 : : * operator we are estimating for. FIXME later.
5731 : : */
5732 : : ListCell *ilist;
5733 : : ListCell *slist;
5734 : :
5735 : : /*
5736 : : * The nullingrels bits within the expression could prevent us from
5737 : : * matching it to expressional index columns or to the expressions in
5738 : : * extended statistics. So strip them out first.
5739 : : */
349 rguo@postgresql.org 5740 [ + + ]: 35176 : if (bms_overlap(varnos, root->outer_join_rels))
5741 : 1548 : node = remove_nulling_relids(node, root->outer_join_rels, NULL);
5742 : :
7974 tgl@sss.pgh.pa.us 5743 [ + + + + : 76089 : foreach(ilist, onerel->indexlist)
+ + ]
5744 : : {
5745 : 42404 : IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
5746 : : ListCell *indexpr_item;
5747 : : int pos;
5748 : :
7875 neilc@samurai.com 5749 : 42404 : indexpr_item = list_head(index->indexprs);
5750 [ + + ]: 42404 : if (indexpr_item == NULL)
7974 tgl@sss.pgh.pa.us 5751 : 39947 : continue; /* no expressions here... */
5752 : :
5753 [ + + ]: 3459 : for (pos = 0; pos < index->ncolumns; pos++)
5754 : : {
5755 [ + + ]: 2493 : if (index->indexkeys[pos] == 0)
5756 : : {
5757 : : Node *indexkey;
5758 : :
7875 neilc@samurai.com 5759 [ - + ]: 2457 : if (indexpr_item == NULL)
7974 tgl@sss.pgh.pa.us 5760 [ # # ]:UBC 0 : elog(ERROR, "too few entries in indexprs list");
7875 neilc@samurai.com 5761 :CBC 2457 : indexkey = (Node *) lfirst(indexpr_item);
7974 tgl@sss.pgh.pa.us 5762 [ + - - + ]: 2457 : if (indexkey && IsA(indexkey, RelabelType))
7974 tgl@sss.pgh.pa.us 5763 :UBC 0 : indexkey = (Node *) ((RelabelType *) indexkey)->arg;
7974 tgl@sss.pgh.pa.us 5764 [ + + ]:CBC 2457 : if (equal(node, indexkey))
5765 : : {
5766 : : /*
5767 : : * Found a match ... is it a unique index? Tests here
5768 : : * should match has_unique_index().
5769 : : */
5770 [ + + ]: 1809 : if (index->unique &&
2811 teodor@sigaev.ru 5771 [ + - + - ]: 219 : index->nkeycolumns == 1 &&
2500 tgl@sss.pgh.pa.us 5772 : 219 : pos == 0 &&
6149 5773 [ - + - - ]: 219 : (index->indpred == NIL || index->predOK))
7974 5774 : 219 : vardata->isunique = true;
5775 : :
5776 : : /*
5777 : : * Has it got stats? We only consider stats for
5778 : : * non-partial indexes, since partial indexes probably
5779 : : * don't reflect whole-relation statistics; the above
5780 : : * check for uniqueness is the only info we take from
5781 : : * a partial index.
5782 : : *
5783 : : * An index stats hook, however, must make its own
5784 : : * decisions about what to do with partial indexes.
5785 : : */
6289 5786 [ - + - - ]: 1809 : if (get_index_stats_hook &&
6289 tgl@sss.pgh.pa.us 5787 :UBC 0 : (*get_index_stats_hook) (root, index->indexoid,
5788 : 0 : pos + 1, vardata))
5789 : : {
5790 : : /*
5791 : : * The hook took control of acquiring a stats
5792 : : * tuple. If it did supply a tuple, it'd better
5793 : : * have supplied a freefunc.
5794 : : */
5795 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
5796 [ # # ]: 0 : !vardata->freefunc)
5797 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
5798 : : }
6149 tgl@sss.pgh.pa.us 5799 [ + - ]:CBC 1809 : else if (index->indpred == NIL)
5800 : : {
6289 5801 : 1809 : vardata->statsTuple =
5785 rhaas@postgresql.org 5802 : 3618 : SearchSysCache3(STATRELATTINH,
5803 : : ObjectIdGetDatum(index->indexoid),
5773 bruce@momjian.us 5804 : 1809 : Int16GetDatum(pos + 1),
5805 : : BoolGetDatum(false));
6289 tgl@sss.pgh.pa.us 5806 : 1809 : vardata->freefunc = ReleaseSysCache;
5807 : :
3148 peter_e@gmx.net 5808 [ + + ]: 1809 : if (HeapTupleIsValid(vardata->statsTuple))
5809 : : {
5810 : : /*
5811 : : * Test if user has permission to access all
5812 : : * rows from the index's table.
5813 : : *
5814 : : * For simplicity, we insist on the whole
5815 : : * table being selectable, rather than trying
5816 : : * to identify which column(s) the index
5817 : : * depends on.
5818 : : *
5819 : : * Note that for an inheritance child,
5820 : : * permissions are checked on the inheritance
5821 : : * root parent, and whole-table select
5822 : : * privilege on the parent doesn't quite
5823 : : * guarantee that the user could read all
5824 : : * columns of the child. But in practice it's
5825 : : * unlikely that any interesting security
5826 : : * violation could result from allowing access
5827 : : * to the expression index's stats, so we
5828 : : * allow it anyway. See similar code in
5829 : : * examine_simple_variable() for additional
5830 : : * comments.
5831 : : */
5832 : 1491 : vardata->acl_ok =
128 dean.a.rasheed@gmail 5833 : 1491 : all_rows_selectable(root,
5834 : 1491 : index->rel->relid,
5835 : : NULL);
5836 : : }
5837 : : else
5838 : : {
5839 : : /* suppress leakproofness checks later */
3148 peter_e@gmx.net 5840 : 318 : vardata->acl_ok = true;
5841 : : }
5842 : : }
7974 tgl@sss.pgh.pa.us 5843 [ + + ]: 1809 : if (vardata->statsTuple)
5844 : 1491 : break;
5845 : : }
2347 5846 : 966 : indexpr_item = lnext(index->indexprs, indexpr_item);
5847 : : }
5848 : : }
7974 5849 [ + + ]: 2457 : if (vardata->statsTuple)
5850 : 1491 : break;
5851 : : }
5852 : :
5853 : : /*
5854 : : * Search extended statistics for one with a matching expression.
5855 : : * There might be multiple ones, so just grab the first one. In the
5856 : : * future, we might consider the statistics target (and pick the most
5857 : : * accurate statistics) and maybe some other parameters.
5858 : : */
1727 tomas.vondra@postgre 5859 [ + + + + : 37234 : foreach(slist, onerel->statlist)
+ + ]
5860 : : {
5861 : 2202 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(slist);
1315 tgl@sss.pgh.pa.us 5862 [ + - ]: 2202 : RangeTblEntry *rte = planner_rt_fetch(onerel->relid, root);
5863 : : ListCell *expr_item;
5864 : : int pos;
5865 : :
5866 : : /*
5867 : : * Stop once we've found statistics for the expression (either
5868 : : * from extended stats, or for an index in the preceding loop).
5869 : : */
1727 tomas.vondra@postgre 5870 [ + + ]: 2202 : if (vardata->statsTuple)
5871 : 144 : break;
5872 : :
5873 : : /* skip stats without per-expression stats */
5874 [ + + ]: 2058 : if (info->kind != STATS_EXT_EXPRESSIONS)
5875 : 1053 : continue;
5876 : :
5877 : : /* skip stats with mismatching stxdinherit value */
1142 tgl@sss.pgh.pa.us 5878 [ + + ]: 1005 : if (info->inherit != rte->inh)
5879 : 3 : continue;
5880 : :
1727 tomas.vondra@postgre 5881 : 1002 : pos = 0;
5882 [ + - + + : 1653 : foreach(expr_item, info->exprs)
+ + ]
5883 : : {
5884 : 1476 : Node *expr = (Node *) lfirst(expr_item);
5885 : :
5886 [ - + ]: 1476 : Assert(expr);
5887 : :
5888 : : /* strip RelabelType before comparing it */
5889 [ + - - + ]: 1476 : if (expr && IsA(expr, RelabelType))
1727 tomas.vondra@postgre 5890 :UBC 0 : expr = (Node *) ((RelabelType *) expr)->arg;
5891 : :
5892 : : /* found a match, see if we can extract pg_statistic row */
1727 tomas.vondra@postgre 5893 [ + + ]:CBC 1476 : if (equal(node, expr))
5894 : : {
5895 : : /*
5896 : : * XXX Not sure if we should cache the tuple somewhere.
5897 : : * Now we just create a new copy every time.
5898 : : */
1431 5899 : 825 : vardata->statsTuple =
5900 : 825 : statext_expressions_load(info->statOid, rte->inh, pos);
5901 : :
5902 : 825 : vardata->freefunc = ReleaseDummy;
5903 : :
5904 : : /*
5905 : : * Test if user has permission to access all rows from the
5906 : : * table.
5907 : : *
5908 : : * For simplicity, we insist on the whole table being
5909 : : * selectable, rather than trying to identify which
5910 : : * column(s) the statistics object depends on.
5911 : : *
5912 : : * Note that for an inheritance child, permissions are
5913 : : * checked on the inheritance root parent, and whole-table
5914 : : * select privilege on the parent doesn't quite guarantee
5915 : : * that the user could read all columns of the child. But
5916 : : * in practice it's unlikely that any interesting security
5917 : : * violation could result from allowing access to the
5918 : : * expression stats, so we allow it anyway. See similar
5919 : : * code in examine_simple_variable() for additional
5920 : : * comments.
5921 : : */
128 dean.a.rasheed@gmail 5922 : 825 : vardata->acl_ok = all_rows_selectable(root,
5923 : : onerel->relid,
5924 : : NULL);
5925 : :
1727 tomas.vondra@postgre 5926 : 825 : break;
5927 : : }
5928 : :
5929 : 651 : pos++;
5930 : : }
5931 : : }
5932 : : }
5933 : :
349 rguo@postgresql.org 5934 : 470037 : bms_free(varnos);
5935 : : }
5936 : :
5937 : : /*
5938 : : * examine_simple_variable
5939 : : * Handle a simple Var for examine_variable
5940 : : *
5941 : : * This is split out as a subroutine so that we can recurse to deal with
5942 : : * Vars referencing subqueries (either sub-SELECT-in-FROM or CTE style).
5943 : : *
5944 : : * We already filled in all the fields of *vardata except for the stats tuple.
5945 : : */
5946 : : static void
5218 tgl@sss.pgh.pa.us 5947 : 1186600 : examine_simple_variable(PlannerInfo *root, Var *var,
5948 : : VariableStatData *vardata)
5949 : : {
5950 : 1186600 : RangeTblEntry *rte = root->simple_rte_array[var->varno];
5951 : :
5952 [ - + ]: 1186600 : Assert(IsA(rte, RangeTblEntry));
5953 : :
5954 [ - + - - ]: 1186600 : if (get_relation_stats_hook &&
5218 tgl@sss.pgh.pa.us 5955 :UBC 0 : (*get_relation_stats_hook) (root, rte, var->varattno, vardata))
5956 : : {
5957 : : /*
5958 : : * The hook took control of acquiring a stats tuple. If it did supply
5959 : : * a tuple, it'd better have supplied a freefunc.
5960 : : */
5961 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
5962 [ # # ]: 0 : !vardata->freefunc)
5963 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
5964 : : }
5218 tgl@sss.pgh.pa.us 5965 [ + + ]:CBC 1186600 : else if (rte->rtekind == RTE_RELATION)
5966 : : {
5967 : : /*
5968 : : * Plain table or parent of an inheritance appendrel, so look up the
5969 : : * column in pg_statistic
5970 : : */
5971 : 1126344 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
5972 : : ObjectIdGetDatum(rte->relid),
5973 : 1126344 : Int16GetDatum(var->varattno),
5974 : 1126344 : BoolGetDatum(rte->inh));
5975 : 1126344 : vardata->freefunc = ReleaseSysCache;
5976 : :
3148 peter_e@gmx.net 5977 [ + + ]: 1126344 : if (HeapTupleIsValid(vardata->statsTuple))
5978 : : {
5979 : : /*
5980 : : * Test if user has permission to read all rows from this column.
5981 : : *
5982 : : * This requires that the user has the appropriate SELECT
5983 : : * privileges and that there are no securityQuals from security
5984 : : * barrier views or RLS policies. If that's not the case, then we
5985 : : * only permit leakproof functions to be passed pg_statistic data
5986 : : * in vardata, otherwise the functions might reveal data that the
5987 : : * user doesn't have permission to see --- see
5988 : : * statistic_proc_security_check().
5989 : : */
5990 : 834912 : vardata->acl_ok =
128 dean.a.rasheed@gmail 5991 : 834912 : all_rows_selectable(root, var->varno,
5992 : 834912 : bms_make_singleton(var->varattno - FirstLowInvalidHeapAttributeNumber));
5993 : : }
5994 : : else
5995 : : {
5996 : : /* suppress any possible leakproofness checks later */
3148 peter_e@gmx.net 5997 : 291432 : vardata->acl_ok = true;
5998 : : }
5999 : : }
761 tgl@sss.pgh.pa.us 6000 [ + + + + ]: 60256 : else if ((rte->rtekind == RTE_SUBQUERY && !rte->inh) ||
6001 [ + + + + ]: 55068 : (rte->rtekind == RTE_CTE && !rte->self_reference))
6002 : : {
6003 : : /*
6004 : : * Plain subquery (not one that was converted to an appendrel) or
6005 : : * non-recursive CTE. In either case, we can try to find out what the
6006 : : * Var refers to within the subquery. We skip this for appendrel and
6007 : : * recursive-CTE cases because any column stats we did find would
6008 : : * likely not be very relevant.
6009 : : */
6010 : : PlannerInfo *subroot;
6011 : : Query *subquery;
6012 : : List *subtlist;
6013 : : TargetEntry *ste;
6014 : :
6015 : : /*
6016 : : * Punt if it's a whole-row var rather than a plain column reference.
6017 : : */
4419 6018 [ - + ]: 8815 : if (var->varattno == InvalidAttrNumber)
4419 tgl@sss.pgh.pa.us 6019 :UBC 0 : return;
6020 : :
6021 : : /*
6022 : : * Otherwise, find the subquery's planner subroot.
6023 : : */
761 tgl@sss.pgh.pa.us 6024 [ + + ]:CBC 8815 : if (rte->rtekind == RTE_SUBQUERY)
6025 : : {
6026 : : RelOptInfo *rel;
6027 : :
6028 : : /*
6029 : : * Fetch RelOptInfo for subquery. Note that we don't change the
6030 : : * rel returned in vardata, since caller expects it to be a rel of
6031 : : * the caller's query level. Because we might already be
6032 : : * recursing, we can't use that rel pointer either, but have to
6033 : : * look up the Var's rel afresh.
6034 : : */
6035 : 5188 : rel = find_base_rel(root, var->varno);
6036 : :
6037 : 5188 : subroot = rel->subroot;
6038 : : }
6039 : : else
6040 : : {
6041 : : /* CTE case is more difficult */
6042 : : PlannerInfo *cteroot;
6043 : : Index levelsup;
6044 : : int ndx;
6045 : : int plan_id;
6046 : : ListCell *lc;
6047 : :
6048 : : /*
6049 : : * Find the referenced CTE, and locate the subroot previously made
6050 : : * for it.
6051 : : */
6052 : 3627 : levelsup = rte->ctelevelsup;
6053 : 3627 : cteroot = root;
6054 [ + + ]: 6820 : while (levelsup-- > 0)
6055 : : {
6056 : 3193 : cteroot = cteroot->parent_root;
6057 [ - + ]: 3193 : if (!cteroot) /* shouldn't happen */
761 tgl@sss.pgh.pa.us 6058 [ # # ]:UBC 0 : elog(ERROR, "bad levelsup for CTE \"%s\"", rte->ctename);
6059 : : }
6060 : :
6061 : : /*
6062 : : * Note: cte_plan_ids can be shorter than cteList, if we are still
6063 : : * working on planning the CTEs (ie, this is a side-reference from
6064 : : * another CTE). So we mustn't use forboth here.
6065 : : */
761 tgl@sss.pgh.pa.us 6066 :CBC 3627 : ndx = 0;
6067 [ + - + - : 4738 : foreach(lc, cteroot->parse->cteList)
+ - ]
6068 : : {
6069 : 4738 : CommonTableExpr *cte = (CommonTableExpr *) lfirst(lc);
6070 : :
6071 [ + + ]: 4738 : if (strcmp(cte->ctename, rte->ctename) == 0)
6072 : 3627 : break;
6073 : 1111 : ndx++;
6074 : : }
6075 [ - + ]: 3627 : if (lc == NULL) /* shouldn't happen */
761 tgl@sss.pgh.pa.us 6076 [ # # ]:UBC 0 : elog(ERROR, "could not find CTE \"%s\"", rte->ctename);
761 tgl@sss.pgh.pa.us 6077 [ - + ]:CBC 3627 : if (ndx >= list_length(cteroot->cte_plan_ids))
761 tgl@sss.pgh.pa.us 6078 [ # # ]:UBC 0 : elog(ERROR, "could not find plan for CTE \"%s\"", rte->ctename);
761 tgl@sss.pgh.pa.us 6079 :CBC 3627 : plan_id = list_nth_int(cteroot->cte_plan_ids, ndx);
6080 [ - + ]: 3627 : if (plan_id <= 0)
761 tgl@sss.pgh.pa.us 6081 [ # # ]:UBC 0 : elog(ERROR, "no plan was made for CTE \"%s\"", rte->ctename);
761 tgl@sss.pgh.pa.us 6082 :CBC 3627 : subroot = list_nth(root->glob->subroots, plan_id - 1);
6083 : : }
6084 : :
6085 : : /* If the subquery hasn't been planned yet, we have to punt */
6086 [ - + ]: 8815 : if (subroot == NULL)
761 tgl@sss.pgh.pa.us 6087 :UBC 0 : return;
761 tgl@sss.pgh.pa.us 6088 [ - + ]:CBC 8815 : Assert(IsA(subroot, PlannerInfo));
6089 : :
6090 : : /*
6091 : : * We must use the subquery parsetree as mangled by the planner, not
6092 : : * the raw version from the RTE, because we need a Var that will refer
6093 : : * to the subroot's live RelOptInfos. For instance, if any subquery
6094 : : * pullup happened during planning, Vars in the targetlist might have
6095 : : * gotten replaced, and we need to see the replacement expressions.
6096 : : */
6097 : 8815 : subquery = subroot->parse;
6098 [ - + ]: 8815 : Assert(IsA(subquery, Query));
6099 : :
6100 : : /*
6101 : : * Punt if subquery uses set operations or grouping sets, as these
6102 : : * will mash underlying columns' stats beyond recognition. (Set ops
6103 : : * are particularly nasty; if we forged ahead, we would return stats
6104 : : * relevant to only the leftmost subselect...) DISTINCT is also
6105 : : * problematic, but we check that later because there is a possibility
6106 : : * of learning something even with it.
6107 : : */
5053 6108 [ + + ]: 8815 : if (subquery->setOperations ||
1660 6109 [ + + ]: 7669 : subquery->groupingSets)
5109 rhaas@postgresql.org 6110 : 1194 : return;
6111 : :
6112 : : /* Get the subquery output expression referenced by the upper Var */
761 tgl@sss.pgh.pa.us 6113 [ + + ]: 7621 : if (subquery->returningList)
6114 : 103 : subtlist = subquery->returningList;
6115 : : else
6116 : 7518 : subtlist = subquery->targetList;
6117 : 7621 : ste = get_tle_by_resno(subtlist, var->varattno);
5218 6118 [ + - - + ]: 7621 : if (ste == NULL || ste->resjunk)
5218 tgl@sss.pgh.pa.us 6119 [ # # ]:UBC 0 : elog(ERROR, "subquery %s does not have attribute %d",
6120 : : rte->eref->aliasname, var->varattno);
5218 tgl@sss.pgh.pa.us 6121 :CBC 7621 : var = (Var *) ste->expr;
6122 : :
6123 : : /*
6124 : : * If subquery uses DISTINCT, we can't make use of any stats for the
6125 : : * variable ... but, if it's the only DISTINCT column, we are entitled
6126 : : * to consider it unique. We do the test this way so that it works
6127 : : * for cases involving DISTINCT ON.
6128 : : */
5053 6129 [ + + ]: 7621 : if (subquery->distinctClause)
6130 : : {
6131 [ + + + + ]: 919 : if (list_length(subquery->distinctClause) == 1 &&
6132 : 308 : targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
6133 : 154 : vardata->isunique = true;
6134 : : /* cannot go further */
6135 : 611 : return;
6136 : : }
6137 : :
6138 : : /* The same idea as with DISTINCT clause works for a GROUP-BY too */
301 akorotkov@postgresql 6139 [ + + ]: 7010 : if (subquery->groupClause)
6140 : : {
6141 [ + + + + ]: 540 : if (list_length(subquery->groupClause) == 1 &&
6142 : 225 : targetIsInSortList(ste, InvalidOid, subquery->groupClause))
6143 : 169 : vardata->isunique = true;
6144 : : /* cannot go further */
6145 : 315 : return;
6146 : : }
6147 : :
6148 : : /*
6149 : : * If the sub-query originated from a view with the security_barrier
6150 : : * attribute, we must not look at the variable's statistics, though it
6151 : : * seems all right to notice the existence of a DISTINCT clause. So
6152 : : * stop here.
6153 : : *
6154 : : * This is probably a harsher restriction than necessary; it's
6155 : : * certainly OK for the selectivity estimator (which is a C function,
6156 : : * and therefore omnipotent anyway) to look at the statistics. But
6157 : : * many selectivity estimators will happily *invoke the operator
6158 : : * function* to try to work out a good estimate - and that's not OK.
6159 : : * So for now, don't dig down for stats.
6160 : : */
5053 tgl@sss.pgh.pa.us 6161 [ + + ]: 6695 : if (rte->security_barrier)
6162 : 705 : return;
6163 : :
6164 : : /* Can only handle a simple Var of subquery's query level */
5218 6165 [ + - + + ]: 5990 : if (var && IsA(var, Var) &&
6166 [ + - ]: 3185 : var->varlevelsup == 0)
6167 : : {
6168 : : /*
6169 : : * OK, recurse into the subquery. Note that the original setting
6170 : : * of vardata->isunique (which will surely be false) is left
6171 : : * unchanged in this situation. That's what we want, since even
6172 : : * if the underlying column is unique, the subquery may have
6173 : : * joined to other tables in a way that creates duplicates.
6174 : : */
761 6175 : 3185 : examine_simple_variable(subroot, var, vardata);
6176 : : }
6177 : : }
6178 : : else
6179 : : {
6180 : : /*
6181 : : * Otherwise, the Var comes from a FUNCTION or VALUES RTE. (We won't
6182 : : * see RTE_JOIN here because join alias Vars have already been
6183 : : * flattened.) There's not much we can do with function outputs, but
6184 : : * maybe someday try to be smarter about VALUES.
6185 : : */
6186 : : }
6187 : : }
6188 : :
6189 : : /*
6190 : : * all_rows_selectable
6191 : : * Test whether the user has permission to select all rows from a given
6192 : : * relation.
6193 : : *
6194 : : * Inputs:
6195 : : * root: the planner info
6196 : : * varno: the index of the relation (assumed to be an RTE_RELATION)
6197 : : * varattnos: the attributes for which permission is required, or NULL if
6198 : : * whole-table access is required
6199 : : *
6200 : : * Returns true if the user has the required select permissions, and there are
6201 : : * no securityQuals from security barrier views or RLS policies.
6202 : : *
6203 : : * Note that if the relation is an inheritance child relation, securityQuals
6204 : : * and access permissions are checked against the inheritance root parent (the
6205 : : * relation actually mentioned in the query) --- see the comments in
6206 : : * expand_single_inheritance_child() for an explanation of why it has to be
6207 : : * done this way.
6208 : : *
6209 : : * If varattnos is non-NULL, its attribute numbers should be offset by
6210 : : * FirstLowInvalidHeapAttributeNumber so that system attributes can be
6211 : : * checked. If varattnos is NULL, only table-level SELECT privileges are
6212 : : * checked, not any column-level privileges.
6213 : : *
6214 : : * Note: if the relation is accessed via a view, this function actually tests
6215 : : * whether the view owner has permission to select from the relation. To
6216 : : * ensure that the current user has permission, it is also necessary to check
6217 : : * that the current user has permission to select from the view, which we do
6218 : : * at planner-startup --- see subquery_planner().
6219 : : *
6220 : : * This is exported so that other estimation functions can use it.
6221 : : */
6222 : : bool
128 dean.a.rasheed@gmail 6223 : 837354 : all_rows_selectable(PlannerInfo *root, Index varno, Bitmapset *varattnos)
6224 : : {
6225 : 837354 : RelOptInfo *rel = find_base_rel_noerr(root, varno);
6226 [ + - ]: 837354 : RangeTblEntry *rte = planner_rt_fetch(varno, root);
6227 : : Oid userid;
6228 : : int varattno;
6229 : :
6230 [ - + ]: 837354 : Assert(rte->rtekind == RTE_RELATION);
6231 : :
6232 : : /*
6233 : : * Determine the user ID to use for privilege checks (either the current
6234 : : * user or the view owner, if we're accessing the table via a view).
6235 : : *
6236 : : * Normally the relation will have an associated RelOptInfo from which we
6237 : : * can find the userid, but it might not if it's a RETURNING Var for an
6238 : : * INSERT target relation. In that case use the RTEPermissionInfo
6239 : : * associated with the RTE.
6240 : : *
6241 : : * If we navigate up to a parent relation, we keep using the same userid,
6242 : : * since it's the same in all relations of a given inheritance tree.
6243 : : */
6244 [ + + ]: 837354 : if (rel)
6245 : 837333 : userid = rel->userid;
6246 : : else
6247 : : {
6248 : : RTEPermissionInfo *perminfo;
6249 : :
6250 : 21 : perminfo = getRTEPermissionInfo(root->parse->rteperminfos, rte);
6251 : 21 : userid = perminfo->checkAsUser;
6252 : : }
6253 [ + + ]: 837354 : if (!OidIsValid(userid))
6254 : 748249 : userid = GetUserId();
6255 : :
6256 : : /*
6257 : : * Permissions and securityQuals must be checked on the table actually
6258 : : * mentioned in the query, so if this is an inheritance child, navigate up
6259 : : * to the inheritance root parent. If the user can read the whole table
6260 : : * or the required columns there, then they can read from the child table
6261 : : * too. For per-column checks, we must find out which of the root
6262 : : * parent's attributes the child relation's attributes correspond to.
6263 : : */
6264 [ + + ]: 837354 : if (root->append_rel_array != NULL)
6265 : : {
6266 : : AppendRelInfo *appinfo;
6267 : :
6268 : 117197 : appinfo = root->append_rel_array[varno];
6269 : :
6270 : : /*
6271 : : * Partitions are mapped to their immediate parent, not the root
6272 : : * parent, so must be ready to walk up multiple AppendRelInfos. But
6273 : : * stop if we hit a parent that is not RTE_RELATION --- that's a
6274 : : * flattened UNION ALL subquery, not an inheritance parent.
6275 : : */
6276 [ + + ]: 218081 : while (appinfo &&
6277 [ + - ]: 101070 : planner_rt_fetch(appinfo->parent_relid,
6278 [ + + ]: 101070 : root)->rtekind == RTE_RELATION)
6279 : : {
6280 : 100884 : Bitmapset *parent_varattnos = NULL;
6281 : :
6282 : : /*
6283 : : * For each child attribute, find the corresponding parent
6284 : : * attribute. In rare cases, the attribute may be local to the
6285 : : * child table, in which case, we've got to live with having no
6286 : : * access to this column.
6287 : : */
6288 : 100884 : varattno = -1;
6289 [ + + ]: 200343 : while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
6290 : : {
6291 : : AttrNumber attno;
6292 : : AttrNumber parent_attno;
6293 : :
6294 : 99459 : attno = varattno + FirstLowInvalidHeapAttributeNumber;
6295 : :
6296 [ + + ]: 99459 : if (attno == InvalidAttrNumber)
6297 : : {
6298 : : /*
6299 : : * Whole-row reference, so must map each column of the
6300 : : * child to the parent table.
6301 : : */
6302 [ + + ]: 18 : for (attno = 1; attno <= appinfo->num_child_cols; attno++)
6303 : : {
6304 : 12 : parent_attno = appinfo->parent_colnos[attno - 1];
6305 [ - + ]: 12 : if (parent_attno == 0)
128 dean.a.rasheed@gmail 6306 :UBC 0 : return false; /* attr is local to child */
6307 : : parent_varattnos =
128 dean.a.rasheed@gmail 6308 :CBC 12 : bms_add_member(parent_varattnos,
6309 : : parent_attno - FirstLowInvalidHeapAttributeNumber);
6310 : : }
6311 : : }
6312 : : else
6313 : : {
6314 [ - + ]: 99453 : if (attno < 0)
6315 : : {
6316 : : /* System attnos are the same in all tables */
128 dean.a.rasheed@gmail 6317 :UBC 0 : parent_attno = attno;
6318 : : }
6319 : : else
6320 : : {
128 dean.a.rasheed@gmail 6321 [ - + ]:CBC 99453 : if (attno > appinfo->num_child_cols)
128 dean.a.rasheed@gmail 6322 :UBC 0 : return false; /* safety check */
128 dean.a.rasheed@gmail 6323 :CBC 99453 : parent_attno = appinfo->parent_colnos[attno - 1];
6324 [ - + ]: 99453 : if (parent_attno == 0)
128 dean.a.rasheed@gmail 6325 :UBC 0 : return false; /* attr is local to child */
6326 : : }
6327 : : parent_varattnos =
128 dean.a.rasheed@gmail 6328 :CBC 99453 : bms_add_member(parent_varattnos,
6329 : : parent_attno - FirstLowInvalidHeapAttributeNumber);
6330 : : }
6331 : : }
6332 : :
6333 : : /* If the parent is itself a child, continue up */
6334 : 100884 : varno = appinfo->parent_relid;
6335 : 100884 : varattnos = parent_varattnos;
6336 : 100884 : appinfo = root->append_rel_array[varno];
6337 : : }
6338 : :
6339 : : /* Perform the access check on this parent rel */
6340 [ + - ]: 117197 : rte = planner_rt_fetch(varno, root);
6341 [ - + ]: 117197 : Assert(rte->rtekind == RTE_RELATION);
6342 : : }
6343 : :
6344 : : /*
6345 : : * For all rows to be accessible, there must be no securityQuals from
6346 : : * security barrier views or RLS policies.
6347 : : */
6348 [ + + ]: 837354 : if (rte->securityQuals != NIL)
6349 : 414 : return false;
6350 : :
6351 : : /*
6352 : : * Test for table-level SELECT privilege.
6353 : : *
6354 : : * If varattnos is non-NULL, this is sufficient to give access to all
6355 : : * requested attributes, even for a child table, since we have verified
6356 : : * that all required child columns have matching parent columns.
6357 : : *
6358 : : * If varattnos is NULL (whole-table access requested), this doesn't
6359 : : * necessarily guarantee that the user can read all columns of a child
6360 : : * table, but we allow it anyway (see comments in examine_variable()) and
6361 : : * don't bother checking any column privileges.
6362 : : */
6363 [ + + ]: 836940 : if (pg_class_aclcheck(rte->relid, userid, ACL_SELECT) == ACLCHECK_OK)
6364 : 836714 : return true;
6365 : :
6366 [ + + ]: 226 : if (varattnos == NULL)
6367 : 6 : return false; /* whole-table access requested */
6368 : :
6369 : : /*
6370 : : * Don't have table-level SELECT privilege, so check per-column
6371 : : * privileges.
6372 : : */
6373 : 220 : varattno = -1;
6374 [ + + ]: 323 : while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
6375 : : {
6376 : 220 : AttrNumber attno = varattno + FirstLowInvalidHeapAttributeNumber;
6377 : :
6378 [ + + ]: 220 : if (attno == InvalidAttrNumber)
6379 : : {
6380 : : /* Whole-row reference, so must have access to all columns */
6381 [ + - ]: 3 : if (pg_attribute_aclcheck_all(rte->relid, userid, ACL_SELECT,
6382 : : ACLMASK_ALL) != ACLCHECK_OK)
6383 : 3 : return false;
6384 : : }
6385 : : else
6386 : : {
6387 [ + + ]: 217 : if (pg_attribute_aclcheck(rte->relid, attno, userid,
6388 : : ACL_SELECT) != ACLCHECK_OK)
6389 : 114 : return false;
6390 : : }
6391 : : }
6392 : :
6393 : : /* If we reach here, have all required column privileges */
6394 : 103 : return true;
6395 : : }
6396 : :
6397 : : /*
6398 : : * examine_indexcol_variable
6399 : : * Try to look up statistical data about an index column/expression.
6400 : : * Fill in a VariableStatData struct to describe the column.
6401 : : *
6402 : : * Inputs:
6403 : : * root: the planner info
6404 : : * index: the index whose column we're interested in
6405 : : * indexcol: 0-based index column number (subscripts index->indexkeys[])
6406 : : *
6407 : : * Outputs: *vardata is filled as follows:
6408 : : * var: the input expression (with any binary relabeling stripped, if
6409 : : * it is or contains a variable; but otherwise the type is preserved)
6410 : : * rel: RelOptInfo for table relation containing variable.
6411 : : * statsTuple: the pg_statistic entry for the variable, if one exists;
6412 : : * otherwise NULL.
6413 : : * freefunc: pointer to a function to release statsTuple with.
6414 : : *
6415 : : * Caller is responsible for doing ReleaseVariableStats() before exiting.
6416 : : */
6417 : : static void
257 pg@bowt.ie 6418 : 406062 : examine_indexcol_variable(PlannerInfo *root, IndexOptInfo *index,
6419 : : int indexcol, VariableStatData *vardata)
6420 : : {
6421 : : AttrNumber colnum;
6422 : : Oid relid;
6423 : :
6424 [ + + ]: 406062 : if (index->indexkeys[indexcol] != 0)
6425 : : {
6426 : : /* Simple variable --- look to stats for the underlying table */
6427 [ + - ]: 404955 : RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
6428 : :
6429 [ - + ]: 404955 : Assert(rte->rtekind == RTE_RELATION);
6430 : 404955 : relid = rte->relid;
6431 [ - + ]: 404955 : Assert(relid != InvalidOid);
6432 : 404955 : colnum = index->indexkeys[indexcol];
6433 : 404955 : vardata->rel = index->rel;
6434 : :
6435 [ - + - - ]: 404955 : if (get_relation_stats_hook &&
257 pg@bowt.ie 6436 :UBC 0 : (*get_relation_stats_hook) (root, rte, colnum, vardata))
6437 : : {
6438 : : /*
6439 : : * The hook took control of acquiring a stats tuple. If it did
6440 : : * supply a tuple, it'd better have supplied a freefunc.
6441 : : */
6442 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6443 [ # # ]: 0 : !vardata->freefunc)
6444 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6445 : : }
6446 : : else
6447 : : {
257 pg@bowt.ie 6448 :CBC 404955 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6449 : : ObjectIdGetDatum(relid),
6450 : : Int16GetDatum(colnum),
6451 : 404955 : BoolGetDatum(rte->inh));
6452 : 404955 : vardata->freefunc = ReleaseSysCache;
6453 : : }
6454 : : }
6455 : : else
6456 : : {
6457 : : /* Expression --- maybe there are stats for the index itself */
6458 : 1107 : relid = index->indexoid;
6459 : 1107 : colnum = indexcol + 1;
6460 : :
6461 [ - + - - ]: 1107 : if (get_index_stats_hook &&
257 pg@bowt.ie 6462 :UBC 0 : (*get_index_stats_hook) (root, relid, colnum, vardata))
6463 : : {
6464 : : /*
6465 : : * The hook took control of acquiring a stats tuple. If it did
6466 : : * supply a tuple, it'd better have supplied a freefunc.
6467 : : */
6468 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6469 [ # # ]: 0 : !vardata->freefunc)
6470 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6471 : : }
6472 : : else
6473 : : {
257 pg@bowt.ie 6474 :CBC 1107 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6475 : : ObjectIdGetDatum(relid),
6476 : : Int16GetDatum(colnum),
6477 : : BoolGetDatum(false));
6478 : 1107 : vardata->freefunc = ReleaseSysCache;
6479 : : }
6480 : : }
6481 : 406062 : }
6482 : :
6483 : : /*
6484 : : * Check whether it is permitted to call func_oid passing some of the
6485 : : * pg_statistic data in vardata. We allow this if either of the following
6486 : : * conditions is met: (1) the user has SELECT privileges on the table or
6487 : : * column underlying the pg_statistic data and there are no securityQuals from
6488 : : * security barrier views or RLS policies, or (2) the function is marked
6489 : : * leakproof.
6490 : : */
6491 : : bool
3148 peter_e@gmx.net 6492 : 583774 : statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
6493 : : {
6494 [ + + ]: 583774 : if (vardata->acl_ok)
128 dean.a.rasheed@gmail 6495 : 582849 : return true; /* have SELECT privs and no securityQuals */
6496 : :
3148 peter_e@gmx.net 6497 [ - + ]: 925 : if (!OidIsValid(func_oid))
3148 peter_e@gmx.net 6498 :UBC 0 : return false;
6499 : :
3148 peter_e@gmx.net 6500 [ + + ]:CBC 925 : if (get_func_leakproof(func_oid))
6501 : 458 : return true;
6502 : :
6503 [ - + ]: 467 : ereport(DEBUG2,
6504 : : (errmsg_internal("not using statistics because function \"%s\" is not leakproof",
6505 : : get_func_name(func_oid))));
6506 : 467 : return false;
6507 : : }
6508 : :
6509 : : /*
6510 : : * get_variable_numdistinct
6511 : : * Estimate the number of distinct values of a variable.
6512 : : *
6513 : : * vardata: results of examine_variable
6514 : : * *isdefault: set to true if the result is a default rather than based on
6515 : : * anything meaningful.
6516 : : *
6517 : : * NB: be careful to produce a positive integral result, since callers may
6518 : : * compare the result to exact integer counts, or might divide by it.
6519 : : */
6520 : : double
5218 tgl@sss.pgh.pa.us 6521 : 832227 : get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
6522 : : {
6523 : : double stadistinct;
3419 6524 : 832227 : double stanullfrac = 0.0;
6525 : : double ntuples;
6526 : :
5218 6527 : 832227 : *isdefault = false;
6528 : :
6529 : : /*
6530 : : * Determine the stadistinct value to use. There are cases where we can
6531 : : * get an estimate even without a pg_statistic entry, or can get a better
6532 : : * value than is in pg_statistic. Grab stanullfrac too if we can find it
6533 : : * (otherwise, assume no nulls, for lack of any better idea).
6534 : : */
7974 6535 [ + + ]: 832227 : if (HeapTupleIsValid(vardata->statsTuple))
6536 : : {
6537 : : /* Use the pg_statistic entry */
6538 : : Form_pg_statistic stats;
6539 : :
6540 : 583953 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
6541 : 583953 : stadistinct = stats->stadistinct;
3419 6542 : 583953 : stanullfrac = stats->stanullfrac;
6543 : : }
7565 6544 [ + + ]: 248274 : else if (vardata->vartype == BOOLOID)
6545 : : {
6546 : : /*
6547 : : * Special-case boolean columns: presumably, two distinct values.
6548 : : *
6549 : : * Are there any other datatypes we should wire in special estimates
6550 : : * for?
6551 : : */
7974 6552 : 301 : stadistinct = 2.0;
6553 : : }
3045 6554 [ + + + + ]: 247973 : else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
6555 : : {
6556 : : /*
6557 : : * If the Var represents a column of a VALUES RTE, assume it's unique.
6558 : : * This could of course be very wrong, but it should tend to be true
6559 : : * in well-written queries. We could consider examining the VALUES'
6560 : : * contents to get some real statistics; but that only works if the
6561 : : * entries are all constants, and it would be pretty expensive anyway.
6562 : : */
6563 : 1764 : stadistinct = -1.0; /* unique (and all non null) */
6564 : : }
6565 : : else
6566 : : {
6567 : : /*
6568 : : * We don't keep statistics for system columns, but in some cases we
6569 : : * can infer distinctness anyway.
6570 : : */
7974 6571 [ + + + + ]: 246209 : if (vardata->var && IsA(vardata->var, Var))
6572 : : {
6573 [ + + + ]: 227182 : switch (((Var *) vardata->var)->varattno)
6574 : : {
6575 : 614 : case SelfItemPointerAttributeNumber:
3419 6576 : 614 : stadistinct = -1.0; /* unique (and all non null) */
7974 6577 : 614 : break;
6578 : 13373 : case TableOidAttributeNumber:
7780 bruce@momjian.us 6579 : 13373 : stadistinct = 1.0; /* only 1 value */
7974 tgl@sss.pgh.pa.us 6580 : 13373 : break;
6581 : 213195 : default:
7780 bruce@momjian.us 6582 : 213195 : stadistinct = 0.0; /* means "unknown" */
7974 tgl@sss.pgh.pa.us 6583 : 213195 : break;
6584 : : }
6585 : : }
6586 : : else
7780 bruce@momjian.us 6587 : 19027 : stadistinct = 0.0; /* means "unknown" */
6588 : :
6589 : : /*
6590 : : * XXX consider using estimate_num_groups on expressions?
6591 : : */
6592 : : }
6593 : :
6594 : : /*
6595 : : * If there is a unique index, DISTINCT or GROUP-BY clause for the
6596 : : * variable, assume it is unique no matter what pg_statistic says; the
6597 : : * statistics could be out of date, or we might have found a partial
6598 : : * unique index that proves the var is unique for this query. However,
6599 : : * we'd better still believe the null-fraction statistic.
6600 : : */
6149 tgl@sss.pgh.pa.us 6601 [ + + ]: 832227 : if (vardata->isunique)
3419 6602 : 207211 : stadistinct = -1.0 * (1.0 - stanullfrac);
6603 : :
6604 : : /*
6605 : : * If we had an absolute estimate, use that.
6606 : : */
7974 6607 [ + + ]: 832227 : if (stadistinct > 0.0)
3793 6608 : 212548 : return clamp_row_est(stadistinct);
6609 : :
6610 : : /*
6611 : : * Otherwise we need to get the relation size; punt if not available.
6612 : : */
7974 6613 [ + + ]: 619679 : if (vardata->rel == NULL)
6614 : : {
5218 6615 : 211 : *isdefault = true;
7974 6616 : 211 : return DEFAULT_NUM_DISTINCT;
6617 : : }
6618 : 619468 : ntuples = vardata->rel->tuples;
6619 [ + + ]: 619468 : if (ntuples <= 0.0)
6620 : : {
5218 6621 : 59900 : *isdefault = true;
7974 6622 : 59900 : return DEFAULT_NUM_DISTINCT;
6623 : : }
6624 : :
6625 : : /*
6626 : : * If we had a relative estimate, use that.
6627 : : */
6628 [ + + ]: 559568 : if (stadistinct < 0.0)
3793 6629 : 409333 : return clamp_row_est(-stadistinct * ntuples);
6630 : :
6631 : : /*
6632 : : * With no data, estimate ndistinct = ntuples if the table is small, else
6633 : : * use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
6634 : : * that the behavior isn't discontinuous.
6635 : : */
7974 6636 [ + + ]: 150235 : if (ntuples < DEFAULT_NUM_DISTINCT)
3793 6637 : 67478 : return clamp_row_est(ntuples);
6638 : :
5218 6639 : 82757 : *isdefault = true;
7974 6640 : 82757 : return DEFAULT_NUM_DISTINCT;
6641 : : }
6642 : :
6643 : : /*
6644 : : * get_variable_range
6645 : : * Estimate the minimum and maximum value of the specified variable.
6646 : : * If successful, store values in *min and *max, and return true.
6647 : : * If no data available, return false.
6648 : : *
6649 : : * sortop is the "<" comparison operator to use. This should generally
6650 : : * be "<" not ">", as only the former is likely to be found in pg_statistic.
6651 : : * The collation must be specified too.
6652 : : */
6653 : : static bool
2021 6654 : 125194 : get_variable_range(PlannerInfo *root, VariableStatData *vardata,
6655 : : Oid sortop, Oid collation,
6656 : : Datum *min, Datum *max)
6657 : : {
6584 6658 : 125194 : Datum tmin = 0;
7974 6659 : 125194 : Datum tmax = 0;
6584 6660 : 125194 : bool have_data = false;
6661 : : int16 typLen;
6662 : : bool typByVal;
6663 : : Oid opfuncoid;
6664 : : FmgrInfo opproc;
6665 : : AttStatsSlot sslot;
6666 : :
6667 : : /*
6668 : : * XXX It's very tempting to try to use the actual column min and max, if
6669 : : * we can get them relatively-cheaply with an index probe. However, since
6670 : : * this function is called many times during join planning, that could
6671 : : * have unpleasant effects on planning speed. Need more investigation
6672 : : * before enabling this.
6673 : : */
6674 : : #ifdef NOT_USED
6675 : : if (get_actual_variable_range(root, vardata, sortop, collation, min, max))
6676 : : return true;
6677 : : #endif
6678 : :
7974 6679 [ + + ]: 125194 : if (!HeapTupleIsValid(vardata->statsTuple))
6680 : : {
6681 : : /* no stats available, so default result */
6682 : 27849 : return false;
6683 : : }
6684 : :
6685 : : /*
6686 : : * If we can't apply the sortop to the stats data, just fail. In
6687 : : * principle, if there's a histogram and no MCVs, we could return the
6688 : : * histogram endpoints without ever applying the sortop ... but it's
6689 : : * probably not worth trying, because whatever the caller wants to do with
6690 : : * the endpoints would likely fail the security check too.
6691 : : */
3148 peter_e@gmx.net 6692 [ - + ]: 97345 : if (!statistic_proc_security_check(vardata,
6693 : 97345 : (opfuncoid = get_opcode(sortop))))
3148 peter_e@gmx.net 6694 :UBC 0 : return false;
6695 : :
2021 tgl@sss.pgh.pa.us 6696 :CBC 97345 : opproc.fn_oid = InvalidOid; /* mark this as not looked up yet */
6697 : :
7974 6698 : 97345 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
6699 : :
6700 : : /*
6701 : : * If there is a histogram with the ordering we want, grab the first and
6702 : : * last values.
6703 : : */
3140 6704 [ + + ]: 97345 : if (get_attstatsslot(&sslot, vardata->statsTuple,
6705 : : STATISTIC_KIND_HISTOGRAM, sortop,
6706 : : ATTSTATSSLOT_VALUES))
6707 : : {
2021 6708 [ + - + - ]: 61829 : if (sslot.stacoll == collation && sslot.nvalues > 0)
6709 : : {
3140 6710 : 61829 : tmin = datumCopy(sslot.values[0], typByVal, typLen);
6711 : 61829 : tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
6584 6712 : 61829 : have_data = true;
6713 : : }
3140 6714 : 61829 : free_attstatsslot(&sslot);
6715 : : }
6716 : :
6717 : : /*
6718 : : * Otherwise, if there is a histogram with some other ordering, scan it
6719 : : * and get the min and max values according to the ordering we want. This
6720 : : * of course may not find values that are really extremal according to our
6721 : : * ordering, but it beats ignoring available data.
6722 : : */
2021 6723 [ + + - + ]: 132861 : if (!have_data &&
6724 : 35516 : get_attstatsslot(&sslot, vardata->statsTuple,
6725 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
6726 : : ATTSTATSSLOT_VALUES))
6727 : : {
2021 tgl@sss.pgh.pa.us 6728 :UBC 0 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
6729 : : collation, typLen, typByVal,
6730 : : &tmin, &tmax, &have_data);
3140 6731 : 0 : free_attstatsslot(&sslot);
6732 : : }
6733 : :
6734 : : /*
6735 : : * If we have most-common-values info, look for extreme MCVs. This is
6736 : : * needed even if we also have a histogram, since the histogram excludes
6737 : : * the MCVs. However, if we *only* have MCVs and no histogram, we should
6738 : : * be pretty wary of deciding that that is a full representation of the
6739 : : * data. Proceed only if the MCVs represent the whole table (to within
6740 : : * roundoff error).
6741 : : */
3140 tgl@sss.pgh.pa.us 6742 [ + + ]:CBC 97345 : if (get_attstatsslot(&sslot, vardata->statsTuple,
6743 : : STATISTIC_KIND_MCV, InvalidOid,
1538 6744 [ + + ]: 97345 : have_data ? ATTSTATSSLOT_VALUES :
6745 : : (ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)))
6746 : : {
6747 : 54980 : bool use_mcvs = have_data;
6748 : :
6749 [ + + ]: 54980 : if (!have_data)
6750 : : {
6751 : 34802 : double sumcommon = 0.0;
6752 : : double nullfrac;
6753 : : int i;
6754 : :
6755 [ + + ]: 263571 : for (i = 0; i < sslot.nnumbers; i++)
6756 : 228769 : sumcommon += sslot.numbers[i];
6757 : 34802 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata->statsTuple))->stanullfrac;
6758 [ + + ]: 34802 : if (sumcommon + nullfrac > 0.99999)
6759 : 33031 : use_mcvs = true;
6760 : : }
6761 : :
6762 [ + + ]: 54980 : if (use_mcvs)
6763 : 53209 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
6764 : : collation, typLen, typByVal,
6765 : : &tmin, &tmax, &have_data);
3140 6766 : 54980 : free_attstatsslot(&sslot);
6767 : : }
6768 : :
6584 6769 : 97345 : *min = tmin;
7974 6770 : 97345 : *max = tmax;
6584 6771 : 97345 : return have_data;
6772 : : }
6773 : :
6774 : : /*
6775 : : * get_stats_slot_range: scan sslot for min/max values
6776 : : *
6777 : : * Subroutine for get_variable_range: update min/max/have_data according
6778 : : * to what we find in the statistics array.
6779 : : */
6780 : : static void
2021 6781 : 53209 : get_stats_slot_range(AttStatsSlot *sslot, Oid opfuncoid, FmgrInfo *opproc,
6782 : : Oid collation, int16 typLen, bool typByVal,
6783 : : Datum *min, Datum *max, bool *p_have_data)
6784 : : {
6785 : 53209 : Datum tmin = *min;
6786 : 53209 : Datum tmax = *max;
6787 : 53209 : bool have_data = *p_have_data;
6788 : 53209 : bool found_tmin = false;
6789 : 53209 : bool found_tmax = false;
6790 : :
6791 : : /* Look up the comparison function, if we didn't already do so */
6792 [ + - ]: 53209 : if (opproc->fn_oid != opfuncoid)
6793 : 53209 : fmgr_info(opfuncoid, opproc);
6794 : :
6795 : : /* Scan all the slot's values */
6796 [ + + ]: 1335247 : for (int i = 0; i < sslot->nvalues; i++)
6797 : : {
6798 [ + + ]: 1282038 : if (!have_data)
6799 : : {
6800 : 33031 : tmin = tmax = sslot->values[i];
6801 : 33031 : found_tmin = found_tmax = true;
6802 : 33031 : *p_have_data = have_data = true;
6803 : 33031 : continue;
6804 : : }
6805 [ + + ]: 1249007 : if (DatumGetBool(FunctionCall2Coll(opproc,
6806 : : collation,
6807 : 1249007 : sslot->values[i], tmin)))
6808 : : {
6809 : 30030 : tmin = sslot->values[i];
6810 : 30030 : found_tmin = true;
6811 : : }
6812 [ + + ]: 1249007 : if (DatumGetBool(FunctionCall2Coll(opproc,
6813 : : collation,
6814 : 1249007 : tmax, sslot->values[i])))
6815 : : {
6816 : 130374 : tmax = sslot->values[i];
6817 : 130374 : found_tmax = true;
6818 : : }
6819 : : }
6820 : :
6821 : : /*
6822 : : * Copy the slot's values, if we found new extreme values.
6823 : : */
6824 [ + + ]: 53209 : if (found_tmin)
6825 : 45164 : *min = datumCopy(tmin, typByVal, typLen);
6826 [ + + ]: 53209 : if (found_tmax)
6827 : 35199 : *max = datumCopy(tmax, typByVal, typLen);
6828 : 53209 : }
6829 : :
6830 : :
6831 : : /*
6832 : : * get_actual_variable_range
6833 : : * Attempt to identify the current *actual* minimum and/or maximum
6834 : : * of the specified variable, by looking for a suitable btree index
6835 : : * and fetching its low and/or high values.
6836 : : * If successful, store values in *min and *max, and return true.
6837 : : * (Either pointer can be NULL if that endpoint isn't needed.)
6838 : : * If unsuccessful, return false.
6839 : : *
6840 : : * sortop is the "<" comparison operator to use.
6841 : : * collation is the required collation.
6842 : : */
6843 : : static bool
5826 6844 : 95045 : get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
6845 : : Oid sortop, Oid collation,
6846 : : Datum *min, Datum *max)
6847 : : {
6848 : 95045 : bool have_data = false;
6849 : 95045 : RelOptInfo *rel = vardata->rel;
6850 : : RangeTblEntry *rte;
6851 : : ListCell *lc;
6852 : :
6853 : : /* No hope if no relation or it doesn't have indexes */
6854 [ + - + + ]: 95045 : if (rel == NULL || rel->indexlist == NIL)
6855 : 6928 : return false;
6856 : : /* If it has indexes it must be a plain relation */
6857 : 88117 : rte = root->simple_rte_array[rel->relid];
6858 [ - + ]: 88117 : Assert(rte->rtekind == RTE_RELATION);
6859 : :
6860 : : /* ignore partitioned tables. Any indexes here are not real indexes */
1073 drowley@postgresql.o 6861 [ + + ]: 88117 : if (rte->relkind == RELKIND_PARTITIONED_TABLE)
6862 : 378 : return false;
6863 : :
6864 : : /* Search through the indexes to see if any match our problem */
5826 tgl@sss.pgh.pa.us 6865 [ + - + + : 169795 : foreach(lc, rel->indexlist)
+ + ]
6866 : : {
6867 : 146409 : IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
6868 : : ScanDirection indexscandir;
6869 : : StrategyNumber strategy;
6870 : :
6871 : : /* Ignore non-ordering indexes */
257 peter@eisentraut.org 6872 [ - + ]: 146409 : if (index->sortopfamily == NULL)
257 peter@eisentraut.org 6873 :UBC 0 : continue;
6874 : :
6875 : : /*
6876 : : * Ignore partial indexes --- we only want stats that cover the entire
6877 : : * relation.
6878 : : */
5826 tgl@sss.pgh.pa.us 6879 [ + + ]:CBC 146409 : if (index->indpred != NIL)
6880 : 144 : continue;
6881 : :
6882 : : /*
6883 : : * The index list might include hypothetical indexes inserted by a
6884 : : * get_relation_info hook --- don't try to access them.
6885 : : */
5418 6886 [ - + ]: 146265 : if (index->hypothetical)
5826 tgl@sss.pgh.pa.us 6887 :UBC 0 : continue;
6888 : :
6889 : : /*
6890 : : * get_actual_variable_endpoint uses the index-only-scan machinery, so
6891 : : * ignore indexes that can't use it on their first column.
6892 : : */
50 peter@eisentraut.org 6893 [ - + ]:CBC 146265 : if (!index->canreturn[0])
50 peter@eisentraut.org 6894 :UBC 0 : continue;
6895 : :
6896 : : /*
6897 : : * The first index column must match the desired variable, sortop, and
6898 : : * collation --- but we can use a descending-order index.
6899 : : */
2021 tgl@sss.pgh.pa.us 6900 [ + + ]:CBC 146265 : if (collation != index->indexcollations[0])
6901 : 18835 : continue; /* test first 'cause it's cheapest */
5826 6902 [ + + ]: 127430 : if (!match_index_to_operand(vardata->var, 0, index))
6903 : 63077 : continue;
257 peter@eisentraut.org 6904 : 64353 : strategy = get_op_opfamily_strategy(sortop, index->sortopfamily[0]);
6905 [ + - - ]: 64353 : switch (IndexAmTranslateStrategy(strategy, index->relam, index->sortopfamily[0], true))
6906 : : {
6907 : 64353 : case COMPARE_LT:
5497 tgl@sss.pgh.pa.us 6908 [ - + ]: 64353 : if (index->reverse_sort[0])
5497 tgl@sss.pgh.pa.us 6909 :UBC 0 : indexscandir = BackwardScanDirection;
6910 : : else
5497 tgl@sss.pgh.pa.us 6911 :CBC 64353 : indexscandir = ForwardScanDirection;
6912 : 64353 : break;
257 peter@eisentraut.org 6913 :UBC 0 : case COMPARE_GT:
5497 tgl@sss.pgh.pa.us 6914 [ # # ]: 0 : if (index->reverse_sort[0])
6915 : 0 : indexscandir = ForwardScanDirection;
6916 : : else
6917 : 0 : indexscandir = BackwardScanDirection;
6918 : 0 : break;
6919 : 0 : default:
6920 : : /* index doesn't match the sortop */
6921 : 0 : continue;
6922 : : }
6923 : :
6924 : : /*
6925 : : * Found a suitable index to extract data from. Set up some data that
6926 : : * can be used by both invocations of get_actual_variable_endpoint.
6927 : : */
6928 : : {
6929 : : MemoryContext tmpcontext;
6930 : : MemoryContext oldcontext;
6931 : : Relation heapRel;
6932 : : Relation indexRel;
6933 : : TupleTableSlot *slot;
6934 : : int16 typLen;
6935 : : bool typByVal;
6936 : : ScanKeyData scankeys[1];
6937 : :
6938 : : /* Make sure any cruft gets recycled when we're done */
2350 tgl@sss.pgh.pa.us 6939 :CBC 64353 : tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
6940 : : "get_actual_variable_range workspace",
6941 : : ALLOCSET_DEFAULT_SIZES);
5826 6942 : 64353 : oldcontext = MemoryContextSwitchTo(tmpcontext);
6943 : :
6944 : : /*
6945 : : * Open the table and index so we can read from them. We should
6946 : : * already have some type of lock on each.
6947 : : */
2522 andres@anarazel.de 6948 : 64353 : heapRel = table_open(rte->relid, NoLock);
2449 tgl@sss.pgh.pa.us 6949 : 64353 : indexRel = index_open(index->indexoid, NoLock);
6950 : :
6951 : : /* build some stuff needed for indexscan execution */
2473 andres@anarazel.de 6952 : 64353 : slot = table_slot_create(heapRel, NULL);
5826 tgl@sss.pgh.pa.us 6953 : 64353 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
6954 : :
6955 : : /* set up an IS NOT NULL scan key so that we ignore nulls */
6956 : 64353 : ScanKeyEntryInitialize(&scankeys[0],
6957 : : SK_ISNULL | SK_SEARCHNOTNULL,
6958 : : 1, /* index col to scan */
6959 : : InvalidStrategy, /* no strategy */
6960 : : InvalidOid, /* no strategy subtype */
6961 : : InvalidOid, /* no collation */
6962 : : InvalidOid, /* no reg proc for this */
6963 : : (Datum) 0); /* constant */
6964 : :
6965 : : /* If min is requested ... */
6966 [ + + ]: 64353 : if (min)
6967 : : {
2350 6968 : 35893 : have_data = get_actual_variable_endpoint(heapRel,
6969 : : indexRel,
6970 : : indexscandir,
6971 : : scankeys,
6972 : : typLen,
6973 : : typByVal,
6974 : : slot,
6975 : : oldcontext,
6976 : : min);
6977 : : }
6978 : : else
6979 : : {
6980 : : /* If min not requested, still want to fetch max */
6981 : 28460 : have_data = true;
6982 : : }
6983 : :
6984 : : /* If max is requested, and we didn't already fail ... */
5826 6985 [ + + + - ]: 64353 : if (max && have_data)
6986 : : {
6987 : : /* scan in the opposite direction; all else is the same */
2350 6988 : 29334 : have_data = get_actual_variable_endpoint(heapRel,
6989 : : indexRel,
6990 : 29334 : -indexscandir,
6991 : : scankeys,
6992 : : typLen,
6993 : : typByVal,
6994 : : slot,
6995 : : oldcontext,
6996 : : max);
6997 : : }
6998 : :
6999 : : /* Clean everything up */
5826 7000 : 64353 : ExecDropSingleTupleTableSlot(slot);
7001 : :
2449 7002 : 64353 : index_close(indexRel, NoLock);
2522 andres@anarazel.de 7003 : 64353 : table_close(heapRel, NoLock);
7004 : :
5826 tgl@sss.pgh.pa.us 7005 : 64353 : MemoryContextSwitchTo(oldcontext);
2350 7006 : 64353 : MemoryContextDelete(tmpcontext);
7007 : :
7008 : : /* And we're done */
5826 7009 : 64353 : break;
7010 : : }
7011 : : }
7012 : :
7013 : 87739 : return have_data;
7014 : : }
7015 : :
7016 : : /*
7017 : : * Get one endpoint datum (min or max depending on indexscandir) from the
7018 : : * specified index. Return true if successful, false if not.
7019 : : * On success, endpoint value is stored to *endpointDatum (and copied into
7020 : : * outercontext).
7021 : : *
7022 : : * scankeys is a 1-element scankey array set up to reject nulls.
7023 : : * typLen/typByVal describe the datatype of the index's first column.
7024 : : * tableslot is a slot suitable to hold table tuples, in case we need
7025 : : * to probe the heap.
7026 : : * (We could compute these values locally, but that would mean computing them
7027 : : * twice when get_actual_variable_range needs both the min and the max.)
7028 : : *
7029 : : * Failure occurs either when the index is empty, or we decide that it's
7030 : : * taking too long to find a suitable tuple.
7031 : : */
7032 : : static bool
2350 7033 : 65227 : get_actual_variable_endpoint(Relation heapRel,
7034 : : Relation indexRel,
7035 : : ScanDirection indexscandir,
7036 : : ScanKey scankeys,
7037 : : int16 typLen,
7038 : : bool typByVal,
7039 : : TupleTableSlot *tableslot,
7040 : : MemoryContext outercontext,
7041 : : Datum *endpointDatum)
7042 : : {
7043 : 65227 : bool have_data = false;
7044 : : SnapshotData SnapshotNonVacuumable;
7045 : : IndexScanDesc index_scan;
7046 : 65227 : Buffer vmbuffer = InvalidBuffer;
1121 7047 : 65227 : BlockNumber last_heap_block = InvalidBlockNumber;
7048 : 65227 : int n_visited_heap_pages = 0;
7049 : : ItemPointer tid;
7050 : : Datum values[INDEX_MAX_KEYS];
7051 : : bool isnull[INDEX_MAX_KEYS];
7052 : : MemoryContext oldcontext;
7053 : :
7054 : : /*
7055 : : * We use the index-only-scan machinery for this. With mostly-static
7056 : : * tables that's a win because it avoids a heap visit. It's also a win
7057 : : * for dynamic data, but the reason is less obvious; read on for details.
7058 : : *
7059 : : * In principle, we should scan the index with our current active
7060 : : * snapshot, which is the best approximation we've got to what the query
7061 : : * will see when executed. But that won't be exact if a new snap is taken
7062 : : * before running the query, and it can be very expensive if a lot of
7063 : : * recently-dead or uncommitted rows exist at the beginning or end of the
7064 : : * index (because we'll laboriously fetch each one and reject it).
7065 : : * Instead, we use SnapshotNonVacuumable. That will accept recently-dead
7066 : : * and uncommitted rows as well as normal visible rows. On the other
7067 : : * hand, it will reject known-dead rows, and thus not give a bogus answer
7068 : : * when the extreme value has been deleted (unless the deletion was quite
7069 : : * recent); that case motivates not using SnapshotAny here.
7070 : : *
7071 : : * A crucial point here is that SnapshotNonVacuumable, with
7072 : : * GlobalVisTestFor(heapRel) as horizon, yields the inverse of the
7073 : : * condition that the indexscan will use to decide that index entries are
7074 : : * killable (see heap_hot_search_buffer()). Therefore, if the snapshot
7075 : : * rejects a tuple (or more precisely, all tuples of a HOT chain) and we
7076 : : * have to continue scanning past it, we know that the indexscan will mark
7077 : : * that index entry killed. That means that the next
7078 : : * get_actual_variable_endpoint() call will not have to re-consider that
7079 : : * index entry. In this way we avoid repetitive work when this function
7080 : : * is used a lot during planning.
7081 : : *
7082 : : * But using SnapshotNonVacuumable creates a hazard of its own. In a
7083 : : * recently-created index, some index entries may point at "broken" HOT
7084 : : * chains in which not all the tuple versions contain data matching the
7085 : : * index entry. The live tuple version(s) certainly do match the index,
7086 : : * but SnapshotNonVacuumable can accept recently-dead tuple versions that
7087 : : * don't match. Hence, if we took data from the selected heap tuple, we
7088 : : * might get a bogus answer that's not close to the index extremal value,
7089 : : * or could even be NULL. We avoid this hazard because we take the data
7090 : : * from the index entry not the heap.
7091 : : *
7092 : : * Despite all this care, there are situations where we might find many
7093 : : * non-visible tuples near the end of the index. We don't want to expend
7094 : : * a huge amount of time here, so we give up once we've read too many heap
7095 : : * pages. When we fail for that reason, the caller will end up using
7096 : : * whatever extremal value is recorded in pg_statistic.
7097 : : */
1953 andres@anarazel.de 7098 : 65227 : InitNonVacuumableSnapshot(SnapshotNonVacuumable,
7099 : : GlobalVisTestFor(heapRel));
7100 : :
2350 tgl@sss.pgh.pa.us 7101 : 65227 : index_scan = index_beginscan(heapRel, indexRel,
7102 : : &SnapshotNonVacuumable, NULL,
7103 : : 1, 0);
7104 : : /* Set it up for index-only scan */
7105 : 65227 : index_scan->xs_want_itup = true;
7106 : 65227 : index_rescan(index_scan, scankeys, 1, NULL, 0);
7107 : :
7108 : : /* Fetch first/next tuple in specified direction */
7109 [ + - ]: 86559 : while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
7110 : : {
1121 7111 : 86559 : BlockNumber block = ItemPointerGetBlockNumber(tid);
7112 : :
2350 7113 [ + + ]: 86559 : if (!VM_ALL_VISIBLE(heapRel,
7114 : : block,
7115 : : &vmbuffer))
7116 : : {
7117 : : /* Rats, we have to visit the heap to check visibility */
7118 [ + + ]: 63216 : if (!index_fetch_heap(index_scan, tableslot))
7119 : : {
7120 : : /*
7121 : : * No visible tuple for this index entry, so we need to
7122 : : * advance to the next entry. Before doing so, count heap
7123 : : * page fetches and give up if we've done too many.
7124 : : *
7125 : : * We don't charge a page fetch if this is the same heap page
7126 : : * as the previous tuple. This is on the conservative side,
7127 : : * since other recently-accessed pages are probably still in
7128 : : * buffers too; but it's good enough for this heuristic.
7129 : : */
7130 : : #define VISITED_PAGES_LIMIT 100
7131 : :
1121 7132 [ + + ]: 21332 : if (block != last_heap_block)
7133 : : {
7134 : 1777 : last_heap_block = block;
7135 : 1777 : n_visited_heap_pages++;
7136 [ - + ]: 1777 : if (n_visited_heap_pages > VISITED_PAGES_LIMIT)
1121 tgl@sss.pgh.pa.us 7137 :UBC 0 : break;
7138 : : }
7139 : :
2350 tgl@sss.pgh.pa.us 7140 :CBC 21332 : continue; /* no visible tuple, try next index entry */
7141 : : }
7142 : :
7143 : : /* We don't actually need the heap tuple for anything */
7144 : 41884 : ExecClearTuple(tableslot);
7145 : :
7146 : : /*
7147 : : * We don't care whether there's more than one visible tuple in
7148 : : * the HOT chain; if any are visible, that's good enough.
7149 : : */
7150 : : }
7151 : :
7152 : : /*
7153 : : * We expect that the index will return data in IndexTuple not
7154 : : * HeapTuple format.
7155 : : */
7156 [ - + ]: 65227 : if (!index_scan->xs_itup)
2350 tgl@sss.pgh.pa.us 7157 [ # # ]:UBC 0 : elog(ERROR, "no data returned for index-only scan");
7158 : :
7159 : : /*
7160 : : * We do not yet support recheck here.
7161 : : */
2350 tgl@sss.pgh.pa.us 7162 [ - + ]:CBC 65227 : if (index_scan->xs_recheck)
257 peter@eisentraut.org 7163 :UBC 0 : break;
7164 : :
7165 : : /* OK to deconstruct the index tuple */
2350 tgl@sss.pgh.pa.us 7166 :CBC 65227 : index_deform_tuple(index_scan->xs_itup,
7167 : : index_scan->xs_itupdesc,
7168 : : values, isnull);
7169 : :
7170 : : /* Shouldn't have got a null, but be careful */
7171 [ - + ]: 65227 : if (isnull[0])
2350 tgl@sss.pgh.pa.us 7172 [ # # ]:UBC 0 : elog(ERROR, "found unexpected null value in index \"%s\"",
7173 : : RelationGetRelationName(indexRel));
7174 : :
7175 : : /* Copy the index column value out to caller's context */
2350 tgl@sss.pgh.pa.us 7176 :CBC 65227 : oldcontext = MemoryContextSwitchTo(outercontext);
7177 : 65227 : *endpointDatum = datumCopy(values[0], typByVal, typLen);
7178 : 65227 : MemoryContextSwitchTo(oldcontext);
7179 : 65227 : have_data = true;
7180 : 65227 : break;
7181 : : }
7182 : :
7183 [ + + ]: 65227 : if (vmbuffer != InvalidBuffer)
7184 : 58869 : ReleaseBuffer(vmbuffer);
7185 : 65227 : index_endscan(index_scan);
7186 : :
7187 : 65227 : return have_data;
7188 : : }
7189 : :
7190 : : /*
7191 : : * find_join_input_rel
7192 : : * Look up the input relation for a join.
7193 : : *
7194 : : * We assume that the input relation's RelOptInfo must have been constructed
7195 : : * already.
7196 : : */
7197 : : static RelOptInfo *
5222 7198 : 5451 : find_join_input_rel(PlannerInfo *root, Relids relids)
7199 : : {
7200 : 5451 : RelOptInfo *rel = NULL;
7201 : :
750 drowley@postgresql.o 7202 [ + - ]: 5451 : if (!bms_is_empty(relids))
7203 : : {
7204 : : int relid;
7205 : :
7206 [ + + ]: 5451 : if (bms_get_singleton_member(relids, &relid))
7207 : 5293 : rel = find_base_rel(root, relid);
7208 : : else
5222 tgl@sss.pgh.pa.us 7209 : 158 : rel = find_join_rel(root, relids);
7210 : : }
7211 : :
7212 [ - + ]: 5451 : if (rel == NULL)
5222 tgl@sss.pgh.pa.us 7213 [ # # ]:UBC 0 : elog(ERROR, "could not find RelOptInfo for given relids");
7214 : :
5222 tgl@sss.pgh.pa.us 7215 :CBC 5451 : return rel;
7216 : : }
7217 : :
7218 : :
7219 : : /*-------------------------------------------------------------------------
7220 : : *
7221 : : * Index cost estimation functions
7222 : : *
7223 : : *-------------------------------------------------------------------------
7224 : : */
7225 : :
7226 : : /*
7227 : : * Extract the actual indexquals (as RestrictInfos) from an IndexClause list
7228 : : */
7229 : : List *
2497 7230 : 414666 : get_quals_from_indexclauses(List *indexclauses)
7231 : : {
2503 7232 : 414666 : List *result = NIL;
7233 : : ListCell *lc;
7234 : :
7235 [ + + + + : 730668 : foreach(lc, indexclauses)
+ + ]
7236 : : {
7237 : 316002 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7238 : : ListCell *lc2;
7239 : :
2498 7240 [ + - + + : 633460 : foreach(lc2, iclause->indexquals)
+ + ]
7241 : : {
7242 : 317458 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7243 : :
7244 : 317458 : result = lappend(result, rinfo);
7245 : : }
7246 : : }
2503 7247 : 414666 : return result;
7248 : : }
7249 : :
7250 : : /*
7251 : : * Compute the total evaluation cost of the comparison operands in a list
7252 : : * of index qual expressions. Since we know these will be evaluated just
7253 : : * once per scan, there's no need to distinguish startup from per-row cost.
7254 : : *
7255 : : * This can be used either on the result of get_quals_from_indexclauses(),
7256 : : * or directly on an indexorderbys list. In both cases, we expect that the
7257 : : * index key expression is on the left side of binary clauses.
7258 : : */
7259 : : Cost
2497 7260 : 822834 : index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
7261 : : {
3942 7262 : 822834 : Cost qual_arg_cost = 0;
7263 : : ListCell *lc;
7264 : :
2497 7265 [ + + + + : 1140523 : foreach(lc, indexquals)
+ + ]
7266 : : {
3942 7267 : 317689 : Expr *clause = (Expr *) lfirst(lc);
7268 : : Node *other_operand;
7269 : : QualCost index_qual_cost;
7270 : :
7271 : : /*
7272 : : * Index quals will have RestrictInfos, indexorderbys won't. Look
7273 : : * through RestrictInfo if present.
7274 : : */
2497 7275 [ + + ]: 317689 : if (IsA(clause, RestrictInfo))
7276 : 317452 : clause = ((RestrictInfo *) clause)->clause;
7277 : :
3942 7278 [ + + ]: 317689 : if (IsA(clause, OpExpr))
7279 : : {
2497 7280 : 310150 : OpExpr *op = (OpExpr *) clause;
7281 : :
7282 : 310150 : other_operand = (Node *) lsecond(op->args);
7283 : : }
7284 [ + + ]: 7539 : else if (IsA(clause, RowCompareExpr))
7285 : : {
7286 : 198 : RowCompareExpr *rc = (RowCompareExpr *) clause;
7287 : :
7288 : 198 : other_operand = (Node *) rc->rargs;
7289 : : }
7290 [ + + ]: 7341 : else if (IsA(clause, ScalarArrayOpExpr))
7291 : : {
7292 : 5878 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
7293 : :
7294 : 5878 : other_operand = (Node *) lsecond(saop->args);
7295 : : }
7296 [ + - ]: 1463 : else if (IsA(clause, NullTest))
7297 : : {
7298 : 1463 : other_operand = NULL;
7299 : : }
7300 : : else
7301 : : {
2497 tgl@sss.pgh.pa.us 7302 [ # # ]:UBC 0 : elog(ERROR, "unsupported indexqual type: %d",
7303 : : (int) nodeTag(clause));
7304 : : other_operand = NULL; /* keep compiler quiet */
7305 : : }
7306 : :
3942 tgl@sss.pgh.pa.us 7307 :CBC 317689 : cost_qual_eval_node(&index_qual_cost, other_operand, root);
7308 : 317689 : qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
7309 : : }
7310 : 822834 : return qual_arg_cost;
7311 : : }
7312 : :
7313 : : void
7500 7314 : 408174 : genericcostestimate(PlannerInfo *root,
7315 : : IndexPath *path,
7316 : : double loop_count,
7317 : : GenericCosts *costs)
7318 : : {
5107 7319 : 408174 : IndexOptInfo *index = path->indexinfo;
2497 7320 : 408174 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
5107 7321 : 408174 : List *indexOrderBys = path->indexorderbys;
7322 : : Cost indexStartupCost;
7323 : : Cost indexTotalCost;
7324 : : Selectivity indexSelectivity;
7325 : : double indexCorrelation;
7326 : : double numIndexPages;
7327 : : double numIndexTuples;
7328 : : double spc_random_page_cost;
7329 : : double num_sa_scans;
7330 : : double num_outer_scans;
7331 : : double num_scans;
7332 : : double qual_op_cost;
7333 : : double qual_arg_cost;
7334 : : List *selectivityQuals;
7335 : : ListCell *l;
7336 : :
7337 : : /*
7338 : : * If the index is partial, AND the index predicate with the explicitly
7339 : : * given indexquals to produce a more accurate idea of the index
7340 : : * selectivity.
7341 : : */
2497 7342 : 408174 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
7343 : :
7344 : : /*
7345 : : * If caller didn't give us an estimate for ScalarArrayOpExpr index scans,
7346 : : * just assume that the number of index descents is the number of distinct
7347 : : * combinations of array elements from all of the scan's SAOP clauses.
7348 : : */
620 pg@bowt.ie 7349 : 408174 : num_sa_scans = costs->num_sa_scans;
7350 [ + + ]: 408174 : if (num_sa_scans < 1)
7351 : : {
7352 : 3952 : num_sa_scans = 1;
7353 [ + + + + : 8296 : foreach(l, indexQuals)
+ + ]
7354 : : {
7355 : 4344 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
7356 : :
7357 [ + + ]: 4344 : if (IsA(rinfo->clause, ScalarArrayOpExpr))
7358 : : {
7359 : 13 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
7360 : 13 : double alength = estimate_array_length(root, lsecond(saop->args));
7361 : :
7362 [ + - ]: 13 : if (alength > 1)
7363 : 13 : num_sa_scans *= alength;
7364 : : }
7365 : : }
7366 : : }
7367 : :
7368 : : /* Estimate the fraction of main-table tuples that will be visited */
4723 tgl@sss.pgh.pa.us 7369 : 408174 : indexSelectivity = clauselist_selectivity(root, selectivityQuals,
7370 : 408174 : index->rel->relid,
7371 : : JOIN_INNER,
7372 : : NULL);
7373 : :
7374 : : /*
7375 : : * If caller didn't give us an estimate, estimate the number of index
7376 : : * tuples that will be visited. We do it in this rather peculiar-looking
7377 : : * way in order to get the right answer for partial indexes.
7378 : : */
7379 : 408174 : numIndexTuples = costs->numIndexTuples;
7492 7380 [ + + ]: 408174 : if (numIndexTuples <= 0.0)
7381 : : {
4723 7382 : 46196 : numIndexTuples = indexSelectivity * index->rel->tuples;
7383 : :
7384 : : /*
7385 : : * The above calculation counts all the tuples visited across all
7386 : : * scans induced by ScalarArrayOpExpr nodes. We want to consider the
7387 : : * average per-indexscan number, so adjust. This is a handy place to
7388 : : * round to integer, too. (If caller supplied tuple estimate, it's
7389 : : * responsible for handling these considerations.)
7390 : : */
6942 7391 : 46196 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
7392 : : }
7393 : :
7394 : : /*
7395 : : * We can bound the number of tuples by the index size in any case. Also,
7396 : : * always estimate at least one tuple is touched, even when
7397 : : * indexSelectivity estimate is tiny.
7398 : : */
7492 7399 [ + + ]: 408174 : if (numIndexTuples > index->tuples)
7400 : 3306 : numIndexTuples = index->tuples;
9383 7401 [ + + ]: 408174 : if (numIndexTuples < 1.0)
7402 : 46575 : numIndexTuples = 1.0;
7403 : :
7404 : : /*
7405 : : * Estimate the number of index pages that will be retrieved.
7406 : : *
7407 : : * We use the simplistic method of taking a pro-rata fraction of the total
7408 : : * number of index pages. In effect, this counts only leaf pages and not
7409 : : * any overhead such as index metapage or upper tree levels.
7410 : : *
7411 : : * In practice access to upper index levels is often nearly free because
7412 : : * those tend to stay in cache under load; moreover, the cost involved is
7413 : : * highly dependent on index type. We therefore ignore such costs here
7414 : : * and leave it to the caller to add a suitable charge if needed.
7415 : : */
7134 7416 [ + + + + ]: 408174 : if (index->pages > 1 && index->tuples > 1)
7417 : 375606 : numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
7418 : : else
9383 7419 : 32568 : numIndexPages = 1.0;
7420 : :
7421 : : /* fetch estimated page cost for tablespace containing index */
5825 rhaas@postgresql.org 7422 : 408174 : get_tablespace_page_costs(index->reltablespace,
7423 : : &spc_random_page_cost,
7424 : : NULL);
7425 : :
7426 : : /*
7427 : : * Now compute the disk access costs.
7428 : : *
7429 : : * The above calculations are all per-index-scan. However, if we are in a
7430 : : * nestloop inner scan, we can expect the scan to be repeated (with
7431 : : * different search keys) for each row of the outer relation. Likewise,
7432 : : * ScalarArrayOpExpr quals result in multiple index scans. This creates
7433 : : * the potential for cache effects to reduce the number of disk page
7434 : : * fetches needed. We want to estimate the average per-scan I/O cost in
7435 : : * the presence of caching.
7436 : : *
7437 : : * We use the Mackert-Lohman formula (see costsize.c for details) to
7438 : : * estimate the total number of page fetches that occur. While this
7439 : : * wasn't what it was designed for, it seems a reasonable model anyway.
7440 : : * Note that we are counting pages not tuples anymore, so we take N = T =
7441 : : * index size, as if there were one "tuple" per page.
7442 : : */
5073 tgl@sss.pgh.pa.us 7443 : 408174 : num_outer_scans = loop_count;
7444 : 408174 : num_scans = num_sa_scans * num_outer_scans;
7445 : :
7109 7446 [ + + ]: 408174 : if (num_scans > 1)
7447 : : {
7448 : : double pages_fetched;
7449 : :
7450 : : /* total page fetches ignoring cache effects */
7134 7451 : 48109 : pages_fetched = numIndexPages * num_scans;
7452 : :
7453 : : /* use Mackert and Lohman formula to adjust for cache effects */
7454 : 48109 : pages_fetched = index_pages_fetched(pages_fetched,
7455 : : index->pages,
7029 7456 : 48109 : (double) index->pages,
7457 : : root);
7458 : :
7459 : : /*
7460 : : * Now compute the total disk access cost, and then report a pro-rated
7461 : : * share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
7462 : : * since that's internal to the indexscan.)
7463 : : */
4723 7464 : 48109 : indexTotalCost = (pages_fetched * spc_random_page_cost)
7465 : : / num_outer_scans;
7466 : : }
7467 : : else
7468 : : {
7469 : : /*
7470 : : * For a single index scan, we just charge spc_random_page_cost per
7471 : : * page touched.
7472 : : */
7473 : 360065 : indexTotalCost = numIndexPages * spc_random_page_cost;
7474 : : }
7475 : :
7476 : : /*
7477 : : * CPU cost: any complex expressions in the indexquals will need to be
7478 : : * evaluated once at the start of the scan to reduce them to runtime keys
7479 : : * to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
7480 : : * CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
7481 : : * indexqual operator. Because we have numIndexTuples as a per-scan
7482 : : * number, we have to multiply by num_sa_scans to get the correct result
7483 : : * for ScalarArrayOpExpr cases. Similarly add in costs for any index
7484 : : * ORDER BY expressions.
7485 : : *
7486 : : * Note: this neglects the possible costs of rechecking lossy operators.
7487 : : * Detecting that that might be needed seems more expensive than it's
7488 : : * worth, though, considering all the other inaccuracies here ...
7489 : : */
2497 7490 : 408174 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
7491 : 408174 : index_other_operands_eval_cost(root, indexOrderBys);
5494 7492 : 408174 : qual_op_cost = cpu_operator_cost *
7493 : 408174 : (list_length(indexQuals) + list_length(indexOrderBys));
7494 : :
4723 7495 : 408174 : indexStartupCost = qual_arg_cost;
7496 : 408174 : indexTotalCost += qual_arg_cost;
7497 : 408174 : indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
7498 : :
7499 : : /*
7500 : : * Generic assumption about index correlation: there isn't any.
7501 : : */
7502 : 408174 : indexCorrelation = 0.0;
7503 : :
7504 : : /*
7505 : : * Return everything to caller.
7506 : : */
7507 : 408174 : costs->indexStartupCost = indexStartupCost;
7508 : 408174 : costs->indexTotalCost = indexTotalCost;
7509 : 408174 : costs->indexSelectivity = indexSelectivity;
7510 : 408174 : costs->indexCorrelation = indexCorrelation;
7511 : 408174 : costs->numIndexPages = numIndexPages;
7512 : 408174 : costs->numIndexTuples = numIndexTuples;
7513 : 408174 : costs->spc_random_page_cost = spc_random_page_cost;
7514 : 408174 : costs->num_sa_scans = num_sa_scans;
7515 : 408174 : }
7516 : :
7517 : : /*
7518 : : * If the index is partial, add its predicate to the given qual list.
7519 : : *
7520 : : * ANDing the index predicate with the explicitly given indexquals produces
7521 : : * a more accurate idea of the index's selectivity. However, we need to be
7522 : : * careful not to insert redundant clauses, because clauselist_selectivity()
7523 : : * is easily fooled into computing a too-low selectivity estimate. Our
7524 : : * approach is to add only the predicate clause(s) that cannot be proven to
7525 : : * be implied by the given indexquals. This successfully handles cases such
7526 : : * as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
7527 : : * There are many other cases where we won't detect redundancy, leading to a
7528 : : * too-low selectivity estimate, which will bias the system in favor of using
7529 : : * partial indexes where possible. That is not necessarily bad though.
7530 : : *
7531 : : * Note that indexQuals contains RestrictInfo nodes while the indpred
7532 : : * does not, so the output list will be mixed. This is OK for both
7533 : : * predicate_implied_by() and clauselist_selectivity(), but might be
7534 : : * problematic if the result were passed to other things.
7535 : : */
7536 : : List *
2497 7537 : 686231 : add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
7538 : : {
4723 7539 : 686231 : List *predExtraQuals = NIL;
7540 : : ListCell *lc;
7541 : :
7542 [ + + ]: 686231 : if (index->indpred == NIL)
7543 : 685227 : return indexQuals;
7544 : :
7545 [ + - + + : 2014 : foreach(lc, index->indpred)
+ + ]
7546 : : {
7547 : 1010 : Node *predQual = (Node *) lfirst(lc);
7548 : 1010 : List *oneQual = list_make1(predQual);
7549 : :
3108 rhaas@postgresql.org 7550 [ + + ]: 1010 : if (!predicate_implied_by(oneQual, indexQuals, false))
4723 tgl@sss.pgh.pa.us 7551 : 906 : predExtraQuals = list_concat(predExtraQuals, oneQual);
7552 : : }
7553 : 1004 : return list_concat(predExtraQuals, indexQuals);
7554 : : }
7555 : :
7556 : : /*
7557 : : * Estimate correlation of btree index's first column.
7558 : : *
7559 : : * If we can get an estimate of the first column's ordering correlation C
7560 : : * from pg_statistic, estimate the index correlation as C for a single-column
7561 : : * index, or C * 0.75 for multiple columns. The idea here is that multiple
7562 : : * columns dilute the importance of the first column's ordering, but don't
7563 : : * negate it entirely.
7564 : : *
7565 : : * We already filled in the stats tuple for *vardata when called.
7566 : : */
7567 : : static double
257 pg@bowt.ie 7568 : 303964 : btcost_correlation(IndexOptInfo *index, VariableStatData *vardata)
7569 : : {
7570 : : Oid sortop;
7571 : : AttStatsSlot sslot;
7572 : 303964 : double indexCorrelation = 0;
7573 : :
7574 [ - + ]: 303964 : Assert(HeapTupleIsValid(vardata->statsTuple));
7575 : :
7576 : 303964 : sortop = get_opfamily_member(index->opfamily[0],
7577 : 303964 : index->opcintype[0],
7578 : 303964 : index->opcintype[0],
7579 : : BTLessStrategyNumber);
7580 [ + - + + ]: 607928 : if (OidIsValid(sortop) &&
7581 : 303964 : get_attstatsslot(&sslot, vardata->statsTuple,
7582 : : STATISTIC_KIND_CORRELATION, sortop,
7583 : : ATTSTATSSLOT_NUMBERS))
7584 : : {
7585 : : double varCorrelation;
7586 : :
7587 [ - + ]: 299940 : Assert(sslot.nnumbers == 1);
7588 : 299940 : varCorrelation = sslot.numbers[0];
7589 : :
7590 [ - + ]: 299940 : if (index->reverse_sort[0])
257 pg@bowt.ie 7591 :UBC 0 : varCorrelation = -varCorrelation;
7592 : :
257 pg@bowt.ie 7593 [ + + ]:CBC 299940 : if (index->nkeycolumns > 1)
7594 : 103665 : indexCorrelation = varCorrelation * 0.75;
7595 : : else
7596 : 196275 : indexCorrelation = varCorrelation;
7597 : :
7598 : 299940 : free_attstatsslot(&sslot);
7599 : : }
7600 : :
7601 : 303964 : return indexCorrelation;
7602 : : }
7603 : :
7604 : : void
3622 tgl@sss.pgh.pa.us 7605 : 404222 : btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7606 : : Cost *indexStartupCost, Cost *indexTotalCost,
7607 : : Selectivity *indexSelectivity, double *indexCorrelation,
7608 : : double *indexPages)
7609 : : {
5107 7610 : 404222 : IndexOptInfo *index = path->indexinfo;
1250 peter@eisentraut.org 7611 : 404222 : GenericCosts costs = {0};
7612 : 404222 : VariableStatData vardata = {0};
7613 : : double numIndexTuples;
7614 : : Cost descentCost;
7615 : : List *indexBoundQuals;
7616 : : List *indexSkipQuals;
7617 : : int indexcol;
7618 : : bool eqQualHere;
7619 : : bool found_row_compare;
7620 : : bool found_array;
7621 : : bool found_is_null_op;
257 pg@bowt.ie 7622 : 404222 : bool have_correlation = false;
7623 : : double num_sa_scans;
7624 : 404222 : double correlation = 0.0;
7625 : : ListCell *lc;
7626 : :
7627 : : /*
7628 : : * For a btree scan, only leading '=' quals plus inequality quals for the
7629 : : * immediately next attribute contribute to index selectivity (these are
7630 : : * the "boundary quals" that determine the starting and stopping points of
7631 : : * the index scan). Additional quals can suppress visits to the heap, so
7632 : : * it's OK to count them in indexSelectivity, but they should not count
7633 : : * for estimating numIndexTuples. So we must examine the given indexquals
7634 : : * to find out which ones count as boundary quals. We rely on the
7635 : : * knowledge that they are given in index column order. Note that nbtree
7636 : : * preprocessing can add skip arrays that act as leading '=' quals in the
7637 : : * absence of ordinary input '=' quals, so in practice _most_ input quals
7638 : : * are able to act as index bound quals (which we take into account here).
7639 : : *
7640 : : * For a RowCompareExpr, we consider only the first column, just as
7641 : : * rowcomparesel() does.
7642 : : *
7643 : : * If there's a SAOP or skip array in the quals, we'll actually perform up
7644 : : * to N index descents (not just one), but the underlying array key's
7645 : : * operator can be considered to act the same as it normally does.
7646 : : */
7492 tgl@sss.pgh.pa.us 7647 : 404222 : indexBoundQuals = NIL;
257 pg@bowt.ie 7648 : 404222 : indexSkipQuals = NIL;
5107 tgl@sss.pgh.pa.us 7649 : 404222 : indexcol = 0;
7492 7650 : 404222 : eqQualHere = false;
257 pg@bowt.ie 7651 : 404222 : found_row_compare = false;
7652 : 404222 : found_array = false;
5829 tgl@sss.pgh.pa.us 7653 : 404222 : found_is_null_op = false;
6942 7654 : 404222 : num_sa_scans = 1;
2497 7655 [ + + + + : 688993 : foreach(lc, path->indexclauses)
+ + ]
7656 : : {
7657 : 303496 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7658 : : ListCell *lc2;
7659 : :
257 pg@bowt.ie 7660 [ + + ]: 303496 : if (indexcol < iclause->indexcol)
7661 : : {
7662 : 58634 : double num_sa_scans_prev_cols = num_sa_scans;
7663 : :
7664 : : /*
7665 : : * Beginning of a new column's quals.
7666 : : *
7667 : : * Skip scans use skip arrays, which are ScalarArrayOp style
7668 : : * arrays that generate their elements procedurally and on demand.
7669 : : * Given a multi-column index on "(a, b)", and an SQL WHERE clause
7670 : : * "WHERE b = 42", a skip scan will effectively use an indexqual
7671 : : * "WHERE a = ANY('{every col a value}') AND b = 42". (Obviously,
7672 : : * the array on "a" must also return "IS NULL" matches, since our
7673 : : * WHERE clause used no strict operator on "a").
7674 : : *
7675 : : * Here we consider how nbtree will backfill skip arrays for any
7676 : : * index columns that lacked an '=' qual. This maintains our
7677 : : * num_sa_scans estimate, and determines if this new column (the
7678 : : * "iclause->indexcol" column, not the prior "indexcol" column)
7679 : : * can have its RestrictInfos/quals added to indexBoundQuals.
7680 : : *
7681 : : * We'll need to handle columns that have inequality quals, where
7682 : : * the skip array generates values from a range constrained by the
7683 : : * quals (not every possible value). We've been maintaining
7684 : : * indexSkipQuals to help with this; it will now contain all of
7685 : : * the prior column's quals (that is, indexcol's quals) when they
7686 : : * might be used for this.
7687 : : */
7688 [ + + ]: 58634 : if (found_row_compare)
7689 : : {
7690 : : /*
7691 : : * Skip arrays can't be added after a RowCompare input qual
7692 : : * due to limitations in nbtree
7693 : : */
7694 : 12 : break;
7695 : : }
7696 [ + + ]: 58622 : if (eqQualHere)
7697 : : {
7698 : : /*
7699 : : * Don't need to add a skip array for an indexcol that already
7700 : : * has an '=' qual/equality constraint
7701 : : */
7702 : 40222 : indexcol++;
7703 : 40222 : indexSkipQuals = NIL;
7704 : : }
5107 tgl@sss.pgh.pa.us 7705 : 58622 : eqQualHere = false;
7706 : :
257 pg@bowt.ie 7707 [ + + ]: 60143 : while (indexcol < iclause->indexcol)
7708 : : {
7709 : : double ndistinct;
7710 : 20234 : bool isdefault = true;
7711 : :
7712 : 20234 : found_array = true;
7713 : :
7714 : : /*
7715 : : * A skipped attribute's ndistinct forms the basis of our
7716 : : * estimate of the total number of "array elements" used by
7717 : : * its skip array at runtime. Look that up first.
7718 : : */
7719 : 20234 : examine_indexcol_variable(root, index, indexcol, &vardata);
7720 : 20234 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
7721 : :
7722 [ + + ]: 20234 : if (indexcol == 0)
7723 : : {
7724 : : /*
7725 : : * Get an estimate of the leading column's correlation in
7726 : : * passing (avoids rereading variable stats below)
7727 : : */
7728 [ + + ]: 18394 : if (HeapTupleIsValid(vardata.statsTuple))
7729 : 11844 : correlation = btcost_correlation(index, &vardata);
7730 : 18394 : have_correlation = true;
7731 : : }
7732 : :
7733 [ + + ]: 20234 : ReleaseVariableStats(vardata);
7734 : :
7735 : : /*
7736 : : * If ndistinct is a default estimate, conservatively assume
7737 : : * that no skipping will happen at runtime
7738 : : */
7739 [ + + ]: 20234 : if (isdefault)
7740 : : {
7741 : 5936 : num_sa_scans = num_sa_scans_prev_cols;
7742 : 18713 : break; /* done building indexBoundQuals */
7743 : : }
7744 : :
7745 : : /*
7746 : : * Apply indexcol's indexSkipQuals selectivity to ndistinct
7747 : : */
7748 [ + + ]: 14298 : if (indexSkipQuals != NIL)
7749 : : {
7750 : : List *partialSkipQuals;
7751 : : Selectivity ndistinctfrac;
7752 : :
7753 : : /*
7754 : : * If the index is partial, AND the index predicate with
7755 : : * the index-bound quals to produce a more accurate idea
7756 : : * of the number of distinct values for prior indexcol
7757 : : */
7758 : 332 : partialSkipQuals = add_predicate_to_index_quals(index,
7759 : : indexSkipQuals);
7760 : :
7761 : 332 : ndistinctfrac = clauselist_selectivity(root, partialSkipQuals,
7762 : 332 : index->rel->relid,
7763 : : JOIN_INNER,
7764 : : NULL);
7765 : :
7766 : : /*
7767 : : * If ndistinctfrac is selective (on its own), the scan is
7768 : : * unlikely to benefit from repositioning itself using
7769 : : * later quals. Do not allow iclause->indexcol's quals to
7770 : : * be added to indexBoundQuals (it would increase descent
7771 : : * costs, without lowering numIndexTuples costs by much).
7772 : : */
7773 [ + + ]: 332 : if (ndistinctfrac < DEFAULT_RANGE_INEQ_SEL)
7774 : : {
7775 : 187 : num_sa_scans = num_sa_scans_prev_cols;
7776 : 187 : break; /* done building indexBoundQuals */
7777 : : }
7778 : :
7779 : : /* Adjust ndistinct downward */
7780 : 145 : ndistinct = rint(ndistinct * ndistinctfrac);
7781 [ + - ]: 145 : ndistinct = Max(ndistinct, 1);
7782 : : }
7783 : :
7784 : : /*
7785 : : * When there's no inequality quals, account for the need to
7786 : : * find an initial value by counting -inf/+inf as a value.
7787 : : *
7788 : : * We don't charge anything extra for possible next/prior key
7789 : : * index probes, which are sometimes used to find the next
7790 : : * valid skip array element (ahead of using the located
7791 : : * element value to relocate the scan to the next position
7792 : : * that might contain matching tuples). It seems hard to do
7793 : : * better here. Use of the skip support infrastructure often
7794 : : * avoids most next/prior key probes. But even when it can't,
7795 : : * there's a decent chance that most individual next/prior key
7796 : : * probes will locate a leaf page whose key space overlaps all
7797 : : * of the scan's keys (even the lower-order keys) -- which
7798 : : * also avoids the need for a separate, extra index descent.
7799 : : * Note also that these probes are much cheaper than non-probe
7800 : : * primitive index scans: they're reliably very selective.
7801 : : */
7802 [ + + ]: 14111 : if (indexSkipQuals == NIL)
7803 : 13966 : ndistinct += 1;
7804 : :
7805 : : /*
7806 : : * Update num_sa_scans estimate by multiplying by ndistinct.
7807 : : *
7808 : : * We make the pessimistic assumption that there is no
7809 : : * naturally occurring cross-column correlation. This is
7810 : : * often wrong, but it seems best to err on the side of not
7811 : : * expecting skipping to be helpful...
7812 : : */
7813 : 14111 : num_sa_scans *= ndistinct;
7814 : :
7815 : : /*
7816 : : * ...but back out of adding this latest group of 1 or more
7817 : : * skip arrays when num_sa_scans exceeds the total number of
7818 : : * index pages (revert to num_sa_scans from before indexcol).
7819 : : * This causes a sharp discontinuity in cost (as a function of
7820 : : * the indexcol's ndistinct), but that is representative of
7821 : : * actual runtime costs.
7822 : : *
7823 : : * Note that skipping is helpful when each primitive index
7824 : : * scan only manages to skip over 1 or 2 irrelevant leaf pages
7825 : : * on average. Skip arrays bring savings in CPU costs due to
7826 : : * the scan not needing to evaluate indexquals against every
7827 : : * tuple, which can greatly exceed any savings in I/O costs.
7828 : : * This test is a test of whether num_sa_scans implies that
7829 : : * we're past the point where the ability to skip ceases to
7830 : : * lower the scan's costs (even qual evaluation CPU costs).
7831 : : */
7832 [ + + ]: 14111 : if (index->pages < num_sa_scans)
7833 : : {
7834 : 12590 : num_sa_scans = num_sa_scans_prev_cols;
7835 : 12590 : break; /* done building indexBoundQuals */
7836 : : }
7837 : :
7838 : 1521 : indexcol++;
7839 : 1521 : indexSkipQuals = NIL;
7840 : : }
7841 : :
7842 : : /*
7843 : : * Finished considering the need to add skip arrays to bridge an
7844 : : * initial eqQualHere gap between the old and new index columns
7845 : : * (or there was no initial eqQualHere gap in the first place).
7846 : : *
7847 : : * If an initial gap could not be bridged, then new column's quals
7848 : : * (i.e. iclause->indexcol's quals) won't go into indexBoundQuals,
7849 : : * and so won't affect our final numIndexTuples estimate.
7850 : : */
2497 tgl@sss.pgh.pa.us 7851 [ + + ]: 58622 : if (indexcol != iclause->indexcol)
257 pg@bowt.ie 7852 : 18713 : break; /* done building indexBoundQuals */
7853 : : }
7854 : :
7855 [ - + ]: 284771 : Assert(indexcol == iclause->indexcol);
7856 : :
7857 : : /* Examine each indexqual associated with this index clause */
2497 tgl@sss.pgh.pa.us 7858 [ + - + + : 570912 : foreach(lc2, iclause->indexquals)
+ + ]
7859 : : {
7860 : 286141 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7861 : 286141 : Expr *clause = rinfo->clause;
7862 : 286141 : Oid clause_op = InvalidOid;
7863 : : int op_strategy;
7864 : :
7865 [ + + ]: 286141 : if (IsA(clause, OpExpr))
7866 : : {
7867 : 279137 : OpExpr *op = (OpExpr *) clause;
7868 : :
7869 : 279137 : clause_op = op->opno;
7870 : : }
7871 [ + + ]: 7004 : else if (IsA(clause, RowCompareExpr))
7872 : : {
7873 : 198 : RowCompareExpr *rc = (RowCompareExpr *) clause;
7874 : :
7875 : 198 : clause_op = linitial_oid(rc->opnos);
257 pg@bowt.ie 7876 : 198 : found_row_compare = true;
7877 : : }
2497 tgl@sss.pgh.pa.us 7878 [ + + ]: 6806 : else if (IsA(clause, ScalarArrayOpExpr))
7879 : : {
7880 : 5664 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
7881 : 5664 : Node *other_operand = (Node *) lsecond(saop->args);
713 7882 : 5664 : double alength = estimate_array_length(root, other_operand);
7883 : :
2497 7884 : 5664 : clause_op = saop->opno;
257 pg@bowt.ie 7885 : 5664 : found_array = true;
7886 : : /* estimate SA descents by indexBoundQuals only */
2497 tgl@sss.pgh.pa.us 7887 [ + + ]: 5664 : if (alength > 1)
7888 : 5510 : num_sa_scans *= alength;
7889 : : }
7890 [ + - ]: 1142 : else if (IsA(clause, NullTest))
7891 : : {
7892 : 1142 : NullTest *nt = (NullTest *) clause;
7893 : :
7894 [ + + ]: 1142 : if (nt->nulltesttype == IS_NULL)
7895 : : {
7896 : 120 : found_is_null_op = true;
7897 : : /* IS NULL is like = for selectivity/skip scan purposes */
7898 : 120 : eqQualHere = true;
7899 : : }
7900 : : }
7901 : : else
2497 tgl@sss.pgh.pa.us 7902 [ # # ]:UBC 0 : elog(ERROR, "unsupported indexqual type: %d",
7903 : : (int) nodeTag(clause));
7904 : :
7905 : : /* check for equality operator */
2497 tgl@sss.pgh.pa.us 7906 [ + + ]:CBC 286141 : if (OidIsValid(clause_op))
7907 : : {
7908 : 284999 : op_strategy = get_op_opfamily_strategy(clause_op,
7909 : 284999 : index->opfamily[indexcol]);
7910 [ - + ]: 284999 : Assert(op_strategy != 0); /* not a member of opfamily?? */
7911 [ + + ]: 284999 : if (op_strategy == BTEqualStrategyNumber)
7912 : 268854 : eqQualHere = true;
7913 : : }
7914 : :
7915 : 286141 : indexBoundQuals = lappend(indexBoundQuals, rinfo);
7916 : :
7917 : : /*
7918 : : * We apply inequality selectivities to estimate index descent
7919 : : * costs with scans that use skip arrays. Save this indexcol's
7920 : : * RestrictInfos if it looks like they'll be needed for that.
7921 : : */
257 pg@bowt.ie 7922 [ + + + + ]: 286141 : if (!eqQualHere && !found_row_compare &&
7923 [ + + ]: 16618 : indexcol < index->nkeycolumns - 1)
7924 : 2852 : indexSkipQuals = lappend(indexSkipQuals, rinfo);
7925 : : }
7926 : : }
7927 : :
7928 : : /*
7929 : : * If index is unique and we found an '=' clause for each column, we can
7930 : : * just assume numIndexTuples = 1 and skip the expensive
7931 : : * clauselist_selectivity calculations. However, an array or NullTest
7932 : : * always invalidates that theory (even when eqQualHere has been set).
7933 : : */
7327 tgl@sss.pgh.pa.us 7934 [ + + ]: 404222 : if (index->unique &&
2811 teodor@sigaev.ru 7935 [ + + + + ]: 330508 : indexcol == index->nkeycolumns - 1 &&
7327 tgl@sss.pgh.pa.us 7936 : 130788 : eqQualHere &&
257 pg@bowt.ie 7937 [ + + ]: 130788 : !found_array &&
5829 tgl@sss.pgh.pa.us 7938 [ + + ]: 127648 : !found_is_null_op)
7492 7939 : 127624 : numIndexTuples = 1.0;
7940 : : else
7941 : : {
7942 : : List *selectivityQuals;
7943 : : Selectivity btreeSelectivity;
7944 : :
7945 : : /*
7946 : : * If the index is partial, AND the index predicate with the
7947 : : * index-bound quals to produce a more accurate idea of the number of
7948 : : * rows covered by the bound conditions.
7949 : : */
2497 7950 : 276598 : selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
7951 : :
5071 7952 : 276598 : btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
7492 7953 : 276598 : index->rel->relid,
7954 : : JOIN_INNER,
7955 : : NULL);
7956 : 276598 : numIndexTuples = btreeSelectivity * index->rel->tuples;
7957 : :
7958 : : /*
7959 : : * btree automatically combines individual array element primitive
7960 : : * index scans whenever the tuples covered by the next set of array
7961 : : * keys are close to tuples covered by the current set. That puts a
7962 : : * natural ceiling on the worst case number of descents -- there
7963 : : * cannot possibly be more than one descent per leaf page scanned.
7964 : : *
7965 : : * Clamp the number of descents to at most 1/3 the number of index
7966 : : * pages. This avoids implausibly high estimates with low selectivity
7967 : : * paths, where scans usually require only one or two descents. This
7968 : : * is most likely to help when there are several SAOP clauses, where
7969 : : * naively accepting the total number of distinct combinations of
7970 : : * array elements as the number of descents would frequently lead to
7971 : : * wild overestimates.
7972 : : *
7973 : : * We somewhat arbitrarily don't just make the cutoff the total number
7974 : : * of leaf pages (we make it 1/3 the total number of pages instead) to
7975 : : * give the btree code credit for its ability to continue on the leaf
7976 : : * level with low selectivity scans.
7977 : : *
7978 : : * Note: num_sa_scans includes both ScalarArrayOp array elements and
7979 : : * skip array elements whose qual affects our numIndexTuples estimate.
7980 : : */
620 pg@bowt.ie 7981 [ + + ]: 276598 : num_sa_scans = Min(num_sa_scans, ceil(index->pages * 0.3333333));
7982 [ + + ]: 276598 : num_sa_scans = Max(num_sa_scans, 1);
7983 : :
7984 : : /*
7985 : : * As in genericcostestimate(), we have to adjust for any array quals
7986 : : * included in indexBoundQuals, and then round to integer.
7987 : : *
7988 : : * It is tempting to make genericcostestimate behave as if array
7989 : : * clauses work in almost the same way as scalar operators during
7990 : : * btree scans, making the top-level scan look like a continuous scan
7991 : : * (as opposed to num_sa_scans-many primitive index scans). After
7992 : : * all, btree scans mostly work like that at runtime. However, such a
7993 : : * scheme would badly bias genericcostestimate's simplistic approach
7994 : : * to calculating numIndexPages through prorating.
7995 : : *
7996 : : * Stick with the approach taken by non-native SAOP scans for now.
7997 : : * genericcostestimate will use the Mackert-Lohman formula to
7998 : : * compensate for repeat page fetches, even though that definitely
7999 : : * won't happen during btree scans (not for leaf pages, at least).
8000 : : * We're usually very pessimistic about the number of primitive index
8001 : : * scans that will be required, but it's not clear how to do better.
8002 : : */
6942 tgl@sss.pgh.pa.us 8003 : 276598 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
8004 : : }
8005 : :
8006 : : /*
8007 : : * Now do generic index cost estimation.
8008 : : */
4723 8009 : 404222 : costs.numIndexTuples = numIndexTuples;
620 pg@bowt.ie 8010 : 404222 : costs.num_sa_scans = num_sa_scans;
8011 : :
2497 tgl@sss.pgh.pa.us 8012 : 404222 : genericcostestimate(root, path, loop_count, &costs);
8013 : :
8014 : : /*
8015 : : * Add a CPU-cost component to represent the costs of initial btree
8016 : : * descent. We don't charge any I/O cost for touching upper btree levels,
8017 : : * since they tend to stay in cache, but we still have to do about log2(N)
8018 : : * comparisons to descend a btree of N leaf tuples. We charge one
8019 : : * cpu_operator_cost per comparison.
8020 : : *
8021 : : * If there are SAOP or skip array keys, charge this once per estimated
8022 : : * index descent. The ones after the first one are not startup cost so
8023 : : * far as the overall plan goes, so just add them to "total" cost.
8024 : : */
4723 8025 [ + + ]: 404222 : if (index->tuples > 1) /* avoid computing log(0) */
8026 : : {
8027 : 376084 : descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
8028 : 376084 : costs.indexStartupCost += descentCost;
8029 : 376084 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8030 : : }
8031 : :
8032 : : /*
8033 : : * Even though we're not charging I/O cost for touching upper btree pages,
8034 : : * it's still reasonable to charge some CPU cost per page descended
8035 : : * through. Moreover, if we had no such charge at all, bloated indexes
8036 : : * would appear to have the same search cost as unbloated ones, at least
8037 : : * in cases where only a single leaf page is expected to be visited. This
8038 : : * cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
8039 : : * touched. The number of such pages is btree tree height plus one (ie,
8040 : : * we charge for the leaf page too). As above, charge once per estimated
8041 : : * SAOP/skip array descent.
8042 : : */
1074 akorotkov@postgresql 8043 : 404222 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4723 tgl@sss.pgh.pa.us 8044 : 404222 : costs.indexStartupCost += descentCost;
8045 : 404222 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8046 : :
257 pg@bowt.ie 8047 [ + + ]: 404222 : if (!have_correlation)
8048 : : {
8049 : 385828 : examine_indexcol_variable(root, index, 0, &vardata);
8050 [ + + ]: 385828 : if (HeapTupleIsValid(vardata.statsTuple))
8051 : 292120 : costs.indexCorrelation = btcost_correlation(index, &vardata);
8052 [ + + ]: 385828 : ReleaseVariableStats(vardata);
8053 : : }
8054 : : else
8055 : : {
8056 : : /* btcost_correlation already called earlier on */
8057 : 18394 : costs.indexCorrelation = correlation;
8058 : : }
8059 : :
4723 tgl@sss.pgh.pa.us 8060 : 404222 : *indexStartupCost = costs.indexStartupCost;
8061 : 404222 : *indexTotalCost = costs.indexTotalCost;
8062 : 404222 : *indexSelectivity = costs.indexSelectivity;
8063 : 404222 : *indexCorrelation = costs.indexCorrelation;
3227 rhaas@postgresql.org 8064 : 404222 : *indexPages = costs.numIndexPages;
10753 scrappy@hub.org 8065 : 404222 : }
8066 : :
8067 : : void
3622 tgl@sss.pgh.pa.us 8068 : 215 : hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8069 : : Cost *indexStartupCost, Cost *indexTotalCost,
8070 : : Selectivity *indexSelectivity, double *indexCorrelation,
8071 : : double *indexPages)
8072 : : {
1250 peter@eisentraut.org 8073 : 215 : GenericCosts costs = {0};
8074 : :
2497 tgl@sss.pgh.pa.us 8075 : 215 : genericcostestimate(root, path, loop_count, &costs);
8076 : :
8077 : : /*
8078 : : * A hash index has no descent costs as such, since the index AM can go
8079 : : * directly to the target bucket after computing the hash value. There
8080 : : * are a couple of other hash-specific costs that we could conceivably add
8081 : : * here, though:
8082 : : *
8083 : : * Ideally we'd charge spc_random_page_cost for each page in the target
8084 : : * bucket, not just the numIndexPages pages that genericcostestimate
8085 : : * thought we'd visit. However in most cases we don't know which bucket
8086 : : * that will be. There's no point in considering the average bucket size
8087 : : * because the hash AM makes sure that's always one page.
8088 : : *
8089 : : * Likewise, we could consider charging some CPU for each index tuple in
8090 : : * the bucket, if we knew how many there were. But the per-tuple cost is
8091 : : * just a hash value comparison, not a general datatype-dependent
8092 : : * comparison, so any such charge ought to be quite a bit less than
8093 : : * cpu_operator_cost; which makes it probably not worth worrying about.
8094 : : *
8095 : : * A bigger issue is that chance hash-value collisions will result in
8096 : : * wasted probes into the heap. We don't currently attempt to model this
8097 : : * cost on the grounds that it's rare, but maybe it's not rare enough.
8098 : : * (Any fix for this ought to consider the generic lossy-operator problem,
8099 : : * though; it's not entirely hash-specific.)
8100 : : */
8101 : :
4723 8102 : 215 : *indexStartupCost = costs.indexStartupCost;
8103 : 215 : *indexTotalCost = costs.indexTotalCost;
8104 : 215 : *indexSelectivity = costs.indexSelectivity;
8105 : 215 : *indexCorrelation = costs.indexCorrelation;
3227 rhaas@postgresql.org 8106 : 215 : *indexPages = costs.numIndexPages;
10705 scrappy@hub.org 8107 : 215 : }
8108 : :
8109 : : void
3622 tgl@sss.pgh.pa.us 8110 : 2439 : gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8111 : : Cost *indexStartupCost, Cost *indexTotalCost,
8112 : : Selectivity *indexSelectivity, double *indexCorrelation,
8113 : : double *indexPages)
8114 : : {
4723 8115 : 2439 : IndexOptInfo *index = path->indexinfo;
1250 peter@eisentraut.org 8116 : 2439 : GenericCosts costs = {0};
8117 : : Cost descentCost;
8118 : :
2497 tgl@sss.pgh.pa.us 8119 : 2439 : genericcostestimate(root, path, loop_count, &costs);
8120 : :
8121 : : /*
8122 : : * We model index descent costs similarly to those for btree, but to do
8123 : : * that we first need an idea of the tree height. We somewhat arbitrarily
8124 : : * assume that the fanout is 100, meaning the tree height is at most
8125 : : * log100(index->pages).
8126 : : *
8127 : : * Although this computation isn't really expensive enough to require
8128 : : * caching, we might as well use index->tree_height to cache it.
8129 : : */
4585 bruce@momjian.us 8130 [ + + ]: 2439 : if (index->tree_height < 0) /* unknown? */
8131 : : {
4723 tgl@sss.pgh.pa.us 8132 [ + + ]: 2432 : if (index->pages > 1) /* avoid computing log(0) */
8133 : 1360 : index->tree_height = (int) (log(index->pages) / log(100.0));
8134 : : else
8135 : 1072 : index->tree_height = 0;
8136 : : }
8137 : :
8138 : : /*
8139 : : * Add a CPU-cost component to represent the costs of initial descent. We
8140 : : * just use log(N) here not log2(N) since the branching factor isn't
8141 : : * necessarily two anyway. As for btree, charge once per SA scan.
8142 : : */
8143 [ + - ]: 2439 : if (index->tuples > 1) /* avoid computing log(0) */
8144 : : {
8145 : 2439 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
8146 : 2439 : costs.indexStartupCost += descentCost;
8147 : 2439 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8148 : : }
8149 : :
8150 : : /*
8151 : : * Likewise add a per-page charge, calculated the same as for btrees.
8152 : : */
1074 akorotkov@postgresql 8153 : 2439 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4723 tgl@sss.pgh.pa.us 8154 : 2439 : costs.indexStartupCost += descentCost;
8155 : 2439 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8156 : :
8157 : 2439 : *indexStartupCost = costs.indexStartupCost;
8158 : 2439 : *indexTotalCost = costs.indexTotalCost;
8159 : 2439 : *indexSelectivity = costs.indexSelectivity;
8160 : 2439 : *indexCorrelation = costs.indexCorrelation;
3227 rhaas@postgresql.org 8161 : 2439 : *indexPages = costs.numIndexPages;
10705 scrappy@hub.org 8162 : 2439 : }
8163 : :
8164 : : void
3622 tgl@sss.pgh.pa.us 8165 : 892 : spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8166 : : Cost *indexStartupCost, Cost *indexTotalCost,
8167 : : Selectivity *indexSelectivity, double *indexCorrelation,
8168 : : double *indexPages)
8169 : : {
4723 8170 : 892 : IndexOptInfo *index = path->indexinfo;
1250 peter@eisentraut.org 8171 : 892 : GenericCosts costs = {0};
8172 : : Cost descentCost;
8173 : :
2497 tgl@sss.pgh.pa.us 8174 : 892 : genericcostestimate(root, path, loop_count, &costs);
8175 : :
8176 : : /*
8177 : : * We model index descent costs similarly to those for btree, but to do
8178 : : * that we first need an idea of the tree height. We somewhat arbitrarily
8179 : : * assume that the fanout is 100, meaning the tree height is at most
8180 : : * log100(index->pages).
8181 : : *
8182 : : * Although this computation isn't really expensive enough to require
8183 : : * caching, we might as well use index->tree_height to cache it.
8184 : : */
4585 bruce@momjian.us 8185 [ + + ]: 892 : if (index->tree_height < 0) /* unknown? */
8186 : : {
4723 tgl@sss.pgh.pa.us 8187 [ + - ]: 889 : if (index->pages > 1) /* avoid computing log(0) */
8188 : 889 : index->tree_height = (int) (log(index->pages) / log(100.0));
8189 : : else
4723 tgl@sss.pgh.pa.us 8190 :UBC 0 : index->tree_height = 0;
8191 : : }
8192 : :
8193 : : /*
8194 : : * Add a CPU-cost component to represent the costs of initial descent. We
8195 : : * just use log(N) here not log2(N) since the branching factor isn't
8196 : : * necessarily two anyway. As for btree, charge once per SA scan.
8197 : : */
4723 tgl@sss.pgh.pa.us 8198 [ + - ]:CBC 892 : if (index->tuples > 1) /* avoid computing log(0) */
8199 : : {
8200 : 892 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
8201 : 892 : costs.indexStartupCost += descentCost;
8202 : 892 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8203 : : }
8204 : :
8205 : : /*
8206 : : * Likewise add a per-page charge, calculated the same as for btrees.
8207 : : */
1074 akorotkov@postgresql 8208 : 892 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4723 tgl@sss.pgh.pa.us 8209 : 892 : costs.indexStartupCost += descentCost;
8210 : 892 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8211 : :
8212 : 892 : *indexStartupCost = costs.indexStartupCost;
8213 : 892 : *indexTotalCost = costs.indexTotalCost;
8214 : 892 : *indexSelectivity = costs.indexSelectivity;
8215 : 892 : *indexCorrelation = costs.indexCorrelation;
3227 rhaas@postgresql.org 8216 : 892 : *indexPages = costs.numIndexPages;
5114 tgl@sss.pgh.pa.us 8217 : 892 : }
8218 : :
8219 : :
8220 : : /*
8221 : : * Support routines for gincostestimate
8222 : : */
8223 : :
8224 : : typedef struct
8225 : : {
8226 : : bool attHasFullScan[INDEX_MAX_KEYS];
8227 : : bool attHasNormalScan[INDEX_MAX_KEYS];
8228 : : double partialEntries;
8229 : : double exactEntries;
8230 : : double searchEntries;
8231 : : double arrayScans;
8232 : : } GinQualCounts;
8233 : :
8234 : : /*
8235 : : * Estimate the number of index terms that need to be searched for while
8236 : : * testing the given GIN query, and increment the counts in *counts
8237 : : * appropriately. If the query is unsatisfiable, return false.
8238 : : */
8239 : : static bool
5111 8240 : 1235 : gincost_pattern(IndexOptInfo *index, int indexcol,
8241 : : Oid clause_op, Datum query,
8242 : : GinQualCounts *counts)
8243 : : {
8244 : : FmgrInfo flinfo;
8245 : : Oid extractProcOid;
8246 : : Oid collation;
8247 : : int strategy_op;
8248 : : Oid lefttype,
8249 : : righttype;
8250 : 1235 : int32 nentries = 0;
8251 : 1235 : bool *partial_matches = NULL;
8252 : 1235 : Pointer *extra_data = NULL;
8253 : 1235 : bool *nullFlags = NULL;
8254 : 1235 : int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
8255 : : int32 i;
8256 : :
2806 teodor@sigaev.ru 8257 [ - + ]: 1235 : Assert(indexcol < index->nkeycolumns);
8258 : :
8259 : : /*
8260 : : * Get the operator's strategy number and declared input data types within
8261 : : * the index opfamily. (We don't need the latter, but we use
8262 : : * get_op_opfamily_properties because it will throw error if it fails to
8263 : : * find a matching pg_amop entry.)
8264 : : */
5111 tgl@sss.pgh.pa.us 8265 : 1235 : get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
8266 : : &strategy_op, &lefttype, &righttype);
8267 : :
8268 : : /*
8269 : : * GIN always uses the "default" support functions, which are those with
8270 : : * lefttype == righttype == the opclass' opcintype (see
8271 : : * IndexSupportInitialize in relcache.c).
8272 : : */
8273 : 1235 : extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
8274 : 1235 : index->opcintype[indexcol],
8275 : 1235 : index->opcintype[indexcol],
8276 : : GIN_EXTRACTQUERY_PROC);
8277 : :
8278 [ - + ]: 1235 : if (!OidIsValid(extractProcOid))
8279 : : {
8280 : : /* should not happen; throw same error as index_getprocinfo */
5111 tgl@sss.pgh.pa.us 8281 [ # # ]:UBC 0 : elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
8282 : : GIN_EXTRACTQUERY_PROC, indexcol + 1,
8283 : : get_rel_name(index->indexoid));
8284 : : }
8285 : :
8286 : : /*
8287 : : * Choose collation to pass to extractProc (should match initGinState).
8288 : : */
4635 tgl@sss.pgh.pa.us 8289 [ + + ]:CBC 1235 : if (OidIsValid(index->indexcollations[indexcol]))
8290 : 207 : collation = index->indexcollations[indexcol];
8291 : : else
8292 : 1028 : collation = DEFAULT_COLLATION_OID;
8293 : :
2088 akorotkov@postgresql 8294 : 1235 : fmgr_info(extractProcOid, &flinfo);
8295 : :
8296 : 1235 : set_fn_opclass_options(&flinfo, index->opclassoptions[indexcol]);
8297 : :
8298 : 1235 : FunctionCall7Coll(&flinfo,
8299 : : collation,
8300 : : query,
8301 : : PointerGetDatum(&nentries),
8302 : : UInt16GetDatum(strategy_op),
8303 : : PointerGetDatum(&partial_matches),
8304 : : PointerGetDatum(&extra_data),
8305 : : PointerGetDatum(&nullFlags),
8306 : : PointerGetDatum(&searchMode));
8307 : :
5111 tgl@sss.pgh.pa.us 8308 [ + + + + ]: 1235 : if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
8309 : : {
8310 : : /* No match is possible */
8311 : 6 : return false;
8312 : : }
8313 : :
8314 [ + + ]: 3333 : for (i = 0; i < nentries; i++)
8315 : : {
8316 : : /*
8317 : : * For partial match we haven't any information to estimate number of
8318 : : * matched entries in index, so, we just estimate it as 100
8319 : : */
8320 [ + + + + ]: 2104 : if (partial_matches && partial_matches[i])
8321 : 347 : counts->partialEntries += 100;
8322 : : else
8323 : 1757 : counts->exactEntries++;
8324 : :
8325 : 2104 : counts->searchEntries++;
8326 : : }
8327 : :
2160 akorotkov@postgresql 8328 [ + + ]: 1229 : if (searchMode == GIN_SEARCH_MODE_DEFAULT)
8329 : : {
8330 : 987 : counts->attHasNormalScan[indexcol] = true;
8331 : : }
8332 [ + + ]: 242 : else if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
8333 : : {
8334 : : /* Treat "include empty" like an exact-match item */
8335 : 22 : counts->attHasNormalScan[indexcol] = true;
5111 tgl@sss.pgh.pa.us 8336 : 22 : counts->exactEntries++;
8337 : 22 : counts->searchEntries++;
8338 : : }
8339 : : else
8340 : : {
8341 : : /* It's GIN_SEARCH_MODE_ALL */
2160 akorotkov@postgresql 8342 : 220 : counts->attHasFullScan[indexcol] = true;
8343 : : }
8344 : :
5111 tgl@sss.pgh.pa.us 8345 : 1229 : return true;
8346 : : }
8347 : :
8348 : : /*
8349 : : * Estimate the number of index terms that need to be searched for while
8350 : : * testing the given GIN index clause, and increment the counts in *counts
8351 : : * appropriately. If the query is unsatisfiable, return false.
8352 : : */
8353 : : static bool
3942 8354 : 1229 : gincost_opexpr(PlannerInfo *root,
8355 : : IndexOptInfo *index,
8356 : : int indexcol,
8357 : : OpExpr *clause,
8358 : : GinQualCounts *counts)
8359 : : {
2497 8360 : 1229 : Oid clause_op = clause->opno;
8361 : 1229 : Node *operand = (Node *) lsecond(clause->args);
8362 : :
8363 : : /* aggressively reduce to a constant, and look through relabeling */
4317 8364 : 1229 : operand = estimate_expression_value(root, operand);
8365 : :
5111 8366 [ - + ]: 1229 : if (IsA(operand, RelabelType))
5111 tgl@sss.pgh.pa.us 8367 :UBC 0 : operand = (Node *) ((RelabelType *) operand)->arg;
8368 : :
8369 : : /*
8370 : : * It's impossible to call extractQuery method for unknown operand. So
8371 : : * unless operand is a Const we can't do much; just assume there will be
8372 : : * one ordinary search entry from the operand at runtime.
8373 : : */
5111 tgl@sss.pgh.pa.us 8374 [ - + ]:CBC 1229 : if (!IsA(operand, Const))
8375 : : {
5111 tgl@sss.pgh.pa.us 8376 :UBC 0 : counts->exactEntries++;
8377 : 0 : counts->searchEntries++;
8378 : 0 : return true;
8379 : : }
8380 : :
8381 : : /* If Const is null, there can be no matches */
5111 tgl@sss.pgh.pa.us 8382 [ - + ]:CBC 1229 : if (((Const *) operand)->constisnull)
5111 tgl@sss.pgh.pa.us 8383 :UBC 0 : return false;
8384 : :
8385 : : /* Otherwise, apply extractQuery and get the actual term counts */
5111 tgl@sss.pgh.pa.us 8386 :CBC 1229 : return gincost_pattern(index, indexcol, clause_op,
8387 : : ((Const *) operand)->constvalue,
8388 : : counts);
8389 : : }
8390 : :
8391 : : /*
8392 : : * Estimate the number of index terms that need to be searched for while
8393 : : * testing the given GIN index clause, and increment the counts in *counts
8394 : : * appropriately. If the query is unsatisfiable, return false.
8395 : : *
8396 : : * A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
8397 : : * each of which involves one value from the RHS array, plus all the
8398 : : * non-array quals (if any). To model this, we average the counts across
8399 : : * the RHS elements, and add the averages to the counts in *counts (which
8400 : : * correspond to per-indexscan costs). We also multiply counts->arrayScans
8401 : : * by N, causing gincostestimate to scale up its estimates accordingly.
8402 : : */
8403 : : static bool
4317 8404 : 3 : gincost_scalararrayopexpr(PlannerInfo *root,
8405 : : IndexOptInfo *index,
8406 : : int indexcol,
8407 : : ScalarArrayOpExpr *clause,
8408 : : double numIndexEntries,
8409 : : GinQualCounts *counts)
8410 : : {
2497 8411 : 3 : Oid clause_op = clause->opno;
8412 : 3 : Node *rightop = (Node *) lsecond(clause->args);
8413 : : ArrayType *arrayval;
8414 : : int16 elmlen;
8415 : : bool elmbyval;
8416 : : char elmalign;
8417 : : int numElems;
8418 : : Datum *elemValues;
8419 : : bool *elemNulls;
8420 : : GinQualCounts arraycounts;
5111 8421 : 3 : int numPossible = 0;
8422 : : int i;
8423 : :
2497 8424 [ - + ]: 3 : Assert(clause->useOr);
8425 : :
8426 : : /* aggressively reduce to a constant, and look through relabeling */
4317 8427 : 3 : rightop = estimate_expression_value(root, rightop);
8428 : :
5111 8429 [ - + ]: 3 : if (IsA(rightop, RelabelType))
5111 tgl@sss.pgh.pa.us 8430 :UBC 0 : rightop = (Node *) ((RelabelType *) rightop)->arg;
8431 : :
8432 : : /*
8433 : : * It's impossible to call extractQuery method for unknown operand. So
8434 : : * unless operand is a Const we can't do much; just assume there will be
8435 : : * one ordinary search entry from each array entry at runtime, and fall
8436 : : * back on a probably-bad estimate of the number of array entries.
8437 : : */
5111 tgl@sss.pgh.pa.us 8438 [ - + ]:CBC 3 : if (!IsA(rightop, Const))
8439 : : {
5111 tgl@sss.pgh.pa.us 8440 :UBC 0 : counts->exactEntries++;
8441 : 0 : counts->searchEntries++;
713 8442 : 0 : counts->arrayScans *= estimate_array_length(root, rightop);
5111 8443 : 0 : return true;
8444 : : }
8445 : :
8446 : : /* If Const is null, there can be no matches */
5111 tgl@sss.pgh.pa.us 8447 [ - + ]:CBC 3 : if (((Const *) rightop)->constisnull)
5111 tgl@sss.pgh.pa.us 8448 :UBC 0 : return false;
8449 : :
8450 : : /* Otherwise, extract the array elements and iterate over them */
5111 tgl@sss.pgh.pa.us 8451 :CBC 3 : arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
8452 : 3 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
8453 : : &elmlen, &elmbyval, &elmalign);
8454 : 3 : deconstruct_array(arrayval,
8455 : : ARR_ELEMTYPE(arrayval),
8456 : : elmlen, elmbyval, elmalign,
8457 : : &elemValues, &elemNulls, &numElems);
8458 : :
8459 : 3 : memset(&arraycounts, 0, sizeof(arraycounts));
8460 : :
8461 [ + + ]: 9 : for (i = 0; i < numElems; i++)
8462 : : {
8463 : : GinQualCounts elemcounts;
8464 : :
8465 : : /* NULL can't match anything, so ignore, as the executor will */
8466 [ - + ]: 6 : if (elemNulls[i])
5111 tgl@sss.pgh.pa.us 8467 :UBC 0 : continue;
8468 : :
8469 : : /* Otherwise, apply extractQuery and get the actual term counts */
5111 tgl@sss.pgh.pa.us 8470 :CBC 6 : memset(&elemcounts, 0, sizeof(elemcounts));
8471 : :
8472 [ + - ]: 6 : if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
8473 : : &elemcounts))
8474 : : {
8475 : : /* We ignore array elements that are unsatisfiable patterns */
8476 : 6 : numPossible++;
8477 : :
2160 akorotkov@postgresql 8478 [ - + ]: 6 : if (elemcounts.attHasFullScan[indexcol] &&
2160 akorotkov@postgresql 8479 [ # # ]:UBC 0 : !elemcounts.attHasNormalScan[indexcol])
8480 : : {
8481 : : /*
8482 : : * Full index scan will be required. We treat this as if
8483 : : * every key in the index had been listed in the query; is
8484 : : * that reasonable?
8485 : : */
5111 tgl@sss.pgh.pa.us 8486 : 0 : elemcounts.partialEntries = 0;
8487 : 0 : elemcounts.exactEntries = numIndexEntries;
8488 : 0 : elemcounts.searchEntries = numIndexEntries;
8489 : : }
5111 tgl@sss.pgh.pa.us 8490 :CBC 6 : arraycounts.partialEntries += elemcounts.partialEntries;
8491 : 6 : arraycounts.exactEntries += elemcounts.exactEntries;
8492 : 6 : arraycounts.searchEntries += elemcounts.searchEntries;
8493 : : }
8494 : : }
8495 : :
8496 [ - + ]: 3 : if (numPossible == 0)
8497 : : {
8498 : : /* No satisfiable patterns in the array */
5111 tgl@sss.pgh.pa.us 8499 :UBC 0 : return false;
8500 : : }
8501 : :
8502 : : /*
8503 : : * Now add the averages to the global counts. This will give us an
8504 : : * estimate of the average number of terms searched for in each indexscan,
8505 : : * including contributions from both array and non-array quals.
8506 : : */
5111 tgl@sss.pgh.pa.us 8507 :CBC 3 : counts->partialEntries += arraycounts.partialEntries / numPossible;
8508 : 3 : counts->exactEntries += arraycounts.exactEntries / numPossible;
8509 : 3 : counts->searchEntries += arraycounts.searchEntries / numPossible;
8510 : :
8511 : 3 : counts->arrayScans *= numPossible;
8512 : :
8513 : 3 : return true;
8514 : : }
8515 : :
8516 : : /*
8517 : : * GIN has search behavior completely different from other index types
8518 : : */
8519 : : void
3622 8520 : 1127 : gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8521 : : Cost *indexStartupCost, Cost *indexTotalCost,
8522 : : Selectivity *indexSelectivity, double *indexCorrelation,
8523 : : double *indexPages)
8524 : : {
5107 8525 : 1127 : IndexOptInfo *index = path->indexinfo;
2497 8526 : 1127 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
8527 : : List *selectivityQuals;
5365 bruce@momjian.us 8528 : 1127 : double numPages = index->pages,
8529 : 1127 : numTuples = index->tuples;
8530 : : double numEntryPages,
8531 : : numDataPages,
8532 : : numPendingPages,
8533 : : numEntries;
8534 : : GinQualCounts counts;
8535 : : bool matchPossible;
8536 : : bool fullIndexScan;
8537 : : double partialScale;
8538 : : double entryPagesFetched,
8539 : : dataPagesFetched,
8540 : : dataPagesFetchedBySel;
8541 : : double qual_op_cost,
8542 : : qual_arg_cost,
8543 : : spc_random_page_cost,
8544 : : outer_scans;
8545 : : Cost descentCost;
8546 : : Relation indexRel;
8547 : : GinStatsData ginStats;
8548 : : ListCell *lc;
8549 : : int i;
8550 : :
8551 : : /*
8552 : : * Obtain statistical information from the meta page, if possible. Else
8553 : : * set ginStats to zeroes, and we'll cope below.
8554 : : */
3669 tgl@sss.pgh.pa.us 8555 [ + - ]: 1127 : if (!index->hypothetical)
8556 : : {
8557 : : /* Lock should have already been obtained in plancat.c */
2449 8558 : 1127 : indexRel = index_open(index->indexoid, NoLock);
3669 8559 : 1127 : ginGetStats(indexRel, &ginStats);
2449 8560 : 1127 : index_close(indexRel, NoLock);
8561 : : }
8562 : : else
8563 : : {
3669 tgl@sss.pgh.pa.us 8564 :UBC 0 : memset(&ginStats, 0, sizeof(ginStats));
8565 : : }
8566 : :
8567 : : /*
8568 : : * Assuming we got valid (nonzero) stats at all, nPendingPages can be
8569 : : * trusted, but the other fields are data as of the last VACUUM. We can
8570 : : * scale them up to account for growth since then, but that method only
8571 : : * goes so far; in the worst case, the stats might be for a completely
8572 : : * empty index, and scaling them will produce pretty bogus numbers.
8573 : : * Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
8574 : : * it's grown more than that, fall back to estimating things only from the
8575 : : * assumed-accurate index size. But we'll trust nPendingPages in any case
8576 : : * so long as it's not clearly insane, ie, more than the index size.
8577 : : */
3638 tgl@sss.pgh.pa.us 8578 [ + - ]:CBC 1127 : if (ginStats.nPendingPages < numPages)
8579 : 1127 : numPendingPages = ginStats.nPendingPages;
8580 : : else
3638 tgl@sss.pgh.pa.us 8581 :UBC 0 : numPendingPages = 0;
8582 : :
3638 tgl@sss.pgh.pa.us 8583 [ + - + - ]:CBC 1127 : if (numPages > 0 && ginStats.nTotalPages <= numPages &&
8584 [ + + ]: 1127 : ginStats.nTotalPages > numPages / 4 &&
8585 [ + - + + ]: 1106 : ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
3669 8586 : 974 : {
8587 : : /*
8588 : : * OK, the stats seem close enough to sane to be trusted. But we
8589 : : * still need to scale them by the ratio numPages / nTotalPages to
8590 : : * account for growth since the last VACUUM.
8591 : : */
5365 bruce@momjian.us 8592 : 974 : double scale = numPages / ginStats.nTotalPages;
8593 : :
3638 tgl@sss.pgh.pa.us 8594 : 974 : numEntryPages = ceil(ginStats.nEntryPages * scale);
8595 : 974 : numDataPages = ceil(ginStats.nDataPages * scale);
8596 : 974 : numEntries = ceil(ginStats.nEntries * scale);
8597 : : /* ensure we didn't round up too much */
8598 [ + + ]: 974 : numEntryPages = Min(numEntryPages, numPages - numPendingPages);
8599 [ + + ]: 974 : numDataPages = Min(numDataPages,
8600 : : numPages - numPendingPages - numEntryPages);
8601 : : }
8602 : : else
8603 : : {
8604 : : /*
8605 : : * We might get here because it's a hypothetical index, or an index
8606 : : * created pre-9.1 and never vacuumed since upgrading (in which case
8607 : : * its stats would read as zeroes), or just because it's grown too
8608 : : * much since the last VACUUM for us to put our faith in scaling.
8609 : : *
8610 : : * Invent some plausible internal statistics based on the index page
8611 : : * count (and clamp that to at least 10 pages, just in case). We
8612 : : * estimate that 90% of the index is entry pages, and the rest is data
8613 : : * pages. Estimate 100 entries per entry page; this is rather bogus
8614 : : * since it'll depend on the size of the keys, but it's more robust
8615 : : * than trying to predict the number of entries per heap tuple.
8616 : : */
3669 8617 [ + + ]: 153 : numPages = Max(numPages, 10);
3638 8618 : 153 : numEntryPages = floor((numPages - numPendingPages) * 0.90);
8619 : 153 : numDataPages = numPages - numPendingPages - numEntryPages;
3669 8620 : 153 : numEntries = floor(numEntryPages * 100);
8621 : : }
8622 : :
8623 : : /* In an empty index, numEntries could be zero. Avoid divide-by-zero */
5354 8624 [ - + ]: 1127 : if (numEntries < 1)
5354 tgl@sss.pgh.pa.us 8625 :UBC 0 : numEntries = 1;
8626 : :
8627 : : /*
8628 : : * If the index is partial, AND the index predicate with the index-bound
8629 : : * quals to produce a more accurate idea of the number of rows covered by
8630 : : * the bound conditions.
8631 : : */
2497 tgl@sss.pgh.pa.us 8632 :CBC 1127 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
8633 : :
8634 : : /* Estimate the fraction of main-table tuples that will be visited */
5540 8635 : 2254 : *indexSelectivity = clauselist_selectivity(root, selectivityQuals,
5365 bruce@momjian.us 8636 : 1127 : index->rel->relid,
8637 : : JOIN_INNER,
8638 : : NULL);
8639 : :
8640 : : /* fetch estimated page cost for tablespace containing index */
8641 : 1127 : get_tablespace_page_costs(index->reltablespace,
8642 : : &spc_random_page_cost,
8643 : : NULL);
8644 : :
8645 : : /*
8646 : : * Generic assumption about index correlation: there isn't any.
8647 : : */
5540 tgl@sss.pgh.pa.us 8648 : 1127 : *indexCorrelation = 0.0;
8649 : :
8650 : : /*
8651 : : * Examine quals to estimate number of search entries & partial matches
8652 : : */
5111 8653 : 1127 : memset(&counts, 0, sizeof(counts));
8654 : 1127 : counts.arrayScans = 1;
8655 : 1127 : matchPossible = true;
8656 : :
2497 8657 [ + - + + : 2359 : foreach(lc, path->indexclauses)
+ + ]
8658 : : {
8659 : 1232 : IndexClause *iclause = lfirst_node(IndexClause, lc);
8660 : : ListCell *lc2;
8661 : :
8662 [ + - + + : 2458 : foreach(lc2, iclause->indexquals)
+ + ]
8663 : : {
8664 : 1232 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
8665 : 1232 : Expr *clause = rinfo->clause;
8666 : :
8667 [ + + ]: 1232 : if (IsA(clause, OpExpr))
8668 : : {
8669 : 1229 : matchPossible = gincost_opexpr(root,
8670 : : index,
8671 : 1229 : iclause->indexcol,
8672 : : (OpExpr *) clause,
8673 : : &counts);
8674 [ + + ]: 1229 : if (!matchPossible)
8675 : 6 : break;
8676 : : }
8677 [ + - ]: 3 : else if (IsA(clause, ScalarArrayOpExpr))
8678 : : {
8679 : 3 : matchPossible = gincost_scalararrayopexpr(root,
8680 : : index,
8681 : 3 : iclause->indexcol,
8682 : : (ScalarArrayOpExpr *) clause,
8683 : : numEntries,
8684 : : &counts);
8685 [ - + ]: 3 : if (!matchPossible)
2497 tgl@sss.pgh.pa.us 8686 :UBC 0 : break;
8687 : : }
8688 : : else
8689 : : {
8690 : : /* shouldn't be anything else for a GIN index */
8691 [ # # ]: 0 : elog(ERROR, "unsupported GIN indexqual type: %d",
8692 : : (int) nodeTag(clause));
8693 : : }
8694 : : }
8695 : : }
8696 : :
8697 : : /* Fall out if there were any provably-unsatisfiable quals */
5111 tgl@sss.pgh.pa.us 8698 [ + + ]:CBC 1127 : if (!matchPossible)
8699 : : {
8700 : 6 : *indexStartupCost = 0;
8701 : 6 : *indexTotalCost = 0;
8702 : 6 : *indexSelectivity = 0;
3622 8703 : 6 : return;
8704 : : }
8705 : :
8706 : : /*
8707 : : * If attribute has a full scan and at the same time doesn't have normal
8708 : : * scan, then we'll have to scan all non-null entries of that attribute.
8709 : : * Currently, we don't have per-attribute statistics for GIN. Thus, we
8710 : : * must assume the whole GIN index has to be scanned in this case.
8711 : : */
2160 akorotkov@postgresql 8712 : 1121 : fullIndexScan = false;
8713 [ + + ]: 2187 : for (i = 0; i < index->nkeycolumns; i++)
8714 : : {
8715 [ + + + + ]: 1235 : if (counts.attHasFullScan[i] && !counts.attHasNormalScan[i])
8716 : : {
8717 : 169 : fullIndexScan = true;
8718 : 169 : break;
8719 : : }
8720 : : }
8721 : :
8722 [ + + - + ]: 1121 : if (fullIndexScan || indexQuals == NIL)
8723 : : {
8724 : : /*
8725 : : * Full index scan will be required. We treat this as if every key in
8726 : : * the index had been listed in the query; is that reasonable?
8727 : : */
5111 tgl@sss.pgh.pa.us 8728 : 169 : counts.partialEntries = 0;
8729 : 169 : counts.exactEntries = numEntries;
8730 : 169 : counts.searchEntries = numEntries;
8731 : : }
8732 : :
8733 : : /* Will we have more than one iteration of a nestloop scan? */
5073 8734 : 1121 : outer_scans = loop_count;
8735 : :
8736 : : /*
8737 : : * Compute cost to begin scan, first of all, pay attention to pending
8738 : : * list.
8739 : : */
5540 8740 : 1121 : entryPagesFetched = numPendingPages;
8741 : :
8742 : : /*
8743 : : * Estimate number of entry pages read. We need to do
8744 : : * counts.searchEntries searches. Use a power function as it should be,
8745 : : * but tuples on leaf pages usually is much greater. Here we include all
8746 : : * searches in entry tree, including search of first entry in partial
8747 : : * match algorithm
8748 : : */
5111 8749 : 1121 : entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
8750 : :
8751 : : /*
8752 : : * Add an estimate of entry pages read by partial match algorithm. It's a
8753 : : * scan over leaf pages in entry tree. We haven't any useful stats here,
8754 : : * so estimate it as proportion. Because counts.partialEntries is really
8755 : : * pretty bogus (see code above), it's possible that it is more than
8756 : : * numEntries; clamp the proportion to ensure sanity.
8757 : : */
3638 8758 : 1121 : partialScale = counts.partialEntries / numEntries;
8759 [ + + ]: 1121 : partialScale = Min(partialScale, 1.0);
8760 : :
8761 : 1121 : entryPagesFetched += ceil(numEntryPages * partialScale);
8762 : :
8763 : : /*
8764 : : * Partial match algorithm reads all data pages before doing actual scan,
8765 : : * so it's a startup cost. Again, we haven't any useful stats here, so
8766 : : * estimate it as proportion.
8767 : : */
8768 : 1121 : dataPagesFetched = ceil(numDataPages * partialScale);
8769 : :
1074 akorotkov@postgresql 8770 : 1121 : *indexStartupCost = 0;
8771 : 1121 : *indexTotalCost = 0;
8772 : :
8773 : : /*
8774 : : * Add a CPU-cost component to represent the costs of initial entry btree
8775 : : * descent. We don't charge any I/O cost for touching upper btree levels,
8776 : : * since they tend to stay in cache, but we still have to do about log2(N)
8777 : : * comparisons to descend a btree of N leaf tuples. We charge one
8778 : : * cpu_operator_cost per comparison.
8779 : : *
8780 : : * If there are ScalarArrayOpExprs, charge this once per SA scan. The
8781 : : * ones after the first one are not startup cost so far as the overall
8782 : : * plan is concerned, so add them only to "total" cost.
8783 : : */
8784 [ + - ]: 1121 : if (numEntries > 1) /* avoid computing log(0) */
8785 : : {
8786 : 1121 : descentCost = ceil(log(numEntries) / log(2.0)) * cpu_operator_cost;
8787 : 1121 : *indexStartupCost += descentCost * counts.searchEntries;
8788 : 1121 : *indexTotalCost += counts.arrayScans * descentCost * counts.searchEntries;
8789 : : }
8790 : :
8791 : : /*
8792 : : * Add a cpu cost per entry-page fetched. This is not amortized over a
8793 : : * loop.
8794 : : */
8795 : 1121 : *indexStartupCost += entryPagesFetched * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8796 : 1121 : *indexTotalCost += entryPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8797 : :
8798 : : /*
8799 : : * Add a cpu cost per data-page fetched. This is also not amortized over a
8800 : : * loop. Since those are the data pages from the partial match algorithm,
8801 : : * charge them as startup cost.
8802 : : */
8803 : 1121 : *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * dataPagesFetched;
8804 : :
8805 : : /*
8806 : : * Since we add the startup cost to the total cost later on, remove the
8807 : : * initial arrayscan from the total.
8808 : : */
8809 : 1121 : *indexTotalCost += dataPagesFetched * (counts.arrayScans - 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8810 : :
8811 : : /*
8812 : : * Calculate cache effects if more than one scan due to nestloops or array
8813 : : * quals. The result is pro-rated per nestloop scan, but the array qual
8814 : : * factor shouldn't be pro-rated (compare genericcostestimate).
8815 : : */
5111 tgl@sss.pgh.pa.us 8816 [ + - + + ]: 1121 : if (outer_scans > 1 || counts.arrayScans > 1)
8817 : : {
8818 : 3 : entryPagesFetched *= outer_scans * counts.arrayScans;
5540 8819 : 3 : entryPagesFetched = index_pages_fetched(entryPagesFetched,
8820 : : (BlockNumber) numEntryPages,
8821 : : numEntryPages, root);
5111 8822 : 3 : entryPagesFetched /= outer_scans;
8823 : 3 : dataPagesFetched *= outer_scans * counts.arrayScans;
5540 8824 : 3 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
8825 : : (BlockNumber) numDataPages,
8826 : : numDataPages, root);
5111 8827 : 3 : dataPagesFetched /= outer_scans;
8828 : : }
8829 : :
8830 : : /*
8831 : : * Here we use random page cost because logically-close pages could be far
8832 : : * apart on disk.
8833 : : */
1074 akorotkov@postgresql 8834 : 1121 : *indexStartupCost += (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
8835 : :
8836 : : /*
8837 : : * Now compute the number of data pages fetched during the scan.
8838 : : *
8839 : : * We assume every entry to have the same number of items, and that there
8840 : : * is no overlap between them. (XXX: tsvector and array opclasses collect
8841 : : * statistics on the frequency of individual keys; it would be nice to use
8842 : : * those here.)
8843 : : */
5111 tgl@sss.pgh.pa.us 8844 : 1121 : dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
8845 : :
8846 : : /*
8847 : : * If there is a lot of overlap among the entries, in particular if one of
8848 : : * the entries is very frequent, the above calculation can grossly
8849 : : * under-estimate. As a simple cross-check, calculate a lower bound based
8850 : : * on the overall selectivity of the quals. At a minimum, we must read
8851 : : * one item pointer for each matching entry.
8852 : : *
8853 : : * The width of each item pointer varies, based on the level of
8854 : : * compression. We don't have statistics on that, but an average of
8855 : : * around 3 bytes per item is fairly typical.
8856 : : */
5540 8857 : 1121 : dataPagesFetchedBySel = ceil(*indexSelectivity *
4298 heikki.linnakangas@i 8858 : 1121 : (numTuples / (BLCKSZ / 3)));
5540 tgl@sss.pgh.pa.us 8859 [ + + ]: 1121 : if (dataPagesFetchedBySel > dataPagesFetched)
8860 : 931 : dataPagesFetched = dataPagesFetchedBySel;
8861 : :
8862 : : /* Add one page cpu-cost to the startup cost */
1074 akorotkov@postgresql 8863 : 1121 : *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * counts.searchEntries;
8864 : :
8865 : : /*
8866 : : * Add once again a CPU-cost for those data pages, before amortizing for
8867 : : * cache.
8868 : : */
8869 : 1121 : *indexTotalCost += dataPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8870 : :
8871 : : /* Account for cache effects, the same as above */
5111 tgl@sss.pgh.pa.us 8872 [ + - + + ]: 1121 : if (outer_scans > 1 || counts.arrayScans > 1)
8873 : : {
8874 : 3 : dataPagesFetched *= outer_scans * counts.arrayScans;
5540 8875 : 3 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
8876 : : (BlockNumber) numDataPages,
8877 : : numDataPages, root);
5111 8878 : 3 : dataPagesFetched /= outer_scans;
8879 : : }
8880 : :
8881 : : /* And apply random_page_cost as the cost per page */
1074 akorotkov@postgresql 8882 : 1121 : *indexTotalCost += *indexStartupCost +
5540 tgl@sss.pgh.pa.us 8883 : 1121 : dataPagesFetched * spc_random_page_cost;
8884 : :
8885 : : /*
8886 : : * Add on index qual eval costs, much as in genericcostestimate. We charge
8887 : : * cpu but we can disregard indexorderbys, since GIN doesn't support
8888 : : * those.
8889 : : */
2497 8890 : 1121 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
8891 : 1121 : qual_op_cost = cpu_operator_cost * list_length(indexQuals);
8892 : :
5540 8893 : 1121 : *indexStartupCost += qual_arg_cost;
8894 : 1121 : *indexTotalCost += qual_arg_cost;
8895 : :
8896 : : /*
8897 : : * Add a cpu cost per search entry, corresponding to the actual visited
8898 : : * entries.
8899 : : */
1074 akorotkov@postgresql 8900 : 1121 : *indexTotalCost += (counts.searchEntries * counts.arrayScans) * (qual_op_cost);
8901 : : /* Now add a cpu cost per tuple in the posting lists / trees */
8902 : 1121 : *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost);
3227 rhaas@postgresql.org 8903 : 1121 : *indexPages = dataPagesFetched;
8904 : : }
8905 : :
8906 : : /*
8907 : : * BRIN has search behavior completely different from other index types
8908 : : */
8909 : : void
3622 tgl@sss.pgh.pa.us 8910 : 5365 : brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8911 : : Cost *indexStartupCost, Cost *indexTotalCost,
8912 : : Selectivity *indexSelectivity, double *indexCorrelation,
8913 : : double *indexPages)
8914 : : {
4058 alvherre@alvh.no-ip. 8915 : 5365 : IndexOptInfo *index = path->indexinfo;
2497 tgl@sss.pgh.pa.us 8916 : 5365 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
4058 alvherre@alvh.no-ip. 8917 : 5365 : double numPages = index->pages;
3177 8918 : 5365 : RelOptInfo *baserel = index->rel;
8919 [ + - ]: 5365 : RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
8920 : : Cost spc_seq_page_cost;
8921 : : Cost spc_random_page_cost;
8922 : : double qual_arg_cost;
8923 : : double qualSelectivity;
8924 : : BrinStatsData statsData;
8925 : : double indexRanges;
8926 : : double minimalRanges;
8927 : : double estimatedRanges;
8928 : : double selec;
8929 : : Relation indexRel;
8930 : : ListCell *l;
8931 : : VariableStatData vardata;
8932 : :
8933 [ - + ]: 5365 : Assert(rte->rtekind == RTE_RELATION);
8934 : :
8935 : : /* fetch estimated page cost for the tablespace containing the index */
4058 8936 : 5365 : get_tablespace_page_costs(index->reltablespace,
8937 : : &spc_random_page_cost,
8938 : : &spc_seq_page_cost);
8939 : :
8940 : : /*
8941 : : * Obtain some data from the index itself, if possible. Otherwise invent
8942 : : * some plausible internal statistics based on the relation page count.
8943 : : */
2218 michael@paquier.xyz 8944 [ + - ]: 5365 : if (!index->hypothetical)
8945 : : {
8946 : : /*
8947 : : * A lock should have already been obtained on the index in plancat.c.
8948 : : */
8949 : 5365 : indexRel = index_open(index->indexoid, NoLock);
8950 : 5365 : brinGetStats(indexRel, &statsData);
8951 : 5365 : index_close(indexRel, NoLock);
8952 : :
8953 : : /* work out the actual number of ranges in the index */
8954 [ + + ]: 5365 : indexRanges = Max(ceil((double) baserel->pages /
8955 : : statsData.pagesPerRange), 1.0);
8956 : : }
8957 : : else
8958 : : {
8959 : : /*
8960 : : * Assume default number of pages per range, and estimate the number
8961 : : * of ranges based on that.
8962 : : */
2218 michael@paquier.xyz 8963 [ # # ]:UBC 0 : indexRanges = Max(ceil((double) baserel->pages /
8964 : : BRIN_DEFAULT_PAGES_PER_RANGE), 1.0);
8965 : :
8966 : 0 : statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE;
8967 : 0 : statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1;
8968 : : }
8969 : :
8970 : : /*
8971 : : * Compute index correlation
8972 : : *
8973 : : * Because we can use all index quals equally when scanning, we can use
8974 : : * the largest correlation (in absolute value) among columns used by the
8975 : : * query. Start at zero, the worst possible case. If we cannot find any
8976 : : * correlation statistics, we will keep it as 0.
8977 : : */
3177 alvherre@alvh.no-ip. 8978 :CBC 5365 : *indexCorrelation = 0;
8979 : :
2497 tgl@sss.pgh.pa.us 8980 [ + - + + : 10731 : foreach(l, path->indexclauses)
+ + ]
8981 : : {
8982 : 5366 : IndexClause *iclause = lfirst_node(IndexClause, l);
8983 : 5366 : AttrNumber attnum = index->indexkeys[iclause->indexcol];
8984 : :
8985 : : /* attempt to lookup stats in relation for this index column */
3177 alvherre@alvh.no-ip. 8986 [ + - ]: 5366 : if (attnum != 0)
8987 : : {
8988 : : /* Simple variable -- look to stats for the underlying table */
8989 [ - + - - ]: 5366 : if (get_relation_stats_hook &&
3177 alvherre@alvh.no-ip. 8990 :UBC 0 : (*get_relation_stats_hook) (root, rte, attnum, &vardata))
8991 : : {
8992 : : /*
8993 : : * The hook took control of acquiring a stats tuple. If it
8994 : : * did supply a tuple, it'd better have supplied a freefunc.
8995 : : */
8996 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
8997 [ # # ]: 0 : elog(ERROR,
8998 : : "no function provided to release variable stats with");
8999 : : }
9000 : : else
9001 : : {
3177 alvherre@alvh.no-ip. 9002 :CBC 5366 : vardata.statsTuple =
9003 : 5366 : SearchSysCache3(STATRELATTINH,
9004 : : ObjectIdGetDatum(rte->relid),
9005 : : Int16GetDatum(attnum),
9006 : : BoolGetDatum(false));
9007 : 5366 : vardata.freefunc = ReleaseSysCache;
9008 : : }
9009 : : }
9010 : : else
9011 : : {
9012 : : /*
9013 : : * Looks like we've found an expression column in the index. Let's
9014 : : * see if there's any stats for it.
9015 : : */
9016 : :
9017 : : /* get the attnum from the 0-based index. */
2497 tgl@sss.pgh.pa.us 9018 :UBC 0 : attnum = iclause->indexcol + 1;
9019 : :
3177 alvherre@alvh.no-ip. 9020 [ # # # # ]: 0 : if (get_index_stats_hook &&
3101 tgl@sss.pgh.pa.us 9021 : 0 : (*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
9022 : : {
9023 : : /*
9024 : : * The hook took control of acquiring a stats tuple. If it
9025 : : * did supply a tuple, it'd better have supplied a freefunc.
9026 : : */
3177 alvherre@alvh.no-ip. 9027 [ # # ]: 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
9028 [ # # ]: 0 : !vardata.freefunc)
9029 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
9030 : : }
9031 : : else
9032 : : {
9033 : 0 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
9034 : : ObjectIdGetDatum(index->indexoid),
9035 : : Int16GetDatum(attnum),
9036 : : BoolGetDatum(false));
9037 : 0 : vardata.freefunc = ReleaseSysCache;
9038 : : }
9039 : : }
9040 : :
3177 alvherre@alvh.no-ip. 9041 [ + + ]:CBC 5366 : if (HeapTupleIsValid(vardata.statsTuple))
9042 : : {
9043 : : AttStatsSlot sslot;
9044 : :
3140 tgl@sss.pgh.pa.us 9045 [ + - ]: 18 : if (get_attstatsslot(&sslot, vardata.statsTuple,
9046 : : STATISTIC_KIND_CORRELATION, InvalidOid,
9047 : : ATTSTATSSLOT_NUMBERS))
9048 : : {
3177 alvherre@alvh.no-ip. 9049 : 18 : double varCorrelation = 0.0;
9050 : :
3140 tgl@sss.pgh.pa.us 9051 [ + - ]: 18 : if (sslot.nnumbers > 0)
1167 peter@eisentraut.org 9052 : 18 : varCorrelation = fabs(sslot.numbers[0]);
9053 : :
3177 alvherre@alvh.no-ip. 9054 [ + - ]: 18 : if (varCorrelation > *indexCorrelation)
9055 : 18 : *indexCorrelation = varCorrelation;
9056 : :
3140 tgl@sss.pgh.pa.us 9057 : 18 : free_attstatsslot(&sslot);
9058 : : }
9059 : : }
9060 : :
3177 alvherre@alvh.no-ip. 9061 [ + + ]: 5366 : ReleaseVariableStats(vardata);
9062 : : }
9063 : :
9064 : 5365 : qualSelectivity = clauselist_selectivity(root, indexQuals,
9065 : 5365 : baserel->relid,
9066 : : JOIN_INNER, NULL);
9067 : :
9068 : : /*
9069 : : * Now calculate the minimum possible ranges we could match with if all of
9070 : : * the rows were in the perfect order in the table's heap.
9071 : : */
9072 : 5365 : minimalRanges = ceil(indexRanges * qualSelectivity);
9073 : :
9074 : : /*
9075 : : * Now estimate the number of ranges that we'll touch by using the
9076 : : * indexCorrelation from the stats. Careful not to divide by zero (note
9077 : : * we're using the absolute value of the correlation).
9078 : : */
9079 [ + + ]: 5365 : if (*indexCorrelation < 1.0e-10)
9080 : 5347 : estimatedRanges = indexRanges;
9081 : : else
9082 [ + + ]: 18 : estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
9083 : :
9084 : : /* we expect to visit this portion of the table */
9085 : 5365 : selec = estimatedRanges / indexRanges;
9086 : :
9087 [ - + - + ]: 5365 : CLAMP_PROBABILITY(selec);
9088 : :
9089 : 5365 : *indexSelectivity = selec;
9090 : :
9091 : : /*
9092 : : * Compute the index qual costs, much as in genericcostestimate, to add to
9093 : : * the index costs. We can disregard indexorderbys, since BRIN doesn't
9094 : : * support those.
9095 : : */
2497 tgl@sss.pgh.pa.us 9096 : 5365 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
9097 : :
9098 : : /*
9099 : : * Compute the startup cost as the cost to read the whole revmap
9100 : : * sequentially, including the cost to execute the index quals.
9101 : : */
3177 alvherre@alvh.no-ip. 9102 : 5365 : *indexStartupCost =
9103 : 5365 : spc_seq_page_cost * statsData.revmapNumPages * loop_count;
4058 9104 : 5365 : *indexStartupCost += qual_arg_cost;
9105 : :
9106 : : /*
9107 : : * To read a BRIN index there might be a bit of back and forth over
9108 : : * regular pages, as revmap might point to them out of sequential order;
9109 : : * calculate the total cost as reading the whole index in random order.
9110 : : */
3177 9111 : 5365 : *indexTotalCost = *indexStartupCost +
9112 : 5365 : spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
9113 : :
9114 : : /*
9115 : : * Charge a small amount per range tuple which we expect to match to. This
9116 : : * is meant to reflect the costs of manipulating the bitmap. The BRIN scan
9117 : : * will set a bit for each page in the range when we find a matching
9118 : : * range, so we must multiply the charge by the number of pages in the
9119 : : * range.
9120 : : */
9121 : 5365 : *indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
9122 : 5365 : statsData.pagesPerRange;
9123 : :
9124 : 5365 : *indexPages = index->pages;
4058 9125 : 5365 : }
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