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1 : : /*-------------------------------------------------------------------------
2 : : *
3 : : * selfuncs.c
4 : : * Selectivity functions and index cost estimation functions for
5 : : * standard operators and index access methods.
6 : : *
7 : : * Selectivity routines are registered in the pg_operator catalog
8 : : * in the "oprrest" and "oprjoin" attributes.
9 : : *
10 : : * Index cost functions are located via the index AM's API struct,
11 : : * which is obtained from the handler function registered in pg_am.
12 : : *
13 : : * Portions Copyright (c) 1996-2026, PostgreSQL Global Development Group
14 : : * Portions Copyright (c) 1994, Regents of the University of California
15 : : *
16 : : *
17 : : * IDENTIFICATION
18 : : * src/backend/utils/adt/selfuncs.c
19 : : *
20 : : *-------------------------------------------------------------------------
21 : : */
22 : :
23 : : /*----------
24 : : * Operator selectivity estimation functions are called to estimate the
25 : : * selectivity of WHERE clauses whose top-level operator is their operator.
26 : : * We divide the problem into two cases:
27 : : * Restriction clause estimation: the clause involves vars of just
28 : : * one relation.
29 : : * Join clause estimation: the clause involves vars of multiple rels.
30 : : * Join selectivity estimation is far more difficult and usually less accurate
31 : : * than restriction estimation.
32 : : *
33 : : * When dealing with the inner scan of a nestloop join, we consider the
34 : : * join's joinclauses as restriction clauses for the inner relation, and
35 : : * treat vars of the outer relation as parameters (a/k/a constants of unknown
36 : : * values). So, restriction estimators need to be able to accept an argument
37 : : * telling which relation is to be treated as the variable.
38 : : *
39 : : * The call convention for a restriction estimator (oprrest function) is
40 : : *
41 : : * Selectivity oprrest (PlannerInfo *root,
42 : : * Oid operator,
43 : : * List *args,
44 : : * int varRelid);
45 : : *
46 : : * root: general information about the query (rtable and RelOptInfo lists
47 : : * are particularly important for the estimator).
48 : : * operator: OID of the specific operator in question.
49 : : * args: argument list from the operator clause.
50 : : * varRelid: if not zero, the relid (rtable index) of the relation to
51 : : * be treated as the variable relation. May be zero if the args list
52 : : * is known to contain vars of only one relation.
53 : : *
54 : : * This is represented at the SQL level (in pg_proc) as
55 : : *
56 : : * float8 oprrest (internal, oid, internal, int4);
57 : : *
58 : : * The result is a selectivity, that is, a fraction (0 to 1) of the rows
59 : : * of the relation that are expected to produce a TRUE result for the
60 : : * given operator.
61 : : *
62 : : * The call convention for a join estimator (oprjoin function) is similar
63 : : * except that varRelid is not needed, and instead join information is
64 : : * supplied:
65 : : *
66 : : * Selectivity oprjoin (PlannerInfo *root,
67 : : * Oid operator,
68 : : * List *args,
69 : : * JoinType jointype,
70 : : * SpecialJoinInfo *sjinfo);
71 : : *
72 : : * float8 oprjoin (internal, oid, internal, int2, internal);
73 : : *
74 : : * (Before Postgres 8.4, join estimators had only the first four of these
75 : : * parameters. That signature is still allowed, but deprecated.) The
76 : : * relationship between jointype and sjinfo is explained in the comments for
77 : : * clause_selectivity() --- the short version is that jointype is usually
78 : : * best ignored in favor of examining sjinfo.
79 : : *
80 : : * Join selectivity for regular inner and outer joins is defined as the
81 : : * fraction (0 to 1) of the cross product of the relations that is expected
82 : : * to produce a TRUE result for the given operator. For both semi and anti
83 : : * joins, however, the selectivity is defined as the fraction of the left-hand
84 : : * side relation's rows that are expected to have a match (ie, at least one
85 : : * row with a TRUE result) in the right-hand side.
86 : : *
87 : : * For both oprrest and oprjoin functions, the operator's input collation OID
88 : : * (if any) is passed using the standard fmgr mechanism, so that the estimator
89 : : * function can fetch it with PG_GET_COLLATION(). Note, however, that all
90 : : * statistics in pg_statistic are currently built using the relevant column's
91 : : * collation.
92 : : *----------
93 : : */
94 : :
95 : : #include "postgres.h"
96 : :
97 : : #include <ctype.h>
98 : : #include <math.h>
99 : :
100 : : #include "access/brin.h"
101 : : #include "access/brin_page.h"
102 : : #include "access/gin.h"
103 : : #include "access/table.h"
104 : : #include "access/tableam.h"
105 : : #include "access/visibilitymap.h"
106 : : #include "catalog/pg_collation.h"
107 : : #include "catalog/pg_operator.h"
108 : : #include "catalog/pg_statistic.h"
109 : : #include "catalog/pg_statistic_ext.h"
110 : : #include "executor/nodeAgg.h"
111 : : #include "miscadmin.h"
112 : : #include "nodes/makefuncs.h"
113 : : #include "nodes/nodeFuncs.h"
114 : : #include "optimizer/clauses.h"
115 : : #include "optimizer/cost.h"
116 : : #include "optimizer/optimizer.h"
117 : : #include "optimizer/pathnode.h"
118 : : #include "optimizer/paths.h"
119 : : #include "optimizer/plancat.h"
120 : : #include "parser/parse_clause.h"
121 : : #include "parser/parse_relation.h"
122 : : #include "parser/parsetree.h"
123 : : #include "rewrite/rewriteManip.h"
124 : : #include "statistics/statistics.h"
125 : : #include "storage/bufmgr.h"
126 : : #include "utils/acl.h"
127 : : #include "utils/array.h"
128 : : #include "utils/builtins.h"
129 : : #include "utils/date.h"
130 : : #include "utils/datum.h"
131 : : #include "utils/fmgroids.h"
132 : : #include "utils/index_selfuncs.h"
133 : : #include "utils/lsyscache.h"
134 : : #include "utils/memutils.h"
135 : : #include "utils/pg_locale.h"
136 : : #include "utils/rel.h"
137 : : #include "utils/selfuncs.h"
138 : : #include "utils/snapmgr.h"
139 : : #include "utils/spccache.h"
140 : : #include "utils/syscache.h"
141 : : #include "utils/timestamp.h"
142 : : #include "utils/typcache.h"
143 : :
144 : : #define DEFAULT_PAGE_CPU_MULTIPLIER 50.0
145 : :
146 : : /*
147 : : * In production builds, switch to hash-based MCV matching when the lists are
148 : : * large enough to amortize hash setup cost. (This threshold is compared to
149 : : * the sum of the lengths of the two MCV lists. This is simplistic but seems
150 : : * to work well enough.) In debug builds, we use a smaller threshold so that
151 : : * the regression tests cover both paths well.
152 : : */
153 : : #ifndef USE_ASSERT_CHECKING
154 : : #define EQJOINSEL_MCV_HASH_THRESHOLD 200
155 : : #else
156 : : #define EQJOINSEL_MCV_HASH_THRESHOLD 20
157 : : #endif
158 : :
159 : : /* Entries in the simplehash hash table used by eqjoinsel_find_matches */
160 : : typedef struct MCVHashEntry
161 : : {
162 : : Datum value; /* the value represented by this entry */
163 : : int index; /* its index in the relevant AttStatsSlot */
164 : : uint32 hash; /* hash code for the Datum */
165 : : char status; /* status code used by simplehash.h */
166 : : } MCVHashEntry;
167 : :
168 : : /* private_data for the simplehash hash table */
169 : : typedef struct MCVHashContext
170 : : {
171 : : FunctionCallInfo equal_fcinfo; /* the equality join operator */
172 : : FunctionCallInfo hash_fcinfo; /* the hash function to use */
173 : : bool op_is_reversed; /* equality compares hash type to probe type */
174 : : bool insert_mode; /* doing inserts or lookups? */
175 : : bool hash_typbyval; /* typbyval of hashed data type */
176 : : int16 hash_typlen; /* typlen of hashed data type */
177 : : } MCVHashContext;
178 : :
179 : : /* forward reference */
180 : : typedef struct MCVHashTable_hash MCVHashTable_hash;
181 : :
182 : : /* Hooks for plugins to get control when we ask for stats */
183 : : get_relation_stats_hook_type get_relation_stats_hook = NULL;
184 : : get_index_stats_hook_type get_index_stats_hook = NULL;
185 : :
186 : : static double eqsel_internal(PG_FUNCTION_ARGS, bool negate);
187 : : static double eqjoinsel_inner(FmgrInfo *eqproc, Oid collation,
188 : : Oid hashLeft, Oid hashRight,
189 : : VariableStatData *vardata1, VariableStatData *vardata2,
190 : : double nd1, double nd2,
191 : : bool isdefault1, bool isdefault2,
192 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
193 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
194 : : bool have_mcvs1, bool have_mcvs2,
195 : : bool *hasmatch1, bool *hasmatch2,
196 : : int *p_nmatches);
197 : : static double eqjoinsel_semi(FmgrInfo *eqproc, Oid collation,
198 : : Oid hashLeft, Oid hashRight,
199 : : bool op_is_reversed,
200 : : VariableStatData *vardata1, VariableStatData *vardata2,
201 : : double nd1, double nd2,
202 : : bool isdefault1, bool isdefault2,
203 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
204 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
205 : : bool have_mcvs1, bool have_mcvs2,
206 : : bool *hasmatch1, bool *hasmatch2,
207 : : int *p_nmatches,
208 : : RelOptInfo *inner_rel);
209 : : static void eqjoinsel_find_matches(FmgrInfo *eqproc, Oid collation,
210 : : Oid hashLeft, Oid hashRight,
211 : : bool op_is_reversed,
212 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
213 : : int nvalues1, int nvalues2,
214 : : bool *hasmatch1, bool *hasmatch2,
215 : : int *p_nmatches, double *p_matchprodfreq);
216 : : static uint32 hash_mcv(MCVHashTable_hash *tab, Datum key);
217 : : static bool mcvs_equal(MCVHashTable_hash *tab, Datum key0, Datum key1);
218 : : static bool estimate_multivariate_ndistinct(PlannerInfo *root,
219 : : RelOptInfo *rel, List **varinfos, double *ndistinct);
220 : : static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
221 : : double *scaledvalue,
222 : : Datum lobound, Datum hibound, Oid boundstypid,
223 : : double *scaledlobound, double *scaledhibound);
224 : : static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
225 : : static void convert_string_to_scalar(char *value,
226 : : double *scaledvalue,
227 : : char *lobound,
228 : : double *scaledlobound,
229 : : char *hibound,
230 : : double *scaledhibound);
231 : : static void convert_bytea_to_scalar(Datum value,
232 : : double *scaledvalue,
233 : : Datum lobound,
234 : : double *scaledlobound,
235 : : Datum hibound,
236 : : double *scaledhibound);
237 : : static double convert_one_string_to_scalar(char *value,
238 : : int rangelo, int rangehi);
239 : : static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
240 : : int rangelo, int rangehi);
241 : : static char *convert_string_datum(Datum value, Oid typid, Oid collid,
242 : : bool *failure);
243 : : static double convert_timevalue_to_scalar(Datum value, Oid typid,
244 : : bool *failure);
245 : : static Node *strip_all_phvs_deep(PlannerInfo *root, Node *node);
246 : : static bool contain_placeholder_walker(Node *node, void *context);
247 : : static Node *strip_all_phvs_mutator(Node *node, void *context);
248 : : static void examine_simple_variable(PlannerInfo *root, Var *var,
249 : : VariableStatData *vardata);
250 : : static void examine_indexcol_variable(PlannerInfo *root, IndexOptInfo *index,
251 : : int indexcol, VariableStatData *vardata);
252 : : static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
253 : : Oid sortop, Oid collation,
254 : : Datum *min, Datum *max);
255 : : static void get_stats_slot_range(AttStatsSlot *sslot,
256 : : Oid opfuncoid, FmgrInfo *opproc,
257 : : Oid collation, int16 typLen, bool typByVal,
258 : : Datum *min, Datum *max, bool *p_have_data);
259 : : static bool get_actual_variable_range(PlannerInfo *root,
260 : : VariableStatData *vardata,
261 : : Oid sortop, Oid collation,
262 : : Datum *min, Datum *max);
263 : : static bool get_actual_variable_endpoint(Relation heapRel,
264 : : Relation indexRel,
265 : : ScanDirection indexscandir,
266 : : ScanKey scankeys,
267 : : int16 typLen,
268 : : bool typByVal,
269 : : TupleTableSlot *tableslot,
270 : : MemoryContext outercontext,
271 : : Datum *endpointDatum);
272 : : static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
273 : : static double btcost_correlation(IndexOptInfo *index,
274 : : VariableStatData *vardata);
275 : :
276 : : /* Define support routines for MCV hash tables */
277 : : #define SH_PREFIX MCVHashTable
278 : : #define SH_ELEMENT_TYPE MCVHashEntry
279 : : #define SH_KEY_TYPE Datum
280 : : #define SH_KEY value
281 : : #define SH_HASH_KEY(tab,key) hash_mcv(tab, key)
282 : : #define SH_EQUAL(tab,key0,key1) mcvs_equal(tab, key0, key1)
283 : : #define SH_SCOPE static inline
284 : : #define SH_STORE_HASH
285 : : #define SH_GET_HASH(tab,ent) (ent)->hash
286 : : #define SH_DEFINE
287 : : #define SH_DECLARE
288 : : #include "lib/simplehash.h"
289 : :
290 : :
291 : : /*
292 : : * eqsel - Selectivity of "=" for any data types.
293 : : *
294 : : * Note: this routine is also used to estimate selectivity for some
295 : : * operators that are not "=" but have comparable selectivity behavior,
296 : : * such as "~=" (geometric approximate-match). Even for "=", we must
297 : : * keep in mind that the left and right datatypes may differ.
298 : : */
299 : : Datum
9414 tgl@sss.pgh.pa.us 300 :CBC 414041 : eqsel(PG_FUNCTION_ARGS)
301 : : {
3207 302 : 414041 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
303 : : }
304 : :
305 : : /*
306 : : * Common code for eqsel() and neqsel()
307 : : */
308 : : static double
309 : 439018 : eqsel_internal(PG_FUNCTION_ARGS, bool negate)
310 : : {
7588 311 : 439018 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
9065 312 : 439018 : Oid operator = PG_GETARG_OID(1);
313 : 439018 : List *args = (List *) PG_GETARG_POINTER(2);
314 : 439018 : int varRelid = PG_GETARG_INT32(3);
2109 315 : 439018 : Oid collation = PG_GET_COLLATION();
316 : : VariableStatData vardata;
317 : : Node *other;
318 : : bool varonleft;
319 : : double selec;
320 : :
321 : : /*
322 : : * When asked about <>, we do the estimation using the corresponding =
323 : : * operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
324 : : */
3207 325 [ + + ]: 439018 : if (negate)
326 : : {
327 : 24977 : operator = get_negator(operator);
328 [ - + ]: 24977 : if (!OidIsValid(operator))
329 : : {
330 : : /* Use default selectivity (should we raise an error instead?) */
3207 tgl@sss.pgh.pa.us 331 :UBC 0 : return 1.0 - DEFAULT_EQ_SEL;
332 : : }
333 : : }
334 : :
335 : : /*
336 : : * If expression is not variable = something or something = variable, then
337 : : * punt and return a default estimate.
338 : : */
8062 tgl@sss.pgh.pa.us 339 [ + + ]:CBC 439018 : if (!get_restriction_variable(root, args, varRelid,
340 : : &vardata, &other, &varonleft))
3207 341 [ + + ]: 2854 : return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
342 : :
343 : : /*
344 : : * We can do a lot better if the something is a constant. (Note: the
345 : : * Const might result from estimation rather than being a simple constant
346 : : * in the query.)
347 : : */
6581 348 [ + + ]: 436161 : if (IsA(other, Const))
2109 349 : 171233 : selec = var_eq_const(&vardata, operator, collation,
6581 350 : 171233 : ((Const *) other)->constvalue,
351 : 171233 : ((Const *) other)->constisnull,
352 : : varonleft, negate);
353 : : else
2109 354 : 264928 : selec = var_eq_non_const(&vardata, operator, collation, other,
355 : : varonleft, negate);
356 : :
6581 357 [ + + ]: 436161 : ReleaseVariableStats(vardata);
358 : :
3207 359 : 436161 : return selec;
360 : : }
361 : :
362 : : /*
363 : : * var_eq_const --- eqsel for var = const case
364 : : *
365 : : * This is exported so that some other estimation functions can use it.
366 : : */
367 : : double
1272 pg@bowt.ie 368 : 194586 : var_eq_const(VariableStatData *vardata, Oid oproid, Oid collation,
369 : : Datum constval, bool constisnull,
370 : : bool varonleft, bool negate)
371 : : {
372 : : double selec;
3207 tgl@sss.pgh.pa.us 373 : 194586 : double nullfrac = 0.0;
374 : : bool isdefault;
375 : : Oid opfuncoid;
376 : :
377 : : /*
378 : : * If the constant is NULL, assume operator is strict and return zero, ie,
379 : : * operator will never return TRUE. (It's zero even for a negator op.)
380 : : */
6581 381 [ + + ]: 194586 : if (constisnull)
382 : 213 : return 0.0;
383 : :
384 : : /*
385 : : * Grab the nullfrac for use below. Note we allow use of nullfrac
386 : : * regardless of security check.
387 : : */
3207 388 [ + + ]: 194373 : if (HeapTupleIsValid(vardata->statsTuple))
389 : : {
390 : : Form_pg_statistic stats;
391 : :
392 : 143849 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
393 : 143849 : nullfrac = stats->stanullfrac;
394 : : }
395 : :
396 : : /*
397 : : * If we matched the var to a unique index, DISTINCT or GROUP-BY clause,
398 : : * assume there is exactly one match regardless of anything else. (This
399 : : * is slightly bogus, since the index or clause's equality operator might
400 : : * be different from ours, but it's much more likely to be right than
401 : : * ignoring the information.)
402 : : */
6237 403 [ + + + - : 194373 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
+ + ]
404 : : {
3207 405 : 45034 : selec = 1.0 / vardata->rel->tuples;
406 : : }
407 [ + + + - ]: 257291 : else if (HeapTupleIsValid(vardata->statsTuple) &&
408 : 107952 : statistic_proc_security_check(vardata,
1272 pg@bowt.ie 409 : 107952 : (opfuncoid = get_opcode(oproid))))
9723 tgl@sss.pgh.pa.us 410 : 107952 : {
411 : : AttStatsSlot sslot;
6581 412 : 107952 : bool match = false;
413 : : int i;
414 : :
415 : : /*
416 : : * Is the constant "=" to any of the column's most common values?
417 : : * (Although the given operator may not really be "=", we will assume
418 : : * that seeing whether it returns TRUE is an appropriate test. If you
419 : : * don't like this, maybe you shouldn't be using eqsel for your
420 : : * operator...)
421 : : */
3228 422 [ + + ]: 107952 : if (get_attstatsslot(&sslot, vardata->statsTuple,
423 : : STATISTIC_KIND_MCV, InvalidOid,
424 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
425 : : {
2154 426 : 97960 : LOCAL_FCINFO(fcinfo, 2);
427 : : FmgrInfo eqproc;
428 : :
3236 peter_e@gmx.net 429 : 97960 : fmgr_info(opfuncoid, &eqproc);
430 : :
431 : : /*
432 : : * Save a few cycles by setting up the fcinfo struct just once.
433 : : * Using FunctionCallInvoke directly also avoids failure if the
434 : : * eqproc returns NULL, though really equality functions should
435 : : * never do that.
436 : : */
2109 tgl@sss.pgh.pa.us 437 : 97960 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
438 : : NULL, NULL);
2154 439 : 97960 : fcinfo->args[0].isnull = false;
440 : 97960 : fcinfo->args[1].isnull = false;
441 : : /* be careful to apply operator right way 'round */
442 [ + + ]: 97960 : if (varonleft)
443 : 97944 : fcinfo->args[1].value = constval;
444 : : else
445 : 16 : fcinfo->args[0].value = constval;
446 : :
3228 447 [ + + ]: 1633663 : for (i = 0; i < sslot.nvalues; i++)
448 : : {
449 : : Datum fresult;
450 : :
6581 451 [ + + ]: 1588140 : if (varonleft)
2154 452 : 1588112 : fcinfo->args[0].value = sslot.values[i];
453 : : else
454 : 28 : fcinfo->args[1].value = sslot.values[i];
455 : 1588140 : fcinfo->isnull = false;
456 : 1588140 : fresult = FunctionCallInvoke(fcinfo);
457 [ + - + + ]: 1588140 : if (!fcinfo->isnull && DatumGetBool(fresult))
458 : : {
459 : 52437 : match = true;
6581 460 : 52437 : break;
461 : : }
462 : : }
463 : : }
464 : : else
465 : : {
466 : : /* no most-common-value info available */
3228 467 : 9992 : i = 0; /* keep compiler quiet */
468 : : }
469 : :
6581 470 [ + + ]: 107952 : if (match)
471 : : {
472 : : /*
473 : : * Constant is "=" to this common value. We know selectivity
474 : : * exactly (or as exactly as ANALYZE could calculate it, anyway).
475 : : */
3228 476 : 52437 : selec = sslot.numbers[i];
477 : : }
478 : : else
479 : : {
480 : : /*
481 : : * Comparison is against a constant that is neither NULL nor any
482 : : * of the common values. Its selectivity cannot be more than
483 : : * this:
484 : : */
6581 485 : 55515 : double sumcommon = 0.0;
486 : : double otherdistinct;
487 : :
3228 488 [ + + ]: 1378873 : for (i = 0; i < sslot.nnumbers; i++)
489 : 1323358 : sumcommon += sslot.numbers[i];
3207 490 : 55515 : selec = 1.0 - sumcommon - nullfrac;
6581 491 [ + + - + ]: 55515 : CLAMP_PROBABILITY(selec);
492 : :
493 : : /*
494 : : * and in fact it's probably a good deal less. We approximate that
495 : : * all the not-common values share this remaining fraction
496 : : * equally, so we divide by the number of other distinct values.
497 : : */
3228 498 : 55515 : otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
499 : 55515 : sslot.nnumbers;
6581 500 [ + + ]: 55515 : if (otherdistinct > 1)
501 : 27592 : selec /= otherdistinct;
502 : :
503 : : /*
504 : : * Another cross-check: selectivity shouldn't be estimated as more
505 : : * than the least common "most common value".
506 : : */
3228 507 [ + + - + ]: 55515 : if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
3228 tgl@sss.pgh.pa.us 508 :UBC 0 : selec = sslot.numbers[sslot.nnumbers - 1];
509 : : }
510 : :
3228 tgl@sss.pgh.pa.us 511 :CBC 107952 : free_attstatsslot(&sslot);
512 : : }
513 : : else
514 : : {
515 : : /*
516 : : * No ANALYZE stats available, so make a guess using estimated number
517 : : * of distinct values and assuming they are equally common. (The guess
518 : : * is unlikely to be very good, but we do know a few special cases.)
519 : : */
5306 520 : 41387 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
521 : : }
522 : :
523 : : /* now adjust if we wanted <> rather than = */
3207 524 [ + + ]: 194373 : if (negate)
525 : 19889 : selec = 1.0 - selec - nullfrac;
526 : :
527 : : /* result should be in range, but make sure... */
6581 528 [ - + - + ]: 194373 : CLAMP_PROBABILITY(selec);
529 : :
530 : 194373 : return selec;
531 : : }
532 : :
533 : : /*
534 : : * var_eq_non_const --- eqsel for var = something-other-than-const case
535 : : *
536 : : * This is exported so that some other estimation functions can use it.
537 : : */
538 : : double
1272 pg@bowt.ie 539 : 264928 : var_eq_non_const(VariableStatData *vardata, Oid oproid, Oid collation,
540 : : Node *other,
541 : : bool varonleft, bool negate)
542 : : {
543 : : double selec;
3207 tgl@sss.pgh.pa.us 544 : 264928 : double nullfrac = 0.0;
545 : : bool isdefault;
546 : :
547 : : /*
548 : : * Grab the nullfrac for use below.
549 : : */
550 [ + + ]: 264928 : if (HeapTupleIsValid(vardata->statsTuple))
551 : : {
552 : : Form_pg_statistic stats;
553 : :
554 : 172513 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
555 : 172513 : nullfrac = stats->stanullfrac;
556 : : }
557 : :
558 : : /*
559 : : * If we matched the var to a unique index, DISTINCT or GROUP-BY clause,
560 : : * assume there is exactly one match regardless of anything else. (This
561 : : * is slightly bogus, since the index or clause's equality operator might
562 : : * be different from ours, but it's much more likely to be right than
563 : : * ignoring the information.)
564 : : */
6237 565 [ + + + - : 264928 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
+ + ]
566 : : {
3207 567 : 93457 : selec = 1.0 / vardata->rel->tuples;
568 : : }
569 [ + + ]: 171471 : else if (HeapTupleIsValid(vardata->statsTuple))
570 : : {
571 : : double ndistinct;
572 : : AttStatsSlot sslot;
573 : :
574 : : /*
575 : : * Search is for a value that we do not know a priori, but we will
576 : : * assume it is not NULL. Estimate the selectivity as non-null
577 : : * fraction divided by number of distinct values, so that we get a
578 : : * result averaged over all possible values whether common or
579 : : * uncommon. (Essentially, we are assuming that the not-yet-known
580 : : * comparison value is equally likely to be any of the possible
581 : : * values, regardless of their frequency in the table. Is that a good
582 : : * idea?)
583 : : */
584 : 89684 : selec = 1.0 - nullfrac;
5306 585 : 89684 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
6581 586 [ + + ]: 89684 : if (ndistinct > 1)
587 : 87630 : selec /= ndistinct;
588 : :
589 : : /*
590 : : * Cross-check: selectivity should never be estimated as more than the
591 : : * most common value's.
592 : : */
3228 593 [ + + ]: 89684 : if (get_attstatsslot(&sslot, vardata->statsTuple,
594 : : STATISTIC_KIND_MCV, InvalidOid,
595 : : ATTSTATSSLOT_NUMBERS))
596 : : {
597 [ + - + + ]: 79483 : if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
598 : 291 : selec = sslot.numbers[0];
599 : 79483 : free_attstatsslot(&sslot);
600 : : }
601 : : }
602 : : else
603 : : {
604 : : /*
605 : : * No ANALYZE stats available, so make a guess using estimated number
606 : : * of distinct values and assuming they are equally common. (The guess
607 : : * is unlikely to be very good, but we do know a few special cases.)
608 : : */
5306 609 : 81787 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
610 : : }
611 : :
612 : : /* now adjust if we wanted <> rather than = */
3207 613 [ + + ]: 264928 : if (negate)
614 : 3745 : selec = 1.0 - selec - nullfrac;
615 : :
616 : : /* result should be in range, but make sure... */
8837 617 [ - + - + ]: 264928 : CLAMP_PROBABILITY(selec);
618 : :
6581 619 : 264928 : return selec;
620 : : }
621 : :
622 : : /*
623 : : * neqsel - Selectivity of "!=" for any data types.
624 : : *
625 : : * This routine is also used for some operators that are not "!="
626 : : * but have comparable selectivity behavior. See above comments
627 : : * for eqsel().
628 : : */
629 : : Datum
9414 630 : 24977 : neqsel(PG_FUNCTION_ARGS)
631 : : {
3207 632 : 24977 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
633 : : }
634 : :
635 : : /*
636 : : * scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
637 : : *
638 : : * This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
639 : : * The isgt and iseq flags distinguish which of the four cases apply.
640 : : *
641 : : * The caller has commuted the clause, if necessary, so that we can treat
642 : : * the variable as being on the left. The caller must also make sure that
643 : : * the other side of the clause is a non-null Const, and dissect that into
644 : : * a value and datatype. (This definition simplifies some callers that
645 : : * want to estimate against a computed value instead of a Const node.)
646 : : *
647 : : * This routine works for any datatype (or pair of datatypes) known to
648 : : * convert_to_scalar(). If it is applied to some other datatype,
649 : : * it will return an approximate estimate based on assuming that the constant
650 : : * value falls in the middle of the bin identified by binary search.
651 : : */
652 : : static double
3105 653 : 202662 : scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
654 : : Oid collation,
655 : : VariableStatData *vardata, Datum constval, Oid consttype)
656 : : {
657 : : Form_pg_statistic stats;
658 : : FmgrInfo opproc;
659 : : double mcv_selec,
660 : : hist_selec,
661 : : sumcommon;
662 : : double selec;
663 : :
8062 664 [ + + ]: 202662 : if (!HeapTupleIsValid(vardata->statsTuple))
665 : : {
666 : : /*
667 : : * No stats are available. Typically this means we have to fall back
668 : : * on the default estimate; but if the variable is CTID then we can
669 : : * make an estimate based on comparing the constant to the table size.
670 : : */
2547 671 [ + - + + ]: 14325 : if (vardata->var && IsA(vardata->var, Var) &&
672 [ + + ]: 11853 : ((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
673 : : {
674 : : ItemPointer itemptr;
675 : : double block;
676 : : double density;
677 : :
678 : : /*
679 : : * If the relation's empty, we're going to include all of it.
680 : : * (This is mostly to avoid divide-by-zero below.)
681 : : */
682 [ - + ]: 1017 : if (vardata->rel->pages == 0)
2547 tgl@sss.pgh.pa.us 683 :UBC 0 : return 1.0;
684 : :
2547 tgl@sss.pgh.pa.us 685 :CBC 1017 : itemptr = (ItemPointer) DatumGetPointer(constval);
686 : 1017 : block = ItemPointerGetBlockNumberNoCheck(itemptr);
687 : :
688 : : /*
689 : : * Determine the average number of tuples per page (density).
690 : : *
691 : : * Since the last page will, on average, be only half full, we can
692 : : * estimate it to have half as many tuples as earlier pages. So
693 : : * give it half the weight of a regular page.
694 : : */
695 : 1017 : density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
696 : :
697 : : /* If target is the last page, use half the density. */
698 [ + + ]: 1017 : if (block >= vardata->rel->pages - 1)
699 : 15 : density *= 0.5;
700 : :
701 : : /*
702 : : * Using the average tuples per page, calculate how far into the
703 : : * page the itemptr is likely to be and adjust block accordingly,
704 : : * by adding that fraction of a whole block (but never more than a
705 : : * whole block, no matter how high the itemptr's offset is). Here
706 : : * we are ignoring the possibility of dead-tuple line pointers,
707 : : * which is fairly bogus, but we lack the info to do better.
708 : : */
709 [ + - ]: 1017 : if (density > 0.0)
710 : : {
711 : 1017 : OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
712 : :
713 [ + + ]: 1017 : block += Min(offset / density, 1.0);
714 : : }
715 : :
716 : : /*
717 : : * Convert relative block number to selectivity. Again, the last
718 : : * page has only half weight.
719 : : */
720 : 1017 : selec = block / (vardata->rel->pages - 0.5);
721 : :
722 : : /*
723 : : * The calculation so far gave us a selectivity for the "<=" case.
724 : : * We'll have one fewer tuple for "<" and one additional tuple for
725 : : * ">=", the latter of which we'll reverse the selectivity for
726 : : * below, so we can simply subtract one tuple for both cases. The
727 : : * cases that need this adjustment can be identified by iseq being
728 : : * equal to isgt.
729 : : */
730 [ + + + - ]: 1017 : if (iseq == isgt && vardata->rel->tuples >= 1.0)
731 : 944 : selec -= (1.0 / vardata->rel->tuples);
732 : :
733 : : /* Finally, reverse the selectivity for the ">", ">=" cases. */
734 [ + + ]: 1017 : if (isgt)
735 : 934 : selec = 1.0 - selec;
736 : :
737 [ + + - + ]: 1017 : CLAMP_PROBABILITY(selec);
738 : 1017 : return selec;
739 : : }
740 : :
741 : : /* no stats available, so default result */
9065 742 : 13308 : return DEFAULT_INEQ_SEL;
743 : : }
8062 744 : 188337 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
745 : :
9065 746 : 188337 : fmgr_info(get_opcode(operator), &opproc);
747 : :
748 : : /*
749 : : * If we have most-common-values info, add up the fractions of the MCV
750 : : * entries that satisfy MCV OP CONST. These fractions contribute directly
751 : : * to the result selectivity. Also add up the total fraction represented
752 : : * by MCV entries.
753 : : */
2109 754 : 188337 : mcv_selec = mcv_selectivity(vardata, &opproc, collation, constval, true,
755 : : &sumcommon);
756 : :
757 : : /*
758 : : * If there is a histogram, determine which bin the constant falls in, and
759 : : * compute the resulting contribution to selectivity.
760 : : */
3105 761 : 188337 : hist_selec = ineq_histogram_selectivity(root, vardata,
762 : : operator, &opproc, isgt, iseq,
763 : : collation,
764 : : constval, consttype);
765 : :
766 : : /*
767 : : * Now merge the results from the MCV and histogram calculations,
768 : : * realizing that the histogram covers only the non-null values that are
769 : : * not listed in MCV.
770 : : */
7369 771 : 188337 : selec = 1.0 - stats->stanullfrac - sumcommon;
772 : :
5914 773 [ + + ]: 188337 : if (hist_selec >= 0.0)
7369 774 : 121621 : selec *= hist_selec;
775 : : else
776 : : {
777 : : /*
778 : : * If no histogram but there are values not accounted for by MCV,
779 : : * arbitrarily assume half of them will match.
780 : : */
781 : 66716 : selec *= 0.5;
782 : : }
783 : :
784 : 188337 : selec += mcv_selec;
785 : :
786 : : /* result should be in range, but make sure... */
787 [ + + + + ]: 188337 : CLAMP_PROBABILITY(selec);
788 : :
789 : 188337 : return selec;
790 : : }
791 : :
792 : : /*
793 : : * mcv_selectivity - Examine the MCV list for selectivity estimates
794 : : *
795 : : * Determine the fraction of the variable's MCV population that satisfies
796 : : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
797 : : * compute the fraction of the total column population represented by the MCV
798 : : * list. This code will work for any boolean-returning predicate operator.
799 : : *
800 : : * The function result is the MCV selectivity, and the fraction of the
801 : : * total population is returned into *sumcommonp. Zeroes are returned
802 : : * if there is no MCV list.
803 : : */
804 : : double
2109 805 : 191533 : mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc, Oid collation,
806 : : Datum constval, bool varonleft,
807 : : double *sumcommonp)
808 : : {
809 : : double mcv_selec,
810 : : sumcommon;
811 : : AttStatsSlot sslot;
812 : : int i;
813 : :
9078 814 : 191533 : mcv_selec = 0.0;
815 : 191533 : sumcommon = 0.0;
816 : :
7369 817 [ + + + + ]: 381834 : if (HeapTupleIsValid(vardata->statsTuple) &&
3236 peter_e@gmx.net 818 [ + + ]: 380437 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3228 tgl@sss.pgh.pa.us 819 : 190136 : get_attstatsslot(&sslot, vardata->statsTuple,
820 : : STATISTIC_KIND_MCV, InvalidOid,
821 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
822 : : {
2154 823 : 104638 : LOCAL_FCINFO(fcinfo, 2);
824 : :
825 : : /*
826 : : * We invoke the opproc "by hand" so that we won't fail on NULL
827 : : * results. Such cases won't arise for normal comparison functions,
828 : : * but generic_restriction_selectivity could perhaps be used with
829 : : * operators that can return NULL. A small side benefit is to not
830 : : * need to re-initialize the fcinfo struct from scratch each time.
831 : : */
2109 832 : 104638 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
833 : : NULL, NULL);
2154 834 : 104638 : fcinfo->args[0].isnull = false;
835 : 104638 : fcinfo->args[1].isnull = false;
836 : : /* be careful to apply operator right way 'round */
837 [ + - ]: 104638 : if (varonleft)
838 : 104638 : fcinfo->args[1].value = constval;
839 : : else
2154 tgl@sss.pgh.pa.us 840 :UBC 0 : fcinfo->args[0].value = constval;
841 : :
3228 tgl@sss.pgh.pa.us 842 [ + + ]:CBC 2650583 : for (i = 0; i < sslot.nvalues; i++)
843 : : {
844 : : Datum fresult;
845 : :
2154 846 [ + - ]: 2545945 : if (varonleft)
847 : 2545945 : fcinfo->args[0].value = sslot.values[i];
848 : : else
2154 tgl@sss.pgh.pa.us 849 :UBC 0 : fcinfo->args[1].value = sslot.values[i];
2154 tgl@sss.pgh.pa.us 850 :CBC 2545945 : fcinfo->isnull = false;
851 : 2545945 : fresult = FunctionCallInvoke(fcinfo);
852 [ + - + + ]: 2545945 : if (!fcinfo->isnull && DatumGetBool(fresult))
3228 853 : 1006786 : mcv_selec += sslot.numbers[i];
854 : 2545945 : sumcommon += sslot.numbers[i];
855 : : }
856 : 104638 : free_attstatsslot(&sslot);
857 : : }
858 : :
7369 859 : 191533 : *sumcommonp = sumcommon;
860 : 191533 : return mcv_selec;
861 : : }
862 : :
863 : : /*
864 : : * histogram_selectivity - Examine the histogram for selectivity estimates
865 : : *
866 : : * Determine the fraction of the variable's histogram entries that satisfy
867 : : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
868 : : *
869 : : * This code will work for any boolean-returning predicate operator, whether
870 : : * or not it has anything to do with the histogram sort operator. We are
871 : : * essentially using the histogram just as a representative sample. However,
872 : : * small histograms are unlikely to be all that representative, so the caller
873 : : * should be prepared to fall back on some other estimation approach when the
874 : : * histogram is missing or very small. It may also be prudent to combine this
875 : : * approach with another one when the histogram is small.
876 : : *
877 : : * If the actual histogram size is not at least min_hist_size, we won't bother
878 : : * to do the calculation at all. Also, if the n_skip parameter is > 0, we
879 : : * ignore the first and last n_skip histogram elements, on the grounds that
880 : : * they are outliers and hence not very representative. Typical values for
881 : : * these parameters are 10 and 1.
882 : : *
883 : : * The function result is the selectivity, or -1 if there is no histogram
884 : : * or it's smaller than min_hist_size.
885 : : *
886 : : * The output parameter *hist_size receives the actual histogram size,
887 : : * or zero if no histogram. Callers may use this number to decide how
888 : : * much faith to put in the function result.
889 : : *
890 : : * Note that the result disregards both the most-common-values (if any) and
891 : : * null entries. The caller is expected to combine this result with
892 : : * statistics for those portions of the column population. It may also be
893 : : * prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
894 : : */
895 : : double
2109 896 : 3196 : histogram_selectivity(VariableStatData *vardata,
897 : : FmgrInfo *opproc, Oid collation,
898 : : Datum constval, bool varonleft,
899 : : int min_hist_size, int n_skip,
900 : : int *hist_size)
901 : : {
902 : : double result;
903 : : AttStatsSlot sslot;
904 : :
905 : : /* check sanity of parameters */
7116 906 [ - + ]: 3196 : Assert(n_skip >= 0);
907 [ - + ]: 3196 : Assert(min_hist_size > 2 * n_skip);
908 : :
909 [ + + + + ]: 5160 : if (HeapTupleIsValid(vardata->statsTuple) &&
3236 peter_e@gmx.net 910 [ + + ]: 3925 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3228 tgl@sss.pgh.pa.us 911 : 1961 : get_attstatsslot(&sslot, vardata->statsTuple,
912 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
913 : : ATTSTATSSLOT_VALUES))
914 : : {
915 : 1913 : *hist_size = sslot.nvalues;
916 [ + + ]: 1913 : if (sslot.nvalues >= min_hist_size)
917 : : {
2154 918 : 936 : LOCAL_FCINFO(fcinfo, 2);
7116 919 : 936 : int nmatch = 0;
920 : : int i;
921 : :
922 : : /*
923 : : * We invoke the opproc "by hand" so that we won't fail on NULL
924 : : * results. Such cases won't arise for normal comparison
925 : : * functions, but generic_restriction_selectivity could perhaps be
926 : : * used with operators that can return NULL. A small side benefit
927 : : * is to not need to re-initialize the fcinfo struct from scratch
928 : : * each time.
929 : : */
2109 930 : 936 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
931 : : NULL, NULL);
2154 932 : 936 : fcinfo->args[0].isnull = false;
933 : 936 : fcinfo->args[1].isnull = false;
934 : : /* be careful to apply operator right way 'round */
935 [ + - ]: 936 : if (varonleft)
936 : 936 : fcinfo->args[1].value = constval;
937 : : else
2154 tgl@sss.pgh.pa.us 938 :UBC 0 : fcinfo->args[0].value = constval;
939 : :
3228 tgl@sss.pgh.pa.us 940 [ + + ]:CBC 74378 : for (i = n_skip; i < sslot.nvalues - n_skip; i++)
941 : : {
942 : : Datum fresult;
943 : :
2154 944 [ + - ]: 73442 : if (varonleft)
945 : 73442 : fcinfo->args[0].value = sslot.values[i];
946 : : else
2154 tgl@sss.pgh.pa.us 947 :UBC 0 : fcinfo->args[1].value = sslot.values[i];
2154 tgl@sss.pgh.pa.us 948 :CBC 73442 : fcinfo->isnull = false;
949 : 73442 : fresult = FunctionCallInvoke(fcinfo);
950 [ + - + + ]: 73442 : if (!fcinfo->isnull && DatumGetBool(fresult))
7116 951 : 5506 : nmatch++;
952 : : }
3228 953 : 936 : result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
954 : : }
955 : : else
7116 956 : 977 : result = -1;
3228 957 : 1913 : free_attstatsslot(&sslot);
958 : : }
959 : : else
960 : : {
6580 961 : 1283 : *hist_size = 0;
7116 962 : 1283 : result = -1;
963 : : }
964 : :
965 : 3196 : return result;
966 : : }
967 : :
968 : : /*
969 : : * generic_restriction_selectivity - Selectivity for almost anything
970 : : *
971 : : * This function estimates selectivity for operators that we don't have any
972 : : * special knowledge about, but are on data types that we collect standard
973 : : * MCV and/or histogram statistics for. (Additional assumptions are that
974 : : * the operator is strict and immutable, or at least stable.)
975 : : *
976 : : * If we have "VAR OP CONST" or "CONST OP VAR", selectivity is estimated by
977 : : * applying the operator to each element of the column's MCV and/or histogram
978 : : * stats, and merging the results using the assumption that the histogram is
979 : : * a reasonable random sample of the column's non-MCV population. Note that
980 : : * if the operator's semantics are related to the histogram ordering, this
981 : : * might not be such a great assumption; other functions such as
982 : : * scalarineqsel() are probably a better match in such cases.
983 : : *
984 : : * Otherwise, fall back to the default selectivity provided by the caller.
985 : : */
986 : : double
2109 987 : 565 : generic_restriction_selectivity(PlannerInfo *root, Oid oproid, Oid collation,
988 : : List *args, int varRelid,
989 : : double default_selectivity)
990 : : {
991 : : double selec;
992 : : VariableStatData vardata;
993 : : Node *other;
994 : : bool varonleft;
995 : :
996 : : /*
997 : : * If expression is not variable OP something or something OP variable,
998 : : * then punt and return the default estimate.
999 : : */
2174 1000 [ - + ]: 565 : if (!get_restriction_variable(root, args, varRelid,
1001 : : &vardata, &other, &varonleft))
2174 tgl@sss.pgh.pa.us 1002 :UBC 0 : return default_selectivity;
1003 : :
1004 : : /*
1005 : : * If the something is a NULL constant, assume operator is strict and
1006 : : * return zero, ie, operator will never return TRUE.
1007 : : */
2174 tgl@sss.pgh.pa.us 1008 [ + - ]:CBC 565 : if (IsA(other, Const) &&
1009 [ - + ]: 565 : ((Const *) other)->constisnull)
1010 : : {
2174 tgl@sss.pgh.pa.us 1011 [ # # ]:UBC 0 : ReleaseVariableStats(vardata);
1012 : 0 : return 0.0;
1013 : : }
1014 : :
2174 tgl@sss.pgh.pa.us 1015 [ + - ]:CBC 565 : if (IsA(other, Const))
1016 : : {
1017 : : /* Variable is being compared to a known non-null constant */
1018 : 565 : Datum constval = ((Const *) other)->constvalue;
1019 : : FmgrInfo opproc;
1020 : : double mcvsum;
1021 : : double mcvsel;
1022 : : double nullfrac;
1023 : : int hist_size;
1024 : :
2154 1025 : 565 : fmgr_info(get_opcode(oproid), &opproc);
1026 : :
1027 : : /*
1028 : : * Calculate the selectivity for the column's most common values.
1029 : : */
2109 1030 : 565 : mcvsel = mcv_selectivity(&vardata, &opproc, collation,
1031 : : constval, varonleft,
1032 : : &mcvsum);
1033 : :
1034 : : /*
1035 : : * If the histogram is large enough, see what fraction of it matches
1036 : : * the query, and assume that's representative of the non-MCV
1037 : : * population. Otherwise use the default selectivity for the non-MCV
1038 : : * population.
1039 : : */
1040 : 565 : selec = histogram_selectivity(&vardata, &opproc, collation,
1041 : : constval, varonleft,
1042 : : 10, 1, &hist_size);
2174 1043 [ + - ]: 565 : if (selec < 0)
1044 : : {
1045 : : /* Nope, fall back on default */
1046 : 565 : selec = default_selectivity;
1047 : : }
2174 tgl@sss.pgh.pa.us 1048 [ # # ]:UBC 0 : else if (hist_size < 100)
1049 : : {
1050 : : /*
1051 : : * For histogram sizes from 10 to 100, we combine the histogram
1052 : : * and default selectivities, putting increasingly more trust in
1053 : : * the histogram for larger sizes.
1054 : : */
1055 : 0 : double hist_weight = hist_size / 100.0;
1056 : :
1057 : 0 : selec = selec * hist_weight +
1058 : 0 : default_selectivity * (1.0 - hist_weight);
1059 : : }
1060 : :
1061 : : /* In any case, don't believe extremely small or large estimates. */
2174 tgl@sss.pgh.pa.us 1062 [ - + ]:CBC 565 : if (selec < 0.0001)
2174 tgl@sss.pgh.pa.us 1063 :UBC 0 : selec = 0.0001;
2174 tgl@sss.pgh.pa.us 1064 [ - + ]:CBC 565 : else if (selec > 0.9999)
2174 tgl@sss.pgh.pa.us 1065 :UBC 0 : selec = 0.9999;
1066 : :
1067 : : /* Don't forget to account for nulls. */
2174 tgl@sss.pgh.pa.us 1068 [ + + ]:CBC 565 : if (HeapTupleIsValid(vardata.statsTuple))
1069 : 42 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
1070 : : else
1071 : 523 : nullfrac = 0.0;
1072 : :
1073 : : /*
1074 : : * Now merge the results from the MCV and histogram calculations,
1075 : : * realizing that the histogram covers only the non-null values that
1076 : : * are not listed in MCV.
1077 : : */
1078 : 565 : selec *= 1.0 - nullfrac - mcvsum;
1079 : 565 : selec += mcvsel;
1080 : : }
1081 : : else
1082 : : {
1083 : : /* Comparison value is not constant, so we can't do anything */
2174 tgl@sss.pgh.pa.us 1084 :UBC 0 : selec = default_selectivity;
1085 : : }
1086 : :
2174 tgl@sss.pgh.pa.us 1087 [ + + ]:CBC 565 : ReleaseVariableStats(vardata);
1088 : :
1089 : : /* result should be in range, but make sure... */
1090 [ - + - + ]: 565 : CLAMP_PROBABILITY(selec);
1091 : :
1092 : 565 : return selec;
1093 : : }
1094 : :
1095 : : /*
1096 : : * ineq_histogram_selectivity - Examine the histogram for scalarineqsel
1097 : : *
1098 : : * Determine the fraction of the variable's histogram population that
1099 : : * satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
1100 : : * The isgt and iseq flags distinguish which of the four cases apply.
1101 : : *
1102 : : * While opproc could be looked up from the operator OID, common callers
1103 : : * also need to call it separately, so we make the caller pass both.
1104 : : *
1105 : : * Returns -1 if there is no histogram (valid results will always be >= 0).
1106 : : *
1107 : : * Note that the result disregards both the most-common-values (if any) and
1108 : : * null entries. The caller is expected to combine this result with
1109 : : * statistics for those portions of the column population.
1110 : : *
1111 : : * This is exported so that some other estimation functions can use it.
1112 : : */
1113 : : double
5914 1114 : 191139 : ineq_histogram_selectivity(PlannerInfo *root,
1115 : : VariableStatData *vardata,
1116 : : Oid opoid, FmgrInfo *opproc, bool isgt, bool iseq,
1117 : : Oid collation,
1118 : : Datum constval, Oid consttype)
1119 : : {
1120 : : double hist_selec;
1121 : : AttStatsSlot sslot;
1122 : :
1123 : 191139 : hist_selec = -1.0;
1124 : :
1125 : : /*
1126 : : * Someday, ANALYZE might store more than one histogram per rel/att,
1127 : : * corresponding to more than one possible sort ordering defined for the
1128 : : * column type. Right now, we know there is only one, so just grab it and
1129 : : * see if it matches the query.
1130 : : *
1131 : : * Note that we can't use opoid as search argument; the staop appearing in
1132 : : * pg_statistic will be for the relevant '<' operator, but what we have
1133 : : * might be some other inequality operator such as '>='. (Even if opoid
1134 : : * is a '<' operator, it could be cross-type.) Hence we must use
1135 : : * comparison_ops_are_compatible() to see if the operators match.
1136 : : */
7369 1137 [ + + + + ]: 381936 : if (HeapTupleIsValid(vardata->statsTuple) &&
3236 peter_e@gmx.net 1138 [ + + ]: 381432 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3228 tgl@sss.pgh.pa.us 1139 : 190635 : get_attstatsslot(&sslot, vardata->statsTuple,
1140 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
1141 : : ATTSTATSSLOT_VALUES))
1142 : : {
2109 1143 [ + - ]: 124079 : if (sslot.nvalues > 1 &&
1144 [ + + + + ]: 248120 : sslot.stacoll == collation &&
1145 : 124041 : comparison_ops_are_compatible(sslot.staop, opoid))
9722 1146 : 123987 : {
1147 : : /*
1148 : : * Use binary search to find the desired location, namely the
1149 : : * right end of the histogram bin containing the comparison value,
1150 : : * which is the leftmost entry for which the comparison operator
1151 : : * succeeds (if isgt) or fails (if !isgt).
1152 : : *
1153 : : * In this loop, we pay no attention to whether the operator iseq
1154 : : * or not; that detail will be mopped up below. (We cannot tell,
1155 : : * anyway, whether the operator thinks the values are equal.)
1156 : : *
1157 : : * If the binary search accesses the first or last histogram
1158 : : * entry, we try to replace that endpoint with the true column min
1159 : : * or max as found by get_actual_variable_range(). This
1160 : : * ameliorates misestimates when the min or max is moving as a
1161 : : * result of changes since the last ANALYZE. Note that this could
1162 : : * result in effectively including MCVs into the histogram that
1163 : : * weren't there before, but we don't try to correct for that.
1164 : : */
1165 : : double histfrac;
7102 bruce@momjian.us 1166 : 123987 : int lobound = 0; /* first possible slot to search */
3189 tgl@sss.pgh.pa.us 1167 : 123987 : int hibound = sslot.nvalues; /* last+1 slot to search */
5914 1168 : 123987 : bool have_end = false;
1169 : :
1170 : : /*
1171 : : * If there are only two histogram entries, we'll want up-to-date
1172 : : * values for both. (If there are more than two, we need at most
1173 : : * one of them to be updated, so we deal with that within the
1174 : : * loop.)
1175 : : */
3228 1176 [ + + ]: 123987 : if (sslot.nvalues == 2)
5914 1177 : 2797 : have_end = get_actual_variable_range(root,
1178 : : vardata,
1179 : : sslot.staop,
1180 : : collation,
1181 : : &sslot.values[0],
3228 1182 : 2797 : &sslot.values[1]);
1183 : :
7116 1184 [ + + ]: 824980 : while (lobound < hibound)
1185 : : {
7102 bruce@momjian.us 1186 : 700993 : int probe = (lobound + hibound) / 2;
1187 : : bool ltcmp;
1188 : :
1189 : : /*
1190 : : * If we find ourselves about to compare to the first or last
1191 : : * histogram entry, first try to replace it with the actual
1192 : : * current min or max (unless we already did so above).
1193 : : */
3228 tgl@sss.pgh.pa.us 1194 [ + + + + ]: 700993 : if (probe == 0 && sslot.nvalues > 2)
5914 1195 : 59271 : have_end = get_actual_variable_range(root,
1196 : : vardata,
1197 : : sslot.staop,
1198 : : collation,
1199 : : &sslot.values[0],
1200 : : NULL);
3228 1201 [ + + + + ]: 641722 : else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
5914 1202 : 43295 : have_end = get_actual_variable_range(root,
1203 : : vardata,
1204 : : sslot.staop,
1205 : : collation,
1206 : : NULL,
3189 1207 : 43295 : &sslot.values[probe]);
1208 : :
5451 1209 : 700993 : ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
1210 : : collation,
3228 1211 : 700993 : sslot.values[probe],
1212 : : constval));
7116 1213 [ + + ]: 700993 : if (isgt)
1214 : 36689 : ltcmp = !ltcmp;
1215 [ + + ]: 700993 : if (ltcmp)
1216 : 273694 : lobound = probe + 1;
1217 : : else
1218 : 427299 : hibound = probe;
1219 : : }
1220 : :
1221 [ + + ]: 123987 : if (lobound <= 0)
1222 : : {
1223 : : /*
1224 : : * Constant is below lower histogram boundary. More
1225 : : * precisely, we have found that no entry in the histogram
1226 : : * satisfies the inequality clause (if !isgt) or they all do
1227 : : * (if isgt). We estimate that that's true of the entire
1228 : : * table, so set histfrac to 0.0 (which we'll flip to 1.0
1229 : : * below, if isgt).
1230 : : */
9078 1231 : 51925 : histfrac = 0.0;
1232 : : }
3228 1233 [ + + ]: 72062 : else if (lobound >= sslot.nvalues)
1234 : : {
1235 : : /*
1236 : : * Inverse case: constant is above upper histogram boundary.
1237 : : */
7116 1238 : 20888 : histfrac = 1.0;
1239 : : }
1240 : : else
1241 : : {
1242 : : /* We have values[i-1] <= constant <= values[i]. */
1243 : 51174 : int i = lobound;
3105 1244 : 51174 : double eq_selec = 0;
1245 : : double val,
1246 : : high,
1247 : : low;
1248 : : double binfrac;
1249 : :
1250 : : /*
1251 : : * In the cases where we'll need it below, obtain an estimate
1252 : : * of the selectivity of "x = constval". We use a calculation
1253 : : * similar to what var_eq_const() does for a non-MCV constant,
1254 : : * ie, estimate that all distinct non-MCV values occur equally
1255 : : * often. But multiplication by "1.0 - sumcommon - nullfrac"
1256 : : * will be done by our caller, so we shouldn't do that here.
1257 : : * Therefore we can't try to clamp the estimate by reference
1258 : : * to the least common MCV; the result would be too small.
1259 : : *
1260 : : * Note: since this is effectively assuming that constval
1261 : : * isn't an MCV, it's logically dubious if constval in fact is
1262 : : * one. But we have to apply *some* correction for equality,
1263 : : * and anyway we cannot tell if constval is an MCV, since we
1264 : : * don't have a suitable equality operator at hand.
1265 : : */
1266 [ + + + + ]: 51174 : if (i == 1 || isgt == iseq)
1267 : : {
1268 : : double otherdistinct;
1269 : : bool isdefault;
1270 : : AttStatsSlot mcvslot;
1271 : :
1272 : : /* Get estimated number of distinct values */
1273 : 21745 : otherdistinct = get_variable_numdistinct(vardata,
1274 : : &isdefault);
1275 : :
1276 : : /* Subtract off the number of known MCVs */
1277 [ + + ]: 21745 : if (get_attstatsslot(&mcvslot, vardata->statsTuple,
1278 : : STATISTIC_KIND_MCV, InvalidOid,
1279 : : ATTSTATSSLOT_NUMBERS))
1280 : : {
1281 : 2713 : otherdistinct -= mcvslot.nnumbers;
1282 : 2713 : free_attstatsslot(&mcvslot);
1283 : : }
1284 : :
1285 : : /* If result doesn't seem sane, leave eq_selec at 0 */
1286 [ + + ]: 21745 : if (otherdistinct > 1)
1287 : 21723 : eq_selec = 1.0 / otherdistinct;
1288 : : }
1289 : :
1290 : : /*
1291 : : * Convert the constant and the two nearest bin boundary
1292 : : * values to a uniform comparison scale, and do a linear
1293 : : * interpolation within this bin.
1294 : : */
2109 1295 [ + - ]: 51174 : if (convert_to_scalar(constval, consttype, collation,
1296 : : &val,
3228 1297 : 51174 : sslot.values[i - 1], sslot.values[i],
1298 : : vardata->vartype,
1299 : : &low, &high))
1300 : : {
7116 1301 [ - + ]: 51174 : if (high <= low)
1302 : : {
1303 : : /* cope if bin boundaries appear identical */
7116 tgl@sss.pgh.pa.us 1304 :UBC 0 : binfrac = 0.5;
1305 : : }
7116 tgl@sss.pgh.pa.us 1306 [ + + ]:CBC 51174 : else if (val <= low)
1307 : 10267 : binfrac = 0.0;
1308 [ + + ]: 40907 : else if (val >= high)
1309 : 1836 : binfrac = 1.0;
1310 : : else
1311 : : {
1312 : 39071 : binfrac = (val - low) / (high - low);
1313 : :
1314 : : /*
1315 : : * Watch out for the possibility that we got a NaN or
1316 : : * Infinity from the division. This can happen
1317 : : * despite the previous checks, if for example "low"
1318 : : * is -Infinity.
1319 : : */
1320 [ + - + - ]: 39071 : if (isnan(binfrac) ||
1321 [ - + ]: 39071 : binfrac < 0.0 || binfrac > 1.0)
7116 tgl@sss.pgh.pa.us 1322 :UBC 0 : binfrac = 0.5;
1323 : : }
1324 : : }
1325 : : else
1326 : : {
1327 : : /*
1328 : : * Ideally we'd produce an error here, on the grounds that
1329 : : * the given operator shouldn't have scalarXXsel
1330 : : * registered as its selectivity func unless we can deal
1331 : : * with its operand types. But currently, all manner of
1332 : : * stuff is invoking scalarXXsel, so give a default
1333 : : * estimate until that can be fixed.
1334 : : */
1335 : 0 : binfrac = 0.5;
1336 : : }
1337 : :
1338 : : /*
1339 : : * Now, compute the overall selectivity across the values
1340 : : * represented by the histogram. We have i-1 full bins and
1341 : : * binfrac partial bin below the constant.
1342 : : */
7116 tgl@sss.pgh.pa.us 1343 :CBC 51174 : histfrac = (double) (i - 1) + binfrac;
3228 1344 : 51174 : histfrac /= (double) (sslot.nvalues - 1);
1345 : :
1346 : : /*
1347 : : * At this point, histfrac is an estimate of the fraction of
1348 : : * the population represented by the histogram that satisfies
1349 : : * "x <= constval". Somewhat remarkably, this statement is
1350 : : * true regardless of which operator we were doing the probes
1351 : : * with, so long as convert_to_scalar() delivers reasonable
1352 : : * results. If the probe constant is equal to some histogram
1353 : : * entry, we would have considered the bin to the left of that
1354 : : * entry if probing with "<" or ">=", or the bin to the right
1355 : : * if probing with "<=" or ">"; but binfrac would have come
1356 : : * out as 1.0 in the first case and 0.0 in the second, leading
1357 : : * to the same histfrac in either case. For probe constants
1358 : : * between histogram entries, we find the same bin and get the
1359 : : * same estimate with any operator.
1360 : : *
1361 : : * The fact that the estimate corresponds to "x <= constval"
1362 : : * and not "x < constval" is because of the way that ANALYZE
1363 : : * constructs the histogram: each entry is, effectively, the
1364 : : * rightmost value in its sample bucket. So selectivity
1365 : : * values that are exact multiples of 1/(histogram_size-1)
1366 : : * should be understood as estimates including a histogram
1367 : : * entry plus everything to its left.
1368 : : *
1369 : : * However, that breaks down for the first histogram entry,
1370 : : * which necessarily is the leftmost value in its sample
1371 : : * bucket. That means the first histogram bin is slightly
1372 : : * narrower than the rest, by an amount equal to eq_selec.
1373 : : * Another way to say that is that we want "x <= leftmost" to
1374 : : * be estimated as eq_selec not zero. So, if we're dealing
1375 : : * with the first bin (i==1), rescale to make that true while
1376 : : * adjusting the rest of that bin linearly.
1377 : : */
3105 1378 [ + + ]: 51174 : if (i == 1)
1379 : 9146 : histfrac += eq_selec * (1.0 - binfrac);
1380 : :
1381 : : /*
1382 : : * "x <= constval" is good if we want an estimate for "<=" or
1383 : : * ">", but if we are estimating for "<" or ">=", we now need
1384 : : * to decrease the estimate by eq_selec.
1385 : : */
1386 [ + + ]: 51174 : if (isgt == iseq)
1387 : 16906 : histfrac -= eq_selec;
1388 : : }
1389 : :
1390 : : /*
1391 : : * Now the estimate is finished for "<" and "<=" cases. If we are
1392 : : * estimating for ">" or ">=", flip it.
1393 : : */
9078 1394 [ + + ]: 123987 : hist_selec = isgt ? (1.0 - histfrac) : histfrac;
1395 : :
1396 : : /*
1397 : : * The histogram boundaries are only approximate to begin with,
1398 : : * and may well be out of date anyway. Therefore, don't believe
1399 : : * extremely small or large selectivity estimates --- unless we
1400 : : * got actual current endpoint values from the table, in which
1401 : : * case just do the usual sanity clamp. Somewhat arbitrarily, we
1402 : : * set the cutoff for other cases at a hundredth of the histogram
1403 : : * resolution.
1404 : : */
5914 1405 [ + + ]: 123987 : if (have_end)
1406 [ + + - + ]: 70934 : CLAMP_PROBABILITY(hist_selec);
1407 : : else
1408 : : {
3105 1409 : 53053 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1410 : :
1411 [ + + ]: 53053 : if (hist_selec < cutoff)
1412 : 18586 : hist_selec = cutoff;
1413 [ + + ]: 34467 : else if (hist_selec > 1.0 - cutoff)
1414 : 12024 : hist_selec = 1.0 - cutoff;
1415 : : }
1416 : : }
2109 1417 [ + - ]: 92 : else if (sslot.nvalues > 1)
1418 : : {
1419 : : /*
1420 : : * If we get here, we have a histogram but it's not sorted the way
1421 : : * we want. Do a brute-force search to see how many of the
1422 : : * entries satisfy the comparison condition, and take that
1423 : : * fraction as our estimate. (This is identical to the inner loop
1424 : : * of histogram_selectivity; maybe share code?)
1425 : : */
1426 : 92 : LOCAL_FCINFO(fcinfo, 2);
1427 : 92 : int nmatch = 0;
1428 : :
1429 : 92 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
1430 : : NULL, NULL);
1431 : 92 : fcinfo->args[0].isnull = false;
1432 : 92 : fcinfo->args[1].isnull = false;
1433 : 92 : fcinfo->args[1].value = constval;
1434 [ + + ]: 481250 : for (int i = 0; i < sslot.nvalues; i++)
1435 : : {
1436 : : Datum fresult;
1437 : :
1438 : 481158 : fcinfo->args[0].value = sslot.values[i];
1439 : 481158 : fcinfo->isnull = false;
1440 : 481158 : fresult = FunctionCallInvoke(fcinfo);
1441 [ + - + + ]: 481158 : if (!fcinfo->isnull && DatumGetBool(fresult))
1442 : 1110 : nmatch++;
1443 : : }
1444 : 92 : hist_selec = ((double) nmatch) / ((double) sslot.nvalues);
1445 : :
1446 : : /*
1447 : : * As above, clamp to a hundredth of the histogram resolution.
1448 : : * This case is surely even less trustworthy than the normal one,
1449 : : * so we shouldn't believe exact 0 or 1 selectivity. (Maybe the
1450 : : * clamp should be more restrictive in this case?)
1451 : : */
1452 : : {
1453 : 92 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1454 : :
1455 [ - + ]: 92 : if (hist_selec < cutoff)
2109 tgl@sss.pgh.pa.us 1456 :UBC 0 : hist_selec = cutoff;
2109 tgl@sss.pgh.pa.us 1457 [ - + ]:CBC 92 : else if (hist_selec > 1.0 - cutoff)
2109 tgl@sss.pgh.pa.us 1458 :UBC 0 : hist_selec = 1.0 - cutoff;
1459 : : }
1460 : : }
1461 : :
3228 tgl@sss.pgh.pa.us 1462 :CBC 124079 : free_attstatsslot(&sslot);
1463 : : }
1464 : :
7369 1465 : 191139 : return hist_selec;
1466 : : }
1467 : :
1468 : : /*
1469 : : * Common wrapper function for the selectivity estimators that simply
1470 : : * invoke scalarineqsel().
1471 : : */
1472 : : static Datum
3105 1473 : 26041 : scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
1474 : : {
7588 1475 : 26041 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
9065 1476 : 26041 : Oid operator = PG_GETARG_OID(1);
1477 : 26041 : List *args = (List *) PG_GETARG_POINTER(2);
1478 : 26041 : int varRelid = PG_GETARG_INT32(3);
2109 1479 : 26041 : Oid collation = PG_GET_COLLATION();
1480 : : VariableStatData vardata;
1481 : : Node *other;
1482 : : bool varonleft;
1483 : : Datum constval;
1484 : : Oid consttype;
1485 : : double selec;
1486 : :
1487 : : /*
1488 : : * If expression is not variable op something or something op variable,
1489 : : * then punt and return a default estimate.
1490 : : */
8062 1491 [ + + ]: 26041 : if (!get_restriction_variable(root, args, varRelid,
1492 : : &vardata, &other, &varonleft))
9065 1493 : 322 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1494 : :
1495 : : /*
1496 : : * Can't do anything useful if the something is not a constant, either.
1497 : : */
8780 1498 [ + + ]: 25719 : if (!IsA(other, Const))
1499 : : {
8062 1500 [ + + ]: 1424 : ReleaseVariableStats(vardata);
8780 1501 : 1424 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1502 : : }
1503 : :
1504 : : /*
1505 : : * If the constant is NULL, assume operator is strict and return zero, ie,
1506 : : * operator will never return TRUE.
1507 : : */
1508 [ + + ]: 24295 : if (((Const *) other)->constisnull)
1509 : : {
8062 1510 [ + + ]: 33 : ReleaseVariableStats(vardata);
8780 1511 : 33 : PG_RETURN_FLOAT8(0.0);
1512 : : }
1513 : 24262 : constval = ((Const *) other)->constvalue;
1514 : 24262 : consttype = ((Const *) other)->consttype;
1515 : :
1516 : : /*
1517 : : * Force the var to be on the left to simplify logic in scalarineqsel.
1518 : : */
3105 1519 [ + + ]: 24262 : if (!varonleft)
1520 : : {
9065 1521 : 192 : operator = get_commutator(operator);
1522 [ - + ]: 192 : if (!operator)
1523 : : {
1524 : : /* Use default selectivity (should we raise an error instead?) */
8062 tgl@sss.pgh.pa.us 1525 [ # # ]:UBC 0 : ReleaseVariableStats(vardata);
9065 1526 : 0 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1527 : : }
3105 tgl@sss.pgh.pa.us 1528 :CBC 192 : isgt = !isgt;
1529 : : }
1530 : :
1531 : : /* The rest of the work is done by scalarineqsel(). */
2109 1532 : 24262 : selec = scalarineqsel(root, operator, isgt, iseq, collation,
1533 : : &vardata, constval, consttype);
1534 : :
8062 1535 [ + + ]: 24262 : ReleaseVariableStats(vardata);
1536 : :
9078 1537 : 24262 : PG_RETURN_FLOAT8((float8) selec);
1538 : : }
1539 : :
1540 : : /*
1541 : : * scalarltsel - Selectivity of "<" for scalars.
1542 : : */
1543 : : Datum
3105 1544 : 7613 : scalarltsel(PG_FUNCTION_ARGS)
1545 : : {
1546 : 7613 : return scalarineqsel_wrapper(fcinfo, false, false);
1547 : : }
1548 : :
1549 : : /*
1550 : : * scalarlesel - Selectivity of "<=" for scalars.
1551 : : */
1552 : : Datum
1553 : 2322 : scalarlesel(PG_FUNCTION_ARGS)
1554 : : {
1555 : 2322 : return scalarineqsel_wrapper(fcinfo, false, true);
1556 : : }
1557 : :
1558 : : /*
1559 : : * scalargtsel - Selectivity of ">" for scalars.
1560 : : */
1561 : : Datum
1562 : 8154 : scalargtsel(PG_FUNCTION_ARGS)
1563 : : {
1564 : 8154 : return scalarineqsel_wrapper(fcinfo, true, false);
1565 : : }
1566 : :
1567 : : /*
1568 : : * scalargesel - Selectivity of ">=" for scalars.
1569 : : */
1570 : : Datum
1571 : 7952 : scalargesel(PG_FUNCTION_ARGS)
1572 : : {
1573 : 7952 : return scalarineqsel_wrapper(fcinfo, true, true);
1574 : : }
1575 : :
1576 : : /*
1577 : : * boolvarsel - Selectivity of Boolean variable.
1578 : : *
1579 : : * This can actually be called on any boolean-valued expression. If it
1580 : : * involves only Vars of the specified relation, and if there are statistics
1581 : : * about the Var or expression (the latter is possible if it's indexed) then
1582 : : * we'll produce a real estimate; otherwise it's just a default.
1583 : : */
1584 : : Selectivity
3825 1585 : 33992 : boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
1586 : : {
1587 : : VariableStatData vardata;
1588 : : double selec;
1589 : :
1590 : 33992 : examine_variable(root, arg, varRelid, &vardata);
1591 [ + + ]: 33992 : if (HeapTupleIsValid(vardata.statsTuple))
1592 : : {
1593 : : /*
1594 : : * A boolean variable V is equivalent to the clause V = 't', so we
1595 : : * compute the selectivity as if that is what we have.
1596 : : */
2109 1597 : 18763 : selec = var_eq_const(&vardata, BooleanEqualOperator, InvalidOid,
1598 : : BoolGetDatum(true), false, true, false);
1599 : : }
176 tgl@sss.pgh.pa.us 1600 [ + + ]:GNC 15229 : else if (is_funcclause(arg))
1601 : : {
1602 : : /*
1603 : : * If we have no stats and it's a function call, estimate 0.3333333.
1604 : : * This seems a pretty unprincipled choice, but Postgres has been
1605 : : * using that estimate for function calls since 1992. The hoariness
1606 : : * of this behavior suggests that we should not be in too much hurry
1607 : : * to use another value.
1608 : : */
1609 : 7008 : selec = 0.3333333;
1610 : : }
1611 : : else
1612 : : {
1613 : : /* Otherwise, the default estimate is 0.5 */
3825 tgl@sss.pgh.pa.us 1614 :CBC 8221 : selec = 0.5;
1615 : : }
1616 [ + + ]: 33992 : ReleaseVariableStats(vardata);
1617 : 33992 : return selec;
1618 : : }
1619 : :
1620 : : /*
1621 : : * booltestsel - Selectivity of BooleanTest Node.
1622 : : */
1623 : : Selectivity
7588 1624 : 507 : booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
1625 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1626 : : {
1627 : : VariableStatData vardata;
1628 : : double selec;
1629 : :
8062 1630 : 507 : examine_variable(root, arg, varRelid, &vardata);
1631 : :
1632 [ + + ]: 507 : if (HeapTupleIsValid(vardata.statsTuple))
1633 : : {
1634 : : Form_pg_statistic stats;
1635 : : double freq_null;
1636 : : AttStatsSlot sslot;
1637 : :
8062 tgl@sss.pgh.pa.us 1638 :GBC 12 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
9029 1639 : 12 : freq_null = stats->stanullfrac;
1640 : :
3228 1641 [ + + ]: 12 : if (get_attstatsslot(&sslot, vardata.statsTuple,
1642 : : STATISTIC_KIND_MCV, InvalidOid,
1643 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
1644 [ + - ]: 6 : && sslot.nnumbers > 0)
9029 1645 : 6 : {
1646 : : double freq_true;
1647 : : double freq_false;
1648 : :
1649 : : /*
1650 : : * Get first MCV frequency and derive frequency for true.
1651 : : */
3228 1652 [ - + ]: 6 : if (DatumGetBool(sslot.values[0]))
3228 tgl@sss.pgh.pa.us 1653 :UBC 0 : freq_true = sslot.numbers[0];
1654 : : else
3228 tgl@sss.pgh.pa.us 1655 :GBC 6 : freq_true = 1.0 - sslot.numbers[0] - freq_null;
1656 : :
1657 : : /*
1658 : : * Next derive frequency for false. Then use these as appropriate
1659 : : * to derive frequency for each case.
1660 : : */
9029 1661 : 6 : freq_false = 1.0 - freq_true - freq_null;
1662 : :
8548 1663 [ - - + - : 6 : switch (booltesttype)
- - - ]
1664 : : {
8907 bruce@momjian.us 1665 :UBC 0 : case IS_UNKNOWN:
1666 : : /* select only NULL values */
9029 tgl@sss.pgh.pa.us 1667 : 0 : selec = freq_null;
1668 : 0 : break;
8907 bruce@momjian.us 1669 : 0 : case IS_NOT_UNKNOWN:
1670 : : /* select non-NULL values */
9029 tgl@sss.pgh.pa.us 1671 : 0 : selec = 1.0 - freq_null;
1672 : 0 : break;
8907 bruce@momjian.us 1673 :GBC 6 : case IS_TRUE:
1674 : : /* select only TRUE values */
9029 tgl@sss.pgh.pa.us 1675 : 6 : selec = freq_true;
1676 : 6 : break;
8907 bruce@momjian.us 1677 :UBC 0 : case IS_NOT_TRUE:
1678 : : /* select non-TRUE values */
9029 tgl@sss.pgh.pa.us 1679 : 0 : selec = 1.0 - freq_true;
1680 : 0 : break;
8907 bruce@momjian.us 1681 : 0 : case IS_FALSE:
1682 : : /* select only FALSE values */
9029 tgl@sss.pgh.pa.us 1683 : 0 : selec = freq_false;
1684 : 0 : break;
8907 bruce@momjian.us 1685 : 0 : case IS_NOT_FALSE:
1686 : : /* select non-FALSE values */
9029 tgl@sss.pgh.pa.us 1687 : 0 : selec = 1.0 - freq_false;
1688 : 0 : break;
8907 bruce@momjian.us 1689 : 0 : default:
8267 tgl@sss.pgh.pa.us 1690 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1691 : : (int) booltesttype);
1692 : : selec = 0.0; /* Keep compiler quiet */
1693 : : break;
1694 : : }
1695 : :
3228 tgl@sss.pgh.pa.us 1696 :GBC 6 : free_attstatsslot(&sslot);
1697 : : }
1698 : : else
1699 : : {
1700 : : /*
1701 : : * No most-common-value info available. Still have null fraction
1702 : : * information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
1703 : : * for null fraction and assume a 50-50 split of TRUE and FALSE.
1704 : : */
8548 1705 [ + - - - : 6 : switch (booltesttype)
- ]
1706 : : {
8907 bruce@momjian.us 1707 : 6 : case IS_UNKNOWN:
1708 : : /* select only NULL values */
9029 tgl@sss.pgh.pa.us 1709 : 6 : selec = freq_null;
1710 : 6 : break;
8907 bruce@momjian.us 1711 :UBC 0 : case IS_NOT_UNKNOWN:
1712 : : /* select non-NULL values */
9029 tgl@sss.pgh.pa.us 1713 : 0 : selec = 1.0 - freq_null;
1714 : 0 : break;
8907 bruce@momjian.us 1715 : 0 : case IS_TRUE:
1716 : : case IS_FALSE:
1717 : : /* Assume we select half of the non-NULL values */
9029 tgl@sss.pgh.pa.us 1718 : 0 : selec = (1.0 - freq_null) / 2.0;
1719 : 0 : break;
4617 1720 : 0 : case IS_NOT_TRUE:
1721 : : case IS_NOT_FALSE:
1722 : : /* Assume we select NULLs plus half of the non-NULLs */
1723 : : /* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
1724 : 0 : selec = (freq_null + 1.0) / 2.0;
1725 : 0 : break;
8907 bruce@momjian.us 1726 : 0 : default:
8267 tgl@sss.pgh.pa.us 1727 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1728 : : (int) booltesttype);
1729 : : selec = 0.0; /* Keep compiler quiet */
1730 : : break;
1731 : : }
1732 : : }
1733 : : }
1734 : : else
1735 : : {
1736 : : /*
1737 : : * If we can't get variable statistics for the argument, perhaps
1738 : : * clause_selectivity can do something with it. We ignore the
1739 : : * possibility of a NULL value when using clause_selectivity, and just
1740 : : * assume the value is either TRUE or FALSE.
1741 : : */
8548 tgl@sss.pgh.pa.us 1742 [ + + + + :CBC 495 : switch (booltesttype)
- ]
1743 : : {
9029 1744 : 24 : case IS_UNKNOWN:
1745 : 24 : selec = DEFAULT_UNK_SEL;
1746 : 24 : break;
1747 : 54 : case IS_NOT_UNKNOWN:
1748 : 54 : selec = DEFAULT_NOT_UNK_SEL;
1749 : 54 : break;
1750 : 126 : case IS_TRUE:
1751 : : case IS_NOT_FALSE:
8062 1752 : 126 : selec = (double) clause_selectivity(root, arg,
1753 : : varRelid,
1754 : : jointype, sjinfo);
1755 : 126 : break;
1756 : 291 : case IS_FALSE:
1757 : : case IS_NOT_TRUE:
1758 : 291 : selec = 1.0 - (double) clause_selectivity(root, arg,
1759 : : varRelid,
1760 : : jointype, sjinfo);
9029 1761 : 291 : break;
9029 tgl@sss.pgh.pa.us 1762 :UBC 0 : default:
8267 1763 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1764 : : (int) booltesttype);
1765 : : selec = 0.0; /* Keep compiler quiet */
1766 : : break;
1767 : : }
1768 : : }
1769 : :
8062 tgl@sss.pgh.pa.us 1770 [ + + ]:CBC 507 : ReleaseVariableStats(vardata);
1771 : :
1772 : : /* result should be in range, but make sure... */
8837 1773 [ - + - + ]: 507 : CLAMP_PROBABILITY(selec);
1774 : :
9029 1775 : 507 : return (Selectivity) selec;
1776 : : }
1777 : :
1778 : : /*
1779 : : * nulltestsel - Selectivity of NullTest Node.
1780 : : */
1781 : : Selectivity
6422 1782 : 9795 : nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
1783 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1784 : : {
1785 : : VariableStatData vardata;
1786 : : double selec;
1787 : :
8062 1788 : 9795 : examine_variable(root, arg, varRelid, &vardata);
1789 : :
1790 [ + + ]: 9795 : if (HeapTupleIsValid(vardata.statsTuple))
1791 : : {
1792 : : Form_pg_statistic stats;
1793 : : double freq_null;
1794 : :
1795 : 5135 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
9029 1796 : 5135 : freq_null = stats->stanullfrac;
1797 : :
8548 1798 [ + + - ]: 5135 : switch (nulltesttype)
1799 : : {
8907 bruce@momjian.us 1800 : 3640 : case IS_NULL:
1801 : :
1802 : : /*
1803 : : * Use freq_null directly.
1804 : : */
9029 tgl@sss.pgh.pa.us 1805 : 3640 : selec = freq_null;
1806 : 3640 : break;
8907 bruce@momjian.us 1807 : 1495 : case IS_NOT_NULL:
1808 : :
1809 : : /*
1810 : : * Select not unknown (not null) values. Calculate from
1811 : : * freq_null.
1812 : : */
9029 tgl@sss.pgh.pa.us 1813 : 1495 : selec = 1.0 - freq_null;
1814 : 1495 : break;
8907 bruce@momjian.us 1815 :UBC 0 : default:
8267 tgl@sss.pgh.pa.us 1816 [ # # ]: 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1817 : : (int) nulltesttype);
1818 : : return (Selectivity) 0; /* keep compiler quiet */
1819 : : }
1820 : : }
2606 tgl@sss.pgh.pa.us 1821 [ + - + + ]:CBC 4660 : else if (vardata.var && IsA(vardata.var, Var) &&
1822 [ + + ]: 4272 : ((Var *) vardata.var)->varattno < 0)
1823 : : {
1824 : : /*
1825 : : * There are no stats for system columns, but we know they are never
1826 : : * NULL.
1827 : : */
1828 [ + - ]: 57 : selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
1829 : : }
1830 : : else
1831 : : {
1832 : : /*
1833 : : * No ANALYZE stats available, so make a guess
1834 : : */
8062 1835 [ + + - ]: 4603 : switch (nulltesttype)
1836 : : {
1837 : 1114 : case IS_NULL:
1838 : 1114 : selec = DEFAULT_UNK_SEL;
1839 : 1114 : break;
1840 : 3489 : case IS_NOT_NULL:
1841 : 3489 : selec = DEFAULT_NOT_UNK_SEL;
1842 : 3489 : break;
8062 tgl@sss.pgh.pa.us 1843 :UBC 0 : default:
1844 [ # # ]: 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1845 : : (int) nulltesttype);
1846 : : return (Selectivity) 0; /* keep compiler quiet */
1847 : : }
1848 : : }
1849 : :
8062 tgl@sss.pgh.pa.us 1850 [ + + ]:CBC 9795 : ReleaseVariableStats(vardata);
1851 : :
1852 : : /* result should be in range, but make sure... */
8837 1853 [ - + - + ]: 9795 : CLAMP_PROBABILITY(selec);
1854 : :
9029 1855 : 9795 : return (Selectivity) selec;
1856 : : }
1857 : :
1858 : : /*
1859 : : * strip_array_coercion - strip binary-compatible relabeling from an array expr
1860 : : *
1861 : : * For array values, the parser normally generates ArrayCoerceExpr conversions,
1862 : : * but it seems possible that RelabelType might show up. Also, the planner
1863 : : * is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
1864 : : * so we need to be ready to deal with more than one level.
1865 : : */
1866 : : static Node *
6986 1867 : 73160 : strip_array_coercion(Node *node)
1868 : : {
1869 : : for (;;)
1870 : : {
3088 1871 [ + - + + ]: 73216 : if (node && IsA(node, ArrayCoerceExpr))
6986 1872 : 56 : {
3088 1873 : 1766 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
1874 : :
1875 : : /*
1876 : : * If the per-element expression is just a RelabelType on top of
1877 : : * CaseTestExpr, then we know it's a binary-compatible relabeling.
1878 : : */
1879 [ + + ]: 1766 : if (IsA(acoerce->elemexpr, RelabelType) &&
1880 [ + - ]: 56 : IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
1881 : 56 : node = (Node *) acoerce->arg;
1882 : : else
1883 : : break;
1884 : : }
6928 1885 [ + - - + ]: 71450 : else if (node && IsA(node, RelabelType))
1886 : : {
1887 : : /* We don't really expect this case, but may as well cope */
6928 tgl@sss.pgh.pa.us 1888 :UBC 0 : node = (Node *) ((RelabelType *) node)->arg;
1889 : : }
1890 : : else
1891 : : break;
1892 : : }
6986 tgl@sss.pgh.pa.us 1893 :CBC 73160 : return node;
1894 : : }
1895 : :
1896 : : /*
1897 : : * scalararraysel - Selectivity of ScalarArrayOpExpr Node.
1898 : : */
1899 : : Selectivity
7415 1900 : 13320 : scalararraysel(PlannerInfo *root,
1901 : : ScalarArrayOpExpr *clause,
1902 : : bool is_join_clause,
1903 : : int varRelid,
1904 : : JoinType jointype,
1905 : : SpecialJoinInfo *sjinfo)
1906 : : {
1907 : 13320 : Oid operator = clause->opno;
1908 : 13320 : bool useOr = clause->useOr;
5125 1909 : 13320 : bool isEquality = false;
1910 : 13320 : bool isInequality = false;
1911 : : Node *leftop;
1912 : : Node *rightop;
1913 : : Oid nominal_element_type;
1914 : : Oid nominal_element_collation;
1915 : : TypeCacheEntry *typentry;
1916 : : RegProcedure oprsel;
1917 : : FmgrInfo oprselproc;
1918 : : Selectivity s1;
1919 : : Selectivity s1disjoint;
1920 : :
1921 : : /* First, deconstruct the expression */
6986 1922 [ - + ]: 13320 : Assert(list_length(clause->args) == 2);
1923 : 13320 : leftop = (Node *) linitial(clause->args);
1924 : 13320 : rightop = (Node *) lsecond(clause->args);
1925 : :
1926 : : /* aggressively reduce both sides to constants */
4405 1927 : 13320 : leftop = estimate_expression_value(root, leftop);
1928 : 13320 : rightop = estimate_expression_value(root, rightop);
1929 : :
1930 : : /* get nominal (after relabeling) element type of rightop */
5624 1931 : 13320 : nominal_element_type = get_base_element_type(exprType(rightop));
6986 1932 [ - + ]: 13320 : if (!OidIsValid(nominal_element_type))
3189 tgl@sss.pgh.pa.us 1933 :UBC 0 : return (Selectivity) 0.5; /* probably shouldn't happen */
1934 : : /* get nominal collation, too, for generating constants */
5469 tgl@sss.pgh.pa.us 1935 :CBC 13320 : nominal_element_collation = exprCollation(rightop);
1936 : :
1937 : : /* look through any binary-compatible relabeling of rightop */
6986 1938 : 13320 : rightop = strip_array_coercion(rightop);
1939 : :
1940 : : /*
1941 : : * Detect whether the operator is the default equality or inequality
1942 : : * operator of the array element type.
1943 : : */
5125 1944 : 13320 : typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
1945 [ + + ]: 13320 : if (OidIsValid(typentry->eq_opr))
1946 : : {
1947 [ + + ]: 13318 : if (operator == typentry->eq_opr)
1948 : 11480 : isEquality = true;
1949 [ + + ]: 1838 : else if (get_negator(operator) == typentry->eq_opr)
1950 : 1555 : isInequality = true;
1951 : : }
1952 : :
1953 : : /*
1954 : : * If it is equality or inequality, we might be able to estimate this as a
1955 : : * form of array containment; for instance "const = ANY(column)" can be
1956 : : * treated as "ARRAY[const] <@ column". scalararraysel_containment tries
1957 : : * that, and returns the selectivity estimate if successful, or -1 if not.
1958 : : */
1959 [ + + + + : 13320 : if ((isEquality || isInequality) && !is_join_clause)
+ + ]
1960 : : {
1961 : 13034 : s1 = scalararraysel_containment(root, leftop, rightop,
1962 : : nominal_element_type,
1963 : : isEquality, useOr, varRelid);
1964 [ + + ]: 13034 : if (s1 >= 0.0)
1965 : 59 : return s1;
1966 : : }
1967 : :
1968 : : /*
1969 : : * Look up the underlying operator's selectivity estimator. Punt if it
1970 : : * hasn't got one.
1971 : : */
1972 [ + + ]: 13261 : if (is_join_clause)
1973 : 1 : oprsel = get_oprjoin(operator);
1974 : : else
1975 : 13260 : oprsel = get_oprrest(operator);
1976 [ + + ]: 13261 : if (!oprsel)
1977 : 2 : return (Selectivity) 0.5;
1978 : 13259 : fmgr_info(oprsel, &oprselproc);
1979 : :
1980 : : /*
1981 : : * In the array-containment check above, we must only believe that an
1982 : : * operator is equality or inequality if it is the default btree equality
1983 : : * operator (or its negator) for the element type, since those are the
1984 : : * operators that array containment will use. But in what follows, we can
1985 : : * be a little laxer, and also believe that any operators using eqsel() or
1986 : : * neqsel() as selectivity estimator act like equality or inequality.
1987 : : */
5121 1988 [ + + + + ]: 13259 : if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
1989 : 11519 : isEquality = true;
1990 [ + + - + ]: 1740 : else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
1991 : 1500 : isInequality = true;
1992 : :
1993 : : /*
1994 : : * We consider three cases:
1995 : : *
1996 : : * 1. rightop is an Array constant: deconstruct the array, apply the
1997 : : * operator's selectivity function for each array element, and merge the
1998 : : * results in the same way that clausesel.c does for AND/OR combinations.
1999 : : *
2000 : : * 2. rightop is an ARRAY[] construct: apply the operator's selectivity
2001 : : * function for each element of the ARRAY[] construct, and merge.
2002 : : *
2003 : : * 3. otherwise, make a guess ...
2004 : : */
7415 2005 [ + - + + ]: 13259 : if (rightop && IsA(rightop, Const))
2006 : 10947 : {
2007 : 10962 : Datum arraydatum = ((Const *) rightop)->constvalue;
2008 : 10962 : bool arrayisnull = ((Const *) rightop)->constisnull;
2009 : : ArrayType *arrayval;
2010 : : int16 elmlen;
2011 : : bool elmbyval;
2012 : : char elmalign;
2013 : : int num_elems;
2014 : : Datum *elem_values;
2015 : : bool *elem_nulls;
2016 : : int i;
2017 : :
2018 [ + + ]: 10962 : if (arrayisnull) /* qual can't succeed if null array */
2019 : 15 : return (Selectivity) 0.0;
2020 : 10947 : arrayval = DatumGetArrayTypeP(arraydatum);
2021 : 10947 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
2022 : : &elmlen, &elmbyval, &elmalign);
2023 : 10947 : deconstruct_array(arrayval,
2024 : : ARR_ELEMTYPE(arrayval),
2025 : : elmlen, elmbyval, elmalign,
2026 : : &elem_values, &elem_nulls, &num_elems);
2027 : :
2028 : : /*
2029 : : * For generic operators, we assume the probability of success is
2030 : : * independent for each array element. But for "= ANY" or "<> ALL",
2031 : : * if the array elements are distinct (which'd typically be the case)
2032 : : * then the probabilities are disjoint, and we should just sum them.
2033 : : *
2034 : : * If we were being really tense we would try to confirm that the
2035 : : * elements are all distinct, but that would be expensive and it
2036 : : * doesn't seem to be worth the cycles; it would amount to penalizing
2037 : : * well-written queries in favor of poorly-written ones. However, we
2038 : : * do protect ourselves a little bit by checking whether the
2039 : : * disjointness assumption leads to an impossible (out of range)
2040 : : * probability; if so, we fall back to the normal calculation.
2041 : : */
5121 2042 [ + + ]: 10947 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
2043 : :
7415 2044 [ + + ]: 45138 : for (i = 0; i < num_elems; i++)
2045 : : {
2046 : : List *args;
2047 : : Selectivity s2;
2048 : :
2049 : 34191 : args = list_make2(leftop,
2050 : : makeConst(nominal_element_type,
2051 : : -1,
2052 : : nominal_element_collation,
2053 : : elmlen,
2054 : : elem_values[i],
2055 : : elem_nulls[i],
2056 : : elmbyval));
6420 2057 [ - + ]: 34191 : if (is_join_clause)
4998 tgl@sss.pgh.pa.us 2058 :UBC 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2059 : : clause->inputcollid,
2060 : : PointerGetDatum(root),
2061 : : ObjectIdGetDatum(operator),
2062 : : PointerGetDatum(args),
2063 : : Int16GetDatum(jointype),
2064 : : PointerGetDatum(sjinfo)));
2065 : : else
4998 tgl@sss.pgh.pa.us 2066 :CBC 34191 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2067 : : clause->inputcollid,
2068 : : PointerGetDatum(root),
2069 : : ObjectIdGetDatum(operator),
2070 : : PointerGetDatum(args),
2071 : : Int32GetDatum(varRelid)));
2072 : :
7415 2073 [ + + ]: 34191 : if (useOr)
2074 : : {
2075 : 29394 : s1 = s1 + s2 - s1 * s2;
5121 2076 [ + + ]: 29394 : if (isEquality)
2077 : 28872 : s1disjoint += s2;
2078 : : }
2079 : : else
2080 : : {
7415 2081 : 4797 : s1 = s1 * s2;
5121 2082 [ + + ]: 4797 : if (isInequality)
2083 : 4641 : s1disjoint += s2 - 1.0;
2084 : : }
2085 : : }
2086 : :
2087 : : /* accept disjoint-probability estimate if in range */
2088 [ + + + + : 10947 : if ((useOr ? isEquality : isInequality) &&
+ + ]
2089 [ + + ]: 10637 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2090 : 10622 : s1 = s1disjoint;
2091 : : }
7415 2092 [ + - + + ]: 2297 : else if (rightop && IsA(rightop, ArrayExpr) &&
2093 [ + - ]: 175 : !((ArrayExpr *) rightop)->multidims)
2094 : 175 : {
2095 : 175 : ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
2096 : : int16 elmlen;
2097 : : bool elmbyval;
2098 : : ListCell *l;
2099 : :
2100 : 175 : get_typlenbyval(arrayexpr->element_typeid,
2101 : : &elmlen, &elmbyval);
2102 : :
2103 : : /*
2104 : : * We use the assumption of disjoint probabilities here too, although
2105 : : * the odds of equal array elements are rather higher if the elements
2106 : : * are not all constants (which they won't be, else constant folding
2107 : : * would have reduced the ArrayExpr to a Const). In this path it's
2108 : : * critical to have the sanity check on the s1disjoint estimate.
2109 : : */
5121 2110 [ + - ]: 175 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
2111 : :
7415 2112 [ + - + + : 642 : foreach(l, arrayexpr->elements)
+ + ]
2113 : : {
6986 2114 : 467 : Node *elem = (Node *) lfirst(l);
2115 : : List *args;
2116 : : Selectivity s2;
2117 : :
2118 : : /*
2119 : : * Theoretically, if elem isn't of nominal_element_type we should
2120 : : * insert a RelabelType, but it seems unlikely that any operator
2121 : : * estimation function would really care ...
2122 : : */
2123 : 467 : args = list_make2(leftop, elem);
6420 2124 [ + + ]: 467 : if (is_join_clause)
4998 2125 : 3 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2126 : : clause->inputcollid,
2127 : : PointerGetDatum(root),
2128 : : ObjectIdGetDatum(operator),
2129 : : PointerGetDatum(args),
2130 : : Int16GetDatum(jointype),
2131 : : PointerGetDatum(sjinfo)));
2132 : : else
2133 : 464 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2134 : : clause->inputcollid,
2135 : : PointerGetDatum(root),
2136 : : ObjectIdGetDatum(operator),
2137 : : PointerGetDatum(args),
2138 : : Int32GetDatum(varRelid)));
2139 : :
7415 2140 [ + - ]: 467 : if (useOr)
2141 : : {
2142 : 467 : s1 = s1 + s2 - s1 * s2;
5121 2143 [ + - ]: 467 : if (isEquality)
2144 : 467 : s1disjoint += s2;
2145 : : }
2146 : : else
2147 : : {
7415 tgl@sss.pgh.pa.us 2148 :UBC 0 : s1 = s1 * s2;
5121 2149 [ # # ]: 0 : if (isInequality)
2150 : 0 : s1disjoint += s2 - 1.0;
2151 : : }
2152 : : }
2153 : :
2154 : : /* accept disjoint-probability estimate if in range */
5121 tgl@sss.pgh.pa.us 2155 [ + - + - :CBC 175 : if ((useOr ? isEquality : isInequality) &&
+ - ]
2156 [ + - ]: 175 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2157 : 175 : s1 = s1disjoint;
2158 : : }
2159 : : else
2160 : : {
2161 : : CaseTestExpr *dummyexpr;
2162 : : List *args;
2163 : : Selectivity s2;
2164 : : int i;
2165 : :
2166 : : /*
2167 : : * We need a dummy rightop to pass to the operator selectivity
2168 : : * routine. It can be pretty much anything that doesn't look like a
2169 : : * constant; CaseTestExpr is a convenient choice.
2170 : : */
7415 2171 : 2122 : dummyexpr = makeNode(CaseTestExpr);
6986 2172 : 2122 : dummyexpr->typeId = nominal_element_type;
7415 2173 : 2122 : dummyexpr->typeMod = -1;
5475 2174 : 2122 : dummyexpr->collation = clause->inputcollid;
7415 2175 : 2122 : args = list_make2(leftop, dummyexpr);
6420 2176 [ - + ]: 2122 : if (is_join_clause)
4998 tgl@sss.pgh.pa.us 2177 :UBC 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2178 : : clause->inputcollid,
2179 : : PointerGetDatum(root),
2180 : : ObjectIdGetDatum(operator),
2181 : : PointerGetDatum(args),
2182 : : Int16GetDatum(jointype),
2183 : : PointerGetDatum(sjinfo)));
2184 : : else
4998 tgl@sss.pgh.pa.us 2185 :CBC 2122 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2186 : : clause->inputcollid,
2187 : : PointerGetDatum(root),
2188 : : ObjectIdGetDatum(operator),
2189 : : PointerGetDatum(args),
2190 : : Int32GetDatum(varRelid)));
7415 2191 [ + - ]: 2122 : s1 = useOr ? 0.0 : 1.0;
2192 : :
2193 : : /*
2194 : : * Arbitrarily assume 10 elements in the eventual array value (see
2195 : : * also estimate_array_length). We don't risk an assumption of
2196 : : * disjoint probabilities here.
2197 : : */
2198 [ + + ]: 23342 : for (i = 0; i < 10; i++)
2199 : : {
2200 [ + - ]: 21220 : if (useOr)
2201 : 21220 : s1 = s1 + s2 - s1 * s2;
2202 : : else
7415 tgl@sss.pgh.pa.us 2203 :UBC 0 : s1 = s1 * s2;
2204 : : }
2205 : : }
2206 : :
2207 : : /* result should be in range, but make sure... */
7415 tgl@sss.pgh.pa.us 2208 [ - + - + ]:CBC 13244 : CLAMP_PROBABILITY(s1);
2209 : :
2210 : 13244 : return s1;
2211 : : }
2212 : :
2213 : : /*
2214 : : * Estimate number of elements in the array yielded by an expression.
2215 : : *
2216 : : * Note: the result is integral, but we use "double" to avoid overflow
2217 : : * concerns. Most callers will use it in double-type expressions anyway.
2218 : : *
2219 : : * Note: in some code paths root can be passed as NULL, resulting in
2220 : : * slightly worse estimates.
2221 : : */
2222 : : double
801 2223 : 59840 : estimate_array_length(PlannerInfo *root, Node *arrayexpr)
2224 : : {
2225 : : /* look through any binary-compatible relabeling of arrayexpr */
6986 2226 : 59840 : arrayexpr = strip_array_coercion(arrayexpr);
2227 : :
7197 2228 [ + - + + ]: 59840 : if (arrayexpr && IsA(arrayexpr, Const))
2229 : : {
2230 : 28311 : Datum arraydatum = ((Const *) arrayexpr)->constvalue;
2231 : 28311 : bool arrayisnull = ((Const *) arrayexpr)->constisnull;
2232 : : ArrayType *arrayval;
2233 : :
2234 [ + + ]: 28311 : if (arrayisnull)
2235 : 45 : return 0;
2236 : 28266 : arrayval = DatumGetArrayTypeP(arraydatum);
2237 : 28266 : return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
2238 : : }
2239 [ + - + + ]: 31529 : else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
2240 [ + - ]: 306 : !((ArrayExpr *) arrayexpr)->multidims)
2241 : : {
2242 : 306 : return list_length(((ArrayExpr *) arrayexpr)->elements);
2243 : : }
522 2244 [ + - + + ]: 31223 : else if (arrayexpr && root)
2245 : : {
2246 : : /* See if we can find any statistics about it */
2247 : : VariableStatData vardata;
2248 : : AttStatsSlot sslot;
801 2249 : 31211 : double nelem = 0;
2250 : :
2251 : 31211 : examine_variable(root, arrayexpr, 0, &vardata);
2252 [ + + ]: 31211 : if (HeapTupleIsValid(vardata.statsTuple))
2253 : : {
2254 : : /*
2255 : : * Found stats, so use the average element count, which is stored
2256 : : * in the last stanumbers element of the DECHIST statistics.
2257 : : * Actually that is the average count of *distinct* elements;
2258 : : * perhaps we should scale it up somewhat?
2259 : : */
2260 [ + + ]: 9565 : if (get_attstatsslot(&sslot, vardata.statsTuple,
2261 : : STATISTIC_KIND_DECHIST, InvalidOid,
2262 : : ATTSTATSSLOT_NUMBERS))
2263 : : {
2264 [ + - ]: 9508 : if (sslot.nnumbers > 0)
2265 : 9508 : nelem = clamp_row_est(sslot.numbers[sslot.nnumbers - 1]);
2266 : 9508 : free_attstatsslot(&sslot);
2267 : : }
2268 : : }
2269 [ + + ]: 31211 : ReleaseVariableStats(vardata);
2270 : :
2271 [ + + ]: 31211 : if (nelem > 0)
2272 : 9508 : return nelem;
2273 : : }
2274 : :
2275 : : /* Else use a default guess --- this should match scalararraysel */
2276 : 21715 : return 10;
2277 : : }
2278 : :
2279 : : /*
2280 : : * rowcomparesel - Selectivity of RowCompareExpr Node.
2281 : : *
2282 : : * We estimate RowCompare selectivity by considering just the first (high
2283 : : * order) columns, which makes it equivalent to an ordinary OpExpr. While
2284 : : * this estimate could be refined by considering additional columns, it
2285 : : * seems unlikely that we could do a lot better without multi-column
2286 : : * statistics.
2287 : : */
2288 : : Selectivity
7365 2289 : 132 : rowcomparesel(PlannerInfo *root,
2290 : : RowCompareExpr *clause,
2291 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
2292 : : {
2293 : : Selectivity s1;
2294 : 132 : Oid opno = linitial_oid(clause->opnos);
4998 2295 : 132 : Oid inputcollid = linitial_oid(clause->inputcollids);
2296 : : List *opargs;
2297 : : bool is_join_clause;
2298 : :
2299 : : /* Build equivalent arg list for single operator */
7365 2300 : 132 : opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
2301 : :
2302 : : /*
2303 : : * Decide if it's a join clause. This should match clausesel.c's
2304 : : * treat_as_join_clause(), except that we intentionally consider only the
2305 : : * leading columns and not the rest of the clause.
2306 : : */
2307 [ + + ]: 132 : if (varRelid != 0)
2308 : : {
2309 : : /*
2310 : : * Caller is forcing restriction mode (eg, because we are examining an
2311 : : * inner indexscan qual).
2312 : : */
6420 2313 : 27 : is_join_clause = false;
2314 : : }
2315 [ + + ]: 105 : else if (sjinfo == NULL)
2316 : : {
2317 : : /*
2318 : : * It must be a restriction clause, since it's being evaluated at a
2319 : : * scan node.
2320 : : */
7365 2321 : 93 : is_join_clause = false;
2322 : : }
2323 : : else
2324 : : {
2325 : : /*
2326 : : * Otherwise, it's a join if there's more than one base relation used.
2327 : : */
1879 2328 : 12 : is_join_clause = (NumRelids(root, (Node *) opargs) > 1);
2329 : : }
2330 : :
7365 2331 [ + + ]: 132 : if (is_join_clause)
2332 : : {
2333 : : /* Estimate selectivity for a join clause. */
2334 : 12 : s1 = join_selectivity(root, opno,
2335 : : opargs,
2336 : : inputcollid,
2337 : : jointype,
2338 : : sjinfo);
2339 : : }
2340 : : else
2341 : : {
2342 : : /* Estimate selectivity for a restriction clause. */
2343 : 120 : s1 = restriction_selectivity(root, opno,
2344 : : opargs,
2345 : : inputcollid,
2346 : : varRelid);
2347 : : }
2348 : :
2349 : 132 : return s1;
2350 : : }
2351 : :
2352 : : /*
2353 : : * eqjoinsel - Join selectivity of "="
2354 : : */
2355 : : Datum
9414 2356 : 155729 : eqjoinsel(PG_FUNCTION_ARGS)
2357 : : {
7588 2358 : 155729 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
9065 2359 : 155729 : Oid operator = PG_GETARG_OID(1);
2360 : 155729 : List *args = (List *) PG_GETARG_POINTER(2);
2361 : :
2362 : : #ifdef NOT_USED
2363 : : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
2364 : : #endif
6420 2365 : 155729 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
2109 2366 : 155729 : Oid collation = PG_GET_COLLATION();
2367 : : double selec;
2368 : : double selec_inner;
2369 : : VariableStatData vardata1;
2370 : : VariableStatData vardata2;
2371 : : double nd1;
2372 : : double nd2;
2373 : : bool isdefault1;
2374 : : bool isdefault2;
2375 : : Oid opfuncoid;
2376 : : FmgrInfo eqproc;
116 tgl@sss.pgh.pa.us 2377 :GNC 155729 : Oid hashLeft = InvalidOid;
2378 : 155729 : Oid hashRight = InvalidOid;
2379 : : AttStatsSlot sslot1;
2380 : : AttStatsSlot sslot2;
2669 tgl@sss.pgh.pa.us 2381 :CBC 155729 : Form_pg_statistic stats1 = NULL;
2382 : 155729 : Form_pg_statistic stats2 = NULL;
2383 : 155729 : bool have_mcvs1 = false;
2384 : 155729 : bool have_mcvs2 = false;
116 tgl@sss.pgh.pa.us 2385 :GNC 155729 : bool *hasmatch1 = NULL;
2386 : 155729 : bool *hasmatch2 = NULL;
2387 : 155729 : int nmatches = 0;
2388 : : bool get_mcv_stats;
2389 : : bool join_is_reversed;
2390 : : RelOptInfo *inner_rel;
2391 : :
6420 tgl@sss.pgh.pa.us 2392 :CBC 155729 : get_join_variables(root, args, sjinfo,
2393 : : &vardata1, &vardata2, &join_is_reversed);
2394 : :
2669 2395 : 155729 : nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
2396 : 155729 : nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
2397 : :
2398 : 155729 : opfuncoid = get_opcode(operator);
2399 : :
2400 : 155729 : memset(&sslot1, 0, sizeof(sslot1));
2401 : 155729 : memset(&sslot2, 0, sizeof(sslot2));
2402 : :
2403 : : /*
2404 : : * There is no use in fetching one side's MCVs if we lack MCVs for the
2405 : : * other side, so do a quick check to verify that both stats exist.
2406 : : */
1213 2407 : 422101 : get_mcv_stats = (HeapTupleIsValid(vardata1.statsTuple) &&
2408 [ + + + + ]: 198767 : HeapTupleIsValid(vardata2.statsTuple) &&
2409 : 88124 : get_attstatsslot(&sslot1, vardata1.statsTuple,
2410 : : STATISTIC_KIND_MCV, InvalidOid,
2411 [ + + + + ]: 266372 : 0) &&
2412 : 40795 : get_attstatsslot(&sslot2, vardata2.statsTuple,
2413 : : STATISTIC_KIND_MCV, InvalidOid,
2414 : : 0));
2415 : :
2669 2416 [ + + ]: 155729 : if (HeapTupleIsValid(vardata1.statsTuple))
2417 : : {
2418 : : /* note we allow use of nullfrac regardless of security check */
2419 : 110643 : stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
1213 2420 [ + + + - ]: 126087 : if (get_mcv_stats &&
2421 : 15444 : statistic_proc_security_check(&vardata1, opfuncoid))
2669 2422 : 15444 : have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
2423 : : STATISTIC_KIND_MCV, InvalidOid,
2424 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2425 : : }
2426 : :
2427 [ + + ]: 155729 : if (HeapTupleIsValid(vardata2.statsTuple))
2428 : : {
2429 : : /* note we allow use of nullfrac regardless of security check */
2430 : 101795 : stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
1213 2431 [ + + + - ]: 117239 : if (get_mcv_stats &&
2432 : 15444 : statistic_proc_security_check(&vardata2, opfuncoid))
2669 2433 : 15444 : have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
2434 : : STATISTIC_KIND_MCV, InvalidOid,
2435 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2436 : : }
2437 : :
2438 : : /* Prepare info usable by both eqjoinsel_inner and eqjoinsel_semi */
116 tgl@sss.pgh.pa.us 2439 [ + + + - ]:GNC 155729 : if (have_mcvs1 && have_mcvs2)
2440 : : {
2441 : 15444 : fmgr_info(opfuncoid, &eqproc);
2442 : 15444 : hasmatch1 = (bool *) palloc0(sslot1.nvalues * sizeof(bool));
2443 : 15444 : hasmatch2 = (bool *) palloc0(sslot2.nvalues * sizeof(bool));
2444 : :
2445 : : /*
2446 : : * If the MCV lists are long enough to justify hashing, try to look up
2447 : : * hash functions for the join operator.
2448 : : */
2449 [ + + ]: 15444 : if ((sslot1.nvalues + sslot2.nvalues) >= EQJOINSEL_MCV_HASH_THRESHOLD)
2450 : 7232 : (void) get_op_hash_functions(operator, &hashLeft, &hashRight);
2451 : : }
2452 : : else
2453 : 140285 : memset(&eqproc, 0, sizeof(eqproc)); /* silence uninit-var warnings */
2454 : :
2455 : : /* We need to compute the inner-join selectivity in all cases */
2456 : 155729 : selec_inner = eqjoinsel_inner(&eqproc, collation,
2457 : : hashLeft, hashRight,
2458 : : &vardata1, &vardata2,
2459 : : nd1, nd2,
2460 : : isdefault1, isdefault2,
2461 : : &sslot1, &sslot2,
2462 : : stats1, stats2,
2463 : : have_mcvs1, have_mcvs2,
2464 : : hasmatch1, hasmatch2,
2465 : : &nmatches);
2466 : :
6420 tgl@sss.pgh.pa.us 2467 [ + + - ]:CBC 155729 : switch (sjinfo->jointype)
2468 : : {
2469 : 148399 : case JOIN_INNER:
2470 : : case JOIN_LEFT:
2471 : : case JOIN_FULL:
2669 2472 : 148399 : selec = selec_inner;
6420 2473 : 148399 : break;
2474 : 7330 : case JOIN_SEMI:
2475 : : case JOIN_ANTI:
2476 : :
2477 : : /*
2478 : : * Look up the join's inner relation. min_righthand is sufficient
2479 : : * information because neither SEMI nor ANTI joins permit any
2480 : : * reassociation into or out of their RHS, so the righthand will
2481 : : * always be exactly that set of rels.
2482 : : */
5309 2483 : 7330 : inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
2484 : :
6420 2485 [ + + ]: 7330 : if (!join_is_reversed)
116 tgl@sss.pgh.pa.us 2486 :GNC 3593 : selec = eqjoinsel_semi(&eqproc, collation,
2487 : : hashLeft, hashRight,
2488 : : false,
2489 : : &vardata1, &vardata2,
2490 : : nd1, nd2,
2491 : : isdefault1, isdefault2,
2492 : : &sslot1, &sslot2,
2493 : : stats1, stats2,
2494 : : have_mcvs1, have_mcvs2,
2495 : : hasmatch1, hasmatch2,
2496 : : &nmatches,
2497 : : inner_rel);
2498 : : else
2499 : 3737 : selec = eqjoinsel_semi(&eqproc, collation,
2500 : : hashLeft, hashRight,
2501 : : true,
2502 : : &vardata2, &vardata1,
2503 : : nd2, nd1,
2504 : : isdefault2, isdefault1,
2505 : : &sslot2, &sslot1,
2506 : : stats2, stats1,
2507 : : have_mcvs2, have_mcvs1,
2508 : : hasmatch2, hasmatch1,
2509 : : &nmatches,
2510 : : inner_rel);
2511 : :
2512 : : /*
2513 : : * We should never estimate the output of a semijoin to be more
2514 : : * rows than we estimate for an inner join with the same input
2515 : : * rels and join condition; it's obviously impossible for that to
2516 : : * happen. The former estimate is N1 * Ssemi while the latter is
2517 : : * N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
2518 : : * this is worthwhile because of the shakier estimation rules we
2519 : : * use in eqjoinsel_semi, particularly in cases where it has to
2520 : : * punt entirely.
2521 : : */
2669 tgl@sss.pgh.pa.us 2522 [ + + ]:CBC 7330 : selec = Min(selec, inner_rel->rows * selec_inner);
6420 2523 : 7330 : break;
6420 tgl@sss.pgh.pa.us 2524 :UBC 0 : default:
2525 : : /* other values not expected here */
2526 [ # # ]: 0 : elog(ERROR, "unrecognized join type: %d",
2527 : : (int) sjinfo->jointype);
2528 : : selec = 0; /* keep compiler quiet */
2529 : : break;
2530 : : }
2531 : :
2669 tgl@sss.pgh.pa.us 2532 :CBC 155729 : free_attstatsslot(&sslot1);
2533 : 155729 : free_attstatsslot(&sslot2);
2534 : :
6420 2535 [ + + ]: 155729 : ReleaseVariableStats(vardata1);
2536 [ + + ]: 155729 : ReleaseVariableStats(vardata2);
2537 : :
116 tgl@sss.pgh.pa.us 2538 [ + + ]:GNC 155729 : if (hasmatch1)
2539 : 15444 : pfree(hasmatch1);
2540 [ + + ]: 155729 : if (hasmatch2)
2541 : 15444 : pfree(hasmatch2);
2542 : :
6420 tgl@sss.pgh.pa.us 2543 [ - + - + ]:CBC 155729 : CLAMP_PROBABILITY(selec);
2544 : :
2545 : 155729 : PG_RETURN_FLOAT8((float8) selec);
2546 : : }
2547 : :
2548 : : /*
2549 : : * eqjoinsel_inner --- eqjoinsel for normal inner join
2550 : : *
2551 : : * In addition to computing the selectivity estimate, this will fill
2552 : : * hasmatch1[], hasmatch2[], and *p_nmatches (if have_mcvs1 && have_mcvs2).
2553 : : * We may be able to re-use that data in eqjoinsel_semi.
2554 : : *
2555 : : * We also use this for LEFT/FULL outer joins; it's not presently clear
2556 : : * that it's worth trying to distinguish them here.
2557 : : */
2558 : : static double
116 tgl@sss.pgh.pa.us 2559 :GNC 155729 : eqjoinsel_inner(FmgrInfo *eqproc, Oid collation,
2560 : : Oid hashLeft, Oid hashRight,
2561 : : VariableStatData *vardata1, VariableStatData *vardata2,
2562 : : double nd1, double nd2,
2563 : : bool isdefault1, bool isdefault2,
2564 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2565 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
2566 : : bool have_mcvs1, bool have_mcvs2,
2567 : : bool *hasmatch1, bool *hasmatch2,
2568 : : int *p_nmatches)
2569 : : {
2570 : : double selec;
2571 : :
8062 tgl@sss.pgh.pa.us 2572 [ + + + - ]:CBC 155729 : if (have_mcvs1 && have_mcvs2)
10416 bruce@momjian.us 2573 : 15444 : {
2574 : : /*
2575 : : * We have most-common-value lists for both relations. Run through
2576 : : * the lists to see which MCVs actually join to each other with the
2577 : : * given operator. This allows us to determine the exact join
2578 : : * selectivity for the portion of the relations represented by the MCV
2579 : : * lists. We still have to estimate for the remaining population, but
2580 : : * in a skewed distribution this gives us a big leg up in accuracy.
2581 : : * For motivation see the analysis in Y. Ioannidis and S.
2582 : : * Christodoulakis, "On the propagation of errors in the size of join
2583 : : * results", Technical Report 1018, Computer Science Dept., University
2584 : : * of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
2585 : : */
8062 tgl@sss.pgh.pa.us 2586 : 15444 : double nullfrac1 = stats1->stanullfrac;
2587 : 15444 : double nullfrac2 = stats2->stanullfrac;
2588 : : double matchprodfreq,
2589 : : matchfreq1,
2590 : : matchfreq2,
2591 : : unmatchfreq1,
2592 : : unmatchfreq2,
2593 : : otherfreq1,
2594 : : otherfreq2,
2595 : : totalsel1,
2596 : : totalsel2;
2597 : : int i,
2598 : : nmatches;
2599 : :
2600 : : /* Fill the match arrays */
116 tgl@sss.pgh.pa.us 2601 :GNC 15444 : eqjoinsel_find_matches(eqproc, collation,
2602 : : hashLeft, hashRight,
2603 : : false,
2604 : : sslot1, sslot2,
2605 : : sslot1->nvalues, sslot2->nvalues,
2606 : : hasmatch1, hasmatch2,
2607 : : p_nmatches, &matchprodfreq);
2608 : 15444 : nmatches = *p_nmatches;
8062 tgl@sss.pgh.pa.us 2609 [ - + - + ]:CBC 15444 : CLAMP_PROBABILITY(matchprodfreq);
2610 : :
2611 : : /* Sum up frequencies of matched and unmatched MCVs */
2612 : 15444 : matchfreq1 = unmatchfreq1 = 0.0;
2669 2613 [ + + ]: 431728 : for (i = 0; i < sslot1->nvalues; i++)
2614 : : {
8062 2615 [ + + ]: 416284 : if (hasmatch1[i])
2669 2616 : 162419 : matchfreq1 += sslot1->numbers[i];
2617 : : else
2618 : 253865 : unmatchfreq1 += sslot1->numbers[i];
2619 : : }
8062 2620 [ - + + + ]: 15444 : CLAMP_PROBABILITY(matchfreq1);
2621 [ - + + + ]: 15444 : CLAMP_PROBABILITY(unmatchfreq1);
2622 : 15444 : matchfreq2 = unmatchfreq2 = 0.0;
2669 2623 [ + + ]: 312852 : for (i = 0; i < sslot2->nvalues; i++)
2624 : : {
8062 2625 [ + + ]: 297408 : if (hasmatch2[i])
2669 2626 : 162419 : matchfreq2 += sslot2->numbers[i];
2627 : : else
2628 : 134989 : unmatchfreq2 += sslot2->numbers[i];
2629 : : }
8062 2630 [ - + + + ]: 15444 : CLAMP_PROBABILITY(matchfreq2);
2631 [ - + - + ]: 15444 : CLAMP_PROBABILITY(unmatchfreq2);
2632 : :
2633 : : /*
2634 : : * Compute total frequency of non-null values that are not in the MCV
2635 : : * lists.
2636 : : */
2637 : 15444 : otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
2638 : 15444 : otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
2639 [ + + - + ]: 15444 : CLAMP_PROBABILITY(otherfreq1);
2640 [ + + - + ]: 15444 : CLAMP_PROBABILITY(otherfreq2);
2641 : :
2642 : : /*
2643 : : * We can estimate the total selectivity from the point of view of
2644 : : * relation 1 as: the known selectivity for matched MCVs, plus
2645 : : * unmatched MCVs that are assumed to match against random members of
2646 : : * relation 2's non-MCV population, plus non-MCV values that are
2647 : : * assumed to match against random members of relation 2's unmatched
2648 : : * MCVs plus non-MCV values.
2649 : : */
2650 : 15444 : totalsel1 = matchprodfreq;
2669 2651 [ + + ]: 15444 : if (nd2 > sslot2->nvalues)
2652 : 3782 : totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
8062 2653 [ + + ]: 15444 : if (nd2 > nmatches)
2654 : 6762 : totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
2655 : 6762 : (nd2 - nmatches);
2656 : : /* Same estimate from the point of view of relation 2. */
2657 : 15444 : totalsel2 = matchprodfreq;
2669 2658 [ + + ]: 15444 : if (nd1 > sslot1->nvalues)
2659 : 4428 : totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
8062 2660 [ + + ]: 15444 : if (nd1 > nmatches)
2661 : 5984 : totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
2662 : 5984 : (nd1 - nmatches);
2663 : :
2664 : : /*
2665 : : * Use the smaller of the two estimates. This can be justified in
2666 : : * essentially the same terms as given below for the no-stats case: to
2667 : : * a first approximation, we are estimating from the point of view of
2668 : : * the relation with smaller nd.
2669 : : */
2670 [ + + ]: 15444 : selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
2671 : : }
2672 : : else
2673 : : {
2674 : : /*
2675 : : * We do not have MCV lists for both sides. Estimate the join
2676 : : * selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
2677 : : * is plausible if we assume that the join operator is strict and the
2678 : : * non-null values are about equally distributed: a given non-null
2679 : : * tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
2680 : : * of rel2, so total join rows are at most
2681 : : * N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
2682 : : * not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
2683 : : * is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
2684 : : * with MIN() is an upper bound. Using the MIN() means we estimate
2685 : : * from the point of view of the relation with smaller nd (since the
2686 : : * larger nd is determining the MIN). It is reasonable to assume that
2687 : : * most tuples in this rel will have join partners, so the bound is
2688 : : * probably reasonably tight and should be taken as-is.
2689 : : *
2690 : : * XXX Can we be smarter if we have an MCV list for just one side? It
2691 : : * seems that if we assume equal distribution for the other side, we
2692 : : * end up with the same answer anyway.
2693 : : */
2694 [ + + ]: 140285 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2695 [ + + ]: 140285 : double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
2696 : :
2697 : 140285 : selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
2698 [ + + ]: 140285 : if (nd1 > nd2)
2699 : 74004 : selec /= nd1;
2700 : : else
2701 : 66281 : selec /= nd2;
2702 : : }
2703 : :
6420 2704 : 155729 : return selec;
2705 : : }
2706 : :
2707 : : /*
2708 : : * eqjoinsel_semi --- eqjoinsel for semi join
2709 : : *
2710 : : * (Also used for anti join, which we are supposed to estimate the same way.)
2711 : : * Caller has ensured that vardata1 is the LHS variable; however, eqproc
2712 : : * is for the original join operator, which might now need to have the inputs
2713 : : * swapped in order to apply correctly. Also, if have_mcvs1 && have_mcvs2
2714 : : * then hasmatch1[], hasmatch2[], and *p_nmatches were filled by
2715 : : * eqjoinsel_inner.
2716 : : */
2717 : : static double
116 tgl@sss.pgh.pa.us 2718 :GNC 7330 : eqjoinsel_semi(FmgrInfo *eqproc, Oid collation,
2719 : : Oid hashLeft, Oid hashRight,
2720 : : bool op_is_reversed,
2721 : : VariableStatData *vardata1, VariableStatData *vardata2,
2722 : : double nd1, double nd2,
2723 : : bool isdefault1, bool isdefault2,
2724 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2725 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
2726 : : bool have_mcvs1, bool have_mcvs2,
2727 : : bool *hasmatch1, bool *hasmatch2,
2728 : : int *p_nmatches,
2729 : : RelOptInfo *inner_rel)
2730 : : {
2731 : : double selec;
2732 : :
2733 : : /*
2734 : : * We clamp nd2 to be not more than what we estimate the inner relation's
2735 : : * size to be. This is intuitively somewhat reasonable since obviously
2736 : : * there can't be more than that many distinct values coming from the
2737 : : * inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
2738 : : * likewise) is that this is the only pathway by which restriction clauses
2739 : : * applied to the inner rel will affect the join result size estimate,
2740 : : * since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
2741 : : * only the outer rel's size. If we clamped nd1 we'd be double-counting
2742 : : * the selectivity of outer-rel restrictions.
2743 : : *
2744 : : * We can apply this clamping both with respect to the base relation from
2745 : : * which the join variable comes (if there is just one), and to the
2746 : : * immediate inner input relation of the current join.
2747 : : *
2748 : : * If we clamp, we can treat nd2 as being a non-default estimate; it's not
2749 : : * great, maybe, but it didn't come out of nowhere either. This is most
2750 : : * helpful when the inner relation is empty and consequently has no stats.
2751 : : */
5309 tgl@sss.pgh.pa.us 2752 [ + + ]:CBC 7330 : if (vardata2->rel)
2753 : : {
3393 2754 [ + + ]: 7327 : if (nd2 >= vardata2->rel->rows)
2755 : : {
2756 : 6202 : nd2 = vardata2->rel->rows;
2757 : 6202 : isdefault2 = false;
2758 : : }
2759 : : }
2760 [ + + ]: 7330 : if (nd2 >= inner_rel->rows)
2761 : : {
2762 : 6175 : nd2 = inner_rel->rows;
2763 : 6175 : isdefault2 = false;
2764 : : }
2765 : :
116 tgl@sss.pgh.pa.us 2766 [ + + + - ]:GNC 7330 : if (have_mcvs1 && have_mcvs2)
6420 tgl@sss.pgh.pa.us 2767 :CBC 316 : {
2768 : : /*
2769 : : * We have most-common-value lists for both relations. Run through
2770 : : * the lists to see which MCVs actually join to each other with the
2771 : : * given operator. This allows us to determine the exact join
2772 : : * selectivity for the portion of the relations represented by the MCV
2773 : : * lists. We still have to estimate for the remaining population, but
2774 : : * in a skewed distribution this gives us a big leg up in accuracy.
2775 : : */
2776 : 316 : double nullfrac1 = stats1->stanullfrac;
2777 : : double matchprodfreq,
2778 : : matchfreq1,
2779 : : uncertainfrac,
2780 : : uncertain;
2781 : : int i,
2782 : : nmatches,
2783 : : clamped_nvalues2;
2784 : :
2785 : : /*
2786 : : * The clamping above could have resulted in nd2 being less than
2787 : : * sslot2->nvalues; in which case, we assume that precisely the nd2
2788 : : * most common values in the relation will appear in the join input,
2789 : : * and so compare to only the first nd2 members of the MCV list. Of
2790 : : * course this is frequently wrong, but it's the best bet we can make.
2791 : : */
2669 2792 [ + + ]: 316 : clamped_nvalues2 = Min(sslot2->nvalues, nd2);
2793 : :
2794 : : /*
2795 : : * If we did not set clamped_nvalues2 to less than sslot2->nvalues,
2796 : : * then the hasmatch1[] and hasmatch2[] match flags computed by
2797 : : * eqjoinsel_inner are still perfectly applicable, so we need not
2798 : : * re-do the matching work. Note that it does not matter if
2799 : : * op_is_reversed: we'd get the same answers.
2800 : : *
2801 : : * If we did clamp, then a different set of sslot2 values is to be
2802 : : * compared, so we have to re-do the matching.
2803 : : */
116 tgl@sss.pgh.pa.us 2804 [ - + ]:GNC 316 : if (clamped_nvalues2 != sslot2->nvalues)
2805 : : {
2806 : : /* Must re-zero the arrays */
116 tgl@sss.pgh.pa.us 2807 :UNC 0 : memset(hasmatch1, 0, sslot1->nvalues * sizeof(bool));
2808 : 0 : memset(hasmatch2, 0, clamped_nvalues2 * sizeof(bool));
2809 : : /* Re-fill the match arrays */
2810 : 0 : eqjoinsel_find_matches(eqproc, collation,
2811 : : hashLeft, hashRight,
2812 : : op_is_reversed,
2813 : : sslot1, sslot2,
2814 : : sslot1->nvalues, clamped_nvalues2,
2815 : : hasmatch1, hasmatch2,
2816 : : p_nmatches, &matchprodfreq);
2817 : : }
116 tgl@sss.pgh.pa.us 2818 :GNC 316 : nmatches = *p_nmatches;
2819 : :
2820 : : /* Sum up frequencies of matched MCVs */
6420 tgl@sss.pgh.pa.us 2821 :CBC 316 : matchfreq1 = 0.0;
2669 2822 [ + + ]: 6976 : for (i = 0; i < sslot1->nvalues; i++)
2823 : : {
6420 2824 [ + + ]: 6660 : if (hasmatch1[i])
2669 2825 : 5714 : matchfreq1 += sslot1->numbers[i];
2826 : : }
6420 2827 [ - + + + ]: 316 : CLAMP_PROBABILITY(matchfreq1);
2828 : :
2829 : : /*
2830 : : * Now we need to estimate the fraction of relation 1 that has at
2831 : : * least one join partner. We know for certain that the matched MCVs
2832 : : * do, so that gives us a lower bound, but we're really in the dark
2833 : : * about everything else. Our crude approach is: if nd1 <= nd2 then
2834 : : * assume all non-null rel1 rows have join partners, else assume for
2835 : : * the uncertain rows that a fraction nd2/nd1 have join partners. We
2836 : : * can discount the known-matched MCVs from the distinct-values counts
2837 : : * before doing the division.
2838 : : *
2839 : : * Crude as the above is, it's completely useless if we don't have
2840 : : * reliable ndistinct values for both sides. Hence, if either nd1 or
2841 : : * nd2 is default, punt and assume half of the uncertain rows have
2842 : : * join partners.
2843 : : */
5306 2844 [ + - + - ]: 316 : if (!isdefault1 && !isdefault2)
2845 : : {
5451 2846 : 316 : nd1 -= nmatches;
2847 : 316 : nd2 -= nmatches;
5309 2848 [ + + - + ]: 316 : if (nd1 <= nd2 || nd2 < 0)
5451 2849 : 298 : uncertainfrac = 1.0;
2850 : : else
2851 : 18 : uncertainfrac = nd2 / nd1;
2852 : : }
2853 : : else
5451 tgl@sss.pgh.pa.us 2854 :UBC 0 : uncertainfrac = 0.5;
5451 tgl@sss.pgh.pa.us 2855 :CBC 316 : uncertain = 1.0 - matchfreq1 - nullfrac1;
2856 [ - + - + ]: 316 : CLAMP_PROBABILITY(uncertain);
2857 : 316 : selec = matchfreq1 + uncertainfrac * uncertain;
2858 : : }
2859 : : else
2860 : : {
2861 : : /*
2862 : : * Without MCV lists for both sides, we can only use the heuristic
2863 : : * about nd1 vs nd2.
2864 : : */
6420 2865 [ + + ]: 7014 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2866 : :
5306 2867 [ + + + + ]: 7014 : if (!isdefault1 && !isdefault2)
2868 : : {
5309 2869 [ + + - + ]: 5269 : if (nd1 <= nd2 || nd2 < 0)
5451 2870 : 2612 : selec = 1.0 - nullfrac1;
2871 : : else
2872 : 2657 : selec = (nd2 / nd1) * (1.0 - nullfrac1);
2873 : : }
2874 : : else
2875 : 1745 : selec = 0.5 * (1.0 - nullfrac1);
2876 : : }
2877 : :
6420 2878 : 7330 : return selec;
2879 : : }
2880 : :
2881 : : /*
2882 : : * Identify matching MCVs for eqjoinsel_inner or eqjoinsel_semi.
2883 : : *
2884 : : * Inputs:
2885 : : * eqproc: FmgrInfo for equality function to use (might be reversed)
2886 : : * collation: OID of collation to use
2887 : : * hashLeft, hashRight: OIDs of hash functions associated with equality op,
2888 : : * or InvalidOid if we're not to use hashing
2889 : : * op_is_reversed: indicates that eqproc compares right type to left type
2890 : : * sslot1, sslot2: MCV values for the lefthand and righthand inputs
2891 : : * nvalues1, nvalues2: number of values to be considered (can be less than
2892 : : * sslotN->nvalues, but not more)
2893 : : * Outputs:
2894 : : * hasmatch1[], hasmatch2[]: pre-zeroed arrays of lengths nvalues1, nvalues2;
2895 : : * entries are set to true if that MCV has a match on the other side
2896 : : * *p_nmatches: receives number of MCV pairs that match
2897 : : * *p_matchprodfreq: receives sum(sslot1->numbers[i] * sslot2->numbers[j])
2898 : : * for matching MCVs
2899 : : *
2900 : : * Note that hashLeft is for the eqproc's left-hand input type, hashRight
2901 : : * for its right, regardless of op_is_reversed.
2902 : : *
2903 : : * Note we assume that each MCV will match at most one member of the other
2904 : : * MCV list. If the operator isn't really equality, there could be multiple
2905 : : * matches --- but we don't look for them, both for speed and because the
2906 : : * math wouldn't add up...
2907 : : */
2908 : : static void
116 tgl@sss.pgh.pa.us 2909 :GNC 15444 : eqjoinsel_find_matches(FmgrInfo *eqproc, Oid collation,
2910 : : Oid hashLeft, Oid hashRight,
2911 : : bool op_is_reversed,
2912 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2913 : : int nvalues1, int nvalues2,
2914 : : bool *hasmatch1, bool *hasmatch2,
2915 : : int *p_nmatches, double *p_matchprodfreq)
2916 : : {
2917 : 15444 : LOCAL_FCINFO(fcinfo, 2);
2918 : 15444 : double matchprodfreq = 0.0;
2919 : 15444 : int nmatches = 0;
2920 : :
2921 : : /*
2922 : : * Save a few cycles by setting up the fcinfo struct just once. Using
2923 : : * FunctionCallInvoke directly also avoids failure if the eqproc returns
2924 : : * NULL, though really equality functions should never do that.
2925 : : */
2926 : 15444 : InitFunctionCallInfoData(*fcinfo, eqproc, 2, collation,
2927 : : NULL, NULL);
2928 : 15444 : fcinfo->args[0].isnull = false;
2929 : 15444 : fcinfo->args[1].isnull = false;
2930 : :
2931 [ + + + - ]: 15444 : if (OidIsValid(hashLeft) && OidIsValid(hashRight))
2932 : 7232 : {
2933 : : /* Use a hash table to speed up the matching */
2934 : 7232 : LOCAL_FCINFO(hash_fcinfo, 1);
2935 : : FmgrInfo hash_proc;
2936 : : MCVHashContext hashContext;
2937 : : MCVHashTable_hash *hashTable;
2938 : : AttStatsSlot *statsProbe;
2939 : : AttStatsSlot *statsHash;
2940 : : bool *hasMatchProbe;
2941 : : bool *hasMatchHash;
2942 : : int nvaluesProbe;
2943 : : int nvaluesHash;
2944 : :
2945 : : /* Make sure we build the hash table on the smaller array. */
2946 [ + + ]: 7232 : if (sslot1->nvalues >= sslot2->nvalues)
2947 : : {
2948 : 5974 : statsProbe = sslot1;
2949 : 5974 : statsHash = sslot2;
2950 : 5974 : hasMatchProbe = hasmatch1;
2951 : 5974 : hasMatchHash = hasmatch2;
2952 : 5974 : nvaluesProbe = nvalues1;
2953 : 5974 : nvaluesHash = nvalues2;
2954 : : }
2955 : : else
2956 : : {
2957 : : /* We'll have to reverse the direction of use of the operator. */
2958 : 1258 : op_is_reversed = !op_is_reversed;
2959 : 1258 : statsProbe = sslot2;
2960 : 1258 : statsHash = sslot1;
2961 : 1258 : hasMatchProbe = hasmatch2;
2962 : 1258 : hasMatchHash = hasmatch1;
2963 : 1258 : nvaluesProbe = nvalues2;
2964 : 1258 : nvaluesHash = nvalues1;
2965 : : }
2966 : :
2967 : : /*
2968 : : * Build the hash table on the smaller array, using the appropriate
2969 : : * hash function for its data type.
2970 : : */
2971 [ + + ]: 7232 : fmgr_info(op_is_reversed ? hashLeft : hashRight, &hash_proc);
2972 : 7232 : InitFunctionCallInfoData(*hash_fcinfo, &hash_proc, 1, collation,
2973 : : NULL, NULL);
2974 : 7232 : hash_fcinfo->args[0].isnull = false;
2975 : :
2976 : 7232 : hashContext.equal_fcinfo = fcinfo;
2977 : 7232 : hashContext.hash_fcinfo = hash_fcinfo;
2978 : 7232 : hashContext.op_is_reversed = op_is_reversed;
2979 : 7232 : hashContext.insert_mode = true;
2980 : 7232 : get_typlenbyval(statsHash->valuetype,
2981 : : &hashContext.hash_typlen,
2982 : : &hashContext.hash_typbyval);
2983 : :
2984 : 7232 : hashTable = MCVHashTable_create(CurrentMemoryContext,
2985 : : nvaluesHash,
2986 : : &hashContext);
2987 : :
2988 [ + + ]: 240025 : for (int i = 0; i < nvaluesHash; i++)
2989 : : {
2990 : 232793 : bool found = false;
2991 : 232793 : MCVHashEntry *entry = MCVHashTable_insert(hashTable,
2992 : 232793 : statsHash->values[i],
2993 : : &found);
2994 : :
2995 : : /*
2996 : : * MCVHashTable_insert will only report "found" if the new value
2997 : : * is equal to some previous one per datum_image_eq(). That
2998 : : * probably shouldn't happen, since we're not expecting duplicates
2999 : : * in the MCV list. If we do find a dup, just ignore it, leaving
3000 : : * the hash entry's index pointing at the first occurrence. That
3001 : : * matches the behavior that the non-hashed code path would have.
3002 : : */
3003 [ + - ]: 232793 : if (likely(!found))
3004 : 232793 : entry->index = i;
3005 : : }
3006 : :
3007 : : /*
3008 : : * Prepare to probe the hash table. If the probe values are of a
3009 : : * different data type, then we need to change hash functions. (This
3010 : : * code relies on the assumption that since we defined SH_STORE_HASH,
3011 : : * simplehash.h will never need to compute hash values for existing
3012 : : * hash table entries.)
3013 : : */
3014 : 7232 : hashContext.insert_mode = false;
3015 [ + + ]: 7232 : if (hashLeft != hashRight)
3016 : : {
3017 [ + + ]: 1052 : fmgr_info(op_is_reversed ? hashRight : hashLeft, &hash_proc);
3018 : : /* Resetting hash_fcinfo is probably unnecessary, but be safe */
3019 : 1052 : InitFunctionCallInfoData(*hash_fcinfo, &hash_proc, 1, collation,
3020 : : NULL, NULL);
3021 : 1052 : hash_fcinfo->args[0].isnull = false;
3022 : : }
3023 : :
3024 : : /* Look up each probe value in turn. */
3025 [ + + ]: 412714 : for (int i = 0; i < nvaluesProbe; i++)
3026 : : {
3027 : 405482 : MCVHashEntry *entry = MCVHashTable_lookup(hashTable,
3028 : 405482 : statsProbe->values[i]);
3029 : :
3030 : : /* As in the other code path, skip already-matched hash entries */
3031 [ + + + - ]: 405482 : if (entry != NULL && !hasMatchHash[entry->index])
3032 : : {
3033 : 130710 : hasMatchHash[entry->index] = hasMatchProbe[i] = true;
3034 : 130710 : nmatches++;
3035 : 130710 : matchprodfreq += statsHash->numbers[entry->index] * statsProbe->numbers[i];
3036 : : }
3037 : : }
3038 : :
3039 : 7232 : MCVHashTable_destroy(hashTable);
3040 : : }
3041 : : else
3042 : : {
3043 : : /* We're not to use hashing, so do it the O(N^2) way */
3044 : : int index1,
3045 : : index2;
3046 : :
3047 : : /* Set up to supply the values in the order the operator expects */
3048 [ - + ]: 8212 : if (op_is_reversed)
3049 : : {
116 tgl@sss.pgh.pa.us 3050 :UNC 0 : index1 = 1;
3051 : 0 : index2 = 0;
3052 : : }
3053 : : else
3054 : : {
116 tgl@sss.pgh.pa.us 3055 :GNC 8212 : index1 = 0;
3056 : 8212 : index2 = 1;
3057 : : }
3058 : :
3059 [ + + ]: 42184 : for (int i = 0; i < nvalues1; i++)
3060 : : {
3061 : 33972 : fcinfo->args[index1].value = sslot1->values[i];
3062 : :
3063 [ + + ]: 112085 : for (int j = 0; j < nvalues2; j++)
3064 : : {
3065 : : Datum fresult;
3066 : :
3067 [ + + ]: 109822 : if (hasmatch2[j])
3068 : 57719 : continue;
3069 : 52103 : fcinfo->args[index2].value = sslot2->values[j];
3070 : 52103 : fcinfo->isnull = false;
3071 : 52103 : fresult = FunctionCallInvoke(fcinfo);
3072 [ + - + + ]: 52103 : if (!fcinfo->isnull && DatumGetBool(fresult))
3073 : : {
3074 : 31709 : hasmatch1[i] = hasmatch2[j] = true;
3075 : 31709 : matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
3076 : 31709 : nmatches++;
3077 : 31709 : break;
3078 : : }
3079 : : }
3080 : : }
3081 : : }
3082 : :
3083 : 15444 : *p_nmatches = nmatches;
3084 : 15444 : *p_matchprodfreq = matchprodfreq;
3085 : 15444 : }
3086 : :
3087 : : /*
3088 : : * Support functions for the hash tables used by eqjoinsel_find_matches
3089 : : */
3090 : : static uint32
3091 : 638275 : hash_mcv(MCVHashTable_hash *tab, Datum key)
3092 : : {
3093 : 638275 : MCVHashContext *context = (MCVHashContext *) tab->private_data;
3094 : 638275 : FunctionCallInfo fcinfo = context->hash_fcinfo;
3095 : : Datum fresult;
3096 : :
3097 : 638275 : fcinfo->args[0].value = key;
3098 : 638275 : fcinfo->isnull = false;
3099 : 638275 : fresult = FunctionCallInvoke(fcinfo);
3100 [ - + ]: 638275 : Assert(!fcinfo->isnull);
3101 : 638275 : return DatumGetUInt32(fresult);
3102 : : }
3103 : :
3104 : : static bool
3105 : 130710 : mcvs_equal(MCVHashTable_hash *tab, Datum key0, Datum key1)
3106 : : {
3107 : 130710 : MCVHashContext *context = (MCVHashContext *) tab->private_data;
3108 : :
3109 [ - + ]: 130710 : if (context->insert_mode)
3110 : : {
3111 : : /*
3112 : : * During the insertion step, any comparisons will be between two
3113 : : * Datums of the hash table's data type, so if the given operator is
3114 : : * cross-type it will be the wrong thing to use. Fortunately, we can
3115 : : * use datum_image_eq instead. The MCV values should all be distinct
3116 : : * anyway, so it's mostly pro-forma to compare them at all.
3117 : : */
116 tgl@sss.pgh.pa.us 3118 :UNC 0 : return datum_image_eq(key0, key1,
3119 : 0 : context->hash_typbyval, context->hash_typlen);
3120 : : }
3121 : : else
3122 : : {
116 tgl@sss.pgh.pa.us 3123 :GNC 130710 : FunctionCallInfo fcinfo = context->equal_fcinfo;
3124 : : Datum fresult;
3125 : :
3126 : : /*
3127 : : * Apply the operator the correct way around. Although simplehash.h
3128 : : * doesn't document this explicitly, during lookups key0 is from the
3129 : : * hash table while key1 is the probe value, so we should compare them
3130 : : * in that order only if op_is_reversed.
3131 : : */
3132 [ + + ]: 130710 : if (context->op_is_reversed)
3133 : : {
3134 : 35874 : fcinfo->args[0].value = key0;
3135 : 35874 : fcinfo->args[1].value = key1;
3136 : : }
3137 : : else
3138 : : {
3139 : 94836 : fcinfo->args[0].value = key1;
3140 : 94836 : fcinfo->args[1].value = key0;
3141 : : }
3142 : 130710 : fcinfo->isnull = false;
3143 : 130710 : fresult = FunctionCallInvoke(fcinfo);
3144 [ + - + - ]: 130710 : return (!fcinfo->isnull && DatumGetBool(fresult));
3145 : : }
3146 : : }
3147 : :
3148 : : /*
3149 : : * neqjoinsel - Join selectivity of "!="
3150 : : */
3151 : : Datum
9414 tgl@sss.pgh.pa.us 3152 :CBC 2495 : neqjoinsel(PG_FUNCTION_ARGS)
3153 : : {
7588 3154 : 2495 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
9058 3155 : 2495 : Oid operator = PG_GETARG_OID(1);
3156 : 2495 : List *args = (List *) PG_GETARG_POINTER(2);
8447 3157 : 2495 : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
6420 3158 : 2495 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
2063 3159 : 2495 : Oid collation = PG_GET_COLLATION();
3160 : : float8 result;
3161 : :
3028 3162 [ + + - + ]: 2495 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
9058 3163 : 833 : {
3164 : : /*
3165 : : * For semi-joins, if there is more than one distinct value in the RHS
3166 : : * relation then every non-null LHS row must find a row to join since
3167 : : * it can only be equal to one of them. We'll assume that there is
3168 : : * always more than one distinct RHS value for the sake of stability,
3169 : : * though in theory we could have special cases for empty RHS
3170 : : * (selectivity = 0) and single-distinct-value RHS (selectivity =
3171 : : * fraction of LHS that has the same value as the single RHS value).
3172 : : *
3173 : : * For anti-joins, if we use the same assumption that there is more
3174 : : * than one distinct key in the RHS relation, then every non-null LHS
3175 : : * row must be suppressed by the anti-join.
3176 : : *
3177 : : * So either way, the selectivity estimate should be 1 - nullfrac.
3178 : : */
3179 : : VariableStatData leftvar;
3180 : : VariableStatData rightvar;
3181 : : bool reversed;
3182 : : HeapTuple statsTuple;
3183 : : double nullfrac;
3184 : :
3028 3185 : 833 : get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
3186 [ + + ]: 833 : statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
3187 [ + + ]: 833 : if (HeapTupleIsValid(statsTuple))
3188 : 712 : nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
3189 : : else
3190 : 121 : nullfrac = 0.0;
3191 [ + + ]: 833 : ReleaseVariableStats(leftvar);
3192 [ + + ]: 833 : ReleaseVariableStats(rightvar);
3193 : :
3194 : 833 : result = 1.0 - nullfrac;
3195 : : }
3196 : : else
3197 : : {
3198 : : /*
3199 : : * We want 1 - eqjoinsel() where the equality operator is the one
3200 : : * associated with this != operator, that is, its negator.
3201 : : */
3202 : 1662 : Oid eqop = get_negator(operator);
3203 : :
3204 [ + - ]: 1662 : if (eqop)
3205 : : {
3206 : : result =
2063 3207 : 1662 : DatumGetFloat8(DirectFunctionCall5Coll(eqjoinsel,
3208 : : collation,
3209 : : PointerGetDatum(root),
3210 : : ObjectIdGetDatum(eqop),
3211 : : PointerGetDatum(args),
3212 : : Int16GetDatum(jointype),
3213 : : PointerGetDatum(sjinfo)));
3214 : : }
3215 : : else
3216 : : {
3217 : : /* Use default selectivity (should we raise an error instead?) */
3028 tgl@sss.pgh.pa.us 3218 :UBC 0 : result = DEFAULT_EQ_SEL;
3219 : : }
3028 tgl@sss.pgh.pa.us 3220 :CBC 1662 : result = 1.0 - result;
3221 : : }
3222 : :
9414 3223 : 2495 : PG_RETURN_FLOAT8(result);
3224 : : }
3225 : :
3226 : : /*
3227 : : * scalarltjoinsel - Join selectivity of "<" for scalars
3228 : : */
3229 : : Datum
3230 : 162 : scalarltjoinsel(PG_FUNCTION_ARGS)
3231 : : {
3232 : 162 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3233 : : }
3234 : :
3235 : : /*
3236 : : * scalarlejoinsel - Join selectivity of "<=" for scalars
3237 : : */
3238 : : Datum
3105 3239 : 138 : scalarlejoinsel(PG_FUNCTION_ARGS)
3240 : : {
3241 : 138 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3242 : : }
3243 : :
3244 : : /*
3245 : : * scalargtjoinsel - Join selectivity of ">" for scalars
3246 : : */
3247 : : Datum
9414 3248 : 144 : scalargtjoinsel(PG_FUNCTION_ARGS)
3249 : : {
3250 : 144 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3251 : : }
3252 : :
3253 : : /*
3254 : : * scalargejoinsel - Join selectivity of ">=" for scalars
3255 : : */
3256 : : Datum
3105 3257 : 92 : scalargejoinsel(PG_FUNCTION_ARGS)
3258 : : {
3259 : 92 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
3260 : : }
3261 : :
3262 : :
3263 : : /*
3264 : : * mergejoinscansel - Scan selectivity of merge join.
3265 : : *
3266 : : * A merge join will stop as soon as it exhausts either input stream.
3267 : : * Therefore, if we can estimate the ranges of both input variables,
3268 : : * we can estimate how much of the input will actually be read. This
3269 : : * can have a considerable impact on the cost when using indexscans.
3270 : : *
3271 : : * Also, we can estimate how much of each input has to be read before the
3272 : : * first join pair is found, which will affect the join's startup time.
3273 : : *
3274 : : * clause should be a clause already known to be mergejoinable. opfamily,
3275 : : * cmptype, and nulls_first specify the sort ordering being used.
3276 : : *
3277 : : * The outputs are:
3278 : : * *leftstart is set to the fraction of the left-hand variable expected
3279 : : * to be scanned before the first join pair is found (0 to 1).
3280 : : * *leftend is set to the fraction of the left-hand variable expected
3281 : : * to be scanned before the join terminates (0 to 1).
3282 : : * *rightstart, *rightend similarly for the right-hand variable.
3283 : : */
3284 : : void
2586 3285 : 82319 : mergejoinscansel(PlannerInfo *root, Node *clause,
3286 : : Oid opfamily, CompareType cmptype, bool nulls_first,
3287 : : Selectivity *leftstart, Selectivity *leftend,
3288 : : Selectivity *rightstart, Selectivity *rightend)
3289 : : {
3290 : : Node *left,
3291 : : *right;
3292 : : VariableStatData leftvar,
3293 : : rightvar;
3294 : : Oid opmethod;
3295 : : int op_strategy;
3296 : : Oid op_lefttype;
3297 : : Oid op_righttype;
3298 : : Oid opno,
3299 : : collation,
3300 : : lsortop,
3301 : : rsortop,
3302 : : lstatop,
3303 : : rstatop,
3304 : : ltop,
3305 : : leop,
3306 : : revltop,
3307 : : revleop;
3308 : : StrategyNumber ltstrat,
3309 : : lestrat,
3310 : : gtstrat,
3311 : : gestrat;
3312 : : bool isgt;
3313 : : Datum leftmin,
3314 : : leftmax,
3315 : : rightmin,
3316 : : rightmax;
3317 : : double selec;
3318 : :
3319 : : /* Set default results if we can't figure anything out. */
3320 : : /* XXX should default "start" fraction be a bit more than 0? */
6672 3321 : 82319 : *leftstart = *rightstart = 0.0;
3322 : 82319 : *leftend = *rightend = 1.0;
3323 : :
3324 : : /* Deconstruct the merge clause */
8780 3325 [ - + ]: 82319 : if (!is_opclause(clause))
8780 tgl@sss.pgh.pa.us 3326 :UBC 0 : return; /* shouldn't happen */
8494 tgl@sss.pgh.pa.us 3327 :CBC 82319 : opno = ((OpExpr *) clause)->opno;
2109 3328 : 82319 : collation = ((OpExpr *) clause)->inputcollid;
8062 3329 : 82319 : left = get_leftop((Expr *) clause);
3330 : 82319 : right = get_rightop((Expr *) clause);
8780 3331 [ - + ]: 82319 : if (!right)
8780 tgl@sss.pgh.pa.us 3332 :UBC 0 : return; /* shouldn't happen */
3333 : :
3334 : : /* Look for stats for the inputs */
8062 tgl@sss.pgh.pa.us 3335 :CBC 82319 : examine_variable(root, left, 0, &leftvar);
3336 : 82319 : examine_variable(root, right, 0, &rightvar);
3337 : :
343 peter@eisentraut.org 3338 : 82319 : opmethod = get_opfamily_method(opfamily);
3339 : :
3340 : : /* Extract the operator's declared left/right datatypes */
5582 tgl@sss.pgh.pa.us 3341 : 82319 : get_op_opfamily_properties(opno, opfamily, false,
3342 : : &op_strategy,
3343 : : &op_lefttype,
3344 : : &op_righttype);
343 peter@eisentraut.org 3345 [ - + ]: 82319 : Assert(IndexAmTranslateStrategy(op_strategy, opmethod, opfamily, true) == COMPARE_EQ);
3346 : :
3347 : : /*
3348 : : * Look up the various operators we need. If we don't find them all, it
3349 : : * probably means the opfamily is broken, but we just fail silently.
3350 : : *
3351 : : * Note: we expect that pg_statistic histograms will be sorted by the '<'
3352 : : * operator, regardless of which sort direction we are considering.
3353 : : */
3354 [ + + - ]: 82319 : switch (cmptype)
3355 : : {
3356 : 82301 : case COMPARE_LT:
6672 tgl@sss.pgh.pa.us 3357 : 82301 : isgt = false;
343 peter@eisentraut.org 3358 : 82301 : ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
3359 : 82301 : lestrat = IndexAmTranslateCompareType(COMPARE_LE, opmethod, opfamily, true);
6672 tgl@sss.pgh.pa.us 3360 [ + + ]: 82301 : if (op_lefttype == op_righttype)
3361 : : {
3362 : : /* easy case */
3363 : 81269 : ltop = get_opfamily_member(opfamily,
3364 : : op_lefttype, op_righttype,
3365 : : ltstrat);
3366 : 81269 : leop = get_opfamily_member(opfamily,
3367 : : op_lefttype, op_righttype,
3368 : : lestrat);
3369 : 81269 : lsortop = ltop;
3370 : 81269 : rsortop = ltop;
3371 : 81269 : lstatop = lsortop;
3372 : 81269 : rstatop = rsortop;
3373 : 81269 : revltop = ltop;
3374 : 81269 : revleop = leop;
3375 : : }
3376 : : else
3377 : : {
3378 : 1032 : ltop = get_opfamily_member(opfamily,
3379 : : op_lefttype, op_righttype,
3380 : : ltstrat);
3381 : 1032 : leop = get_opfamily_member(opfamily,
3382 : : op_lefttype, op_righttype,
3383 : : lestrat);
3384 : 1032 : lsortop = get_opfamily_member(opfamily,
3385 : : op_lefttype, op_lefttype,
3386 : : ltstrat);
3387 : 1032 : rsortop = get_opfamily_member(opfamily,
3388 : : op_righttype, op_righttype,
3389 : : ltstrat);
3390 : 1032 : lstatop = lsortop;
3391 : 1032 : rstatop = rsortop;
3392 : 1032 : revltop = get_opfamily_member(opfamily,
3393 : : op_righttype, op_lefttype,
3394 : : ltstrat);
3395 : 1032 : revleop = get_opfamily_member(opfamily,
3396 : : op_righttype, op_lefttype,
3397 : : lestrat);
3398 : : }
7022 3399 : 82301 : break;
343 peter@eisentraut.org 3400 : 18 : case COMPARE_GT:
3401 : : /* descending-order case */
6672 tgl@sss.pgh.pa.us 3402 : 18 : isgt = true;
343 peter@eisentraut.org 3403 : 18 : ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
3404 : 18 : gtstrat = IndexAmTranslateCompareType(COMPARE_GT, opmethod, opfamily, true);
3405 : 18 : gestrat = IndexAmTranslateCompareType(COMPARE_GE, opmethod, opfamily, true);
6672 tgl@sss.pgh.pa.us 3406 [ + - ]: 18 : if (op_lefttype == op_righttype)
3407 : : {
3408 : : /* easy case */
3409 : 18 : ltop = get_opfamily_member(opfamily,
3410 : : op_lefttype, op_righttype,
3411 : : gtstrat);
3412 : 18 : leop = get_opfamily_member(opfamily,
3413 : : op_lefttype, op_righttype,
3414 : : gestrat);
3415 : 18 : lsortop = ltop;
3416 : 18 : rsortop = ltop;
3417 : 18 : lstatop = get_opfamily_member(opfamily,
3418 : : op_lefttype, op_lefttype,
3419 : : ltstrat);
3420 : 18 : rstatop = lstatop;
3421 : 18 : revltop = ltop;
3422 : 18 : revleop = leop;
3423 : : }
3424 : : else
3425 : : {
6672 tgl@sss.pgh.pa.us 3426 :UBC 0 : ltop = get_opfamily_member(opfamily,
3427 : : op_lefttype, op_righttype,
3428 : : gtstrat);
3429 : 0 : leop = get_opfamily_member(opfamily,
3430 : : op_lefttype, op_righttype,
3431 : : gestrat);
3432 : 0 : lsortop = get_opfamily_member(opfamily,
3433 : : op_lefttype, op_lefttype,
3434 : : gtstrat);
3435 : 0 : rsortop = get_opfamily_member(opfamily,
3436 : : op_righttype, op_righttype,
3437 : : gtstrat);
3438 : 0 : lstatop = get_opfamily_member(opfamily,
3439 : : op_lefttype, op_lefttype,
3440 : : ltstrat);
3441 : 0 : rstatop = get_opfamily_member(opfamily,
3442 : : op_righttype, op_righttype,
3443 : : ltstrat);
3444 : 0 : revltop = get_opfamily_member(opfamily,
3445 : : op_righttype, op_lefttype,
3446 : : gtstrat);
3447 : 0 : revleop = get_opfamily_member(opfamily,
3448 : : op_righttype, op_lefttype,
3449 : : gestrat);
3450 : : }
7022 tgl@sss.pgh.pa.us 3451 :CBC 18 : break;
7022 tgl@sss.pgh.pa.us 3452 :UBC 0 : default:
3453 : 0 : goto fail; /* shouldn't get here */
3454 : : }
3455 : :
7022 tgl@sss.pgh.pa.us 3456 [ + - + - ]:CBC 82319 : if (!OidIsValid(lsortop) ||
3457 [ + - ]: 82319 : !OidIsValid(rsortop) ||
6672 3458 [ + - ]: 82319 : !OidIsValid(lstatop) ||
3459 [ + + ]: 82319 : !OidIsValid(rstatop) ||
3460 [ + - ]: 82313 : !OidIsValid(ltop) ||
7022 3461 [ + - ]: 82313 : !OidIsValid(leop) ||
6672 3462 [ - + ]: 82313 : !OidIsValid(revltop) ||
3463 : : !OidIsValid(revleop))
7022 3464 : 6 : goto fail; /* insufficient info in catalogs */
3465 : :
3466 : : /* Try to get ranges of both inputs */
6672 3467 [ + + ]: 82313 : if (!isgt)
3468 : : {
2109 3469 [ + + ]: 82295 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3470 : : &leftmin, &leftmax))
6672 3471 : 24214 : goto fail; /* no range available from stats */
2109 3472 [ + + ]: 58081 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3473 : : &rightmin, &rightmax))
6672 3474 : 13484 : goto fail; /* no range available from stats */
3475 : : }
3476 : : else
3477 : : {
3478 : : /* need to swap the max and min */
2109 3479 [ + + ]: 18 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3480 : : &leftmax, &leftmin))
6672 3481 : 15 : goto fail; /* no range available from stats */
2109 3482 [ - + ]: 3 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3483 : : &rightmax, &rightmin))
6672 tgl@sss.pgh.pa.us 3484 :UBC 0 : goto fail; /* no range available from stats */
3485 : : }
3486 : :
3487 : : /*
3488 : : * Now, the fraction of the left variable that will be scanned is the
3489 : : * fraction that's <= the right-side maximum value. But only believe
3490 : : * non-default estimates, else stick with our 1.0.
3491 : : */
2109 tgl@sss.pgh.pa.us 3492 :CBC 44600 : selec = scalarineqsel(root, leop, isgt, true, collation, &leftvar,
3493 : : rightmax, op_righttype);
8780 3494 [ + + ]: 44600 : if (selec != DEFAULT_INEQ_SEL)
6672 3495 : 44597 : *leftend = selec;
3496 : :
3497 : : /* And similarly for the right variable. */
2109 3498 : 44600 : selec = scalarineqsel(root, revleop, isgt, true, collation, &rightvar,
3499 : : leftmax, op_lefttype);
8780 3500 [ + - ]: 44600 : if (selec != DEFAULT_INEQ_SEL)
6672 3501 : 44600 : *rightend = selec;
3502 : :
3503 : : /*
3504 : : * Only one of the two "end" fractions can really be less than 1.0;
3505 : : * believe the smaller estimate and reset the other one to exactly 1.0. If
3506 : : * we get exactly equal estimates (as can easily happen with self-joins),
3507 : : * believe neither.
3508 : : */
3509 [ + + ]: 44600 : if (*leftend > *rightend)
3510 : 12912 : *leftend = 1.0;
3511 [ + + ]: 31688 : else if (*leftend < *rightend)
3512 : 18190 : *rightend = 1.0;
3513 : : else
3514 : 13498 : *leftend = *rightend = 1.0;
3515 : :
3516 : : /*
3517 : : * Also, the fraction of the left variable that will be scanned before the
3518 : : * first join pair is found is the fraction that's < the right-side
3519 : : * minimum value. But only believe non-default estimates, else stick with
3520 : : * our own default.
3521 : : */
2109 3522 : 44600 : selec = scalarineqsel(root, ltop, isgt, false, collation, &leftvar,
3523 : : rightmin, op_righttype);
6672 3524 [ + - ]: 44600 : if (selec != DEFAULT_INEQ_SEL)
3525 : 44600 : *leftstart = selec;
3526 : :
3527 : : /* And similarly for the right variable. */
2109 3528 : 44600 : selec = scalarineqsel(root, revltop, isgt, false, collation, &rightvar,
3529 : : leftmin, op_lefttype);
6672 3530 [ + - ]: 44600 : if (selec != DEFAULT_INEQ_SEL)
3531 : 44600 : *rightstart = selec;
3532 : :
3533 : : /*
3534 : : * Only one of the two "start" fractions can really be more than zero;
3535 : : * believe the larger estimate and reset the other one to exactly 0.0. If
3536 : : * we get exactly equal estimates (as can easily happen with self-joins),
3537 : : * believe neither.
3538 : : */
3539 [ + + ]: 44600 : if (*leftstart < *rightstart)
3540 : 9819 : *leftstart = 0.0;
3541 [ + + ]: 34781 : else if (*leftstart > *rightstart)
3542 : 13596 : *rightstart = 0.0;
3543 : : else
3544 : 21185 : *leftstart = *rightstart = 0.0;
3545 : :
3546 : : /*
3547 : : * If the sort order is nulls-first, we're going to have to skip over any
3548 : : * nulls too. These would not have been counted by scalarineqsel, and we
3549 : : * can safely add in this fraction regardless of whether we believe
3550 : : * scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
3551 : : */
3552 [ + + ]: 44600 : if (nulls_first)
3553 : : {
3554 : : Form_pg_statistic stats;
3555 : :
3556 [ + - ]: 3 : if (HeapTupleIsValid(leftvar.statsTuple))
3557 : : {
3558 : 3 : stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
3559 : 3 : *leftstart += stats->stanullfrac;
3560 [ - + - + ]: 3 : CLAMP_PROBABILITY(*leftstart);
3561 : 3 : *leftend += stats->stanullfrac;
3562 [ - + - + ]: 3 : CLAMP_PROBABILITY(*leftend);
3563 : : }
3564 [ + - ]: 3 : if (HeapTupleIsValid(rightvar.statsTuple))
3565 : : {
6992 3566 : 3 : stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple);
6672 3567 : 3 : *rightstart += stats->stanullfrac;
3568 [ - + - + ]: 3 : CLAMP_PROBABILITY(*rightstart);
3569 : 3 : *rightend += stats->stanullfrac;
3570 [ - + - + ]: 3 : CLAMP_PROBABILITY(*rightend);
3571 : : }
3572 : : }
3573 : :
3574 : : /* Disbelieve start >= end, just in case that can happen */
3575 [ + + ]: 44600 : if (*leftstart >= *leftend)
3576 : : {
3577 : 73 : *leftstart = 0.0;
3578 : 73 : *leftend = 1.0;
3579 : : }
3580 [ + + ]: 44600 : if (*rightstart >= *rightend)
3581 : : {
3582 : 743 : *rightstart = 0.0;
3583 : 743 : *rightend = 1.0;
3584 : : }
3585 : :
8062 3586 : 43857 : fail:
3587 [ + + ]: 82319 : ReleaseVariableStats(leftvar);
3588 [ + + ]: 82319 : ReleaseVariableStats(rightvar);
3589 : : }
3590 : :
3591 : :
3592 : : /*
3593 : : * matchingsel -- generic matching-operator selectivity support
3594 : : *
3595 : : * Use these for any operators that (a) are on data types for which we collect
3596 : : * standard statistics, and (b) have behavior for which the default estimate
3597 : : * (twice DEFAULT_EQ_SEL) is sane. Typically that is good for match-like
3598 : : * operators.
3599 : : */
3600 : :
3601 : : Datum
2174 3602 : 565 : matchingsel(PG_FUNCTION_ARGS)
3603 : : {
3604 : 565 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
3605 : 565 : Oid operator = PG_GETARG_OID(1);
3606 : 565 : List *args = (List *) PG_GETARG_POINTER(2);
3607 : 565 : int varRelid = PG_GETARG_INT32(3);
2109 3608 : 565 : Oid collation = PG_GET_COLLATION();
3609 : : double selec;
3610 : :
3611 : : /* Use generic restriction selectivity logic. */
3612 : 565 : selec = generic_restriction_selectivity(root, operator, collation,
3613 : : args, varRelid,
3614 : : DEFAULT_MATCHING_SEL);
3615 : :
2174 3616 : 565 : PG_RETURN_FLOAT8((float8) selec);
3617 : : }
3618 : :
3619 : : Datum
3620 : 3 : matchingjoinsel(PG_FUNCTION_ARGS)
3621 : : {
3622 : : /* Just punt, for the moment. */
3623 : 3 : PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
3624 : : }
3625 : :
3626 : :
3627 : : /*
3628 : : * Helper routine for estimate_num_groups: add an item to a list of
3629 : : * GroupVarInfos, but only if it's not known equal to any of the existing
3630 : : * entries.
3631 : : */
3632 : : typedef struct
3633 : : {
3634 : : Node *var; /* might be an expression, not just a Var */
3635 : : RelOptInfo *rel; /* relation it belongs to */
3636 : : double ndistinct; /* # distinct values */
3637 : : bool isdefault; /* true if DEFAULT_NUM_DISTINCT was used */
3638 : : } GroupVarInfo;
3639 : :
3640 : : static List *
7588 3641 : 233017 : add_unique_group_var(PlannerInfo *root, List *varinfos,
3642 : : Node *var, VariableStatData *vardata)
3643 : : {
3644 : : GroupVarInfo *varinfo;
3645 : : double ndistinct;
3646 : : bool isdefault;
3647 : : ListCell *lc;
3648 : :
5306 3649 : 233017 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
3650 : :
3651 : : /*
3652 : : * The nullingrels bits within the var could cause the same var to be
3653 : : * counted multiple times if it's marked with different nullingrels. They
3654 : : * could also prevent us from matching the var to the expressions in
3655 : : * extended statistics (see estimate_multivariate_ndistinct). So strip
3656 : : * them out first.
3657 : : */
437 rguo@postgresql.org 3658 : 233017 : var = remove_nulling_relids(var, root->outer_join_rels, NULL);
3659 : :
2435 tgl@sss.pgh.pa.us 3660 [ + + + + : 282100 : foreach(lc, varinfos)
+ + ]
3661 : : {
7848 3662 : 49658 : varinfo = (GroupVarInfo *) lfirst(lc);
3663 : :
3664 : : /* Drop exact duplicates */
3665 [ + + ]: 49658 : if (equal(var, varinfo->var))
3666 : 575 : return varinfos;
3667 : :
3668 : : /*
3669 : : * Drop known-equal vars, but only if they belong to different
3670 : : * relations (see comments for estimate_num_groups). We aren't too
3671 : : * fussy about the semantics of "equal" here.
3672 : : */
3673 [ + + + + ]: 52119 : if (vardata->rel != varinfo->rel &&
593 rguo@postgresql.org 3674 : 2898 : exprs_known_equal(root, var, varinfo->var, InvalidOid))
3675 : : {
7848 tgl@sss.pgh.pa.us 3676 [ + + ]: 150 : if (varinfo->ndistinct <= ndistinct)
3677 : : {
3678 : : /* Keep older item, forget new one */
3679 : 138 : return varinfos;
3680 : : }
3681 : : else
3682 : : {
3683 : : /* Delete the older item */
2435 3684 : 12 : varinfos = foreach_delete_current(varinfos, lc);
3685 : : }
3686 : : }
3687 : : }
3688 : :
95 michael@paquier.xyz 3689 :GNC 232442 : varinfo = palloc_object(GroupVarInfo);
3690 : :
7848 tgl@sss.pgh.pa.us 3691 :CBC 232442 : varinfo->var = var;
3692 : 232442 : varinfo->rel = vardata->rel;
3693 : 232442 : varinfo->ndistinct = ndistinct;
1811 drowley@postgresql.o 3694 : 232442 : varinfo->isdefault = isdefault;
7848 tgl@sss.pgh.pa.us 3695 : 232442 : varinfos = lappend(varinfos, varinfo);
3696 : 232442 : return varinfos;
3697 : : }
3698 : :
3699 : : /*
3700 : : * estimate_num_groups - Estimate number of groups in a grouped query
3701 : : *
3702 : : * Given a query having a GROUP BY clause, estimate how many groups there
3703 : : * will be --- ie, the number of distinct combinations of the GROUP BY
3704 : : * expressions.
3705 : : *
3706 : : * This routine is also used to estimate the number of rows emitted by
3707 : : * a DISTINCT filtering step; that is an isomorphic problem. (Note:
3708 : : * actually, we only use it for DISTINCT when there's no grouping or
3709 : : * aggregation ahead of the DISTINCT.)
3710 : : *
3711 : : * Inputs:
3712 : : * root - the query
3713 : : * groupExprs - list of expressions being grouped by
3714 : : * input_rows - number of rows estimated to arrive at the group/unique
3715 : : * filter step
3716 : : * pgset - NULL, or a List** pointing to a grouping set to filter the
3717 : : * groupExprs against
3718 : : *
3719 : : * Outputs:
3720 : : * estinfo - When passed as non-NULL, the function will set bits in the
3721 : : * "flags" field in order to provide callers with additional information
3722 : : * about the estimation. Currently, we only set the SELFLAG_USED_DEFAULT
3723 : : * bit if we used any default values in the estimation.
3724 : : *
3725 : : * Given the lack of any cross-correlation statistics in the system, it's
3726 : : * impossible to do anything really trustworthy with GROUP BY conditions
3727 : : * involving multiple Vars. We should however avoid assuming the worst
3728 : : * case (all possible cross-product terms actually appear as groups) since
3729 : : * very often the grouped-by Vars are highly correlated. Our current approach
3730 : : * is as follows:
3731 : : * 1. Expressions yielding boolean are assumed to contribute two groups,
3732 : : * independently of their content, and are ignored in the subsequent
3733 : : * steps. This is mainly because tests like "col IS NULL" break the
3734 : : * heuristic used in step 2 especially badly.
3735 : : * 2. Reduce the given expressions to a list of unique Vars used. For
3736 : : * example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
3737 : : * It is clearly correct not to count the same Var more than once.
3738 : : * It is also reasonable to treat f(x) the same as x: f() cannot
3739 : : * increase the number of distinct values (unless it is volatile,
3740 : : * which we consider unlikely for grouping), but it probably won't
3741 : : * reduce the number of distinct values much either.
3742 : : * As a special case, if a GROUP BY expression can be matched to an
3743 : : * expressional index for which we have statistics, then we treat the
3744 : : * whole expression as though it were just a Var.
3745 : : * 3. If the list contains Vars of different relations that are known equal
3746 : : * due to equivalence classes, then drop all but one of the Vars from each
3747 : : * known-equal set, keeping the one with smallest estimated # of values
3748 : : * (since the extra values of the others can't appear in joined rows).
3749 : : * Note the reason we only consider Vars of different relations is that
3750 : : * if we considered ones of the same rel, we'd be double-counting the
3751 : : * restriction selectivity of the equality in the next step.
3752 : : * 4. For Vars within a single source rel, we multiply together the numbers
3753 : : * of values, clamp to the number of rows in the rel (divided by 10 if
3754 : : * more than one Var), and then multiply by a factor based on the
3755 : : * selectivity of the restriction clauses for that rel. When there's
3756 : : * more than one Var, the initial product is probably too high (it's the
3757 : : * worst case) but clamping to a fraction of the rel's rows seems to be a
3758 : : * helpful heuristic for not letting the estimate get out of hand. (The
3759 : : * factor of 10 is derived from pre-Postgres-7.4 practice.) The factor
3760 : : * we multiply by to adjust for the restriction selectivity assumes that
3761 : : * the restriction clauses are independent of the grouping, which may not
3762 : : * be a valid assumption, but it's hard to do better.
3763 : : * 5. If there are Vars from multiple rels, we repeat step 4 for each such
3764 : : * rel, and multiply the results together.
3765 : : * Note that rels not containing grouped Vars are ignored completely, as are
3766 : : * join clauses. Such rels cannot increase the number of groups, and we
3767 : : * assume such clauses do not reduce the number either (somewhat bogus,
3768 : : * but we don't have the info to do better).
3769 : : */
3770 : : double
3956 andres@anarazel.de 3771 : 201440 : estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
3772 : : List **pgset, EstimationInfo *estinfo)
3773 : : {
1259 tgl@sss.pgh.pa.us 3774 : 201440 : List *varinfos = NIL;
3032 3775 : 201440 : double srf_multiplier = 1.0;
3776 : : double numdistinct;
3777 : : ListCell *l;
3778 : : int i;
3779 : :
3780 : : /* Zero the estinfo output parameter, if non-NULL */
1811 drowley@postgresql.o 3781 [ + + ]: 201440 : if (estinfo != NULL)
3782 : 175852 : memset(estinfo, 0, sizeof(EstimationInfo));
3783 : :
3784 : : /*
3785 : : * We don't ever want to return an estimate of zero groups, as that tends
3786 : : * to lead to division-by-zero and other unpleasantness. The input_rows
3787 : : * estimate is usually already at least 1, but clamp it just in case it
3788 : : * isn't.
3789 : : */
4692 tgl@sss.pgh.pa.us 3790 : 201440 : input_rows = clamp_row_est(input_rows);
3791 : :
3792 : : /*
3793 : : * If no grouping columns, there's exactly one group. (This can't happen
3794 : : * for normal cases with GROUP BY or DISTINCT, but it is possible for
3795 : : * corner cases with set operations.)
3796 : : */
1306 3797 [ + + + + : 201440 : if (groupExprs == NIL || (pgset && *pgset == NIL))
+ + ]
5088 3798 : 586 : return 1.0;
3799 : :
3800 : : /*
3801 : : * Count groups derived from boolean grouping expressions. For other
3802 : : * expressions, find the unique Vars used, treating an expression as a Var
3803 : : * if we can find stats for it. For each one, record the statistical
3804 : : * estimate of number of distinct values (total in its table, without
3805 : : * regard for filtering).
3806 : : */
6460 3807 : 200854 : numdistinct = 1.0;
3808 : :
1259 3809 : 200854 : i = 0;
8455 3810 [ + - + + : 433144 : foreach(l, groupExprs)
+ + ]
3811 : : {
3812 : 232314 : Node *groupexpr = (Node *) lfirst(l);
3813 : : double this_srf_multiplier;
3814 : : VariableStatData vardata;
3815 : : List *varshere;
3816 : : ListCell *l2;
3817 : :
3818 : : /* is expression in this grouping set? */
3956 andres@anarazel.de 3819 [ + + + + ]: 232314 : if (pgset && !list_member_int(*pgset, i++))
3820 : 193345 : continue;
3821 : :
3822 : : /*
3823 : : * Set-returning functions in grouping columns are a bit problematic.
3824 : : * The code below will effectively ignore their SRF nature and come up
3825 : : * with a numdistinct estimate as though they were scalar functions.
3826 : : * We compensate by scaling up the end result by the largest SRF
3827 : : * rowcount estimate. (This will be an overestimate if the SRF
3828 : : * produces multiple copies of any output value, but it seems best to
3829 : : * assume the SRF's outputs are distinct. In any case, it's probably
3830 : : * pointless to worry too much about this without much better
3831 : : * estimates for SRF output rowcounts than we have today.)
3832 : : */
2591 tgl@sss.pgh.pa.us 3833 : 231908 : this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
3032 3834 [ + + ]: 231908 : if (srf_multiplier < this_srf_multiplier)
3835 : 78 : srf_multiplier = this_srf_multiplier;
3836 : :
3837 : : /* Short-circuit for expressions returning boolean */
6460 3838 [ + + ]: 231908 : if (exprType(groupexpr) == BOOLOID)
3839 : : {
3840 : 183 : numdistinct *= 2.0;
3841 : 183 : continue;
3842 : : }
3843 : :
3844 : : /*
3845 : : * If examine_variable is able to deduce anything about the GROUP BY
3846 : : * expression, treat it as a single variable even if it's really more
3847 : : * complicated.
3848 : : *
3849 : : * XXX This has the consequence that if there's a statistics object on
3850 : : * the expression, we don't split it into individual Vars. This
3851 : : * affects our selection of statistics in
3852 : : * estimate_multivariate_ndistinct, because it's probably better to
3853 : : * use more accurate estimate for each expression and treat them as
3854 : : * independent, than to combine estimates for the extracted variables
3855 : : * when we don't know how that relates to the expressions.
3856 : : */
7848 3857 : 231725 : examine_variable(root, groupexpr, 0, &vardata);
6377 3858 [ + + + + ]: 231725 : if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
3859 : : {
7848 3860 : 192436 : varinfos = add_unique_group_var(root, varinfos,
3861 : : groupexpr, &vardata);
3862 [ + + ]: 192436 : ReleaseVariableStats(vardata);
3863 : 192436 : continue;
3864 : : }
3865 [ - + ]: 39289 : ReleaseVariableStats(vardata);
3866 : :
3867 : : /*
3868 : : * Else pull out the component Vars. Handle PlaceHolderVars by
3869 : : * recursing into their arguments (effectively assuming that the
3870 : : * PlaceHolderVar doesn't change the number of groups, which boils
3871 : : * down to ignoring the possible addition of nulls to the result set).
3872 : : */
5360 3873 : 39289 : varshere = pull_var_clause(groupexpr,
3874 : : PVC_RECURSE_AGGREGATES |
3875 : : PVC_RECURSE_WINDOWFUNCS |
3876 : : PVC_RECURSE_PLACEHOLDERS);
3877 : :
3878 : : /*
3879 : : * If we find any variable-free GROUP BY item, then either it is a
3880 : : * constant (and we can ignore it) or it contains a volatile function;
3881 : : * in the latter case we punt and assume that each input row will
3882 : : * yield a distinct group.
3883 : : */
8517 3884 [ + + ]: 39289 : if (varshere == NIL)
3885 : : {
3886 [ + + ]: 344 : if (contain_volatile_functions(groupexpr))
3887 : 24 : return input_rows;
3888 : 320 : continue;
3889 : : }
3890 : :
3891 : : /*
3892 : : * Else add variables to varinfos list
3893 : : */
7848 3894 [ + - + + : 79526 : foreach(l2, varshere)
+ + ]
3895 : : {
3896 : 40581 : Node *var = (Node *) lfirst(l2);
3897 : :
3898 : 40581 : examine_variable(root, var, 0, &vardata);
3899 : 40581 : varinfos = add_unique_group_var(root, varinfos, var, &vardata);
3900 [ + + ]: 40581 : ReleaseVariableStats(vardata);
3901 : : }
3902 : : }
3903 : :
3904 : : /*
3905 : : * If now no Vars, we must have an all-constant or all-boolean GROUP BY
3906 : : * list.
3907 : : */
3908 [ + + ]: 200830 : if (varinfos == NIL)
3909 : : {
3910 : : /* Apply SRF multiplier as we would do in the long path */
3032 3911 : 235 : numdistinct *= srf_multiplier;
3912 : : /* Round off */
3913 : 235 : numdistinct = ceil(numdistinct);
3914 : : /* Guard against out-of-range answers */
6460 3915 [ + + ]: 235 : if (numdistinct > input_rows)
3916 : 43 : numdistinct = input_rows;
3032 3917 [ - + ]: 235 : if (numdistinct < 1.0)
3032 tgl@sss.pgh.pa.us 3918 :UBC 0 : numdistinct = 1.0;
6460 tgl@sss.pgh.pa.us 3919 :CBC 235 : return numdistinct;
3920 : : }
3921 : :
3922 : : /*
3923 : : * Group Vars by relation and estimate total numdistinct.
3924 : : *
3925 : : * For each iteration of the outer loop, we process the frontmost Var in
3926 : : * varinfos, plus all other Vars in the same relation. We remove these
3927 : : * Vars from the newvarinfos list for the next iteration. This is the
3928 : : * easiest way to group Vars of same rel together.
3929 : : */
3930 : : do
3931 : : {
7848 3932 : 202065 : GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
3933 : 202065 : RelOptInfo *rel = varinfo1->rel;
3278 alvherre@alvh.no-ip. 3934 : 202065 : double reldistinct = 1;
7712 tgl@sss.pgh.pa.us 3935 : 202065 : double relmaxndistinct = reldistinct;
3275 alvherre@alvh.no-ip. 3936 : 202065 : int relvarcount = 0;
8259 bruce@momjian.us 3937 : 202065 : List *newvarinfos = NIL;
3278 alvherre@alvh.no-ip. 3938 : 202065 : List *relvarinfos = NIL;
3939 : :
3940 : : /*
3941 : : * Split the list of varinfos in two - one for the current rel, one
3942 : : * for remaining Vars on other rels.
3943 : : */
2433 tgl@sss.pgh.pa.us 3944 : 202065 : relvarinfos = lappend(relvarinfos, varinfo1);
1994 3945 [ + - + + : 234605 : for_each_from(l, varinfos, 1)
+ + ]
3946 : : {
7848 3947 : 32540 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3948 : :
3949 [ + + ]: 32540 : if (varinfo2->rel == varinfo1->rel)
3950 : : {
3951 : : /* varinfos on current rel */
2433 3952 : 30365 : relvarinfos = lappend(relvarinfos, varinfo2);
3953 : : }
3954 : : else
3955 : : {
3956 : : /* not time to process varinfo2 yet */
3957 : 2175 : newvarinfos = lappend(newvarinfos, varinfo2);
3958 : : }
3959 : : }
3960 : :
3961 : : /*
3962 : : * Get the numdistinct estimate for the Vars of this rel. We
3963 : : * iteratively search for multivariate n-distinct with maximum number
3964 : : * of vars; assuming that each var group is independent of the others,
3965 : : * we multiply them together. Any remaining relvarinfos after no more
3966 : : * multivariate matches are found are assumed independent too, so
3967 : : * their individual ndistinct estimates are multiplied also.
3968 : : *
3969 : : * While iterating, count how many separate numdistinct values we
3970 : : * apply. We apply a fudge factor below, but only if we multiplied
3971 : : * more than one such values.
3972 : : */
3278 alvherre@alvh.no-ip. 3973 [ + + ]: 404193 : while (relvarinfos)
3974 : : {
3975 : : double mvndistinct;
3976 : :
3977 [ + + ]: 202128 : if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
3978 : : &mvndistinct))
3979 : : {
3980 : 207 : reldistinct *= mvndistinct;
3981 [ + + ]: 207 : if (relmaxndistinct < mvndistinct)
3982 : 201 : relmaxndistinct = mvndistinct;
3275 3983 : 207 : relvarcount++;
3984 : : }
3985 : : else
3986 : : {
3224 bruce@momjian.us 3987 [ + - + + : 433913 : foreach(l, relvarinfos)
+ + ]
3988 : : {
3278 alvherre@alvh.no-ip. 3989 : 231992 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3990 : :
3991 : 231992 : reldistinct *= varinfo2->ndistinct;
3992 [ + + ]: 231992 : if (relmaxndistinct < varinfo2->ndistinct)
3993 : 206141 : relmaxndistinct = varinfo2->ndistinct;
3994 : 231992 : relvarcount++;
3995 : :
3996 : : /*
3997 : : * When varinfo2's isdefault is set then we'd better set
3998 : : * the SELFLAG_USED_DEFAULT bit in the EstimationInfo.
3999 : : */
1811 drowley@postgresql.o 4000 [ + + + + ]: 231992 : if (estinfo != NULL && varinfo2->isdefault)
4001 : 9453 : estinfo->flags |= SELFLAG_USED_DEFAULT;
4002 : : }
4003 : :
4004 : : /* we're done with this relation */
3278 alvherre@alvh.no-ip. 4005 : 201921 : relvarinfos = NIL;
4006 : : }
4007 : : }
4008 : :
4009 : : /*
4010 : : * Sanity check --- don't divide by zero if empty relation.
4011 : : */
3268 rhaas@postgresql.org 4012 [ + + - + ]: 202065 : Assert(IS_SIMPLE_REL(rel));
8186 tgl@sss.pgh.pa.us 4013 [ + + ]: 202065 : if (rel->tuples > 0)
4014 : : {
4015 : : /*
4016 : : * Clamp to size of rel, or size of rel / 10 if multiple Vars. The
4017 : : * fudge factor is because the Vars are probably correlated but we
4018 : : * don't know by how much. We should never clamp to less than the
4019 : : * largest ndistinct value for any of the Vars, though, since
4020 : : * there will surely be at least that many groups.
4021 : : */
7716 4022 : 201542 : double clamp = rel->tuples;
4023 : :
4024 [ + + ]: 201542 : if (relvarcount > 1)
4025 : : {
4026 : 27615 : clamp *= 0.1;
7712 4027 [ + + ]: 27615 : if (clamp < relmaxndistinct)
4028 : : {
4029 : 26273 : clamp = relmaxndistinct;
4030 : : /* for sanity in case some ndistinct is too large: */
4031 [ + + ]: 26273 : if (clamp > rel->tuples)
4032 : 39 : clamp = rel->tuples;
4033 : : }
4034 : : }
7716 4035 [ + + ]: 201542 : if (reldistinct > clamp)
4036 : 22474 : reldistinct = clamp;
4037 : :
4038 : : /*
4039 : : * Update the estimate based on the restriction selectivity,
4040 : : * guarding against division by zero when reldistinct is zero.
4041 : : * Also skip this if we know that we are returning all rows.
4042 : : */
3632 dean.a.rasheed@gmail 4043 [ + - + + ]: 201542 : if (reldistinct > 0 && rel->rows < rel->tuples)
4044 : : {
4045 : : /*
4046 : : * Given a table containing N rows with n distinct values in a
4047 : : * uniform distribution, if we select p rows at random then
4048 : : * the expected number of distinct values selected is
4049 : : *
4050 : : * n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
4051 : : *
4052 : : * = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
4053 : : *
4054 : : * See "Approximating block accesses in database
4055 : : * organizations", S. B. Yao, Communications of the ACM,
4056 : : * Volume 20 Issue 4, April 1977 Pages 260-261.
4057 : : *
4058 : : * Alternatively, re-arranging the terms from the factorials,
4059 : : * this may be written as
4060 : : *
4061 : : * n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
4062 : : *
4063 : : * This form of the formula is more efficient to compute in
4064 : : * the common case where p is larger than N/n. Additionally,
4065 : : * as pointed out by Dell'Era, if i << N for all terms in the
4066 : : * product, it can be approximated by
4067 : : *
4068 : : * n * (1 - ((N-p)/N)^(N/n))
4069 : : *
4070 : : * See "Expected distinct values when selecting from a bag
4071 : : * without replacement", Alberto Dell'Era,
4072 : : * http://www.adellera.it/investigations/distinct_balls/.
4073 : : *
4074 : : * The condition i << N is equivalent to n >> 1, so this is a
4075 : : * good approximation when the number of distinct values in
4076 : : * the table is large. It turns out that this formula also
4077 : : * works well even when n is small.
4078 : : */
4079 : 67829 : reldistinct *=
4080 : 67829 : (1 - pow((rel->tuples - rel->rows) / rel->tuples,
4081 : 67829 : rel->tuples / reldistinct));
4082 : : }
4083 : 201542 : reldistinct = clamp_row_est(reldistinct);
4084 : :
4085 : : /*
4086 : : * Update estimate of total distinct groups.
4087 : : */
8186 tgl@sss.pgh.pa.us 4088 : 201542 : numdistinct *= reldistinct;
4089 : : }
4090 : :
8517 4091 : 202065 : varinfos = newvarinfos;
4092 [ + + ]: 202065 : } while (varinfos != NIL);
4093 : :
4094 : : /* Now we can account for the effects of any SRFs */
3032 4095 : 200595 : numdistinct *= srf_multiplier;
4096 : :
4097 : : /* Round off */
8448 4098 : 200595 : numdistinct = ceil(numdistinct);
4099 : :
4100 : : /* Guard against out-of-range answers */
8517 4101 [ + + ]: 200595 : if (numdistinct > input_rows)
4102 : 42900 : numdistinct = input_rows;
4103 [ - + ]: 200595 : if (numdistinct < 1.0)
8517 tgl@sss.pgh.pa.us 4104 :UBC 0 : numdistinct = 1.0;
4105 : :
8517 tgl@sss.pgh.pa.us 4106 :CBC 200595 : return numdistinct;
4107 : : }
4108 : :
4109 : : /*
4110 : : * Try to estimate the bucket size of the hash join inner side when the join
4111 : : * condition contains two or more clauses by employing extended statistics.
4112 : : *
4113 : : * The main idea of this approach is that the distinct value generated by
4114 : : * multivariate estimation on two or more columns would provide less bucket size
4115 : : * than estimation on one separate column.
4116 : : *
4117 : : * IMPORTANT: It is crucial to synchronize the approach of combining different
4118 : : * estimations with the caller's method.
4119 : : *
4120 : : * Return a list of clauses that didn't fetch any extended statistics.
4121 : : */
4122 : : List *
370 akorotkov@postgresql 4123 : 252574 : estimate_multivariate_bucketsize(PlannerInfo *root, RelOptInfo *inner,
4124 : : List *hashclauses,
4125 : : Selectivity *innerbucketsize)
4126 : : {
4127 : : List *clauses;
4128 : : List *otherclauses;
4129 : : double ndistinct;
4130 : :
4131 [ + + ]: 252574 : if (list_length(hashclauses) <= 1)
4132 : : {
4133 : : /*
4134 : : * Nothing to do for a single clause. Could we employ univariate
4135 : : * extended stat here?
4136 : : */
4137 : 232162 : return hashclauses;
4138 : : }
4139 : :
4140 : : /* "clauses" is the list of hashclauses we've not dealt with yet */
239 tgl@sss.pgh.pa.us 4141 :GNC 20412 : clauses = list_copy(hashclauses);
4142 : : /* "otherclauses" holds clauses we are going to return to caller */
4143 : 20412 : otherclauses = NIL;
4144 : : /* current estimate of ndistinct */
4145 : 20412 : ndistinct = 1.0;
370 akorotkov@postgresql 4146 [ + + ]:CBC 40830 : while (clauses != NIL)
4147 : : {
4148 : : ListCell *lc;
4149 : 20418 : int relid = -1;
4150 : 20418 : List *varinfos = NIL;
4151 : 20418 : List *origin_rinfos = NIL;
4152 : : double mvndistinct;
4153 : : List *origin_varinfos;
4154 : 20418 : int group_relid = -1;
4155 : 20418 : RelOptInfo *group_rel = NULL;
4156 : : ListCell *lc1,
4157 : : *lc2;
4158 : :
4159 : : /*
4160 : : * Find clauses, referencing the same single base relation and try to
4161 : : * estimate such a group with extended statistics. Create varinfo for
4162 : : * an approved clause, push it to otherclauses, if it can't be
4163 : : * estimated here or ignore to process at the next iteration.
4164 : : */
4165 [ + + + + : 61536 : foreach(lc, clauses)
+ + ]
4166 : : {
4167 : 41118 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
4168 : : Node *expr;
4169 : : Relids relids;
4170 : : GroupVarInfo *varinfo;
4171 : :
4172 : : /*
4173 : : * Find the inner side of the join, which we need to estimate the
4174 : : * number of buckets. Use outer_is_left because the
4175 : : * clause_sides_match_join routine has called on hash clauses.
4176 : : */
4177 : 82236 : relids = rinfo->outer_is_left ?
4178 [ + + ]: 41118 : rinfo->right_relids : rinfo->left_relids;
4179 : 82236 : expr = rinfo->outer_is_left ?
4180 [ + + ]: 41118 : get_rightop(rinfo->clause) : get_leftop(rinfo->clause);
4181 : :
4182 [ + + ]: 41118 : if (bms_get_singleton_member(relids, &relid) &&
4183 [ + + ]: 39595 : root->simple_rel_array[relid]->statlist != NIL)
4184 : 24 : {
326 4185 : 30 : bool is_duplicate = false;
4186 : :
4187 : : /*
4188 : : * This inner-side expression references only one relation.
4189 : : * Extended statistics on this clause can exist.
4190 : : */
370 4191 [ + + ]: 30 : if (group_relid < 0)
4192 : : {
4193 : 15 : RangeTblEntry *rte = root->simple_rte_array[relid];
4194 : :
4195 [ + - - + ]: 15 : if (!rte || (rte->relkind != RELKIND_RELATION &&
370 akorotkov@postgresql 4196 [ # # ]:UBC 0 : rte->relkind != RELKIND_MATVIEW &&
4197 [ # # ]: 0 : rte->relkind != RELKIND_FOREIGN_TABLE &&
4198 [ # # ]: 0 : rte->relkind != RELKIND_PARTITIONED_TABLE))
4199 : : {
4200 : : /* Extended statistics can't exist in principle */
4201 : 0 : otherclauses = lappend(otherclauses, rinfo);
4202 : 0 : clauses = foreach_delete_current(clauses, lc);
4203 : 0 : continue;
4204 : : }
4205 : :
370 akorotkov@postgresql 4206 :CBC 15 : group_relid = relid;
4207 : 15 : group_rel = root->simple_rel_array[relid];
4208 : : }
4209 [ - + ]: 15 : else if (group_relid != relid)
4210 : : {
4211 : : /*
4212 : : * Being in the group forming state we don't need other
4213 : : * clauses.
4214 : : */
370 akorotkov@postgresql 4215 :UBC 0 : continue;
4216 : : }
4217 : :
4218 : : /*
4219 : : * We're going to add the new clause to the varinfos list. We
4220 : : * might re-use add_unique_group_var(), but we don't do so for
4221 : : * two reasons.
4222 : : *
4223 : : * 1) We must keep the origin_rinfos list ordered exactly the
4224 : : * same way as varinfos.
4225 : : *
4226 : : * 2) add_unique_group_var() is designed for
4227 : : * estimate_num_groups(), where a larger number of groups is
4228 : : * worse. While estimating the number of hash buckets, we
4229 : : * have the opposite: a lesser number of groups is worse.
4230 : : * Therefore, we don't have to remove "known equal" vars: the
4231 : : * removed var may valuably contribute to the multivariate
4232 : : * statistics to grow the number of groups.
4233 : : */
4234 : :
4235 : : /*
4236 : : * Clear nullingrels to correctly match hash keys. See
4237 : : * add_unique_group_var()'s comment for details.
4238 : : */
326 akorotkov@postgresql 4239 :CBC 30 : expr = remove_nulling_relids(expr, root->outer_join_rels, NULL);
4240 : :
4241 : : /*
4242 : : * Detect and exclude exact duplicates from the list of hash
4243 : : * keys (like add_unique_group_var does).
4244 : : */
4245 [ + + + + : 42 : foreach(lc1, varinfos)
+ + ]
4246 : : {
4247 : 18 : varinfo = (GroupVarInfo *) lfirst(lc1);
4248 : :
4249 [ + + ]: 18 : if (!equal(expr, varinfo->var))
4250 : 12 : continue;
4251 : :
4252 : 6 : is_duplicate = true;
4253 : 6 : break;
4254 : : }
4255 : :
4256 [ + + ]: 30 : if (is_duplicate)
4257 : : {
4258 : : /*
4259 : : * Skip exact duplicates. Adding them to the otherclauses
4260 : : * list also doesn't make sense.
4261 : : */
4262 : 6 : continue;
4263 : : }
4264 : :
4265 : : /*
4266 : : * Initialize GroupVarInfo. We only use it to call
4267 : : * estimate_multivariate_ndistinct(), which doesn't care about
4268 : : * ndistinct and isdefault fields. Thus, skip these fields.
4269 : : */
95 michael@paquier.xyz 4270 :GNC 24 : varinfo = palloc0_object(GroupVarInfo);
370 akorotkov@postgresql 4271 :CBC 24 : varinfo->var = expr;
4272 : 24 : varinfo->rel = root->simple_rel_array[relid];
4273 : 24 : varinfos = lappend(varinfos, varinfo);
4274 : :
4275 : : /*
4276 : : * Remember the link to RestrictInfo for the case the clause
4277 : : * is failed to be estimated.
4278 : : */
4279 : 24 : origin_rinfos = lappend(origin_rinfos, rinfo);
4280 : : }
4281 : : else
4282 : : {
4283 : : /* This clause can't be estimated with extended statistics */
4284 : 41088 : otherclauses = lappend(otherclauses, rinfo);
4285 : : }
4286 : :
4287 : 41112 : clauses = foreach_delete_current(clauses, lc);
4288 : : }
4289 : :
4290 [ + + ]: 20418 : if (list_length(varinfos) < 2)
4291 : : {
4292 : : /*
4293 : : * Multivariate statistics doesn't apply to single columns except
4294 : : * for expressions, but it has not been implemented yet.
4295 : : */
4296 : 20412 : otherclauses = list_concat(otherclauses, origin_rinfos);
4297 : 20412 : list_free_deep(varinfos);
4298 : 20412 : list_free(origin_rinfos);
4299 : 20412 : continue;
4300 : : }
4301 : :
4302 [ - + ]: 6 : Assert(group_rel != NULL);
4303 : :
4304 : : /* Employ the extended statistics. */
4305 : 6 : origin_varinfos = varinfos;
4306 : : for (;;)
4307 : 6 : {
4308 : 12 : bool estimated = estimate_multivariate_ndistinct(root,
4309 : : group_rel,
4310 : : &varinfos,
4311 : : &mvndistinct);
4312 : :
4313 [ + + ]: 12 : if (!estimated)
4314 : 6 : break;
4315 : :
4316 : : /*
4317 : : * We've got an estimation. Use ndistinct value in a consistent
4318 : : * way - according to the caller's logic (see
4319 : : * final_cost_hashjoin).
4320 : : */
4321 [ + - ]: 6 : if (ndistinct < mvndistinct)
4322 : 6 : ndistinct = mvndistinct;
4323 [ - + ]: 6 : Assert(ndistinct >= 1.0);
4324 : : }
4325 : :
4326 [ - + ]: 6 : Assert(list_length(origin_varinfos) == list_length(origin_rinfos));
4327 : :
4328 : : /* Collect unmatched clauses as otherclauses. */
4329 [ + - + + : 21 : forboth(lc1, origin_varinfos, lc2, origin_rinfos)
+ - + + +
+ + - +
+ ]
4330 : : {
4331 : 15 : GroupVarInfo *vinfo = lfirst(lc1);
4332 : :
4333 [ + - ]: 15 : if (!list_member_ptr(varinfos, vinfo))
4334 : : /* Already estimated */
4335 : 15 : continue;
4336 : :
4337 : : /* Can't be estimated here - push to the returning list */
370 akorotkov@postgresql 4338 :UBC 0 : otherclauses = lappend(otherclauses, lfirst(lc2));
4339 : : }
4340 : : }
4341 : :
370 akorotkov@postgresql 4342 :CBC 20412 : *innerbucketsize = 1.0 / ndistinct;
4343 : 20412 : return otherclauses;
4344 : : }
4345 : :
4346 : : /*
4347 : : * Estimate hash bucket statistics when the specified expression is used
4348 : : * as a hash key for the given number of buckets.
4349 : : *
4350 : : * This attempts to determine two values:
4351 : : *
4352 : : * 1. The frequency of the most common value of the expression (returns
4353 : : * zero into *mcv_freq if we can't get that). This will be frequency
4354 : : * relative to the entire underlying table.
4355 : : *
4356 : : * 2. The "bucketsize fraction", ie, average number of entries in a bucket
4357 : : * divided by total number of tuples to be hashed.
4358 : : *
4359 : : * XXX This is really pretty bogus since we're effectively assuming that the
4360 : : * distribution of hash keys will be the same after applying restriction
4361 : : * clauses as it was in the underlying relation. However, we are not nearly
4362 : : * smart enough to figure out how the restrict clauses might change the
4363 : : * distribution, so this will have to do for now.
4364 : : *
4365 : : * We are passed the number of buckets the executor will use for the given
4366 : : * input relation. If the data were perfectly distributed, with the same
4367 : : * number of tuples going into each available bucket, then the bucketsize
4368 : : * fraction would be 1/nbuckets. But this happy state of affairs will occur
4369 : : * only if (a) there are at least nbuckets distinct data values, and (b)
4370 : : * we have a not-too-skewed data distribution. Otherwise the buckets will
4371 : : * be nonuniformly occupied. If the other relation in the join has a key
4372 : : * distribution similar to this one's, then the most-loaded buckets are
4373 : : * exactly those that will be probed most often. Therefore, the "average"
4374 : : * bucket size for costing purposes should really be taken as something close
4375 : : * to the "worst case" bucket size. We try to estimate this by adjusting the
4376 : : * fraction if there are too few distinct data values, and then clamping to
4377 : : * at least the bucket size implied by the most common value's frequency.
4378 : : *
4379 : : * If no statistics are available, use a default estimate of 0.1. This will
4380 : : * discourage use of a hash rather strongly if the inner relation is large,
4381 : : * which is what we want. We do not want to hash unless we know that the
4382 : : * inner rel is well-dispersed (or the alternatives seem much worse).
4383 : : *
4384 : : * The caller should also check that the mcv_freq is not so large that the
4385 : : * most common value would by itself require an impractically large bucket.
4386 : : * In a hash join, the executor can split buckets if they get too big, but
4387 : : * obviously that doesn't help for a bucket that contains many duplicates of
4388 : : * the same value.
4389 : : */
4390 : : void
3134 tgl@sss.pgh.pa.us 4391 : 116095 : estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
4392 : : Selectivity *mcv_freq,
4393 : : Selectivity *bucketsize_frac)
4394 : : {
4395 : : VariableStatData vardata;
4396 : : double estfract,
4397 : : ndistinct;
4398 : : bool isdefault;
4399 : : AttStatsSlot sslot;
4400 : :
8062 4401 : 116095 : examine_variable(root, hashkey, 0, &vardata);
4402 : :
4403 : : /* Initialize *mcv_freq to "unknown" */
3134 4404 : 116095 : *mcv_freq = 0.0;
4405 : :
4406 : : /* Look up the frequency of the most common value, if available */
4407 [ + + ]: 116095 : if (HeapTupleIsValid(vardata.statsTuple))
4408 : : {
4409 [ + + ]: 81066 : if (get_attstatsslot(&sslot, vardata.statsTuple,
4410 : : STATISTIC_KIND_MCV, InvalidOid,
4411 : : ATTSTATSSLOT_NUMBERS))
4412 : : {
4413 : : /*
4414 : : * The first MCV stat is for the most common value.
4415 : : */
4416 [ + - ]: 46551 : if (sslot.nnumbers > 0)
4417 : 46551 : *mcv_freq = sslot.numbers[0];
4418 : 46551 : free_attstatsslot(&sslot);
4419 : : }
76 tgl@sss.pgh.pa.us 4420 [ + + ]:GNC 34515 : else if (get_attstatsslot(&sslot, vardata.statsTuple,
4421 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
4422 : : 0))
4423 : : {
4424 : : /*
4425 : : * If there are no recorded MCVs, but we do have a histogram, then
4426 : : * assume that ANALYZE determined that the column is unique.
4427 : : */
14 4428 [ + - + + ]: 33174 : if (vardata.rel && vardata.rel->tuples > 0)
4429 : 33165 : *mcv_freq = 1.0 / vardata.rel->tuples;
4430 : : }
4431 : : }
4432 : :
4433 : : /* Get number of distinct values */
5306 tgl@sss.pgh.pa.us 4434 :CBC 116095 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
4435 : :
4436 : : /*
4437 : : * If ndistinct isn't real, punt. We normally return 0.1, but if the
4438 : : * mcv_freq is known to be even higher than that, use it instead.
4439 : : */
4440 [ + + ]: 116095 : if (isdefault)
4441 : : {
3134 4442 [ + - ]: 16572 : *bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
5306 4443 [ + + ]: 16572 : ReleaseVariableStats(vardata);
3134 4444 : 16572 : return;
4445 : : }
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 : : */
3640 4455 [ + - + + ]: 99523 : if (vardata.rel && vardata.rel->tuples > 0)
4456 : : {
8062 4457 : 99497 : ndistinct *= vardata.rel->rows / vardata.rel->tuples;
3640 4458 : 99497 : 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 : : */
7679 4466 [ + + ]: 99523 : if (ndistinct > nbuckets)
4467 : 33 : estfract = 1.0 / nbuckets;
4468 : : else
8062 4469 : 99490 : estfract = 1.0 / ndistinct;
4470 : :
4471 : : /*
4472 : : * Clamp the bucketsize fraction to be not less than the MCV frequency,
4473 : : * since whichever bucket the MCV values end up in will have at least that
4474 : : * size. This has no effect if *mcv_freq is still zero.
4475 : : */
11 tgl@sss.pgh.pa.us 4476 [ + + ]:GNC 99523 : estfract = Max(estfract, *mcv_freq);
4477 : :
3134 tgl@sss.pgh.pa.us 4478 :CBC 99523 : *bucketsize_frac = (Selectivity) estfract;
4479 : :
4480 [ + + ]: 99523 : ReleaseVariableStats(vardata);
4481 : : }
4482 : :
4483 : : /*
4484 : : * estimate_hashagg_tablesize
4485 : : * estimate the number of bytes that a hash aggregate hashtable will
4486 : : * require based on the agg_costs, path width and number of groups.
4487 : : *
4488 : : * We return the result as "double" to forestall any possible overflow
4489 : : * problem in the multiplication by dNumGroups.
4490 : : *
4491 : : * XXX this may be over-estimating the size now that hashagg knows to omit
4492 : : * unneeded columns from the hashtable. Also for mixed-mode grouping sets,
4493 : : * grouping columns not in the hashed set are counted here even though hashagg
4494 : : * won't store them. Is this a problem?
4495 : : */
4496 : : double
1937 heikki.linnakangas@i 4497 : 1332 : estimate_hashagg_tablesize(PlannerInfo *root, Path *path,
4498 : : const AggClauseCosts *agg_costs, double dNumGroups)
4499 : : {
4500 : : Size hashentrysize;
4501 : :
4502 : 1332 : hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
4503 : 1332 : path->pathtarget->width,
4504 : 1332 : agg_costs->transitionSpace);
4505 : :
4506 : : /*
4507 : : * Note that this disregards the effect of fill-factor and growth policy
4508 : : * of the hash table. That's probably ok, given that the default
4509 : : * fill-factor is relatively high. It'd be hard to meaningfully factor in
4510 : : * "double-in-size" growth policies here.
4511 : : */
2579 tgl@sss.pgh.pa.us 4512 : 1332 : return hashentrysize * dNumGroups;
4513 : : }
4514 : :
4515 : :
4516 : : /*-------------------------------------------------------------------------
4517 : : *
4518 : : * Support routines
4519 : : *
4520 : : *-------------------------------------------------------------------------
4521 : : */
4522 : :
4523 : : /*
4524 : : * Find the best matching ndistinct extended statistics for the given list of
4525 : : * GroupVarInfos.
4526 : : *
4527 : : * Callers must ensure that the given GroupVarInfos all belong to 'rel' and
4528 : : * the GroupVarInfos list does not contain any duplicate Vars or expressions.
4529 : : *
4530 : : * When statistics are found that match > 1 of the given GroupVarInfo, the
4531 : : * *ndistinct parameter is set according to the ndistinct estimate and a new
4532 : : * list is built with the matching GroupVarInfos removed, which is output via
4533 : : * the *varinfos parameter before returning true. When no matching stats are
4534 : : * found, false is returned and the *varinfos and *ndistinct parameters are
4535 : : * left untouched.
4536 : : */
4537 : : static bool
3278 alvherre@alvh.no-ip. 4538 : 202140 : estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
4539 : : List **varinfos, double *ndistinct)
4540 : : {
4541 : : ListCell *lc;
4542 : : int nmatches_vars;
4543 : : int nmatches_exprs;
4544 : 202140 : Oid statOid = InvalidOid;
4545 : : MVNDistinct *stats;
1815 tomas.vondra@postgre 4546 : 202140 : StatisticExtInfo *matched_info = NULL;
1230 tgl@sss.pgh.pa.us 4547 [ + - ]: 202140 : RangeTblEntry *rte = planner_rt_fetch(rel->relid, root);
4548 : :
4549 : : /* bail out immediately if the table has no extended statistics */
3278 alvherre@alvh.no-ip. 4550 [ + + ]: 202140 : if (!rel->statlist)
4551 : 201857 : return false;
4552 : :
4553 : : /* look for the ndistinct statistics object matching the most vars */
1815 tomas.vondra@postgre 4554 : 283 : nmatches_vars = 0; /* we require at least two matches */
4555 : 283 : nmatches_exprs = 0;
3278 alvherre@alvh.no-ip. 4556 [ + - + + : 1127 : foreach(lc, rel->statlist)
+ + ]
4557 : : {
4558 : : ListCell *lc2;
4559 : 844 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
1815 tomas.vondra@postgre 4560 : 844 : int nshared_vars = 0;
4561 : 844 : int nshared_exprs = 0;
4562 : :
4563 : : /* skip statistics of other kinds */
3278 alvherre@alvh.no-ip. 4564 [ + + ]: 844 : if (info->kind != STATS_EXT_NDISTINCT)
4565 : 399 : continue;
4566 : :
4567 : : /* skip statistics with mismatching stxdinherit value */
1230 tgl@sss.pgh.pa.us 4568 [ + + ]: 445 : if (info->inherit != rte->inh)
4569 : 15 : continue;
4570 : :
4571 : : /*
4572 : : * Determine how many expressions (and variables in non-matched
4573 : : * expressions) match. We'll then use these numbers to pick the
4574 : : * statistics object that best matches the clauses.
4575 : : */
1815 tomas.vondra@postgre 4576 [ + + + + : 1361 : foreach(lc2, *varinfos)
+ + ]
4577 : : {
4578 : : ListCell *lc3;
4579 : 931 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
4580 : : AttrNumber attnum;
4581 : :
4582 [ - + ]: 931 : Assert(varinfo->rel == rel);
4583 : :
4584 : : /* simple Var, search in statistics keys directly */
4585 [ + + ]: 931 : if (IsA(varinfo->var, Var))
4586 : : {
4587 : 748 : attnum = ((Var *) varinfo->var)->varattno;
4588 : :
4589 : : /*
4590 : : * Ignore system attributes - we don't support statistics on
4591 : : * them, so can't match them (and it'd fail as the values are
4592 : : * negative).
4593 : : */
4594 [ + + ]: 748 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4595 : 6 : continue;
4596 : :
4597 [ + + ]: 742 : if (bms_is_member(attnum, info->keys))
4598 : 438 : nshared_vars++;
4599 : :
4600 : 742 : continue;
4601 : : }
4602 : :
4603 : : /* expression - see if it's in the statistics object */
4604 [ + + + + : 330 : foreach(lc3, info->exprs)
+ + ]
4605 : : {
4606 : 264 : Node *expr = (Node *) lfirst(lc3);
4607 : :
4608 [ + + ]: 264 : if (equal(varinfo->var, expr))
4609 : : {
4610 : 117 : nshared_exprs++;
4611 : 117 : break;
4612 : : }
4613 : : }
4614 : : }
4615 : :
4616 : : /*
4617 : : * The ndistinct extended statistics contain estimates for a minimum
4618 : : * of pairs of columns which the statistics are defined on and
4619 : : * certainly not single columns. Here we skip unless we managed to
4620 : : * match to at least two columns.
4621 : : */
4622 [ + + ]: 430 : if (nshared_vars + nshared_exprs < 2)
4623 : 199 : continue;
4624 : :
4625 : : /*
4626 : : * Check if these statistics are a better match than the previous best
4627 : : * match and if so, take note of the StatisticExtInfo.
4628 : : *
4629 : : * The statslist is sorted by statOid, so the StatisticExtInfo we
4630 : : * select as the best match is deterministic even when multiple sets
4631 : : * of statistics match equally as well.
4632 : : */
4633 [ + + + - ]: 231 : if ((nshared_exprs > nmatches_exprs) ||
4634 [ + + ]: 177 : (((nshared_exprs == nmatches_exprs)) && (nshared_vars > nmatches_vars)))
4635 : : {
3278 alvherre@alvh.no-ip. 4636 : 219 : statOid = info->statOid;
1815 tomas.vondra@postgre 4637 : 219 : nmatches_vars = nshared_vars;
4638 : 219 : nmatches_exprs = nshared_exprs;
4639 : 219 : matched_info = info;
4640 : : }
4641 : : }
4642 : :
4643 : : /* No match? */
3278 alvherre@alvh.no-ip. 4644 [ + + ]: 283 : if (statOid == InvalidOid)
4645 : 70 : return false;
4646 : :
1815 tomas.vondra@postgre 4647 [ - + ]: 213 : Assert(nmatches_vars + nmatches_exprs > 1);
4648 : :
1519 4649 : 213 : stats = statext_ndistinct_load(statOid, rte->inh);
4650 : :
4651 : : /*
4652 : : * If we have a match, search it for the specific item that matches (there
4653 : : * must be one), and construct the output values.
4654 : : */
3278 alvherre@alvh.no-ip. 4655 [ + - ]: 213 : if (stats)
4656 : : {
4657 : : int i;
3224 bruce@momjian.us 4658 : 213 : List *newlist = NIL;
3278 alvherre@alvh.no-ip. 4659 : 213 : MVNDistinctItem *item = NULL;
4660 : : ListCell *lc2;
1815 tomas.vondra@postgre 4661 : 213 : Bitmapset *matched = NULL;
4662 : : AttrNumber attnum_offset;
4663 : :
4664 : : /*
4665 : : * How much we need to offset the attnums? If there are no
4666 : : * expressions, no offset is needed. Otherwise offset enough to move
4667 : : * the lowest one (which is equal to number of expressions) to 1.
4668 : : */
4669 [ + + ]: 213 : if (matched_info->exprs)
4670 : 75 : attnum_offset = (list_length(matched_info->exprs) + 1);
4671 : : else
4672 : 138 : attnum_offset = 0;
4673 : :
4674 : : /* see what actually matched */
4675 [ + - + + : 744 : foreach(lc2, *varinfos)
+ + ]
4676 : : {
4677 : : ListCell *lc3;
4678 : : int idx;
4679 : 531 : bool found = false;
4680 : :
4681 : 531 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
4682 : :
4683 : : /*
4684 : : * Process a simple Var expression, by matching it to keys
4685 : : * directly. If there's a matching expression, we'll try matching
4686 : : * it later.
4687 : : */
4688 [ + + ]: 531 : if (IsA(varinfo->var, Var))
4689 : : {
4690 : 438 : AttrNumber attnum = ((Var *) varinfo->var)->varattno;
4691 : :
4692 : : /*
4693 : : * Ignore expressions on system attributes. Can't rely on the
4694 : : * bms check for negative values.
4695 : : */
4696 [ + + ]: 438 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4697 : 3 : continue;
4698 : :
4699 : : /* Is the variable covered by the statistics object? */
4700 [ + + ]: 435 : if (!bms_is_member(attnum, matched_info->keys))
4701 : 60 : continue;
4702 : :
4703 : 375 : attnum = attnum + attnum_offset;
4704 : :
4705 : : /* ensure sufficient offset */
4706 [ - + ]: 375 : Assert(AttrNumberIsForUserDefinedAttr(attnum));
4707 : :
4708 : 375 : matched = bms_add_member(matched, attnum);
4709 : :
4710 : 375 : found = true;
4711 : : }
4712 : :
4713 : : /*
4714 : : * XXX Maybe we should allow searching the expressions even if we
4715 : : * found an attribute matching the expression? That would handle
4716 : : * trivial expressions like "(a)" but it seems fairly useless.
4717 : : */
4718 [ + + ]: 468 : if (found)
4719 : 375 : continue;
4720 : :
4721 : : /* expression - see if it's in the statistics object */
4722 : 93 : idx = 0;
4723 [ + + + + : 153 : foreach(lc3, matched_info->exprs)
+ + ]
4724 : : {
4725 : 138 : Node *expr = (Node *) lfirst(lc3);
4726 : :
4727 [ + + ]: 138 : if (equal(varinfo->var, expr))
4728 : : {
4729 : 78 : AttrNumber attnum = -(idx + 1);
4730 : :
4731 : 78 : attnum = attnum + attnum_offset;
4732 : :
4733 : : /* ensure sufficient offset */
4734 [ - + ]: 78 : Assert(AttrNumberIsForUserDefinedAttr(attnum));
4735 : :
4736 : 78 : matched = bms_add_member(matched, attnum);
4737 : :
4738 : : /* there should be just one matching expression */
4739 : 78 : break;
4740 : : }
4741 : :
4742 : 60 : idx++;
4743 : : }
4744 : : }
4745 : :
4746 : : /* Find the specific item that exactly matches the combination */
3278 alvherre@alvh.no-ip. 4747 [ + - ]: 432 : for (i = 0; i < stats->nitems; i++)
4748 : : {
4749 : : int j;
4750 : 432 : MVNDistinctItem *tmpitem = &stats->items[i];
4751 : :
1815 tomas.vondra@postgre 4752 [ + + ]: 432 : if (tmpitem->nattributes != bms_num_members(matched))
4753 : 81 : continue;
4754 : :
4755 : : /* assume it's the right item */
4756 : 351 : item = tmpitem;
4757 : :
4758 : : /* check that all item attributes/expressions fit the match */
4759 [ + + ]: 846 : for (j = 0; j < tmpitem->nattributes; j++)
4760 : : {
4761 : 633 : AttrNumber attnum = tmpitem->attributes[j];
4762 : :
4763 : : /*
4764 : : * Thanks to how we constructed the matched bitmap above, we
4765 : : * can just offset all attnums the same way.
4766 : : */
4767 : 633 : attnum = attnum + attnum_offset;
4768 : :
4769 [ + + ]: 633 : if (!bms_is_member(attnum, matched))
4770 : : {
4771 : : /* nah, it's not this item */
4772 : 138 : item = NULL;
4773 : 138 : break;
4774 : : }
4775 : : }
4776 : :
4777 : : /*
4778 : : * If the item has all the matched attributes, we know it's the
4779 : : * right one - there can't be a better one. matching more.
4780 : : */
4781 [ + + ]: 351 : if (item)
4782 : 213 : break;
4783 : : }
4784 : :
4785 : : /*
4786 : : * Make sure we found an item. There has to be one, because ndistinct
4787 : : * statistics includes all combinations of attributes.
4788 : : */
3278 alvherre@alvh.no-ip. 4789 [ - + ]: 213 : if (!item)
3278 alvherre@alvh.no-ip. 4790 [ # # ]:UBC 0 : elog(ERROR, "corrupt MVNDistinct entry");
4791 : :
4792 : : /* Form the output varinfo list, keeping only unmatched ones */
3278 alvherre@alvh.no-ip. 4793 [ + - + + :CBC 744 : foreach(lc, *varinfos)
+ + ]
4794 : : {
4795 : 531 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
4796 : : ListCell *lc3;
1815 tomas.vondra@postgre 4797 : 531 : bool found = false;
4798 : :
4799 : : /*
4800 : : * Let's look at plain variables first, because it's the most
4801 : : * common case and the check is quite cheap. We can simply get the
4802 : : * attnum and check (with an offset) matched bitmap.
4803 : : */
4804 [ + + ]: 531 : if (IsA(varinfo->var, Var))
3278 alvherre@alvh.no-ip. 4805 : 435 : {
1815 tomas.vondra@postgre 4806 : 438 : AttrNumber attnum = ((Var *) varinfo->var)->varattno;
4807 : :
4808 : : /*
4809 : : * If it's a system attribute, we're done. We don't support
4810 : : * extended statistics on system attributes, so it's clearly
4811 : : * not matched. Just keep the expression and continue.
4812 : : */
4813 [ + + ]: 438 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4814 : : {
4815 : 3 : newlist = lappend(newlist, varinfo);
4816 : 3 : continue;
4817 : : }
4818 : :
4819 : : /* apply the same offset as above */
4820 : 435 : attnum += attnum_offset;
4821 : :
4822 : : /* if it's not matched, keep the varinfo */
4823 [ + + ]: 435 : if (!bms_is_member(attnum, matched))
4824 : 60 : newlist = lappend(newlist, varinfo);
4825 : :
4826 : : /* The rest of the loop deals with complex expressions. */
3278 alvherre@alvh.no-ip. 4827 : 435 : continue;
4828 : : }
4829 : :
4830 : : /*
4831 : : * Process complex expressions, not just simple Vars.
4832 : : *
4833 : : * First, we search for an exact match of an expression. If we
4834 : : * find one, we can just discard the whole GroupVarInfo, with all
4835 : : * the variables we extracted from it.
4836 : : *
4837 : : * Otherwise we inspect the individual vars, and try matching it
4838 : : * to variables in the item.
4839 : : */
1815 tomas.vondra@postgre 4840 [ + + + + : 153 : foreach(lc3, matched_info->exprs)
+ + ]
4841 : : {
4842 : 138 : Node *expr = (Node *) lfirst(lc3);
4843 : :
4844 [ + + ]: 138 : if (equal(varinfo->var, expr))
4845 : : {
4846 : 78 : found = true;
4847 : 78 : break;
4848 : : }
4849 : : }
4850 : :
4851 : : /* found exact match, skip */
4852 [ + + ]: 93 : if (found)
2311 4853 : 78 : continue;
4854 : :
1815 4855 : 15 : newlist = lappend(newlist, varinfo);
4856 : : }
4857 : :
3278 alvherre@alvh.no-ip. 4858 : 213 : *varinfos = newlist;
4859 : 213 : *ndistinct = item->ndistinct;
4860 : 213 : return true;
4861 : : }
4862 : :
3278 alvherre@alvh.no-ip. 4863 :UBC 0 : return false;
4864 : : }
4865 : :
4866 : : /*
4867 : : * convert_to_scalar
4868 : : * Convert non-NULL values of the indicated types to the comparison
4869 : : * scale needed by scalarineqsel().
4870 : : * Returns "true" if successful.
4871 : : *
4872 : : * XXX this routine is a hack: ideally we should look up the conversion
4873 : : * subroutines in pg_type.
4874 : : *
4875 : : * All numeric datatypes are simply converted to their equivalent
4876 : : * "double" values. (NUMERIC values that are outside the range of "double"
4877 : : * are clamped to +/- HUGE_VAL.)
4878 : : *
4879 : : * String datatypes are converted by convert_string_to_scalar(),
4880 : : * which is explained below. The reason why this routine deals with
4881 : : * three values at a time, not just one, is that we need it for strings.
4882 : : *
4883 : : * The bytea datatype is just enough different from strings that it has
4884 : : * to be treated separately.
4885 : : *
4886 : : * The several datatypes representing absolute times are all converted
4887 : : * to Timestamp, which is actually an int64, and then we promote that to
4888 : : * a double. Note this will give correct results even for the "special"
4889 : : * values of Timestamp, since those are chosen to compare correctly;
4890 : : * see timestamp_cmp.
4891 : : *
4892 : : * The several datatypes representing relative times (intervals) are all
4893 : : * converted to measurements expressed in seconds.
4894 : : */
4895 : : static bool
2648 tgl@sss.pgh.pa.us 4896 :CBC 51174 : convert_to_scalar(Datum value, Oid valuetypid, Oid collid, double *scaledvalue,
4897 : : Datum lobound, Datum hibound, Oid boundstypid,
4898 : : double *scaledlobound, double *scaledhibound)
4899 : : {
2934 4900 : 51174 : bool failure = false;
4901 : :
4902 : : /*
4903 : : * Both the valuetypid and the boundstypid should exactly match the
4904 : : * declared input type(s) of the operator we are invoked for. However,
4905 : : * extensions might try to use scalarineqsel as estimator for operators
4906 : : * with input type(s) we don't handle here; in such cases, we want to
4907 : : * return false, not fail. In any case, we mustn't assume that valuetypid
4908 : : * and boundstypid are identical.
4909 : : *
4910 : : * XXX The histogram we are interpolating between points of could belong
4911 : : * to a column that's only binary-compatible with the declared type. In
4912 : : * essence we are assuming that the semantics of binary-compatible types
4913 : : * are enough alike that we can use a histogram generated with one type's
4914 : : * operators to estimate selectivity for the other's. This is outright
4915 : : * wrong in some cases --- in particular signed versus unsigned
4916 : : * interpretation could trip us up. But it's useful enough in the
4917 : : * majority of cases that we do it anyway. Should think about more
4918 : : * rigorous ways to do it.
4919 : : */
9464 4920 [ + + - - : 51174 : switch (valuetypid)
- - ]
4921 : : {
4922 : : /*
4923 : : * Built-in numeric types
4924 : : */
9065 4925 : 47510 : case BOOLOID:
4926 : : case INT2OID:
4927 : : case INT4OID:
4928 : : case INT8OID:
4929 : : case FLOAT4OID:
4930 : : case FLOAT8OID:
4931 : : case NUMERICOID:
4932 : : case OIDOID:
4933 : : case REGPROCOID:
4934 : : case REGPROCEDUREOID:
4935 : : case REGOPEROID:
4936 : : case REGOPERATOROID:
4937 : : case REGCLASSOID:
4938 : : case REGTYPEOID:
4939 : : case REGCOLLATIONOID:
4940 : : case REGCONFIGOID:
4941 : : case REGDICTIONARYOID:
4942 : : case REGROLEOID:
4943 : : case REGNAMESPACEOID:
4944 : : case REGDATABASEOID:
2934 4945 : 47510 : *scaledvalue = convert_numeric_to_scalar(value, valuetypid,
4946 : : &failure);
4947 : 47510 : *scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
4948 : : &failure);
4949 : 47510 : *scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
4950 : : &failure);
4951 : 47510 : return !failure;
4952 : :
4953 : : /*
4954 : : * Built-in string types
4955 : : */
9547 4956 : 3664 : case CHAROID:
4957 : : case BPCHAROID:
4958 : : case VARCHAROID:
4959 : : case TEXTOID:
4960 : : case NAMEOID:
4961 : : {
2934 4962 : 3664 : char *valstr = convert_string_datum(value, valuetypid,
4963 : : collid, &failure);
4964 : 3664 : char *lostr = convert_string_datum(lobound, boundstypid,
4965 : : collid, &failure);
4966 : 3664 : char *histr = convert_string_datum(hibound, boundstypid,
4967 : : collid, &failure);
4968 : :
4969 : : /*
4970 : : * Bail out if any of the values is not of string type. We
4971 : : * might leak converted strings for the other value(s), but
4972 : : * that's not worth troubling over.
4973 : : */
4974 [ - + ]: 3664 : if (failure)
2934 tgl@sss.pgh.pa.us 4975 :UBC 0 : return false;
4976 : :
9124 bruce@momjian.us 4977 :CBC 3664 : convert_string_to_scalar(valstr, scaledvalue,
4978 : : lostr, scaledlobound,
4979 : : histr, scaledhibound);
4980 : 3664 : pfree(valstr);
4981 : 3664 : pfree(lostr);
4982 : 3664 : pfree(histr);
4983 : 3664 : return true;
4984 : : }
4985 : :
4986 : : /*
4987 : : * Built-in bytea type
4988 : : */
8980 tgl@sss.pgh.pa.us 4989 :UBC 0 : case BYTEAOID:
4990 : : {
4991 : : /* We only support bytea vs bytea comparison */
2934 4992 [ # # ]: 0 : if (boundstypid != BYTEAOID)
4993 : 0 : return false;
8980 4994 : 0 : convert_bytea_to_scalar(value, scaledvalue,
4995 : : lobound, scaledlobound,
4996 : : hibound, scaledhibound);
4997 : 0 : return true;
4998 : : }
4999 : :
5000 : : /*
5001 : : * Built-in time types
5002 : : */
9514 5003 : 0 : case TIMESTAMPOID:
5004 : : case TIMESTAMPTZOID:
5005 : : case DATEOID:
5006 : : case INTERVALOID:
5007 : : case TIMEOID:
5008 : : case TIMETZOID:
2934 5009 : 0 : *scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
5010 : : &failure);
5011 : 0 : *scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
5012 : : &failure);
5013 : 0 : *scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
5014 : : &failure);
5015 : 0 : return !failure;
5016 : :
5017 : : /*
5018 : : * Built-in network types
5019 : : */
9045 5020 : 0 : case INETOID:
5021 : : case CIDROID:
5022 : : case MACADDROID:
5023 : : case MACADDR8OID:
2934 5024 : 0 : *scaledvalue = convert_network_to_scalar(value, valuetypid,
5025 : : &failure);
5026 : 0 : *scaledlobound = convert_network_to_scalar(lobound, boundstypid,
5027 : : &failure);
5028 : 0 : *scaledhibound = convert_network_to_scalar(hibound, boundstypid,
5029 : : &failure);
5030 : 0 : return !failure;
5031 : : }
5032 : : /* Don't know how to convert */
7477 5033 : 0 : *scaledvalue = *scaledlobound = *scaledhibound = 0;
9722 5034 : 0 : return false;
5035 : : }
5036 : :
5037 : : /*
5038 : : * Do convert_to_scalar()'s work for any numeric data type.
5039 : : *
5040 : : * On failure (e.g., unsupported typid), set *failure to true;
5041 : : * otherwise, that variable is not changed.
5042 : : */
5043 : : static double
2934 tgl@sss.pgh.pa.us 5044 :CBC 142530 : convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
5045 : : {
9464 5046 [ - + + - : 142530 : switch (typid)
- + - +
- ]
5047 : : {
9078 tgl@sss.pgh.pa.us 5048 :UBC 0 : case BOOLOID:
9422 5049 : 0 : return (double) DatumGetBool(value);
9464 tgl@sss.pgh.pa.us 5050 :CBC 6 : case INT2OID:
5051 : 6 : return (double) DatumGetInt16(value);
5052 : 16446 : case INT4OID:
5053 : 16446 : return (double) DatumGetInt32(value);
9464 tgl@sss.pgh.pa.us 5054 :UBC 0 : case INT8OID:
9422 5055 : 0 : return (double) DatumGetInt64(value);
9464 5056 : 0 : case FLOAT4OID:
9422 5057 : 0 : return (double) DatumGetFloat4(value);
9464 tgl@sss.pgh.pa.us 5058 :CBC 27 : case FLOAT8OID:
9422 5059 : 27 : return (double) DatumGetFloat8(value);
9464 tgl@sss.pgh.pa.us 5060 :UBC 0 : case NUMERICOID:
5061 : : /* Note: out-of-range values will be clamped to +-HUGE_VAL */
8919 5062 : 0 : return (double)
5063 : 0 : DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
5064 : : value));
9464 tgl@sss.pgh.pa.us 5065 :CBC 126051 : case OIDOID:
5066 : : case REGPROCOID:
5067 : : case REGPROCEDUREOID:
5068 : : case REGOPEROID:
5069 : : case REGOPERATOROID:
5070 : : case REGCLASSOID:
5071 : : case REGTYPEOID:
5072 : : case REGCOLLATIONOID:
5073 : : case REGCONFIGOID:
5074 : : case REGDICTIONARYOID:
5075 : : case REGROLEOID:
5076 : : case REGNAMESPACEOID:
5077 : : case REGDATABASEOID:
5078 : : /* we can treat OIDs as integers... */
5079 : 126051 : return (double) DatumGetObjectId(value);
5080 : : }
5081 : :
2934 tgl@sss.pgh.pa.us 5082 :UBC 0 : *failure = true;
9464 5083 : 0 : return 0;
5084 : : }
5085 : :
5086 : : /*
5087 : : * Do convert_to_scalar()'s work for any character-string data type.
5088 : : *
5089 : : * String datatypes are converted to a scale that ranges from 0 to 1,
5090 : : * where we visualize the bytes of the string as fractional digits.
5091 : : *
5092 : : * We do not want the base to be 256, however, since that tends to
5093 : : * generate inflated selectivity estimates; few databases will have
5094 : : * occurrences of all 256 possible byte values at each position.
5095 : : * Instead, use the smallest and largest byte values seen in the bounds
5096 : : * as the estimated range for each byte, after some fudging to deal with
5097 : : * the fact that we probably aren't going to see the full range that way.
5098 : : *
5099 : : * An additional refinement is that we discard any common prefix of the
5100 : : * three strings before computing the scaled values. This allows us to
5101 : : * "zoom in" when we encounter a narrow data range. An example is a phone
5102 : : * number database where all the values begin with the same area code.
5103 : : * (Actually, the bounds will be adjacent histogram-bin-boundary values,
5104 : : * so this is more likely to happen than you might think.)
5105 : : */
5106 : : static void
7477 tgl@sss.pgh.pa.us 5107 :CBC 3664 : convert_string_to_scalar(char *value,
5108 : : double *scaledvalue,
5109 : : char *lobound,
5110 : : double *scaledlobound,
5111 : : char *hibound,
5112 : : double *scaledhibound)
5113 : : {
5114 : : int rangelo,
5115 : : rangehi;
5116 : : char *sptr;
5117 : :
5118 : 3664 : rangelo = rangehi = (unsigned char) hibound[0];
9464 5119 [ + + ]: 47917 : for (sptr = lobound; *sptr; sptr++)
5120 : : {
7477 5121 [ + + ]: 44253 : if (rangelo > (unsigned char) *sptr)
5122 : 8730 : rangelo = (unsigned char) *sptr;
5123 [ + + ]: 44253 : if (rangehi < (unsigned char) *sptr)
5124 : 4492 : rangehi = (unsigned char) *sptr;
5125 : : }
9464 5126 [ + + ]: 40223 : for (sptr = hibound; *sptr; sptr++)
5127 : : {
7477 5128 [ + + ]: 36559 : if (rangelo > (unsigned char) *sptr)
5129 : 574 : rangelo = (unsigned char) *sptr;
5130 [ + + ]: 36559 : if (rangehi < (unsigned char) *sptr)
5131 : 1574 : rangehi = (unsigned char) *sptr;
5132 : : }
5133 : : /* If range includes any upper-case ASCII chars, make it include all */
9464 5134 [ + + + + ]: 3664 : if (rangelo <= 'Z' && rangehi >= 'A')
5135 : : {
5136 [ + + ]: 835 : if (rangelo > 'A')
5137 : 111 : rangelo = 'A';
5138 [ + + ]: 835 : if (rangehi < 'Z')
5139 : 240 : rangehi = 'Z';
5140 : : }
5141 : : /* Ditto lower-case */
5142 [ + - + + ]: 3664 : if (rangelo <= 'z' && rangehi >= 'a')
5143 : : {
5144 [ + + ]: 3401 : if (rangelo > 'a')
5145 : 14 : rangelo = 'a';
5146 [ + + ]: 3401 : if (rangehi < 'z')
5147 : 3359 : rangehi = 'z';
5148 : : }
5149 : : /* Ditto digits */
5150 [ + + + - ]: 3664 : if (rangelo <= '9' && rangehi >= '0')
5151 : : {
5152 [ + + ]: 507 : if (rangelo > '0')
5153 : 439 : rangelo = '0';
5154 [ + + ]: 507 : if (rangehi < '9')
5155 : 17 : rangehi = '9';
5156 : : }
5157 : :
5158 : : /*
5159 : : * If range includes less than 10 chars, assume we have not got enough
5160 : : * data, and make it include regular ASCII set.
5161 : : */
5162 [ - + ]: 3664 : if (rangehi - rangelo < 9)
5163 : : {
9464 tgl@sss.pgh.pa.us 5164 :UBC 0 : rangelo = ' ';
5165 : 0 : rangehi = 127;
5166 : : }
5167 : :
5168 : : /*
5169 : : * Now strip any common prefix of the three strings.
5170 : : */
9464 tgl@sss.pgh.pa.us 5171 [ + + ]:CBC 7906 : while (*lobound)
5172 : : {
5173 [ + + + - ]: 7896 : if (*lobound != *hibound || *lobound != *value)
5174 : : break;
5175 : 4242 : lobound++, hibound++, value++;
5176 : : }
5177 : :
5178 : : /*
5179 : : * Now we can do the conversions.
5180 : : */
5181 : 3664 : *scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
5182 : 3664 : *scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
5183 : 3664 : *scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
5184 : 3664 : }
5185 : :
5186 : : static double
7477 5187 : 10992 : convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
5188 : : {
5189 : 10992 : int slen = strlen(value);
5190 : : double num,
5191 : : denom,
5192 : : base;
5193 : :
9464 5194 [ + + ]: 10992 : if (slen <= 0)
9464 tgl@sss.pgh.pa.us 5195 :GBC 10 : return 0.0; /* empty string has scalar value 0 */
5196 : :
5197 : : /*
5198 : : * There seems little point in considering more than a dozen bytes from
5199 : : * the string. Since base is at least 10, that will give us nominal
5200 : : * resolution of at least 12 decimal digits, which is surely far more
5201 : : * precision than this estimation technique has got anyway (especially in
5202 : : * non-C locales). Also, even with the maximum possible base of 256, this
5203 : : * ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
5204 : : * overflow on any known machine.
5205 : : */
3857 tgl@sss.pgh.pa.us 5206 [ + + ]:CBC 10982 : if (slen > 12)
5207 : 2637 : slen = 12;
5208 : :
5209 : : /* Convert initial characters to fraction */
9464 5210 : 10982 : base = rangehi - rangelo + 1;
5211 : 10982 : num = 0.0;
5212 : 10982 : denom = base;
5213 [ + + ]: 89568 : while (slen-- > 0)
5214 : : {
7477 5215 : 78586 : int ch = (unsigned char) *value++;
5216 : :
9464 5217 [ + + ]: 78586 : if (ch < rangelo)
9124 bruce@momjian.us 5218 : 52 : ch = rangelo - 1;
9464 tgl@sss.pgh.pa.us 5219 [ - + ]: 78534 : else if (ch > rangehi)
9124 bruce@momjian.us 5220 :UBC 0 : ch = rangehi + 1;
9464 tgl@sss.pgh.pa.us 5221 :CBC 78586 : num += ((double) (ch - rangelo)) / denom;
5222 : 78586 : denom *= base;
5223 : : }
5224 : :
5225 : 10982 : return num;
5226 : : }
5227 : :
5228 : : /*
5229 : : * Convert a string-type Datum into a palloc'd, null-terminated string.
5230 : : *
5231 : : * On failure (e.g., unsupported typid), set *failure to true;
5232 : : * otherwise, that variable is not changed. (We'll return NULL on failure.)
5233 : : *
5234 : : * When using a non-C locale, we must pass the string through pg_strxfrm()
5235 : : * before continuing, so as to generate correct locale-specific results.
5236 : : */
5237 : : static char *
2648 5238 : 10992 : convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
5239 : : {
5240 : : char *val;
5241 : : pg_locale_t mylocale;
5242 : :
9464 5243 [ - + + - ]: 10992 : switch (typid)
5244 : : {
9464 tgl@sss.pgh.pa.us 5245 :UBC 0 : case CHAROID:
5246 : 0 : val = (char *) palloc(2);
5247 : 0 : val[0] = DatumGetChar(value);
5248 : 0 : val[1] = '\0';
5249 : 0 : break;
9464 tgl@sss.pgh.pa.us 5250 :CBC 3402 : case BPCHAROID:
5251 : : case VARCHAROID:
5252 : : case TEXTOID:
6564 5253 : 3402 : val = TextDatumGetCString(value);
5254 : 3402 : break;
9464 5255 : 7590 : case NAMEOID:
5256 : : {
9124 bruce@momjian.us 5257 : 7590 : NameData *nm = (NameData *) DatumGetPointer(value);
5258 : :
5259 : 7590 : val = pstrdup(NameStr(*nm));
5260 : 7590 : break;
5261 : : }
9464 tgl@sss.pgh.pa.us 5262 :UBC 0 : default:
2934 5263 : 0 : *failure = true;
9464 5264 : 0 : return NULL;
5265 : : }
5266 : :
557 jdavis@postgresql.or 5267 :CBC 10992 : mylocale = pg_newlocale_from_collation(collid);
5268 : :
5269 [ + + ]: 10992 : if (!mylocale->collate_is_c)
5270 : : {
5271 : : char *xfrmstr;
5272 : : size_t xfrmlen;
5273 : : size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
5274 : :
5275 : : /*
5276 : : * XXX: We could guess at a suitable output buffer size and only call
5277 : : * pg_strxfrm() twice if our guess is too small.
5278 : : *
5279 : : * XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
5280 : : * bogus data or set an error. This is not really a problem unless it
5281 : : * crashes since it will only give an estimation error and nothing
5282 : : * fatal.
5283 : : *
5284 : : * XXX: we do not check pg_strxfrm_enabled(). On some platforms and in
5285 : : * some cases, libc strxfrm() may return the wrong results, but that
5286 : : * will only lead to an estimation error.
5287 : : */
586 5288 : 45 : xfrmlen = pg_strxfrm(NULL, val, 0, mylocale);
5289 : : #ifdef WIN32
5290 : :
5291 : : /*
5292 : : * On Windows, strxfrm returns INT_MAX when an error occurs. Instead
5293 : : * of trying to allocate this much memory (and fail), just return the
5294 : : * original string unmodified as if we were in the C locale.
5295 : : */
5296 : : if (xfrmlen == INT_MAX)
5297 : : return val;
5298 : : #endif
8277 tgl@sss.pgh.pa.us 5299 : 45 : xfrmstr = (char *) palloc(xfrmlen + 1);
586 jdavis@postgresql.or 5300 : 45 : xfrmlen2 = pg_strxfrm(xfrmstr, val, xfrmlen + 1, mylocale);
5301 : :
5302 : : /*
5303 : : * Some systems (e.g., glibc) can return a smaller value from the
5304 : : * second call than the first; thus the Assert must be <= not ==.
5305 : : */
8277 tgl@sss.pgh.pa.us 5306 [ - + ]: 45 : Assert(xfrmlen2 <= xfrmlen);
8747 peter_e@gmx.net 5307 : 45 : pfree(val);
5308 : 45 : val = xfrmstr;
5309 : : }
5310 : :
7477 tgl@sss.pgh.pa.us 5311 : 10992 : return val;
5312 : : }
5313 : :
5314 : : /*
5315 : : * Do convert_to_scalar()'s work for any bytea data type.
5316 : : *
5317 : : * Very similar to convert_string_to_scalar except we can't assume
5318 : : * null-termination and therefore pass explicit lengths around.
5319 : : *
5320 : : * Also, assumptions about likely "normal" ranges of characters have been
5321 : : * removed - a data range of 0..255 is always used, for now. (Perhaps
5322 : : * someday we will add information about actual byte data range to
5323 : : * pg_statistic.)
5324 : : */
5325 : : static void
8980 tgl@sss.pgh.pa.us 5326 :UBC 0 : convert_bytea_to_scalar(Datum value,
5327 : : double *scaledvalue,
5328 : : Datum lobound,
5329 : : double *scaledlobound,
5330 : : Datum hibound,
5331 : : double *scaledhibound)
5332 : : {
2934 5333 : 0 : bytea *valuep = DatumGetByteaPP(value);
5334 : 0 : bytea *loboundp = DatumGetByteaPP(lobound);
5335 : 0 : bytea *hiboundp = DatumGetByteaPP(hibound);
5336 : : int rangelo,
5337 : : rangehi,
5338 [ # # # # : 0 : valuelen = VARSIZE_ANY_EXHDR(valuep),
# # # # #
# ]
5339 [ # # # # : 0 : loboundlen = VARSIZE_ANY_EXHDR(loboundp),
# # # # #
# ]
5340 [ # # # # : 0 : hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
# # # # #
# ]
5341 : : i,
5342 : : minlen;
5343 [ # # ]: 0 : unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
5344 [ # # ]: 0 : unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
5345 [ # # ]: 0 : unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
5346 : :
5347 : : /*
5348 : : * Assume bytea data is uniformly distributed across all byte values.
5349 : : */
8980 5350 : 0 : rangelo = 0;
5351 : 0 : rangehi = 255;
5352 : :
5353 : : /*
5354 : : * Now strip any common prefix of the three strings.
5355 : : */
5356 : 0 : minlen = Min(Min(valuelen, loboundlen), hiboundlen);
5357 [ # # ]: 0 : for (i = 0; i < minlen; i++)
5358 : : {
5359 [ # # # # ]: 0 : if (*lostr != *histr || *lostr != *valstr)
5360 : : break;
5361 : 0 : lostr++, histr++, valstr++;
5362 : 0 : loboundlen--, hiboundlen--, valuelen--;
5363 : : }
5364 : :
5365 : : /*
5366 : : * Now we can do the conversions.
5367 : : */
5368 : 0 : *scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
5369 : 0 : *scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
5370 : 0 : *scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
5371 : 0 : }
5372 : :
5373 : : static double
5374 : 0 : convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
5375 : : int rangelo, int rangehi)
5376 : : {
5377 : : double num,
5378 : : denom,
5379 : : base;
5380 : :
5381 [ # # ]: 0 : if (valuelen <= 0)
5382 : 0 : return 0.0; /* empty string has scalar value 0 */
5383 : :
5384 : : /*
5385 : : * Since base is 256, need not consider more than about 10 chars (even
5386 : : * this many seems like overkill)
5387 : : */
5388 [ # # ]: 0 : if (valuelen > 10)
5389 : 0 : valuelen = 10;
5390 : :
5391 : : /* Convert initial characters to fraction */
5392 : 0 : base = rangehi - rangelo + 1;
5393 : 0 : num = 0.0;
5394 : 0 : denom = base;
5395 [ # # ]: 0 : while (valuelen-- > 0)
5396 : : {
5397 : 0 : int ch = *value++;
5398 : :
5399 [ # # ]: 0 : if (ch < rangelo)
5400 : 0 : ch = rangelo - 1;
5401 [ # # ]: 0 : else if (ch > rangehi)
5402 : 0 : ch = rangehi + 1;
5403 : 0 : num += ((double) (ch - rangelo)) / denom;
5404 : 0 : denom *= base;
5405 : : }
5406 : :
5407 : 0 : return num;
5408 : : }
5409 : :
5410 : : /*
5411 : : * Do convert_to_scalar()'s work for any timevalue data type.
5412 : : *
5413 : : * On failure (e.g., unsupported typid), set *failure to true;
5414 : : * otherwise, that variable is not changed.
5415 : : */
5416 : : static double
2934 5417 : 0 : convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
5418 : : {
9464 5419 [ # # # # : 0 : switch (typid)
# # # ]
5420 : : {
9065 5421 : 0 : case TIMESTAMPOID:
9410 5422 : 0 : return DatumGetTimestamp(value);
8929 5423 : 0 : case TIMESTAMPTZOID:
5424 : 0 : return DatumGetTimestampTz(value);
9464 5425 : 0 : case DATEOID:
5556 5426 : 0 : return date2timestamp_no_overflow(DatumGetDateADT(value));
9464 5427 : 0 : case INTERVALOID:
5428 : : {
9124 bruce@momjian.us 5429 : 0 : Interval *interval = DatumGetIntervalP(value);
5430 : :
5431 : : /*
5432 : : * Convert the month part of Interval to days using assumed
5433 : : * average month length of 365.25/12.0 days. Not too
5434 : : * accurate, but plenty good enough for our purposes.
5435 : : *
5436 : : * This also works for infinite intervals, which just have all
5437 : : * fields set to INT_MIN/INT_MAX, and so will produce a result
5438 : : * smaller/larger than any finite interval.
5439 : : */
7456 5440 : 0 : return interval->time + interval->day * (double) USECS_PER_DAY +
5441 : 0 : interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
5442 : : }
9464 tgl@sss.pgh.pa.us 5443 : 0 : case TIMEOID:
9410 5444 : 0 : return DatumGetTimeADT(value);
9045 5445 : 0 : case TIMETZOID:
5446 : : {
5447 : 0 : TimeTzADT *timetz = DatumGetTimeTzADTP(value);
5448 : :
5449 : : /* use GMT-equivalent time */
8729 lockhart@fourpalms.o 5450 : 0 : return (double) (timetz->time + (timetz->zone * 1000000.0));
5451 : : }
5452 : : }
5453 : :
2934 tgl@sss.pgh.pa.us 5454 : 0 : *failure = true;
9464 5455 : 0 : return 0;
5456 : : }
5457 : :
5458 : :
5459 : : /*
5460 : : * get_restriction_variable
5461 : : * Examine the args of a restriction clause to see if it's of the
5462 : : * form (variable op pseudoconstant) or (pseudoconstant op variable),
5463 : : * where "variable" could be either a Var or an expression in vars of a
5464 : : * single relation. If so, extract information about the variable,
5465 : : * and also indicate which side it was on and the other argument.
5466 : : *
5467 : : * Inputs:
5468 : : * root: the planner info
5469 : : * args: clause argument list
5470 : : * varRelid: see specs for restriction selectivity functions
5471 : : *
5472 : : * Outputs: (these are valid only if true is returned)
5473 : : * *vardata: gets information about variable (see examine_variable)
5474 : : * *other: gets other clause argument, aggressively reduced to a constant
5475 : : * *varonleft: set true if variable is on the left, false if on the right
5476 : : *
5477 : : * Returns true if a variable is identified, otherwise false.
5478 : : *
5479 : : * Note: if there are Vars on both sides of the clause, we must fail, because
5480 : : * callers are expecting that the other side will act like a pseudoconstant.
5481 : : */
5482 : : bool
7588 tgl@sss.pgh.pa.us 5483 :CBC 474854 : get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
5484 : : VariableStatData *vardata, Node **other,
5485 : : bool *varonleft)
5486 : : {
5487 : : Node *left,
5488 : : *right;
5489 : : VariableStatData rdata;
5490 : :
5491 : : /* Fail if not a binary opclause (probably shouldn't happen) */
7959 neilc@samurai.com 5492 [ - + ]: 474854 : if (list_length(args) != 2)
8062 tgl@sss.pgh.pa.us 5493 :UBC 0 : return false;
5494 : :
7963 neilc@samurai.com 5495 :CBC 474854 : left = (Node *) linitial(args);
8062 tgl@sss.pgh.pa.us 5496 : 474854 : right = (Node *) lsecond(args);
5497 : :
5498 : : /*
5499 : : * Examine both sides. Note that when varRelid is nonzero, Vars of other
5500 : : * relations will be treated as pseudoconstants.
5501 : : */
5502 : 474854 : examine_variable(root, left, varRelid, vardata);
5503 : 474854 : examine_variable(root, right, varRelid, &rdata);
5504 : :
5505 : : /*
5506 : : * If one side is a variable and the other not, we win.
5507 : : */
5508 [ + + + + ]: 474854 : if (vardata->rel && rdata.rel == NULL)
5509 : : {
5510 : 422869 : *varonleft = true;
6964 5511 : 422869 : *other = estimate_expression_value(root, rdata.var);
5512 : : /* Assume we need no ReleaseVariableStats(rdata) here */
8062 5513 : 422866 : return true;
5514 : : }
5515 : :
5516 [ + + + + ]: 51985 : if (vardata->rel == NULL && rdata.rel)
5517 : : {
5518 : 48587 : *varonleft = false;
6964 5519 : 48587 : *other = estimate_expression_value(root, vardata->var);
5520 : : /* Assume we need no ReleaseVariableStats(*vardata) here */
8062 5521 : 48587 : *vardata = rdata;
5522 : 48587 : return true;
5523 : : }
5524 : :
5525 : : /* Oops, clause has wrong structure (probably var op var) */
5526 [ + + ]: 3398 : ReleaseVariableStats(*vardata);
5527 [ + + ]: 3398 : ReleaseVariableStats(rdata);
5528 : :
5529 : 3398 : return false;
5530 : : }
5531 : :
5532 : : /*
5533 : : * get_join_variables
5534 : : * Apply examine_variable() to each side of a join clause.
5535 : : * Also, attempt to identify whether the join clause has the same
5536 : : * or reversed sense compared to the SpecialJoinInfo.
5537 : : *
5538 : : * We consider the join clause "normal" if it is "lhs_var OP rhs_var",
5539 : : * or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
5540 : : * where we can't tell for sure, we default to assuming it's normal.
5541 : : */
5542 : : void
6420 5543 : 156562 : get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
5544 : : VariableStatData *vardata1, VariableStatData *vardata2,
5545 : : bool *join_is_reversed)
5546 : : {
5547 : : Node *left,
5548 : : *right;
5549 : :
7959 neilc@samurai.com 5550 [ - + ]: 156562 : if (list_length(args) != 2)
8062 tgl@sss.pgh.pa.us 5551 [ # # ]:UBC 0 : elog(ERROR, "join operator should take two arguments");
5552 : :
7963 neilc@samurai.com 5553 :CBC 156562 : left = (Node *) linitial(args);
9065 tgl@sss.pgh.pa.us 5554 : 156562 : right = (Node *) lsecond(args);
5555 : :
8062 5556 : 156562 : examine_variable(root, left, 0, vardata1);
5557 : 156562 : examine_variable(root, right, 0, vardata2);
5558 : :
6420 5559 [ + + + + ]: 312995 : if (vardata1->rel &&
5560 : 156433 : bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
3189 5561 : 52500 : *join_is_reversed = true; /* var1 is on RHS */
6420 5562 [ + + + + ]: 207999 : else if (vardata2->rel &&
5563 : 103937 : bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
3189 5564 : 171 : *join_is_reversed = true; /* var2 is on LHS */
5565 : : else
6420 5566 : 103891 : *join_is_reversed = false;
8062 5567 : 156562 : }
5568 : :
5569 : : /* statext_expressions_load copies the tuple, so just pfree it. */
5570 : : static void
1815 tomas.vondra@postgre 5571 : 831 : ReleaseDummy(HeapTuple tuple)
5572 : : {
5573 : 831 : pfree(tuple);
5574 : 831 : }
5575 : :
5576 : : /*
5577 : : * examine_variable
5578 : : * Try to look up statistical data about an expression.
5579 : : * Fill in a VariableStatData struct to describe the expression.
5580 : : *
5581 : : * Inputs:
5582 : : * root: the planner info
5583 : : * node: the expression tree to examine
5584 : : * varRelid: see specs for restriction selectivity functions
5585 : : *
5586 : : * Outputs: *vardata is filled as follows:
5587 : : * var: the input expression (with any phvs or binary relabeling stripped,
5588 : : * if it is or contains a variable; but otherwise unchanged)
5589 : : * rel: RelOptInfo for relation containing variable; NULL if expression
5590 : : * contains no Vars (NOTE this could point to a RelOptInfo of a
5591 : : * subquery, not one in the current query).
5592 : : * statsTuple: the pg_statistic entry for the variable, if one exists;
5593 : : * otherwise NULL.
5594 : : * freefunc: pointer to a function to release statsTuple with.
5595 : : * vartype: exposed type of the expression; this should always match
5596 : : * the declared input type of the operator we are estimating for.
5597 : : * atttype, atttypmod: actual type/typmod of the "var" expression. This is
5598 : : * commonly the same as the exposed type of the variable argument,
5599 : : * but can be different in binary-compatible-type cases.
5600 : : * isunique: true if we were able to match the var to a unique index, a
5601 : : * single-column DISTINCT or GROUP-BY clause, implying its values are
5602 : : * unique for this query. (Caution: this should be trusted for
5603 : : * statistical purposes only, since we do not check indimmediate nor
5604 : : * verify that the exact same definition of equality applies.)
5605 : : * acl_ok: true if current user has permission to read all table rows from
5606 : : * the column(s) underlying the pg_statistic entry. This is consulted by
5607 : : * statistic_proc_security_check().
5608 : : *
5609 : : * Caller is responsible for doing ReleaseVariableStats() before exiting.
5610 : : */
5611 : : void
7588 tgl@sss.pgh.pa.us 5612 : 1904410 : examine_variable(PlannerInfo *root, Node *node, int varRelid,
5613 : : VariableStatData *vardata)
5614 : : {
5615 : : Node *basenode;
5616 : : Relids varnos;
5617 : : Relids basevarnos;
5618 : : RelOptInfo *onerel;
5619 : :
5620 : : /* Make sure we don't return dangling pointers in vardata */
8062 5621 [ + - + - : 13330870 : MemSet(vardata, 0, sizeof(VariableStatData));
+ - + - +
+ ]
5622 : :
5623 : : /* Save the exposed type of the expression */
7653 5624 : 1904410 : vardata->vartype = exprType(node);
5625 : :
5626 : : /*
5627 : : * PlaceHolderVars are transparent for the purpose of statistics lookup;
5628 : : * they do not alter the value distribution of the underlying expression.
5629 : : * However, they can obscure the structure, preventing us from recognizing
5630 : : * matches to base columns, index expressions, or extended statistics. So
5631 : : * strip them out first.
5632 : : */
76 rguo@postgresql.org 5633 : 1904410 : basenode = strip_all_phvs_deep(root, node);
5634 : :
5635 : : /*
5636 : : * Look inside any binary-compatible relabeling. We need to handle nested
5637 : : * RelabelType nodes here, because the prior stripping of PlaceHolderVars
5638 : : * may have brought separate RelabelTypes into adjacency.
5639 : : */
5640 [ + + ]: 1929260 : while (IsA(basenode, RelabelType))
5641 : 24850 : basenode = (Node *) ((RelabelType *) basenode)->arg;
5642 : :
5643 : : /* Fast path for a simple Var */
7659 tgl@sss.pgh.pa.us 5644 [ + + + + ]: 1904410 : if (IsA(basenode, Var) &&
5645 [ + + ]: 489519 : (varRelid == 0 || varRelid == ((Var *) basenode)->varno))
5646 : : {
5647 : 1367661 : Var *var = (Var *) basenode;
5648 : :
5649 : : /* Set up result fields other than the stats tuple */
76 rguo@postgresql.org 5650 : 1367661 : vardata->var = basenode; /* return Var without phvs or relabeling */
8062 tgl@sss.pgh.pa.us 5651 : 1367661 : vardata->rel = find_base_rel(root, var->varno);
5652 : 1367661 : vardata->atttype = var->vartype;
5653 : 1367661 : vardata->atttypmod = var->vartypmod;
6237 5654 : 1367661 : vardata->isunique = has_unique_index(vardata->rel, var->varattno);
5655 : :
5656 : : /* Try to locate some stats */
5306 5657 : 1367661 : examine_simple_variable(root, var, vardata);
5658 : :
8062 5659 : 1367661 : 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 : : */
1879 5667 : 536749 : varnos = pull_varnos(root, basenode);
437 rguo@postgresql.org 5668 : 536749 : basevarnos = bms_difference(varnos, root->outer_join_rels);
5669 : :
8062 tgl@sss.pgh.pa.us 5670 : 536749 : onerel = NULL;
5671 : :
437 rguo@postgresql.org 5672 [ + + ]: 536749 : 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 [ + + ]: 283485 : if (bms_get_singleton_member(basevarnos, &relid))
5682 : : {
838 drowley@postgresql.o 5683 [ + + + + ]: 281032 : if (varRelid == 0 || varRelid == relid)
5684 : : {
5685 : 36642 : onerel = find_base_rel(root, relid);
8062 tgl@sss.pgh.pa.us 5686 : 36642 : vardata->rel = onerel;
76 rguo@postgresql.org 5687 : 36642 : node = basenode; /* strip any phvs or relabeling */
5688 : : }
5689 : : /* else treat it as a constant */
5690 : : }
5691 : : else
5692 : : {
5693 : : /* varnos has multiple relids */
8062 tgl@sss.pgh.pa.us 5694 [ + + ]: 2453 : if (varRelid == 0)
5695 : : {
5696 : : /* treat it as a variable of a join relation */
5697 : 1791 : vardata->rel = find_join_rel(root, varnos);
76 rguo@postgresql.org 5698 : 1791 : node = basenode; /* strip any phvs or relabeling */
5699 : : }
8062 tgl@sss.pgh.pa.us 5700 [ + + ]: 662 : else if (bms_is_member(varRelid, varnos))
5701 : : {
5702 : : /* ignore the vars belonging to other relations */
5703 : 605 : vardata->rel = find_base_rel(root, varRelid);
76 rguo@postgresql.org 5704 : 605 : node = basenode; /* strip any phvs or relabeling */
5705 : : /* note: no point in expressional-index search here */
5706 : : }
5707 : : /* else treat it as a constant */
5708 : : }
5709 : : }
5710 : :
437 5711 : 536749 : bms_free(basevarnos);
5712 : :
7659 tgl@sss.pgh.pa.us 5713 : 536749 : vardata->var = node;
8062 5714 : 536749 : vardata->atttype = exprType(node);
5715 : 536749 : vardata->atttypmod = exprTypmod(node);
5716 : :
5717 [ + + ]: 536749 : 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 : : */
437 rguo@postgresql.org 5740 [ + + ]: 36642 : if (bms_overlap(varnos, root->outer_join_rels))
5741 : 1300 : node = remove_nulling_relids(node, root->outer_join_rels, NULL);
5742 : :
8062 tgl@sss.pgh.pa.us 5743 [ + + + + : 81630 : foreach(ilist, onerel->indexlist)
+ + ]
5744 : : {
5745 : 46479 : IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
5746 : : ListCell *indexpr_item;
5747 : : int pos;
5748 : :
7963 neilc@samurai.com 5749 : 46479 : indexpr_item = list_head(index->indexprs);
5750 [ + + ]: 46479 : if (indexpr_item == NULL)
8062 tgl@sss.pgh.pa.us 5751 : 44043 : continue; /* no expressions here... */
5752 : :
5753 [ + + ]: 3417 : for (pos = 0; pos < index->ncolumns; pos++)
5754 : : {
5755 [ + + ]: 2472 : if (index->indexkeys[pos] == 0)
5756 : : {
5757 : : Node *indexkey;
5758 : :
7963 neilc@samurai.com 5759 [ - + ]: 2436 : if (indexpr_item == NULL)
8062 tgl@sss.pgh.pa.us 5760 [ # # ]:UBC 0 : elog(ERROR, "too few entries in indexprs list");
7963 neilc@samurai.com 5761 :CBC 2436 : indexkey = (Node *) lfirst(indexpr_item);
8062 tgl@sss.pgh.pa.us 5762 [ + - - + ]: 2436 : if (indexkey && IsA(indexkey, RelabelType))
8062 tgl@sss.pgh.pa.us 5763 :UBC 0 : indexkey = (Node *) ((RelabelType *) indexkey)->arg;
8062 tgl@sss.pgh.pa.us 5764 [ + + ]:CBC 2436 : 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 &&
2899 teodor@sigaev.ru 5771 [ + - + - ]: 219 : index->nkeycolumns == 1 &&
2588 tgl@sss.pgh.pa.us 5772 : 219 : pos == 0 &&
6237 5773 [ - + - - ]: 219 : (index->indpred == NIL || index->predOK))
8062 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 : : */
6377 5786 [ - + - - ]: 1809 : if (get_index_stats_hook &&
6377 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 : : }
6237 tgl@sss.pgh.pa.us 5799 [ + - ]:CBC 1809 : else if (index->indpred == NIL)
5800 : : {
6377 5801 : 1809 : vardata->statsTuple =
5873 rhaas@postgresql.org 5802 : 3618 : SearchSysCache3(STATRELATTINH,
5803 : : ObjectIdGetDatum(index->indexoid),
5861 bruce@momjian.us 5804 : 1809 : Int16GetDatum(pos + 1),
5805 : : BoolGetDatum(false));
6377 tgl@sss.pgh.pa.us 5806 : 1809 : vardata->freefunc = ReleaseSysCache;
5807 : :
3236 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 =
216 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 */
3236 peter_e@gmx.net 5840 : 318 : vardata->acl_ok = true;
5841 : : }
5842 : : }
8062 tgl@sss.pgh.pa.us 5843 [ + + ]: 1809 : if (vardata->statsTuple)
5844 : 1491 : break;
5845 : : }
2435 5846 : 945 : indexpr_item = lnext(index->indexprs, indexpr_item);
5847 : : }
5848 : : }
8062 5849 [ + + ]: 2436 : 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 : : */
1815 tomas.vondra@postgre 5859 [ + + + + : 38728 : foreach(slist, onerel->statlist)
+ + ]
5860 : : {
5861 : 2230 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(slist);
1403 tgl@sss.pgh.pa.us 5862 [ + - ]: 2230 : 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 : : */
1815 tomas.vondra@postgre 5870 [ + + ]: 2230 : if (vardata->statsTuple)
5871 : 144 : break;
5872 : :
5873 : : /* skip stats without per-expression stats */
5874 [ + + ]: 2086 : if (info->kind != STATS_EXT_EXPRESSIONS)
5875 : 1074 : continue;
5876 : :
5877 : : /* skip stats with mismatching stxdinherit value */
1230 tgl@sss.pgh.pa.us 5878 [ + + ]: 1012 : if (info->inherit != rte->inh)
5879 : 3 : continue;
5880 : :
1815 tomas.vondra@postgre 5881 : 1009 : pos = 0;
5882 [ + - + + : 1660 : foreach(expr_item, info->exprs)
+ + ]
5883 : : {
5884 : 1483 : Node *expr = (Node *) lfirst(expr_item);
5885 : :
5886 [ - + ]: 1483 : Assert(expr);
5887 : :
5888 : : /* strip RelabelType before comparing it */
5889 [ + - - + ]: 1483 : if (expr && IsA(expr, RelabelType))
1815 tomas.vondra@postgre 5890 :UBC 0 : expr = (Node *) ((RelabelType *) expr)->arg;
5891 : :
5892 : : /* found a match, see if we can extract pg_statistic row */
1815 tomas.vondra@postgre 5893 [ + + ]:CBC 1483 : 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 : : */
1519 5899 : 832 : vardata->statsTuple =
5900 : 832 : statext_expressions_load(info->statOid, rte->inh, pos);
5901 : :
5902 : : /* Nothing to release if no data found */
13 michael@paquier.xyz 5903 [ + + ]: 832 : if (vardata->statsTuple != NULL)
5904 : : {
5905 : 831 : vardata->freefunc = ReleaseDummy;
5906 : : }
5907 : :
5908 : : /*
5909 : : * Test if user has permission to access all rows from the
5910 : : * table.
5911 : : *
5912 : : * For simplicity, we insist on the whole table being
5913 : : * selectable, rather than trying to identify which
5914 : : * column(s) the statistics object depends on.
5915 : : *
5916 : : * Note that for an inheritance child, permissions are
5917 : : * checked on the inheritance root parent, and whole-table
5918 : : * select privilege on the parent doesn't quite guarantee
5919 : : * that the user could read all columns of the child. But
5920 : : * in practice it's unlikely that any interesting security
5921 : : * violation could result from allowing access to the
5922 : : * expression stats, so we allow it anyway. See similar
5923 : : * code in examine_simple_variable() for additional
5924 : : * comments.
5925 : : */
216 dean.a.rasheed@gmail 5926 : 832 : vardata->acl_ok = all_rows_selectable(root,
5927 : : onerel->relid,
5928 : : NULL);
5929 : :
1815 tomas.vondra@postgre 5930 : 832 : break;
5931 : : }
5932 : :
5933 : 651 : pos++;
5934 : : }
5935 : : }
5936 : : }
5937 : :
437 rguo@postgresql.org 5938 : 536749 : bms_free(varnos);
5939 : : }
5940 : :
5941 : : /*
5942 : : * strip_all_phvs_deep
5943 : : * Deeply strip all PlaceHolderVars in an expression.
5944 : :
5945 : : * As a performance optimization, we first use a lightweight walker to check
5946 : : * for the presence of any PlaceHolderVars. The expensive mutator is invoked
5947 : : * only if a PlaceHolderVar is found, avoiding unnecessary memory allocation
5948 : : * and tree copying in the common case where no PlaceHolderVars are present.
5949 : : */
5950 : : static Node *
76 5951 : 1904410 : strip_all_phvs_deep(PlannerInfo *root, Node *node)
5952 : : {
5953 : : /* If there are no PHVs anywhere, we needn't work hard */
5954 [ + + ]: 1904410 : if (root->glob->lastPHId == 0)
5955 : 1886981 : return node;
5956 : :
5957 [ + + ]: 17429 : if (!contain_placeholder_walker(node, NULL))
5958 : 15209 : return node;
5959 : 2220 : return strip_all_phvs_mutator(node, NULL);
5960 : : }
5961 : :
5962 : : /*
5963 : : * contain_placeholder_walker
5964 : : * Lightweight walker to check if an expression contains any
5965 : : * PlaceHolderVars
5966 : : */
5967 : : static bool
5968 : 19752 : contain_placeholder_walker(Node *node, void *context)
5969 : : {
5970 [ + + ]: 19752 : if (node == NULL)
5971 : 105 : return false;
5972 [ + + ]: 19647 : if (IsA(node, PlaceHolderVar))
5973 : 2220 : return true;
5974 : :
5975 : 17427 : return expression_tree_walker(node, contain_placeholder_walker, context);
5976 : : }
5977 : :
5978 : : /*
5979 : : * strip_all_phvs_mutator
5980 : : * Mutator to deeply strip all PlaceHolderVars
5981 : : */
5982 : : static Node *
5983 : 5985 : strip_all_phvs_mutator(Node *node, void *context)
5984 : : {
5985 [ + + ]: 5985 : if (node == NULL)
5986 : 24 : return NULL;
5987 [ + + ]: 5961 : if (IsA(node, PlaceHolderVar))
5988 : : {
5989 : : /* Strip it and recurse into its contained expression */
5990 : 2295 : PlaceHolderVar *phv = (PlaceHolderVar *) node;
5991 : :
5992 : 2295 : return strip_all_phvs_mutator((Node *) phv->phexpr, context);
5993 : : }
5994 : :
5995 : 3666 : return expression_tree_mutator(node, strip_all_phvs_mutator, context);
5996 : : }
5997 : :
5998 : : /*
5999 : : * examine_simple_variable
6000 : : * Handle a simple Var for examine_variable
6001 : : *
6002 : : * This is split out as a subroutine so that we can recurse to deal with
6003 : : * Vars referencing subqueries (either sub-SELECT-in-FROM or CTE style).
6004 : : *
6005 : : * We already filled in all the fields of *vardata except for the stats tuple.
6006 : : */
6007 : : static void
5306 tgl@sss.pgh.pa.us 6008 : 1373095 : examine_simple_variable(PlannerInfo *root, Var *var,
6009 : : VariableStatData *vardata)
6010 : : {
6011 : 1373095 : RangeTblEntry *rte = root->simple_rte_array[var->varno];
6012 : :
6013 [ - + ]: 1373095 : Assert(IsA(rte, RangeTblEntry));
6014 : :
6015 [ - + - - ]: 1373095 : if (get_relation_stats_hook &&
5306 tgl@sss.pgh.pa.us 6016 :UBC 0 : (*get_relation_stats_hook) (root, rte, var->varattno, vardata))
6017 : : {
6018 : : /*
6019 : : * The hook took control of acquiring a stats tuple. If it did supply
6020 : : * a tuple, it'd better have supplied a freefunc.
6021 : : */
6022 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6023 [ # # ]: 0 : !vardata->freefunc)
6024 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6025 : : }
5306 tgl@sss.pgh.pa.us 6026 [ + + ]:CBC 1373095 : else if (rte->rtekind == RTE_RELATION)
6027 : : {
6028 : : /*
6029 : : * Plain table or parent of an inheritance appendrel, so look up the
6030 : : * column in pg_statistic
6031 : : */
6032 : 1302945 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6033 : : ObjectIdGetDatum(rte->relid),
6034 : 1302945 : Int16GetDatum(var->varattno),
6035 : 1302945 : BoolGetDatum(rte->inh));
6036 : 1302945 : vardata->freefunc = ReleaseSysCache;
6037 : :
3236 peter_e@gmx.net 6038 [ + + ]: 1302945 : if (HeapTupleIsValid(vardata->statsTuple))
6039 : : {
6040 : : /*
6041 : : * Test if user has permission to read all rows from this column.
6042 : : *
6043 : : * This requires that the user has the appropriate SELECT
6044 : : * privileges and that there are no securityQuals from security
6045 : : * barrier views or RLS policies. If that's not the case, then we
6046 : : * only permit leakproof functions to be passed pg_statistic data
6047 : : * in vardata, otherwise the functions might reveal data that the
6048 : : * user doesn't have permission to see --- see
6049 : : * statistic_proc_security_check().
6050 : : */
6051 : 934422 : vardata->acl_ok =
216 dean.a.rasheed@gmail 6052 : 934422 : all_rows_selectable(root, var->varno,
6053 : 934422 : bms_make_singleton(var->varattno - FirstLowInvalidHeapAttributeNumber));
6054 : : }
6055 : : else
6056 : : {
6057 : : /* suppress any possible leakproofness checks later */
3236 peter_e@gmx.net 6058 : 368523 : vardata->acl_ok = true;
6059 : : }
6060 : : }
849 tgl@sss.pgh.pa.us 6061 [ + + + + ]: 70150 : else if ((rte->rtekind == RTE_SUBQUERY && !rte->inh) ||
6062 [ + + + + ]: 64696 : (rte->rtekind == RTE_CTE && !rte->self_reference))
6063 : : {
6064 : : /*
6065 : : * Plain subquery (not one that was converted to an appendrel) or
6066 : : * non-recursive CTE. In either case, we can try to find out what the
6067 : : * Var refers to within the subquery. We skip this for appendrel and
6068 : : * recursive-CTE cases because any column stats we did find would
6069 : : * likely not be very relevant.
6070 : : */
6071 : : PlannerInfo *subroot;
6072 : : Query *subquery;
6073 : : List *subtlist;
6074 : : TargetEntry *ste;
6075 : :
6076 : : /*
6077 : : * Punt if it's a whole-row var rather than a plain column reference.
6078 : : */
4507 6079 [ - + ]: 11392 : if (var->varattno == InvalidAttrNumber)
4507 tgl@sss.pgh.pa.us 6080 :UBC 0 : return;
6081 : :
6082 : : /*
6083 : : * Otherwise, find the subquery's planner subroot.
6084 : : */
849 tgl@sss.pgh.pa.us 6085 [ + + ]:CBC 11392 : if (rte->rtekind == RTE_SUBQUERY)
6086 : : {
6087 : : RelOptInfo *rel;
6088 : :
6089 : : /*
6090 : : * Fetch RelOptInfo for subquery. Note that we don't change the
6091 : : * rel returned in vardata, since caller expects it to be a rel of
6092 : : * the caller's query level. Because we might already be
6093 : : * recursing, we can't use that rel pointer either, but have to
6094 : : * look up the Var's rel afresh.
6095 : : */
6096 : 5454 : rel = find_base_rel(root, var->varno);
6097 : :
6098 : 5454 : subroot = rel->subroot;
6099 : : }
6100 : : else
6101 : : {
6102 : : /* CTE case is more difficult */
6103 : : PlannerInfo *cteroot;
6104 : : Index levelsup;
6105 : : int ndx;
6106 : : int plan_id;
6107 : : ListCell *lc;
6108 : :
6109 : : /*
6110 : : * Find the referenced CTE, and locate the subroot previously made
6111 : : * for it.
6112 : : */
6113 : 5938 : levelsup = rte->ctelevelsup;
6114 : 5938 : cteroot = root;
6115 [ + + ]: 13619 : while (levelsup-- > 0)
6116 : : {
6117 : 7681 : cteroot = cteroot->parent_root;
6118 [ - + ]: 7681 : if (!cteroot) /* shouldn't happen */
849 tgl@sss.pgh.pa.us 6119 [ # # ]:UBC 0 : elog(ERROR, "bad levelsup for CTE \"%s\"", rte->ctename);
6120 : : }
6121 : :
6122 : : /*
6123 : : * Note: cte_plan_ids can be shorter than cteList, if we are still
6124 : : * working on planning the CTEs (ie, this is a side-reference from
6125 : : * another CTE). So we mustn't use forboth here.
6126 : : */
849 tgl@sss.pgh.pa.us 6127 :CBC 5938 : ndx = 0;
6128 [ + - + - : 8173 : foreach(lc, cteroot->parse->cteList)
+ - ]
6129 : : {
6130 : 8173 : CommonTableExpr *cte = (CommonTableExpr *) lfirst(lc);
6131 : :
6132 [ + + ]: 8173 : if (strcmp(cte->ctename, rte->ctename) == 0)
6133 : 5938 : break;
6134 : 2235 : ndx++;
6135 : : }
6136 [ - + ]: 5938 : if (lc == NULL) /* shouldn't happen */
849 tgl@sss.pgh.pa.us 6137 [ # # ]:UBC 0 : elog(ERROR, "could not find CTE \"%s\"", rte->ctename);
849 tgl@sss.pgh.pa.us 6138 [ - + ]:CBC 5938 : if (ndx >= list_length(cteroot->cte_plan_ids))
849 tgl@sss.pgh.pa.us 6139 [ # # ]:UBC 0 : elog(ERROR, "could not find plan for CTE \"%s\"", rte->ctename);
849 tgl@sss.pgh.pa.us 6140 :CBC 5938 : plan_id = list_nth_int(cteroot->cte_plan_ids, ndx);
6141 [ - + ]: 5938 : if (plan_id <= 0)
849 tgl@sss.pgh.pa.us 6142 [ # # ]:UBC 0 : elog(ERROR, "no plan was made for CTE \"%s\"", rte->ctename);
849 tgl@sss.pgh.pa.us 6143 :CBC 5938 : subroot = list_nth(root->glob->subroots, plan_id - 1);
6144 : : }
6145 : :
6146 : : /* If the subquery hasn't been planned yet, we have to punt */
6147 [ - + ]: 11392 : if (subroot == NULL)
849 tgl@sss.pgh.pa.us 6148 :UBC 0 : return;
849 tgl@sss.pgh.pa.us 6149 [ - + ]:CBC 11392 : Assert(IsA(subroot, PlannerInfo));
6150 : :
6151 : : /*
6152 : : * We must use the subquery parsetree as mangled by the planner, not
6153 : : * the raw version from the RTE, because we need a Var that will refer
6154 : : * to the subroot's live RelOptInfos. For instance, if any subquery
6155 : : * pullup happened during planning, Vars in the targetlist might have
6156 : : * gotten replaced, and we need to see the replacement expressions.
6157 : : */
6158 : 11392 : subquery = subroot->parse;
6159 [ - + ]: 11392 : Assert(IsA(subquery, Query));
6160 : :
6161 : : /*
6162 : : * Punt if subquery uses set operations or grouping sets, as these
6163 : : * will mash underlying columns' stats beyond recognition. (Set ops
6164 : : * are particularly nasty; if we forged ahead, we would return stats
6165 : : * relevant to only the leftmost subselect...) DISTINCT is also
6166 : : * problematic, but we check that later because there is a possibility
6167 : : * of learning something even with it.
6168 : : */
5141 6169 [ + + ]: 11392 : if (subquery->setOperations ||
1748 6170 [ + + ]: 10176 : subquery->groupingSets)
5197 rhaas@postgresql.org 6171 : 1264 : return;
6172 : :
6173 : : /* Get the subquery output expression referenced by the upper Var */
849 tgl@sss.pgh.pa.us 6174 [ + + ]: 10128 : if (subquery->returningList)
6175 : 103 : subtlist = subquery->returningList;
6176 : : else
6177 : 10025 : subtlist = subquery->targetList;
6178 : 10128 : ste = get_tle_by_resno(subtlist, var->varattno);
5306 6179 [ + - - + ]: 10128 : if (ste == NULL || ste->resjunk)
5306 tgl@sss.pgh.pa.us 6180 [ # # ]:UBC 0 : elog(ERROR, "subquery %s does not have attribute %d",
6181 : : rte->eref->aliasname, var->varattno);
5306 tgl@sss.pgh.pa.us 6182 :CBC 10128 : var = (Var *) ste->expr;
6183 : :
6184 : : /*
6185 : : * If subquery uses DISTINCT, we can't make use of any stats for the
6186 : : * variable ... but, if it's the only DISTINCT column, we are entitled
6187 : : * to consider it unique. We do the test this way so that it works
6188 : : * for cases involving DISTINCT ON.
6189 : : */
5141 6190 [ + + ]: 10128 : if (subquery->distinctClause)
6191 : : {
6192 [ + + + + ]: 897 : if (list_length(subquery->distinctClause) == 1 &&
6193 : 297 : targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
6194 : 150 : vardata->isunique = true;
6195 : : /* cannot go further */
6196 : 600 : return;
6197 : : }
6198 : :
6199 : : /* The same idea as with DISTINCT clause works for a GROUP-BY too */
389 akorotkov@postgresql 6200 [ + + ]: 9528 : if (subquery->groupClause)
6201 : : {
6202 [ + + + + ]: 588 : if (list_length(subquery->groupClause) == 1 &&
6203 : 249 : targetIsInSortList(ste, InvalidOid, subquery->groupClause))
6204 : 193 : vardata->isunique = true;
6205 : : /* cannot go further */
6206 : 339 : return;
6207 : : }
6208 : :
6209 : : /*
6210 : : * If the sub-query originated from a view with the security_barrier
6211 : : * attribute, we must not look at the variable's statistics, though it
6212 : : * seems all right to notice the existence of a DISTINCT clause. So
6213 : : * stop here.
6214 : : *
6215 : : * This is probably a harsher restriction than necessary; it's
6216 : : * certainly OK for the selectivity estimator (which is a C function,
6217 : : * and therefore omnipotent anyway) to look at the statistics. But
6218 : : * many selectivity estimators will happily *invoke the operator
6219 : : * function* to try to work out a good estimate - and that's not OK.
6220 : : * So for now, don't dig down for stats.
6221 : : */
5141 tgl@sss.pgh.pa.us 6222 [ + + ]: 9189 : if (rte->security_barrier)
6223 : 705 : return;
6224 : :
6225 : : /* Can only handle a simple Var of subquery's query level */
5306 6226 [ + - + + ]: 8484 : if (var && IsA(var, Var) &&
6227 [ + - ]: 5434 : var->varlevelsup == 0)
6228 : : {
6229 : : /*
6230 : : * OK, recurse into the subquery. Note that the original setting
6231 : : * of vardata->isunique (which will surely be false) is left
6232 : : * unchanged in this situation. That's what we want, since even
6233 : : * if the underlying column is unique, the subquery may have
6234 : : * joined to other tables in a way that creates duplicates.
6235 : : */
849 6236 : 5434 : examine_simple_variable(subroot, var, vardata);
6237 : : }
6238 : : }
6239 : : else
6240 : : {
6241 : : /*
6242 : : * Otherwise, the Var comes from a FUNCTION or VALUES RTE. (We won't
6243 : : * see RTE_JOIN here because join alias Vars have already been
6244 : : * flattened.) There's not much we can do with function outputs, but
6245 : : * maybe someday try to be smarter about VALUES.
6246 : : */
6247 : : }
6248 : : }
6249 : :
6250 : : /*
6251 : : * all_rows_selectable
6252 : : * Test whether the user has permission to select all rows from a given
6253 : : * relation.
6254 : : *
6255 : : * Inputs:
6256 : : * root: the planner info
6257 : : * varno: the index of the relation (assumed to be an RTE_RELATION)
6258 : : * varattnos: the attributes for which permission is required, or NULL if
6259 : : * whole-table access is required
6260 : : *
6261 : : * Returns true if the user has the required select permissions, and there are
6262 : : * no securityQuals from security barrier views or RLS policies.
6263 : : *
6264 : : * Note that if the relation is an inheritance child relation, securityQuals
6265 : : * and access permissions are checked against the inheritance root parent (the
6266 : : * relation actually mentioned in the query) --- see the comments in
6267 : : * expand_single_inheritance_child() for an explanation of why it has to be
6268 : : * done this way.
6269 : : *
6270 : : * If varattnos is non-NULL, its attribute numbers should be offset by
6271 : : * FirstLowInvalidHeapAttributeNumber so that system attributes can be
6272 : : * checked. If varattnos is NULL, only table-level SELECT privileges are
6273 : : * checked, not any column-level privileges.
6274 : : *
6275 : : * Note: if the relation is accessed via a view, this function actually tests
6276 : : * whether the view owner has permission to select from the relation. To
6277 : : * ensure that the current user has permission, it is also necessary to check
6278 : : * that the current user has permission to select from the view, which we do
6279 : : * at planner-startup --- see subquery_planner().
6280 : : *
6281 : : * This is exported so that other estimation functions can use it.
6282 : : */
6283 : : bool
216 dean.a.rasheed@gmail 6284 : 936871 : all_rows_selectable(PlannerInfo *root, Index varno, Bitmapset *varattnos)
6285 : : {
6286 : 936871 : RelOptInfo *rel = find_base_rel_noerr(root, varno);
6287 [ + - ]: 936871 : RangeTblEntry *rte = planner_rt_fetch(varno, root);
6288 : : Oid userid;
6289 : : int varattno;
6290 : :
6291 [ - + ]: 936871 : Assert(rte->rtekind == RTE_RELATION);
6292 : :
6293 : : /*
6294 : : * Determine the user ID to use for privilege checks (either the current
6295 : : * user or the view owner, if we're accessing the table via a view).
6296 : : *
6297 : : * Normally the relation will have an associated RelOptInfo from which we
6298 : : * can find the userid, but it might not if it's a RETURNING Var for an
6299 : : * INSERT target relation. In that case use the RTEPermissionInfo
6300 : : * associated with the RTE.
6301 : : *
6302 : : * If we navigate up to a parent relation, we keep using the same userid,
6303 : : * since it's the same in all relations of a given inheritance tree.
6304 : : */
6305 [ + + ]: 936871 : if (rel)
6306 : 936850 : userid = rel->userid;
6307 : : else
6308 : : {
6309 : : RTEPermissionInfo *perminfo;
6310 : :
6311 : 21 : perminfo = getRTEPermissionInfo(root->parse->rteperminfos, rte);
6312 : 21 : userid = perminfo->checkAsUser;
6313 : : }
6314 [ + + ]: 936871 : if (!OidIsValid(userid))
6315 : 830001 : userid = GetUserId();
6316 : :
6317 : : /*
6318 : : * Permissions and securityQuals must be checked on the table actually
6319 : : * mentioned in the query, so if this is an inheritance child, navigate up
6320 : : * to the inheritance root parent. If the user can read the whole table
6321 : : * or the required columns there, then they can read from the child table
6322 : : * too. For per-column checks, we must find out which of the root
6323 : : * parent's attributes the child relation's attributes correspond to.
6324 : : */
6325 [ + + ]: 936871 : if (root->append_rel_array != NULL)
6326 : : {
6327 : : AppendRelInfo *appinfo;
6328 : :
6329 : 118097 : appinfo = root->append_rel_array[varno];
6330 : :
6331 : : /*
6332 : : * Partitions are mapped to their immediate parent, not the root
6333 : : * parent, so must be ready to walk up multiple AppendRelInfos. But
6334 : : * stop if we hit a parent that is not RTE_RELATION --- that's a
6335 : : * flattened UNION ALL subquery, not an inheritance parent.
6336 : : */
6337 [ + + ]: 219658 : while (appinfo &&
6338 [ + - ]: 101747 : planner_rt_fetch(appinfo->parent_relid,
6339 [ + + ]: 101747 : root)->rtekind == RTE_RELATION)
6340 : : {
6341 : 101561 : Bitmapset *parent_varattnos = NULL;
6342 : :
6343 : : /*
6344 : : * For each child attribute, find the corresponding parent
6345 : : * attribute. In rare cases, the attribute may be local to the
6346 : : * child table, in which case, we've got to live with having no
6347 : : * access to this column.
6348 : : */
6349 : 101561 : varattno = -1;
6350 [ + + ]: 201697 : while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
6351 : : {
6352 : : AttrNumber attno;
6353 : : AttrNumber parent_attno;
6354 : :
6355 : 100136 : attno = varattno + FirstLowInvalidHeapAttributeNumber;
6356 : :
6357 [ + + ]: 100136 : if (attno == InvalidAttrNumber)
6358 : : {
6359 : : /*
6360 : : * Whole-row reference, so must map each column of the
6361 : : * child to the parent table.
6362 : : */
6363 [ + + ]: 18 : for (attno = 1; attno <= appinfo->num_child_cols; attno++)
6364 : : {
6365 : 12 : parent_attno = appinfo->parent_colnos[attno - 1];
6366 [ - + ]: 12 : if (parent_attno == 0)
216 dean.a.rasheed@gmail 6367 :UBC 0 : return false; /* attr is local to child */
6368 : : parent_varattnos =
216 dean.a.rasheed@gmail 6369 :CBC 12 : bms_add_member(parent_varattnos,
6370 : : parent_attno - FirstLowInvalidHeapAttributeNumber);
6371 : : }
6372 : : }
6373 : : else
6374 : : {
6375 [ - + ]: 100130 : if (attno < 0)
6376 : : {
6377 : : /* System attnos are the same in all tables */
216 dean.a.rasheed@gmail 6378 :UBC 0 : parent_attno = attno;
6379 : : }
6380 : : else
6381 : : {
216 dean.a.rasheed@gmail 6382 [ - + ]:CBC 100130 : if (attno > appinfo->num_child_cols)
216 dean.a.rasheed@gmail 6383 :UBC 0 : return false; /* safety check */
216 dean.a.rasheed@gmail 6384 :CBC 100130 : parent_attno = appinfo->parent_colnos[attno - 1];
6385 [ - + ]: 100130 : if (parent_attno == 0)
216 dean.a.rasheed@gmail 6386 :UBC 0 : return false; /* attr is local to child */
6387 : : }
6388 : : parent_varattnos =
216 dean.a.rasheed@gmail 6389 :CBC 100130 : bms_add_member(parent_varattnos,
6390 : : parent_attno - FirstLowInvalidHeapAttributeNumber);
6391 : : }
6392 : : }
6393 : :
6394 : : /* If the parent is itself a child, continue up */
6395 : 101561 : varno = appinfo->parent_relid;
6396 : 101561 : varattnos = parent_varattnos;
6397 : 101561 : appinfo = root->append_rel_array[varno];
6398 : : }
6399 : :
6400 : : /* Perform the access check on this parent rel */
6401 [ + - ]: 118097 : rte = planner_rt_fetch(varno, root);
6402 [ - + ]: 118097 : Assert(rte->rtekind == RTE_RELATION);
6403 : : }
6404 : :
6405 : : /*
6406 : : * For all rows to be accessible, there must be no securityQuals from
6407 : : * security barrier views or RLS policies.
6408 : : */
6409 [ + + ]: 936871 : if (rte->securityQuals != NIL)
6410 : 414 : return false;
6411 : :
6412 : : /*
6413 : : * Test for table-level SELECT privilege.
6414 : : *
6415 : : * If varattnos is non-NULL, this is sufficient to give access to all
6416 : : * requested attributes, even for a child table, since we have verified
6417 : : * that all required child columns have matching parent columns.
6418 : : *
6419 : : * If varattnos is NULL (whole-table access requested), this doesn't
6420 : : * necessarily guarantee that the user can read all columns of a child
6421 : : * table, but we allow it anyway (see comments in examine_variable()) and
6422 : : * don't bother checking any column privileges.
6423 : : */
6424 [ + + ]: 936457 : if (pg_class_aclcheck(rte->relid, userid, ACL_SELECT) == ACLCHECK_OK)
6425 : 936231 : return true;
6426 : :
6427 [ + + ]: 226 : if (varattnos == NULL)
6428 : 6 : return false; /* whole-table access requested */
6429 : :
6430 : : /*
6431 : : * Don't have table-level SELECT privilege, so check per-column
6432 : : * privileges.
6433 : : */
6434 : 220 : varattno = -1;
6435 [ + + ]: 323 : while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
6436 : : {
6437 : 220 : AttrNumber attno = varattno + FirstLowInvalidHeapAttributeNumber;
6438 : :
6439 [ + + ]: 220 : if (attno == InvalidAttrNumber)
6440 : : {
6441 : : /* Whole-row reference, so must have access to all columns */
6442 [ + - ]: 3 : if (pg_attribute_aclcheck_all(rte->relid, userid, ACL_SELECT,
6443 : : ACLMASK_ALL) != ACLCHECK_OK)
6444 : 3 : return false;
6445 : : }
6446 : : else
6447 : : {
6448 [ + + ]: 217 : if (pg_attribute_aclcheck(rte->relid, attno, userid,
6449 : : ACL_SELECT) != ACLCHECK_OK)
6450 : 114 : return false;
6451 : : }
6452 : : }
6453 : :
6454 : : /* If we reach here, have all required column privileges */
6455 : 103 : return true;
6456 : : }
6457 : :
6458 : : /*
6459 : : * examine_indexcol_variable
6460 : : * Try to look up statistical data about an index column/expression.
6461 : : * Fill in a VariableStatData struct to describe the column.
6462 : : *
6463 : : * Inputs:
6464 : : * root: the planner info
6465 : : * index: the index whose column we're interested in
6466 : : * indexcol: 0-based index column number (subscripts index->indexkeys[])
6467 : : *
6468 : : * Outputs: *vardata is filled as follows:
6469 : : * var: the input expression (with any binary relabeling stripped, if
6470 : : * it is or contains a variable; but otherwise the type is preserved)
6471 : : * rel: RelOptInfo for table relation containing variable.
6472 : : * statsTuple: the pg_statistic entry for the variable, if one exists;
6473 : : * otherwise NULL.
6474 : : * freefunc: pointer to a function to release statsTuple with.
6475 : : *
6476 : : * Caller is responsible for doing ReleaseVariableStats() before exiting.
6477 : : */
6478 : : static void
345 pg@bowt.ie 6479 : 474550 : examine_indexcol_variable(PlannerInfo *root, IndexOptInfo *index,
6480 : : int indexcol, VariableStatData *vardata)
6481 : : {
6482 : : AttrNumber colnum;
6483 : : Oid relid;
6484 : :
6485 [ + + ]: 474550 : if (index->indexkeys[indexcol] != 0)
6486 : : {
6487 : : /* Simple variable --- look to stats for the underlying table */
6488 [ + - ]: 473443 : RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
6489 : :
6490 [ - + ]: 473443 : Assert(rte->rtekind == RTE_RELATION);
6491 : 473443 : relid = rte->relid;
6492 [ - + ]: 473443 : Assert(relid != InvalidOid);
6493 : 473443 : colnum = index->indexkeys[indexcol];
6494 : 473443 : vardata->rel = index->rel;
6495 : :
6496 [ - + - - ]: 473443 : if (get_relation_stats_hook &&
345 pg@bowt.ie 6497 :UBC 0 : (*get_relation_stats_hook) (root, rte, colnum, vardata))
6498 : : {
6499 : : /*
6500 : : * The hook took control of acquiring a stats tuple. If it did
6501 : : * supply a tuple, it'd better have supplied a freefunc.
6502 : : */
6503 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6504 [ # # ]: 0 : !vardata->freefunc)
6505 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6506 : : }
6507 : : else
6508 : : {
345 pg@bowt.ie 6509 :CBC 473443 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6510 : : ObjectIdGetDatum(relid),
6511 : : Int16GetDatum(colnum),
6512 : 473443 : BoolGetDatum(rte->inh));
6513 : 473443 : vardata->freefunc = ReleaseSysCache;
6514 : : }
6515 : : }
6516 : : else
6517 : : {
6518 : : /* Expression --- maybe there are stats for the index itself */
6519 : 1107 : relid = index->indexoid;
6520 : 1107 : colnum = indexcol + 1;
6521 : :
6522 [ - + - - ]: 1107 : if (get_index_stats_hook &&
345 pg@bowt.ie 6523 :UBC 0 : (*get_index_stats_hook) (root, relid, colnum, vardata))
6524 : : {
6525 : : /*
6526 : : * The hook took control of acquiring a stats tuple. If it did
6527 : : * supply a tuple, it'd better have supplied a freefunc.
6528 : : */
6529 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6530 [ # # ]: 0 : !vardata->freefunc)
6531 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6532 : : }
6533 : : else
6534 : : {
345 pg@bowt.ie 6535 :CBC 1107 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6536 : : ObjectIdGetDatum(relid),
6537 : : Int16GetDatum(colnum),
6538 : : BoolGetDatum(false));
6539 : 1107 : vardata->freefunc = ReleaseSysCache;
6540 : : }
6541 : : }
6542 : 474550 : }
6543 : :
6544 : : /*
6545 : : * Check whether it is permitted to call func_oid passing some of the
6546 : : * pg_statistic data in vardata. We allow this if either of the following
6547 : : * conditions is met: (1) the user has SELECT privileges on the table or
6548 : : * column underlying the pg_statistic data and there are no securityQuals from
6549 : : * security barrier views or RLS policies, or (2) the function is marked
6550 : : * leakproof.
6551 : : */
6552 : : bool
3236 peter_e@gmx.net 6553 : 630012 : statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
6554 : : {
6555 [ + + ]: 630012 : if (vardata->acl_ok)
216 dean.a.rasheed@gmail 6556 : 629087 : return true; /* have SELECT privs and no securityQuals */
6557 : :
3236 peter_e@gmx.net 6558 [ - + ]: 925 : if (!OidIsValid(func_oid))
3236 peter_e@gmx.net 6559 :UBC 0 : return false;
6560 : :
3236 peter_e@gmx.net 6561 [ + + ]:CBC 925 : if (get_func_leakproof(func_oid))
6562 : 458 : return true;
6563 : :
6564 [ - + ]: 467 : ereport(DEBUG2,
6565 : : (errmsg_internal("not using statistics because function \"%s\" is not leakproof",
6566 : : get_func_name(func_oid))));
6567 : 467 : return false;
6568 : : }
6569 : :
6570 : : /*
6571 : : * get_variable_numdistinct
6572 : : * Estimate the number of distinct values of a variable.
6573 : : *
6574 : : * vardata: results of examine_variable
6575 : : * *isdefault: set to true if the result is a default rather than based on
6576 : : * anything meaningful.
6577 : : *
6578 : : * NB: be careful to produce a positive integral result, since callers may
6579 : : * compare the result to exact integer counts, or might divide by it.
6580 : : */
6581 : : double
5306 tgl@sss.pgh.pa.us 6582 : 974599 : get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
6583 : : {
6584 : : double stadistinct;
3507 6585 : 974599 : double stanullfrac = 0.0;
6586 : : double ntuples;
6587 : :
5306 6588 : 974599 : *isdefault = false;
6589 : :
6590 : : /*
6591 : : * Determine the stadistinct value to use. There are cases where we can
6592 : : * get an estimate even without a pg_statistic entry, or can get a better
6593 : : * value than is in pg_statistic. Grab stanullfrac too if we can find it
6594 : : * (otherwise, assume no nulls, for lack of any better idea).
6595 : : */
8062 6596 [ + + ]: 974599 : if (HeapTupleIsValid(vardata->statsTuple))
6597 : : {
6598 : : /* Use the pg_statistic entry */
6599 : : Form_pg_statistic stats;
6600 : :
6601 : 659701 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
6602 : 659701 : stadistinct = stats->stadistinct;
3507 6603 : 659701 : stanullfrac = stats->stanullfrac;
6604 : : }
7653 6605 [ + + ]: 314898 : else if (vardata->vartype == BOOLOID)
6606 : : {
6607 : : /*
6608 : : * Special-case boolean columns: presumably, two distinct values.
6609 : : *
6610 : : * Are there any other datatypes we should wire in special estimates
6611 : : * for?
6612 : : */
8062 6613 : 476 : stadistinct = 2.0;
6614 : : }
3133 6615 [ + + + + ]: 314422 : else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
6616 : : {
6617 : : /*
6618 : : * If the Var represents a column of a VALUES RTE, assume it's unique.
6619 : : * This could of course be very wrong, but it should tend to be true
6620 : : * in well-written queries. We could consider examining the VALUES'
6621 : : * contents to get some real statistics; but that only works if the
6622 : : * entries are all constants, and it would be pretty expensive anyway.
6623 : : */
6624 : 1977 : stadistinct = -1.0; /* unique (and all non null) */
6625 : : }
6626 : : else
6627 : : {
6628 : : /*
6629 : : * We don't keep statistics for system columns, but in some cases we
6630 : : * can infer distinctness anyway.
6631 : : */
8062 6632 [ + + + + ]: 312445 : if (vardata->var && IsA(vardata->var, Var))
6633 : : {
6634 [ + + + ]: 290897 : switch (((Var *) vardata->var)->varattno)
6635 : : {
6636 : 623 : case SelfItemPointerAttributeNumber:
3507 6637 : 623 : stadistinct = -1.0; /* unique (and all non null) */
8062 6638 : 623 : break;
6639 : 17714 : case TableOidAttributeNumber:
7868 bruce@momjian.us 6640 : 17714 : stadistinct = 1.0; /* only 1 value */
8062 tgl@sss.pgh.pa.us 6641 : 17714 : break;
6642 : 272560 : default:
7868 bruce@momjian.us 6643 : 272560 : stadistinct = 0.0; /* means "unknown" */
8062 tgl@sss.pgh.pa.us 6644 : 272560 : break;
6645 : : }
6646 : : }
6647 : : else
7868 bruce@momjian.us 6648 : 21548 : stadistinct = 0.0; /* means "unknown" */
6649 : :
6650 : : /*
6651 : : * XXX consider using estimate_num_groups on expressions?
6652 : : */
6653 : : }
6654 : :
6655 : : /*
6656 : : * If there is a unique index, DISTINCT or GROUP-BY clause for the
6657 : : * variable, assume it is unique no matter what pg_statistic says; the
6658 : : * statistics could be out of date, or we might have found a partial
6659 : : * unique index that proves the var is unique for this query. However,
6660 : : * we'd better still believe the null-fraction statistic.
6661 : : */
6237 tgl@sss.pgh.pa.us 6662 [ + + ]: 974599 : if (vardata->isunique)
3507 6663 : 248139 : stadistinct = -1.0 * (1.0 - stanullfrac);
6664 : :
6665 : : /*
6666 : : * If we had an absolute estimate, use that.
6667 : : */
8062 6668 [ + + ]: 974599 : if (stadistinct > 0.0)
3881 6669 : 229508 : return clamp_row_est(stadistinct);
6670 : :
6671 : : /*
6672 : : * Otherwise we need to get the relation size; punt if not available.
6673 : : */
8062 6674 [ + + ]: 745091 : if (vardata->rel == NULL)
6675 : : {
5306 6676 : 357 : *isdefault = true;
8062 6677 : 357 : return DEFAULT_NUM_DISTINCT;
6678 : : }
6679 : 744734 : ntuples = vardata->rel->tuples;
6680 [ + + ]: 744734 : if (ntuples <= 0.0)
6681 : : {
5306 6682 : 89785 : *isdefault = true;
8062 6683 : 89785 : return DEFAULT_NUM_DISTINCT;
6684 : : }
6685 : :
6686 : : /*
6687 : : * If we had a relative estimate, use that.
6688 : : */
6689 [ + + ]: 654949 : if (stadistinct < 0.0)
3881 6690 : 479378 : return clamp_row_est(-stadistinct * ntuples);
6691 : :
6692 : : /*
6693 : : * With no data, estimate ndistinct = ntuples if the table is small, else
6694 : : * use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
6695 : : * that the behavior isn't discontinuous.
6696 : : */
8062 6697 [ + + ]: 175571 : if (ntuples < DEFAULT_NUM_DISTINCT)
3881 6698 : 81650 : return clamp_row_est(ntuples);
6699 : :
5306 6700 : 93921 : *isdefault = true;
8062 6701 : 93921 : return DEFAULT_NUM_DISTINCT;
6702 : : }
6703 : :
6704 : : /*
6705 : : * get_variable_range
6706 : : * Estimate the minimum and maximum value of the specified variable.
6707 : : * If successful, store values in *min and *max, and return true.
6708 : : * If no data available, return false.
6709 : : *
6710 : : * sortop is the "<" comparison operator to use. This should generally
6711 : : * be "<" not ">", as only the former is likely to be found in pg_statistic.
6712 : : * The collation must be specified too.
6713 : : */
6714 : : static bool
2109 6715 : 140397 : get_variable_range(PlannerInfo *root, VariableStatData *vardata,
6716 : : Oid sortop, Oid collation,
6717 : : Datum *min, Datum *max)
6718 : : {
6672 6719 : 140397 : Datum tmin = 0;
8062 6720 : 140397 : Datum tmax = 0;
6672 6721 : 140397 : bool have_data = false;
6722 : : int16 typLen;
6723 : : bool typByVal;
6724 : : Oid opfuncoid;
6725 : : FmgrInfo opproc;
6726 : : AttStatsSlot sslot;
6727 : :
6728 : : /*
6729 : : * XXX It's very tempting to try to use the actual column min and max, if
6730 : : * we can get them relatively-cheaply with an index probe. However, since
6731 : : * this function is called many times during join planning, that could
6732 : : * have unpleasant effects on planning speed. Need more investigation
6733 : : * before enabling this.
6734 : : */
6735 : : #ifdef NOT_USED
6736 : : if (get_actual_variable_range(root, vardata, sortop, collation, min, max))
6737 : : return true;
6738 : : #endif
6739 : :
8062 6740 [ + + ]: 140397 : if (!HeapTupleIsValid(vardata->statsTuple))
6741 : : {
6742 : : /* no stats available, so default result */
6743 : 34735 : return false;
6744 : : }
6745 : :
6746 : : /*
6747 : : * If we can't apply the sortop to the stats data, just fail. In
6748 : : * principle, if there's a histogram and no MCVs, we could return the
6749 : : * histogram endpoints without ever applying the sortop ... but it's
6750 : : * probably not worth trying, because whatever the caller wants to do with
6751 : : * the endpoints would likely fail the security check too.
6752 : : */
3236 peter_e@gmx.net 6753 [ - + ]: 105662 : if (!statistic_proc_security_check(vardata,
6754 : 105662 : (opfuncoid = get_opcode(sortop))))
3236 peter_e@gmx.net 6755 :UBC 0 : return false;
6756 : :
2109 tgl@sss.pgh.pa.us 6757 :CBC 105662 : opproc.fn_oid = InvalidOid; /* mark this as not looked up yet */
6758 : :
8062 6759 : 105662 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
6760 : :
6761 : : /*
6762 : : * If there is a histogram with the ordering we want, grab the first and
6763 : : * last values.
6764 : : */
3228 6765 [ + + ]: 105662 : if (get_attstatsslot(&sslot, vardata->statsTuple,
6766 : : STATISTIC_KIND_HISTOGRAM, sortop,
6767 : : ATTSTATSSLOT_VALUES))
6768 : : {
2109 6769 [ + - + - ]: 68632 : if (sslot.stacoll == collation && sslot.nvalues > 0)
6770 : : {
3228 6771 : 68632 : tmin = datumCopy(sslot.values[0], typByVal, typLen);
6772 : 68632 : tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
6672 6773 : 68632 : have_data = true;
6774 : : }
3228 6775 : 68632 : free_attstatsslot(&sslot);
6776 : : }
6777 : :
6778 : : /*
6779 : : * Otherwise, if there is a histogram with some other ordering, scan it
6780 : : * and get the min and max values according to the ordering we want. This
6781 : : * of course may not find values that are really extremal according to our
6782 : : * ordering, but it beats ignoring available data.
6783 : : */
2109 6784 [ + + - + ]: 142692 : if (!have_data &&
6785 : 37030 : get_attstatsslot(&sslot, vardata->statsTuple,
6786 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
6787 : : ATTSTATSSLOT_VALUES))
6788 : : {
2109 tgl@sss.pgh.pa.us 6789 :UBC 0 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
6790 : : collation, typLen, typByVal,
6791 : : &tmin, &tmax, &have_data);
3228 6792 : 0 : free_attstatsslot(&sslot);
6793 : : }
6794 : :
6795 : : /*
6796 : : * If we have most-common-values info, look for extreme MCVs. This is
6797 : : * needed even if we also have a histogram, since the histogram excludes
6798 : : * the MCVs. However, if we *only* have MCVs and no histogram, we should
6799 : : * be pretty wary of deciding that that is a full representation of the
6800 : : * data. Proceed only if the MCVs represent the whole table (to within
6801 : : * roundoff error).
6802 : : */
3228 tgl@sss.pgh.pa.us 6803 [ + + ]:CBC 105662 : if (get_attstatsslot(&sslot, vardata->statsTuple,
6804 : : STATISTIC_KIND_MCV, InvalidOid,
1626 6805 [ + + ]: 105662 : have_data ? ATTSTATSSLOT_VALUES :
6806 : : (ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)))
6807 : : {
6808 : 59478 : bool use_mcvs = have_data;
6809 : :
6810 [ + + ]: 59478 : if (!have_data)
6811 : : {
6812 : 36191 : double sumcommon = 0.0;
6813 : : double nullfrac;
6814 : : int i;
6815 : :
6816 [ + + ]: 281051 : for (i = 0; i < sslot.nnumbers; i++)
6817 : 244860 : sumcommon += sslot.numbers[i];
6818 : 36191 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata->statsTuple))->stanullfrac;
6819 [ + + ]: 36191 : if (sumcommon + nullfrac > 0.99999)
6820 : 34052 : use_mcvs = true;
6821 : : }
6822 : :
6823 [ + + ]: 59478 : if (use_mcvs)
6824 : 57339 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
6825 : : collation, typLen, typByVal,
6826 : : &tmin, &tmax, &have_data);
3228 6827 : 59478 : free_attstatsslot(&sslot);
6828 : : }
6829 : :
6672 6830 : 105662 : *min = tmin;
8062 6831 : 105662 : *max = tmax;
6672 6832 : 105662 : return have_data;
6833 : : }
6834 : :
6835 : : /*
6836 : : * get_stats_slot_range: scan sslot for min/max values
6837 : : *
6838 : : * Subroutine for get_variable_range: update min/max/have_data according
6839 : : * to what we find in the statistics array.
6840 : : */
6841 : : static void
2109 6842 : 57339 : get_stats_slot_range(AttStatsSlot *sslot, Oid opfuncoid, FmgrInfo *opproc,
6843 : : Oid collation, int16 typLen, bool typByVal,
6844 : : Datum *min, Datum *max, bool *p_have_data)
6845 : : {
6846 : 57339 : Datum tmin = *min;
6847 : 57339 : Datum tmax = *max;
6848 : 57339 : bool have_data = *p_have_data;
6849 : 57339 : bool found_tmin = false;
6850 : 57339 : bool found_tmax = false;
6851 : :
6852 : : /* Look up the comparison function, if we didn't already do so */
6853 [ + - ]: 57339 : if (opproc->fn_oid != opfuncoid)
6854 : 57339 : fmgr_info(opfuncoid, opproc);
6855 : :
6856 : : /* Scan all the slot's values */
6857 [ + + ]: 1496956 : for (int i = 0; i < sslot->nvalues; i++)
6858 : : {
6859 [ + + ]: 1439617 : if (!have_data)
6860 : : {
6861 : 34052 : tmin = tmax = sslot->values[i];
6862 : 34052 : found_tmin = found_tmax = true;
6863 : 34052 : *p_have_data = have_data = true;
6864 : 34052 : continue;
6865 : : }
6866 [ + + ]: 1405565 : if (DatumGetBool(FunctionCall2Coll(opproc,
6867 : : collation,
6868 : 1405565 : sslot->values[i], tmin)))
6869 : : {
6870 : 34922 : tmin = sslot->values[i];
6871 : 34922 : found_tmin = true;
6872 : : }
6873 [ + + ]: 1405565 : if (DatumGetBool(FunctionCall2Coll(opproc,
6874 : : collation,
6875 : 1405565 : tmax, sslot->values[i])))
6876 : : {
6877 : 134719 : tmax = sslot->values[i];
6878 : 134719 : found_tmax = true;
6879 : : }
6880 : : }
6881 : :
6882 : : /*
6883 : : * Copy the slot's values, if we found new extreme values.
6884 : : */
6885 [ + + ]: 57339 : if (found_tmin)
6886 : 48013 : *min = datumCopy(tmin, typByVal, typLen);
6887 [ + + ]: 57339 : if (found_tmax)
6888 : 36838 : *max = datumCopy(tmax, typByVal, typLen);
6889 : 57339 : }
6890 : :
6891 : :
6892 : : /*
6893 : : * get_actual_variable_range
6894 : : * Attempt to identify the current *actual* minimum and/or maximum
6895 : : * of the specified variable, by looking for a suitable btree index
6896 : : * and fetching its low and/or high values.
6897 : : * If successful, store values in *min and *max, and return true.
6898 : : * (Either pointer can be NULL if that endpoint isn't needed.)
6899 : : * If unsuccessful, return false.
6900 : : *
6901 : : * sortop is the "<" comparison operator to use.
6902 : : * collation is the required collation.
6903 : : */
6904 : : static bool
5914 6905 : 105363 : get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
6906 : : Oid sortop, Oid collation,
6907 : : Datum *min, Datum *max)
6908 : : {
6909 : 105363 : bool have_data = false;
6910 : 105363 : RelOptInfo *rel = vardata->rel;
6911 : : RangeTblEntry *rte;
6912 : : ListCell *lc;
6913 : :
6914 : : /* No hope if no relation or it doesn't have indexes */
6915 [ + - + + ]: 105363 : if (rel == NULL || rel->indexlist == NIL)
6916 : 6932 : return false;
6917 : : /* If it has indexes it must be a plain relation */
6918 : 98431 : rte = root->simple_rte_array[rel->relid];
6919 [ - + ]: 98431 : Assert(rte->rtekind == RTE_RELATION);
6920 : :
6921 : : /* ignore partitioned tables. Any indexes here are not real indexes */
1161 drowley@postgresql.o 6922 [ + + ]: 98431 : if (rte->relkind == RELKIND_PARTITIONED_TABLE)
6923 : 430 : return false;
6924 : :
6925 : : /* Search through the indexes to see if any match our problem */
5914 tgl@sss.pgh.pa.us 6926 [ + - + + : 193521 : foreach(lc, rel->indexlist)
+ + ]
6927 : : {
6928 : 166454 : IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
6929 : : ScanDirection indexscandir;
6930 : : StrategyNumber strategy;
6931 : :
6932 : : /* Ignore non-ordering indexes */
345 peter@eisentraut.org 6933 [ + + ]: 166454 : if (index->sortopfamily == NULL)
345 peter@eisentraut.org 6934 :GBC 3 : continue;
6935 : :
6936 : : /*
6937 : : * Ignore partial indexes --- we only want stats that cover the entire
6938 : : * relation.
6939 : : */
5914 tgl@sss.pgh.pa.us 6940 [ + + ]:CBC 166451 : if (index->indpred != NIL)
6941 : 144 : continue;
6942 : :
6943 : : /*
6944 : : * The index list might include hypothetical indexes inserted by a
6945 : : * get_relation_info hook --- don't try to access them.
6946 : : */
5506 6947 [ - + ]: 166307 : if (index->hypothetical)
5914 tgl@sss.pgh.pa.us 6948 :UBC 0 : continue;
6949 : :
6950 : : /*
6951 : : * get_actual_variable_endpoint uses the index-only-scan machinery, so
6952 : : * ignore indexes that can't use it on their first column.
6953 : : */
138 peter@eisentraut.org 6954 [ - + ]:CBC 166307 : if (!index->canreturn[0])
138 peter@eisentraut.org 6955 :UBC 0 : continue;
6956 : :
6957 : : /*
6958 : : * The first index column must match the desired variable, sortop, and
6959 : : * collation --- but we can use a descending-order index.
6960 : : */
2109 tgl@sss.pgh.pa.us 6961 [ + + ]:CBC 166307 : if (collation != index->indexcollations[0])
6962 : 22230 : continue; /* test first 'cause it's cheapest */
5914 6963 [ + + ]: 144077 : if (!match_index_to_operand(vardata->var, 0, index))
6964 : 73143 : continue;
345 peter@eisentraut.org 6965 : 70934 : strategy = get_op_opfamily_strategy(sortop, index->sortopfamily[0]);
6966 [ + - - ]: 70934 : switch (IndexAmTranslateStrategy(strategy, index->relam, index->sortopfamily[0], true))
6967 : : {
6968 : 70934 : case COMPARE_LT:
5585 tgl@sss.pgh.pa.us 6969 [ - + ]: 70934 : if (index->reverse_sort[0])
5585 tgl@sss.pgh.pa.us 6970 :UBC 0 : indexscandir = BackwardScanDirection;
6971 : : else
5585 tgl@sss.pgh.pa.us 6972 :CBC 70934 : indexscandir = ForwardScanDirection;
6973 : 70934 : break;
345 peter@eisentraut.org 6974 :UBC 0 : case COMPARE_GT:
5585 tgl@sss.pgh.pa.us 6975 [ # # ]: 0 : if (index->reverse_sort[0])
6976 : 0 : indexscandir = ForwardScanDirection;
6977 : : else
6978 : 0 : indexscandir = BackwardScanDirection;
6979 : 0 : break;
6980 : 0 : default:
6981 : : /* index doesn't match the sortop */
6982 : 0 : continue;
6983 : : }
6984 : :
6985 : : /*
6986 : : * Found a suitable index to extract data from. Set up some data that
6987 : : * can be used by both invocations of get_actual_variable_endpoint.
6988 : : */
6989 : : {
6990 : : MemoryContext tmpcontext;
6991 : : MemoryContext oldcontext;
6992 : : Relation heapRel;
6993 : : Relation indexRel;
6994 : : TupleTableSlot *slot;
6995 : : int16 typLen;
6996 : : bool typByVal;
6997 : : ScanKeyData scankeys[1];
6998 : :
6999 : : /* Make sure any cruft gets recycled when we're done */
2438 tgl@sss.pgh.pa.us 7000 :CBC 70934 : tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
7001 : : "get_actual_variable_range workspace",
7002 : : ALLOCSET_DEFAULT_SIZES);
5914 7003 : 70934 : oldcontext = MemoryContextSwitchTo(tmpcontext);
7004 : :
7005 : : /*
7006 : : * Open the table and index so we can read from them. We should
7007 : : * already have some type of lock on each.
7008 : : */
2610 andres@anarazel.de 7009 : 70934 : heapRel = table_open(rte->relid, NoLock);
2537 tgl@sss.pgh.pa.us 7010 : 70934 : indexRel = index_open(index->indexoid, NoLock);
7011 : :
7012 : : /* build some stuff needed for indexscan execution */
2561 andres@anarazel.de 7013 : 70934 : slot = table_slot_create(heapRel, NULL);
5914 tgl@sss.pgh.pa.us 7014 : 70934 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
7015 : :
7016 : : /* set up an IS NOT NULL scan key so that we ignore nulls */
7017 : 70934 : ScanKeyEntryInitialize(&scankeys[0],
7018 : : SK_ISNULL | SK_SEARCHNOTNULL,
7019 : : 1, /* index col to scan */
7020 : : InvalidStrategy, /* no strategy */
7021 : : InvalidOid, /* no strategy subtype */
7022 : : InvalidOid, /* no collation */
7023 : : InvalidOid, /* no reg proc for this */
7024 : : (Datum) 0); /* constant */
7025 : :
7026 : : /* If min is requested ... */
7027 [ + + ]: 70934 : if (min)
7028 : : {
2438 7029 : 38802 : have_data = get_actual_variable_endpoint(heapRel,
7030 : : indexRel,
7031 : : indexscandir,
7032 : : scankeys,
7033 : : typLen,
7034 : : typByVal,
7035 : : slot,
7036 : : oldcontext,
7037 : : min);
7038 : : }
7039 : : else
7040 : : {
7041 : : /* If min not requested, still want to fetch max */
7042 : 32132 : have_data = true;
7043 : : }
7044 : :
7045 : : /* If max is requested, and we didn't already fail ... */
5914 7046 [ + + + - ]: 70934 : if (max && have_data)
7047 : : {
7048 : : /* scan in the opposite direction; all else is the same */
2438 7049 : 33182 : have_data = get_actual_variable_endpoint(heapRel,
7050 : : indexRel,
7051 : 33182 : -indexscandir,
7052 : : scankeys,
7053 : : typLen,
7054 : : typByVal,
7055 : : slot,
7056 : : oldcontext,
7057 : : max);
7058 : : }
7059 : :
7060 : : /* Clean everything up */
5914 7061 : 70934 : ExecDropSingleTupleTableSlot(slot);
7062 : :
2537 7063 : 70934 : index_close(indexRel, NoLock);
2610 andres@anarazel.de 7064 : 70934 : table_close(heapRel, NoLock);
7065 : :
5914 tgl@sss.pgh.pa.us 7066 : 70934 : MemoryContextSwitchTo(oldcontext);
2438 7067 : 70934 : MemoryContextDelete(tmpcontext);
7068 : :
7069 : : /* And we're done */
5914 7070 : 70934 : break;
7071 : : }
7072 : : }
7073 : :
7074 : 98001 : return have_data;
7075 : : }
7076 : :
7077 : : /*
7078 : : * Get one endpoint datum (min or max depending on indexscandir) from the
7079 : : * specified index. Return true if successful, false if not.
7080 : : * On success, endpoint value is stored to *endpointDatum (and copied into
7081 : : * outercontext).
7082 : : *
7083 : : * scankeys is a 1-element scankey array set up to reject nulls.
7084 : : * typLen/typByVal describe the datatype of the index's first column.
7085 : : * tableslot is a slot suitable to hold table tuples, in case we need
7086 : : * to probe the heap.
7087 : : * (We could compute these values locally, but that would mean computing them
7088 : : * twice when get_actual_variable_range needs both the min and the max.)
7089 : : *
7090 : : * Failure occurs either when the index is empty, or we decide that it's
7091 : : * taking too long to find a suitable tuple.
7092 : : */
7093 : : static bool
2438 7094 : 71984 : get_actual_variable_endpoint(Relation heapRel,
7095 : : Relation indexRel,
7096 : : ScanDirection indexscandir,
7097 : : ScanKey scankeys,
7098 : : int16 typLen,
7099 : : bool typByVal,
7100 : : TupleTableSlot *tableslot,
7101 : : MemoryContext outercontext,
7102 : : Datum *endpointDatum)
7103 : : {
7104 : 71984 : bool have_data = false;
7105 : : SnapshotData SnapshotNonVacuumable;
7106 : : IndexScanDesc index_scan;
7107 : 71984 : Buffer vmbuffer = InvalidBuffer;
1209 7108 : 71984 : BlockNumber last_heap_block = InvalidBlockNumber;
7109 : 71984 : int n_visited_heap_pages = 0;
7110 : : ItemPointer tid;
7111 : : Datum values[INDEX_MAX_KEYS];
7112 : : bool isnull[INDEX_MAX_KEYS];
7113 : : MemoryContext oldcontext;
7114 : :
7115 : : /*
7116 : : * We use the index-only-scan machinery for this. With mostly-static
7117 : : * tables that's a win because it avoids a heap visit. It's also a win
7118 : : * for dynamic data, but the reason is less obvious; read on for details.
7119 : : *
7120 : : * In principle, we should scan the index with our current active
7121 : : * snapshot, which is the best approximation we've got to what the query
7122 : : * will see when executed. But that won't be exact if a new snap is taken
7123 : : * before running the query, and it can be very expensive if a lot of
7124 : : * recently-dead or uncommitted rows exist at the beginning or end of the
7125 : : * index (because we'll laboriously fetch each one and reject it).
7126 : : * Instead, we use SnapshotNonVacuumable. That will accept recently-dead
7127 : : * and uncommitted rows as well as normal visible rows. On the other
7128 : : * hand, it will reject known-dead rows, and thus not give a bogus answer
7129 : : * when the extreme value has been deleted (unless the deletion was quite
7130 : : * recent); that case motivates not using SnapshotAny here.
7131 : : *
7132 : : * A crucial point here is that SnapshotNonVacuumable, with
7133 : : * GlobalVisTestFor(heapRel) as horizon, yields the inverse of the
7134 : : * condition that the indexscan will use to decide that index entries are
7135 : : * killable (see heap_hot_search_buffer()). Therefore, if the snapshot
7136 : : * rejects a tuple (or more precisely, all tuples of a HOT chain) and we
7137 : : * have to continue scanning past it, we know that the indexscan will mark
7138 : : * that index entry killed. That means that the next
7139 : : * get_actual_variable_endpoint() call will not have to re-consider that
7140 : : * index entry. In this way we avoid repetitive work when this function
7141 : : * is used a lot during planning.
7142 : : *
7143 : : * But using SnapshotNonVacuumable creates a hazard of its own. In a
7144 : : * recently-created index, some index entries may point at "broken" HOT
7145 : : * chains in which not all the tuple versions contain data matching the
7146 : : * index entry. The live tuple version(s) certainly do match the index,
7147 : : * but SnapshotNonVacuumable can accept recently-dead tuple versions that
7148 : : * don't match. Hence, if we took data from the selected heap tuple, we
7149 : : * might get a bogus answer that's not close to the index extremal value,
7150 : : * or could even be NULL. We avoid this hazard because we take the data
7151 : : * from the index entry not the heap.
7152 : : *
7153 : : * Despite all this care, there are situations where we might find many
7154 : : * non-visible tuples near the end of the index. We don't want to expend
7155 : : * a huge amount of time here, so we give up once we've read too many heap
7156 : : * pages. When we fail for that reason, the caller will end up using
7157 : : * whatever extremal value is recorded in pg_statistic.
7158 : : */
2041 andres@anarazel.de 7159 : 71984 : InitNonVacuumableSnapshot(SnapshotNonVacuumable,
7160 : : GlobalVisTestFor(heapRel));
7161 : :
2438 tgl@sss.pgh.pa.us 7162 : 71984 : index_scan = index_beginscan(heapRel, indexRel,
7163 : : &SnapshotNonVacuumable, NULL,
7164 : : 1, 0);
7165 : : /* Set it up for index-only scan */
7166 : 71984 : index_scan->xs_want_itup = true;
7167 : 71984 : index_rescan(index_scan, scankeys, 1, NULL, 0);
7168 : :
7169 : : /* Fetch first/next tuple in specified direction */
7170 [ + - ]: 89026 : while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
7171 : : {
1209 7172 : 89026 : BlockNumber block = ItemPointerGetBlockNumber(tid);
7173 : :
2438 7174 [ + + ]: 89026 : if (!VM_ALL_VISIBLE(heapRel,
7175 : : block,
7176 : : &vmbuffer))
7177 : : {
7178 : : /* Rats, we have to visit the heap to check visibility */
7179 [ + + ]: 61300 : if (!index_fetch_heap(index_scan, tableslot))
7180 : : {
7181 : : /*
7182 : : * No visible tuple for this index entry, so we need to
7183 : : * advance to the next entry. Before doing so, count heap
7184 : : * page fetches and give up if we've done too many.
7185 : : *
7186 : : * We don't charge a page fetch if this is the same heap page
7187 : : * as the previous tuple. This is on the conservative side,
7188 : : * since other recently-accessed pages are probably still in
7189 : : * buffers too; but it's good enough for this heuristic.
7190 : : */
7191 : : #define VISITED_PAGES_LIMIT 100
7192 : :
1209 7193 [ + + ]: 17042 : if (block != last_heap_block)
7194 : : {
7195 : 1824 : last_heap_block = block;
7196 : 1824 : n_visited_heap_pages++;
7197 [ - + ]: 1824 : if (n_visited_heap_pages > VISITED_PAGES_LIMIT)
1209 tgl@sss.pgh.pa.us 7198 :UBC 0 : break;
7199 : : }
7200 : :
2438 tgl@sss.pgh.pa.us 7201 :CBC 17042 : continue; /* no visible tuple, try next index entry */
7202 : : }
7203 : :
7204 : : /* We don't actually need the heap tuple for anything */
7205 : 44258 : ExecClearTuple(tableslot);
7206 : :
7207 : : /*
7208 : : * We don't care whether there's more than one visible tuple in
7209 : : * the HOT chain; if any are visible, that's good enough.
7210 : : */
7211 : : }
7212 : :
7213 : : /*
7214 : : * We expect that the index will return data in IndexTuple not
7215 : : * HeapTuple format.
7216 : : */
7217 [ - + ]: 71984 : if (!index_scan->xs_itup)
2438 tgl@sss.pgh.pa.us 7218 [ # # ]:UBC 0 : elog(ERROR, "no data returned for index-only scan");
7219 : :
7220 : : /*
7221 : : * We do not yet support recheck here.
7222 : : */
2438 tgl@sss.pgh.pa.us 7223 [ - + ]:CBC 71984 : if (index_scan->xs_recheck)
345 peter@eisentraut.org 7224 :UBC 0 : break;
7225 : :
7226 : : /* OK to deconstruct the index tuple */
2438 tgl@sss.pgh.pa.us 7227 :CBC 71984 : index_deform_tuple(index_scan->xs_itup,
7228 : : index_scan->xs_itupdesc,
7229 : : values, isnull);
7230 : :
7231 : : /* Shouldn't have got a null, but be careful */
7232 [ - + ]: 71984 : if (isnull[0])
2438 tgl@sss.pgh.pa.us 7233 [ # # ]:UBC 0 : elog(ERROR, "found unexpected null value in index \"%s\"",
7234 : : RelationGetRelationName(indexRel));
7235 : :
7236 : : /* Copy the index column value out to caller's context */
2438 tgl@sss.pgh.pa.us 7237 :CBC 71984 : oldcontext = MemoryContextSwitchTo(outercontext);
7238 : 71984 : *endpointDatum = datumCopy(values[0], typByVal, typLen);
7239 : 71984 : MemoryContextSwitchTo(oldcontext);
7240 : 71984 : have_data = true;
7241 : 71984 : break;
7242 : : }
7243 : :
7244 [ + + ]: 71984 : if (vmbuffer != InvalidBuffer)
7245 : 65600 : ReleaseBuffer(vmbuffer);
7246 : 71984 : index_endscan(index_scan);
7247 : :
7248 : 71984 : return have_data;
7249 : : }
7250 : :
7251 : : /*
7252 : : * find_join_input_rel
7253 : : * Look up the input relation for a join.
7254 : : *
7255 : : * We assume that the input relation's RelOptInfo must have been constructed
7256 : : * already.
7257 : : */
7258 : : static RelOptInfo *
5310 7259 : 7330 : find_join_input_rel(PlannerInfo *root, Relids relids)
7260 : : {
7261 : 7330 : RelOptInfo *rel = NULL;
7262 : :
838 drowley@postgresql.o 7263 [ + - ]: 7330 : if (!bms_is_empty(relids))
7264 : : {
7265 : : int relid;
7266 : :
7267 [ + + ]: 7330 : if (bms_get_singleton_member(relids, &relid))
7268 : 7157 : rel = find_base_rel(root, relid);
7269 : : else
5310 tgl@sss.pgh.pa.us 7270 : 173 : rel = find_join_rel(root, relids);
7271 : : }
7272 : :
7273 [ - + ]: 7330 : if (rel == NULL)
5310 tgl@sss.pgh.pa.us 7274 [ # # ]:UBC 0 : elog(ERROR, "could not find RelOptInfo for given relids");
7275 : :
5310 tgl@sss.pgh.pa.us 7276 :CBC 7330 : return rel;
7277 : : }
7278 : :
7279 : :
7280 : : /*-------------------------------------------------------------------------
7281 : : *
7282 : : * Index cost estimation functions
7283 : : *
7284 : : *-------------------------------------------------------------------------
7285 : : */
7286 : :
7287 : : /*
7288 : : * Extract the actual indexquals (as RestrictInfos) from an IndexClause list
7289 : : */
7290 : : List *
2585 7291 : 482952 : get_quals_from_indexclauses(List *indexclauses)
7292 : : {
2591 7293 : 482952 : List *result = NIL;
7294 : : ListCell *lc;
7295 : :
7296 [ + + + + : 846396 : foreach(lc, indexclauses)
+ + ]
7297 : : {
7298 : 363444 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7299 : : ListCell *lc2;
7300 : :
2586 7301 [ + - + + : 728351 : foreach(lc2, iclause->indexquals)
+ + ]
7302 : : {
7303 : 364907 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7304 : :
7305 : 364907 : result = lappend(result, rinfo);
7306 : : }
7307 : : }
2591 7308 : 482952 : return result;
7309 : : }
7310 : :
7311 : : /*
7312 : : * Compute the total evaluation cost of the comparison operands in a list
7313 : : * of index qual expressions. Since we know these will be evaluated just
7314 : : * once per scan, there's no need to distinguish startup from per-row cost.
7315 : : *
7316 : : * This can be used either on the result of get_quals_from_indexclauses(),
7317 : : * or directly on an indexorderbys list. In both cases, we expect that the
7318 : : * index key expression is on the left side of binary clauses.
7319 : : */
7320 : : Cost
2585 7321 : 959398 : index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
7322 : : {
4030 7323 : 959398 : Cost qual_arg_cost = 0;
7324 : : ListCell *lc;
7325 : :
2585 7326 [ + + + + : 1324536 : foreach(lc, indexquals)
+ + ]
7327 : : {
4030 7328 : 365138 : Expr *clause = (Expr *) lfirst(lc);
7329 : : Node *other_operand;
7330 : : QualCost index_qual_cost;
7331 : :
7332 : : /*
7333 : : * Index quals will have RestrictInfos, indexorderbys won't. Look
7334 : : * through RestrictInfo if present.
7335 : : */
2585 7336 [ + + ]: 365138 : if (IsA(clause, RestrictInfo))
7337 : 364901 : clause = ((RestrictInfo *) clause)->clause;
7338 : :
4030 7339 [ + + ]: 365138 : if (IsA(clause, OpExpr))
7340 : : {
2585 7341 : 355103 : OpExpr *op = (OpExpr *) clause;
7342 : :
7343 : 355103 : other_operand = (Node *) lsecond(op->args);
7344 : : }
7345 [ + + ]: 10035 : else if (IsA(clause, RowCompareExpr))
7346 : : {
7347 : 198 : RowCompareExpr *rc = (RowCompareExpr *) clause;
7348 : :
7349 : 198 : other_operand = (Node *) rc->rargs;
7350 : : }
7351 [ + + ]: 9837 : else if (IsA(clause, ScalarArrayOpExpr))
7352 : : {
7353 : 8374 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
7354 : :
7355 : 8374 : other_operand = (Node *) lsecond(saop->args);
7356 : : }
7357 [ + - ]: 1463 : else if (IsA(clause, NullTest))
7358 : : {
7359 : 1463 : other_operand = NULL;
7360 : : }
7361 : : else
7362 : : {
2585 tgl@sss.pgh.pa.us 7363 [ # # ]:UBC 0 : elog(ERROR, "unsupported indexqual type: %d",
7364 : : (int) nodeTag(clause));
7365 : : other_operand = NULL; /* keep compiler quiet */
7366 : : }
7367 : :
4030 tgl@sss.pgh.pa.us 7368 :CBC 365138 : cost_qual_eval_node(&index_qual_cost, other_operand, root);
7369 : 365138 : qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
7370 : : }
7371 : 959398 : return qual_arg_cost;
7372 : : }
7373 : :
7374 : : void
7588 7375 : 476452 : genericcostestimate(PlannerInfo *root,
7376 : : IndexPath *path,
7377 : : double loop_count,
7378 : : GenericCosts *costs)
7379 : : {
5195 7380 : 476452 : IndexOptInfo *index = path->indexinfo;
2585 7381 : 476452 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
5195 7382 : 476452 : List *indexOrderBys = path->indexorderbys;
7383 : : Cost indexStartupCost;
7384 : : Cost indexTotalCost;
7385 : : Selectivity indexSelectivity;
7386 : : double indexCorrelation;
7387 : : double numIndexPages;
7388 : : double numIndexTuples;
7389 : : double spc_random_page_cost;
7390 : : double num_sa_scans;
7391 : : double num_outer_scans;
7392 : : double num_scans;
7393 : : double qual_op_cost;
7394 : : double qual_arg_cost;
7395 : : List *selectivityQuals;
7396 : : ListCell *l;
7397 : :
7398 : : /*
7399 : : * If the index is partial, AND the index predicate with the explicitly
7400 : : * given indexquals to produce a more accurate idea of the index
7401 : : * selectivity.
7402 : : */
2585 7403 : 476452 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
7404 : :
7405 : : /*
7406 : : * If caller didn't give us an estimate for ScalarArrayOpExpr index scans,
7407 : : * just assume that the number of index descents is the number of distinct
7408 : : * combinations of array elements from all of the scan's SAOP clauses.
7409 : : */
708 pg@bowt.ie 7410 : 476452 : num_sa_scans = costs->num_sa_scans;
7411 [ + + ]: 476452 : if (num_sa_scans < 1)
7412 : : {
7413 : 3952 : num_sa_scans = 1;
7414 [ + + + + : 8296 : foreach(l, indexQuals)
+ + ]
7415 : : {
7416 : 4344 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
7417 : :
7418 [ + + ]: 4344 : if (IsA(rinfo->clause, ScalarArrayOpExpr))
7419 : : {
7420 : 13 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
7421 : 13 : double alength = estimate_array_length(root, lsecond(saop->args));
7422 : :
7423 [ + - ]: 13 : if (alength > 1)
7424 : 13 : num_sa_scans *= alength;
7425 : : }
7426 : : }
7427 : : }
7428 : :
7429 : : /* Estimate the fraction of main-table tuples that will be visited */
4811 tgl@sss.pgh.pa.us 7430 : 476452 : indexSelectivity = clauselist_selectivity(root, selectivityQuals,
7431 : 476452 : index->rel->relid,
7432 : : JOIN_INNER,
7433 : : NULL);
7434 : :
7435 : : /*
7436 : : * If caller didn't give us an estimate, estimate the number of index
7437 : : * tuples that will be visited. We do it in this rather peculiar-looking
7438 : : * way in order to get the right answer for partial indexes.
7439 : : */
7440 : 476452 : numIndexTuples = costs->numIndexTuples;
7580 7441 [ + + ]: 476452 : if (numIndexTuples <= 0.0)
7442 : : {
4811 7443 : 62725 : numIndexTuples = indexSelectivity * index->rel->tuples;
7444 : :
7445 : : /*
7446 : : * The above calculation counts all the tuples visited across all
7447 : : * scans induced by ScalarArrayOpExpr nodes. We want to consider the
7448 : : * average per-indexscan number, so adjust. This is a handy place to
7449 : : * round to integer, too. (If caller supplied tuple estimate, it's
7450 : : * responsible for handling these considerations.)
7451 : : */
7030 7452 : 62725 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
7453 : : }
7454 : :
7455 : : /*
7456 : : * We can bound the number of tuples by the index size in any case. Also,
7457 : : * always estimate at least one tuple is touched, even when
7458 : : * indexSelectivity estimate is tiny.
7459 : : */
7580 7460 [ + + ]: 476452 : if (numIndexTuples > index->tuples)
7461 : 5840 : numIndexTuples = index->tuples;
9471 7462 [ + + ]: 476452 : if (numIndexTuples < 1.0)
7463 : 65638 : numIndexTuples = 1.0;
7464 : :
7465 : : /*
7466 : : * Estimate the number of index pages that will be retrieved.
7467 : : *
7468 : : * We use the simplistic method of taking a pro-rata fraction of the total
7469 : : * number of index pages. In effect, this counts only leaf pages and not
7470 : : * any overhead such as index metapage or upper tree levels.
7471 : : *
7472 : : * In practice access to upper index levels is often nearly free because
7473 : : * those tend to stay in cache under load; moreover, the cost involved is
7474 : : * highly dependent on index type. We therefore ignore such costs here
7475 : : * and leave it to the caller to add a suitable charge if needed.
7476 : : */
7222 7477 [ + + + + ]: 476452 : if (index->pages > 1 && index->tuples > 1)
7478 : 427079 : numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
7479 : : else
9471 7480 : 49373 : numIndexPages = 1.0;
7481 : :
7482 : : /* fetch estimated page cost for tablespace containing index */
5913 rhaas@postgresql.org 7483 : 476452 : get_tablespace_page_costs(index->reltablespace,
7484 : : &spc_random_page_cost,
7485 : : NULL);
7486 : :
7487 : : /*
7488 : : * Now compute the disk access costs.
7489 : : *
7490 : : * The above calculations are all per-index-scan. However, if we are in a
7491 : : * nestloop inner scan, we can expect the scan to be repeated (with
7492 : : * different search keys) for each row of the outer relation. Likewise,
7493 : : * ScalarArrayOpExpr quals result in multiple index scans. This creates
7494 : : * the potential for cache effects to reduce the number of disk page
7495 : : * fetches needed. We want to estimate the average per-scan I/O cost in
7496 : : * the presence of caching.
7497 : : *
7498 : : * We use the Mackert-Lohman formula (see costsize.c for details) to
7499 : : * estimate the total number of page fetches that occur. While this
7500 : : * wasn't what it was designed for, it seems a reasonable model anyway.
7501 : : * Note that we are counting pages not tuples anymore, so we take N = T =
7502 : : * index size, as if there were one "tuple" per page.
7503 : : */
5161 tgl@sss.pgh.pa.us 7504 : 476452 : num_outer_scans = loop_count;
7505 : 476452 : num_scans = num_sa_scans * num_outer_scans;
7506 : :
7197 7507 [ + + ]: 476452 : if (num_scans > 1)
7508 : : {
7509 : : double pages_fetched;
7510 : :
7511 : : /* total page fetches ignoring cache effects */
7222 7512 : 57684 : pages_fetched = numIndexPages * num_scans;
7513 : :
7514 : : /* use Mackert and Lohman formula to adjust for cache effects */
7515 : 57684 : pages_fetched = index_pages_fetched(pages_fetched,
7516 : : index->pages,
7117 7517 : 57684 : (double) index->pages,
7518 : : root);
7519 : :
7520 : : /*
7521 : : * Now compute the total disk access cost, and then report a pro-rated
7522 : : * share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
7523 : : * since that's internal to the indexscan.)
7524 : : */
4811 7525 : 57684 : indexTotalCost = (pages_fetched * spc_random_page_cost)
7526 : : / num_outer_scans;
7527 : : }
7528 : : else
7529 : : {
7530 : : /*
7531 : : * For a single index scan, we just charge spc_random_page_cost per
7532 : : * page touched.
7533 : : */
7534 : 418768 : indexTotalCost = numIndexPages * spc_random_page_cost;
7535 : : }
7536 : :
7537 : : /*
7538 : : * CPU cost: any complex expressions in the indexquals will need to be
7539 : : * evaluated once at the start of the scan to reduce them to runtime keys
7540 : : * to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
7541 : : * CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
7542 : : * indexqual operator. Because we have numIndexTuples as a per-scan
7543 : : * number, we have to multiply by num_sa_scans to get the correct result
7544 : : * for ScalarArrayOpExpr cases. Similarly add in costs for any index
7545 : : * ORDER BY expressions.
7546 : : *
7547 : : * Note: this neglects the possible costs of rechecking lossy operators.
7548 : : * Detecting that that might be needed seems more expensive than it's
7549 : : * worth, though, considering all the other inaccuracies here ...
7550 : : */
2585 7551 : 476452 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
7552 : 476452 : index_other_operands_eval_cost(root, indexOrderBys);
5582 7553 : 476452 : qual_op_cost = cpu_operator_cost *
7554 : 476452 : (list_length(indexQuals) + list_length(indexOrderBys));
7555 : :
4811 7556 : 476452 : indexStartupCost = qual_arg_cost;
7557 : 476452 : indexTotalCost += qual_arg_cost;
7558 : 476452 : indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
7559 : :
7560 : : /*
7561 : : * Generic assumption about index correlation: there isn't any.
7562 : : */
7563 : 476452 : indexCorrelation = 0.0;
7564 : :
7565 : : /*
7566 : : * Return everything to caller.
7567 : : */
7568 : 476452 : costs->indexStartupCost = indexStartupCost;
7569 : 476452 : costs->indexTotalCost = indexTotalCost;
7570 : 476452 : costs->indexSelectivity = indexSelectivity;
7571 : 476452 : costs->indexCorrelation = indexCorrelation;
7572 : 476452 : costs->numIndexPages = numIndexPages;
7573 : 476452 : costs->numIndexTuples = numIndexTuples;
7574 : 476452 : costs->spc_random_page_cost = spc_random_page_cost;
7575 : 476452 : costs->num_sa_scans = num_sa_scans;
7576 : 476452 : }
7577 : :
7578 : : /*
7579 : : * If the index is partial, add its predicate to the given qual list.
7580 : : *
7581 : : * ANDing the index predicate with the explicitly given indexquals produces
7582 : : * a more accurate idea of the index's selectivity. However, we need to be
7583 : : * careful not to insert redundant clauses, because clauselist_selectivity()
7584 : : * is easily fooled into computing a too-low selectivity estimate. Our
7585 : : * approach is to add only the predicate clause(s) that cannot be proven to
7586 : : * be implied by the given indexquals. This successfully handles cases such
7587 : : * as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
7588 : : * There are many other cases where we won't detect redundancy, leading to a
7589 : : * too-low selectivity estimate, which will bias the system in favor of using
7590 : : * partial indexes where possible. That is not necessarily bad though.
7591 : : *
7592 : : * Note that indexQuals contains RestrictInfo nodes while the indpred
7593 : : * does not, so the output list will be mixed. This is OK for both
7594 : : * predicate_implied_by() and clauselist_selectivity(), but might be
7595 : : * problematic if the result were passed to other things.
7596 : : */
7597 : : List *
2585 7598 : 809551 : add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
7599 : : {
4811 7600 : 809551 : List *predExtraQuals = NIL;
7601 : : ListCell *lc;
7602 : :
7603 [ + + ]: 809551 : if (index->indpred == NIL)
7604 : 808542 : return indexQuals;
7605 : :
7606 [ + - + + : 2024 : foreach(lc, index->indpred)
+ + ]
7607 : : {
7608 : 1015 : Node *predQual = (Node *) lfirst(lc);
7609 : 1015 : List *oneQual = list_make1(predQual);
7610 : :
3196 rhaas@postgresql.org 7611 [ + + ]: 1015 : if (!predicate_implied_by(oneQual, indexQuals, false))
4811 tgl@sss.pgh.pa.us 7612 : 906 : predExtraQuals = list_concat(predExtraQuals, oneQual);
7613 : : }
7614 : 1009 : return list_concat(predExtraQuals, indexQuals);
7615 : : }
7616 : :
7617 : : /*
7618 : : * Estimate correlation of btree index's first column.
7619 : : *
7620 : : * If we can get an estimate of the first column's ordering correlation C
7621 : : * from pg_statistic, estimate the index correlation as C for a single-column
7622 : : * index, or C * 0.75 for multiple columns. The idea here is that multiple
7623 : : * columns dilute the importance of the first column's ordering, but don't
7624 : : * negate it entirely.
7625 : : *
7626 : : * We already filled in the stats tuple for *vardata when called.
7627 : : */
7628 : : static double
345 pg@bowt.ie 7629 : 335924 : btcost_correlation(IndexOptInfo *index, VariableStatData *vardata)
7630 : : {
7631 : : Oid sortop;
7632 : : AttStatsSlot sslot;
7633 : 335924 : double indexCorrelation = 0;
7634 : :
7635 [ - + ]: 335924 : Assert(HeapTupleIsValid(vardata->statsTuple));
7636 : :
7637 : 335924 : sortop = get_opfamily_member(index->opfamily[0],
7638 : 335924 : index->opcintype[0],
7639 : 335924 : index->opcintype[0],
7640 : : BTLessStrategyNumber);
7641 [ + - + + ]: 671848 : if (OidIsValid(sortop) &&
7642 : 335924 : get_attstatsslot(&sslot, vardata->statsTuple,
7643 : : STATISTIC_KIND_CORRELATION, sortop,
7644 : : ATTSTATSSLOT_NUMBERS))
7645 : : {
7646 : : double varCorrelation;
7647 : :
7648 [ - + ]: 330850 : Assert(sslot.nnumbers == 1);
7649 : 330850 : varCorrelation = sslot.numbers[0];
7650 : :
7651 [ - + ]: 330850 : if (index->reverse_sort[0])
345 pg@bowt.ie 7652 :UBC 0 : varCorrelation = -varCorrelation;
7653 : :
345 pg@bowt.ie 7654 [ + + ]:CBC 330850 : if (index->nkeycolumns > 1)
7655 : 115375 : indexCorrelation = varCorrelation * 0.75;
7656 : : else
7657 : 215475 : indexCorrelation = varCorrelation;
7658 : :
7659 : 330850 : free_attstatsslot(&sslot);
7660 : : }
7661 : :
7662 : 335924 : return indexCorrelation;
7663 : : }
7664 : :
7665 : : void
3710 tgl@sss.pgh.pa.us 7666 : 472500 : btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7667 : : Cost *indexStartupCost, Cost *indexTotalCost,
7668 : : Selectivity *indexSelectivity, double *indexCorrelation,
7669 : : double *indexPages)
7670 : : {
5195 7671 : 472500 : IndexOptInfo *index = path->indexinfo;
1338 peter@eisentraut.org 7672 : 472500 : GenericCosts costs = {0};
7673 : 472500 : VariableStatData vardata = {0};
7674 : : double numIndexTuples;
7675 : : Cost descentCost;
7676 : : List *indexBoundQuals;
7677 : : List *indexSkipQuals;
7678 : : int indexcol;
7679 : : bool eqQualHere;
7680 : : bool found_row_compare;
7681 : : bool found_array;
7682 : : bool found_is_null_op;
345 pg@bowt.ie 7683 : 472500 : bool have_correlation = false;
7684 : : double num_sa_scans;
7685 : 472500 : double correlation = 0.0;
7686 : : ListCell *lc;
7687 : :
7688 : : /*
7689 : : * For a btree scan, only leading '=' quals plus inequality quals for the
7690 : : * immediately next attribute contribute to index selectivity (these are
7691 : : * the "boundary quals" that determine the starting and stopping points of
7692 : : * the index scan). Additional quals can suppress visits to the heap, so
7693 : : * it's OK to count them in indexSelectivity, but they should not count
7694 : : * for estimating numIndexTuples. So we must examine the given indexquals
7695 : : * to find out which ones count as boundary quals. We rely on the
7696 : : * knowledge that they are given in index column order. Note that nbtree
7697 : : * preprocessing can add skip arrays that act as leading '=' quals in the
7698 : : * absence of ordinary input '=' quals, so in practice _most_ input quals
7699 : : * are able to act as index bound quals (which we take into account here).
7700 : : *
7701 : : * For a RowCompareExpr, we consider only the first column, just as
7702 : : * rowcomparesel() does.
7703 : : *
7704 : : * If there's a SAOP or skip array in the quals, we'll actually perform up
7705 : : * to N index descents (not just one), but the underlying array key's
7706 : : * operator can be considered to act the same as it normally does.
7707 : : */
7580 tgl@sss.pgh.pa.us 7708 : 472500 : indexBoundQuals = NIL;
345 pg@bowt.ie 7709 : 472500 : indexSkipQuals = NIL;
5195 tgl@sss.pgh.pa.us 7710 : 472500 : indexcol = 0;
7580 7711 : 472500 : eqQualHere = false;
345 pg@bowt.ie 7712 : 472500 : found_row_compare = false;
7713 : 472500 : found_array = false;
5917 tgl@sss.pgh.pa.us 7714 : 472500 : found_is_null_op = false;
7030 7715 : 472500 : num_sa_scans = 1;
2585 7716 [ + + + + : 800611 : foreach(lc, path->indexclauses)
+ + ]
7717 : : {
7718 : 350370 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7719 : : ListCell *lc2;
7720 : :
345 pg@bowt.ie 7721 [ + + ]: 350370 : if (indexcol < iclause->indexcol)
7722 : : {
7723 : 71486 : double num_sa_scans_prev_cols = num_sa_scans;
7724 : :
7725 : : /*
7726 : : * Beginning of a new column's quals.
7727 : : *
7728 : : * Skip scans use skip arrays, which are ScalarArrayOp style
7729 : : * arrays that generate their elements procedurally and on demand.
7730 : : * Given a multi-column index on "(a, b)", and an SQL WHERE clause
7731 : : * "WHERE b = 42", a skip scan will effectively use an indexqual
7732 : : * "WHERE a = ANY('{every col a value}') AND b = 42". (Obviously,
7733 : : * the array on "a" must also return "IS NULL" matches, since our
7734 : : * WHERE clause used no strict operator on "a").
7735 : : *
7736 : : * Here we consider how nbtree will backfill skip arrays for any
7737 : : * index columns that lacked an '=' qual. This maintains our
7738 : : * num_sa_scans estimate, and determines if this new column (the
7739 : : * "iclause->indexcol" column, not the prior "indexcol" column)
7740 : : * can have its RestrictInfos/quals added to indexBoundQuals.
7741 : : *
7742 : : * We'll need to handle columns that have inequality quals, where
7743 : : * the skip array generates values from a range constrained by the
7744 : : * quals (not every possible value). We've been maintaining
7745 : : * indexSkipQuals to help with this; it will now contain all of
7746 : : * the prior column's quals (that is, indexcol's quals) when they
7747 : : * might be used for this.
7748 : : */
7749 [ + + ]: 71486 : if (found_row_compare)
7750 : : {
7751 : : /*
7752 : : * Skip arrays can't be added after a RowCompare input qual
7753 : : * due to limitations in nbtree
7754 : : */
7755 : 12 : break;
7756 : : }
7757 [ + + ]: 71474 : if (eqQualHere)
7758 : : {
7759 : : /*
7760 : : * Don't need to add a skip array for an indexcol that already
7761 : : * has an '=' qual/equality constraint
7762 : : */
7763 : 49607 : indexcol++;
7764 : 49607 : indexSkipQuals = NIL;
7765 : : }
5195 tgl@sss.pgh.pa.us 7766 : 71474 : eqQualHere = false;
7767 : :
345 pg@bowt.ie 7768 [ + + ]: 73138 : while (indexcol < iclause->indexcol)
7769 : : {
7770 : : double ndistinct;
7771 : 23911 : bool isdefault = true;
7772 : :
7773 : 23911 : found_array = true;
7774 : :
7775 : : /*
7776 : : * A skipped attribute's ndistinct forms the basis of our
7777 : : * estimate of the total number of "array elements" used by
7778 : : * its skip array at runtime. Look that up first.
7779 : : */
7780 : 23911 : examine_indexcol_variable(root, index, indexcol, &vardata);
7781 : 23911 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
7782 : :
7783 [ + + ]: 23911 : if (indexcol == 0)
7784 : : {
7785 : : /*
7786 : : * Get an estimate of the leading column's correlation in
7787 : : * passing (avoids rereading variable stats below)
7788 : : */
7789 [ + + ]: 21861 : if (HeapTupleIsValid(vardata.statsTuple))
7790 : 13140 : correlation = btcost_correlation(index, &vardata);
7791 : 21861 : have_correlation = true;
7792 : : }
7793 : :
7794 [ + + ]: 23911 : ReleaseVariableStats(vardata);
7795 : :
7796 : : /*
7797 : : * If ndistinct is a default estimate, conservatively assume
7798 : : * that no skipping will happen at runtime
7799 : : */
7800 [ + + ]: 23911 : if (isdefault)
7801 : : {
7802 : 7218 : num_sa_scans = num_sa_scans_prev_cols;
7803 : 22247 : break; /* done building indexBoundQuals */
7804 : : }
7805 : :
7806 : : /*
7807 : : * Apply indexcol's indexSkipQuals selectivity to ndistinct
7808 : : */
7809 [ + + ]: 16693 : if (indexSkipQuals != NIL)
7810 : : {
7811 : : List *partialSkipQuals;
7812 : : Selectivity ndistinctfrac;
7813 : :
7814 : : /*
7815 : : * If the index is partial, AND the index predicate with
7816 : : * the index-bound quals to produce a more accurate idea
7817 : : * of the number of distinct values for prior indexcol
7818 : : */
7819 : 332 : partialSkipQuals = add_predicate_to_index_quals(index,
7820 : : indexSkipQuals);
7821 : :
7822 : 332 : ndistinctfrac = clauselist_selectivity(root, partialSkipQuals,
7823 : 332 : index->rel->relid,
7824 : : JOIN_INNER,
7825 : : NULL);
7826 : :
7827 : : /*
7828 : : * If ndistinctfrac is selective (on its own), the scan is
7829 : : * unlikely to benefit from repositioning itself using
7830 : : * later quals. Do not allow iclause->indexcol's quals to
7831 : : * be added to indexBoundQuals (it would increase descent
7832 : : * costs, without lowering numIndexTuples costs by much).
7833 : : */
7834 [ + + ]: 332 : if (ndistinctfrac < DEFAULT_RANGE_INEQ_SEL)
7835 : : {
7836 : 187 : num_sa_scans = num_sa_scans_prev_cols;
7837 : 187 : break; /* done building indexBoundQuals */
7838 : : }
7839 : :
7840 : : /* Adjust ndistinct downward */
7841 : 145 : ndistinct = rint(ndistinct * ndistinctfrac);
7842 [ + - ]: 145 : ndistinct = Max(ndistinct, 1);
7843 : : }
7844 : :
7845 : : /*
7846 : : * When there's no inequality quals, account for the need to
7847 : : * find an initial value by counting -inf/+inf as a value.
7848 : : *
7849 : : * We don't charge anything extra for possible next/prior key
7850 : : * index probes, which are sometimes used to find the next
7851 : : * valid skip array element (ahead of using the located
7852 : : * element value to relocate the scan to the next position
7853 : : * that might contain matching tuples). It seems hard to do
7854 : : * better here. Use of the skip support infrastructure often
7855 : : * avoids most next/prior key probes. But even when it can't,
7856 : : * there's a decent chance that most individual next/prior key
7857 : : * probes will locate a leaf page whose key space overlaps all
7858 : : * of the scan's keys (even the lower-order keys) -- which
7859 : : * also avoids the need for a separate, extra index descent.
7860 : : * Note also that these probes are much cheaper than non-probe
7861 : : * primitive index scans: they're reliably very selective.
7862 : : */
7863 [ + + ]: 16506 : if (indexSkipQuals == NIL)
7864 : 16361 : ndistinct += 1;
7865 : :
7866 : : /*
7867 : : * Update num_sa_scans estimate by multiplying by ndistinct.
7868 : : *
7869 : : * We make the pessimistic assumption that there is no
7870 : : * naturally occurring cross-column correlation. This is
7871 : : * often wrong, but it seems best to err on the side of not
7872 : : * expecting skipping to be helpful...
7873 : : */
7874 : 16506 : num_sa_scans *= ndistinct;
7875 : :
7876 : : /*
7877 : : * ...but back out of adding this latest group of 1 or more
7878 : : * skip arrays when num_sa_scans exceeds the total number of
7879 : : * index pages (revert to num_sa_scans from before indexcol).
7880 : : * This causes a sharp discontinuity in cost (as a function of
7881 : : * the indexcol's ndistinct), but that is representative of
7882 : : * actual runtime costs.
7883 : : *
7884 : : * Note that skipping is helpful when each primitive index
7885 : : * scan only manages to skip over 1 or 2 irrelevant leaf pages
7886 : : * on average. Skip arrays bring savings in CPU costs due to
7887 : : * the scan not needing to evaluate indexquals against every
7888 : : * tuple, which can greatly exceed any savings in I/O costs.
7889 : : * This test is a test of whether num_sa_scans implies that
7890 : : * we're past the point where the ability to skip ceases to
7891 : : * lower the scan's costs (even qual evaluation CPU costs).
7892 : : */
7893 [ + + ]: 16506 : if (index->pages < num_sa_scans)
7894 : : {
7895 : 14842 : num_sa_scans = num_sa_scans_prev_cols;
7896 : 14842 : break; /* done building indexBoundQuals */
7897 : : }
7898 : :
7899 : 1664 : indexcol++;
7900 : 1664 : indexSkipQuals = NIL;
7901 : : }
7902 : :
7903 : : /*
7904 : : * Finished considering the need to add skip arrays to bridge an
7905 : : * initial eqQualHere gap between the old and new index columns
7906 : : * (or there was no initial eqQualHere gap in the first place).
7907 : : *
7908 : : * If an initial gap could not be bridged, then new column's quals
7909 : : * (i.e. iclause->indexcol's quals) won't go into indexBoundQuals,
7910 : : * and so won't affect our final numIndexTuples estimate.
7911 : : */
2585 tgl@sss.pgh.pa.us 7912 [ + + ]: 71474 : if (indexcol != iclause->indexcol)
345 pg@bowt.ie 7913 : 22247 : break; /* done building indexBoundQuals */
7914 : : }
7915 : :
7916 [ - + ]: 328111 : Assert(indexcol == iclause->indexcol);
7917 : :
7918 : : /* Examine each indexqual associated with this index clause */
2585 tgl@sss.pgh.pa.us 7919 [ + - + + : 657595 : foreach(lc2, iclause->indexquals)
+ + ]
7920 : : {
7921 : 329484 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7922 : 329484 : Expr *clause = rinfo->clause;
7923 : 329484 : Oid clause_op = InvalidOid;
7924 : : int op_strategy;
7925 : :
7926 [ + + ]: 329484 : if (IsA(clause, OpExpr))
7927 : : {
7928 : 319984 : OpExpr *op = (OpExpr *) clause;
7929 : :
7930 : 319984 : clause_op = op->opno;
7931 : : }
7932 [ + + ]: 9500 : else if (IsA(clause, RowCompareExpr))
7933 : : {
7934 : 198 : RowCompareExpr *rc = (RowCompareExpr *) clause;
7935 : :
7936 : 198 : clause_op = linitial_oid(rc->opnos);
345 pg@bowt.ie 7937 : 198 : found_row_compare = true;
7938 : : }
2585 tgl@sss.pgh.pa.us 7939 [ + + ]: 9302 : else if (IsA(clause, ScalarArrayOpExpr))
7940 : : {
7941 : 8160 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
7942 : 8160 : Node *other_operand = (Node *) lsecond(saop->args);
801 7943 : 8160 : double alength = estimate_array_length(root, other_operand);
7944 : :
2585 7945 : 8160 : clause_op = saop->opno;
345 pg@bowt.ie 7946 : 8160 : found_array = true;
7947 : : /* estimate SA descents by indexBoundQuals only */
2585 tgl@sss.pgh.pa.us 7948 [ + + ]: 8160 : if (alength > 1)
7949 : 8006 : num_sa_scans *= alength;
7950 : : }
7951 [ + - ]: 1142 : else if (IsA(clause, NullTest))
7952 : : {
7953 : 1142 : NullTest *nt = (NullTest *) clause;
7954 : :
7955 [ + + ]: 1142 : if (nt->nulltesttype == IS_NULL)
7956 : : {
7957 : 120 : found_is_null_op = true;
7958 : : /* IS NULL is like = for selectivity/skip scan purposes */
7959 : 120 : eqQualHere = true;
7960 : : }
7961 : : }
7962 : : else
2585 tgl@sss.pgh.pa.us 7963 [ # # ]:UBC 0 : elog(ERROR, "unsupported indexqual type: %d",
7964 : : (int) nodeTag(clause));
7965 : :
7966 : : /* check for equality operator */
2585 tgl@sss.pgh.pa.us 7967 [ + + ]:CBC 329484 : if (OidIsValid(clause_op))
7968 : : {
7969 : 328342 : op_strategy = get_op_opfamily_strategy(clause_op,
7970 : 328342 : index->opfamily[indexcol]);
7971 [ - + ]: 328342 : Assert(op_strategy != 0); /* not a member of opfamily?? */
7972 [ + + ]: 328342 : if (op_strategy == BTEqualStrategyNumber)
7973 : 311720 : eqQualHere = true;
7974 : : }
7975 : :
7976 : 329484 : indexBoundQuals = lappend(indexBoundQuals, rinfo);
7977 : :
7978 : : /*
7979 : : * We apply inequality selectivities to estimate index descent
7980 : : * costs with scans that use skip arrays. Save this indexcol's
7981 : : * RestrictInfos if it looks like they'll be needed for that.
7982 : : */
345 pg@bowt.ie 7983 [ + + + + ]: 329484 : if (!eqQualHere && !found_row_compare &&
7984 [ + + ]: 17095 : indexcol < index->nkeycolumns - 1)
7985 : 2852 : indexSkipQuals = lappend(indexSkipQuals, rinfo);
7986 : : }
7987 : : }
7988 : :
7989 : : /*
7990 : : * If index is unique and we found an '=' clause for each column, we can
7991 : : * just assume numIndexTuples = 1 and skip the expensive
7992 : : * clauselist_selectivity calculations. However, an array or NullTest
7993 : : * always invalidates that theory (even when eqQualHere has been set).
7994 : : */
7415 tgl@sss.pgh.pa.us 7995 [ + + ]: 472500 : if (index->unique &&
2899 teodor@sigaev.ru 7996 [ + + + + ]: 383663 : indexcol == index->nkeycolumns - 1 &&
7415 tgl@sss.pgh.pa.us 7997 : 144730 : eqQualHere &&
345 pg@bowt.ie 7998 [ + + ]: 144730 : !found_array &&
5917 tgl@sss.pgh.pa.us 7999 [ + + ]: 140889 : !found_is_null_op)
7580 8000 : 140865 : numIndexTuples = 1.0;
8001 : : else
8002 : : {
8003 : : List *selectivityQuals;
8004 : : Selectivity btreeSelectivity;
8005 : :
8006 : : /*
8007 : : * If the index is partial, AND the index predicate with the
8008 : : * index-bound quals to produce a more accurate idea of the number of
8009 : : * rows covered by the bound conditions.
8010 : : */
2585 8011 : 331635 : selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
8012 : :
5159 8013 : 331635 : btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
7580 8014 : 331635 : index->rel->relid,
8015 : : JOIN_INNER,
8016 : : NULL);
8017 : 331635 : numIndexTuples = btreeSelectivity * index->rel->tuples;
8018 : :
8019 : : /*
8020 : : * btree automatically combines individual array element primitive
8021 : : * index scans whenever the tuples covered by the next set of array
8022 : : * keys are close to tuples covered by the current set. That puts a
8023 : : * natural ceiling on the worst case number of descents -- there
8024 : : * cannot possibly be more than one descent per leaf page scanned.
8025 : : *
8026 : : * Clamp the number of descents to at most 1/3 the number of index
8027 : : * pages. This avoids implausibly high estimates with low selectivity
8028 : : * paths, where scans usually require only one or two descents. This
8029 : : * is most likely to help when there are several SAOP clauses, where
8030 : : * naively accepting the total number of distinct combinations of
8031 : : * array elements as the number of descents would frequently lead to
8032 : : * wild overestimates.
8033 : : *
8034 : : * We somewhat arbitrarily don't just make the cutoff the total number
8035 : : * of leaf pages (we make it 1/3 the total number of pages instead) to
8036 : : * give the btree code credit for its ability to continue on the leaf
8037 : : * level with low selectivity scans.
8038 : : *
8039 : : * Note: num_sa_scans includes both ScalarArrayOp array elements and
8040 : : * skip array elements whose qual affects our numIndexTuples estimate.
8041 : : */
708 pg@bowt.ie 8042 [ + + ]: 331635 : num_sa_scans = Min(num_sa_scans, ceil(index->pages * 0.3333333));
8043 [ + + ]: 331635 : num_sa_scans = Max(num_sa_scans, 1);
8044 : :
8045 : : /*
8046 : : * As in genericcostestimate(), we have to adjust for any array quals
8047 : : * included in indexBoundQuals, and then round to integer.
8048 : : *
8049 : : * It is tempting to make genericcostestimate behave as if array
8050 : : * clauses work in almost the same way as scalar operators during
8051 : : * btree scans, making the top-level scan look like a continuous scan
8052 : : * (as opposed to num_sa_scans-many primitive index scans). After
8053 : : * all, btree scans mostly work like that at runtime. However, such a
8054 : : * scheme would badly bias genericcostestimate's simplistic approach
8055 : : * to calculating numIndexPages through prorating.
8056 : : *
8057 : : * Stick with the approach taken by non-native SAOP scans for now.
8058 : : * genericcostestimate will use the Mackert-Lohman formula to
8059 : : * compensate for repeat page fetches, even though that definitely
8060 : : * won't happen during btree scans (not for leaf pages, at least).
8061 : : * We're usually very pessimistic about the number of primitive index
8062 : : * scans that will be required, but it's not clear how to do better.
8063 : : */
7030 tgl@sss.pgh.pa.us 8064 : 331635 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
8065 : : }
8066 : :
8067 : : /*
8068 : : * Now do generic index cost estimation.
8069 : : */
4811 8070 : 472500 : costs.numIndexTuples = numIndexTuples;
708 pg@bowt.ie 8071 : 472500 : costs.num_sa_scans = num_sa_scans;
8072 : :
2585 tgl@sss.pgh.pa.us 8073 : 472500 : genericcostestimate(root, path, loop_count, &costs);
8074 : :
8075 : : /*
8076 : : * Add a CPU-cost component to represent the costs of initial btree
8077 : : * descent. We don't charge any I/O cost for touching upper btree levels,
8078 : : * since they tend to stay in cache, but we still have to do about log2(N)
8079 : : * comparisons to descend a btree of N leaf tuples. We charge one
8080 : : * cpu_operator_cost per comparison.
8081 : : *
8082 : : * If there are SAOP or skip array keys, charge this once per estimated
8083 : : * index descent. The ones after the first one are not startup cost so
8084 : : * far as the overall plan goes, so just add them to "total" cost.
8085 : : */
4811 8086 [ + + ]: 472500 : if (index->tuples > 1) /* avoid computing log(0) */
8087 : : {
8088 : 427577 : descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
8089 : 427577 : costs.indexStartupCost += descentCost;
8090 : 427577 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8091 : : }
8092 : :
8093 : : /*
8094 : : * Even though we're not charging I/O cost for touching upper btree pages,
8095 : : * it's still reasonable to charge some CPU cost per page descended
8096 : : * through. Moreover, if we had no such charge at all, bloated indexes
8097 : : * would appear to have the same search cost as unbloated ones, at least
8098 : : * in cases where only a single leaf page is expected to be visited. This
8099 : : * cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
8100 : : * touched. The number of such pages is btree tree height plus one (ie,
8101 : : * we charge for the leaf page too). As above, charge once per estimated
8102 : : * SAOP/skip array descent.
8103 : : */
1162 akorotkov@postgresql 8104 : 472500 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4811 tgl@sss.pgh.pa.us 8105 : 472500 : costs.indexStartupCost += descentCost;
8106 : 472500 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8107 : :
345 pg@bowt.ie 8108 [ + + ]: 472500 : if (!have_correlation)
8109 : : {
8110 : 450639 : examine_indexcol_variable(root, index, 0, &vardata);
8111 [ + + ]: 450639 : if (HeapTupleIsValid(vardata.statsTuple))
8112 : 322784 : costs.indexCorrelation = btcost_correlation(index, &vardata);
8113 [ + + ]: 450639 : ReleaseVariableStats(vardata);
8114 : : }
8115 : : else
8116 : : {
8117 : : /* btcost_correlation already called earlier on */
8118 : 21861 : costs.indexCorrelation = correlation;
8119 : : }
8120 : :
4811 tgl@sss.pgh.pa.us 8121 : 472500 : *indexStartupCost = costs.indexStartupCost;
8122 : 472500 : *indexTotalCost = costs.indexTotalCost;
8123 : 472500 : *indexSelectivity = costs.indexSelectivity;
8124 : 472500 : *indexCorrelation = costs.indexCorrelation;
3315 rhaas@postgresql.org 8125 : 472500 : *indexPages = costs.numIndexPages;
10841 scrappy@hub.org 8126 : 472500 : }
8127 : :
8128 : : void
3710 tgl@sss.pgh.pa.us 8129 : 215 : hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8130 : : Cost *indexStartupCost, Cost *indexTotalCost,
8131 : : Selectivity *indexSelectivity, double *indexCorrelation,
8132 : : double *indexPages)
8133 : : {
1338 peter@eisentraut.org 8134 : 215 : GenericCosts costs = {0};
8135 : :
2585 tgl@sss.pgh.pa.us 8136 : 215 : genericcostestimate(root, path, loop_count, &costs);
8137 : :
8138 : : /*
8139 : : * A hash index has no descent costs as such, since the index AM can go
8140 : : * directly to the target bucket after computing the hash value. There
8141 : : * are a couple of other hash-specific costs that we could conceivably add
8142 : : * here, though:
8143 : : *
8144 : : * Ideally we'd charge spc_random_page_cost for each page in the target
8145 : : * bucket, not just the numIndexPages pages that genericcostestimate
8146 : : * thought we'd visit. However in most cases we don't know which bucket
8147 : : * that will be. There's no point in considering the average bucket size
8148 : : * because the hash AM makes sure that's always one page.
8149 : : *
8150 : : * Likewise, we could consider charging some CPU for each index tuple in
8151 : : * the bucket, if we knew how many there were. But the per-tuple cost is
8152 : : * just a hash value comparison, not a general datatype-dependent
8153 : : * comparison, so any such charge ought to be quite a bit less than
8154 : : * cpu_operator_cost; which makes it probably not worth worrying about.
8155 : : *
8156 : : * A bigger issue is that chance hash-value collisions will result in
8157 : : * wasted probes into the heap. We don't currently attempt to model this
8158 : : * cost on the grounds that it's rare, but maybe it's not rare enough.
8159 : : * (Any fix for this ought to consider the generic lossy-operator problem,
8160 : : * though; it's not entirely hash-specific.)
8161 : : */
8162 : :
4811 8163 : 215 : *indexStartupCost = costs.indexStartupCost;
8164 : 215 : *indexTotalCost = costs.indexTotalCost;
8165 : 215 : *indexSelectivity = costs.indexSelectivity;
8166 : 215 : *indexCorrelation = costs.indexCorrelation;
3315 rhaas@postgresql.org 8167 : 215 : *indexPages = costs.numIndexPages;
10793 scrappy@hub.org 8168 : 215 : }
8169 : :
8170 : : void
3710 tgl@sss.pgh.pa.us 8171 : 2439 : gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8172 : : Cost *indexStartupCost, Cost *indexTotalCost,
8173 : : Selectivity *indexSelectivity, double *indexCorrelation,
8174 : : double *indexPages)
8175 : : {
4811 8176 : 2439 : IndexOptInfo *index = path->indexinfo;
1338 peter@eisentraut.org 8177 : 2439 : GenericCosts costs = {0};
8178 : : Cost descentCost;
8179 : :
2585 tgl@sss.pgh.pa.us 8180 : 2439 : genericcostestimate(root, path, loop_count, &costs);
8181 : :
8182 : : /*
8183 : : * We model index descent costs similarly to those for btree, but to do
8184 : : * that we first need an idea of the tree height. We somewhat arbitrarily
8185 : : * assume that the fanout is 100, meaning the tree height is at most
8186 : : * log100(index->pages).
8187 : : *
8188 : : * Although this computation isn't really expensive enough to require
8189 : : * caching, we might as well use index->tree_height to cache it.
8190 : : */
4673 bruce@momjian.us 8191 [ + + ]: 2439 : if (index->tree_height < 0) /* unknown? */
8192 : : {
4811 tgl@sss.pgh.pa.us 8193 [ + + ]: 2432 : if (index->pages > 1) /* avoid computing log(0) */
8194 : 1360 : index->tree_height = (int) (log(index->pages) / log(100.0));
8195 : : else
8196 : 1072 : index->tree_height = 0;
8197 : : }
8198 : :
8199 : : /*
8200 : : * Add a CPU-cost component to represent the costs of initial descent. We
8201 : : * just use log(N) here not log2(N) since the branching factor isn't
8202 : : * necessarily two anyway. As for btree, charge once per SA scan.
8203 : : */
8204 [ + - ]: 2439 : if (index->tuples > 1) /* avoid computing log(0) */
8205 : : {
8206 : 2439 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
8207 : 2439 : costs.indexStartupCost += descentCost;
8208 : 2439 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8209 : : }
8210 : :
8211 : : /*
8212 : : * Likewise add a per-page charge, calculated the same as for btrees.
8213 : : */
1162 akorotkov@postgresql 8214 : 2439 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4811 tgl@sss.pgh.pa.us 8215 : 2439 : costs.indexStartupCost += descentCost;
8216 : 2439 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8217 : :
8218 : 2439 : *indexStartupCost = costs.indexStartupCost;
8219 : 2439 : *indexTotalCost = costs.indexTotalCost;
8220 : 2439 : *indexSelectivity = costs.indexSelectivity;
8221 : 2439 : *indexCorrelation = costs.indexCorrelation;
3315 rhaas@postgresql.org 8222 : 2439 : *indexPages = costs.numIndexPages;
10793 scrappy@hub.org 8223 : 2439 : }
8224 : :
8225 : : void
3710 tgl@sss.pgh.pa.us 8226 : 892 : spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8227 : : Cost *indexStartupCost, Cost *indexTotalCost,
8228 : : Selectivity *indexSelectivity, double *indexCorrelation,
8229 : : double *indexPages)
8230 : : {
4811 8231 : 892 : IndexOptInfo *index = path->indexinfo;
1338 peter@eisentraut.org 8232 : 892 : GenericCosts costs = {0};
8233 : : Cost descentCost;
8234 : :
2585 tgl@sss.pgh.pa.us 8235 : 892 : genericcostestimate(root, path, loop_count, &costs);
8236 : :
8237 : : /*
8238 : : * We model index descent costs similarly to those for btree, but to do
8239 : : * that we first need an idea of the tree height. We somewhat arbitrarily
8240 : : * assume that the fanout is 100, meaning the tree height is at most
8241 : : * log100(index->pages).
8242 : : *
8243 : : * Although this computation isn't really expensive enough to require
8244 : : * caching, we might as well use index->tree_height to cache it.
8245 : : */
4673 bruce@momjian.us 8246 [ + + ]: 892 : if (index->tree_height < 0) /* unknown? */
8247 : : {
4811 tgl@sss.pgh.pa.us 8248 [ + - ]: 889 : if (index->pages > 1) /* avoid computing log(0) */
8249 : 889 : index->tree_height = (int) (log(index->pages) / log(100.0));
8250 : : else
4811 tgl@sss.pgh.pa.us 8251 :UBC 0 : index->tree_height = 0;
8252 : : }
8253 : :
8254 : : /*
8255 : : * Add a CPU-cost component to represent the costs of initial descent. We
8256 : : * just use log(N) here not log2(N) since the branching factor isn't
8257 : : * necessarily two anyway. As for btree, charge once per SA scan.
8258 : : */
4811 tgl@sss.pgh.pa.us 8259 [ + - ]:CBC 892 : if (index->tuples > 1) /* avoid computing log(0) */
8260 : : {
8261 : 892 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
8262 : 892 : costs.indexStartupCost += descentCost;
8263 : 892 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8264 : : }
8265 : :
8266 : : /*
8267 : : * Likewise add a per-page charge, calculated the same as for btrees.
8268 : : */
1162 akorotkov@postgresql 8269 : 892 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4811 tgl@sss.pgh.pa.us 8270 : 892 : costs.indexStartupCost += descentCost;
8271 : 892 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
8272 : :
8273 : 892 : *indexStartupCost = costs.indexStartupCost;
8274 : 892 : *indexTotalCost = costs.indexTotalCost;
8275 : 892 : *indexSelectivity = costs.indexSelectivity;
8276 : 892 : *indexCorrelation = costs.indexCorrelation;
3315 rhaas@postgresql.org 8277 : 892 : *indexPages = costs.numIndexPages;
5202 tgl@sss.pgh.pa.us 8278 : 892 : }
8279 : :
8280 : :
8281 : : /*
8282 : : * Support routines for gincostestimate
8283 : : */
8284 : :
8285 : : typedef struct
8286 : : {
8287 : : bool attHasFullScan[INDEX_MAX_KEYS];
8288 : : bool attHasNormalScan[INDEX_MAX_KEYS];
8289 : : double partialEntries;
8290 : : double exactEntries;
8291 : : double searchEntries;
8292 : : double arrayScans;
8293 : : } GinQualCounts;
8294 : :
8295 : : /*
8296 : : * Estimate the number of index terms that need to be searched for while
8297 : : * testing the given GIN query, and increment the counts in *counts
8298 : : * appropriately. If the query is unsatisfiable, return false.
8299 : : */
8300 : : static bool
5199 8301 : 1240 : gincost_pattern(IndexOptInfo *index, int indexcol,
8302 : : Oid clause_op, Datum query,
8303 : : GinQualCounts *counts)
8304 : : {
8305 : : FmgrInfo flinfo;
8306 : : Oid extractProcOid;
8307 : : Oid collation;
8308 : : int strategy_op;
8309 : : Oid lefttype,
8310 : : righttype;
8311 : 1240 : int32 nentries = 0;
8312 : 1240 : bool *partial_matches = NULL;
8313 : 1240 : Pointer *extra_data = NULL;
8314 : 1240 : bool *nullFlags = NULL;
8315 : 1240 : int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
8316 : : int32 i;
8317 : :
2894 teodor@sigaev.ru 8318 [ - + ]: 1240 : Assert(indexcol < index->nkeycolumns);
8319 : :
8320 : : /*
8321 : : * Get the operator's strategy number and declared input data types within
8322 : : * the index opfamily. (We don't need the latter, but we use
8323 : : * get_op_opfamily_properties because it will throw error if it fails to
8324 : : * find a matching pg_amop entry.)
8325 : : */
5199 tgl@sss.pgh.pa.us 8326 : 1240 : get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
8327 : : &strategy_op, &lefttype, &righttype);
8328 : :
8329 : : /*
8330 : : * GIN always uses the "default" support functions, which are those with
8331 : : * lefttype == righttype == the opclass' opcintype (see
8332 : : * IndexSupportInitialize in relcache.c).
8333 : : */
8334 : 1240 : extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
8335 : 1240 : index->opcintype[indexcol],
8336 : 1240 : index->opcintype[indexcol],
8337 : : GIN_EXTRACTQUERY_PROC);
8338 : :
8339 [ - + ]: 1240 : if (!OidIsValid(extractProcOid))
8340 : : {
8341 : : /* should not happen; throw same error as index_getprocinfo */
5199 tgl@sss.pgh.pa.us 8342 [ # # ]:UBC 0 : elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
8343 : : GIN_EXTRACTQUERY_PROC, indexcol + 1,
8344 : : get_rel_name(index->indexoid));
8345 : : }
8346 : :
8347 : : /*
8348 : : * Choose collation to pass to extractProc (should match initGinState).
8349 : : */
4723 tgl@sss.pgh.pa.us 8350 [ + + ]:CBC 1240 : if (OidIsValid(index->indexcollations[indexcol]))
8351 : 207 : collation = index->indexcollations[indexcol];
8352 : : else
8353 : 1033 : collation = DEFAULT_COLLATION_OID;
8354 : :
2176 akorotkov@postgresql 8355 : 1240 : fmgr_info(extractProcOid, &flinfo);
8356 : :
8357 : 1240 : set_fn_opclass_options(&flinfo, index->opclassoptions[indexcol]);
8358 : :
8359 : 1240 : FunctionCall7Coll(&flinfo,
8360 : : collation,
8361 : : query,
8362 : : PointerGetDatum(&nentries),
8363 : : UInt16GetDatum(strategy_op),
8364 : : PointerGetDatum(&partial_matches),
8365 : : PointerGetDatum(&extra_data),
8366 : : PointerGetDatum(&nullFlags),
8367 : : PointerGetDatum(&searchMode));
8368 : :
5199 tgl@sss.pgh.pa.us 8369 [ + + + + ]: 1240 : if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
8370 : : {
8371 : : /* No match is possible */
8372 : 6 : return false;
8373 : : }
8374 : :
8375 [ + + ]: 4838 : for (i = 0; i < nentries; i++)
8376 : : {
8377 : : /*
8378 : : * For partial match we haven't any information to estimate number of
8379 : : * matched entries in index, so, we just estimate it as 100
8380 : : */
8381 [ + + + + ]: 3604 : if (partial_matches && partial_matches[i])
8382 : 347 : counts->partialEntries += 100;
8383 : : else
8384 : 3257 : counts->exactEntries++;
8385 : :
8386 : 3604 : counts->searchEntries++;
8387 : : }
8388 : :
2248 akorotkov@postgresql 8389 [ + + ]: 1234 : if (searchMode == GIN_SEARCH_MODE_DEFAULT)
8390 : : {
8391 : 992 : counts->attHasNormalScan[indexcol] = true;
8392 : : }
8393 [ + + ]: 242 : else if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
8394 : : {
8395 : : /* Treat "include empty" like an exact-match item */
8396 : 22 : counts->attHasNormalScan[indexcol] = true;
5199 tgl@sss.pgh.pa.us 8397 : 22 : counts->exactEntries++;
8398 : 22 : counts->searchEntries++;
8399 : : }
8400 : : else
8401 : : {
8402 : : /* It's GIN_SEARCH_MODE_ALL */
2248 akorotkov@postgresql 8403 : 220 : counts->attHasFullScan[indexcol] = true;
8404 : : }
8405 : :
5199 tgl@sss.pgh.pa.us 8406 : 1234 : return true;
8407 : : }
8408 : :
8409 : : /*
8410 : : * Estimate the number of index terms that need to be searched for while
8411 : : * testing the given GIN index clause, and increment the counts in *counts
8412 : : * appropriately. If the query is unsatisfiable, return false.
8413 : : */
8414 : : static bool
4030 8415 : 1234 : gincost_opexpr(PlannerInfo *root,
8416 : : IndexOptInfo *index,
8417 : : int indexcol,
8418 : : OpExpr *clause,
8419 : : GinQualCounts *counts)
8420 : : {
2585 8421 : 1234 : Oid clause_op = clause->opno;
8422 : 1234 : Node *operand = (Node *) lsecond(clause->args);
8423 : :
8424 : : /* aggressively reduce to a constant, and look through relabeling */
4405 8425 : 1234 : operand = estimate_expression_value(root, operand);
8426 : :
5199 8427 [ - + ]: 1234 : if (IsA(operand, RelabelType))
5199 tgl@sss.pgh.pa.us 8428 :UBC 0 : operand = (Node *) ((RelabelType *) operand)->arg;
8429 : :
8430 : : /*
8431 : : * It's impossible to call extractQuery method for unknown operand. So
8432 : : * unless operand is a Const we can't do much; just assume there will be
8433 : : * one ordinary search entry from the operand at runtime.
8434 : : */
5199 tgl@sss.pgh.pa.us 8435 [ - + ]:CBC 1234 : if (!IsA(operand, Const))
8436 : : {
5199 tgl@sss.pgh.pa.us 8437 :UBC 0 : counts->exactEntries++;
8438 : 0 : counts->searchEntries++;
8439 : 0 : return true;
8440 : : }
8441 : :
8442 : : /* If Const is null, there can be no matches */
5199 tgl@sss.pgh.pa.us 8443 [ - + ]:CBC 1234 : if (((Const *) operand)->constisnull)
5199 tgl@sss.pgh.pa.us 8444 :UBC 0 : return false;
8445 : :
8446 : : /* Otherwise, apply extractQuery and get the actual term counts */
5199 tgl@sss.pgh.pa.us 8447 :CBC 1234 : return gincost_pattern(index, indexcol, clause_op,
8448 : : ((Const *) operand)->constvalue,
8449 : : counts);
8450 : : }
8451 : :
8452 : : /*
8453 : : * Estimate the number of index terms that need to be searched for while
8454 : : * testing the given GIN index clause, and increment the counts in *counts
8455 : : * appropriately. If the query is unsatisfiable, return false.
8456 : : *
8457 : : * A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
8458 : : * each of which involves one value from the RHS array, plus all the
8459 : : * non-array quals (if any). To model this, we average the counts across
8460 : : * the RHS elements, and add the averages to the counts in *counts (which
8461 : : * correspond to per-indexscan costs). We also multiply counts->arrayScans
8462 : : * by N, causing gincostestimate to scale up its estimates accordingly.
8463 : : */
8464 : : static bool
4405 8465 : 3 : gincost_scalararrayopexpr(PlannerInfo *root,
8466 : : IndexOptInfo *index,
8467 : : int indexcol,
8468 : : ScalarArrayOpExpr *clause,
8469 : : double numIndexEntries,
8470 : : GinQualCounts *counts)
8471 : : {
2585 8472 : 3 : Oid clause_op = clause->opno;
8473 : 3 : Node *rightop = (Node *) lsecond(clause->args);
8474 : : ArrayType *arrayval;
8475 : : int16 elmlen;
8476 : : bool elmbyval;
8477 : : char elmalign;
8478 : : int numElems;
8479 : : Datum *elemValues;
8480 : : bool *elemNulls;
8481 : : GinQualCounts arraycounts;
5199 8482 : 3 : int numPossible = 0;
8483 : : int i;
8484 : :
2585 8485 [ - + ]: 3 : Assert(clause->useOr);
8486 : :
8487 : : /* aggressively reduce to a constant, and look through relabeling */
4405 8488 : 3 : rightop = estimate_expression_value(root, rightop);
8489 : :
5199 8490 [ - + ]: 3 : if (IsA(rightop, RelabelType))
5199 tgl@sss.pgh.pa.us 8491 :UBC 0 : rightop = (Node *) ((RelabelType *) rightop)->arg;
8492 : :
8493 : : /*
8494 : : * It's impossible to call extractQuery method for unknown operand. So
8495 : : * unless operand is a Const we can't do much; just assume there will be
8496 : : * one ordinary search entry from each array entry at runtime, and fall
8497 : : * back on a probably-bad estimate of the number of array entries.
8498 : : */
5199 tgl@sss.pgh.pa.us 8499 [ - + ]:CBC 3 : if (!IsA(rightop, Const))
8500 : : {
5199 tgl@sss.pgh.pa.us 8501 :UBC 0 : counts->exactEntries++;
8502 : 0 : counts->searchEntries++;
801 8503 : 0 : counts->arrayScans *= estimate_array_length(root, rightop);
5199 8504 : 0 : return true;
8505 : : }
8506 : :
8507 : : /* If Const is null, there can be no matches */
5199 tgl@sss.pgh.pa.us 8508 [ - + ]:CBC 3 : if (((Const *) rightop)->constisnull)
5199 tgl@sss.pgh.pa.us 8509 :UBC 0 : return false;
8510 : :
8511 : : /* Otherwise, extract the array elements and iterate over them */
5199 tgl@sss.pgh.pa.us 8512 :CBC 3 : arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
8513 : 3 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
8514 : : &elmlen, &elmbyval, &elmalign);
8515 : 3 : deconstruct_array(arrayval,
8516 : : ARR_ELEMTYPE(arrayval),
8517 : : elmlen, elmbyval, elmalign,
8518 : : &elemValues, &elemNulls, &numElems);
8519 : :
8520 : 3 : memset(&arraycounts, 0, sizeof(arraycounts));
8521 : :
8522 [ + + ]: 9 : for (i = 0; i < numElems; i++)
8523 : : {
8524 : : GinQualCounts elemcounts;
8525 : :
8526 : : /* NULL can't match anything, so ignore, as the executor will */
8527 [ - + ]: 6 : if (elemNulls[i])
5199 tgl@sss.pgh.pa.us 8528 :UBC 0 : continue;
8529 : :
8530 : : /* Otherwise, apply extractQuery and get the actual term counts */
5199 tgl@sss.pgh.pa.us 8531 :CBC 6 : memset(&elemcounts, 0, sizeof(elemcounts));
8532 : :
8533 [ + - ]: 6 : if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
8534 : : &elemcounts))
8535 : : {
8536 : : /* We ignore array elements that are unsatisfiable patterns */
8537 : 6 : numPossible++;
8538 : :
2248 akorotkov@postgresql 8539 [ - + ]: 6 : if (elemcounts.attHasFullScan[indexcol] &&
2248 akorotkov@postgresql 8540 [ # # ]:UBC 0 : !elemcounts.attHasNormalScan[indexcol])
8541 : : {
8542 : : /*
8543 : : * Full index scan will be required. We treat this as if
8544 : : * every key in the index had been listed in the query; is
8545 : : * that reasonable?
8546 : : */
5199 tgl@sss.pgh.pa.us 8547 : 0 : elemcounts.partialEntries = 0;
8548 : 0 : elemcounts.exactEntries = numIndexEntries;
8549 : 0 : elemcounts.searchEntries = numIndexEntries;
8550 : : }
5199 tgl@sss.pgh.pa.us 8551 :CBC 6 : arraycounts.partialEntries += elemcounts.partialEntries;
8552 : 6 : arraycounts.exactEntries += elemcounts.exactEntries;
8553 : 6 : arraycounts.searchEntries += elemcounts.searchEntries;
8554 : : }
8555 : : }
8556 : :
8557 [ - + ]: 3 : if (numPossible == 0)
8558 : : {
8559 : : /* No satisfiable patterns in the array */
5199 tgl@sss.pgh.pa.us 8560 :UBC 0 : return false;
8561 : : }
8562 : :
8563 : : /*
8564 : : * Now add the averages to the global counts. This will give us an
8565 : : * estimate of the average number of terms searched for in each indexscan,
8566 : : * including contributions from both array and non-array quals.
8567 : : */
5199 tgl@sss.pgh.pa.us 8568 :CBC 3 : counts->partialEntries += arraycounts.partialEntries / numPossible;
8569 : 3 : counts->exactEntries += arraycounts.exactEntries / numPossible;
8570 : 3 : counts->searchEntries += arraycounts.searchEntries / numPossible;
8571 : :
8572 : 3 : counts->arrayScans *= numPossible;
8573 : :
8574 : 3 : return true;
8575 : : }
8576 : :
8577 : : /*
8578 : : * GIN has search behavior completely different from other index types
8579 : : */
8580 : : void
3710 8581 : 1132 : gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8582 : : Cost *indexStartupCost, Cost *indexTotalCost,
8583 : : Selectivity *indexSelectivity, double *indexCorrelation,
8584 : : double *indexPages)
8585 : : {
5195 8586 : 1132 : IndexOptInfo *index = path->indexinfo;
2585 8587 : 1132 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
8588 : : List *selectivityQuals;
5453 bruce@momjian.us 8589 : 1132 : double numPages = index->pages,
8590 : 1132 : numTuples = index->tuples;
8591 : : double numEntryPages,
8592 : : numDataPages,
8593 : : numPendingPages,
8594 : : numEntries;
8595 : : GinQualCounts counts;
8596 : : bool matchPossible;
8597 : : bool fullIndexScan;
8598 : : double partialScale;
8599 : : double entryPagesFetched,
8600 : : dataPagesFetched,
8601 : : dataPagesFetchedBySel;
8602 : : double qual_op_cost,
8603 : : qual_arg_cost,
8604 : : spc_random_page_cost,
8605 : : outer_scans;
8606 : : Cost descentCost;
8607 : : Relation indexRel;
8608 : : GinStatsData ginStats;
8609 : : ListCell *lc;
8610 : : int i;
8611 : :
8612 : : /*
8613 : : * Obtain statistical information from the meta page, if possible. Else
8614 : : * set ginStats to zeroes, and we'll cope below.
8615 : : */
3757 tgl@sss.pgh.pa.us 8616 [ + - ]: 1132 : if (!index->hypothetical)
8617 : : {
8618 : : /* Lock should have already been obtained in plancat.c */
2537 8619 : 1132 : indexRel = index_open(index->indexoid, NoLock);
3757 8620 : 1132 : ginGetStats(indexRel, &ginStats);
2537 8621 : 1132 : index_close(indexRel, NoLock);
8622 : : }
8623 : : else
8624 : : {
3757 tgl@sss.pgh.pa.us 8625 :UBC 0 : memset(&ginStats, 0, sizeof(ginStats));
8626 : : }
8627 : :
8628 : : /*
8629 : : * Assuming we got valid (nonzero) stats at all, nPendingPages can be
8630 : : * trusted, but the other fields are data as of the last VACUUM. We can
8631 : : * scale them up to account for growth since then, but that method only
8632 : : * goes so far; in the worst case, the stats might be for a completely
8633 : : * empty index, and scaling them will produce pretty bogus numbers.
8634 : : * Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
8635 : : * it's grown more than that, fall back to estimating things only from the
8636 : : * assumed-accurate index size. But we'll trust nPendingPages in any case
8637 : : * so long as it's not clearly insane, ie, more than the index size.
8638 : : */
3726 tgl@sss.pgh.pa.us 8639 [ + - ]:CBC 1132 : if (ginStats.nPendingPages < numPages)
8640 : 1132 : numPendingPages = ginStats.nPendingPages;
8641 : : else
3726 tgl@sss.pgh.pa.us 8642 :UBC 0 : numPendingPages = 0;
8643 : :
3726 tgl@sss.pgh.pa.us 8644 [ + - + - ]:CBC 1132 : if (numPages > 0 && ginStats.nTotalPages <= numPages &&
8645 [ + + ]: 1132 : ginStats.nTotalPages > numPages / 4 &&
8646 [ + - + + ]: 1106 : ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
3757 8647 : 974 : {
8648 : : /*
8649 : : * OK, the stats seem close enough to sane to be trusted. But we
8650 : : * still need to scale them by the ratio numPages / nTotalPages to
8651 : : * account for growth since the last VACUUM.
8652 : : */
5453 bruce@momjian.us 8653 : 974 : double scale = numPages / ginStats.nTotalPages;
8654 : :
3726 tgl@sss.pgh.pa.us 8655 : 974 : numEntryPages = ceil(ginStats.nEntryPages * scale);
8656 : 974 : numDataPages = ceil(ginStats.nDataPages * scale);
8657 : 974 : numEntries = ceil(ginStats.nEntries * scale);
8658 : : /* ensure we didn't round up too much */
8659 [ + + ]: 974 : numEntryPages = Min(numEntryPages, numPages - numPendingPages);
8660 [ + + ]: 974 : numDataPages = Min(numDataPages,
8661 : : numPages - numPendingPages - numEntryPages);
8662 : : }
8663 : : else
8664 : : {
8665 : : /*
8666 : : * We might get here because it's a hypothetical index, or an index
8667 : : * created pre-9.1 and never vacuumed since upgrading (in which case
8668 : : * its stats would read as zeroes), or just because it's grown too
8669 : : * much since the last VACUUM for us to put our faith in scaling.
8670 : : *
8671 : : * Invent some plausible internal statistics based on the index page
8672 : : * count (and clamp that to at least 10 pages, just in case). We
8673 : : * estimate that 90% of the index is entry pages, and the rest is data
8674 : : * pages. Estimate 100 entries per entry page; this is rather bogus
8675 : : * since it'll depend on the size of the keys, but it's more robust
8676 : : * than trying to predict the number of entries per heap tuple.
8677 : : */
3757 8678 [ + + ]: 158 : numPages = Max(numPages, 10);
3726 8679 : 158 : numEntryPages = floor((numPages - numPendingPages) * 0.90);
8680 : 158 : numDataPages = numPages - numPendingPages - numEntryPages;
3757 8681 : 158 : numEntries = floor(numEntryPages * 100);
8682 : : }
8683 : :
8684 : : /* In an empty index, numEntries could be zero. Avoid divide-by-zero */
5442 8685 [ - + ]: 1132 : if (numEntries < 1)
5442 tgl@sss.pgh.pa.us 8686 :UBC 0 : numEntries = 1;
8687 : :
8688 : : /*
8689 : : * If the index is partial, AND the index predicate with the index-bound
8690 : : * quals to produce a more accurate idea of the number of rows covered by
8691 : : * the bound conditions.
8692 : : */
2585 tgl@sss.pgh.pa.us 8693 :CBC 1132 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
8694 : :
8695 : : /* Estimate the fraction of main-table tuples that will be visited */
5628 8696 : 2264 : *indexSelectivity = clauselist_selectivity(root, selectivityQuals,
5453 bruce@momjian.us 8697 : 1132 : index->rel->relid,
8698 : : JOIN_INNER,
8699 : : NULL);
8700 : :
8701 : : /* fetch estimated page cost for tablespace containing index */
8702 : 1132 : get_tablespace_page_costs(index->reltablespace,
8703 : : &spc_random_page_cost,
8704 : : NULL);
8705 : :
8706 : : /*
8707 : : * Generic assumption about index correlation: there isn't any.
8708 : : */
5628 tgl@sss.pgh.pa.us 8709 : 1132 : *indexCorrelation = 0.0;
8710 : :
8711 : : /*
8712 : : * Examine quals to estimate number of search entries & partial matches
8713 : : */
5199 8714 : 1132 : memset(&counts, 0, sizeof(counts));
8715 : 1132 : counts.arrayScans = 1;
8716 : 1132 : matchPossible = true;
8717 : :
2585 8718 [ + - + + : 2369 : foreach(lc, path->indexclauses)
+ + ]
8719 : : {
8720 : 1237 : IndexClause *iclause = lfirst_node(IndexClause, lc);
8721 : : ListCell *lc2;
8722 : :
8723 [ + - + + : 2468 : foreach(lc2, iclause->indexquals)
+ + ]
8724 : : {
8725 : 1237 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
8726 : 1237 : Expr *clause = rinfo->clause;
8727 : :
8728 [ + + ]: 1237 : if (IsA(clause, OpExpr))
8729 : : {
8730 : 1234 : matchPossible = gincost_opexpr(root,
8731 : : index,
8732 : 1234 : iclause->indexcol,
8733 : : (OpExpr *) clause,
8734 : : &counts);
8735 [ + + ]: 1234 : if (!matchPossible)
8736 : 6 : break;
8737 : : }
8738 [ + - ]: 3 : else if (IsA(clause, ScalarArrayOpExpr))
8739 : : {
8740 : 3 : matchPossible = gincost_scalararrayopexpr(root,
8741 : : index,
8742 : 3 : iclause->indexcol,
8743 : : (ScalarArrayOpExpr *) clause,
8744 : : numEntries,
8745 : : &counts);
8746 [ - + ]: 3 : if (!matchPossible)
2585 tgl@sss.pgh.pa.us 8747 :UBC 0 : break;
8748 : : }
8749 : : else
8750 : : {
8751 : : /* shouldn't be anything else for a GIN index */
8752 [ # # ]: 0 : elog(ERROR, "unsupported GIN indexqual type: %d",
8753 : : (int) nodeTag(clause));
8754 : : }
8755 : : }
8756 : : }
8757 : :
8758 : : /* Fall out if there were any provably-unsatisfiable quals */
5199 tgl@sss.pgh.pa.us 8759 [ + + ]:CBC 1132 : if (!matchPossible)
8760 : : {
8761 : 6 : *indexStartupCost = 0;
8762 : 6 : *indexTotalCost = 0;
8763 : 6 : *indexSelectivity = 0;
3710 8764 : 6 : return;
8765 : : }
8766 : :
8767 : : /*
8768 : : * If attribute has a full scan and at the same time doesn't have normal
8769 : : * scan, then we'll have to scan all non-null entries of that attribute.
8770 : : * Currently, we don't have per-attribute statistics for GIN. Thus, we
8771 : : * must assume the whole GIN index has to be scanned in this case.
8772 : : */
2248 akorotkov@postgresql 8773 : 1126 : fullIndexScan = false;
8774 [ + + ]: 2197 : for (i = 0; i < index->nkeycolumns; i++)
8775 : : {
8776 [ + + + + ]: 1240 : if (counts.attHasFullScan[i] && !counts.attHasNormalScan[i])
8777 : : {
8778 : 169 : fullIndexScan = true;
8779 : 169 : break;
8780 : : }
8781 : : }
8782 : :
8783 [ + + - + ]: 1126 : if (fullIndexScan || indexQuals == NIL)
8784 : : {
8785 : : /*
8786 : : * Full index scan will be required. We treat this as if every key in
8787 : : * the index had been listed in the query; is that reasonable?
8788 : : */
5199 tgl@sss.pgh.pa.us 8789 : 169 : counts.partialEntries = 0;
8790 : 169 : counts.exactEntries = numEntries;
8791 : 169 : counts.searchEntries = numEntries;
8792 : : }
8793 : :
8794 : : /* Will we have more than one iteration of a nestloop scan? */
5161 8795 : 1126 : outer_scans = loop_count;
8796 : :
8797 : : /*
8798 : : * Compute cost to begin scan, first of all, pay attention to pending
8799 : : * list.
8800 : : */
5628 8801 : 1126 : entryPagesFetched = numPendingPages;
8802 : :
8803 : : /*
8804 : : * Estimate number of entry pages read. We need to do
8805 : : * counts.searchEntries searches. Use a power function as it should be,
8806 : : * but tuples on leaf pages usually is much greater. Here we include all
8807 : : * searches in entry tree, including search of first entry in partial
8808 : : * match algorithm
8809 : : */
5199 8810 : 1126 : entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
8811 : :
8812 : : /*
8813 : : * Add an estimate of entry pages read by partial match algorithm. It's a
8814 : : * scan over leaf pages in entry tree. We haven't any useful stats here,
8815 : : * so estimate it as proportion. Because counts.partialEntries is really
8816 : : * pretty bogus (see code above), it's possible that it is more than
8817 : : * numEntries; clamp the proportion to ensure sanity.
8818 : : */
3726 8819 : 1126 : partialScale = counts.partialEntries / numEntries;
8820 [ + + ]: 1126 : partialScale = Min(partialScale, 1.0);
8821 : :
8822 : 1126 : entryPagesFetched += ceil(numEntryPages * partialScale);
8823 : :
8824 : : /*
8825 : : * Partial match algorithm reads all data pages before doing actual scan,
8826 : : * so it's a startup cost. Again, we haven't any useful stats here, so
8827 : : * estimate it as proportion.
8828 : : */
8829 : 1126 : dataPagesFetched = ceil(numDataPages * partialScale);
8830 : :
1162 akorotkov@postgresql 8831 : 1126 : *indexStartupCost = 0;
8832 : 1126 : *indexTotalCost = 0;
8833 : :
8834 : : /*
8835 : : * Add a CPU-cost component to represent the costs of initial entry btree
8836 : : * descent. We don't charge any I/O cost for touching upper btree levels,
8837 : : * since they tend to stay in cache, but we still have to do about log2(N)
8838 : : * comparisons to descend a btree of N leaf tuples. We charge one
8839 : : * cpu_operator_cost per comparison.
8840 : : *
8841 : : * If there are ScalarArrayOpExprs, charge this once per SA scan. The
8842 : : * ones after the first one are not startup cost so far as the overall
8843 : : * plan is concerned, so add them only to "total" cost.
8844 : : */
8845 [ + - ]: 1126 : if (numEntries > 1) /* avoid computing log(0) */
8846 : : {
8847 : 1126 : descentCost = ceil(log(numEntries) / log(2.0)) * cpu_operator_cost;
8848 : 1126 : *indexStartupCost += descentCost * counts.searchEntries;
8849 : 1126 : *indexTotalCost += counts.arrayScans * descentCost * counts.searchEntries;
8850 : : }
8851 : :
8852 : : /*
8853 : : * Add a cpu cost per entry-page fetched. This is not amortized over a
8854 : : * loop.
8855 : : */
8856 : 1126 : *indexStartupCost += entryPagesFetched * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8857 : 1126 : *indexTotalCost += entryPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8858 : :
8859 : : /*
8860 : : * Add a cpu cost per data-page fetched. This is also not amortized over a
8861 : : * loop. Since those are the data pages from the partial match algorithm,
8862 : : * charge them as startup cost.
8863 : : */
8864 : 1126 : *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * dataPagesFetched;
8865 : :
8866 : : /*
8867 : : * Since we add the startup cost to the total cost later on, remove the
8868 : : * initial arrayscan from the total.
8869 : : */
8870 : 1126 : *indexTotalCost += dataPagesFetched * (counts.arrayScans - 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8871 : :
8872 : : /*
8873 : : * Calculate cache effects if more than one scan due to nestloops or array
8874 : : * quals. The result is pro-rated per nestloop scan, but the array qual
8875 : : * factor shouldn't be pro-rated (compare genericcostestimate).
8876 : : */
5199 tgl@sss.pgh.pa.us 8877 [ + - + + ]: 1126 : if (outer_scans > 1 || counts.arrayScans > 1)
8878 : : {
8879 : 3 : entryPagesFetched *= outer_scans * counts.arrayScans;
5628 8880 : 3 : entryPagesFetched = index_pages_fetched(entryPagesFetched,
8881 : : (BlockNumber) numEntryPages,
8882 : : numEntryPages, root);
5199 8883 : 3 : entryPagesFetched /= outer_scans;
8884 : 3 : dataPagesFetched *= outer_scans * counts.arrayScans;
5628 8885 : 3 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
8886 : : (BlockNumber) numDataPages,
8887 : : numDataPages, root);
5199 8888 : 3 : dataPagesFetched /= outer_scans;
8889 : : }
8890 : :
8891 : : /*
8892 : : * Here we use random page cost because logically-close pages could be far
8893 : : * apart on disk.
8894 : : */
1162 akorotkov@postgresql 8895 : 1126 : *indexStartupCost += (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
8896 : :
8897 : : /*
8898 : : * Now compute the number of data pages fetched during the scan.
8899 : : *
8900 : : * We assume every entry to have the same number of items, and that there
8901 : : * is no overlap between them. (XXX: tsvector and array opclasses collect
8902 : : * statistics on the frequency of individual keys; it would be nice to use
8903 : : * those here.)
8904 : : */
5199 tgl@sss.pgh.pa.us 8905 : 1126 : dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
8906 : :
8907 : : /*
8908 : : * If there is a lot of overlap among the entries, in particular if one of
8909 : : * the entries is very frequent, the above calculation can grossly
8910 : : * under-estimate. As a simple cross-check, calculate a lower bound based
8911 : : * on the overall selectivity of the quals. At a minimum, we must read
8912 : : * one item pointer for each matching entry.
8913 : : *
8914 : : * The width of each item pointer varies, based on the level of
8915 : : * compression. We don't have statistics on that, but an average of
8916 : : * around 3 bytes per item is fairly typical.
8917 : : */
5628 8918 : 1126 : dataPagesFetchedBySel = ceil(*indexSelectivity *
4386 heikki.linnakangas@i 8919 : 1126 : (numTuples / (BLCKSZ / 3)));
5628 tgl@sss.pgh.pa.us 8920 [ + + ]: 1126 : if (dataPagesFetchedBySel > dataPagesFetched)
8921 : 933 : dataPagesFetched = dataPagesFetchedBySel;
8922 : :
8923 : : /* Add one page cpu-cost to the startup cost */
1162 akorotkov@postgresql 8924 : 1126 : *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * counts.searchEntries;
8925 : :
8926 : : /*
8927 : : * Add once again a CPU-cost for those data pages, before amortizing for
8928 : : * cache.
8929 : : */
8930 : 1126 : *indexTotalCost += dataPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8931 : :
8932 : : /* Account for cache effects, the same as above */
5199 tgl@sss.pgh.pa.us 8933 [ + - + + ]: 1126 : if (outer_scans > 1 || counts.arrayScans > 1)
8934 : : {
8935 : 3 : dataPagesFetched *= outer_scans * counts.arrayScans;
5628 8936 : 3 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
8937 : : (BlockNumber) numDataPages,
8938 : : numDataPages, root);
5199 8939 : 3 : dataPagesFetched /= outer_scans;
8940 : : }
8941 : :
8942 : : /* And apply random_page_cost as the cost per page */
1162 akorotkov@postgresql 8943 : 1126 : *indexTotalCost += *indexStartupCost +
5628 tgl@sss.pgh.pa.us 8944 : 1126 : dataPagesFetched * spc_random_page_cost;
8945 : :
8946 : : /*
8947 : : * Add on index qual eval costs, much as in genericcostestimate. We charge
8948 : : * cpu but we can disregard indexorderbys, since GIN doesn't support
8949 : : * those.
8950 : : */
2585 8951 : 1126 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
8952 : 1126 : qual_op_cost = cpu_operator_cost * list_length(indexQuals);
8953 : :
5628 8954 : 1126 : *indexStartupCost += qual_arg_cost;
8955 : 1126 : *indexTotalCost += qual_arg_cost;
8956 : :
8957 : : /*
8958 : : * Add a cpu cost per search entry, corresponding to the actual visited
8959 : : * entries.
8960 : : */
1162 akorotkov@postgresql 8961 : 1126 : *indexTotalCost += (counts.searchEntries * counts.arrayScans) * (qual_op_cost);
8962 : : /* Now add a cpu cost per tuple in the posting lists / trees */
8963 : 1126 : *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost);
3315 rhaas@postgresql.org 8964 : 1126 : *indexPages = dataPagesFetched;
8965 : : }
8966 : :
8967 : : /*
8968 : : * BRIN has search behavior completely different from other index types
8969 : : */
8970 : : void
3710 tgl@sss.pgh.pa.us 8971 : 5368 : brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8972 : : Cost *indexStartupCost, Cost *indexTotalCost,
8973 : : Selectivity *indexSelectivity, double *indexCorrelation,
8974 : : double *indexPages)
8975 : : {
4146 alvherre@alvh.no-ip. 8976 : 5368 : IndexOptInfo *index = path->indexinfo;
2585 tgl@sss.pgh.pa.us 8977 : 5368 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
4146 alvherre@alvh.no-ip. 8978 : 5368 : double numPages = index->pages;
3265 8979 : 5368 : RelOptInfo *baserel = index->rel;
8980 [ + - ]: 5368 : RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
8981 : : Cost spc_seq_page_cost;
8982 : : Cost spc_random_page_cost;
8983 : : double qual_arg_cost;
8984 : : double qualSelectivity;
8985 : : BrinStatsData statsData;
8986 : : double indexRanges;
8987 : : double minimalRanges;
8988 : : double estimatedRanges;
8989 : : double selec;
8990 : : Relation indexRel;
8991 : : ListCell *l;
8992 : : VariableStatData vardata;
8993 : :
8994 [ - + ]: 5368 : Assert(rte->rtekind == RTE_RELATION);
8995 : :
8996 : : /* fetch estimated page cost for the tablespace containing the index */
4146 8997 : 5368 : get_tablespace_page_costs(index->reltablespace,
8998 : : &spc_random_page_cost,
8999 : : &spc_seq_page_cost);
9000 : :
9001 : : /*
9002 : : * Obtain some data from the index itself, if possible. Otherwise invent
9003 : : * some plausible internal statistics based on the relation page count.
9004 : : */
2306 michael@paquier.xyz 9005 [ + - ]: 5368 : if (!index->hypothetical)
9006 : : {
9007 : : /*
9008 : : * A lock should have already been obtained on the index in plancat.c.
9009 : : */
9010 : 5368 : indexRel = index_open(index->indexoid, NoLock);
9011 : 5368 : brinGetStats(indexRel, &statsData);
9012 : 5368 : index_close(indexRel, NoLock);
9013 : :
9014 : : /* work out the actual number of ranges in the index */
9015 [ + + ]: 5368 : indexRanges = Max(ceil((double) baserel->pages /
9016 : : statsData.pagesPerRange), 1.0);
9017 : : }
9018 : : else
9019 : : {
9020 : : /*
9021 : : * Assume default number of pages per range, and estimate the number
9022 : : * of ranges based on that.
9023 : : */
2306 michael@paquier.xyz 9024 [ # # ]:UBC 0 : indexRanges = Max(ceil((double) baserel->pages /
9025 : : BRIN_DEFAULT_PAGES_PER_RANGE), 1.0);
9026 : :
9027 : 0 : statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE;
9028 : 0 : statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1;
9029 : : }
9030 : :
9031 : : /*
9032 : : * Compute index correlation
9033 : : *
9034 : : * Because we can use all index quals equally when scanning, we can use
9035 : : * the largest correlation (in absolute value) among columns used by the
9036 : : * query. Start at zero, the worst possible case. If we cannot find any
9037 : : * correlation statistics, we will keep it as 0.
9038 : : */
3265 alvherre@alvh.no-ip. 9039 :CBC 5368 : *indexCorrelation = 0;
9040 : :
2585 tgl@sss.pgh.pa.us 9041 [ + - + + : 10737 : foreach(l, path->indexclauses)
+ + ]
9042 : : {
9043 : 5369 : IndexClause *iclause = lfirst_node(IndexClause, l);
9044 : 5369 : AttrNumber attnum = index->indexkeys[iclause->indexcol];
9045 : :
9046 : : /* attempt to lookup stats in relation for this index column */
3265 alvherre@alvh.no-ip. 9047 [ + - ]: 5369 : if (attnum != 0)
9048 : : {
9049 : : /* Simple variable -- look to stats for the underlying table */
9050 [ - + - - ]: 5369 : if (get_relation_stats_hook &&
3265 alvherre@alvh.no-ip. 9051 :UBC 0 : (*get_relation_stats_hook) (root, rte, attnum, &vardata))
9052 : : {
9053 : : /*
9054 : : * The hook took control of acquiring a stats tuple. If it
9055 : : * did supply a tuple, it'd better have supplied a freefunc.
9056 : : */
9057 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
9058 [ # # ]: 0 : elog(ERROR,
9059 : : "no function provided to release variable stats with");
9060 : : }
9061 : : else
9062 : : {
3265 alvherre@alvh.no-ip. 9063 :CBC 5369 : vardata.statsTuple =
9064 : 5369 : SearchSysCache3(STATRELATTINH,
9065 : : ObjectIdGetDatum(rte->relid),
9066 : : Int16GetDatum(attnum),
9067 : : BoolGetDatum(false));
9068 : 5369 : vardata.freefunc = ReleaseSysCache;
9069 : : }
9070 : : }
9071 : : else
9072 : : {
9073 : : /*
9074 : : * Looks like we've found an expression column in the index. Let's
9075 : : * see if there's any stats for it.
9076 : : */
9077 : :
9078 : : /* get the attnum from the 0-based index. */
2585 tgl@sss.pgh.pa.us 9079 :UBC 0 : attnum = iclause->indexcol + 1;
9080 : :
3265 alvherre@alvh.no-ip. 9081 [ # # # # ]: 0 : if (get_index_stats_hook &&
3189 tgl@sss.pgh.pa.us 9082 : 0 : (*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
9083 : : {
9084 : : /*
9085 : : * The hook took control of acquiring a stats tuple. If it
9086 : : * did supply a tuple, it'd better have supplied a freefunc.
9087 : : */
3265 alvherre@alvh.no-ip. 9088 [ # # ]: 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
9089 [ # # ]: 0 : !vardata.freefunc)
9090 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
9091 : : }
9092 : : else
9093 : : {
9094 : 0 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
9095 : : ObjectIdGetDatum(index->indexoid),
9096 : : Int16GetDatum(attnum),
9097 : : BoolGetDatum(false));
9098 : 0 : vardata.freefunc = ReleaseSysCache;
9099 : : }
9100 : : }
9101 : :
3265 alvherre@alvh.no-ip. 9102 [ + + ]:CBC 5369 : if (HeapTupleIsValid(vardata.statsTuple))
9103 : : {
9104 : : AttStatsSlot sslot;
9105 : :
3228 tgl@sss.pgh.pa.us 9106 [ + - ]: 21 : if (get_attstatsslot(&sslot, vardata.statsTuple,
9107 : : STATISTIC_KIND_CORRELATION, InvalidOid,
9108 : : ATTSTATSSLOT_NUMBERS))
9109 : : {
3265 alvherre@alvh.no-ip. 9110 : 21 : double varCorrelation = 0.0;
9111 : :
3228 tgl@sss.pgh.pa.us 9112 [ + - ]: 21 : if (sslot.nnumbers > 0)
1255 peter@eisentraut.org 9113 : 21 : varCorrelation = fabs(sslot.numbers[0]);
9114 : :
3265 alvherre@alvh.no-ip. 9115 [ + - ]: 21 : if (varCorrelation > *indexCorrelation)
9116 : 21 : *indexCorrelation = varCorrelation;
9117 : :
3228 tgl@sss.pgh.pa.us 9118 : 21 : free_attstatsslot(&sslot);
9119 : : }
9120 : : }
9121 : :
3265 alvherre@alvh.no-ip. 9122 [ + + ]: 5369 : ReleaseVariableStats(vardata);
9123 : : }
9124 : :
9125 : 5368 : qualSelectivity = clauselist_selectivity(root, indexQuals,
9126 : 5368 : baserel->relid,
9127 : : JOIN_INNER, NULL);
9128 : :
9129 : : /*
9130 : : * Now calculate the minimum possible ranges we could match with if all of
9131 : : * the rows were in the perfect order in the table's heap.
9132 : : */
9133 : 5368 : minimalRanges = ceil(indexRanges * qualSelectivity);
9134 : :
9135 : : /*
9136 : : * Now estimate the number of ranges that we'll touch by using the
9137 : : * indexCorrelation from the stats. Careful not to divide by zero (note
9138 : : * we're using the absolute value of the correlation).
9139 : : */
9140 [ + + ]: 5368 : if (*indexCorrelation < 1.0e-10)
9141 : 5347 : estimatedRanges = indexRanges;
9142 : : else
9143 [ + + ]: 21 : estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
9144 : :
9145 : : /* we expect to visit this portion of the table */
9146 : 5368 : selec = estimatedRanges / indexRanges;
9147 : :
9148 [ - + - + ]: 5368 : CLAMP_PROBABILITY(selec);
9149 : :
9150 : 5368 : *indexSelectivity = selec;
9151 : :
9152 : : /*
9153 : : * Compute the index qual costs, much as in genericcostestimate, to add to
9154 : : * the index costs. We can disregard indexorderbys, since BRIN doesn't
9155 : : * support those.
9156 : : */
2585 tgl@sss.pgh.pa.us 9157 : 5368 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
9158 : :
9159 : : /*
9160 : : * Compute the startup cost as the cost to read the whole revmap
9161 : : * sequentially, including the cost to execute the index quals.
9162 : : */
3265 alvherre@alvh.no-ip. 9163 : 5368 : *indexStartupCost =
9164 : 5368 : spc_seq_page_cost * statsData.revmapNumPages * loop_count;
4146 9165 : 5368 : *indexStartupCost += qual_arg_cost;
9166 : :
9167 : : /*
9168 : : * To read a BRIN index there might be a bit of back and forth over
9169 : : * regular pages, as revmap might point to them out of sequential order;
9170 : : * calculate the total cost as reading the whole index in random order.
9171 : : */
3265 9172 : 5368 : *indexTotalCost = *indexStartupCost +
9173 : 5368 : spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
9174 : :
9175 : : /*
9176 : : * Charge a small amount per range tuple which we expect to match to. This
9177 : : * is meant to reflect the costs of manipulating the bitmap. The BRIN scan
9178 : : * will set a bit for each page in the range when we find a matching
9179 : : * range, so we must multiply the charge by the number of pages in the
9180 : : * range.
9181 : : */
9182 : 5368 : *indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
9183 : 5368 : statsData.pagesPerRange;
9184 : :
9185 : 5368 : *indexPages = index->pages;
4146 9186 : 5368 : }
|
