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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) 1996-2025, PostgreSQL Global Development Group
14 : : * Portions Copyright (c) 1994, Regents of the University of California
15 : : *
16 : : *
17 : : * IDENTIFICATION
18 : : * src/backend/utils/adt/selfuncs.c
19 : : *
20 : : *-------------------------------------------------------------------------
21 : : */
22 : :
23 : : /*----------
24 : : * Operator selectivity estimation functions are called to estimate the
25 : : * selectivity of WHERE clauses whose top-level operator is their operator.
26 : : * We divide the problem into two cases:
27 : : * Restriction clause estimation: the clause involves vars of just
28 : : * one relation.
29 : : * Join clause estimation: the clause involves vars of multiple rels.
30 : : * Join selectivity estimation is far more difficult and usually less accurate
31 : : * than restriction estimation.
32 : : *
33 : : * When dealing with the inner scan of a nestloop join, we consider the
34 : : * join's joinclauses as restriction clauses for the inner relation, and
35 : : * treat vars of the outer relation as parameters (a/k/a constants of unknown
36 : : * values). So, restriction estimators need to be able to accept an argument
37 : : * telling which relation is to be treated as the variable.
38 : : *
39 : : * The call convention for a restriction estimator (oprrest function) is
40 : : *
41 : : * Selectivity oprrest (PlannerInfo *root,
42 : : * Oid operator,
43 : : * List *args,
44 : : * int varRelid);
45 : : *
46 : : * root: general information about the query (rtable and RelOptInfo lists
47 : : * are particularly important for the estimator).
48 : : * operator: OID of the specific operator in question.
49 : : * args: argument list from the operator clause.
50 : : * varRelid: if not zero, the relid (rtable index) of the relation to
51 : : * be treated as the variable relation. May be zero if the args list
52 : : * is known to contain vars of only one relation.
53 : : *
54 : : * This is represented at the SQL level (in pg_proc) as
55 : : *
56 : : * float8 oprrest (internal, oid, internal, int4);
57 : : *
58 : : * The result is a selectivity, that is, a fraction (0 to 1) of the rows
59 : : * of the relation that are expected to produce a TRUE result for the
60 : : * given operator.
61 : : *
62 : : * The call convention for a join estimator (oprjoin function) is similar
63 : : * except that varRelid is not needed, and instead join information is
64 : : * supplied:
65 : : *
66 : : * Selectivity oprjoin (PlannerInfo *root,
67 : : * Oid operator,
68 : : * List *args,
69 : : * JoinType jointype,
70 : : * SpecialJoinInfo *sjinfo);
71 : : *
72 : : * float8 oprjoin (internal, oid, internal, int2, internal);
73 : : *
74 : : * (Before Postgres 8.4, join estimators had only the first four of these
75 : : * parameters. That signature is still allowed, but deprecated.) The
76 : : * relationship between jointype and sjinfo is explained in the comments for
77 : : * clause_selectivity() --- the short version is that jointype is usually
78 : : * best ignored in favor of examining sjinfo.
79 : : *
80 : : * Join selectivity for regular inner and outer joins is defined as the
81 : : * fraction (0 to 1) of the cross product of the relations that is expected
82 : : * to produce a TRUE result for the given operator. For both semi and anti
83 : : * joins, however, the selectivity is defined as the fraction of the left-hand
84 : : * side relation's rows that are expected to have a match (ie, at least one
85 : : * row with a TRUE result) in the right-hand side.
86 : : *
87 : : * For both oprrest and oprjoin functions, the operator's input collation OID
88 : : * (if any) is passed using the standard fmgr mechanism, so that the estimator
89 : : * function can fetch it with PG_GET_COLLATION(). Note, however, that all
90 : : * statistics in pg_statistic are currently built using the relevant column's
91 : : * collation.
92 : : *----------
93 : : */
94 : :
95 : : #include "postgres.h"
96 : :
97 : : #include <ctype.h>
98 : : #include <math.h>
99 : :
100 : : #include "access/brin.h"
101 : : #include "access/brin_page.h"
102 : : #include "access/gin.h"
103 : : #include "access/table.h"
104 : : #include "access/tableam.h"
105 : : #include "access/visibilitymap.h"
106 : : #include "catalog/pg_collation.h"
107 : : #include "catalog/pg_operator.h"
108 : : #include "catalog/pg_statistic.h"
109 : : #include "catalog/pg_statistic_ext.h"
110 : : #include "executor/nodeAgg.h"
111 : : #include "miscadmin.h"
112 : : #include "nodes/makefuncs.h"
113 : : #include "nodes/nodeFuncs.h"
114 : : #include "optimizer/clauses.h"
115 : : #include "optimizer/cost.h"
116 : : #include "optimizer/optimizer.h"
117 : : #include "optimizer/pathnode.h"
118 : : #include "optimizer/paths.h"
119 : : #include "optimizer/plancat.h"
120 : : #include "parser/parse_clause.h"
121 : : #include "parser/parse_relation.h"
122 : : #include "parser/parsetree.h"
123 : : #include "rewrite/rewriteManip.h"
124 : : #include "statistics/statistics.h"
125 : : #include "storage/bufmgr.h"
126 : : #include "utils/acl.h"
127 : : #include "utils/array.h"
128 : : #include "utils/builtins.h"
129 : : #include "utils/date.h"
130 : : #include "utils/datum.h"
131 : : #include "utils/fmgroids.h"
132 : : #include "utils/index_selfuncs.h"
133 : : #include "utils/lsyscache.h"
134 : : #include "utils/memutils.h"
135 : : #include "utils/pg_locale.h"
136 : : #include "utils/rel.h"
137 : : #include "utils/selfuncs.h"
138 : : #include "utils/snapmgr.h"
139 : : #include "utils/spccache.h"
140 : : #include "utils/syscache.h"
141 : : #include "utils/timestamp.h"
142 : : #include "utils/typcache.h"
143 : :
144 : : #define DEFAULT_PAGE_CPU_MULTIPLIER 50.0
145 : :
146 : : /* Hooks for plugins to get control when we ask for stats */
147 : : get_relation_stats_hook_type get_relation_stats_hook = NULL;
148 : : get_index_stats_hook_type get_index_stats_hook = NULL;
149 : :
150 : : static double eqsel_internal(PG_FUNCTION_ARGS, bool negate);
151 : : static double eqjoinsel_inner(Oid opfuncoid, Oid collation,
152 : : VariableStatData *vardata1, VariableStatData *vardata2,
153 : : double nd1, double nd2,
154 : : bool isdefault1, bool isdefault2,
155 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
156 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
157 : : bool have_mcvs1, bool have_mcvs2);
158 : : static double eqjoinsel_semi(Oid opfuncoid, Oid collation,
159 : : VariableStatData *vardata1, VariableStatData *vardata2,
160 : : double nd1, double nd2,
161 : : bool isdefault1, bool isdefault2,
162 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
163 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
164 : : bool have_mcvs1, bool have_mcvs2,
165 : : RelOptInfo *inner_rel);
166 : : static bool estimate_multivariate_ndistinct(PlannerInfo *root,
167 : : RelOptInfo *rel, List **varinfos, double *ndistinct);
168 : : static bool convert_to_scalar(Datum value, Oid valuetypid, Oid collid,
169 : : double *scaledvalue,
170 : : Datum lobound, Datum hibound, Oid boundstypid,
171 : : double *scaledlobound, double *scaledhibound);
172 : : static double convert_numeric_to_scalar(Datum value, Oid typid, bool *failure);
173 : : static void convert_string_to_scalar(char *value,
174 : : double *scaledvalue,
175 : : char *lobound,
176 : : double *scaledlobound,
177 : : char *hibound,
178 : : double *scaledhibound);
179 : : static void convert_bytea_to_scalar(Datum value,
180 : : double *scaledvalue,
181 : : Datum lobound,
182 : : double *scaledlobound,
183 : : Datum hibound,
184 : : double *scaledhibound);
185 : : static double convert_one_string_to_scalar(char *value,
186 : : int rangelo, int rangehi);
187 : : static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
188 : : int rangelo, int rangehi);
189 : : static char *convert_string_datum(Datum value, Oid typid, Oid collid,
190 : : bool *failure);
191 : : static double convert_timevalue_to_scalar(Datum value, Oid typid,
192 : : bool *failure);
193 : : static void examine_simple_variable(PlannerInfo *root, Var *var,
194 : : VariableStatData *vardata);
195 : : static void examine_indexcol_variable(PlannerInfo *root, IndexOptInfo *index,
196 : : int indexcol, VariableStatData *vardata);
197 : : static bool get_variable_range(PlannerInfo *root, VariableStatData *vardata,
198 : : Oid sortop, Oid collation,
199 : : Datum *min, Datum *max);
200 : : static void get_stats_slot_range(AttStatsSlot *sslot,
201 : : Oid opfuncoid, FmgrInfo *opproc,
202 : : Oid collation, int16 typLen, bool typByVal,
203 : : Datum *min, Datum *max, bool *p_have_data);
204 : : static bool get_actual_variable_range(PlannerInfo *root,
205 : : VariableStatData *vardata,
206 : : Oid sortop, Oid collation,
207 : : Datum *min, Datum *max);
208 : : static bool get_actual_variable_endpoint(Relation heapRel,
209 : : Relation indexRel,
210 : : ScanDirection indexscandir,
211 : : ScanKey scankeys,
212 : : int16 typLen,
213 : : bool typByVal,
214 : : TupleTableSlot *tableslot,
215 : : MemoryContext outercontext,
216 : : Datum *endpointDatum);
217 : : static RelOptInfo *find_join_input_rel(PlannerInfo *root, Relids relids);
218 : : static double btcost_correlation(IndexOptInfo *index,
219 : : VariableStatData *vardata);
220 : :
221 : :
222 : : /*
223 : : * eqsel - Selectivity of "=" for any data types.
224 : : *
225 : : * Note: this routine is also used to estimate selectivity for some
226 : : * operators that are not "=" but have comparable selectivity behavior,
227 : : * such as "~=" (geometric approximate-match). Even for "=", we must
228 : : * keep in mind that the left and right datatypes may differ.
229 : : */
230 : : Datum
9224 tgl@sss.pgh.pa.us 231 :CBC 316187 : eqsel(PG_FUNCTION_ARGS)
232 : : {
3017 233 : 316187 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, false));
234 : : }
235 : :
236 : : /*
237 : : * Common code for eqsel() and neqsel()
238 : : */
239 : : static double
240 : 338735 : eqsel_internal(PG_FUNCTION_ARGS, bool negate)
241 : : {
7398 242 : 338735 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
8875 243 : 338735 : Oid operator = PG_GETARG_OID(1);
244 : 338735 : List *args = (List *) PG_GETARG_POINTER(2);
245 : 338735 : int varRelid = PG_GETARG_INT32(3);
1919 246 : 338735 : Oid collation = PG_GET_COLLATION();
247 : : VariableStatData vardata;
248 : : Node *other;
249 : : bool varonleft;
250 : : double selec;
251 : :
252 : : /*
253 : : * When asked about <>, we do the estimation using the corresponding =
254 : : * operator, then convert to <> via "1.0 - eq_selectivity - nullfrac".
255 : : */
3017 256 [ + + ]: 338735 : if (negate)
257 : : {
258 : 22548 : operator = get_negator(operator);
259 [ - + ]: 22548 : if (!OidIsValid(operator))
260 : : {
261 : : /* Use default selectivity (should we raise an error instead?) */
3017 tgl@sss.pgh.pa.us 262 :UBC 0 : return 1.0 - DEFAULT_EQ_SEL;
263 : : }
264 : : }
265 : :
266 : : /*
267 : : * If expression is not variable = something or something = variable, then
268 : : * punt and return a default estimate.
269 : : */
7872 tgl@sss.pgh.pa.us 270 [ + + ]:CBC 338735 : if (!get_restriction_variable(root, args, varRelid,
271 : : &vardata, &other, &varonleft))
3017 272 [ + + ]: 2589 : return negate ? (1.0 - DEFAULT_EQ_SEL) : DEFAULT_EQ_SEL;
273 : :
274 : : /*
275 : : * We can do a lot better if the something is a constant. (Note: the
276 : : * Const might result from estimation rather than being a simple constant
277 : : * in the query.)
278 : : */
6391 279 [ + + ]: 336143 : if (IsA(other, Const))
1919 280 : 149744 : selec = var_eq_const(&vardata, operator, collation,
6391 281 : 149744 : ((Const *) other)->constvalue,
282 : 149744 : ((Const *) other)->constisnull,
283 : : varonleft, negate);
284 : : else
1919 285 : 186399 : selec = var_eq_non_const(&vardata, operator, collation, other,
286 : : varonleft, negate);
287 : :
6391 288 [ + + ]: 336143 : ReleaseVariableStats(vardata);
289 : :
3017 290 : 336143 : return selec;
291 : : }
292 : :
293 : : /*
294 : : * var_eq_const --- eqsel for var = const case
295 : : *
296 : : * This is exported so that some other estimation functions can use it.
297 : : */
298 : : double
1082 pg@bowt.ie 299 : 171664 : var_eq_const(VariableStatData *vardata, Oid oproid, Oid collation,
300 : : Datum constval, bool constisnull,
301 : : bool varonleft, bool negate)
302 : : {
303 : : double selec;
3017 tgl@sss.pgh.pa.us 304 : 171664 : double nullfrac = 0.0;
305 : : bool isdefault;
306 : : Oid opfuncoid;
307 : :
308 : : /*
309 : : * If the constant is NULL, assume operator is strict and return zero, ie,
310 : : * operator will never return TRUE. (It's zero even for a negator op.)
311 : : */
6391 312 [ + + ]: 171664 : if (constisnull)
313 : 202 : return 0.0;
314 : :
315 : : /*
316 : : * Grab the nullfrac for use below. Note we allow use of nullfrac
317 : : * regardless of security check.
318 : : */
3017 319 [ + + ]: 171462 : if (HeapTupleIsValid(vardata->statsTuple))
320 : : {
321 : : Form_pg_statistic stats;
322 : :
323 : 132092 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
324 : 132092 : nullfrac = stats->stanullfrac;
325 : : }
326 : :
327 : : /*
328 : : * If we matched the var to a unique index, DISTINCT or GROUP-BY clause,
329 : : * assume there is exactly one match regardless of anything else. (This
330 : : * is slightly bogus, since the index or clause's equality operator might
331 : : * be different from ours, but it's much more likely to be right than
332 : : * ignoring the information.)
333 : : */
6047 334 [ + + + - : 171462 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
+ + ]
335 : : {
3017 336 : 42725 : selec = 1.0 / vardata->rel->tuples;
337 : : }
338 [ + + + - ]: 226560 : else if (HeapTupleIsValid(vardata->statsTuple) &&
339 : 97823 : statistic_proc_security_check(vardata,
1082 pg@bowt.ie 340 : 97823 : (opfuncoid = get_opcode(oproid))))
9533 tgl@sss.pgh.pa.us 341 : 97823 : {
342 : : AttStatsSlot sslot;
6391 343 : 97823 : bool match = false;
344 : : int i;
345 : :
346 : : /*
347 : : * Is the constant "=" to any of the column's most common values?
348 : : * (Although the given operator may not really be "=", we will assume
349 : : * that seeing whether it returns TRUE is an appropriate test. If you
350 : : * don't like this, maybe you shouldn't be using eqsel for your
351 : : * operator...)
352 : : */
3038 353 [ + + ]: 97823 : if (get_attstatsslot(&sslot, vardata->statsTuple,
354 : : STATISTIC_KIND_MCV, InvalidOid,
355 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
356 : : {
1964 357 : 88570 : LOCAL_FCINFO(fcinfo, 2);
358 : : FmgrInfo eqproc;
359 : :
3046 peter_e@gmx.net 360 : 88570 : fmgr_info(opfuncoid, &eqproc);
361 : :
362 : : /*
363 : : * Save a few cycles by setting up the fcinfo struct just once.
364 : : * Using FunctionCallInvoke directly also avoids failure if the
365 : : * eqproc returns NULL, though really equality functions should
366 : : * never do that.
367 : : */
1919 tgl@sss.pgh.pa.us 368 : 88570 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
369 : : NULL, NULL);
1964 370 : 88570 : fcinfo->args[0].isnull = false;
371 : 88570 : fcinfo->args[1].isnull = false;
372 : : /* be careful to apply operator right way 'round */
373 [ + + ]: 88570 : if (varonleft)
374 : 88554 : fcinfo->args[1].value = constval;
375 : : else
376 : 16 : fcinfo->args[0].value = constval;
377 : :
3038 378 [ + + ]: 1561017 : for (i = 0; i < sslot.nvalues; i++)
379 : : {
380 : : Datum fresult;
381 : :
6391 382 [ + + ]: 1519780 : if (varonleft)
1964 383 : 1519752 : fcinfo->args[0].value = sslot.values[i];
384 : : else
385 : 28 : fcinfo->args[1].value = sslot.values[i];
386 : 1519780 : fcinfo->isnull = false;
387 : 1519780 : fresult = FunctionCallInvoke(fcinfo);
388 [ + - + + ]: 1519780 : if (!fcinfo->isnull && DatumGetBool(fresult))
389 : : {
390 : 47333 : match = true;
6391 391 : 47333 : break;
392 : : }
393 : : }
394 : : }
395 : : else
396 : : {
397 : : /* no most-common-value info available */
3038 398 : 9253 : i = 0; /* keep compiler quiet */
399 : : }
400 : :
6391 401 [ + + ]: 97823 : if (match)
402 : : {
403 : : /*
404 : : * Constant is "=" to this common value. We know selectivity
405 : : * exactly (or as exactly as ANALYZE could calculate it, anyway).
406 : : */
3038 407 : 47333 : selec = sslot.numbers[i];
408 : : }
409 : : else
410 : : {
411 : : /*
412 : : * Comparison is against a constant that is neither NULL nor any
413 : : * of the common values. Its selectivity cannot be more than
414 : : * this:
415 : : */
6391 416 : 50490 : double sumcommon = 0.0;
417 : : double otherdistinct;
418 : :
3038 419 [ + + ]: 1330414 : for (i = 0; i < sslot.nnumbers; i++)
420 : 1279924 : sumcommon += sslot.numbers[i];
3017 421 : 50490 : selec = 1.0 - sumcommon - nullfrac;
6391 422 [ + + - + ]: 50490 : CLAMP_PROBABILITY(selec);
423 : :
424 : : /*
425 : : * and in fact it's probably a good deal less. We approximate that
426 : : * all the not-common values share this remaining fraction
427 : : * equally, so we divide by the number of other distinct values.
428 : : */
3038 429 : 50490 : otherdistinct = get_variable_numdistinct(vardata, &isdefault) -
430 : 50490 : sslot.nnumbers;
6391 431 [ + + ]: 50490 : if (otherdistinct > 1)
432 : 26103 : selec /= otherdistinct;
433 : :
434 : : /*
435 : : * Another cross-check: selectivity shouldn't be estimated as more
436 : : * than the least common "most common value".
437 : : */
3038 438 [ + + - + ]: 50490 : if (sslot.nnumbers > 0 && selec > sslot.numbers[sslot.nnumbers - 1])
3038 tgl@sss.pgh.pa.us 439 :UBC 0 : selec = sslot.numbers[sslot.nnumbers - 1];
440 : : }
441 : :
3038 tgl@sss.pgh.pa.us 442 :CBC 97823 : free_attstatsslot(&sslot);
443 : : }
444 : : else
445 : : {
446 : : /*
447 : : * No ANALYZE stats available, so make a guess using estimated number
448 : : * of distinct values and assuming they are equally common. (The guess
449 : : * is unlikely to be very good, but we do know a few special cases.)
450 : : */
5116 451 : 30914 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
452 : : }
453 : :
454 : : /* now adjust if we wanted <> rather than = */
3017 455 [ + + ]: 171462 : if (negate)
456 : 18145 : selec = 1.0 - selec - nullfrac;
457 : :
458 : : /* result should be in range, but make sure... */
6391 459 [ - + - + ]: 171462 : CLAMP_PROBABILITY(selec);
460 : :
461 : 171462 : return selec;
462 : : }
463 : :
464 : : /*
465 : : * var_eq_non_const --- eqsel for var = something-other-than-const case
466 : : *
467 : : * This is exported so that some other estimation functions can use it.
468 : : */
469 : : double
1082 pg@bowt.ie 470 : 186399 : var_eq_non_const(VariableStatData *vardata, Oid oproid, Oid collation,
471 : : Node *other,
472 : : bool varonleft, bool negate)
473 : : {
474 : : double selec;
3017 tgl@sss.pgh.pa.us 475 : 186399 : double nullfrac = 0.0;
476 : : bool isdefault;
477 : :
478 : : /*
479 : : * Grab the nullfrac for use below.
480 : : */
481 [ + + ]: 186399 : if (HeapTupleIsValid(vardata->statsTuple))
482 : : {
483 : : Form_pg_statistic stats;
484 : :
485 : 141568 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
486 : 141568 : nullfrac = stats->stanullfrac;
487 : : }
488 : :
489 : : /*
490 : : * If we matched the var to a unique index, DISTINCT or GROUP-BY clause,
491 : : * assume there is exactly one match regardless of anything else. (This
492 : : * is slightly bogus, since the index or clause's equality operator might
493 : : * be different from ours, but it's much more likely to be right than
494 : : * ignoring the information.)
495 : : */
6047 496 [ + + + - : 186399 : if (vardata->isunique && vardata->rel && vardata->rel->tuples >= 1.0)
+ + ]
497 : : {
3017 498 : 74588 : selec = 1.0 / vardata->rel->tuples;
499 : : }
500 [ + + ]: 111811 : else if (HeapTupleIsValid(vardata->statsTuple))
501 : : {
502 : : double ndistinct;
503 : : AttStatsSlot sslot;
504 : :
505 : : /*
506 : : * Search is for a value that we do not know a priori, but we will
507 : : * assume it is not NULL. Estimate the selectivity as non-null
508 : : * fraction divided by number of distinct values, so that we get a
509 : : * result averaged over all possible values whether common or
510 : : * uncommon. (Essentially, we are assuming that the not-yet-known
511 : : * comparison value is equally likely to be any of the possible
512 : : * values, regardless of their frequency in the table. Is that a good
513 : : * idea?)
514 : : */
515 : 73866 : selec = 1.0 - nullfrac;
5116 516 : 73866 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
6391 517 [ + + ]: 73866 : if (ndistinct > 1)
518 : 72003 : selec /= ndistinct;
519 : :
520 : : /*
521 : : * Cross-check: selectivity should never be estimated as more than the
522 : : * most common value's.
523 : : */
3038 524 [ + + ]: 73866 : if (get_attstatsslot(&sslot, vardata->statsTuple,
525 : : STATISTIC_KIND_MCV, InvalidOid,
526 : : ATTSTATSSLOT_NUMBERS))
527 : : {
528 [ + - + + ]: 64354 : if (sslot.nnumbers > 0 && selec > sslot.numbers[0])
529 : 279 : selec = sslot.numbers[0];
530 : 64354 : free_attstatsslot(&sslot);
531 : : }
532 : : }
533 : : else
534 : : {
535 : : /*
536 : : * No ANALYZE stats available, so make a guess using estimated number
537 : : * of distinct values and assuming they are equally common. (The guess
538 : : * is unlikely to be very good, but we do know a few special cases.)
539 : : */
5116 540 : 37945 : selec = 1.0 / get_variable_numdistinct(vardata, &isdefault);
541 : : }
542 : :
543 : : /* now adjust if we wanted <> rather than = */
3017 544 [ + + ]: 186399 : if (negate)
545 : 3241 : selec = 1.0 - selec - nullfrac;
546 : :
547 : : /* result should be in range, but make sure... */
8647 548 [ - + - + ]: 186399 : CLAMP_PROBABILITY(selec);
549 : :
6391 550 : 186399 : return selec;
551 : : }
552 : :
553 : : /*
554 : : * neqsel - Selectivity of "!=" for any data types.
555 : : *
556 : : * This routine is also used for some operators that are not "!="
557 : : * but have comparable selectivity behavior. See above comments
558 : : * for eqsel().
559 : : */
560 : : Datum
9224 561 : 22548 : neqsel(PG_FUNCTION_ARGS)
562 : : {
3017 563 : 22548 : PG_RETURN_FLOAT8((float8) eqsel_internal(fcinfo, true));
564 : : }
565 : :
566 : : /*
567 : : * scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
568 : : *
569 : : * This is the guts of scalarltsel/scalarlesel/scalargtsel/scalargesel.
570 : : * The isgt and iseq flags distinguish which of the four cases apply.
571 : : *
572 : : * The caller has commuted the clause, if necessary, so that we can treat
573 : : * the variable as being on the left. The caller must also make sure that
574 : : * the other side of the clause is a non-null Const, and dissect that into
575 : : * a value and datatype. (This definition simplifies some callers that
576 : : * want to estimate against a computed value instead of a Const node.)
577 : : *
578 : : * This routine works for any datatype (or pair of datatypes) known to
579 : : * convert_to_scalar(). If it is applied to some other datatype,
580 : : * it will return an approximate estimate based on assuming that the constant
581 : : * value falls in the middle of the bin identified by binary search.
582 : : */
583 : : static double
2915 584 : 154818 : scalarineqsel(PlannerInfo *root, Oid operator, bool isgt, bool iseq,
585 : : Oid collation,
586 : : VariableStatData *vardata, Datum constval, Oid consttype)
587 : : {
588 : : Form_pg_statistic stats;
589 : : FmgrInfo opproc;
590 : : double mcv_selec,
591 : : hist_selec,
592 : : sumcommon;
593 : : double selec;
594 : :
7872 595 [ + + ]: 154818 : if (!HeapTupleIsValid(vardata->statsTuple))
596 : : {
597 : : /*
598 : : * No stats are available. Typically this means we have to fall back
599 : : * on the default estimate; but if the variable is CTID then we can
600 : : * make an estimate based on comparing the constant to the table size.
601 : : */
2357 602 [ + - + + ]: 11234 : if (vardata->var && IsA(vardata->var, Var) &&
603 [ + + ]: 8831 : ((Var *) vardata->var)->varattno == SelfItemPointerAttributeNumber)
604 : : {
605 : : ItemPointer itemptr;
606 : : double block;
607 : : double density;
608 : :
609 : : /*
610 : : * If the relation's empty, we're going to include all of it.
611 : : * (This is mostly to avoid divide-by-zero below.)
612 : : */
613 [ - + ]: 974 : if (vardata->rel->pages == 0)
2357 tgl@sss.pgh.pa.us 614 :UBC 0 : return 1.0;
615 : :
2357 tgl@sss.pgh.pa.us 616 :CBC 974 : itemptr = (ItemPointer) DatumGetPointer(constval);
617 : 974 : block = ItemPointerGetBlockNumberNoCheck(itemptr);
618 : :
619 : : /*
620 : : * Determine the average number of tuples per page (density).
621 : : *
622 : : * Since the last page will, on average, be only half full, we can
623 : : * estimate it to have half as many tuples as earlier pages. So
624 : : * give it half the weight of a regular page.
625 : : */
626 : 974 : density = vardata->rel->tuples / (vardata->rel->pages - 0.5);
627 : :
628 : : /* If target is the last page, use half the density. */
629 [ + + ]: 974 : if (block >= vardata->rel->pages - 1)
630 : 15 : density *= 0.5;
631 : :
632 : : /*
633 : : * Using the average tuples per page, calculate how far into the
634 : : * page the itemptr is likely to be and adjust block accordingly,
635 : : * by adding that fraction of a whole block (but never more than a
636 : : * whole block, no matter how high the itemptr's offset is). Here
637 : : * we are ignoring the possibility of dead-tuple line pointers,
638 : : * which is fairly bogus, but we lack the info to do better.
639 : : */
640 [ + - ]: 974 : if (density > 0.0)
641 : : {
642 : 974 : OffsetNumber offset = ItemPointerGetOffsetNumberNoCheck(itemptr);
643 : :
644 [ + + ]: 974 : block += Min(offset / density, 1.0);
645 : : }
646 : :
647 : : /*
648 : : * Convert relative block number to selectivity. Again, the last
649 : : * page has only half weight.
650 : : */
651 : 974 : selec = block / (vardata->rel->pages - 0.5);
652 : :
653 : : /*
654 : : * The calculation so far gave us a selectivity for the "<=" case.
655 : : * We'll have one fewer tuple for "<" and one additional tuple for
656 : : * ">=", the latter of which we'll reverse the selectivity for
657 : : * below, so we can simply subtract one tuple for both cases. The
658 : : * cases that need this adjustment can be identified by iseq being
659 : : * equal to isgt.
660 : : */
661 [ + + + - ]: 974 : if (iseq == isgt && vardata->rel->tuples >= 1.0)
662 : 918 : selec -= (1.0 / vardata->rel->tuples);
663 : :
664 : : /* Finally, reverse the selectivity for the ">", ">=" cases. */
665 [ + + ]: 974 : if (isgt)
666 : 917 : selec = 1.0 - selec;
667 : :
668 [ + + - + ]: 974 : CLAMP_PROBABILITY(selec);
669 : 974 : return selec;
670 : : }
671 : :
672 : : /* no stats available, so default result */
8875 673 : 10260 : return DEFAULT_INEQ_SEL;
674 : : }
7872 675 : 143584 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
676 : :
8875 677 : 143584 : fmgr_info(get_opcode(operator), &opproc);
678 : :
679 : : /*
680 : : * If we have most-common-values info, add up the fractions of the MCV
681 : : * entries that satisfy MCV OP CONST. These fractions contribute directly
682 : : * to the result selectivity. Also add up the total fraction represented
683 : : * by MCV entries.
684 : : */
1919 685 : 143584 : mcv_selec = mcv_selectivity(vardata, &opproc, collation, constval, true,
686 : : &sumcommon);
687 : :
688 : : /*
689 : : * If there is a histogram, determine which bin the constant falls in, and
690 : : * compute the resulting contribution to selectivity.
691 : : */
2915 692 : 143584 : hist_selec = ineq_histogram_selectivity(root, vardata,
693 : : operator, &opproc, isgt, iseq,
694 : : collation,
695 : : constval, consttype);
696 : :
697 : : /*
698 : : * Now merge the results from the MCV and histogram calculations,
699 : : * realizing that the histogram covers only the non-null values that are
700 : : * not listed in MCV.
701 : : */
7179 702 : 143584 : selec = 1.0 - stats->stanullfrac - sumcommon;
703 : :
5724 704 [ + + ]: 143584 : if (hist_selec >= 0.0)
7179 705 : 108053 : selec *= hist_selec;
706 : : else
707 : : {
708 : : /*
709 : : * If no histogram but there are values not accounted for by MCV,
710 : : * arbitrarily assume half of them will match.
711 : : */
712 : 35531 : selec *= 0.5;
713 : : }
714 : :
715 : 143584 : selec += mcv_selec;
716 : :
717 : : /* result should be in range, but make sure... */
718 [ + + + + ]: 143584 : CLAMP_PROBABILITY(selec);
719 : :
720 : 143584 : return selec;
721 : : }
722 : :
723 : : /*
724 : : * mcv_selectivity - Examine the MCV list for selectivity estimates
725 : : *
726 : : * Determine the fraction of the variable's MCV population that satisfies
727 : : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
728 : : * compute the fraction of the total column population represented by the MCV
729 : : * list. This code will work for any boolean-returning predicate operator.
730 : : *
731 : : * The function result is the MCV selectivity, and the fraction of the
732 : : * total population is returned into *sumcommonp. Zeroes are returned
733 : : * if there is no MCV list.
734 : : */
735 : : double
1919 736 : 146673 : mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc, Oid collation,
737 : : Datum constval, bool varonleft,
738 : : double *sumcommonp)
739 : : {
740 : : double mcv_selec,
741 : : sumcommon;
742 : : AttStatsSlot sslot;
743 : : int i;
744 : :
8888 745 : 146673 : mcv_selec = 0.0;
746 : 146673 : sumcommon = 0.0;
747 : :
7179 748 [ + + + + ]: 292116 : if (HeapTupleIsValid(vardata->statsTuple) &&
3046 peter_e@gmx.net 749 [ + + ]: 290721 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3038 tgl@sss.pgh.pa.us 750 : 145278 : get_attstatsslot(&sslot, vardata->statsTuple,
751 : : STATISTIC_KIND_MCV, InvalidOid,
752 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS))
753 : : {
1964 754 : 68614 : LOCAL_FCINFO(fcinfo, 2);
755 : :
756 : : /*
757 : : * We invoke the opproc "by hand" so that we won't fail on NULL
758 : : * results. Such cases won't arise for normal comparison functions,
759 : : * but generic_restriction_selectivity could perhaps be used with
760 : : * operators that can return NULL. A small side benefit is to not
761 : : * need to re-initialize the fcinfo struct from scratch each time.
762 : : */
1919 763 : 68614 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
764 : : NULL, NULL);
1964 765 : 68614 : fcinfo->args[0].isnull = false;
766 : 68614 : fcinfo->args[1].isnull = false;
767 : : /* be careful to apply operator right way 'round */
768 [ + - ]: 68614 : if (varonleft)
769 : 68614 : fcinfo->args[1].value = constval;
770 : : else
1964 tgl@sss.pgh.pa.us 771 :UBC 0 : fcinfo->args[0].value = constval;
772 : :
3038 tgl@sss.pgh.pa.us 773 [ + + ]:CBC 2095383 : for (i = 0; i < sslot.nvalues; i++)
774 : : {
775 : : Datum fresult;
776 : :
1964 777 [ + - ]: 2026769 : if (varonleft)
778 : 2026769 : fcinfo->args[0].value = sslot.values[i];
779 : : else
1964 tgl@sss.pgh.pa.us 780 :UBC 0 : fcinfo->args[1].value = sslot.values[i];
1964 tgl@sss.pgh.pa.us 781 :CBC 2026769 : fcinfo->isnull = false;
782 : 2026769 : fresult = FunctionCallInvoke(fcinfo);
783 [ + - + + ]: 2026769 : if (!fcinfo->isnull && DatumGetBool(fresult))
3038 784 : 758808 : mcv_selec += sslot.numbers[i];
785 : 2026769 : sumcommon += sslot.numbers[i];
786 : : }
787 : 68614 : free_attstatsslot(&sslot);
788 : : }
789 : :
7179 790 : 146673 : *sumcommonp = sumcommon;
791 : 146673 : return mcv_selec;
792 : : }
793 : :
794 : : /*
795 : : * histogram_selectivity - Examine the histogram for selectivity estimates
796 : : *
797 : : * Determine the fraction of the variable's histogram entries that satisfy
798 : : * the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
799 : : *
800 : : * This code will work for any boolean-returning predicate operator, whether
801 : : * or not it has anything to do with the histogram sort operator. We are
802 : : * essentially using the histogram just as a representative sample. However,
803 : : * small histograms are unlikely to be all that representative, so the caller
804 : : * should be prepared to fall back on some other estimation approach when the
805 : : * histogram is missing or very small. It may also be prudent to combine this
806 : : * approach with another one when the histogram is small.
807 : : *
808 : : * If the actual histogram size is not at least min_hist_size, we won't bother
809 : : * to do the calculation at all. Also, if the n_skip parameter is > 0, we
810 : : * ignore the first and last n_skip histogram elements, on the grounds that
811 : : * they are outliers and hence not very representative. Typical values for
812 : : * these parameters are 10 and 1.
813 : : *
814 : : * The function result is the selectivity, or -1 if there is no histogram
815 : : * or it's smaller than min_hist_size.
816 : : *
817 : : * The output parameter *hist_size receives the actual histogram size,
818 : : * or zero if no histogram. Callers may use this number to decide how
819 : : * much faith to put in the function result.
820 : : *
821 : : * Note that the result disregards both the most-common-values (if any) and
822 : : * null entries. The caller is expected to combine this result with
823 : : * statistics for those portions of the column population. It may also be
824 : : * prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
825 : : */
826 : : double
1919 827 : 3089 : histogram_selectivity(VariableStatData *vardata,
828 : : FmgrInfo *opproc, Oid collation,
829 : : Datum constval, bool varonleft,
830 : : int min_hist_size, int n_skip,
831 : : int *hist_size)
832 : : {
833 : : double result;
834 : : AttStatsSlot sslot;
835 : :
836 : : /* check sanity of parameters */
6926 837 [ - + ]: 3089 : Assert(n_skip >= 0);
838 [ - + ]: 3089 : Assert(min_hist_size > 2 * n_skip);
839 : :
840 [ + + + + ]: 4948 : if (HeapTupleIsValid(vardata->statsTuple) &&
3046 peter_e@gmx.net 841 [ + + ]: 3715 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3038 tgl@sss.pgh.pa.us 842 : 1856 : get_attstatsslot(&sslot, vardata->statsTuple,
843 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
844 : : ATTSTATSSLOT_VALUES))
845 : : {
846 : 1809 : *hist_size = sslot.nvalues;
847 [ + + ]: 1809 : if (sslot.nvalues >= min_hist_size)
848 : : {
1964 849 : 893 : LOCAL_FCINFO(fcinfo, 2);
6926 850 : 893 : int nmatch = 0;
851 : : int i;
852 : :
853 : : /*
854 : : * We invoke the opproc "by hand" so that we won't fail on NULL
855 : : * results. Such cases won't arise for normal comparison
856 : : * functions, but generic_restriction_selectivity could perhaps be
857 : : * used with operators that can return NULL. A small side benefit
858 : : * is to not need to re-initialize the fcinfo struct from scratch
859 : : * each time.
860 : : */
1919 861 : 893 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
862 : : NULL, NULL);
1964 863 : 893 : fcinfo->args[0].isnull = false;
864 : 893 : fcinfo->args[1].isnull = false;
865 : : /* be careful to apply operator right way 'round */
866 [ + - ]: 893 : if (varonleft)
867 : 893 : fcinfo->args[1].value = constval;
868 : : else
1964 tgl@sss.pgh.pa.us 869 :UBC 0 : fcinfo->args[0].value = constval;
870 : :
3038 tgl@sss.pgh.pa.us 871 [ + + ]:CBC 73945 : for (i = n_skip; i < sslot.nvalues - n_skip; i++)
872 : : {
873 : : Datum fresult;
874 : :
1964 875 [ + - ]: 73052 : if (varonleft)
876 : 73052 : fcinfo->args[0].value = sslot.values[i];
877 : : else
1964 tgl@sss.pgh.pa.us 878 :UBC 0 : fcinfo->args[1].value = sslot.values[i];
1964 tgl@sss.pgh.pa.us 879 :CBC 73052 : fcinfo->isnull = false;
880 : 73052 : fresult = FunctionCallInvoke(fcinfo);
881 [ + - + + ]: 73052 : if (!fcinfo->isnull && DatumGetBool(fresult))
6926 882 : 4987 : nmatch++;
883 : : }
3038 884 : 893 : result = ((double) nmatch) / ((double) (sslot.nvalues - 2 * n_skip));
885 : : }
886 : : else
6926 887 : 916 : result = -1;
3038 888 : 1809 : free_attstatsslot(&sslot);
889 : : }
890 : : else
891 : : {
6390 892 : 1280 : *hist_size = 0;
6926 893 : 1280 : result = -1;
894 : : }
895 : :
896 : 3089 : return result;
897 : : }
898 : :
899 : : /*
900 : : * generic_restriction_selectivity - Selectivity for almost anything
901 : : *
902 : : * This function estimates selectivity for operators that we don't have any
903 : : * special knowledge about, but are on data types that we collect standard
904 : : * MCV and/or histogram statistics for. (Additional assumptions are that
905 : : * the operator is strict and immutable, or at least stable.)
906 : : *
907 : : * If we have "VAR OP CONST" or "CONST OP VAR", selectivity is estimated by
908 : : * applying the operator to each element of the column's MCV and/or histogram
909 : : * stats, and merging the results using the assumption that the histogram is
910 : : * a reasonable random sample of the column's non-MCV population. Note that
911 : : * if the operator's semantics are related to the histogram ordering, this
912 : : * might not be such a great assumption; other functions such as
913 : : * scalarineqsel() are probably a better match in such cases.
914 : : *
915 : : * Otherwise, fall back to the default selectivity provided by the caller.
916 : : */
917 : : double
1919 918 : 565 : generic_restriction_selectivity(PlannerInfo *root, Oid oproid, Oid collation,
919 : : List *args, int varRelid,
920 : : double default_selectivity)
921 : : {
922 : : double selec;
923 : : VariableStatData vardata;
924 : : Node *other;
925 : : bool varonleft;
926 : :
927 : : /*
928 : : * If expression is not variable OP something or something OP variable,
929 : : * then punt and return the default estimate.
930 : : */
1984 931 [ - + ]: 565 : if (!get_restriction_variable(root, args, varRelid,
932 : : &vardata, &other, &varonleft))
1984 tgl@sss.pgh.pa.us 933 :UBC 0 : return default_selectivity;
934 : :
935 : : /*
936 : : * If the something is a NULL constant, assume operator is strict and
937 : : * return zero, ie, operator will never return TRUE.
938 : : */
1984 tgl@sss.pgh.pa.us 939 [ + - ]:CBC 565 : if (IsA(other, Const) &&
940 [ - + ]: 565 : ((Const *) other)->constisnull)
941 : : {
1984 tgl@sss.pgh.pa.us 942 [ # # ]:UBC 0 : ReleaseVariableStats(vardata);
943 : 0 : return 0.0;
944 : : }
945 : :
1984 tgl@sss.pgh.pa.us 946 [ + - ]:CBC 565 : if (IsA(other, Const))
947 : : {
948 : : /* Variable is being compared to a known non-null constant */
949 : 565 : Datum constval = ((Const *) other)->constvalue;
950 : : FmgrInfo opproc;
951 : : double mcvsum;
952 : : double mcvsel;
953 : : double nullfrac;
954 : : int hist_size;
955 : :
1964 956 : 565 : fmgr_info(get_opcode(oproid), &opproc);
957 : :
958 : : /*
959 : : * Calculate the selectivity for the column's most common values.
960 : : */
1919 961 : 565 : mcvsel = mcv_selectivity(&vardata, &opproc, collation,
962 : : constval, varonleft,
963 : : &mcvsum);
964 : :
965 : : /*
966 : : * If the histogram is large enough, see what fraction of it matches
967 : : * the query, and assume that's representative of the non-MCV
968 : : * population. Otherwise use the default selectivity for the non-MCV
969 : : * population.
970 : : */
971 : 565 : selec = histogram_selectivity(&vardata, &opproc, collation,
972 : : constval, varonleft,
973 : : 10, 1, &hist_size);
1984 974 [ + - ]: 565 : if (selec < 0)
975 : : {
976 : : /* Nope, fall back on default */
977 : 565 : selec = default_selectivity;
978 : : }
1984 tgl@sss.pgh.pa.us 979 [ # # ]:UBC 0 : else if (hist_size < 100)
980 : : {
981 : : /*
982 : : * For histogram sizes from 10 to 100, we combine the histogram
983 : : * and default selectivities, putting increasingly more trust in
984 : : * the histogram for larger sizes.
985 : : */
986 : 0 : double hist_weight = hist_size / 100.0;
987 : :
988 : 0 : selec = selec * hist_weight +
989 : 0 : default_selectivity * (1.0 - hist_weight);
990 : : }
991 : :
992 : : /* In any case, don't believe extremely small or large estimates. */
1984 tgl@sss.pgh.pa.us 993 [ - + ]:CBC 565 : if (selec < 0.0001)
1984 tgl@sss.pgh.pa.us 994 :UBC 0 : selec = 0.0001;
1984 tgl@sss.pgh.pa.us 995 [ - + ]:CBC 565 : else if (selec > 0.9999)
1984 tgl@sss.pgh.pa.us 996 :UBC 0 : selec = 0.9999;
997 : :
998 : : /* Don't forget to account for nulls. */
1984 tgl@sss.pgh.pa.us 999 [ + + ]:CBC 565 : if (HeapTupleIsValid(vardata.statsTuple))
1000 : 42 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
1001 : : else
1002 : 523 : nullfrac = 0.0;
1003 : :
1004 : : /*
1005 : : * Now merge the results from the MCV and histogram calculations,
1006 : : * realizing that the histogram covers only the non-null values that
1007 : : * are not listed in MCV.
1008 : : */
1009 : 565 : selec *= 1.0 - nullfrac - mcvsum;
1010 : 565 : selec += mcvsel;
1011 : : }
1012 : : else
1013 : : {
1014 : : /* Comparison value is not constant, so we can't do anything */
1984 tgl@sss.pgh.pa.us 1015 :UBC 0 : selec = default_selectivity;
1016 : : }
1017 : :
1984 tgl@sss.pgh.pa.us 1018 [ + + ]:CBC 565 : ReleaseVariableStats(vardata);
1019 : :
1020 : : /* result should be in range, but make sure... */
1021 [ - + - + ]: 565 : CLAMP_PROBABILITY(selec);
1022 : :
1023 : 565 : return selec;
1024 : : }
1025 : :
1026 : : /*
1027 : : * ineq_histogram_selectivity - Examine the histogram for scalarineqsel
1028 : : *
1029 : : * Determine the fraction of the variable's histogram population that
1030 : : * satisfies the inequality condition, ie, VAR < (or <=, >, >=) CONST.
1031 : : * The isgt and iseq flags distinguish which of the four cases apply.
1032 : : *
1033 : : * While opproc could be looked up from the operator OID, common callers
1034 : : * also need to call it separately, so we make the caller pass both.
1035 : : *
1036 : : * Returns -1 if there is no histogram (valid results will always be >= 0).
1037 : : *
1038 : : * Note that the result disregards both the most-common-values (if any) and
1039 : : * null entries. The caller is expected to combine this result with
1040 : : * statistics for those portions of the column population.
1041 : : *
1042 : : * This is exported so that some other estimation functions can use it.
1043 : : */
1044 : : double
5724 1045 : 146130 : ineq_histogram_selectivity(PlannerInfo *root,
1046 : : VariableStatData *vardata,
1047 : : Oid opoid, FmgrInfo *opproc, bool isgt, bool iseq,
1048 : : Oid collation,
1049 : : Datum constval, Oid consttype)
1050 : : {
1051 : : double hist_selec;
1052 : : AttStatsSlot sslot;
1053 : :
1054 : 146130 : hist_selec = -1.0;
1055 : :
1056 : : /*
1057 : : * Someday, ANALYZE might store more than one histogram per rel/att,
1058 : : * corresponding to more than one possible sort ordering defined for the
1059 : : * column type. Right now, we know there is only one, so just grab it and
1060 : : * see if it matches the query.
1061 : : *
1062 : : * Note that we can't use opoid as search argument; the staop appearing in
1063 : : * pg_statistic will be for the relevant '<' operator, but what we have
1064 : : * might be some other inequality operator such as '>='. (Even if opoid
1065 : : * is a '<' operator, it could be cross-type.) Hence we must use
1066 : : * comparison_ops_are_compatible() to see if the operators match.
1067 : : */
7179 1068 [ + + + + ]: 291915 : if (HeapTupleIsValid(vardata->statsTuple) &&
3046 peter_e@gmx.net 1069 [ + + ]: 291408 : statistic_proc_security_check(vardata, opproc->fn_oid) &&
3038 tgl@sss.pgh.pa.us 1070 : 145623 : get_attstatsslot(&sslot, vardata->statsTuple,
1071 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
1072 : : ATTSTATSSLOT_VALUES))
1073 : : {
1919 1074 [ + - ]: 110253 : if (sslot.nvalues > 1 &&
1075 [ + + + + ]: 220468 : sslot.stacoll == collation &&
1076 : 110215 : comparison_ops_are_compatible(sslot.staop, opoid))
9532 1077 : 110161 : {
1078 : : /*
1079 : : * Use binary search to find the desired location, namely the
1080 : : * right end of the histogram bin containing the comparison value,
1081 : : * which is the leftmost entry for which the comparison operator
1082 : : * succeeds (if isgt) or fails (if !isgt).
1083 : : *
1084 : : * In this loop, we pay no attention to whether the operator iseq
1085 : : * or not; that detail will be mopped up below. (We cannot tell,
1086 : : * anyway, whether the operator thinks the values are equal.)
1087 : : *
1088 : : * If the binary search accesses the first or last histogram
1089 : : * entry, we try to replace that endpoint with the true column min
1090 : : * or max as found by get_actual_variable_range(). This
1091 : : * ameliorates misestimates when the min or max is moving as a
1092 : : * result of changes since the last ANALYZE. Note that this could
1093 : : * result in effectively including MCVs into the histogram that
1094 : : * weren't there before, but we don't try to correct for that.
1095 : : */
1096 : : double histfrac;
6912 bruce@momjian.us 1097 : 110161 : int lobound = 0; /* first possible slot to search */
2999 tgl@sss.pgh.pa.us 1098 : 110161 : int hibound = sslot.nvalues; /* last+1 slot to search */
5724 1099 : 110161 : bool have_end = false;
1100 : :
1101 : : /*
1102 : : * If there are only two histogram entries, we'll want up-to-date
1103 : : * values for both. (If there are more than two, we need at most
1104 : : * one of them to be updated, so we deal with that within the
1105 : : * loop.)
1106 : : */
3038 1107 [ + + ]: 110161 : if (sslot.nvalues == 2)
5724 1108 : 2487 : have_end = get_actual_variable_range(root,
1109 : : vardata,
1110 : : sslot.staop,
1111 : : collation,
1112 : : &sslot.values[0],
3038 1113 : 2487 : &sslot.values[1]);
1114 : :
6926 1115 [ + + ]: 732786 : while (lobound < hibound)
1116 : : {
6912 bruce@momjian.us 1117 : 622625 : int probe = (lobound + hibound) / 2;
1118 : : bool ltcmp;
1119 : :
1120 : : /*
1121 : : * If we find ourselves about to compare to the first or last
1122 : : * histogram entry, first try to replace it with the actual
1123 : : * current min or max (unless we already did so above).
1124 : : */
3038 tgl@sss.pgh.pa.us 1125 [ + + + + ]: 622625 : if (probe == 0 && sslot.nvalues > 2)
5724 1126 : 53743 : have_end = get_actual_variable_range(root,
1127 : : vardata,
1128 : : sslot.staop,
1129 : : collation,
1130 : : &sslot.values[0],
1131 : : NULL);
3038 1132 [ + + + + ]: 568882 : else if (probe == sslot.nvalues - 1 && sslot.nvalues > 2)
5724 1133 : 37402 : have_end = get_actual_variable_range(root,
1134 : : vardata,
1135 : : sslot.staop,
1136 : : collation,
1137 : : NULL,
2999 1138 : 37402 : &sslot.values[probe]);
1139 : :
5261 1140 : 622625 : ltcmp = DatumGetBool(FunctionCall2Coll(opproc,
1141 : : collation,
3038 1142 : 622625 : sslot.values[probe],
1143 : : constval));
6926 1144 [ + + ]: 622625 : if (isgt)
1145 : 34527 : ltcmp = !ltcmp;
1146 [ + + ]: 622625 : if (ltcmp)
1147 : 238655 : lobound = probe + 1;
1148 : : else
1149 : 383970 : hibound = probe;
1150 : : }
1151 : :
1152 [ + + ]: 110161 : if (lobound <= 0)
1153 : : {
1154 : : /*
1155 : : * Constant is below lower histogram boundary. More
1156 : : * precisely, we have found that no entry in the histogram
1157 : : * satisfies the inequality clause (if !isgt) or they all do
1158 : : * (if isgt). We estimate that that's true of the entire
1159 : : * table, so set histfrac to 0.0 (which we'll flip to 1.0
1160 : : * below, if isgt).
1161 : : */
8888 1162 : 46787 : histfrac = 0.0;
1163 : : }
3038 1164 [ + + ]: 63374 : else if (lobound >= sslot.nvalues)
1165 : : {
1166 : : /*
1167 : : * Inverse case: constant is above upper histogram boundary.
1168 : : */
6926 1169 : 17685 : histfrac = 1.0;
1170 : : }
1171 : : else
1172 : : {
1173 : : /* We have values[i-1] <= constant <= values[i]. */
1174 : 45689 : int i = lobound;
2915 1175 : 45689 : double eq_selec = 0;
1176 : : double val,
1177 : : high,
1178 : : low;
1179 : : double binfrac;
1180 : :
1181 : : /*
1182 : : * In the cases where we'll need it below, obtain an estimate
1183 : : * of the selectivity of "x = constval". We use a calculation
1184 : : * similar to what var_eq_const() does for a non-MCV constant,
1185 : : * ie, estimate that all distinct non-MCV values occur equally
1186 : : * often. But multiplication by "1.0 - sumcommon - nullfrac"
1187 : : * will be done by our caller, so we shouldn't do that here.
1188 : : * Therefore we can't try to clamp the estimate by reference
1189 : : * to the least common MCV; the result would be too small.
1190 : : *
1191 : : * Note: since this is effectively assuming that constval
1192 : : * isn't an MCV, it's logically dubious if constval in fact is
1193 : : * one. But we have to apply *some* correction for equality,
1194 : : * and anyway we cannot tell if constval is an MCV, since we
1195 : : * don't have a suitable equality operator at hand.
1196 : : */
1197 [ + + + + ]: 45689 : if (i == 1 || isgt == iseq)
1198 : : {
1199 : : double otherdistinct;
1200 : : bool isdefault;
1201 : : AttStatsSlot mcvslot;
1202 : :
1203 : : /* Get estimated number of distinct values */
1204 : 19613 : otherdistinct = get_variable_numdistinct(vardata,
1205 : : &isdefault);
1206 : :
1207 : : /* Subtract off the number of known MCVs */
1208 [ + + ]: 19613 : if (get_attstatsslot(&mcvslot, vardata->statsTuple,
1209 : : STATISTIC_KIND_MCV, InvalidOid,
1210 : : ATTSTATSSLOT_NUMBERS))
1211 : : {
1212 : 2309 : otherdistinct -= mcvslot.nnumbers;
1213 : 2309 : free_attstatsslot(&mcvslot);
1214 : : }
1215 : :
1216 : : /* If result doesn't seem sane, leave eq_selec at 0 */
1217 [ + + ]: 19613 : if (otherdistinct > 1)
1218 : 19592 : eq_selec = 1.0 / otherdistinct;
1219 : : }
1220 : :
1221 : : /*
1222 : : * Convert the constant and the two nearest bin boundary
1223 : : * values to a uniform comparison scale, and do a linear
1224 : : * interpolation within this bin.
1225 : : */
1919 1226 [ + - ]: 45689 : if (convert_to_scalar(constval, consttype, collation,
1227 : : &val,
3038 1228 : 45689 : sslot.values[i - 1], sslot.values[i],
1229 : : vardata->vartype,
1230 : : &low, &high))
1231 : : {
6926 1232 [ - + ]: 45689 : if (high <= low)
1233 : : {
1234 : : /* cope if bin boundaries appear identical */
6926 tgl@sss.pgh.pa.us 1235 :UBC 0 : binfrac = 0.5;
1236 : : }
6926 tgl@sss.pgh.pa.us 1237 [ + + ]:CBC 45689 : else if (val <= low)
1238 : 9611 : binfrac = 0.0;
1239 [ + + ]: 36078 : else if (val >= high)
1240 : 1330 : binfrac = 1.0;
1241 : : else
1242 : : {
1243 : 34748 : binfrac = (val - low) / (high - low);
1244 : :
1245 : : /*
1246 : : * Watch out for the possibility that we got a NaN or
1247 : : * Infinity from the division. This can happen
1248 : : * despite the previous checks, if for example "low"
1249 : : * is -Infinity.
1250 : : */
1251 [ + - + - ]: 34748 : if (isnan(binfrac) ||
1252 [ - + ]: 34748 : binfrac < 0.0 || binfrac > 1.0)
6926 tgl@sss.pgh.pa.us 1253 :UBC 0 : binfrac = 0.5;
1254 : : }
1255 : : }
1256 : : else
1257 : : {
1258 : : /*
1259 : : * Ideally we'd produce an error here, on the grounds that
1260 : : * the given operator shouldn't have scalarXXsel
1261 : : * registered as its selectivity func unless we can deal
1262 : : * with its operand types. But currently, all manner of
1263 : : * stuff is invoking scalarXXsel, so give a default
1264 : : * estimate until that can be fixed.
1265 : : */
1266 : 0 : binfrac = 0.5;
1267 : : }
1268 : :
1269 : : /*
1270 : : * Now, compute the overall selectivity across the values
1271 : : * represented by the histogram. We have i-1 full bins and
1272 : : * binfrac partial bin below the constant.
1273 : : */
6926 tgl@sss.pgh.pa.us 1274 :CBC 45689 : histfrac = (double) (i - 1) + binfrac;
3038 1275 : 45689 : histfrac /= (double) (sslot.nvalues - 1);
1276 : :
1277 : : /*
1278 : : * At this point, histfrac is an estimate of the fraction of
1279 : : * the population represented by the histogram that satisfies
1280 : : * "x <= constval". Somewhat remarkably, this statement is
1281 : : * true regardless of which operator we were doing the probes
1282 : : * with, so long as convert_to_scalar() delivers reasonable
1283 : : * results. If the probe constant is equal to some histogram
1284 : : * entry, we would have considered the bin to the left of that
1285 : : * entry if probing with "<" or ">=", or the bin to the right
1286 : : * if probing with "<=" or ">"; but binfrac would have come
1287 : : * out as 1.0 in the first case and 0.0 in the second, leading
1288 : : * to the same histfrac in either case. For probe constants
1289 : : * between histogram entries, we find the same bin and get the
1290 : : * same estimate with any operator.
1291 : : *
1292 : : * The fact that the estimate corresponds to "x <= constval"
1293 : : * and not "x < constval" is because of the way that ANALYZE
1294 : : * constructs the histogram: each entry is, effectively, the
1295 : : * rightmost value in its sample bucket. So selectivity
1296 : : * values that are exact multiples of 1/(histogram_size-1)
1297 : : * should be understood as estimates including a histogram
1298 : : * entry plus everything to its left.
1299 : : *
1300 : : * However, that breaks down for the first histogram entry,
1301 : : * which necessarily is the leftmost value in its sample
1302 : : * bucket. That means the first histogram bin is slightly
1303 : : * narrower than the rest, by an amount equal to eq_selec.
1304 : : * Another way to say that is that we want "x <= leftmost" to
1305 : : * be estimated as eq_selec not zero. So, if we're dealing
1306 : : * with the first bin (i==1), rescale to make that true while
1307 : : * adjusting the rest of that bin linearly.
1308 : : */
2915 1309 [ + + ]: 45689 : if (i == 1)
1310 : 8409 : histfrac += eq_selec * (1.0 - binfrac);
1311 : :
1312 : : /*
1313 : : * "x <= constval" is good if we want an estimate for "<=" or
1314 : : * ">", but if we are estimating for "<" or ">=", we now need
1315 : : * to decrease the estimate by eq_selec.
1316 : : */
1317 [ + + ]: 45689 : if (isgt == iseq)
1318 : 14953 : histfrac -= eq_selec;
1319 : : }
1320 : :
1321 : : /*
1322 : : * Now the estimate is finished for "<" and "<=" cases. If we are
1323 : : * estimating for ">" or ">=", flip it.
1324 : : */
8888 1325 [ + + ]: 110161 : hist_selec = isgt ? (1.0 - histfrac) : histfrac;
1326 : :
1327 : : /*
1328 : : * The histogram boundaries are only approximate to begin with,
1329 : : * and may well be out of date anyway. Therefore, don't believe
1330 : : * extremely small or large selectivity estimates --- unless we
1331 : : * got actual current endpoint values from the table, in which
1332 : : * case just do the usual sanity clamp. Somewhat arbitrarily, we
1333 : : * set the cutoff for other cases at a hundredth of the histogram
1334 : : * resolution.
1335 : : */
5724 1336 [ + + ]: 110161 : if (have_end)
1337 [ + + - + ]: 61965 : CLAMP_PROBABILITY(hist_selec);
1338 : : else
1339 : : {
2915 1340 : 48196 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1341 : :
1342 [ + + ]: 48196 : if (hist_selec < cutoff)
1343 : 17186 : hist_selec = cutoff;
1344 [ + + ]: 31010 : else if (hist_selec > 1.0 - cutoff)
1345 : 11620 : hist_selec = 1.0 - cutoff;
1346 : : }
1347 : : }
1919 1348 [ + - ]: 92 : else if (sslot.nvalues > 1)
1349 : : {
1350 : : /*
1351 : : * If we get here, we have a histogram but it's not sorted the way
1352 : : * we want. Do a brute-force search to see how many of the
1353 : : * entries satisfy the comparison condition, and take that
1354 : : * fraction as our estimate. (This is identical to the inner loop
1355 : : * of histogram_selectivity; maybe share code?)
1356 : : */
1357 : 92 : LOCAL_FCINFO(fcinfo, 2);
1358 : 92 : int nmatch = 0;
1359 : :
1360 : 92 : InitFunctionCallInfoData(*fcinfo, opproc, 2, collation,
1361 : : NULL, NULL);
1362 : 92 : fcinfo->args[0].isnull = false;
1363 : 92 : fcinfo->args[1].isnull = false;
1364 : 92 : fcinfo->args[1].value = constval;
1365 [ + + ]: 481258 : for (int i = 0; i < sslot.nvalues; i++)
1366 : : {
1367 : : Datum fresult;
1368 : :
1369 : 481166 : fcinfo->args[0].value = sslot.values[i];
1370 : 481166 : fcinfo->isnull = false;
1371 : 481166 : fresult = FunctionCallInvoke(fcinfo);
1372 [ + - + + ]: 481166 : if (!fcinfo->isnull && DatumGetBool(fresult))
1373 : 1116 : nmatch++;
1374 : : }
1375 : 92 : hist_selec = ((double) nmatch) / ((double) sslot.nvalues);
1376 : :
1377 : : /*
1378 : : * As above, clamp to a hundredth of the histogram resolution.
1379 : : * This case is surely even less trustworthy than the normal one,
1380 : : * so we shouldn't believe exact 0 or 1 selectivity. (Maybe the
1381 : : * clamp should be more restrictive in this case?)
1382 : : */
1383 : : {
1384 : 92 : double cutoff = 0.01 / (double) (sslot.nvalues - 1);
1385 : :
1386 [ - + ]: 92 : if (hist_selec < cutoff)
1919 tgl@sss.pgh.pa.us 1387 :UBC 0 : hist_selec = cutoff;
1919 tgl@sss.pgh.pa.us 1388 [ - + ]:CBC 92 : else if (hist_selec > 1.0 - cutoff)
1919 tgl@sss.pgh.pa.us 1389 :UBC 0 : hist_selec = 1.0 - cutoff;
1390 : : }
1391 : : }
1392 : :
3038 tgl@sss.pgh.pa.us 1393 :CBC 110253 : free_attstatsslot(&sslot);
1394 : : }
1395 : :
7179 1396 : 146130 : return hist_selec;
1397 : : }
1398 : :
1399 : : /*
1400 : : * Common wrapper function for the selectivity estimators that simply
1401 : : * invoke scalarineqsel().
1402 : : */
1403 : : static Datum
2915 1404 : 22404 : scalarineqsel_wrapper(PG_FUNCTION_ARGS, bool isgt, bool iseq)
1405 : : {
7398 1406 : 22404 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
8875 1407 : 22404 : Oid operator = PG_GETARG_OID(1);
1408 : 22404 : List *args = (List *) PG_GETARG_POINTER(2);
1409 : 22404 : int varRelid = PG_GETARG_INT32(3);
1919 1410 : 22404 : Oid collation = PG_GET_COLLATION();
1411 : : VariableStatData vardata;
1412 : : Node *other;
1413 : : bool varonleft;
1414 : : Datum constval;
1415 : : Oid consttype;
1416 : : double selec;
1417 : :
1418 : : /*
1419 : : * If expression is not variable op something or something op variable,
1420 : : * then punt and return a default estimate.
1421 : : */
7872 1422 [ + + ]: 22404 : if (!get_restriction_variable(root, args, varRelid,
1423 : : &vardata, &other, &varonleft))
8875 1424 : 325 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1425 : :
1426 : : /*
1427 : : * Can't do anything useful if the something is not a constant, either.
1428 : : */
8590 1429 [ + + ]: 22079 : if (!IsA(other, Const))
1430 : : {
7872 1431 [ + + ]: 1388 : ReleaseVariableStats(vardata);
8590 1432 : 1388 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1433 : : }
1434 : :
1435 : : /*
1436 : : * If the constant is NULL, assume operator is strict and return zero, ie,
1437 : : * operator will never return TRUE.
1438 : : */
1439 [ + + ]: 20691 : if (((Const *) other)->constisnull)
1440 : : {
7872 1441 [ + + ]: 33 : ReleaseVariableStats(vardata);
8590 1442 : 33 : PG_RETURN_FLOAT8(0.0);
1443 : : }
1444 : 20658 : constval = ((Const *) other)->constvalue;
1445 : 20658 : consttype = ((Const *) other)->consttype;
1446 : :
1447 : : /*
1448 : : * Force the var to be on the left to simplify logic in scalarineqsel.
1449 : : */
2915 1450 [ + + ]: 20658 : if (!varonleft)
1451 : : {
8875 1452 : 183 : operator = get_commutator(operator);
1453 [ - + ]: 183 : if (!operator)
1454 : : {
1455 : : /* Use default selectivity (should we raise an error instead?) */
7872 tgl@sss.pgh.pa.us 1456 [ # # ]:UBC 0 : ReleaseVariableStats(vardata);
8875 1457 : 0 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
1458 : : }
2915 tgl@sss.pgh.pa.us 1459 :CBC 183 : isgt = !isgt;
1460 : : }
1461 : :
1462 : : /* The rest of the work is done by scalarineqsel(). */
1919 1463 : 20658 : selec = scalarineqsel(root, operator, isgt, iseq, collation,
1464 : : &vardata, constval, consttype);
1465 : :
7872 1466 [ + + ]: 20658 : ReleaseVariableStats(vardata);
1467 : :
8888 1468 : 20658 : PG_RETURN_FLOAT8((float8) selec);
1469 : : }
1470 : :
1471 : : /*
1472 : : * scalarltsel - Selectivity of "<" for scalars.
1473 : : */
1474 : : Datum
2915 1475 : 7514 : scalarltsel(PG_FUNCTION_ARGS)
1476 : : {
1477 : 7514 : return scalarineqsel_wrapper(fcinfo, false, false);
1478 : : }
1479 : :
1480 : : /*
1481 : : * scalarlesel - Selectivity of "<=" for scalars.
1482 : : */
1483 : : Datum
1484 : 2273 : scalarlesel(PG_FUNCTION_ARGS)
1485 : : {
1486 : 2273 : return scalarineqsel_wrapper(fcinfo, false, true);
1487 : : }
1488 : :
1489 : : /*
1490 : : * scalargtsel - Selectivity of ">" for scalars.
1491 : : */
1492 : : Datum
1493 : 7628 : scalargtsel(PG_FUNCTION_ARGS)
1494 : : {
1495 : 7628 : return scalarineqsel_wrapper(fcinfo, true, false);
1496 : : }
1497 : :
1498 : : /*
1499 : : * scalargesel - Selectivity of ">=" for scalars.
1500 : : */
1501 : : Datum
1502 : 4989 : scalargesel(PG_FUNCTION_ARGS)
1503 : : {
1504 : 4989 : return scalarineqsel_wrapper(fcinfo, true, true);
1505 : : }
1506 : :
1507 : : /*
1508 : : * boolvarsel - Selectivity of Boolean variable.
1509 : : *
1510 : : * This can actually be called on any boolean-valued expression. If it
1511 : : * involves only Vars of the specified relation, and if there are statistics
1512 : : * about the Var or expression (the latter is possible if it's indexed) then
1513 : : * we'll produce a real estimate; otherwise it's just a default.
1514 : : */
1515 : : Selectivity
3635 1516 : 21419 : boolvarsel(PlannerInfo *root, Node *arg, int varRelid)
1517 : : {
1518 : : VariableStatData vardata;
1519 : : double selec;
1520 : :
1521 : 21419 : examine_variable(root, arg, varRelid, &vardata);
1522 [ + + ]: 21419 : if (HeapTupleIsValid(vardata.statsTuple))
1523 : : {
1524 : : /*
1525 : : * A boolean variable V is equivalent to the clause V = 't', so we
1526 : : * compute the selectivity as if that is what we have.
1527 : : */
1919 1528 : 17553 : selec = var_eq_const(&vardata, BooleanEqualOperator, InvalidOid,
1529 : : BoolGetDatum(true), false, true, false);
1530 : : }
1531 : : else
1532 : : {
1533 : : /* Otherwise, the default estimate is 0.5 */
3635 1534 : 3866 : selec = 0.5;
1535 : : }
1536 [ + + ]: 21419 : ReleaseVariableStats(vardata);
1537 : 21419 : return selec;
1538 : : }
1539 : :
1540 : : /*
1541 : : * booltestsel - Selectivity of BooleanTest Node.
1542 : : */
1543 : : Selectivity
7398 1544 : 443 : booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
1545 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1546 : : {
1547 : : VariableStatData vardata;
1548 : : double selec;
1549 : :
7872 1550 : 443 : examine_variable(root, arg, varRelid, &vardata);
1551 : :
1552 [ - + ]: 443 : if (HeapTupleIsValid(vardata.statsTuple))
1553 : : {
1554 : : Form_pg_statistic stats;
1555 : : double freq_null;
1556 : : AttStatsSlot sslot;
1557 : :
7872 tgl@sss.pgh.pa.us 1558 :UBC 0 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
8839 1559 : 0 : freq_null = stats->stanullfrac;
1560 : :
3038 1561 [ # # ]: 0 : if (get_attstatsslot(&sslot, vardata.statsTuple,
1562 : : STATISTIC_KIND_MCV, InvalidOid,
1563 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)
1564 [ # # ]: 0 : && sslot.nnumbers > 0)
8839 1565 : 0 : {
1566 : : double freq_true;
1567 : : double freq_false;
1568 : :
1569 : : /*
1570 : : * Get first MCV frequency and derive frequency for true.
1571 : : */
3038 1572 [ # # ]: 0 : if (DatumGetBool(sslot.values[0]))
1573 : 0 : freq_true = sslot.numbers[0];
1574 : : else
1575 : 0 : freq_true = 1.0 - sslot.numbers[0] - freq_null;
1576 : :
1577 : : /*
1578 : : * Next derive frequency for false. Then use these as appropriate
1579 : : * to derive frequency for each case.
1580 : : */
8839 1581 : 0 : freq_false = 1.0 - freq_true - freq_null;
1582 : :
8358 1583 [ # # # # : 0 : switch (booltesttype)
# # # ]
1584 : : {
8717 bruce@momjian.us 1585 : 0 : case IS_UNKNOWN:
1586 : : /* select only NULL values */
8839 tgl@sss.pgh.pa.us 1587 : 0 : selec = freq_null;
1588 : 0 : break;
8717 bruce@momjian.us 1589 : 0 : case IS_NOT_UNKNOWN:
1590 : : /* select non-NULL values */
8839 tgl@sss.pgh.pa.us 1591 : 0 : selec = 1.0 - freq_null;
1592 : 0 : break;
8717 bruce@momjian.us 1593 : 0 : case IS_TRUE:
1594 : : /* select only TRUE values */
8839 tgl@sss.pgh.pa.us 1595 : 0 : selec = freq_true;
1596 : 0 : break;
8717 bruce@momjian.us 1597 : 0 : case IS_NOT_TRUE:
1598 : : /* select non-TRUE values */
8839 tgl@sss.pgh.pa.us 1599 : 0 : selec = 1.0 - freq_true;
1600 : 0 : break;
8717 bruce@momjian.us 1601 : 0 : case IS_FALSE:
1602 : : /* select only FALSE values */
8839 tgl@sss.pgh.pa.us 1603 : 0 : selec = freq_false;
1604 : 0 : break;
8717 bruce@momjian.us 1605 : 0 : case IS_NOT_FALSE:
1606 : : /* select non-FALSE values */
8839 tgl@sss.pgh.pa.us 1607 : 0 : selec = 1.0 - freq_false;
1608 : 0 : break;
8717 bruce@momjian.us 1609 : 0 : default:
8077 tgl@sss.pgh.pa.us 1610 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1611 : : (int) booltesttype);
1612 : : selec = 0.0; /* Keep compiler quiet */
1613 : : break;
1614 : : }
1615 : :
3038 1616 : 0 : free_attstatsslot(&sslot);
1617 : : }
1618 : : else
1619 : : {
1620 : : /*
1621 : : * No most-common-value info available. Still have null fraction
1622 : : * information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
1623 : : * for null fraction and assume a 50-50 split of TRUE and FALSE.
1624 : : */
8358 1625 [ # # # # : 0 : switch (booltesttype)
# ]
1626 : : {
8717 bruce@momjian.us 1627 : 0 : case IS_UNKNOWN:
1628 : : /* select only NULL values */
8839 tgl@sss.pgh.pa.us 1629 : 0 : selec = freq_null;
1630 : 0 : break;
8717 bruce@momjian.us 1631 : 0 : case IS_NOT_UNKNOWN:
1632 : : /* select non-NULL values */
8839 tgl@sss.pgh.pa.us 1633 : 0 : selec = 1.0 - freq_null;
1634 : 0 : break;
8717 bruce@momjian.us 1635 : 0 : case IS_TRUE:
1636 : : case IS_FALSE:
1637 : : /* Assume we select half of the non-NULL values */
8839 tgl@sss.pgh.pa.us 1638 : 0 : selec = (1.0 - freq_null) / 2.0;
1639 : 0 : break;
4427 1640 : 0 : case IS_NOT_TRUE:
1641 : : case IS_NOT_FALSE:
1642 : : /* Assume we select NULLs plus half of the non-NULLs */
1643 : : /* equiv. to freq_null + (1.0 - freq_null) / 2.0 */
1644 : 0 : selec = (freq_null + 1.0) / 2.0;
1645 : 0 : break;
8717 bruce@momjian.us 1646 : 0 : default:
8077 tgl@sss.pgh.pa.us 1647 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1648 : : (int) booltesttype);
1649 : : selec = 0.0; /* Keep compiler quiet */
1650 : : break;
1651 : : }
1652 : : }
1653 : : }
1654 : : else
1655 : : {
1656 : : /*
1657 : : * If we can't get variable statistics for the argument, perhaps
1658 : : * clause_selectivity can do something with it. We ignore the
1659 : : * possibility of a NULL value when using clause_selectivity, and just
1660 : : * assume the value is either TRUE or FALSE.
1661 : : */
8358 tgl@sss.pgh.pa.us 1662 [ + + + + :CBC 443 : switch (booltesttype)
- ]
1663 : : {
8839 1664 : 24 : case IS_UNKNOWN:
1665 : 24 : selec = DEFAULT_UNK_SEL;
1666 : 24 : break;
1667 : 54 : case IS_NOT_UNKNOWN:
1668 : 54 : selec = DEFAULT_NOT_UNK_SEL;
1669 : 54 : break;
1670 : 126 : case IS_TRUE:
1671 : : case IS_NOT_FALSE:
7872 1672 : 126 : selec = (double) clause_selectivity(root, arg,
1673 : : varRelid,
1674 : : jointype, sjinfo);
1675 : 126 : break;
1676 : 239 : case IS_FALSE:
1677 : : case IS_NOT_TRUE:
1678 : 239 : selec = 1.0 - (double) clause_selectivity(root, arg,
1679 : : varRelid,
1680 : : jointype, sjinfo);
8839 1681 : 239 : break;
8839 tgl@sss.pgh.pa.us 1682 :UBC 0 : default:
8077 1683 [ # # ]: 0 : elog(ERROR, "unrecognized booltesttype: %d",
1684 : : (int) booltesttype);
1685 : : selec = 0.0; /* Keep compiler quiet */
1686 : : break;
1687 : : }
1688 : : }
1689 : :
7872 tgl@sss.pgh.pa.us 1690 [ - + ]:CBC 443 : ReleaseVariableStats(vardata);
1691 : :
1692 : : /* result should be in range, but make sure... */
8647 1693 [ - + - + ]: 443 : CLAMP_PROBABILITY(selec);
1694 : :
8839 1695 : 443 : return (Selectivity) selec;
1696 : : }
1697 : :
1698 : : /*
1699 : : * nulltestsel - Selectivity of NullTest Node.
1700 : : */
1701 : : Selectivity
6232 1702 : 8884 : nulltestsel(PlannerInfo *root, NullTestType nulltesttype, Node *arg,
1703 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
1704 : : {
1705 : : VariableStatData vardata;
1706 : : double selec;
1707 : :
7872 1708 : 8884 : examine_variable(root, arg, varRelid, &vardata);
1709 : :
1710 [ + + ]: 8884 : if (HeapTupleIsValid(vardata.statsTuple))
1711 : : {
1712 : : Form_pg_statistic stats;
1713 : : double freq_null;
1714 : :
1715 : 4910 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
8839 1716 : 4910 : freq_null = stats->stanullfrac;
1717 : :
8358 1718 [ + + - ]: 4910 : switch (nulltesttype)
1719 : : {
8717 bruce@momjian.us 1720 : 3612 : case IS_NULL:
1721 : :
1722 : : /*
1723 : : * Use freq_null directly.
1724 : : */
8839 tgl@sss.pgh.pa.us 1725 : 3612 : selec = freq_null;
1726 : 3612 : break;
8717 bruce@momjian.us 1727 : 1298 : case IS_NOT_NULL:
1728 : :
1729 : : /*
1730 : : * Select not unknown (not null) values. Calculate from
1731 : : * freq_null.
1732 : : */
8839 tgl@sss.pgh.pa.us 1733 : 1298 : selec = 1.0 - freq_null;
1734 : 1298 : break;
8717 bruce@momjian.us 1735 :UBC 0 : default:
8077 tgl@sss.pgh.pa.us 1736 [ # # ]: 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1737 : : (int) nulltesttype);
1738 : : return (Selectivity) 0; /* keep compiler quiet */
1739 : : }
1740 : : }
2416 tgl@sss.pgh.pa.us 1741 [ + - + + ]:CBC 3974 : else if (vardata.var && IsA(vardata.var, Var) &&
1742 [ + + ]: 3598 : ((Var *) vardata.var)->varattno < 0)
1743 : : {
1744 : : /*
1745 : : * There are no stats for system columns, but we know they are never
1746 : : * NULL.
1747 : : */
1748 [ + - ]: 30 : selec = (nulltesttype == IS_NULL) ? 0.0 : 1.0;
1749 : : }
1750 : : else
1751 : : {
1752 : : /*
1753 : : * No ANALYZE stats available, so make a guess
1754 : : */
7872 1755 [ + + - ]: 3944 : switch (nulltesttype)
1756 : : {
1757 : 1053 : case IS_NULL:
1758 : 1053 : selec = DEFAULT_UNK_SEL;
1759 : 1053 : break;
1760 : 2891 : case IS_NOT_NULL:
1761 : 2891 : selec = DEFAULT_NOT_UNK_SEL;
1762 : 2891 : break;
7872 tgl@sss.pgh.pa.us 1763 :UBC 0 : default:
1764 [ # # ]: 0 : elog(ERROR, "unrecognized nulltesttype: %d",
1765 : : (int) nulltesttype);
1766 : : return (Selectivity) 0; /* keep compiler quiet */
1767 : : }
1768 : : }
1769 : :
7872 tgl@sss.pgh.pa.us 1770 [ + + ]:CBC 8884 : ReleaseVariableStats(vardata);
1771 : :
1772 : : /* result should be in range, but make sure... */
8647 1773 [ - + - + ]: 8884 : CLAMP_PROBABILITY(selec);
1774 : :
8839 1775 : 8884 : return (Selectivity) selec;
1776 : : }
1777 : :
1778 : : /*
1779 : : * strip_array_coercion - strip binary-compatible relabeling from an array expr
1780 : : *
1781 : : * For array values, the parser normally generates ArrayCoerceExpr conversions,
1782 : : * but it seems possible that RelabelType might show up. Also, the planner
1783 : : * is not currently tense about collapsing stacked ArrayCoerceExpr nodes,
1784 : : * so we need to be ready to deal with more than one level.
1785 : : */
1786 : : static Node *
6796 1787 : 62214 : strip_array_coercion(Node *node)
1788 : : {
1789 : : for (;;)
1790 : : {
2898 1791 [ + - + + ]: 62255 : if (node && IsA(node, ArrayCoerceExpr))
6796 1792 : 41 : {
2898 1793 : 1477 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
1794 : :
1795 : : /*
1796 : : * If the per-element expression is just a RelabelType on top of
1797 : : * CaseTestExpr, then we know it's a binary-compatible relabeling.
1798 : : */
1799 [ + + ]: 1477 : if (IsA(acoerce->elemexpr, RelabelType) &&
1800 [ + - ]: 41 : IsA(((RelabelType *) acoerce->elemexpr)->arg, CaseTestExpr))
1801 : 41 : node = (Node *) acoerce->arg;
1802 : : else
1803 : : break;
1804 : : }
6738 1805 [ + - - + ]: 60778 : else if (node && IsA(node, RelabelType))
1806 : : {
1807 : : /* We don't really expect this case, but may as well cope */
6738 tgl@sss.pgh.pa.us 1808 :UBC 0 : node = (Node *) ((RelabelType *) node)->arg;
1809 : : }
1810 : : else
1811 : : break;
1812 : : }
6796 tgl@sss.pgh.pa.us 1813 :CBC 62214 : return node;
1814 : : }
1815 : :
1816 : : /*
1817 : : * scalararraysel - Selectivity of ScalarArrayOpExpr Node.
1818 : : */
1819 : : Selectivity
7225 1820 : 10564 : scalararraysel(PlannerInfo *root,
1821 : : ScalarArrayOpExpr *clause,
1822 : : bool is_join_clause,
1823 : : int varRelid,
1824 : : JoinType jointype,
1825 : : SpecialJoinInfo *sjinfo)
1826 : : {
1827 : 10564 : Oid operator = clause->opno;
1828 : 10564 : bool useOr = clause->useOr;
4935 1829 : 10564 : bool isEquality = false;
1830 : 10564 : bool isInequality = false;
1831 : : Node *leftop;
1832 : : Node *rightop;
1833 : : Oid nominal_element_type;
1834 : : Oid nominal_element_collation;
1835 : : TypeCacheEntry *typentry;
1836 : : RegProcedure oprsel;
1837 : : FmgrInfo oprselproc;
1838 : : Selectivity s1;
1839 : : Selectivity s1disjoint;
1840 : :
1841 : : /* First, deconstruct the expression */
6796 1842 [ - + ]: 10564 : Assert(list_length(clause->args) == 2);
1843 : 10564 : leftop = (Node *) linitial(clause->args);
1844 : 10564 : rightop = (Node *) lsecond(clause->args);
1845 : :
1846 : : /* aggressively reduce both sides to constants */
4215 1847 : 10564 : leftop = estimate_expression_value(root, leftop);
1848 : 10564 : rightop = estimate_expression_value(root, rightop);
1849 : :
1850 : : /* get nominal (after relabeling) element type of rightop */
5434 1851 : 10564 : nominal_element_type = get_base_element_type(exprType(rightop));
6796 1852 [ - + ]: 10564 : if (!OidIsValid(nominal_element_type))
2999 tgl@sss.pgh.pa.us 1853 :UBC 0 : return (Selectivity) 0.5; /* probably shouldn't happen */
1854 : : /* get nominal collation, too, for generating constants */
5279 tgl@sss.pgh.pa.us 1855 :CBC 10564 : nominal_element_collation = exprCollation(rightop);
1856 : :
1857 : : /* look through any binary-compatible relabeling of rightop */
6796 1858 : 10564 : rightop = strip_array_coercion(rightop);
1859 : :
1860 : : /*
1861 : : * Detect whether the operator is the default equality or inequality
1862 : : * operator of the array element type.
1863 : : */
4935 1864 : 10564 : typentry = lookup_type_cache(nominal_element_type, TYPECACHE_EQ_OPR);
1865 [ + + ]: 10564 : if (OidIsValid(typentry->eq_opr))
1866 : : {
1867 [ + + ]: 10562 : if (operator == typentry->eq_opr)
1868 : 8922 : isEquality = true;
1869 [ + + ]: 1640 : else if (get_negator(operator) == typentry->eq_opr)
1870 : 1357 : isInequality = true;
1871 : : }
1872 : :
1873 : : /*
1874 : : * If it is equality or inequality, we might be able to estimate this as a
1875 : : * form of array containment; for instance "const = ANY(column)" can be
1876 : : * treated as "ARRAY[const] <@ column". scalararraysel_containment tries
1877 : : * that, and returns the selectivity estimate if successful, or -1 if not.
1878 : : */
1879 [ + + + + : 10564 : if ((isEquality || isInequality) && !is_join_clause)
+ - ]
1880 : : {
1881 : 10279 : s1 = scalararraysel_containment(root, leftop, rightop,
1882 : : nominal_element_type,
1883 : : isEquality, useOr, varRelid);
1884 [ + + ]: 10279 : if (s1 >= 0.0)
1885 : 59 : return s1;
1886 : : }
1887 : :
1888 : : /*
1889 : : * Look up the underlying operator's selectivity estimator. Punt if it
1890 : : * hasn't got one.
1891 : : */
1892 [ - + ]: 10505 : if (is_join_clause)
4935 tgl@sss.pgh.pa.us 1893 :UBC 0 : oprsel = get_oprjoin(operator);
1894 : : else
4935 tgl@sss.pgh.pa.us 1895 :CBC 10505 : oprsel = get_oprrest(operator);
1896 [ + + ]: 10505 : if (!oprsel)
1897 : 2 : return (Selectivity) 0.5;
1898 : 10503 : fmgr_info(oprsel, &oprselproc);
1899 : :
1900 : : /*
1901 : : * In the array-containment check above, we must only believe that an
1902 : : * operator is equality or inequality if it is the default btree equality
1903 : : * operator (or its negator) for the element type, since those are the
1904 : : * operators that array containment will use. But in what follows, we can
1905 : : * be a little laxer, and also believe that any operators using eqsel() or
1906 : : * neqsel() as selectivity estimator act like equality or inequality.
1907 : : */
4931 1908 [ + + - + ]: 10503 : if (oprsel == F_EQSEL || oprsel == F_EQJOINSEL)
1909 : 8961 : isEquality = true;
1910 [ + + - + ]: 1542 : else if (oprsel == F_NEQSEL || oprsel == F_NEQJOINSEL)
1911 : 1302 : isInequality = true;
1912 : :
1913 : : /*
1914 : : * We consider three cases:
1915 : : *
1916 : : * 1. rightop is an Array constant: deconstruct the array, apply the
1917 : : * operator's selectivity function for each array element, and merge the
1918 : : * results in the same way that clausesel.c does for AND/OR combinations.
1919 : : *
1920 : : * 2. rightop is an ARRAY[] construct: apply the operator's selectivity
1921 : : * function for each element of the ARRAY[] construct, and merge.
1922 : : *
1923 : : * 3. otherwise, make a guess ...
1924 : : */
7225 1925 [ + - + + ]: 10503 : if (rightop && IsA(rightop, Const))
1926 : 8417 : {
1927 : 8432 : Datum arraydatum = ((Const *) rightop)->constvalue;
1928 : 8432 : bool arrayisnull = ((Const *) rightop)->constisnull;
1929 : : ArrayType *arrayval;
1930 : : int16 elmlen;
1931 : : bool elmbyval;
1932 : : char elmalign;
1933 : : int num_elems;
1934 : : Datum *elem_values;
1935 : : bool *elem_nulls;
1936 : : int i;
1937 : :
1938 [ + + ]: 8432 : if (arrayisnull) /* qual can't succeed if null array */
1939 : 15 : return (Selectivity) 0.0;
1940 : 8417 : arrayval = DatumGetArrayTypeP(arraydatum);
1941 : 8417 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
1942 : : &elmlen, &elmbyval, &elmalign);
1943 : 8417 : deconstruct_array(arrayval,
1944 : : ARR_ELEMTYPE(arrayval),
1945 : : elmlen, elmbyval, elmalign,
1946 : : &elem_values, &elem_nulls, &num_elems);
1947 : :
1948 : : /*
1949 : : * For generic operators, we assume the probability of success is
1950 : : * independent for each array element. But for "= ANY" or "<> ALL",
1951 : : * if the array elements are distinct (which'd typically be the case)
1952 : : * then the probabilities are disjoint, and we should just sum them.
1953 : : *
1954 : : * If we were being really tense we would try to confirm that the
1955 : : * elements are all distinct, but that would be expensive and it
1956 : : * doesn't seem to be worth the cycles; it would amount to penalizing
1957 : : * well-written queries in favor of poorly-written ones. However, we
1958 : : * do protect ourselves a little bit by checking whether the
1959 : : * disjointness assumption leads to an impossible (out of range)
1960 : : * probability; if so, we fall back to the normal calculation.
1961 : : */
4931 1962 [ + + ]: 8417 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
1963 : :
7225 1964 [ + + ]: 36961 : for (i = 0; i < num_elems; i++)
1965 : : {
1966 : : List *args;
1967 : : Selectivity s2;
1968 : :
1969 : 28544 : args = list_make2(leftop,
1970 : : makeConst(nominal_element_type,
1971 : : -1,
1972 : : nominal_element_collation,
1973 : : elmlen,
1974 : : elem_values[i],
1975 : : elem_nulls[i],
1976 : : elmbyval));
6230 1977 [ - + ]: 28544 : if (is_join_clause)
4808 tgl@sss.pgh.pa.us 1978 :UBC 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
1979 : : clause->inputcollid,
1980 : : PointerGetDatum(root),
1981 : : ObjectIdGetDatum(operator),
1982 : : PointerGetDatum(args),
1983 : : Int16GetDatum(jointype),
1984 : : PointerGetDatum(sjinfo)));
1985 : : else
4808 tgl@sss.pgh.pa.us 1986 :CBC 28544 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
1987 : : clause->inputcollid,
1988 : : PointerGetDatum(root),
1989 : : ObjectIdGetDatum(operator),
1990 : : PointerGetDatum(args),
1991 : : Int32GetDatum(varRelid)));
1992 : :
7225 1993 [ + + ]: 28544 : if (useOr)
1994 : : {
1995 : 24343 : s1 = s1 + s2 - s1 * s2;
4931 1996 [ + + ]: 24343 : if (isEquality)
1997 : 23821 : s1disjoint += s2;
1998 : : }
1999 : : else
2000 : : {
7225 2001 : 4201 : s1 = s1 * s2;
4931 2002 [ + + ]: 4201 : if (isInequality)
2003 : 4045 : s1disjoint += s2 - 1.0;
2004 : : }
2005 : : }
2006 : :
2007 : : /* accept disjoint-probability estimate if in range */
2008 [ + + + + : 8417 : if ((useOr ? isEquality : isInequality) &&
+ + ]
2009 [ + + ]: 8098 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2010 : 8083 : s1 = s1disjoint;
2011 : : }
7225 2012 [ + - + + ]: 2071 : else if (rightop && IsA(rightop, ArrayExpr) &&
2013 [ + - ]: 147 : !((ArrayExpr *) rightop)->multidims)
2014 : 147 : {
2015 : 147 : ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
2016 : : int16 elmlen;
2017 : : bool elmbyval;
2018 : : ListCell *l;
2019 : :
2020 : 147 : get_typlenbyval(arrayexpr->element_typeid,
2021 : : &elmlen, &elmbyval);
2022 : :
2023 : : /*
2024 : : * We use the assumption of disjoint probabilities here too, although
2025 : : * the odds of equal array elements are rather higher if the elements
2026 : : * are not all constants (which they won't be, else constant folding
2027 : : * would have reduced the ArrayExpr to a Const). In this path it's
2028 : : * critical to have the sanity check on the s1disjoint estimate.
2029 : : */
4931 2030 [ + - ]: 147 : s1 = s1disjoint = (useOr ? 0.0 : 1.0);
2031 : :
7225 2032 [ + - + + : 530 : foreach(l, arrayexpr->elements)
+ + ]
2033 : : {
6796 2034 : 383 : Node *elem = (Node *) lfirst(l);
2035 : : List *args;
2036 : : Selectivity s2;
2037 : :
2038 : : /*
2039 : : * Theoretically, if elem isn't of nominal_element_type we should
2040 : : * insert a RelabelType, but it seems unlikely that any operator
2041 : : * estimation function would really care ...
2042 : : */
2043 : 383 : args = list_make2(leftop, elem);
6230 2044 [ - + ]: 383 : if (is_join_clause)
4808 tgl@sss.pgh.pa.us 2045 :UBC 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2046 : : clause->inputcollid,
2047 : : PointerGetDatum(root),
2048 : : ObjectIdGetDatum(operator),
2049 : : PointerGetDatum(args),
2050 : : Int16GetDatum(jointype),
2051 : : PointerGetDatum(sjinfo)));
2052 : : else
4808 tgl@sss.pgh.pa.us 2053 :CBC 383 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2054 : : clause->inputcollid,
2055 : : PointerGetDatum(root),
2056 : : ObjectIdGetDatum(operator),
2057 : : PointerGetDatum(args),
2058 : : Int32GetDatum(varRelid)));
2059 : :
7225 2060 [ + - ]: 383 : if (useOr)
2061 : : {
2062 : 383 : s1 = s1 + s2 - s1 * s2;
4931 2063 [ + - ]: 383 : if (isEquality)
2064 : 383 : s1disjoint += s2;
2065 : : }
2066 : : else
2067 : : {
7225 tgl@sss.pgh.pa.us 2068 :UBC 0 : s1 = s1 * s2;
4931 2069 [ # # ]: 0 : if (isInequality)
2070 : 0 : s1disjoint += s2 - 1.0;
2071 : : }
2072 : : }
2073 : :
2074 : : /* accept disjoint-probability estimate if in range */
4931 tgl@sss.pgh.pa.us 2075 [ + - + - :CBC 147 : if ((useOr ? isEquality : isInequality) &&
+ - ]
2076 [ + - ]: 147 : s1disjoint >= 0.0 && s1disjoint <= 1.0)
2077 : 147 : s1 = s1disjoint;
2078 : : }
2079 : : else
2080 : : {
2081 : : CaseTestExpr *dummyexpr;
2082 : : List *args;
2083 : : Selectivity s2;
2084 : : int i;
2085 : :
2086 : : /*
2087 : : * We need a dummy rightop to pass to the operator selectivity
2088 : : * routine. It can be pretty much anything that doesn't look like a
2089 : : * constant; CaseTestExpr is a convenient choice.
2090 : : */
7225 2091 : 1924 : dummyexpr = makeNode(CaseTestExpr);
6796 2092 : 1924 : dummyexpr->typeId = nominal_element_type;
7225 2093 : 1924 : dummyexpr->typeMod = -1;
5285 2094 : 1924 : dummyexpr->collation = clause->inputcollid;
7225 2095 : 1924 : args = list_make2(leftop, dummyexpr);
6230 2096 [ - + ]: 1924 : if (is_join_clause)
4808 tgl@sss.pgh.pa.us 2097 :UBC 0 : s2 = DatumGetFloat8(FunctionCall5Coll(&oprselproc,
2098 : : clause->inputcollid,
2099 : : PointerGetDatum(root),
2100 : : ObjectIdGetDatum(operator),
2101 : : PointerGetDatum(args),
2102 : : Int16GetDatum(jointype),
2103 : : PointerGetDatum(sjinfo)));
2104 : : else
4808 tgl@sss.pgh.pa.us 2105 :CBC 1924 : s2 = DatumGetFloat8(FunctionCall4Coll(&oprselproc,
2106 : : clause->inputcollid,
2107 : : PointerGetDatum(root),
2108 : : ObjectIdGetDatum(operator),
2109 : : PointerGetDatum(args),
2110 : : Int32GetDatum(varRelid)));
7225 2111 [ + - ]: 1924 : s1 = useOr ? 0.0 : 1.0;
2112 : :
2113 : : /*
2114 : : * Arbitrarily assume 10 elements in the eventual array value (see
2115 : : * also estimate_array_length). We don't risk an assumption of
2116 : : * disjoint probabilities here.
2117 : : */
2118 [ + + ]: 21164 : for (i = 0; i < 10; i++)
2119 : : {
2120 [ + - ]: 19240 : if (useOr)
2121 : 19240 : s1 = s1 + s2 - s1 * s2;
2122 : : else
7225 tgl@sss.pgh.pa.us 2123 :UBC 0 : s1 = s1 * s2;
2124 : : }
2125 : : }
2126 : :
2127 : : /* result should be in range, but make sure... */
7225 tgl@sss.pgh.pa.us 2128 [ - + - + ]:CBC 10488 : CLAMP_PROBABILITY(s1);
2129 : :
2130 : 10488 : return s1;
2131 : : }
2132 : :
2133 : : /*
2134 : : * Estimate number of elements in the array yielded by an expression.
2135 : : *
2136 : : * Note: the result is integral, but we use "double" to avoid overflow
2137 : : * concerns. Most callers will use it in double-type expressions anyway.
2138 : : *
2139 : : * Note: in some code paths root can be passed as NULL, resulting in
2140 : : * slightly worse estimates.
2141 : : */
2142 : : double
611 2143 : 51650 : estimate_array_length(PlannerInfo *root, Node *arrayexpr)
2144 : : {
2145 : : /* look through any binary-compatible relabeling of arrayexpr */
6796 2146 : 51650 : arrayexpr = strip_array_coercion(arrayexpr);
2147 : :
7007 2148 [ + - + + ]: 51650 : if (arrayexpr && IsA(arrayexpr, Const))
2149 : : {
2150 : 22678 : Datum arraydatum = ((Const *) arrayexpr)->constvalue;
2151 : 22678 : bool arrayisnull = ((Const *) arrayexpr)->constisnull;
2152 : : ArrayType *arrayval;
2153 : :
2154 [ + + ]: 22678 : if (arrayisnull)
2155 : 45 : return 0;
2156 : 22633 : arrayval = DatumGetArrayTypeP(arraydatum);
2157 : 22633 : return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
2158 : : }
2159 [ + - + + ]: 28972 : else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
2160 [ + - ]: 249 : !((ArrayExpr *) arrayexpr)->multidims)
2161 : : {
2162 : 249 : return list_length(((ArrayExpr *) arrayexpr)->elements);
2163 : : }
332 2164 [ + - + + ]: 28723 : else if (arrayexpr && root)
2165 : : {
2166 : : /* See if we can find any statistics about it */
2167 : : VariableStatData vardata;
2168 : : AttStatsSlot sslot;
611 2169 : 28711 : double nelem = 0;
2170 : :
2171 : 28711 : examine_variable(root, arrayexpr, 0, &vardata);
2172 [ + + ]: 28711 : if (HeapTupleIsValid(vardata.statsTuple))
2173 : : {
2174 : : /*
2175 : : * Found stats, so use the average element count, which is stored
2176 : : * in the last stanumbers element of the DECHIST statistics.
2177 : : * Actually that is the average count of *distinct* elements;
2178 : : * perhaps we should scale it up somewhat?
2179 : : */
2180 [ + + ]: 7228 : if (get_attstatsslot(&sslot, vardata.statsTuple,
2181 : : STATISTIC_KIND_DECHIST, InvalidOid,
2182 : : ATTSTATSSLOT_NUMBERS))
2183 : : {
2184 [ + - ]: 7171 : if (sslot.nnumbers > 0)
2185 : 7171 : nelem = clamp_row_est(sslot.numbers[sslot.nnumbers - 1]);
2186 : 7171 : free_attstatsslot(&sslot);
2187 : : }
2188 : : }
2189 [ + + ]: 28711 : ReleaseVariableStats(vardata);
2190 : :
2191 [ + + ]: 28711 : if (nelem > 0)
2192 : 7171 : return nelem;
2193 : : }
2194 : :
2195 : : /* Else use a default guess --- this should match scalararraysel */
2196 : 21552 : return 10;
2197 : : }
2198 : :
2199 : : /*
2200 : : * rowcomparesel - Selectivity of RowCompareExpr Node.
2201 : : *
2202 : : * We estimate RowCompare selectivity by considering just the first (high
2203 : : * order) columns, which makes it equivalent to an ordinary OpExpr. While
2204 : : * this estimate could be refined by considering additional columns, it
2205 : : * seems unlikely that we could do a lot better without multi-column
2206 : : * statistics.
2207 : : */
2208 : : Selectivity
7175 2209 : 126 : rowcomparesel(PlannerInfo *root,
2210 : : RowCompareExpr *clause,
2211 : : int varRelid, JoinType jointype, SpecialJoinInfo *sjinfo)
2212 : : {
2213 : : Selectivity s1;
2214 : 126 : Oid opno = linitial_oid(clause->opnos);
4808 2215 : 126 : Oid inputcollid = linitial_oid(clause->inputcollids);
2216 : : List *opargs;
2217 : : bool is_join_clause;
2218 : :
2219 : : /* Build equivalent arg list for single operator */
7175 2220 : 126 : opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
2221 : :
2222 : : /*
2223 : : * Decide if it's a join clause. This should match clausesel.c's
2224 : : * treat_as_join_clause(), except that we intentionally consider only the
2225 : : * leading columns and not the rest of the clause.
2226 : : */
2227 [ + + ]: 126 : if (varRelid != 0)
2228 : : {
2229 : : /*
2230 : : * Caller is forcing restriction mode (eg, because we are examining an
2231 : : * inner indexscan qual).
2232 : : */
6230 2233 : 27 : is_join_clause = false;
2234 : : }
2235 [ + + ]: 99 : else if (sjinfo == NULL)
2236 : : {
2237 : : /*
2238 : : * It must be a restriction clause, since it's being evaluated at a
2239 : : * scan node.
2240 : : */
7175 2241 : 93 : is_join_clause = false;
2242 : : }
2243 : : else
2244 : : {
2245 : : /*
2246 : : * Otherwise, it's a join if there's more than one base relation used.
2247 : : */
1689 2248 : 6 : is_join_clause = (NumRelids(root, (Node *) opargs) > 1);
2249 : : }
2250 : :
7175 2251 [ + + ]: 126 : if (is_join_clause)
2252 : : {
2253 : : /* Estimate selectivity for a join clause. */
2254 : 6 : s1 = join_selectivity(root, opno,
2255 : : opargs,
2256 : : inputcollid,
2257 : : jointype,
2258 : : sjinfo);
2259 : : }
2260 : : else
2261 : : {
2262 : : /* Estimate selectivity for a restriction clause. */
2263 : 120 : s1 = restriction_selectivity(root, opno,
2264 : : opargs,
2265 : : inputcollid,
2266 : : varRelid);
2267 : : }
2268 : :
2269 : 126 : return s1;
2270 : : }
2271 : :
2272 : : /*
2273 : : * eqjoinsel - Join selectivity of "="
2274 : : */
2275 : : Datum
9224 2276 : 112508 : eqjoinsel(PG_FUNCTION_ARGS)
2277 : : {
7398 2278 : 112508 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
8875 2279 : 112508 : Oid operator = PG_GETARG_OID(1);
2280 : 112508 : List *args = (List *) PG_GETARG_POINTER(2);
2281 : :
2282 : : #ifdef NOT_USED
2283 : : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
2284 : : #endif
6230 2285 : 112508 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
1919 2286 : 112508 : Oid collation = PG_GET_COLLATION();
2287 : : double selec;
2288 : : double selec_inner;
2289 : : VariableStatData vardata1;
2290 : : VariableStatData vardata2;
2291 : : double nd1;
2292 : : double nd2;
2293 : : bool isdefault1;
2294 : : bool isdefault2;
2295 : : Oid opfuncoid;
2296 : : AttStatsSlot sslot1;
2297 : : AttStatsSlot sslot2;
2479 2298 : 112508 : Form_pg_statistic stats1 = NULL;
2299 : 112508 : Form_pg_statistic stats2 = NULL;
2300 : 112508 : bool have_mcvs1 = false;
2301 : 112508 : bool have_mcvs2 = false;
2302 : : bool get_mcv_stats;
2303 : : bool join_is_reversed;
2304 : : RelOptInfo *inner_rel;
2305 : :
6230 2306 : 112508 : get_join_variables(root, args, sjinfo,
2307 : : &vardata1, &vardata2, &join_is_reversed);
2308 : :
2479 2309 : 112508 : nd1 = get_variable_numdistinct(&vardata1, &isdefault1);
2310 : 112508 : nd2 = get_variable_numdistinct(&vardata2, &isdefault2);
2311 : :
2312 : 112508 : opfuncoid = get_opcode(operator);
2313 : :
2314 : 112508 : memset(&sslot1, 0, sizeof(sslot1));
2315 : 112508 : memset(&sslot2, 0, sizeof(sslot2));
2316 : :
2317 : : /*
2318 : : * There is no use in fetching one side's MCVs if we lack MCVs for the
2319 : : * other side, so do a quick check to verify that both stats exist.
2320 : : */
1023 2321 : 316755 : get_mcv_stats = (HeapTupleIsValid(vardata1.statsTuple) &&
2322 [ + + + + ]: 162791 : HeapTupleIsValid(vardata2.statsTuple) &&
2323 : 71052 : get_attstatsslot(&sslot1, vardata1.statsTuple,
2324 : : STATISTIC_KIND_MCV, InvalidOid,
2325 [ + + + + ]: 204247 : 0) &&
2326 : 27368 : get_attstatsslot(&sslot2, vardata2.statsTuple,
2327 : : STATISTIC_KIND_MCV, InvalidOid,
2328 : : 0));
2329 : :
2479 2330 [ + + ]: 112508 : if (HeapTupleIsValid(vardata1.statsTuple))
2331 : : {
2332 : : /* note we allow use of nullfrac regardless of security check */
2333 : 91739 : stats1 = (Form_pg_statistic) GETSTRUCT(vardata1.statsTuple);
1023 2334 [ + + + - ]: 99217 : if (get_mcv_stats &&
2335 : 7478 : statistic_proc_security_check(&vardata1, opfuncoid))
2479 2336 : 7478 : have_mcvs1 = get_attstatsslot(&sslot1, vardata1.statsTuple,
2337 : : STATISTIC_KIND_MCV, InvalidOid,
2338 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2339 : : }
2340 : :
2341 [ + + ]: 112508 : if (HeapTupleIsValid(vardata2.statsTuple))
2342 : : {
2343 : : /* note we allow use of nullfrac regardless of security check */
2344 : 76819 : stats2 = (Form_pg_statistic) GETSTRUCT(vardata2.statsTuple);
1023 2345 [ + + + - ]: 84297 : if (get_mcv_stats &&
2346 : 7478 : statistic_proc_security_check(&vardata2, opfuncoid))
2479 2347 : 7478 : have_mcvs2 = get_attstatsslot(&sslot2, vardata2.statsTuple,
2348 : : STATISTIC_KIND_MCV, InvalidOid,
2349 : : ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS);
2350 : : }
2351 : :
2352 : : /* We need to compute the inner-join selectivity in all cases */
1919 2353 : 112508 : selec_inner = eqjoinsel_inner(opfuncoid, collation,
2354 : : &vardata1, &vardata2,
2355 : : nd1, nd2,
2356 : : isdefault1, isdefault2,
2357 : : &sslot1, &sslot2,
2358 : : stats1, stats2,
2359 : : have_mcvs1, have_mcvs2);
2360 : :
6230 2361 [ + + - ]: 112508 : switch (sjinfo->jointype)
2362 : : {
2363 : 107151 : case JOIN_INNER:
2364 : : case JOIN_LEFT:
2365 : : case JOIN_FULL:
2479 2366 : 107151 : selec = selec_inner;
6230 2367 : 107151 : break;
2368 : 5357 : case JOIN_SEMI:
2369 : : case JOIN_ANTI:
2370 : :
2371 : : /*
2372 : : * Look up the join's inner relation. min_righthand is sufficient
2373 : : * information because neither SEMI nor ANTI joins permit any
2374 : : * reassociation into or out of their RHS, so the righthand will
2375 : : * always be exactly that set of rels.
2376 : : */
5119 2377 : 5357 : inner_rel = find_join_input_rel(root, sjinfo->min_righthand);
2378 : :
6230 2379 [ + + ]: 5357 : if (!join_is_reversed)
1919 2380 : 3300 : selec = eqjoinsel_semi(opfuncoid, collation,
2381 : : &vardata1, &vardata2,
2382 : : nd1, nd2,
2383 : : isdefault1, isdefault2,
2384 : : &sslot1, &sslot2,
2385 : : stats1, stats2,
2386 : : have_mcvs1, have_mcvs2,
2387 : : inner_rel);
2388 : : else
2389 : : {
2479 2390 : 2057 : Oid commop = get_commutator(operator);
2391 [ + - ]: 2057 : Oid commopfuncoid = OidIsValid(commop) ? get_opcode(commop) : InvalidOid;
2392 : :
1919 2393 : 2057 : selec = eqjoinsel_semi(commopfuncoid, collation,
2394 : : &vardata2, &vardata1,
2395 : : nd2, nd1,
2396 : : isdefault2, isdefault1,
2397 : : &sslot2, &sslot1,
2398 : : stats2, stats1,
2399 : : have_mcvs2, have_mcvs1,
2400 : : inner_rel);
2401 : : }
2402 : :
2403 : : /*
2404 : : * We should never estimate the output of a semijoin to be more
2405 : : * rows than we estimate for an inner join with the same input
2406 : : * rels and join condition; it's obviously impossible for that to
2407 : : * happen. The former estimate is N1 * Ssemi while the latter is
2408 : : * N1 * N2 * Sinner, so we may clamp Ssemi <= N2 * Sinner. Doing
2409 : : * this is worthwhile because of the shakier estimation rules we
2410 : : * use in eqjoinsel_semi, particularly in cases where it has to
2411 : : * punt entirely.
2412 : : */
2479 2413 [ + + ]: 5357 : selec = Min(selec, inner_rel->rows * selec_inner);
6230 2414 : 5357 : break;
6230 tgl@sss.pgh.pa.us 2415 :UBC 0 : default:
2416 : : /* other values not expected here */
2417 [ # # ]: 0 : elog(ERROR, "unrecognized join type: %d",
2418 : : (int) sjinfo->jointype);
2419 : : selec = 0; /* keep compiler quiet */
2420 : : break;
2421 : : }
2422 : :
2479 tgl@sss.pgh.pa.us 2423 :CBC 112508 : free_attstatsslot(&sslot1);
2424 : 112508 : free_attstatsslot(&sslot2);
2425 : :
6230 2426 [ + + ]: 112508 : ReleaseVariableStats(vardata1);
2427 [ + + ]: 112508 : ReleaseVariableStats(vardata2);
2428 : :
2429 [ - + - + ]: 112508 : CLAMP_PROBABILITY(selec);
2430 : :
2431 : 112508 : PG_RETURN_FLOAT8((float8) selec);
2432 : : }
2433 : :
2434 : : /*
2435 : : * eqjoinsel_inner --- eqjoinsel for normal inner join
2436 : : *
2437 : : * We also use this for LEFT/FULL outer joins; it's not presently clear
2438 : : * that it's worth trying to distinguish them here.
2439 : : */
2440 : : static double
1919 2441 : 112508 : eqjoinsel_inner(Oid opfuncoid, Oid collation,
2442 : : VariableStatData *vardata1, VariableStatData *vardata2,
2443 : : double nd1, double nd2,
2444 : : bool isdefault1, bool isdefault2,
2445 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2446 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
2447 : : bool have_mcvs1, bool have_mcvs2)
2448 : : {
2449 : : double selec;
2450 : :
7872 2451 [ + + + - ]: 112508 : if (have_mcvs1 && have_mcvs2)
10226 bruce@momjian.us 2452 : 7478 : {
2453 : : /*
2454 : : * We have most-common-value lists for both relations. Run through
2455 : : * the lists to see which MCVs actually join to each other with the
2456 : : * given operator. This allows us to determine the exact join
2457 : : * selectivity for the portion of the relations represented by the MCV
2458 : : * lists. We still have to estimate for the remaining population, but
2459 : : * in a skewed distribution this gives us a big leg up in accuracy.
2460 : : * For motivation see the analysis in Y. Ioannidis and S.
2461 : : * Christodoulakis, "On the propagation of errors in the size of join
2462 : : * results", Technical Report 1018, Computer Science Dept., University
2463 : : * of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
2464 : : */
1964 tgl@sss.pgh.pa.us 2465 : 7478 : LOCAL_FCINFO(fcinfo, 2);
2466 : : FmgrInfo eqproc;
2467 : : bool *hasmatch1;
2468 : : bool *hasmatch2;
7872 2469 : 7478 : double nullfrac1 = stats1->stanullfrac;
2470 : 7478 : double nullfrac2 = stats2->stanullfrac;
2471 : : double matchprodfreq,
2472 : : matchfreq1,
2473 : : matchfreq2,
2474 : : unmatchfreq1,
2475 : : unmatchfreq2,
2476 : : otherfreq1,
2477 : : otherfreq2,
2478 : : totalsel1,
2479 : : totalsel2;
2480 : : int i,
2481 : : nmatches;
2482 : :
3046 peter_e@gmx.net 2483 : 7478 : fmgr_info(opfuncoid, &eqproc);
2484 : :
2485 : : /*
2486 : : * Save a few cycles by setting up the fcinfo struct just once. Using
2487 : : * FunctionCallInvoke directly also avoids failure if the eqproc
2488 : : * returns NULL, though really equality functions should never do
2489 : : * that.
2490 : : */
1919 tgl@sss.pgh.pa.us 2491 : 7478 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
2492 : : NULL, NULL);
1964 2493 : 7478 : fcinfo->args[0].isnull = false;
2494 : 7478 : fcinfo->args[1].isnull = false;
2495 : :
2479 2496 : 7478 : hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
2497 : 7478 : hasmatch2 = (bool *) palloc0(sslot2->nvalues * sizeof(bool));
2498 : :
2499 : : /*
2500 : : * Note we assume that each MCV will match at most one member of the
2501 : : * other MCV list. If the operator isn't really equality, there could
2502 : : * be multiple matches --- but we don't look for them, both for speed
2503 : : * and because the math wouldn't add up...
2504 : : */
7872 2505 : 7478 : matchprodfreq = 0.0;
2506 : 7478 : nmatches = 0;
2479 2507 [ + + ]: 298174 : for (i = 0; i < sslot1->nvalues; i++)
2508 : : {
2509 : : int j;
2510 : :
1964 2511 : 290696 : fcinfo->args[0].value = sslot1->values[i];
2512 : :
2479 2513 [ + + ]: 11503709 : for (j = 0; j < sslot2->nvalues; j++)
2514 : : {
2515 : : Datum fresult;
2516 : :
7872 2517 [ + + ]: 11310522 : if (hasmatch2[j])
2518 : 2914677 : continue;
1964 2519 : 8395845 : fcinfo->args[1].value = sslot2->values[j];
2520 : 8395845 : fcinfo->isnull = false;
2521 : 8395845 : fresult = FunctionCallInvoke(fcinfo);
2522 [ + - + + ]: 8395845 : if (!fcinfo->isnull && DatumGetBool(fresult))
2523 : : {
7872 2524 : 97509 : hasmatch1[i] = hasmatch2[j] = true;
2479 2525 : 97509 : matchprodfreq += sslot1->numbers[i] * sslot2->numbers[j];
7872 2526 : 97509 : nmatches++;
2527 : 97509 : break;
2528 : : }
2529 : : }
2530 : : }
2531 [ - + - + ]: 7478 : CLAMP_PROBABILITY(matchprodfreq);
2532 : : /* Sum up frequencies of matched and unmatched MCVs */
2533 : 7478 : matchfreq1 = unmatchfreq1 = 0.0;
2479 2534 [ + + ]: 298174 : for (i = 0; i < sslot1->nvalues; i++)
2535 : : {
7872 2536 [ + + ]: 290696 : if (hasmatch1[i])
2479 2537 : 97509 : matchfreq1 += sslot1->numbers[i];
2538 : : else
2539 : 193187 : unmatchfreq1 += sslot1->numbers[i];
2540 : : }
7872 2541 [ - + + + ]: 7478 : CLAMP_PROBABILITY(matchfreq1);
2542 [ - + + + ]: 7478 : CLAMP_PROBABILITY(unmatchfreq1);
2543 : 7478 : matchfreq2 = unmatchfreq2 = 0.0;
2479 2544 [ + + ]: 211859 : for (i = 0; i < sslot2->nvalues; i++)
2545 : : {
7872 2546 [ + + ]: 204381 : if (hasmatch2[i])
2479 2547 : 97509 : matchfreq2 += sslot2->numbers[i];
2548 : : else
2549 : 106872 : unmatchfreq2 += sslot2->numbers[i];
2550 : : }
7872 2551 [ - + + + ]: 7478 : CLAMP_PROBABILITY(matchfreq2);
2552 [ - + + + ]: 7478 : CLAMP_PROBABILITY(unmatchfreq2);
2553 : 7478 : pfree(hasmatch1);
2554 : 7478 : pfree(hasmatch2);
2555 : :
2556 : : /*
2557 : : * Compute total frequency of non-null values that are not in the MCV
2558 : : * lists.
2559 : : */
2560 : 7478 : otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
2561 : 7478 : otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
2562 [ + + - + ]: 7478 : CLAMP_PROBABILITY(otherfreq1);
2563 [ + + - + ]: 7478 : CLAMP_PROBABILITY(otherfreq2);
2564 : :
2565 : : /*
2566 : : * We can estimate the total selectivity from the point of view of
2567 : : * relation 1 as: the known selectivity for matched MCVs, plus
2568 : : * unmatched MCVs that are assumed to match against random members of
2569 : : * relation 2's non-MCV population, plus non-MCV values that are
2570 : : * assumed to match against random members of relation 2's unmatched
2571 : : * MCVs plus non-MCV values.
2572 : : */
2573 : 7478 : totalsel1 = matchprodfreq;
2479 2574 [ + + ]: 7478 : if (nd2 > sslot2->nvalues)
2575 : 3394 : totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - sslot2->nvalues);
7872 2576 [ + + ]: 7478 : if (nd2 > nmatches)
2577 : 5828 : totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
2578 : 5828 : (nd2 - nmatches);
2579 : : /* Same estimate from the point of view of relation 2. */
2580 : 7478 : totalsel2 = matchprodfreq;
2479 2581 [ + + ]: 7478 : if (nd1 > sslot1->nvalues)
2582 : 3506 : totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - sslot1->nvalues);
7872 2583 [ + + ]: 7478 : if (nd1 > nmatches)
2584 : 5194 : totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
2585 : 5194 : (nd1 - nmatches);
2586 : :
2587 : : /*
2588 : : * Use the smaller of the two estimates. This can be justified in
2589 : : * essentially the same terms as given below for the no-stats case: to
2590 : : * a first approximation, we are estimating from the point of view of
2591 : : * the relation with smaller nd.
2592 : : */
2593 [ + + ]: 7478 : selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
2594 : : }
2595 : : else
2596 : : {
2597 : : /*
2598 : : * We do not have MCV lists for both sides. Estimate the join
2599 : : * selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
2600 : : * is plausible if we assume that the join operator is strict and the
2601 : : * non-null values are about equally distributed: a given non-null
2602 : : * tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
2603 : : * of rel2, so total join rows are at most
2604 : : * N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
2605 : : * not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
2606 : : * is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
2607 : : * with MIN() is an upper bound. Using the MIN() means we estimate
2608 : : * from the point of view of the relation with smaller nd (since the
2609 : : * larger nd is determining the MIN). It is reasonable to assume that
2610 : : * most tuples in this rel will have join partners, so the bound is
2611 : : * probably reasonably tight and should be taken as-is.
2612 : : *
2613 : : * XXX Can we be smarter if we have an MCV list for just one side? It
2614 : : * seems that if we assume equal distribution for the other side, we
2615 : : * end up with the same answer anyway.
2616 : : */
2617 [ + + ]: 105030 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2618 [ + + ]: 105030 : double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
2619 : :
2620 : 105030 : selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
2621 [ + + ]: 105030 : if (nd1 > nd2)
2622 : 56046 : selec /= nd1;
2623 : : else
2624 : 48984 : selec /= nd2;
2625 : : }
2626 : :
6230 2627 : 112508 : return selec;
2628 : : }
2629 : :
2630 : : /*
2631 : : * eqjoinsel_semi --- eqjoinsel for semi join
2632 : : *
2633 : : * (Also used for anti join, which we are supposed to estimate the same way.)
2634 : : * Caller has ensured that vardata1 is the LHS variable.
2635 : : * Unlike eqjoinsel_inner, we have to cope with opfuncoid being InvalidOid.
2636 : : */
2637 : : static double
1919 2638 : 5357 : eqjoinsel_semi(Oid opfuncoid, Oid collation,
2639 : : VariableStatData *vardata1, VariableStatData *vardata2,
2640 : : double nd1, double nd2,
2641 : : bool isdefault1, bool isdefault2,
2642 : : AttStatsSlot *sslot1, AttStatsSlot *sslot2,
2643 : : Form_pg_statistic stats1, Form_pg_statistic stats2,
2644 : : bool have_mcvs1, bool have_mcvs2,
2645 : : RelOptInfo *inner_rel)
2646 : : {
2647 : : double selec;
2648 : :
2649 : : /*
2650 : : * We clamp nd2 to be not more than what we estimate the inner relation's
2651 : : * size to be. This is intuitively somewhat reasonable since obviously
2652 : : * there can't be more than that many distinct values coming from the
2653 : : * inner rel. The reason for the asymmetry (ie, that we don't clamp nd1
2654 : : * likewise) is that this is the only pathway by which restriction clauses
2655 : : * applied to the inner rel will affect the join result size estimate,
2656 : : * since set_joinrel_size_estimates will multiply SEMI/ANTI selectivity by
2657 : : * only the outer rel's size. If we clamped nd1 we'd be double-counting
2658 : : * the selectivity of outer-rel restrictions.
2659 : : *
2660 : : * We can apply this clamping both with respect to the base relation from
2661 : : * which the join variable comes (if there is just one), and to the
2662 : : * immediate inner input relation of the current join.
2663 : : *
2664 : : * If we clamp, we can treat nd2 as being a non-default estimate; it's not
2665 : : * great, maybe, but it didn't come out of nowhere either. This is most
2666 : : * helpful when the inner relation is empty and consequently has no stats.
2667 : : */
5119 2668 [ + + ]: 5357 : if (vardata2->rel)
2669 : : {
3203 2670 [ + + ]: 5354 : if (nd2 >= vardata2->rel->rows)
2671 : : {
2672 : 4294 : nd2 = vardata2->rel->rows;
2673 : 4294 : isdefault2 = false;
2674 : : }
2675 : : }
2676 [ + + ]: 5357 : if (nd2 >= inner_rel->rows)
2677 : : {
2678 : 4267 : nd2 = inner_rel->rows;
2679 : 4267 : isdefault2 = false;
2680 : : }
2681 : :
2479 2682 [ + + + - : 5357 : if (have_mcvs1 && have_mcvs2 && OidIsValid(opfuncoid))
+ - ]
6230 2683 : 312 : {
2684 : : /*
2685 : : * We have most-common-value lists for both relations. Run through
2686 : : * the lists to see which MCVs actually join to each other with the
2687 : : * given operator. This allows us to determine the exact join
2688 : : * selectivity for the portion of the relations represented by the MCV
2689 : : * lists. We still have to estimate for the remaining population, but
2690 : : * in a skewed distribution this gives us a big leg up in accuracy.
2691 : : */
1964 2692 : 312 : LOCAL_FCINFO(fcinfo, 2);
2693 : : FmgrInfo eqproc;
2694 : : bool *hasmatch1;
2695 : : bool *hasmatch2;
6230 2696 : 312 : double nullfrac1 = stats1->stanullfrac;
2697 : : double matchfreq1,
2698 : : uncertainfrac,
2699 : : uncertain;
2700 : : int i,
2701 : : nmatches,
2702 : : clamped_nvalues2;
2703 : :
2704 : : /*
2705 : : * The clamping above could have resulted in nd2 being less than
2706 : : * sslot2->nvalues; in which case, we assume that precisely the nd2
2707 : : * most common values in the relation will appear in the join input,
2708 : : * and so compare to only the first nd2 members of the MCV list. Of
2709 : : * course this is frequently wrong, but it's the best bet we can make.
2710 : : */
2479 2711 [ + + ]: 312 : clamped_nvalues2 = Min(sslot2->nvalues, nd2);
2712 : :
3046 peter_e@gmx.net 2713 : 312 : fmgr_info(opfuncoid, &eqproc);
2714 : :
2715 : : /*
2716 : : * Save a few cycles by setting up the fcinfo struct just once. Using
2717 : : * FunctionCallInvoke directly also avoids failure if the eqproc
2718 : : * returns NULL, though really equality functions should never do
2719 : : * that.
2720 : : */
1919 tgl@sss.pgh.pa.us 2721 : 312 : InitFunctionCallInfoData(*fcinfo, &eqproc, 2, collation,
2722 : : NULL, NULL);
1964 2723 : 312 : fcinfo->args[0].isnull = false;
2724 : 312 : fcinfo->args[1].isnull = false;
2725 : :
2479 2726 : 312 : hasmatch1 = (bool *) palloc0(sslot1->nvalues * sizeof(bool));
5119 2727 : 312 : hasmatch2 = (bool *) palloc0(clamped_nvalues2 * sizeof(bool));
2728 : :
2729 : : /*
2730 : : * Note we assume that each MCV will match at most one member of the
2731 : : * other MCV list. If the operator isn't really equality, there could
2732 : : * be multiple matches --- but we don't look for them, both for speed
2733 : : * and because the math wouldn't add up...
2734 : : */
6230 2735 : 312 : nmatches = 0;
2479 2736 [ + + ]: 6660 : for (i = 0; i < sslot1->nvalues; i++)
2737 : : {
2738 : : int j;
2739 : :
1964 2740 : 6348 : fcinfo->args[0].value = sslot1->values[i];
2741 : :
5119 2742 [ + + ]: 257152 : for (j = 0; j < clamped_nvalues2; j++)
2743 : : {
2744 : : Datum fresult;
2745 : :
6230 2746 [ + + ]: 256205 : if (hasmatch2[j])
2747 : 193616 : continue;
1964 2748 : 62589 : fcinfo->args[1].value = sslot2->values[j];
2749 : 62589 : fcinfo->isnull = false;
2750 : 62589 : fresult = FunctionCallInvoke(fcinfo);
2751 [ + - + + ]: 62589 : if (!fcinfo->isnull && DatumGetBool(fresult))
2752 : : {
6230 2753 : 5401 : hasmatch1[i] = hasmatch2[j] = true;
2754 : 5401 : nmatches++;
2755 : 5401 : break;
2756 : : }
2757 : : }
2758 : : }
2759 : : /* Sum up frequencies of matched MCVs */
2760 : 312 : matchfreq1 = 0.0;
2479 2761 [ + + ]: 6660 : for (i = 0; i < sslot1->nvalues; i++)
2762 : : {
6230 2763 [ + + ]: 6348 : if (hasmatch1[i])
2479 2764 : 5401 : matchfreq1 += sslot1->numbers[i];
2765 : : }
6230 2766 [ - + + + ]: 312 : CLAMP_PROBABILITY(matchfreq1);
2767 : 312 : pfree(hasmatch1);
2768 : 312 : pfree(hasmatch2);
2769 : :
2770 : : /*
2771 : : * Now we need to estimate the fraction of relation 1 that has at
2772 : : * least one join partner. We know for certain that the matched MCVs
2773 : : * do, so that gives us a lower bound, but we're really in the dark
2774 : : * about everything else. Our crude approach is: if nd1 <= nd2 then
2775 : : * assume all non-null rel1 rows have join partners, else assume for
2776 : : * the uncertain rows that a fraction nd2/nd1 have join partners. We
2777 : : * can discount the known-matched MCVs from the distinct-values counts
2778 : : * before doing the division.
2779 : : *
2780 : : * Crude as the above is, it's completely useless if we don't have
2781 : : * reliable ndistinct values for both sides. Hence, if either nd1 or
2782 : : * nd2 is default, punt and assume half of the uncertain rows have
2783 : : * join partners.
2784 : : */
5116 2785 [ + - + - ]: 312 : if (!isdefault1 && !isdefault2)
2786 : : {
5261 2787 : 312 : nd1 -= nmatches;
2788 : 312 : nd2 -= nmatches;
5119 2789 [ + + - + ]: 312 : if (nd1 <= nd2 || nd2 < 0)
5261 2790 : 297 : uncertainfrac = 1.0;
2791 : : else
2792 : 15 : uncertainfrac = nd2 / nd1;
2793 : : }
2794 : : else
5261 tgl@sss.pgh.pa.us 2795 :UBC 0 : uncertainfrac = 0.5;
5261 tgl@sss.pgh.pa.us 2796 :CBC 312 : uncertain = 1.0 - matchfreq1 - nullfrac1;
2797 [ - + - + ]: 312 : CLAMP_PROBABILITY(uncertain);
2798 : 312 : selec = matchfreq1 + uncertainfrac * uncertain;
2799 : : }
2800 : : else
2801 : : {
2802 : : /*
2803 : : * Without MCV lists for both sides, we can only use the heuristic
2804 : : * about nd1 vs nd2.
2805 : : */
6230 2806 [ + + ]: 5045 : double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
2807 : :
5116 2808 [ + + + + ]: 5045 : if (!isdefault1 && !isdefault2)
2809 : : {
5119 2810 [ + + - + ]: 3889 : if (nd1 <= nd2 || nd2 < 0)
5261 2811 : 2300 : selec = 1.0 - nullfrac1;
2812 : : else
2813 : 1589 : selec = (nd2 / nd1) * (1.0 - nullfrac1);
2814 : : }
2815 : : else
2816 : 1156 : selec = 0.5 * (1.0 - nullfrac1);
2817 : : }
2818 : :
6230 2819 : 5357 : return selec;
2820 : : }
2821 : :
2822 : : /*
2823 : : * neqjoinsel - Join selectivity of "!="
2824 : : */
2825 : : Datum
9224 2826 : 1864 : neqjoinsel(PG_FUNCTION_ARGS)
2827 : : {
7398 2828 : 1864 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
8868 2829 : 1864 : Oid operator = PG_GETARG_OID(1);
2830 : 1864 : List *args = (List *) PG_GETARG_POINTER(2);
8257 2831 : 1864 : JoinType jointype = (JoinType) PG_GETARG_INT16(3);
6230 2832 : 1864 : SpecialJoinInfo *sjinfo = (SpecialJoinInfo *) PG_GETARG_POINTER(4);
1873 2833 : 1864 : Oid collation = PG_GET_COLLATION();
2834 : : float8 result;
2835 : :
2838 2836 [ + + - + ]: 1864 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
8868 2837 : 631 : {
2838 : : /*
2839 : : * For semi-joins, if there is more than one distinct value in the RHS
2840 : : * relation then every non-null LHS row must find a row to join since
2841 : : * it can only be equal to one of them. We'll assume that there is
2842 : : * always more than one distinct RHS value for the sake of stability,
2843 : : * though in theory we could have special cases for empty RHS
2844 : : * (selectivity = 0) and single-distinct-value RHS (selectivity =
2845 : : * fraction of LHS that has the same value as the single RHS value).
2846 : : *
2847 : : * For anti-joins, if we use the same assumption that there is more
2848 : : * than one distinct key in the RHS relation, then every non-null LHS
2849 : : * row must be suppressed by the anti-join.
2850 : : *
2851 : : * So either way, the selectivity estimate should be 1 - nullfrac.
2852 : : */
2853 : : VariableStatData leftvar;
2854 : : VariableStatData rightvar;
2855 : : bool reversed;
2856 : : HeapTuple statsTuple;
2857 : : double nullfrac;
2858 : :
2838 2859 : 631 : get_join_variables(root, args, sjinfo, &leftvar, &rightvar, &reversed);
2860 [ + + ]: 631 : statsTuple = reversed ? rightvar.statsTuple : leftvar.statsTuple;
2861 [ + + ]: 631 : if (HeapTupleIsValid(statsTuple))
2862 : 515 : nullfrac = ((Form_pg_statistic) GETSTRUCT(statsTuple))->stanullfrac;
2863 : : else
2864 : 116 : nullfrac = 0.0;
2865 [ + + ]: 631 : ReleaseVariableStats(leftvar);
2866 [ + + ]: 631 : ReleaseVariableStats(rightvar);
2867 : :
2868 : 631 : result = 1.0 - nullfrac;
2869 : : }
2870 : : else
2871 : : {
2872 : : /*
2873 : : * We want 1 - eqjoinsel() where the equality operator is the one
2874 : : * associated with this != operator, that is, its negator.
2875 : : */
2876 : 1233 : Oid eqop = get_negator(operator);
2877 : :
2878 [ + - ]: 1233 : if (eqop)
2879 : : {
2880 : : result =
1873 2881 : 1233 : DatumGetFloat8(DirectFunctionCall5Coll(eqjoinsel,
2882 : : collation,
2883 : : PointerGetDatum(root),
2884 : : ObjectIdGetDatum(eqop),
2885 : : PointerGetDatum(args),
2886 : : Int16GetDatum(jointype),
2887 : : PointerGetDatum(sjinfo)));
2888 : : }
2889 : : else
2890 : : {
2891 : : /* Use default selectivity (should we raise an error instead?) */
2838 tgl@sss.pgh.pa.us 2892 :UBC 0 : result = DEFAULT_EQ_SEL;
2893 : : }
2838 tgl@sss.pgh.pa.us 2894 :CBC 1233 : result = 1.0 - result;
2895 : : }
2896 : :
9224 2897 : 1864 : PG_RETURN_FLOAT8(result);
2898 : : }
2899 : :
2900 : : /*
2901 : : * scalarltjoinsel - Join selectivity of "<" for scalars
2902 : : */
2903 : : Datum
2904 : 162 : scalarltjoinsel(PG_FUNCTION_ARGS)
2905 : : {
2906 : 162 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2907 : : }
2908 : :
2909 : : /*
2910 : : * scalarlejoinsel - Join selectivity of "<=" for scalars
2911 : : */
2912 : : Datum
2915 2913 : 138 : scalarlejoinsel(PG_FUNCTION_ARGS)
2914 : : {
2915 : 138 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2916 : : }
2917 : :
2918 : : /*
2919 : : * scalargtjoinsel - Join selectivity of ">" for scalars
2920 : : */
2921 : : Datum
9224 2922 : 138 : scalargtjoinsel(PG_FUNCTION_ARGS)
2923 : : {
2924 : 138 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2925 : : }
2926 : :
2927 : : /*
2928 : : * scalargejoinsel - Join selectivity of ">=" for scalars
2929 : : */
2930 : : Datum
2915 2931 : 92 : scalargejoinsel(PG_FUNCTION_ARGS)
2932 : : {
2933 : 92 : PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
2934 : : }
2935 : :
2936 : :
2937 : : /*
2938 : : * mergejoinscansel - Scan selectivity of merge join.
2939 : : *
2940 : : * A merge join will stop as soon as it exhausts either input stream.
2941 : : * Therefore, if we can estimate the ranges of both input variables,
2942 : : * we can estimate how much of the input will actually be read. This
2943 : : * can have a considerable impact on the cost when using indexscans.
2944 : : *
2945 : : * Also, we can estimate how much of each input has to be read before the
2946 : : * first join pair is found, which will affect the join's startup time.
2947 : : *
2948 : : * clause should be a clause already known to be mergejoinable. opfamily,
2949 : : * cmptype, and nulls_first specify the sort ordering being used.
2950 : : *
2951 : : * The outputs are:
2952 : : * *leftstart is set to the fraction of the left-hand variable expected
2953 : : * to be scanned before the first join pair is found (0 to 1).
2954 : : * *leftend is set to the fraction of the left-hand variable expected
2955 : : * to be scanned before the join terminates (0 to 1).
2956 : : * *rightstart, *rightend similarly for the right-hand variable.
2957 : : */
2958 : : void
2396 2959 : 57255 : mergejoinscansel(PlannerInfo *root, Node *clause,
2960 : : Oid opfamily, CompareType cmptype, bool nulls_first,
2961 : : Selectivity *leftstart, Selectivity *leftend,
2962 : : Selectivity *rightstart, Selectivity *rightend)
2963 : : {
2964 : : Node *left,
2965 : : *right;
2966 : : VariableStatData leftvar,
2967 : : rightvar;
2968 : : Oid opmethod;
2969 : : int op_strategy;
2970 : : Oid op_lefttype;
2971 : : Oid op_righttype;
2972 : : Oid opno,
2973 : : collation,
2974 : : lsortop,
2975 : : rsortop,
2976 : : lstatop,
2977 : : rstatop,
2978 : : ltop,
2979 : : leop,
2980 : : revltop,
2981 : : revleop;
2982 : : StrategyNumber ltstrat,
2983 : : lestrat,
2984 : : gtstrat,
2985 : : gestrat;
2986 : : bool isgt;
2987 : : Datum leftmin,
2988 : : leftmax,
2989 : : rightmin,
2990 : : rightmax;
2991 : : double selec;
2992 : :
2993 : : /* Set default results if we can't figure anything out. */
2994 : : /* XXX should default "start" fraction be a bit more than 0? */
6482 2995 : 57255 : *leftstart = *rightstart = 0.0;
2996 : 57255 : *leftend = *rightend = 1.0;
2997 : :
2998 : : /* Deconstruct the merge clause */
8590 2999 [ - + ]: 57255 : if (!is_opclause(clause))
8590 tgl@sss.pgh.pa.us 3000 :UBC 0 : return; /* shouldn't happen */
8304 tgl@sss.pgh.pa.us 3001 :CBC 57255 : opno = ((OpExpr *) clause)->opno;
1919 3002 : 57255 : collation = ((OpExpr *) clause)->inputcollid;
7872 3003 : 57255 : left = get_leftop((Expr *) clause);
3004 : 57255 : right = get_rightop((Expr *) clause);
8590 3005 [ - + ]: 57255 : if (!right)
8590 tgl@sss.pgh.pa.us 3006 :UBC 0 : return; /* shouldn't happen */
3007 : :
3008 : : /* Look for stats for the inputs */
7872 tgl@sss.pgh.pa.us 3009 :CBC 57255 : examine_variable(root, left, 0, &leftvar);
3010 : 57255 : examine_variable(root, right, 0, &rightvar);
3011 : :
153 peter@eisentraut.org 3012 : 57255 : opmethod = get_opfamily_method(opfamily);
3013 : :
3014 : : /* Extract the operator's declared left/right datatypes */
5392 tgl@sss.pgh.pa.us 3015 : 57255 : get_op_opfamily_properties(opno, opfamily, false,
3016 : : &op_strategy,
3017 : : &op_lefttype,
3018 : : &op_righttype);
153 peter@eisentraut.org 3019 [ - + ]: 57255 : Assert(IndexAmTranslateStrategy(op_strategy, opmethod, opfamily, true) == COMPARE_EQ);
3020 : :
3021 : : /*
3022 : : * Look up the various operators we need. If we don't find them all, it
3023 : : * probably means the opfamily is broken, but we just fail silently.
3024 : : *
3025 : : * Note: we expect that pg_statistic histograms will be sorted by the '<'
3026 : : * operator, regardless of which sort direction we are considering.
3027 : : */
3028 [ + + - ]: 57255 : switch (cmptype)
3029 : : {
3030 : 57219 : case COMPARE_LT:
6482 tgl@sss.pgh.pa.us 3031 : 57219 : isgt = false;
153 peter@eisentraut.org 3032 : 57219 : ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
3033 : 57219 : lestrat = IndexAmTranslateCompareType(COMPARE_LE, opmethod, opfamily, true);
6482 tgl@sss.pgh.pa.us 3034 [ + + ]: 57219 : if (op_lefttype == op_righttype)
3035 : : {
3036 : : /* easy case */
3037 : 56516 : ltop = get_opfamily_member(opfamily,
3038 : : op_lefttype, op_righttype,
3039 : : ltstrat);
3040 : 56516 : leop = get_opfamily_member(opfamily,
3041 : : op_lefttype, op_righttype,
3042 : : lestrat);
3043 : 56516 : lsortop = ltop;
3044 : 56516 : rsortop = ltop;
3045 : 56516 : lstatop = lsortop;
3046 : 56516 : rstatop = rsortop;
3047 : 56516 : revltop = ltop;
3048 : 56516 : revleop = leop;
3049 : : }
3050 : : else
3051 : : {
3052 : 703 : ltop = get_opfamily_member(opfamily,
3053 : : op_lefttype, op_righttype,
3054 : : ltstrat);
3055 : 703 : leop = get_opfamily_member(opfamily,
3056 : : op_lefttype, op_righttype,
3057 : : lestrat);
3058 : 703 : lsortop = get_opfamily_member(opfamily,
3059 : : op_lefttype, op_lefttype,
3060 : : ltstrat);
3061 : 703 : rsortop = get_opfamily_member(opfamily,
3062 : : op_righttype, op_righttype,
3063 : : ltstrat);
3064 : 703 : lstatop = lsortop;
3065 : 703 : rstatop = rsortop;
3066 : 703 : revltop = get_opfamily_member(opfamily,
3067 : : op_righttype, op_lefttype,
3068 : : ltstrat);
3069 : 703 : revleop = get_opfamily_member(opfamily,
3070 : : op_righttype, op_lefttype,
3071 : : lestrat);
3072 : : }
6832 3073 : 57219 : break;
153 peter@eisentraut.org 3074 : 36 : case COMPARE_GT:
3075 : : /* descending-order case */
6482 tgl@sss.pgh.pa.us 3076 : 36 : isgt = true;
153 peter@eisentraut.org 3077 : 36 : ltstrat = IndexAmTranslateCompareType(COMPARE_LT, opmethod, opfamily, true);
3078 : 36 : gtstrat = IndexAmTranslateCompareType(COMPARE_GT, opmethod, opfamily, true);
3079 : 36 : gestrat = IndexAmTranslateCompareType(COMPARE_GE, opmethod, opfamily, true);
6482 tgl@sss.pgh.pa.us 3080 [ + - ]: 36 : if (op_lefttype == op_righttype)
3081 : : {
3082 : : /* easy case */
3083 : 36 : ltop = get_opfamily_member(opfamily,
3084 : : op_lefttype, op_righttype,
3085 : : gtstrat);
3086 : 36 : leop = get_opfamily_member(opfamily,
3087 : : op_lefttype, op_righttype,
3088 : : gestrat);
3089 : 36 : lsortop = ltop;
3090 : 36 : rsortop = ltop;
3091 : 36 : lstatop = get_opfamily_member(opfamily,
3092 : : op_lefttype, op_lefttype,
3093 : : ltstrat);
3094 : 36 : rstatop = lstatop;
3095 : 36 : revltop = ltop;
3096 : 36 : revleop = leop;
3097 : : }
3098 : : else
3099 : : {
6482 tgl@sss.pgh.pa.us 3100 :UBC 0 : ltop = get_opfamily_member(opfamily,
3101 : : op_lefttype, op_righttype,
3102 : : gtstrat);
3103 : 0 : leop = get_opfamily_member(opfamily,
3104 : : op_lefttype, op_righttype,
3105 : : gestrat);
3106 : 0 : lsortop = get_opfamily_member(opfamily,
3107 : : op_lefttype, op_lefttype,
3108 : : gtstrat);
3109 : 0 : rsortop = get_opfamily_member(opfamily,
3110 : : op_righttype, op_righttype,
3111 : : gtstrat);
3112 : 0 : lstatop = get_opfamily_member(opfamily,
3113 : : op_lefttype, op_lefttype,
3114 : : ltstrat);
3115 : 0 : rstatop = get_opfamily_member(opfamily,
3116 : : op_righttype, op_righttype,
3117 : : ltstrat);
3118 : 0 : revltop = get_opfamily_member(opfamily,
3119 : : op_righttype, op_lefttype,
3120 : : gtstrat);
3121 : 0 : revleop = get_opfamily_member(opfamily,
3122 : : op_righttype, op_lefttype,
3123 : : gestrat);
3124 : : }
6832 tgl@sss.pgh.pa.us 3125 :CBC 36 : break;
6832 tgl@sss.pgh.pa.us 3126 :UBC 0 : default:
3127 : 0 : goto fail; /* shouldn't get here */
3128 : : }
3129 : :
6832 tgl@sss.pgh.pa.us 3130 [ + - + - ]:CBC 57255 : if (!OidIsValid(lsortop) ||
3131 [ + - ]: 57255 : !OidIsValid(rsortop) ||
6482 3132 [ + - ]: 57255 : !OidIsValid(lstatop) ||
3133 [ + + ]: 57255 : !OidIsValid(rstatop) ||
3134 [ + - ]: 57249 : !OidIsValid(ltop) ||
6832 3135 [ + - ]: 57249 : !OidIsValid(leop) ||
6482 3136 [ - + ]: 57249 : !OidIsValid(revltop) ||
3137 : : !OidIsValid(revleop))
6832 3138 : 6 : goto fail; /* insufficient info in catalogs */
3139 : :
3140 : : /* Try to get ranges of both inputs */
6482 3141 [ + + ]: 57249 : if (!isgt)
3142 : : {
1919 3143 [ + + ]: 57213 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3144 : : &leftmin, &leftmax))
6482 3145 : 11507 : goto fail; /* no range available from stats */
1919 3146 [ + + ]: 45706 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3147 : : &rightmin, &rightmax))
6482 3148 : 12187 : goto fail; /* no range available from stats */
3149 : : }
3150 : : else
3151 : : {
3152 : : /* need to swap the max and min */
1919 3153 [ + + ]: 36 : if (!get_variable_range(root, &leftvar, lstatop, collation,
3154 : : &leftmax, &leftmin))
6482 3155 : 15 : goto fail; /* no range available from stats */
1919 3156 [ - + ]: 21 : if (!get_variable_range(root, &rightvar, rstatop, collation,
3157 : : &rightmax, &rightmin))
6482 tgl@sss.pgh.pa.us 3158 :UBC 0 : goto fail; /* no range available from stats */
3159 : : }
3160 : :
3161 : : /*
3162 : : * Now, the fraction of the left variable that will be scanned is the
3163 : : * fraction that's <= the right-side maximum value. But only believe
3164 : : * non-default estimates, else stick with our 1.0.
3165 : : */
1919 tgl@sss.pgh.pa.us 3166 :CBC 33540 : selec = scalarineqsel(root, leop, isgt, true, collation, &leftvar,
3167 : : rightmax, op_righttype);
8590 3168 [ + + ]: 33540 : if (selec != DEFAULT_INEQ_SEL)
6482 3169 : 33537 : *leftend = selec;
3170 : :
3171 : : /* And similarly for the right variable. */
1919 3172 : 33540 : selec = scalarineqsel(root, revleop, isgt, true, collation, &rightvar,
3173 : : leftmax, op_lefttype);
8590 3174 [ + - ]: 33540 : if (selec != DEFAULT_INEQ_SEL)
6482 3175 : 33540 : *rightend = selec;
3176 : :
3177 : : /*
3178 : : * Only one of the two "end" fractions can really be less than 1.0;
3179 : : * believe the smaller estimate and reset the other one to exactly 1.0. If
3180 : : * we get exactly equal estimates (as can easily happen with self-joins),
3181 : : * believe neither.
3182 : : */
3183 [ + + ]: 33540 : if (*leftend > *rightend)
3184 : 12151 : *leftend = 1.0;
3185 [ + + ]: 21389 : else if (*leftend < *rightend)
3186 : 16904 : *rightend = 1.0;
3187 : : else
3188 : 4485 : *leftend = *rightend = 1.0;
3189 : :
3190 : : /*
3191 : : * Also, the fraction of the left variable that will be scanned before the
3192 : : * first join pair is found is the fraction that's < the right-side
3193 : : * minimum value. But only believe non-default estimates, else stick with
3194 : : * our own default.
3195 : : */
1919 3196 : 33540 : selec = scalarineqsel(root, ltop, isgt, false, collation, &leftvar,
3197 : : rightmin, op_righttype);
6482 3198 [ + - ]: 33540 : if (selec != DEFAULT_INEQ_SEL)
3199 : 33540 : *leftstart = selec;
3200 : :
3201 : : /* And similarly for the right variable. */
1919 3202 : 33540 : selec = scalarineqsel(root, revltop, isgt, false, collation, &rightvar,
3203 : : leftmin, op_lefttype);
6482 3204 [ + - ]: 33540 : if (selec != DEFAULT_INEQ_SEL)
3205 : 33540 : *rightstart = selec;
3206 : :
3207 : : /*
3208 : : * Only one of the two "start" fractions can really be more than zero;
3209 : : * believe the larger estimate and reset the other one to exactly 0.0. If
3210 : : * we get exactly equal estimates (as can easily happen with self-joins),
3211 : : * believe neither.
3212 : : */
3213 [ + + ]: 33540 : if (*leftstart < *rightstart)
3214 : 9024 : *leftstart = 0.0;
3215 [ + + ]: 24516 : else if (*leftstart > *rightstart)
3216 : 12523 : *rightstart = 0.0;
3217 : : else
3218 : 11993 : *leftstart = *rightstart = 0.0;
3219 : :
3220 : : /*
3221 : : * If the sort order is nulls-first, we're going to have to skip over any
3222 : : * nulls too. These would not have been counted by scalarineqsel, and we
3223 : : * can safely add in this fraction regardless of whether we believe
3224 : : * scalarineqsel's results or not. But be sure to clamp the sum to 1.0!
3225 : : */
3226 [ + + ]: 33540 : if (nulls_first)
3227 : : {
3228 : : Form_pg_statistic stats;
3229 : :
3230 [ + - ]: 21 : if (HeapTupleIsValid(leftvar.statsTuple))
3231 : : {
3232 : 21 : stats = (Form_pg_statistic) GETSTRUCT(leftvar.statsTuple);
3233 : 21 : *leftstart += stats->stanullfrac;
3234 [ - + - + ]: 21 : CLAMP_PROBABILITY(*leftstart);
3235 : 21 : *leftend += stats->stanullfrac;
3236 [ - + - + ]: 21 : CLAMP_PROBABILITY(*leftend);
3237 : : }
3238 [ + - ]: 21 : if (HeapTupleIsValid(rightvar.statsTuple))
3239 : : {
6802 3240 : 21 : stats = (Form_pg_statistic) GETSTRUCT(rightvar.statsTuple);
6482 3241 : 21 : *rightstart += stats->stanullfrac;
3242 [ - + - + ]: 21 : CLAMP_PROBABILITY(*rightstart);
3243 : 21 : *rightend += stats->stanullfrac;
3244 [ - + - + ]: 21 : CLAMP_PROBABILITY(*rightend);
3245 : : }
3246 : : }
3247 : :
3248 : : /* Disbelieve start >= end, just in case that can happen */
3249 [ + + ]: 33540 : if (*leftstart >= *leftend)
3250 : : {
3251 : 90 : *leftstart = 0.0;
3252 : 90 : *leftend = 1.0;
3253 : : }
3254 [ + + ]: 33540 : if (*rightstart >= *rightend)
3255 : : {
3256 : 563 : *rightstart = 0.0;
3257 : 563 : *rightend = 1.0;
3258 : : }
3259 : :
7872 3260 : 32977 : fail:
3261 [ + + ]: 57255 : ReleaseVariableStats(leftvar);
3262 [ + + ]: 57255 : ReleaseVariableStats(rightvar);
3263 : : }
3264 : :
3265 : :
3266 : : /*
3267 : : * matchingsel -- generic matching-operator selectivity support
3268 : : *
3269 : : * Use these for any operators that (a) are on data types for which we collect
3270 : : * standard statistics, and (b) have behavior for which the default estimate
3271 : : * (twice DEFAULT_EQ_SEL) is sane. Typically that is good for match-like
3272 : : * operators.
3273 : : */
3274 : :
3275 : : Datum
1984 3276 : 565 : matchingsel(PG_FUNCTION_ARGS)
3277 : : {
3278 : 565 : PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
3279 : 565 : Oid operator = PG_GETARG_OID(1);
3280 : 565 : List *args = (List *) PG_GETARG_POINTER(2);
3281 : 565 : int varRelid = PG_GETARG_INT32(3);
1919 3282 : 565 : Oid collation = PG_GET_COLLATION();
3283 : : double selec;
3284 : :
3285 : : /* Use generic restriction selectivity logic. */
3286 : 565 : selec = generic_restriction_selectivity(root, operator, collation,
3287 : : args, varRelid,
3288 : : DEFAULT_MATCHING_SEL);
3289 : :
1984 3290 : 565 : PG_RETURN_FLOAT8((float8) selec);
3291 : : }
3292 : :
3293 : : Datum
3294 : 3 : matchingjoinsel(PG_FUNCTION_ARGS)
3295 : : {
3296 : : /* Just punt, for the moment. */
3297 : 3 : PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
3298 : : }
3299 : :
3300 : :
3301 : : /*
3302 : : * Helper routine for estimate_num_groups: add an item to a list of
3303 : : * GroupVarInfos, but only if it's not known equal to any of the existing
3304 : : * entries.
3305 : : */
3306 : : typedef struct
3307 : : {
3308 : : Node *var; /* might be an expression, not just a Var */
3309 : : RelOptInfo *rel; /* relation it belongs to */
3310 : : double ndistinct; /* # distinct values */
3311 : : bool isdefault; /* true if DEFAULT_NUM_DISTINCT was used */
3312 : : } GroupVarInfo;
3313 : :
3314 : : static List *
7398 3315 : 181797 : add_unique_group_var(PlannerInfo *root, List *varinfos,
3316 : : Node *var, VariableStatData *vardata)
3317 : : {
3318 : : GroupVarInfo *varinfo;
3319 : : double ndistinct;
3320 : : bool isdefault;
3321 : : ListCell *lc;
3322 : :
5116 3323 : 181797 : ndistinct = get_variable_numdistinct(vardata, &isdefault);
3324 : :
3325 : : /*
3326 : : * The nullingrels bits within the var could cause the same var to be
3327 : : * counted multiple times if it's marked with different nullingrels. They
3328 : : * could also prevent us from matching the var to the expressions in
3329 : : * extended statistics (see estimate_multivariate_ndistinct). So strip
3330 : : * them out first.
3331 : : */
247 rguo@postgresql.org 3332 : 181797 : var = remove_nulling_relids(var, root->outer_join_rels, NULL);
3333 : :
2245 tgl@sss.pgh.pa.us 3334 [ + + + + : 221140 : foreach(lc, varinfos)
+ + ]
3335 : : {
7658 3336 : 39869 : varinfo = (GroupVarInfo *) lfirst(lc);
3337 : :
3338 : : /* Drop exact duplicates */
3339 [ + + ]: 39869 : if (equal(var, varinfo->var))
3340 : 526 : return varinfos;
3341 : :
3342 : : /*
3343 : : * Drop known-equal vars, but only if they belong to different
3344 : : * relations (see comments for estimate_num_groups). We aren't too
3345 : : * fussy about the semantics of "equal" here.
3346 : : */
3347 [ + + + + ]: 41894 : if (vardata->rel != varinfo->rel &&
403 rguo@postgresql.org 3348 : 2443 : exprs_known_equal(root, var, varinfo->var, InvalidOid))
3349 : : {
7658 tgl@sss.pgh.pa.us 3350 [ + + ]: 120 : if (varinfo->ndistinct <= ndistinct)
3351 : : {
3352 : : /* Keep older item, forget new one */
3353 : 108 : return varinfos;
3354 : : }
3355 : : else
3356 : : {
3357 : : /* Delete the older item */
2245 3358 : 12 : varinfos = foreach_delete_current(varinfos, lc);
3359 : : }
3360 : : }
3361 : : }
3362 : :
7658 3363 : 181271 : varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
3364 : :
3365 : 181271 : varinfo->var = var;
3366 : 181271 : varinfo->rel = vardata->rel;
3367 : 181271 : varinfo->ndistinct = ndistinct;
1621 drowley@postgresql.o 3368 : 181271 : varinfo->isdefault = isdefault;
7658 tgl@sss.pgh.pa.us 3369 : 181271 : varinfos = lappend(varinfos, varinfo);
3370 : 181271 : return varinfos;
3371 : : }
3372 : :
3373 : : /*
3374 : : * estimate_num_groups - Estimate number of groups in a grouped query
3375 : : *
3376 : : * Given a query having a GROUP BY clause, estimate how many groups there
3377 : : * will be --- ie, the number of distinct combinations of the GROUP BY
3378 : : * expressions.
3379 : : *
3380 : : * This routine is also used to estimate the number of rows emitted by
3381 : : * a DISTINCT filtering step; that is an isomorphic problem. (Note:
3382 : : * actually, we only use it for DISTINCT when there's no grouping or
3383 : : * aggregation ahead of the DISTINCT.)
3384 : : *
3385 : : * Inputs:
3386 : : * root - the query
3387 : : * groupExprs - list of expressions being grouped by
3388 : : * input_rows - number of rows estimated to arrive at the group/unique
3389 : : * filter step
3390 : : * pgset - NULL, or a List** pointing to a grouping set to filter the
3391 : : * groupExprs against
3392 : : *
3393 : : * Outputs:
3394 : : * estinfo - When passed as non-NULL, the function will set bits in the
3395 : : * "flags" field in order to provide callers with additional information
3396 : : * about the estimation. Currently, we only set the SELFLAG_USED_DEFAULT
3397 : : * bit if we used any default values in the estimation.
3398 : : *
3399 : : * Given the lack of any cross-correlation statistics in the system, it's
3400 : : * impossible to do anything really trustworthy with GROUP BY conditions
3401 : : * involving multiple Vars. We should however avoid assuming the worst
3402 : : * case (all possible cross-product terms actually appear as groups) since
3403 : : * very often the grouped-by Vars are highly correlated. Our current approach
3404 : : * is as follows:
3405 : : * 1. Expressions yielding boolean are assumed to contribute two groups,
3406 : : * independently of their content, and are ignored in the subsequent
3407 : : * steps. This is mainly because tests like "col IS NULL" break the
3408 : : * heuristic used in step 2 especially badly.
3409 : : * 2. Reduce the given expressions to a list of unique Vars used. For
3410 : : * example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
3411 : : * It is clearly correct not to count the same Var more than once.
3412 : : * It is also reasonable to treat f(x) the same as x: f() cannot
3413 : : * increase the number of distinct values (unless it is volatile,
3414 : : * which we consider unlikely for grouping), but it probably won't
3415 : : * reduce the number of distinct values much either.
3416 : : * As a special case, if a GROUP BY expression can be matched to an
3417 : : * expressional index for which we have statistics, then we treat the
3418 : : * whole expression as though it were just a Var.
3419 : : * 3. If the list contains Vars of different relations that are known equal
3420 : : * due to equivalence classes, then drop all but one of the Vars from each
3421 : : * known-equal set, keeping the one with smallest estimated # of values
3422 : : * (since the extra values of the others can't appear in joined rows).
3423 : : * Note the reason we only consider Vars of different relations is that
3424 : : * if we considered ones of the same rel, we'd be double-counting the
3425 : : * restriction selectivity of the equality in the next step.
3426 : : * 4. For Vars within a single source rel, we multiply together the numbers
3427 : : * of values, clamp to the number of rows in the rel (divided by 10 if
3428 : : * more than one Var), and then multiply by a factor based on the
3429 : : * selectivity of the restriction clauses for that rel. When there's
3430 : : * more than one Var, the initial product is probably too high (it's the
3431 : : * worst case) but clamping to a fraction of the rel's rows seems to be a
3432 : : * helpful heuristic for not letting the estimate get out of hand. (The
3433 : : * factor of 10 is derived from pre-Postgres-7.4 practice.) The factor
3434 : : * we multiply by to adjust for the restriction selectivity assumes that
3435 : : * the restriction clauses are independent of the grouping, which may not
3436 : : * be a valid assumption, but it's hard to do better.
3437 : : * 5. If there are Vars from multiple rels, we repeat step 4 for each such
3438 : : * rel, and multiply the results together.
3439 : : * Note that rels not containing grouped Vars are ignored completely, as are
3440 : : * join clauses. Such rels cannot increase the number of groups, and we
3441 : : * assume such clauses do not reduce the number either (somewhat bogus,
3442 : : * but we don't have the info to do better).
3443 : : */
3444 : : double
3766 andres@anarazel.de 3445 : 157470 : estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
3446 : : List **pgset, EstimationInfo *estinfo)
3447 : : {
1069 tgl@sss.pgh.pa.us 3448 : 157470 : List *varinfos = NIL;
2842 3449 : 157470 : double srf_multiplier = 1.0;
3450 : : double numdistinct;
3451 : : ListCell *l;
3452 : : int i;
3453 : :
3454 : : /* Zero the estinfo output parameter, if non-NULL */
1621 drowley@postgresql.o 3455 [ + + ]: 157470 : if (estinfo != NULL)
3456 : 137139 : memset(estinfo, 0, sizeof(EstimationInfo));
3457 : :
3458 : : /*
3459 : : * We don't ever want to return an estimate of zero groups, as that tends
3460 : : * to lead to division-by-zero and other unpleasantness. The input_rows
3461 : : * estimate is usually already at least 1, but clamp it just in case it
3462 : : * isn't.
3463 : : */
4502 tgl@sss.pgh.pa.us 3464 : 157470 : input_rows = clamp_row_est(input_rows);
3465 : :
3466 : : /*
3467 : : * If no grouping columns, there's exactly one group. (This can't happen
3468 : : * for normal cases with GROUP BY or DISTINCT, but it is possible for
3469 : : * corner cases with set operations.)
3470 : : */
1116 3471 [ + + + + : 157470 : if (groupExprs == NIL || (pgset && *pgset == NIL))
+ + ]
4898 3472 : 535 : return 1.0;
3473 : :
3474 : : /*
3475 : : * Count groups derived from boolean grouping expressions. For other
3476 : : * expressions, find the unique Vars used, treating an expression as a Var
3477 : : * if we can find stats for it. For each one, record the statistical
3478 : : * estimate of number of distinct values (total in its table, without
3479 : : * regard for filtering).
3480 : : */
6270 3481 : 156935 : numdistinct = 1.0;
3482 : :
1069 3483 : 156935 : i = 0;
8265 3484 [ + - + + : 337864 : foreach(l, groupExprs)
+ + ]
3485 : : {
3486 : 180953 : Node *groupexpr = (Node *) lfirst(l);
3487 : : double this_srf_multiplier;
3488 : : VariableStatData vardata;
3489 : : List *varshere;
3490 : : ListCell *l2;
3491 : :
3492 : : /* is expression in this grouping set? */
3766 andres@anarazel.de 3493 [ + + + + ]: 180953 : if (pgset && !list_member_int(*pgset, i++))
3494 : 148498 : continue;
3495 : :
3496 : : /*
3497 : : * Set-returning functions in grouping columns are a bit problematic.
3498 : : * The code below will effectively ignore their SRF nature and come up
3499 : : * with a numdistinct estimate as though they were scalar functions.
3500 : : * We compensate by scaling up the end result by the largest SRF
3501 : : * rowcount estimate. (This will be an overestimate if the SRF
3502 : : * produces multiple copies of any output value, but it seems best to
3503 : : * assume the SRF's outputs are distinct. In any case, it's probably
3504 : : * pointless to worry too much about this without much better
3505 : : * estimates for SRF output rowcounts than we have today.)
3506 : : */
2401 tgl@sss.pgh.pa.us 3507 : 180559 : this_srf_multiplier = expression_returns_set_rows(root, groupexpr);
2842 3508 [ + + ]: 180559 : if (srf_multiplier < this_srf_multiplier)
3509 : 66 : srf_multiplier = this_srf_multiplier;
3510 : :
3511 : : /* Short-circuit for expressions returning boolean */
6270 3512 [ + + ]: 180559 : if (exprType(groupexpr) == BOOLOID)
3513 : : {
3514 : 99 : numdistinct *= 2.0;
3515 : 99 : continue;
3516 : : }
3517 : :
3518 : : /*
3519 : : * If examine_variable is able to deduce anything about the GROUP BY
3520 : : * expression, treat it as a single variable even if it's really more
3521 : : * complicated.
3522 : : *
3523 : : * XXX This has the consequence that if there's a statistics object on
3524 : : * the expression, we don't split it into individual Vars. This
3525 : : * affects our selection of statistics in
3526 : : * estimate_multivariate_ndistinct, because it's probably better to
3527 : : * use more accurate estimate for each expression and treat them as
3528 : : * independent, than to combine estimates for the extracted variables
3529 : : * when we don't know how that relates to the expressions.
3530 : : */
7658 3531 : 180460 : examine_variable(root, groupexpr, 0, &vardata);
6187 3532 [ + + + + ]: 180460 : if (HeapTupleIsValid(vardata.statsTuple) || vardata.isunique)
3533 : : {
7658 3534 : 147666 : varinfos = add_unique_group_var(root, varinfos,
3535 : : groupexpr, &vardata);
3536 [ + + ]: 147666 : ReleaseVariableStats(vardata);
3537 : 147666 : continue;
3538 : : }
3539 [ - + ]: 32794 : ReleaseVariableStats(vardata);
3540 : :
3541 : : /*
3542 : : * Else pull out the component Vars. Handle PlaceHolderVars by
3543 : : * recursing into their arguments (effectively assuming that the
3544 : : * PlaceHolderVar doesn't change the number of groups, which boils
3545 : : * down to ignoring the possible addition of nulls to the result set).
3546 : : */
5170 3547 : 32794 : varshere = pull_var_clause(groupexpr,
3548 : : PVC_RECURSE_AGGREGATES |
3549 : : PVC_RECURSE_WINDOWFUNCS |
3550 : : PVC_RECURSE_PLACEHOLDERS);
3551 : :
3552 : : /*
3553 : : * If we find any variable-free GROUP BY item, then either it is a
3554 : : * constant (and we can ignore it) or it contains a volatile function;
3555 : : * in the latter case we punt and assume that each input row will
3556 : : * yield a distinct group.
3557 : : */
8327 3558 [ + + ]: 32794 : if (varshere == NIL)
3559 : : {
3560 [ + + ]: 363 : if (contain_volatile_functions(groupexpr))
3561 : 24 : return input_rows;
3562 : 339 : continue;
3563 : : }
3564 : :
3565 : : /*
3566 : : * Else add variables to varinfos list
3567 : : */
7658 3568 [ + - + + : 66562 : foreach(l2, varshere)
+ + ]
3569 : : {
3570 : 34131 : Node *var = (Node *) lfirst(l2);
3571 : :
3572 : 34131 : examine_variable(root, var, 0, &vardata);
3573 : 34131 : varinfos = add_unique_group_var(root, varinfos, var, &vardata);
3574 [ + + ]: 34131 : ReleaseVariableStats(vardata);
3575 : : }
3576 : : }
3577 : :
3578 : : /*
3579 : : * If now no Vars, we must have an all-constant or all-boolean GROUP BY
3580 : : * list.
3581 : : */
3582 [ + + ]: 156911 : if (varinfos == NIL)
3583 : : {
3584 : : /* Apply SRF multiplier as we would do in the long path */
2842 3585 : 197 : numdistinct *= srf_multiplier;
3586 : : /* Round off */
3587 : 197 : numdistinct = ceil(numdistinct);
3588 : : /* Guard against out-of-range answers */
6270 3589 [ + + ]: 197 : if (numdistinct > input_rows)
3590 : 22 : numdistinct = input_rows;
2842 3591 [ - + ]: 197 : if (numdistinct < 1.0)
2842 tgl@sss.pgh.pa.us 3592 :UBC 0 : numdistinct = 1.0;
6270 tgl@sss.pgh.pa.us 3593 :CBC 197 : return numdistinct;
3594 : : }
3595 : :
3596 : : /*
3597 : : * Group Vars by relation and estimate total numdistinct.
3598 : : *
3599 : : * For each iteration of the outer loop, we process the frontmost Var in
3600 : : * varinfos, plus all other Vars in the same relation. We remove these
3601 : : * Vars from the newvarinfos list for the next iteration. This is the
3602 : : * easiest way to group Vars of same rel together.
3603 : : */
3604 : : do
3605 : : {
7658 3606 : 157721 : GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
3607 : 157721 : RelOptInfo *rel = varinfo1->rel;
3088 alvherre@alvh.no-ip. 3608 : 157721 : double reldistinct = 1;
7522 tgl@sss.pgh.pa.us 3609 : 157721 : double relmaxndistinct = reldistinct;
3085 alvherre@alvh.no-ip. 3610 : 157721 : int relvarcount = 0;
8069 bruce@momjian.us 3611 : 157721 : List *newvarinfos = NIL;
3088 alvherre@alvh.no-ip. 3612 : 157721 : List *relvarinfos = NIL;
3613 : :
3614 : : /*
3615 : : * Split the list of varinfos in two - one for the current rel, one
3616 : : * for remaining Vars on other rels.
3617 : : */
2243 tgl@sss.pgh.pa.us 3618 : 157721 : relvarinfos = lappend(relvarinfos, varinfo1);
1804 3619 [ + - + + : 183249 : for_each_from(l, varinfos, 1)
+ + ]
3620 : : {
7658 3621 : 25528 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3622 : :
3623 [ + + ]: 25528 : if (varinfo2->rel == varinfo1->rel)
3624 : : {
3625 : : /* varinfos on current rel */
2243 3626 : 23538 : relvarinfos = lappend(relvarinfos, varinfo2);
3627 : : }
3628 : : else
3629 : : {
3630 : : /* not time to process varinfo2 yet */
3631 : 1990 : newvarinfos = lappend(newvarinfos, varinfo2);
3632 : : }
3633 : : }
3634 : :
3635 : : /*
3636 : : * Get the numdistinct estimate for the Vars of this rel. We
3637 : : * iteratively search for multivariate n-distinct with maximum number
3638 : : * of vars; assuming that each var group is independent of the others,
3639 : : * we multiply them together. Any remaining relvarinfos after no more
3640 : : * multivariate matches are found are assumed independent too, so
3641 : : * their individual ndistinct estimates are multiplied also.
3642 : : *
3643 : : * While iterating, count how many separate numdistinct values we
3644 : : * apply. We apply a fudge factor below, but only if we multiplied
3645 : : * more than one such values.
3646 : : */
3088 alvherre@alvh.no-ip. 3647 [ + + ]: 315505 : while (relvarinfos)
3648 : : {
3649 : : double mvndistinct;
3650 : :
3651 [ + + ]: 157784 : if (estimate_multivariate_ndistinct(root, rel, &relvarinfos,
3652 : : &mvndistinct))
3653 : : {
3654 : 201 : reldistinct *= mvndistinct;
3655 [ + + ]: 201 : if (relmaxndistinct < mvndistinct)
3656 : 195 : relmaxndistinct = mvndistinct;
3085 3657 : 201 : relvarcount++;
3658 : : }
3659 : : else
3660 : : {
3034 bruce@momjian.us 3661 [ + - + + : 338416 : foreach(l, relvarinfos)
+ + ]
3662 : : {
3088 alvherre@alvh.no-ip. 3663 : 180833 : GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
3664 : :
3665 : 180833 : reldistinct *= varinfo2->ndistinct;
3666 [ + + ]: 180833 : if (relmaxndistinct < varinfo2->ndistinct)
3667 : 158164 : relmaxndistinct = varinfo2->ndistinct;
3668 : 180833 : relvarcount++;
3669 : :
3670 : : /*
3671 : : * When varinfo2's isdefault is set then we'd better set
3672 : : * the SELFLAG_USED_DEFAULT bit in the EstimationInfo.
3673 : : */
1621 drowley@postgresql.o 3674 [ + + + + ]: 180833 : if (estinfo != NULL && varinfo2->isdefault)
3675 : 8581 : estinfo->flags |= SELFLAG_USED_DEFAULT;
3676 : : }
3677 : :
3678 : : /* we're done with this relation */
3088 alvherre@alvh.no-ip. 3679 : 157583 : relvarinfos = NIL;
3680 : : }
3681 : : }
3682 : :
3683 : : /*
3684 : : * Sanity check --- don't divide by zero if empty relation.
3685 : : */
3078 rhaas@postgresql.org 3686 [ + + - + ]: 157721 : Assert(IS_SIMPLE_REL(rel));
7996 tgl@sss.pgh.pa.us 3687 [ + + ]: 157721 : if (rel->tuples > 0)
3688 : : {
3689 : : /*
3690 : : * Clamp to size of rel, or size of rel / 10 if multiple Vars. The
3691 : : * fudge factor is because the Vars are probably correlated but we
3692 : : * don't know by how much. We should never clamp to less than the
3693 : : * largest ndistinct value for any of the Vars, though, since
3694 : : * there will surely be at least that many groups.
3695 : : */
7526 3696 : 157208 : double clamp = rel->tuples;
3697 : :
3698 [ + + ]: 157208 : if (relvarcount > 1)
3699 : : {
3700 : 21266 : clamp *= 0.1;
7522 3701 [ + + ]: 21266 : if (clamp < relmaxndistinct)
3702 : : {
3703 : 20219 : clamp = relmaxndistinct;
3704 : : /* for sanity in case some ndistinct is too large: */
3705 [ + + ]: 20219 : if (clamp > rel->tuples)
3706 : 39 : clamp = rel->tuples;
3707 : : }
3708 : : }
7526 3709 [ + + ]: 157208 : if (reldistinct > clamp)
3710 : 17486 : reldistinct = clamp;
3711 : :
3712 : : /*
3713 : : * Update the estimate based on the restriction selectivity,
3714 : : * guarding against division by zero when reldistinct is zero.
3715 : : * Also skip this if we know that we are returning all rows.
3716 : : */
3442 dean.a.rasheed@gmail 3717 [ + - + + ]: 157208 : if (reldistinct > 0 && rel->rows < rel->tuples)
3718 : : {
3719 : : /*
3720 : : * Given a table containing N rows with n distinct values in a
3721 : : * uniform distribution, if we select p rows at random then
3722 : : * the expected number of distinct values selected is
3723 : : *
3724 : : * n * (1 - product((N-N/n-i)/(N-i), i=0..p-1))
3725 : : *
3726 : : * = n * (1 - (N-N/n)! / (N-N/n-p)! * (N-p)! / N!)
3727 : : *
3728 : : * See "Approximating block accesses in database
3729 : : * organizations", S. B. Yao, Communications of the ACM,
3730 : : * Volume 20 Issue 4, April 1977 Pages 260-261.
3731 : : *
3732 : : * Alternatively, re-arranging the terms from the factorials,
3733 : : * this may be written as
3734 : : *
3735 : : * n * (1 - product((N-p-i)/(N-i), i=0..N/n-1))
3736 : : *
3737 : : * This form of the formula is more efficient to compute in
3738 : : * the common case where p is larger than N/n. Additionally,
3739 : : * as pointed out by Dell'Era, if i << N for all terms in the
3740 : : * product, it can be approximated by
3741 : : *
3742 : : * n * (1 - ((N-p)/N)^(N/n))
3743 : : *
3744 : : * See "Expected distinct values when selecting from a bag
3745 : : * without replacement", Alberto Dell'Era,
3746 : : * http://www.adellera.it/investigations/distinct_balls/.
3747 : : *
3748 : : * The condition i << N is equivalent to n >> 1, so this is a
3749 : : * good approximation when the number of distinct values in
3750 : : * the table is large. It turns out that this formula also
3751 : : * works well even when n is small.
3752 : : */
3753 : 51928 : reldistinct *=
3754 : 51928 : (1 - pow((rel->tuples - rel->rows) / rel->tuples,
3755 : 51928 : rel->tuples / reldistinct));
3756 : : }
3757 : 157208 : reldistinct = clamp_row_est(reldistinct);
3758 : :
3759 : : /*
3760 : : * Update estimate of total distinct groups.
3761 : : */
7996 tgl@sss.pgh.pa.us 3762 : 157208 : numdistinct *= reldistinct;
3763 : : }
3764 : :
8327 3765 : 157721 : varinfos = newvarinfos;
3766 [ + + ]: 157721 : } while (varinfos != NIL);
3767 : :
3768 : : /* Now we can account for the effects of any SRFs */
2842 3769 : 156714 : numdistinct *= srf_multiplier;
3770 : :
3771 : : /* Round off */
8258 3772 : 156714 : numdistinct = ceil(numdistinct);
3773 : :
3774 : : /* Guard against out-of-range answers */
8327 3775 [ + + ]: 156714 : if (numdistinct > input_rows)
3776 : 34421 : numdistinct = input_rows;
3777 [ - + ]: 156714 : if (numdistinct < 1.0)
8327 tgl@sss.pgh.pa.us 3778 :UBC 0 : numdistinct = 1.0;
3779 : :
8327 tgl@sss.pgh.pa.us 3780 :CBC 156714 : return numdistinct;
3781 : : }
3782 : :
3783 : : /*
3784 : : * Try to estimate the bucket size of the hash join inner side when the join
3785 : : * condition contains two or more clauses by employing extended statistics.
3786 : : *
3787 : : * The main idea of this approach is that the distinct value generated by
3788 : : * multivariate estimation on two or more columns would provide less bucket size
3789 : : * than estimation on one separate column.
3790 : : *
3791 : : * IMPORTANT: It is crucial to synchronize the approach of combining different
3792 : : * estimations with the caller's method.
3793 : : *
3794 : : * Return a list of clauses that didn't fetch any extended statistics.
3795 : : */
3796 : : List *
180 akorotkov@postgresql 3797 : 139423 : estimate_multivariate_bucketsize(PlannerInfo *root, RelOptInfo *inner,
3798 : : List *hashclauses,
3799 : : Selectivity *innerbucketsize)
3800 : : {
3801 : : List *clauses;
3802 : : List *otherclauses;
3803 : : double ndistinct;
3804 : :
3805 [ + + ]: 139423 : if (list_length(hashclauses) <= 1)
3806 : : {
3807 : : /*
3808 : : * Nothing to do for a single clause. Could we employ univariate
3809 : : * extended stat here?
3810 : : */
3811 : 123073 : return hashclauses;
3812 : : }
3813 : :
3814 : : /* "clauses" is the list of hashclauses we've not dealt with yet */
49 tgl@sss.pgh.pa.us 3815 :GNC 16350 : clauses = list_copy(hashclauses);
3816 : : /* "otherclauses" holds clauses we are going to return to caller */
3817 : 16350 : otherclauses = NIL;
3818 : : /* current estimate of ndistinct */
3819 : 16350 : ndistinct = 1.0;
180 akorotkov@postgresql 3820 [ + + ]:CBC 32706 : while (clauses != NIL)
3821 : : {
3822 : : ListCell *lc;
3823 : 16356 : int relid = -1;
3824 : 16356 : List *varinfos = NIL;
3825 : 16356 : List *origin_rinfos = NIL;
3826 : : double mvndistinct;
3827 : : List *origin_varinfos;
3828 : 16356 : int group_relid = -1;
3829 : 16356 : RelOptInfo *group_rel = NULL;
3830 : : ListCell *lc1,
3831 : : *lc2;
3832 : :
3833 : : /*
3834 : : * Find clauses, referencing the same single base relation and try to
3835 : : * estimate such a group with extended statistics. Create varinfo for
3836 : : * an approved clause, push it to otherclauses, if it can't be
3837 : : * estimated here or ignore to process at the next iteration.
3838 : : */
3839 [ + + + + : 49374 : foreach(lc, clauses)
+ + ]
3840 : : {
3841 : 33018 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
3842 : : Node *expr;
3843 : : Relids relids;
3844 : : GroupVarInfo *varinfo;
3845 : :
3846 : : /*
3847 : : * Find the inner side of the join, which we need to estimate the
3848 : : * number of buckets. Use outer_is_left because the
3849 : : * clause_sides_match_join routine has called on hash clauses.
3850 : : */
3851 : 66036 : relids = rinfo->outer_is_left ?
3852 [ + + ]: 33018 : rinfo->right_relids : rinfo->left_relids;
3853 : 66036 : expr = rinfo->outer_is_left ?
3854 [ + + ]: 33018 : get_rightop(rinfo->clause) : get_leftop(rinfo->clause);
3855 : :
3856 [ + + ]: 33018 : if (bms_get_singleton_member(relids, &relid) &&
3857 [ + + ]: 31699 : root->simple_rel_array[relid]->statlist != NIL)
3858 : 24 : {
136 3859 : 30 : bool is_duplicate = false;
3860 : :
3861 : : /*
3862 : : * This inner-side expression references only one relation.
3863 : : * Extended statistics on this clause can exist.
3864 : : */
180 3865 [ + + ]: 30 : if (group_relid < 0)
3866 : : {
3867 : 15 : RangeTblEntry *rte = root->simple_rte_array[relid];
3868 : :
3869 [ + - - + ]: 15 : if (!rte || (rte->relkind != RELKIND_RELATION &&
180 akorotkov@postgresql 3870 [ # # ]:UBC 0 : rte->relkind != RELKIND_MATVIEW &&
3871 [ # # ]: 0 : rte->relkind != RELKIND_FOREIGN_TABLE &&
3872 [ # # ]: 0 : rte->relkind != RELKIND_PARTITIONED_TABLE))
3873 : : {
3874 : : /* Extended statistics can't exist in principle */
3875 : 0 : otherclauses = lappend(otherclauses, rinfo);
3876 : 0 : clauses = foreach_delete_current(clauses, lc);
3877 : 0 : continue;
3878 : : }
3879 : :
180 akorotkov@postgresql 3880 :CBC 15 : group_relid = relid;
3881 : 15 : group_rel = root->simple_rel_array[relid];
3882 : : }
3883 [ - + ]: 15 : else if (group_relid != relid)
3884 : : {
3885 : : /*
3886 : : * Being in the group forming state we don't need other
3887 : : * clauses.
3888 : : */
180 akorotkov@postgresql 3889 :UBC 0 : continue;
3890 : : }
3891 : :
3892 : : /*
3893 : : * We're going to add the new clause to the varinfos list. We
3894 : : * might re-use add_unique_group_var(), but we don't do so for
3895 : : * two reasons.
3896 : : *
3897 : : * 1) We must keep the origin_rinfos list ordered exactly the
3898 : : * same way as varinfos.
3899 : : *
3900 : : * 2) add_unique_group_var() is designed for
3901 : : * estimate_num_groups(), where a larger number of groups is
3902 : : * worse. While estimating the number of hash buckets, we
3903 : : * have the opposite: a lesser number of groups is worse.
3904 : : * Therefore, we don't have to remove "known equal" vars: the
3905 : : * removed var may valuably contribute to the multivariate
3906 : : * statistics to grow the number of groups.
3907 : : */
3908 : :
3909 : : /*
3910 : : * Clear nullingrels to correctly match hash keys. See
3911 : : * add_unique_group_var()'s comment for details.
3912 : : */
136 akorotkov@postgresql 3913 :CBC 30 : expr = remove_nulling_relids(expr, root->outer_join_rels, NULL);
3914 : :
3915 : : /*
3916 : : * Detect and exclude exact duplicates from the list of hash
3917 : : * keys (like add_unique_group_var does).
3918 : : */
3919 [ + + + + : 42 : foreach(lc1, varinfos)
+ + ]
3920 : : {
3921 : 18 : varinfo = (GroupVarInfo *) lfirst(lc1);
3922 : :
3923 [ + + ]: 18 : if (!equal(expr, varinfo->var))
3924 : 12 : continue;
3925 : :
3926 : 6 : is_duplicate = true;
3927 : 6 : break;
3928 : : }
3929 : :
3930 [ + + ]: 30 : if (is_duplicate)
3931 : : {
3932 : : /*
3933 : : * Skip exact duplicates. Adding them to the otherclauses
3934 : : * list also doesn't make sense.
3935 : : */
3936 : 6 : continue;
3937 : : }
3938 : :
3939 : : /*
3940 : : * Initialize GroupVarInfo. We only use it to call
3941 : : * estimate_multivariate_ndistinct(), which doesn't care about
3942 : : * ndistinct and isdefault fields. Thus, skip these fields.
3943 : : */
3944 : 24 : varinfo = (GroupVarInfo *) palloc0(sizeof(GroupVarInfo));
180 3945 : 24 : varinfo->var = expr;
3946 : 24 : varinfo->rel = root->simple_rel_array[relid];
3947 : 24 : varinfos = lappend(varinfos, varinfo);
3948 : :
3949 : : /*
3950 : : * Remember the link to RestrictInfo for the case the clause
3951 : : * is failed to be estimated.
3952 : : */
3953 : 24 : origin_rinfos = lappend(origin_rinfos, rinfo);
3954 : : }
3955 : : else
3956 : : {
3957 : : /* This clause can't be estimated with extended statistics */
3958 : 32988 : otherclauses = lappend(otherclauses, rinfo);
3959 : : }
3960 : :
3961 : 33012 : clauses = foreach_delete_current(clauses, lc);
3962 : : }
3963 : :
3964 [ + + ]: 16356 : if (list_length(varinfos) < 2)
3965 : : {
3966 : : /*
3967 : : * Multivariate statistics doesn't apply to single columns except
3968 : : * for expressions, but it has not been implemented yet.
3969 : : */
3970 : 16350 : otherclauses = list_concat(otherclauses, origin_rinfos);
3971 : 16350 : list_free_deep(varinfos);
3972 : 16350 : list_free(origin_rinfos);
3973 : 16350 : continue;
3974 : : }
3975 : :
3976 [ - + ]: 6 : Assert(group_rel != NULL);
3977 : :
3978 : : /* Employ the extended statistics. */
3979 : 6 : origin_varinfos = varinfos;
3980 : : for (;;)
3981 : 6 : {
3982 : 12 : bool estimated = estimate_multivariate_ndistinct(root,
3983 : : group_rel,
3984 : : &varinfos,
3985 : : &mvndistinct);
3986 : :
3987 [ + + ]: 12 : if (!estimated)
3988 : 6 : break;
3989 : :
3990 : : /*
3991 : : * We've got an estimation. Use ndistinct value in a consistent
3992 : : * way - according to the caller's logic (see
3993 : : * final_cost_hashjoin).
3994 : : */
3995 [ + - ]: 6 : if (ndistinct < mvndistinct)
3996 : 6 : ndistinct = mvndistinct;
3997 [ - + ]: 6 : Assert(ndistinct >= 1.0);
3998 : : }
3999 : :
4000 [ - + ]: 6 : Assert(list_length(origin_varinfos) == list_length(origin_rinfos));
4001 : :
4002 : : /* Collect unmatched clauses as otherclauses. */
4003 [ + - + + : 21 : forboth(lc1, origin_varinfos, lc2, origin_rinfos)
+ - + + +
+ + - +
+ ]
4004 : : {
4005 : 15 : GroupVarInfo *vinfo = lfirst(lc1);
4006 : :
4007 [ + - ]: 15 : if (!list_member_ptr(varinfos, vinfo))
4008 : : /* Already estimated */
4009 : 15 : continue;
4010 : :
4011 : : /* Can't be estimated here - push to the returning list */
180 akorotkov@postgresql 4012 :UBC 0 : otherclauses = lappend(otherclauses, lfirst(lc2));
4013 : : }
4014 : : }
4015 : :
180 akorotkov@postgresql 4016 :CBC 16350 : *innerbucketsize = 1.0 / ndistinct;
4017 : 16350 : return otherclauses;
4018 : : }
4019 : :
4020 : : /*
4021 : : * Estimate hash bucket statistics when the specified expression is used
4022 : : * as a hash key for the given number of buckets.
4023 : : *
4024 : : * This attempts to determine two values:
4025 : : *
4026 : : * 1. The frequency of the most common value of the expression (returns
4027 : : * zero into *mcv_freq if we can't get that).
4028 : : *
4029 : : * 2. The "bucketsize fraction", ie, average number of entries in a bucket
4030 : : * divided by total tuples in relation.
4031 : : *
4032 : : * XXX This is really pretty bogus since we're effectively assuming that the
4033 : : * distribution of hash keys will be the same after applying restriction
4034 : : * clauses as it was in the underlying relation. However, we are not nearly
4035 : : * smart enough to figure out how the restrict clauses might change the
4036 : : * distribution, so this will have to do for now.
4037 : : *
4038 : : * We are passed the number of buckets the executor will use for the given
4039 : : * input relation. If the data were perfectly distributed, with the same
4040 : : * number of tuples going into each available bucket, then the bucketsize
4041 : : * fraction would be 1/nbuckets. But this happy state of affairs will occur
4042 : : * only if (a) there are at least nbuckets distinct data values, and (b)
4043 : : * we have a not-too-skewed data distribution. Otherwise the buckets will
4044 : : * be nonuniformly occupied. If the other relation in the join has a key
4045 : : * distribution similar to this one's, then the most-loaded buckets are
4046 : : * exactly those that will be probed most often. Therefore, the "average"
4047 : : * bucket size for costing purposes should really be taken as something close
4048 : : * to the "worst case" bucket size. We try to estimate this by adjusting the
4049 : : * fraction if there are too few distinct data values, and then scaling up
4050 : : * by the ratio of the most common value's frequency to the average frequency.
4051 : : *
4052 : : * If no statistics are available, use a default estimate of 0.1. This will
4053 : : * discourage use of a hash rather strongly if the inner relation is large,
4054 : : * which is what we want. We do not want to hash unless we know that the
4055 : : * inner rel is well-dispersed (or the alternatives seem much worse).
4056 : : *
4057 : : * The caller should also check that the mcv_freq is not so large that the
4058 : : * most common value would by itself require an impractically large bucket.
4059 : : * In a hash join, the executor can split buckets if they get too big, but
4060 : : * obviously that doesn't help for a bucket that contains many duplicates of
4061 : : * the same value.
4062 : : */
4063 : : void
2944 tgl@sss.pgh.pa.us 4064 : 82235 : estimate_hash_bucket_stats(PlannerInfo *root, Node *hashkey, double nbuckets,
4065 : : Selectivity *mcv_freq,
4066 : : Selectivity *bucketsize_frac)
4067 : : {
4068 : : VariableStatData vardata;
4069 : : double estfract,
4070 : : ndistinct,
4071 : : stanullfrac,
4072 : : avgfreq;
4073 : : bool isdefault;
4074 : : AttStatsSlot sslot;
4075 : :
7872 4076 : 82235 : examine_variable(root, hashkey, 0, &vardata);
4077 : :
4078 : : /* Look up the frequency of the most common value, if available */
2944 4079 : 82235 : *mcv_freq = 0.0;
4080 : :
4081 [ + + ]: 82235 : if (HeapTupleIsValid(vardata.statsTuple))
4082 : : {
4083 [ + + ]: 58616 : if (get_attstatsslot(&sslot, vardata.statsTuple,
4084 : : STATISTIC_KIND_MCV, InvalidOid,
4085 : : ATTSTATSSLOT_NUMBERS))
4086 : : {
4087 : : /*
4088 : : * The first MCV stat is for the most common value.
4089 : : */
4090 [ + - ]: 29336 : if (sslot.nnumbers > 0)
4091 : 29336 : *mcv_freq = sslot.numbers[0];
4092 : 29336 : free_attstatsslot(&sslot);
4093 : : }
4094 : : }
4095 : :
4096 : : /* Get number of distinct values */
5116 4097 : 82235 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
4098 : :
4099 : : /*
4100 : : * If ndistinct isn't real, punt. We normally return 0.1, but if the
4101 : : * mcv_freq is known to be even higher than that, use it instead.
4102 : : */
4103 [ + + ]: 82235 : if (isdefault)
4104 : : {
2944 4105 [ + - ]: 10247 : *bucketsize_frac = (Selectivity) Max(0.1, *mcv_freq);
5116 4106 [ + + ]: 10247 : ReleaseVariableStats(vardata);
2944 4107 : 10247 : return;
4108 : : }
4109 : :
4110 : : /* Get fraction that are null */
7872 4111 [ + + ]: 71988 : if (HeapTupleIsValid(vardata.statsTuple))
4112 : : {
4113 : : Form_pg_statistic stats;
4114 : :
4115 : 58607 : stats = (Form_pg_statistic) GETSTRUCT(vardata.statsTuple);
4116 : 58607 : stanullfrac = stats->stanullfrac;
4117 : : }
4118 : : else
4119 : 13381 : stanullfrac = 0.0;
4120 : :
4121 : : /* Compute avg freq of all distinct data values in raw relation */
4122 : 71988 : avgfreq = (1.0 - stanullfrac) / ndistinct;
4123 : :
4124 : : /*
4125 : : * Adjust ndistinct to account for restriction clauses. Observe we are
4126 : : * assuming that the data distribution is affected uniformly by the
4127 : : * restriction clauses!
4128 : : *
4129 : : * XXX Possibly better way, but much more expensive: multiply by
4130 : : * selectivity of rel's restriction clauses that mention the target Var.
4131 : : */
3450 4132 [ + - + + ]: 71988 : if (vardata.rel && vardata.rel->tuples > 0)
4133 : : {
7872 4134 : 71981 : ndistinct *= vardata.rel->rows / vardata.rel->tuples;
3450 4135 : 71981 : ndistinct = clamp_row_est(ndistinct);
4136 : : }
4137 : :
4138 : : /*
4139 : : * Initial estimate of bucketsize fraction is 1/nbuckets as long as the
4140 : : * number of buckets is less than the expected number of distinct values;
4141 : : * otherwise it is 1/ndistinct.
4142 : : */
7489 4143 [ + + ]: 71988 : if (ndistinct > nbuckets)
4144 : 42 : estfract = 1.0 / nbuckets;
4145 : : else
7872 4146 : 71946 : estfract = 1.0 / ndistinct;
4147 : :
4148 : : /*
4149 : : * Adjust estimated bucketsize upward to account for skewed distribution.
4150 : : */
2944 4151 [ + + + + ]: 71988 : if (avgfreq > 0.0 && *mcv_freq > avgfreq)
4152 : 26019 : estfract *= *mcv_freq / avgfreq;
4153 : :
4154 : : /*
4155 : : * Clamp bucketsize to sane range (the above adjustment could easily
4156 : : * produce an out-of-range result). We set the lower bound a little above
4157 : : * zero, since zero isn't a very sane result.
4158 : : */
7872 4159 [ - + ]: 71988 : if (estfract < 1.0e-6)
7872 tgl@sss.pgh.pa.us 4160 :UBC 0 : estfract = 1.0e-6;
7872 tgl@sss.pgh.pa.us 4161 [ + + ]:CBC 71988 : else if (estfract > 1.0)
4162 : 18312 : estfract = 1.0;
4163 : :
2944 4164 : 71988 : *bucketsize_frac = (Selectivity) estfract;
4165 : :
4166 [ + + ]: 71988 : ReleaseVariableStats(vardata);
4167 : : }
4168 : :
4169 : : /*
4170 : : * estimate_hashagg_tablesize
4171 : : * estimate the number of bytes that a hash aggregate hashtable will
4172 : : * require based on the agg_costs, path width and number of groups.
4173 : : *
4174 : : * We return the result as "double" to forestall any possible overflow
4175 : : * problem in the multiplication by dNumGroups.
4176 : : *
4177 : : * XXX this may be over-estimating the size now that hashagg knows to omit
4178 : : * unneeded columns from the hashtable. Also for mixed-mode grouping sets,
4179 : : * grouping columns not in the hashed set are counted here even though hashagg
4180 : : * won't store them. Is this a problem?
4181 : : */
4182 : : double
1747 heikki.linnakangas@i 4183 : 1175 : estimate_hashagg_tablesize(PlannerInfo *root, Path *path,
4184 : : const AggClauseCosts *agg_costs, double dNumGroups)
4185 : : {
4186 : : Size hashentrysize;
4187 : :
4188 : 1175 : hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
4189 : 1175 : path->pathtarget->width,
4190 : 1175 : agg_costs->transitionSpace);
4191 : :
4192 : : /*
4193 : : * Note that this disregards the effect of fill-factor and growth policy
4194 : : * of the hash table. That's probably ok, given that the default
4195 : : * fill-factor is relatively high. It'd be hard to meaningfully factor in
4196 : : * "double-in-size" growth policies here.
4197 : : */
2389 tgl@sss.pgh.pa.us 4198 : 1175 : return hashentrysize * dNumGroups;
4199 : : }
4200 : :
4201 : :
4202 : : /*-------------------------------------------------------------------------
4203 : : *
4204 : : * Support routines
4205 : : *
4206 : : *-------------------------------------------------------------------------
4207 : : */
4208 : :
4209 : : /*
4210 : : * Find the best matching ndistinct extended statistics for the given list of
4211 : : * GroupVarInfos.
4212 : : *
4213 : : * Callers must ensure that the given GroupVarInfos all belong to 'rel' and
4214 : : * the GroupVarInfos list does not contain any duplicate Vars or expressions.
4215 : : *
4216 : : * When statistics are found that match > 1 of the given GroupVarInfo, the
4217 : : * *ndistinct parameter is set according to the ndistinct estimate and a new
4218 : : * list is built with the matching GroupVarInfos removed, which is output via
4219 : : * the *varinfos parameter before returning true. When no matching stats are
4220 : : * found, false is returned and the *varinfos and *ndistinct parameters are
4221 : : * left untouched.
4222 : : */
4223 : : static bool
3088 alvherre@alvh.no-ip. 4224 : 157796 : estimate_multivariate_ndistinct(PlannerInfo *root, RelOptInfo *rel,
4225 : : List **varinfos, double *ndistinct)
4226 : : {
4227 : : ListCell *lc;
4228 : : int nmatches_vars;
4229 : : int nmatches_exprs;
4230 : 157796 : Oid statOid = InvalidOid;
4231 : : MVNDistinct *stats;
1625 tomas.vondra@postgre 4232 : 157796 : StatisticExtInfo *matched_info = NULL;
1040 tgl@sss.pgh.pa.us 4233 [ + - ]: 157796 : RangeTblEntry *rte = planner_rt_fetch(rel->relid, root);
4234 : :
4235 : : /* bail out immediately if the table has no extended statistics */
3088 alvherre@alvh.no-ip. 4236 [ + + ]: 157796 : if (!rel->statlist)
4237 : 157520 : return false;
4238 : :
4239 : : /* look for the ndistinct statistics object matching the most vars */
1625 tomas.vondra@postgre 4240 : 276 : nmatches_vars = 0; /* we require at least two matches */
4241 : 276 : nmatches_exprs = 0;
3088 alvherre@alvh.no-ip. 4242 [ + - + + : 1086 : foreach(lc, rel->statlist)
+ + ]
4243 : : {
4244 : : ListCell *lc2;
4245 : 810 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(lc);
1625 tomas.vondra@postgre 4246 : 810 : int nshared_vars = 0;
4247 : 810 : int nshared_exprs = 0;
4248 : :
4249 : : /* skip statistics of other kinds */
3088 alvherre@alvh.no-ip. 4250 [ + + ]: 810 : if (info->kind != STATS_EXT_NDISTINCT)
4251 : 375 : continue;
4252 : :
4253 : : /* skip statistics with mismatching stxdinherit value */
1040 tgl@sss.pgh.pa.us 4254 [ + + ]: 435 : if (info->inherit != rte->inh)
4255 : 12 : continue;
4256 : :
4257 : : /*
4258 : : * Determine how many expressions (and variables in non-matched
4259 : : * expressions) match. We'll then use these numbers to pick the
4260 : : * statistics object that best matches the clauses.
4261 : : */
1625 tomas.vondra@postgre 4262 [ + + + + : 1341 : foreach(lc2, *varinfos)
+ + ]
4263 : : {
4264 : : ListCell *lc3;
4265 : 918 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
4266 : : AttrNumber attnum;
4267 : :
4268 [ - + ]: 918 : Assert(varinfo->rel == rel);
4269 : :
4270 : : /* simple Var, search in statistics keys directly */
4271 [ + + ]: 918 : if (IsA(varinfo->var, Var))
4272 : : {
4273 : 735 : attnum = ((Var *) varinfo->var)->varattno;
4274 : :
4275 : : /*
4276 : : * Ignore system attributes - we don't support statistics on
4277 : : * them, so can't match them (and it'd fail as the values are
4278 : : * negative).
4279 : : */
4280 [ + + ]: 735 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4281 : 6 : continue;
4282 : :
4283 [ + + ]: 729 : if (bms_is_member(attnum, info->keys))
4284 : 426 : nshared_vars++;
4285 : :
4286 : 729 : continue;
4287 : : }
4288 : :
4289 : : /* expression - see if it's in the statistics object */
4290 [ + + + + : 330 : foreach(lc3, info->exprs)
+ + ]
4291 : : {
4292 : 264 : Node *expr = (Node *) lfirst(lc3);
4293 : :
4294 [ + + ]: 264 : if (equal(varinfo->var, expr))
4295 : : {
4296 : 117 : nshared_exprs++;
4297 : 117 : break;
4298 : : }
4299 : : }
4300 : : }
4301 : :
4302 : : /*
4303 : : * The ndistinct extended statistics contain estimates for a minimum
4304 : : * of pairs of columns which the statistics are defined on and
4305 : : * certainly not single columns. Here we skip unless we managed to
4306 : : * match to at least two columns.
4307 : : */
4308 [ + + ]: 423 : if (nshared_vars + nshared_exprs < 2)
4309 : 198 : continue;
4310 : :
4311 : : /*
4312 : : * Check if these statistics are a better match than the previous best
4313 : : * match and if so, take note of the StatisticExtInfo.
4314 : : *
4315 : : * The statslist is sorted by statOid, so the StatisticExtInfo we
4316 : : * select as the best match is deterministic even when multiple sets
4317 : : * of statistics match equally as well.
4318 : : */
4319 [ + + + - ]: 225 : if ((nshared_exprs > nmatches_exprs) ||
4320 [ + + ]: 171 : (((nshared_exprs == nmatches_exprs)) && (nshared_vars > nmatches_vars)))
4321 : : {
3088 alvherre@alvh.no-ip. 4322 : 213 : statOid = info->statOid;
1625 tomas.vondra@postgre 4323 : 213 : nmatches_vars = nshared_vars;
4324 : 213 : nmatches_exprs = nshared_exprs;
4325 : 213 : matched_info = info;
4326 : : }
4327 : : }
4328 : :
4329 : : /* No match? */
3088 alvherre@alvh.no-ip. 4330 [ + + ]: 276 : if (statOid == InvalidOid)
4331 : 69 : return false;
4332 : :
1625 tomas.vondra@postgre 4333 [ - + ]: 207 : Assert(nmatches_vars + nmatches_exprs > 1);
4334 : :
1329 4335 : 207 : stats = statext_ndistinct_load(statOid, rte->inh);
4336 : :
4337 : : /*
4338 : : * If we have a match, search it for the specific item that matches (there
4339 : : * must be one), and construct the output values.
4340 : : */
3088 alvherre@alvh.no-ip. 4341 [ + - ]: 207 : if (stats)
4342 : : {
4343 : : int i;
3034 bruce@momjian.us 4344 : 207 : List *newlist = NIL;
3088 alvherre@alvh.no-ip. 4345 : 207 : MVNDistinctItem *item = NULL;
4346 : : ListCell *lc2;
1625 tomas.vondra@postgre 4347 : 207 : Bitmapset *matched = NULL;
4348 : : AttrNumber attnum_offset;
4349 : :
4350 : : /*
4351 : : * How much we need to offset the attnums? If there are no
4352 : : * expressions, no offset is needed. Otherwise offset enough to move
4353 : : * the lowest one (which is equal to number of expressions) to 1.
4354 : : */
4355 [ + + ]: 207 : if (matched_info->exprs)
4356 : 72 : attnum_offset = (list_length(matched_info->exprs) + 1);
4357 : : else
4358 : 135 : attnum_offset = 0;
4359 : :
4360 : : /* see what actually matched */
4361 [ + - + + : 726 : foreach(lc2, *varinfos)
+ + ]
4362 : : {
4363 : : ListCell *lc3;
4364 : : int idx;
4365 : 519 : bool found = false;
4366 : :
4367 : 519 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc2);
4368 : :
4369 : : /*
4370 : : * Process a simple Var expression, by matching it to keys
4371 : : * directly. If there's a matching expression, we'll try matching
4372 : : * it later.
4373 : : */
4374 [ + + ]: 519 : if (IsA(varinfo->var, Var))
4375 : : {
4376 : 426 : AttrNumber attnum = ((Var *) varinfo->var)->varattno;
4377 : :
4378 : : /*
4379 : : * Ignore expressions on system attributes. Can't rely on the
4380 : : * bms check for negative values.
4381 : : */
4382 [ + + ]: 426 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4383 : 3 : continue;
4384 : :
4385 : : /* Is the variable covered by the statistics object? */
4386 [ + + ]: 423 : if (!bms_is_member(attnum, matched_info->keys))
4387 : 60 : continue;
4388 : :
4389 : 363 : attnum = attnum + attnum_offset;
4390 : :
4391 : : /* ensure sufficient offset */
4392 [ - + ]: 363 : Assert(AttrNumberIsForUserDefinedAttr(attnum));
4393 : :
4394 : 363 : matched = bms_add_member(matched, attnum);
4395 : :
4396 : 363 : found = true;
4397 : : }
4398 : :
4399 : : /*
4400 : : * XXX Maybe we should allow searching the expressions even if we
4401 : : * found an attribute matching the expression? That would handle
4402 : : * trivial expressions like "(a)" but it seems fairly useless.
4403 : : */
4404 [ + + ]: 456 : if (found)
4405 : 363 : continue;
4406 : :
4407 : : /* expression - see if it's in the statistics object */
4408 : 93 : idx = 0;
4409 [ + + + + : 153 : foreach(lc3, matched_info->exprs)
+ + ]
4410 : : {
4411 : 138 : Node *expr = (Node *) lfirst(lc3);
4412 : :
4413 [ + + ]: 138 : if (equal(varinfo->var, expr))
4414 : : {
4415 : 78 : AttrNumber attnum = -(idx + 1);
4416 : :
4417 : 78 : attnum = attnum + attnum_offset;
4418 : :
4419 : : /* ensure sufficient offset */
4420 [ - + ]: 78 : Assert(AttrNumberIsForUserDefinedAttr(attnum));
4421 : :
4422 : 78 : matched = bms_add_member(matched, attnum);
4423 : :
4424 : : /* there should be just one matching expression */
4425 : 78 : break;
4426 : : }
4427 : :
4428 : 60 : idx++;
4429 : : }
4430 : : }
4431 : :
4432 : : /* Find the specific item that exactly matches the combination */
3088 alvherre@alvh.no-ip. 4433 [ + - ]: 426 : for (i = 0; i < stats->nitems; i++)
4434 : : {
4435 : : int j;
4436 : 426 : MVNDistinctItem *tmpitem = &stats->items[i];
4437 : :
1625 tomas.vondra@postgre 4438 [ + + ]: 426 : if (tmpitem->nattributes != bms_num_members(matched))
4439 : 81 : continue;
4440 : :
4441 : : /* assume it's the right item */
4442 : 345 : item = tmpitem;
4443 : :
4444 : : /* check that all item attributes/expressions fit the match */
4445 [ + + ]: 828 : for (j = 0; j < tmpitem->nattributes; j++)
4446 : : {
4447 : 621 : AttrNumber attnum = tmpitem->attributes[j];
4448 : :
4449 : : /*
4450 : : * Thanks to how we constructed the matched bitmap above, we
4451 : : * can just offset all attnums the same way.
4452 : : */
4453 : 621 : attnum = attnum + attnum_offset;
4454 : :
4455 [ + + ]: 621 : if (!bms_is_member(attnum, matched))
4456 : : {
4457 : : /* nah, it's not this item */
4458 : 138 : item = NULL;
4459 : 138 : break;
4460 : : }
4461 : : }
4462 : :
4463 : : /*
4464 : : * If the item has all the matched attributes, we know it's the
4465 : : * right one - there can't be a better one. matching more.
4466 : : */
4467 [ + + ]: 345 : if (item)
4468 : 207 : break;
4469 : : }
4470 : :
4471 : : /*
4472 : : * Make sure we found an item. There has to be one, because ndistinct
4473 : : * statistics includes all combinations of attributes.
4474 : : */
3088 alvherre@alvh.no-ip. 4475 [ - + ]: 207 : if (!item)
3088 alvherre@alvh.no-ip. 4476 [ # # ]:UBC 0 : elog(ERROR, "corrupt MVNDistinct entry");
4477 : :
4478 : : /* Form the output varinfo list, keeping only unmatched ones */
3088 alvherre@alvh.no-ip. 4479 [ + - + + :CBC 726 : foreach(lc, *varinfos)
+ + ]
4480 : : {
4481 : 519 : GroupVarInfo *varinfo = (GroupVarInfo *) lfirst(lc);
4482 : : ListCell *lc3;
1625 tomas.vondra@postgre 4483 : 519 : bool found = false;
4484 : :
4485 : : /*
4486 : : * Let's look at plain variables first, because it's the most
4487 : : * common case and the check is quite cheap. We can simply get the
4488 : : * attnum and check (with an offset) matched bitmap.
4489 : : */
4490 [ + + ]: 519 : if (IsA(varinfo->var, Var))
3088 alvherre@alvh.no-ip. 4491 : 423 : {
1625 tomas.vondra@postgre 4492 : 426 : AttrNumber attnum = ((Var *) varinfo->var)->varattno;
4493 : :
4494 : : /*
4495 : : * If it's a system attribute, we're done. We don't support
4496 : : * extended statistics on system attributes, so it's clearly
4497 : : * not matched. Just keep the expression and continue.
4498 : : */
4499 [ + + ]: 426 : if (!AttrNumberIsForUserDefinedAttr(attnum))
4500 : : {
4501 : 3 : newlist = lappend(newlist, varinfo);
4502 : 3 : continue;
4503 : : }
4504 : :
4505 : : /* apply the same offset as above */
4506 : 423 : attnum += attnum_offset;
4507 : :
4508 : : /* if it's not matched, keep the varinfo */
4509 [ + + ]: 423 : if (!bms_is_member(attnum, matched))
4510 : 60 : newlist = lappend(newlist, varinfo);
4511 : :
4512 : : /* The rest of the loop deals with complex expressions. */
3088 alvherre@alvh.no-ip. 4513 : 423 : continue;
4514 : : }
4515 : :
4516 : : /*
4517 : : * Process complex expressions, not just simple Vars.
4518 : : *
4519 : : * First, we search for an exact match of an expression. If we
4520 : : * find one, we can just discard the whole GroupVarInfo, with all
4521 : : * the variables we extracted from it.
4522 : : *
4523 : : * Otherwise we inspect the individual vars, and try matching it
4524 : : * to variables in the item.
4525 : : */
1625 tomas.vondra@postgre 4526 [ + + + + : 153 : foreach(lc3, matched_info->exprs)
+ + ]
4527 : : {
4528 : 138 : Node *expr = (Node *) lfirst(lc3);
4529 : :
4530 [ + + ]: 138 : if (equal(varinfo->var, expr))
4531 : : {
4532 : 78 : found = true;
4533 : 78 : break;
4534 : : }
4535 : : }
4536 : :
4537 : : /* found exact match, skip */
4538 [ + + ]: 93 : if (found)
2121 4539 : 78 : continue;
4540 : :
1625 4541 : 15 : newlist = lappend(newlist, varinfo);
4542 : : }
4543 : :
3088 alvherre@alvh.no-ip. 4544 : 207 : *varinfos = newlist;
4545 : 207 : *ndistinct = item->ndistinct;
4546 : 207 : return true;
4547 : : }
4548 : :
3088 alvherre@alvh.no-ip. 4549 :UBC 0 : return false;
4550 : : }
4551 : :
4552 : : /*
4553 : : * convert_to_scalar
4554 : : * Convert non-NULL values of the indicated types to the comparison
4555 : : * scale needed by scalarineqsel().
4556 : : * Returns "true" if successful.
4557 : : *
4558 : : * XXX this routine is a hack: ideally we should look up the conversion
4559 : : * subroutines in pg_type.
4560 : : *
4561 : : * All numeric datatypes are simply converted to their equivalent
4562 : : * "double" values. (NUMERIC values that are outside the range of "double"
4563 : : * are clamped to +/- HUGE_VAL.)
4564 : : *
4565 : : * String datatypes are converted by convert_string_to_scalar(),
4566 : : * which is explained below. The reason why this routine deals with
4567 : : * three values at a time, not just one, is that we need it for strings.
4568 : : *
4569 : : * The bytea datatype is just enough different from strings that it has
4570 : : * to be treated separately.
4571 : : *
4572 : : * The several datatypes representing absolute times are all converted
4573 : : * to Timestamp, which is actually an int64, and then we promote that to
4574 : : * a double. Note this will give correct results even for the "special"
4575 : : * values of Timestamp, since those are chosen to compare correctly;
4576 : : * see timestamp_cmp.
4577 : : *
4578 : : * The several datatypes representing relative times (intervals) are all
4579 : : * converted to measurements expressed in seconds.
4580 : : */
4581 : : static bool
2458 tgl@sss.pgh.pa.us 4582 :CBC 45689 : convert_to_scalar(Datum value, Oid valuetypid, Oid collid, double *scaledvalue,
4583 : : Datum lobound, Datum hibound, Oid boundstypid,
4584 : : double *scaledlobound, double *scaledhibound)
4585 : : {
2744 4586 : 45689 : bool failure = false;
4587 : :
4588 : : /*
4589 : : * Both the valuetypid and the boundstypid should exactly match the
4590 : : * declared input type(s) of the operator we are invoked for. However,
4591 : : * extensions might try to use scalarineqsel as estimator for operators
4592 : : * with input type(s) we don't handle here; in such cases, we want to
4593 : : * return false, not fail. In any case, we mustn't assume that valuetypid
4594 : : * and boundstypid are identical.
4595 : : *
4596 : : * XXX The histogram we are interpolating between points of could belong
4597 : : * to a column that's only binary-compatible with the declared type. In
4598 : : * essence we are assuming that the semantics of binary-compatible types
4599 : : * are enough alike that we can use a histogram generated with one type's
4600 : : * operators to estimate selectivity for the other's. This is outright
4601 : : * wrong in some cases --- in particular signed versus unsigned
4602 : : * interpretation could trip us up. But it's useful enough in the
4603 : : * majority of cases that we do it anyway. Should think about more
4604 : : * rigorous ways to do it.
4605 : : */
9274 4606 [ + + - - : 45689 : switch (valuetypid)
- - ]
4607 : : {
4608 : : /*
4609 : : * Built-in numeric types
4610 : : */
8875 4611 : 42247 : case BOOLOID:
4612 : : case INT2OID:
4613 : : case INT4OID:
4614 : : case INT8OID:
4615 : : case FLOAT4OID:
4616 : : case FLOAT8OID:
4617 : : case NUMERICOID:
4618 : : case OIDOID:
4619 : : case REGPROCOID:
4620 : : case REGPROCEDUREOID:
4621 : : case REGOPEROID:
4622 : : case REGOPERATOROID:
4623 : : case REGCLASSOID:
4624 : : case REGTYPEOID:
4625 : : case REGCOLLATIONOID:
4626 : : case REGCONFIGOID:
4627 : : case REGDICTIONARYOID:
4628 : : case REGROLEOID:
4629 : : case REGNAMESPACEOID:
4630 : : case REGDATABASEOID:
2744 4631 : 42247 : *scaledvalue = convert_numeric_to_scalar(value, valuetypid,
4632 : : &failure);
4633 : 42247 : *scaledlobound = convert_numeric_to_scalar(lobound, boundstypid,
4634 : : &failure);
4635 : 42247 : *scaledhibound = convert_numeric_to_scalar(hibound, boundstypid,
4636 : : &failure);
4637 : 42247 : return !failure;
4638 : :
4639 : : /*
4640 : : * Built-in string types
4641 : : */
9357 4642 : 3442 : case CHAROID:
4643 : : case BPCHAROID:
4644 : : case VARCHAROID:
4645 : : case TEXTOID:
4646 : : case NAMEOID:
4647 : : {
2744 4648 : 3442 : char *valstr = convert_string_datum(value, valuetypid,
4649 : : collid, &failure);
4650 : 3442 : char *lostr = convert_string_datum(lobound, boundstypid,
4651 : : collid, &failure);
4652 : 3442 : char *histr = convert_string_datum(hibound, boundstypid,
4653 : : collid, &failure);
4654 : :
4655 : : /*
4656 : : * Bail out if any of the values is not of string type. We
4657 : : * might leak converted strings for the other value(s), but
4658 : : * that's not worth troubling over.
4659 : : */
4660 [ - + ]: 3442 : if (failure)
2744 tgl@sss.pgh.pa.us 4661 :UBC 0 : return false;
4662 : :
8934 bruce@momjian.us 4663 :CBC 3442 : convert_string_to_scalar(valstr, scaledvalue,
4664 : : lostr, scaledlobound,
4665 : : histr, scaledhibound);
4666 : 3442 : pfree(valstr);
4667 : 3442 : pfree(lostr);
4668 : 3442 : pfree(histr);
4669 : 3442 : return true;
4670 : : }
4671 : :
4672 : : /*
4673 : : * Built-in bytea type
4674 : : */
8790 tgl@sss.pgh.pa.us 4675 :UBC 0 : case BYTEAOID:
4676 : : {
4677 : : /* We only support bytea vs bytea comparison */
2744 4678 [ # # ]: 0 : if (boundstypid != BYTEAOID)
4679 : 0 : return false;
8790 4680 : 0 : convert_bytea_to_scalar(value, scaledvalue,
4681 : : lobound, scaledlobound,
4682 : : hibound, scaledhibound);
4683 : 0 : return true;
4684 : : }
4685 : :
4686 : : /*
4687 : : * Built-in time types
4688 : : */
9324 4689 : 0 : case TIMESTAMPOID:
4690 : : case TIMESTAMPTZOID:
4691 : : case DATEOID:
4692 : : case INTERVALOID:
4693 : : case TIMEOID:
4694 : : case TIMETZOID:
2744 4695 : 0 : *scaledvalue = convert_timevalue_to_scalar(value, valuetypid,
4696 : : &failure);
4697 : 0 : *scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid,
4698 : : &failure);
4699 : 0 : *scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid,
4700 : : &failure);
4701 : 0 : return !failure;
4702 : :
4703 : : /*
4704 : : * Built-in network types
4705 : : */
8855 4706 : 0 : case INETOID:
4707 : : case CIDROID:
4708 : : case MACADDROID:
4709 : : case MACADDR8OID:
2744 4710 : 0 : *scaledvalue = convert_network_to_scalar(value, valuetypid,
4711 : : &failure);
4712 : 0 : *scaledlobound = convert_network_to_scalar(lobound, boundstypid,
4713 : : &failure);
4714 : 0 : *scaledhibound = convert_network_to_scalar(hibound, boundstypid,
4715 : : &failure);
4716 : 0 : return !failure;
4717 : : }
4718 : : /* Don't know how to convert */
7287 4719 : 0 : *scaledvalue = *scaledlobound = *scaledhibound = 0;
9532 4720 : 0 : return false;
4721 : : }
4722 : :
4723 : : /*
4724 : : * Do convert_to_scalar()'s work for any numeric data type.
4725 : : *
4726 : : * On failure (e.g., unsupported typid), set *failure to true;
4727 : : * otherwise, that variable is not changed.
4728 : : */
4729 : : static double
2744 tgl@sss.pgh.pa.us 4730 :CBC 126741 : convert_numeric_to_scalar(Datum value, Oid typid, bool *failure)
4731 : : {
9274 4732 [ - + + - : 126741 : switch (typid)
- + - +
- ]
4733 : : {
8888 tgl@sss.pgh.pa.us 4734 :UBC 0 : case BOOLOID:
9232 4735 : 0 : return (double) DatumGetBool(value);
9274 tgl@sss.pgh.pa.us 4736 :CBC 6 : case INT2OID:
4737 : 6 : return (double) DatumGetInt16(value);
4738 : 15705 : case INT4OID:
4739 : 15705 : return (double) DatumGetInt32(value);
9274 tgl@sss.pgh.pa.us 4740 :UBC 0 : case INT8OID:
9232 4741 : 0 : return (double) DatumGetInt64(value);
9274 4742 : 0 : case FLOAT4OID:
9232 4743 : 0 : return (double) DatumGetFloat4(value);
9274 tgl@sss.pgh.pa.us 4744 :CBC 27 : case FLOAT8OID:
9232 4745 : 27 : return (double) DatumGetFloat8(value);
9274 tgl@sss.pgh.pa.us 4746 :UBC 0 : case NUMERICOID:
4747 : : /* Note: out-of-range values will be clamped to +-HUGE_VAL */
8729 4748 : 0 : return (double)
4749 : 0 : DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
4750 : : value));
9274 tgl@sss.pgh.pa.us 4751 :CBC 111003 : case OIDOID:
4752 : : case REGPROCOID:
4753 : : case REGPROCEDUREOID:
4754 : : case REGOPEROID:
4755 : : case REGOPERATOROID:
4756 : : case REGCLASSOID:
4757 : : case REGTYPEOID:
4758 : : case REGCOLLATIONOID:
4759 : : case REGCONFIGOID:
4760 : : case REGDICTIONARYOID:
4761 : : case REGROLEOID:
4762 : : case REGNAMESPACEOID:
4763 : : case REGDATABASEOID:
4764 : : /* we can treat OIDs as integers... */
4765 : 111003 : return (double) DatumGetObjectId(value);
4766 : : }
4767 : :
2744 tgl@sss.pgh.pa.us 4768 :UBC 0 : *failure = true;
9274 4769 : 0 : return 0;
4770 : : }
4771 : :
4772 : : /*
4773 : : * Do convert_to_scalar()'s work for any character-string data type.
4774 : : *
4775 : : * String datatypes are converted to a scale that ranges from 0 to 1,
4776 : : * where we visualize the bytes of the string as fractional digits.
4777 : : *
4778 : : * We do not want the base to be 256, however, since that tends to
4779 : : * generate inflated selectivity estimates; few databases will have
4780 : : * occurrences of all 256 possible byte values at each position.
4781 : : * Instead, use the smallest and largest byte values seen in the bounds
4782 : : * as the estimated range for each byte, after some fudging to deal with
4783 : : * the fact that we probably aren't going to see the full range that way.
4784 : : *
4785 : : * An additional refinement is that we discard any common prefix of the
4786 : : * three strings before computing the scaled values. This allows us to
4787 : : * "zoom in" when we encounter a narrow data range. An example is a phone
4788 : : * number database where all the values begin with the same area code.
4789 : : * (Actually, the bounds will be adjacent histogram-bin-boundary values,
4790 : : * so this is more likely to happen than you might think.)
4791 : : */
4792 : : static void
7287 tgl@sss.pgh.pa.us 4793 :CBC 3442 : convert_string_to_scalar(char *value,
4794 : : double *scaledvalue,
4795 : : char *lobound,
4796 : : double *scaledlobound,
4797 : : char *hibound,
4798 : : double *scaledhibound)
4799 : : {
4800 : : int rangelo,
4801 : : rangehi;
4802 : : char *sptr;
4803 : :
4804 : 3442 : rangelo = rangehi = (unsigned char) hibound[0];
9274 4805 [ + + ]: 44164 : for (sptr = lobound; *sptr; sptr++)
4806 : : {
7287 4807 [ + + ]: 40722 : if (rangelo > (unsigned char) *sptr)
4808 : 8331 : rangelo = (unsigned char) *sptr;
4809 [ + + ]: 40722 : if (rangehi < (unsigned char) *sptr)
4810 : 4202 : rangehi = (unsigned char) *sptr;
4811 : : }
9274 4812 [ + + ]: 37487 : for (sptr = hibound; *sptr; sptr++)
4813 : : {
7287 4814 [ + + ]: 34045 : if (rangelo > (unsigned char) *sptr)
4815 : 396 : rangelo = (unsigned char) *sptr;
4816 [ + + ]: 34045 : if (rangehi < (unsigned char) *sptr)
4817 : 1795 : rangehi = (unsigned char) *sptr;
4818 : : }
4819 : : /* If range includes any upper-case ASCII chars, make it include all */
9274 4820 [ + + + + ]: 3442 : if (rangelo <= 'Z' && rangehi >= 'A')
4821 : : {
4822 [ + + ]: 697 : if (rangelo > 'A')
4823 : 111 : rangelo = 'A';
4824 [ + + ]: 697 : if (rangehi < 'Z')
4825 : 240 : rangehi = 'Z';
4826 : : }
4827 : : /* Ditto lower-case */
4828 [ + - + + ]: 3442 : if (rangelo <= 'z' && rangehi >= 'a')
4829 : : {
4830 [ + + ]: 3179 : if (rangelo > 'a')
4831 : 14 : rangelo = 'a';
4832 [ + + ]: 3179 : if (rangehi < 'z')
4833 : 3140 : rangehi = 'z';
4834 : : }
4835 : : /* Ditto digits */
4836 [ + + + - ]: 3442 : if (rangelo <= '9' && rangehi >= '0')
4837 : : {
4838 [ + + ]: 369 : if (rangelo > '0')
4839 : 328 : rangelo = '0';
4840 [ + + ]: 369 : if (rangehi < '9')
4841 : 17 : rangehi = '9';
4842 : : }
4843 : :
4844 : : /*
4845 : : * If range includes less than 10 chars, assume we have not got enough
4846 : : * data, and make it include regular ASCII set.
4847 : : */
4848 [ - + ]: 3442 : if (rangehi - rangelo < 9)
4849 : : {
9274 tgl@sss.pgh.pa.us 4850 :UBC 0 : rangelo = ' ';
4851 : 0 : rangehi = 127;
4852 : : }
4853 : :
4854 : : /*
4855 : : * Now strip any common prefix of the three strings.
4856 : : */
9274 tgl@sss.pgh.pa.us 4857 [ + - ]:CBC 7215 : while (*lobound)
4858 : : {
4859 [ + + + - ]: 7215 : if (*lobound != *hibound || *lobound != *value)
4860 : : break;
4861 : 3773 : lobound++, hibound++, value++;
4862 : : }
4863 : :
4864 : : /*
4865 : : * Now we can do the conversions.
4866 : : */
4867 : 3442 : *scaledvalue = convert_one_string_to_scalar(value, rangelo, rangehi);
4868 : 3442 : *scaledlobound = convert_one_string_to_scalar(lobound, rangelo, rangehi);
4869 : 3442 : *scaledhibound = convert_one_string_to_scalar(hibound, rangelo, rangehi);
4870 : 3442 : }
4871 : :
4872 : : static double
7287 4873 : 10326 : convert_one_string_to_scalar(char *value, int rangelo, int rangehi)
4874 : : {
4875 : 10326 : int slen = strlen(value);
4876 : : double num,
4877 : : denom,
4878 : : base;
4879 : :
9274 4880 [ - + ]: 10326 : if (slen <= 0)
9274 tgl@sss.pgh.pa.us 4881 :LBC (10) : return 0.0; /* empty string has scalar value 0 */
4882 : :
4883 : : /*
4884 : : * There seems little point in considering more than a dozen bytes from
4885 : : * the string. Since base is at least 10, that will give us nominal
4886 : : * resolution of at least 12 decimal digits, which is surely far more
4887 : : * precision than this estimation technique has got anyway (especially in
4888 : : * non-C locales). Also, even with the maximum possible base of 256, this
4889 : : * ensures denom cannot grow larger than 256^13 = 2.03e31, which will not
4890 : : * overflow on any known machine.
4891 : : */
3667 tgl@sss.pgh.pa.us 4892 [ + + ]:CBC 10326 : if (slen > 12)
4893 : 2408 : slen = 12;
4894 : :
4895 : : /* Convert initial characters to fraction */
9274 4896 : 10326 : base = rangehi - rangelo + 1;
4897 : 10326 : num = 0.0;
4898 : 10326 : denom = base;
4899 [ + + ]: 84716 : while (slen-- > 0)
4900 : : {
7287 4901 : 74390 : int ch = (unsigned char) *value++;
4902 : :
9274 4903 [ + + ]: 74390 : if (ch < rangelo)
8934 bruce@momjian.us 4904 : 56 : ch = rangelo - 1;
9274 tgl@sss.pgh.pa.us 4905 [ - + ]: 74334 : else if (ch > rangehi)
8934 bruce@momjian.us 4906 :UBC 0 : ch = rangehi + 1;
9274 tgl@sss.pgh.pa.us 4907 :CBC 74390 : num += ((double) (ch - rangelo)) / denom;
4908 : 74390 : denom *= base;
4909 : : }
4910 : :
4911 : 10326 : return num;
4912 : : }
4913 : :
4914 : : /*
4915 : : * Convert a string-type Datum into a palloc'd, null-terminated string.
4916 : : *
4917 : : * On failure (e.g., unsupported typid), set *failure to true;
4918 : : * otherwise, that variable is not changed. (We'll return NULL on failure.)
4919 : : *
4920 : : * When using a non-C locale, we must pass the string through pg_strxfrm()
4921 : : * before continuing, so as to generate correct locale-specific results.
4922 : : */
4923 : : static char *
2458 4924 : 10326 : convert_string_datum(Datum value, Oid typid, Oid collid, bool *failure)
4925 : : {
4926 : : char *val;
4927 : : pg_locale_t mylocale;
4928 : :
9274 4929 [ - + + - ]: 10326 : switch (typid)
4930 : : {
9274 tgl@sss.pgh.pa.us 4931 :UBC 0 : case CHAROID:
4932 : 0 : val = (char *) palloc(2);
4933 : 0 : val[0] = DatumGetChar(value);
4934 : 0 : val[1] = '\0';
4935 : 0 : break;
9274 tgl@sss.pgh.pa.us 4936 :CBC 3174 : case BPCHAROID:
4937 : : case VARCHAROID:
4938 : : case TEXTOID:
6374 4939 : 3174 : val = TextDatumGetCString(value);
4940 : 3174 : break;
9274 4941 : 7152 : case NAMEOID:
4942 : : {
8934 bruce@momjian.us 4943 : 7152 : NameData *nm = (NameData *) DatumGetPointer(value);
4944 : :
4945 : 7152 : val = pstrdup(NameStr(*nm));
4946 : 7152 : break;
4947 : : }
9274 tgl@sss.pgh.pa.us 4948 :UBC 0 : default:
2744 4949 : 0 : *failure = true;
9274 4950 : 0 : return NULL;
4951 : : }
4952 : :
367 jdavis@postgresql.or 4953 :CBC 10326 : mylocale = pg_newlocale_from_collation(collid);
4954 : :
4955 [ + + ]: 10326 : if (!mylocale->collate_is_c)
4956 : : {
4957 : : char *xfrmstr;
4958 : : size_t xfrmlen;
4959 : : size_t xfrmlen2 PG_USED_FOR_ASSERTS_ONLY;
4960 : :
4961 : : /*
4962 : : * XXX: We could guess at a suitable output buffer size and only call
4963 : : * pg_strxfrm() twice if our guess is too small.
4964 : : *
4965 : : * XXX: strxfrm doesn't support UTF-8 encoding on Win32, it can return
4966 : : * bogus data or set an error. This is not really a problem unless it
4967 : : * crashes since it will only give an estimation error and nothing
4968 : : * fatal.
4969 : : *
4970 : : * XXX: we do not check pg_strxfrm_enabled(). On some platforms and in
4971 : : * some cases, libc strxfrm() may return the wrong results, but that
4972 : : * will only lead to an estimation error.
4973 : : */
396 4974 : 36 : xfrmlen = pg_strxfrm(NULL, val, 0, mylocale);
4975 : : #ifdef WIN32
4976 : :
4977 : : /*
4978 : : * On Windows, strxfrm returns INT_MAX when an error occurs. Instead
4979 : : * of trying to allocate this much memory (and fail), just return the
4980 : : * original string unmodified as if we were in the C locale.
4981 : : */
4982 : : if (xfrmlen == INT_MAX)
4983 : : return val;
4984 : : #endif
8087 tgl@sss.pgh.pa.us 4985 : 36 : xfrmstr = (char *) palloc(xfrmlen + 1);
396 jdavis@postgresql.or 4986 : 36 : xfrmlen2 = pg_strxfrm(xfrmstr, val, xfrmlen + 1, mylocale);
4987 : :
4988 : : /*
4989 : : * Some systems (e.g., glibc) can return a smaller value from the
4990 : : * second call than the first; thus the Assert must be <= not ==.
4991 : : */
8087 tgl@sss.pgh.pa.us 4992 [ - + ]: 36 : Assert(xfrmlen2 <= xfrmlen);
8557 peter_e@gmx.net 4993 : 36 : pfree(val);
4994 : 36 : val = xfrmstr;
4995 : : }
4996 : :
7287 tgl@sss.pgh.pa.us 4997 : 10326 : return val;
4998 : : }
4999 : :
5000 : : /*
5001 : : * Do convert_to_scalar()'s work for any bytea data type.
5002 : : *
5003 : : * Very similar to convert_string_to_scalar except we can't assume
5004 : : * null-termination and therefore pass explicit lengths around.
5005 : : *
5006 : : * Also, assumptions about likely "normal" ranges of characters have been
5007 : : * removed - a data range of 0..255 is always used, for now. (Perhaps
5008 : : * someday we will add information about actual byte data range to
5009 : : * pg_statistic.)
5010 : : */
5011 : : static void
8790 tgl@sss.pgh.pa.us 5012 :UBC 0 : convert_bytea_to_scalar(Datum value,
5013 : : double *scaledvalue,
5014 : : Datum lobound,
5015 : : double *scaledlobound,
5016 : : Datum hibound,
5017 : : double *scaledhibound)
5018 : : {
2744 5019 : 0 : bytea *valuep = DatumGetByteaPP(value);
5020 : 0 : bytea *loboundp = DatumGetByteaPP(lobound);
5021 : 0 : bytea *hiboundp = DatumGetByteaPP(hibound);
5022 : : int rangelo,
5023 : : rangehi,
5024 [ # # # # : 0 : valuelen = VARSIZE_ANY_EXHDR(valuep),
# # # # #
# ]
5025 [ # # # # : 0 : loboundlen = VARSIZE_ANY_EXHDR(loboundp),
# # # # #
# ]
5026 [ # # # # : 0 : hiboundlen = VARSIZE_ANY_EXHDR(hiboundp),
# # # # #
# ]
5027 : : i,
5028 : : minlen;
5029 [ # # ]: 0 : unsigned char *valstr = (unsigned char *) VARDATA_ANY(valuep);
5030 [ # # ]: 0 : unsigned char *lostr = (unsigned char *) VARDATA_ANY(loboundp);
5031 [ # # ]: 0 : unsigned char *histr = (unsigned char *) VARDATA_ANY(hiboundp);
5032 : :
5033 : : /*
5034 : : * Assume bytea data is uniformly distributed across all byte values.
5035 : : */
8790 5036 : 0 : rangelo = 0;
5037 : 0 : rangehi = 255;
5038 : :
5039 : : /*
5040 : : * Now strip any common prefix of the three strings.
5041 : : */
5042 : 0 : minlen = Min(Min(valuelen, loboundlen), hiboundlen);
5043 [ # # ]: 0 : for (i = 0; i < minlen; i++)
5044 : : {
5045 [ # # # # ]: 0 : if (*lostr != *histr || *lostr != *valstr)
5046 : : break;
5047 : 0 : lostr++, histr++, valstr++;
5048 : 0 : loboundlen--, hiboundlen--, valuelen--;
5049 : : }
5050 : :
5051 : : /*
5052 : : * Now we can do the conversions.
5053 : : */
5054 : 0 : *scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
5055 : 0 : *scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
5056 : 0 : *scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
5057 : 0 : }
5058 : :
5059 : : static double
5060 : 0 : convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
5061 : : int rangelo, int rangehi)
5062 : : {
5063 : : double num,
5064 : : denom,
5065 : : base;
5066 : :
5067 [ # # ]: 0 : if (valuelen <= 0)
5068 : 0 : return 0.0; /* empty string has scalar value 0 */
5069 : :
5070 : : /*
5071 : : * Since base is 256, need not consider more than about 10 chars (even
5072 : : * this many seems like overkill)
5073 : : */
5074 [ # # ]: 0 : if (valuelen > 10)
5075 : 0 : valuelen = 10;
5076 : :
5077 : : /* Convert initial characters to fraction */
5078 : 0 : base = rangehi - rangelo + 1;
5079 : 0 : num = 0.0;
5080 : 0 : denom = base;
5081 [ # # ]: 0 : while (valuelen-- > 0)
5082 : : {
5083 : 0 : int ch = *value++;
5084 : :
5085 [ # # ]: 0 : if (ch < rangelo)
5086 : 0 : ch = rangelo - 1;
5087 [ # # ]: 0 : else if (ch > rangehi)
5088 : 0 : ch = rangehi + 1;
5089 : 0 : num += ((double) (ch - rangelo)) / denom;
5090 : 0 : denom *= base;
5091 : : }
5092 : :
5093 : 0 : return num;
5094 : : }
5095 : :
5096 : : /*
5097 : : * Do convert_to_scalar()'s work for any timevalue data type.
5098 : : *
5099 : : * On failure (e.g., unsupported typid), set *failure to true;
5100 : : * otherwise, that variable is not changed.
5101 : : */
5102 : : static double
2744 5103 : 0 : convert_timevalue_to_scalar(Datum value, Oid typid, bool *failure)
5104 : : {
9274 5105 [ # # # # : 0 : switch (typid)
# # # ]
5106 : : {
8875 5107 : 0 : case TIMESTAMPOID:
9220 5108 : 0 : return DatumGetTimestamp(value);
8739 5109 : 0 : case TIMESTAMPTZOID:
5110 : 0 : return DatumGetTimestampTz(value);
9274 5111 : 0 : case DATEOID:
5366 5112 : 0 : return date2timestamp_no_overflow(DatumGetDateADT(value));
9274 5113 : 0 : case INTERVALOID:
5114 : : {
8934 bruce@momjian.us 5115 : 0 : Interval *interval = DatumGetIntervalP(value);
5116 : :
5117 : : /*
5118 : : * Convert the month part of Interval to days using assumed
5119 : : * average month length of 365.25/12.0 days. Not too
5120 : : * accurate, but plenty good enough for our purposes.
5121 : : *
5122 : : * This also works for infinite intervals, which just have all
5123 : : * fields set to INT_MIN/INT_MAX, and so will produce a result
5124 : : * smaller/larger than any finite interval.
5125 : : */
7266 5126 : 0 : return interval->time + interval->day * (double) USECS_PER_DAY +
5127 : 0 : interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
5128 : : }
9274 tgl@sss.pgh.pa.us 5129 : 0 : case TIMEOID:
9220 5130 : 0 : return DatumGetTimeADT(value);
8855 5131 : 0 : case TIMETZOID:
5132 : : {
5133 : 0 : TimeTzADT *timetz = DatumGetTimeTzADTP(value);
5134 : :
5135 : : /* use GMT-equivalent time */
8539 lockhart@fourpalms.o 5136 : 0 : return (double) (timetz->time + (timetz->zone * 1000000.0));
5137 : : }
5138 : : }
5139 : :
2744 tgl@sss.pgh.pa.us 5140 : 0 : *failure = true;
9274 5141 : 0 : return 0;
5142 : : }
5143 : :
5144 : :
5145 : : /*
5146 : : * get_restriction_variable
5147 : : * Examine the args of a restriction clause to see if it's of the
5148 : : * form (variable op pseudoconstant) or (pseudoconstant op variable),
5149 : : * where "variable" could be either a Var or an expression in vars of a
5150 : : * single relation. If so, extract information about the variable,
5151 : : * and also indicate which side it was on and the other argument.
5152 : : *
5153 : : * Inputs:
5154 : : * root: the planner info
5155 : : * args: clause argument list
5156 : : * varRelid: see specs for restriction selectivity functions
5157 : : *
5158 : : * Outputs: (these are valid only if true is returned)
5159 : : * *vardata: gets information about variable (see examine_variable)
5160 : : * *other: gets other clause argument, aggressively reduced to a constant
5161 : : * *varonleft: set true if variable is on the left, false if on the right
5162 : : *
5163 : : * Returns true if a variable is identified, otherwise false.
5164 : : *
5165 : : * Note: if there are Vars on both sides of the clause, we must fail, because
5166 : : * callers are expecting that the other side will act like a pseudoconstant.
5167 : : */
5168 : : bool
7398 tgl@sss.pgh.pa.us 5169 :CBC 370638 : get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
5170 : : VariableStatData *vardata, Node **other,
5171 : : bool *varonleft)
5172 : : {
5173 : : Node *left,
5174 : : *right;
5175 : : VariableStatData rdata;
5176 : :
5177 : : /* Fail if not a binary opclause (probably shouldn't happen) */
7769 neilc@samurai.com 5178 [ - + ]: 370638 : if (list_length(args) != 2)
7872 tgl@sss.pgh.pa.us 5179 :UBC 0 : return false;
5180 : :
7773 neilc@samurai.com 5181 :CBC 370638 : left = (Node *) linitial(args);
7872 tgl@sss.pgh.pa.us 5182 : 370638 : right = (Node *) lsecond(args);
5183 : :
5184 : : /*
5185 : : * Examine both sides. Note that when varRelid is nonzero, Vars of other
5186 : : * relations will be treated as pseudoconstants.
5187 : : */
5188 : 370638 : examine_variable(root, left, varRelid, vardata);
5189 : 370638 : examine_variable(root, right, varRelid, &rdata);
5190 : :
5191 : : /*
5192 : : * If one side is a variable and the other not, we win.
5193 : : */
5194 [ + + + + ]: 370638 : if (vardata->rel && rdata.rel == NULL)
5195 : : {
5196 : 333422 : *varonleft = true;
6774 5197 : 333422 : *other = estimate_expression_value(root, rdata.var);
5198 : : /* Assume we need no ReleaseVariableStats(rdata) here */
7872 5199 : 333419 : return true;
5200 : : }
5201 : :
5202 [ + + + + ]: 37216 : if (vardata->rel == NULL && rdata.rel)
5203 : : {
5204 : 34092 : *varonleft = false;
6774 5205 : 34092 : *other = estimate_expression_value(root, vardata->var);
5206 : : /* Assume we need no ReleaseVariableStats(*vardata) here */
7872 5207 : 34092 : *vardata = rdata;
5208 : 34092 : return true;
5209 : : }
5210 : :
5211 : : /* Oops, clause has wrong structure (probably var op var) */
5212 [ + + ]: 3124 : ReleaseVariableStats(*vardata);
5213 [ + + ]: 3124 : ReleaseVariableStats(rdata);
5214 : :
5215 : 3124 : return false;
5216 : : }
5217 : :
5218 : : /*
5219 : : * get_join_variables
5220 : : * Apply examine_variable() to each side of a join clause.
5221 : : * Also, attempt to identify whether the join clause has the same
5222 : : * or reversed sense compared to the SpecialJoinInfo.
5223 : : *
5224 : : * We consider the join clause "normal" if it is "lhs_var OP rhs_var",
5225 : : * or "reversed" if it is "rhs_var OP lhs_var". In complicated cases
5226 : : * where we can't tell for sure, we default to assuming it's normal.
5227 : : */
5228 : : void
6230 5229 : 113139 : get_join_variables(PlannerInfo *root, List *args, SpecialJoinInfo *sjinfo,
5230 : : VariableStatData *vardata1, VariableStatData *vardata2,
5231 : : bool *join_is_reversed)
5232 : : {
5233 : : Node *left,
5234 : : *right;
5235 : :
7769 neilc@samurai.com 5236 [ - + ]: 113139 : if (list_length(args) != 2)
7872 tgl@sss.pgh.pa.us 5237 [ # # ]:UBC 0 : elog(ERROR, "join operator should take two arguments");
5238 : :
7773 neilc@samurai.com 5239 :CBC 113139 : left = (Node *) linitial(args);
8875 tgl@sss.pgh.pa.us 5240 : 113139 : right = (Node *) lsecond(args);
5241 : :
7872 5242 : 113139 : examine_variable(root, left, 0, vardata1);
5243 : 113139 : examine_variable(root, right, 0, vardata2);
5244 : :
6230 5245 [ + + + + ]: 226188 : if (vardata1->rel &&
5246 : 113049 : bms_is_subset(vardata1->rel->relids, sjinfo->syn_righthand))
2999 5247 : 41810 : *join_is_reversed = true; /* var1 is on RHS */
6230 5248 [ + + + + ]: 142585 : else if (vardata2->rel &&
5249 : 71256 : bms_is_subset(vardata2->rel->relids, sjinfo->syn_lefthand))
2999 5250 : 66 : *join_is_reversed = true; /* var2 is on LHS */
5251 : : else
6230 5252 : 71263 : *join_is_reversed = false;
7872 5253 : 113139 : }
5254 : :
5255 : : /* statext_expressions_load copies the tuple, so just pfree it. */
5256 : : static void
1625 tomas.vondra@postgre 5257 : 822 : ReleaseDummy(HeapTuple tuple)
5258 : : {
5259 : 822 : pfree(tuple);
5260 : 822 : }
5261 : :
5262 : : /*
5263 : : * examine_variable
5264 : : * Try to look up statistical data about an expression.
5265 : : * Fill in a VariableStatData struct to describe the expression.
5266 : : *
5267 : : * Inputs:
5268 : : * root: the planner info
5269 : : * node: the expression tree to examine
5270 : : * varRelid: see specs for restriction selectivity functions
5271 : : *
5272 : : * Outputs: *vardata is filled as follows:
5273 : : * var: the input expression (with any binary relabeling stripped, if
5274 : : * it is or contains a variable; but otherwise the type is preserved)
5275 : : * rel: RelOptInfo for relation containing variable; NULL if expression
5276 : : * contains no Vars (NOTE this could point to a RelOptInfo of a
5277 : : * subquery, not one in the current query).
5278 : : * statsTuple: the pg_statistic entry for the variable, if one exists;
5279 : : * otherwise NULL.
5280 : : * freefunc: pointer to a function to release statsTuple with.
5281 : : * vartype: exposed type of the expression; this should always match
5282 : : * the declared input type of the operator we are estimating for.
5283 : : * atttype, atttypmod: actual type/typmod of the "var" expression. This is
5284 : : * commonly the same as the exposed type of the variable argument,
5285 : : * but can be different in binary-compatible-type cases.
5286 : : * isunique: true if we were able to match the var to a unique index, a
5287 : : * single-column DISTINCT or GROUP-BY clause, implying its values are
5288 : : * unique for this query. (Caution: this should be trusted for
5289 : : * statistical purposes only, since we do not check indimmediate nor
5290 : : * verify that the exact same definition of equality applies.)
5291 : : * acl_ok: true if current user has permission to read all table rows from
5292 : : * the column(s) underlying the pg_statistic entry. This is consulted by
5293 : : * statistic_proc_security_check().
5294 : : *
5295 : : * Caller is responsible for doing ReleaseVariableStats() before exiting.
5296 : : */
5297 : : void
7398 tgl@sss.pgh.pa.us 5298 : 1448626 : examine_variable(PlannerInfo *root, Node *node, int varRelid,
5299 : : VariableStatData *vardata)
5300 : : {
5301 : : Node *basenode;
5302 : : Relids varnos;
5303 : : Relids basevarnos;
5304 : : RelOptInfo *onerel;
5305 : :
5306 : : /* Make sure we don't return dangling pointers in vardata */
7872 5307 [ + - + - : 10140382 : MemSet(vardata, 0, sizeof(VariableStatData));
+ - + - +
+ ]
5308 : :
5309 : : /* Save the exposed type of the expression */
7463 5310 : 1448626 : vardata->vartype = exprType(node);
5311 : :
5312 : : /* Look inside any binary-compatible relabeling */
5313 : :
7872 5314 [ + + ]: 1448626 : if (IsA(node, RelabelType))
7469 5315 : 23383 : basenode = (Node *) ((RelabelType *) node)->arg;
5316 : : else
5317 : 1425243 : basenode = node;
5318 : :
5319 : : /* Fast path for a simple Var */
5320 : :
5321 [ + + + + ]: 1448626 : if (IsA(basenode, Var) &&
5322 [ + + ]: 334651 : (varRelid == 0 || varRelid == ((Var *) basenode)->varno))
5323 : : {
5324 : 1028492 : Var *var = (Var *) basenode;
5325 : :
5326 : : /* Set up result fields other than the stats tuple */
5327 : 1028492 : vardata->var = basenode; /* return Var without relabeling */
7872 5328 : 1028492 : vardata->rel = find_base_rel(root, var->varno);
5329 : 1028492 : vardata->atttype = var->vartype;
5330 : 1028492 : vardata->atttypmod = var->vartypmod;
6047 5331 : 1028492 : vardata->isunique = has_unique_index(vardata->rel, var->varattno);
5332 : :
5333 : : /* Try to locate some stats */
5116 5334 : 1028492 : examine_simple_variable(root, var, vardata);
5335 : :
7872 5336 : 1028492 : return;
5337 : : }
5338 : :
5339 : : /*
5340 : : * Okay, it's a more complicated expression. Determine variable
5341 : : * membership. Note that when varRelid isn't zero, only vars of that
5342 : : * relation are considered "real" vars.
5343 : : */
1689 5344 : 420134 : varnos = pull_varnos(root, basenode);
247 rguo@postgresql.org 5345 : 420134 : basevarnos = bms_difference(varnos, root->outer_join_rels);
5346 : :
7872 tgl@sss.pgh.pa.us 5347 : 420134 : onerel = NULL;
5348 : :
247 rguo@postgresql.org 5349 [ + + ]: 420134 : if (bms_is_empty(basevarnos))
5350 : : {
5351 : : /* No Vars at all ... must be pseudo-constant clause */
5352 : : }
5353 : : else
5354 : : {
5355 : : int relid;
5356 : :
5357 : : /* Check if the expression is in vars of a single base relation */
5358 [ + + ]: 199800 : if (bms_get_singleton_member(basevarnos, &relid))
5359 : : {
648 drowley@postgresql.o 5360 [ + + + + ]: 197700 : if (varRelid == 0 || varRelid == relid)
5361 : : {
5362 : 29728 : onerel = find_base_rel(root, relid);
7872 tgl@sss.pgh.pa.us 5363 : 29728 : vardata->rel = onerel;
7266 bruce@momjian.us 5364 : 29728 : node = basenode; /* strip any relabeling */
5365 : : }
5366 : : /* else treat it as a constant */
5367 : : }
5368 : : else
5369 : : {
5370 : : /* varnos has multiple relids */
7872 tgl@sss.pgh.pa.us 5371 [ + + ]: 2100 : if (varRelid == 0)
5372 : : {
5373 : : /* treat it as a variable of a join relation */
5374 : 1926 : vardata->rel = find_join_rel(root, varnos);
7266 bruce@momjian.us 5375 : 1926 : node = basenode; /* strip any relabeling */
5376 : : }
7872 tgl@sss.pgh.pa.us 5377 [ + + ]: 174 : else if (bms_is_member(varRelid, varnos))
5378 : : {
5379 : : /* ignore the vars belonging to other relations */
5380 : 87 : vardata->rel = find_base_rel(root, varRelid);
7266 bruce@momjian.us 5381 : 87 : node = basenode; /* strip any relabeling */
5382 : : /* note: no point in expressional-index search here */
5383 : : }
5384 : : /* else treat it as a constant */
5385 : : }
5386 : : }
5387 : :
247 rguo@postgresql.org 5388 : 420134 : bms_free(basevarnos);
5389 : :
7469 tgl@sss.pgh.pa.us 5390 : 420134 : vardata->var = node;
7872 5391 : 420134 : vardata->atttype = exprType(node);
5392 : 420134 : vardata->atttypmod = exprTypmod(node);
5393 : :
5394 [ + + ]: 420134 : if (onerel)
5395 : : {
5396 : : /*
5397 : : * We have an expression in vars of a single relation. Try to match
5398 : : * it to expressional index columns, in hopes of finding some
5399 : : * statistics.
5400 : : *
5401 : : * Note that we consider all index columns including INCLUDE columns,
5402 : : * since there could be stats for such columns. But the test for
5403 : : * uniqueness needs to be warier.
5404 : : *
5405 : : * XXX it's conceivable that there are multiple matches with different
5406 : : * index opfamilies; if so, we need to pick one that matches the
5407 : : * operator we are estimating for. FIXME later.
5408 : : */
5409 : : ListCell *ilist;
5410 : : ListCell *slist;
5411 : :
5412 : : /*
5413 : : * The nullingrels bits within the expression could prevent us from
5414 : : * matching it to expressional index columns or to the expressions in
5415 : : * extended statistics. So strip them out first.
5416 : : */
247 rguo@postgresql.org 5417 [ + + ]: 29728 : if (bms_overlap(varnos, root->outer_join_rels))
5418 : 1530 : node = remove_nulling_relids(node, root->outer_join_rels, NULL);
5419 : :
7872 tgl@sss.pgh.pa.us 5420 [ + + + + : 59350 : foreach(ilist, onerel->indexlist)
+ + ]
5421 : : {
5422 : 31101 : IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
5423 : : ListCell *indexpr_item;
5424 : : int pos;
5425 : :
7773 neilc@samurai.com 5426 : 31101 : indexpr_item = list_head(index->indexprs);
5427 [ + + ]: 31101 : if (indexpr_item == NULL)
7872 tgl@sss.pgh.pa.us 5428 : 28656 : continue; /* no expressions here... */
5429 : :
5430 [ + + ]: 3447 : for (pos = 0; pos < index->ncolumns; pos++)
5431 : : {
5432 [ + + ]: 2481 : if (index->indexkeys[pos] == 0)
5433 : : {
5434 : : Node *indexkey;
5435 : :
7773 neilc@samurai.com 5436 [ - + ]: 2445 : if (indexpr_item == NULL)
7872 tgl@sss.pgh.pa.us 5437 [ # # ]:UBC 0 : elog(ERROR, "too few entries in indexprs list");
7773 neilc@samurai.com 5438 :CBC 2445 : indexkey = (Node *) lfirst(indexpr_item);
7872 tgl@sss.pgh.pa.us 5439 [ + - - + ]: 2445 : if (indexkey && IsA(indexkey, RelabelType))
7872 tgl@sss.pgh.pa.us 5440 :UBC 0 : indexkey = (Node *) ((RelabelType *) indexkey)->arg;
7872 tgl@sss.pgh.pa.us 5441 [ + + ]:CBC 2445 : if (equal(node, indexkey))
5442 : : {
5443 : : /*
5444 : : * Found a match ... is it a unique index? Tests here
5445 : : * should match has_unique_index().
5446 : : */
5447 [ + + ]: 1797 : if (index->unique &&
2709 teodor@sigaev.ru 5448 [ + - + - ]: 219 : index->nkeycolumns == 1 &&
2398 tgl@sss.pgh.pa.us 5449 : 219 : pos == 0 &&
6047 5450 [ - + - - ]: 219 : (index->indpred == NIL || index->predOK))
7872 5451 : 219 : vardata->isunique = true;
5452 : :
5453 : : /*
5454 : : * Has it got stats? We only consider stats for
5455 : : * non-partial indexes, since partial indexes probably
5456 : : * don't reflect whole-relation statistics; the above
5457 : : * check for uniqueness is the only info we take from
5458 : : * a partial index.
5459 : : *
5460 : : * An index stats hook, however, must make its own
5461 : : * decisions about what to do with partial indexes.
5462 : : */
6187 5463 [ - + - - ]: 1797 : if (get_index_stats_hook &&
6187 tgl@sss.pgh.pa.us 5464 :UBC 0 : (*get_index_stats_hook) (root, index->indexoid,
5465 : 0 : pos + 1, vardata))
5466 : : {
5467 : : /*
5468 : : * The hook took control of acquiring a stats
5469 : : * tuple. If it did supply a tuple, it'd better
5470 : : * have supplied a freefunc.
5471 : : */
5472 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
5473 [ # # ]: 0 : !vardata->freefunc)
5474 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
5475 : : }
6047 tgl@sss.pgh.pa.us 5476 [ + - ]:CBC 1797 : else if (index->indpred == NIL)
5477 : : {
6187 5478 : 1797 : vardata->statsTuple =
5683 rhaas@postgresql.org 5479 : 3594 : SearchSysCache3(STATRELATTINH,
5480 : : ObjectIdGetDatum(index->indexoid),
5671 bruce@momjian.us 5481 : 1797 : Int16GetDatum(pos + 1),
5482 : : BoolGetDatum(false));
6187 tgl@sss.pgh.pa.us 5483 : 1797 : vardata->freefunc = ReleaseSysCache;
5484 : :
3046 peter_e@gmx.net 5485 [ + + ]: 1797 : if (HeapTupleIsValid(vardata->statsTuple))
5486 : : {
5487 : : /*
5488 : : * Test if user has permission to access all
5489 : : * rows from the index's table.
5490 : : *
5491 : : * For simplicity, we insist on the whole
5492 : : * table being selectable, rather than trying
5493 : : * to identify which column(s) the index
5494 : : * depends on.
5495 : : *
5496 : : * Note that for an inheritance child,
5497 : : * permissions are checked on the inheritance
5498 : : * root parent, and whole-table select
5499 : : * privilege on the parent doesn't quite
5500 : : * guarantee that the user could read all
5501 : : * columns of the child. But in practice it's
5502 : : * unlikely that any interesting security
5503 : : * violation could result from allowing access
5504 : : * to the expression index's stats, so we
5505 : : * allow it anyway. See similar code in
5506 : : * examine_simple_variable() for additional
5507 : : * comments.
5508 : : */
5509 : 1479 : vardata->acl_ok =
26 dean.a.rasheed@gmail 5510 : 1479 : all_rows_selectable(root,
5511 : 1479 : index->rel->relid,
5512 : : NULL);
5513 : : }
5514 : : else
5515 : : {
5516 : : /* suppress leakproofness checks later */
3046 peter_e@gmx.net 5517 : 318 : vardata->acl_ok = true;
5518 : : }
5519 : : }
7872 tgl@sss.pgh.pa.us 5520 [ + + ]: 1797 : if (vardata->statsTuple)
5521 : 1479 : break;
5522 : : }
2245 5523 : 966 : indexpr_item = lnext(index->indexprs, indexpr_item);
5524 : : }
5525 : : }
7872 5526 [ + + ]: 2445 : if (vardata->statsTuple)
5527 : 1479 : break;
5528 : : }
5529 : :
5530 : : /*
5531 : : * Search extended statistics for one with a matching expression.
5532 : : * There might be multiple ones, so just grab the first one. In the
5533 : : * future, we might consider the statistics target (and pick the most
5534 : : * accurate statistics) and maybe some other parameters.
5535 : : */
1625 tomas.vondra@postgre 5536 [ + + + + : 31777 : foreach(slist, onerel->statlist)
+ + ]
5537 : : {
5538 : 2193 : StatisticExtInfo *info = (StatisticExtInfo *) lfirst(slist);
1213 tgl@sss.pgh.pa.us 5539 [ + - ]: 2193 : RangeTblEntry *rte = planner_rt_fetch(onerel->relid, root);
5540 : : ListCell *expr_item;
5541 : : int pos;
5542 : :
5543 : : /*
5544 : : * Stop once we've found statistics for the expression (either
5545 : : * from extended stats, or for an index in the preceding loop).
5546 : : */
1625 tomas.vondra@postgre 5547 [ + + ]: 2193 : if (vardata->statsTuple)
5548 : 144 : break;
5549 : :
5550 : : /* skip stats without per-expression stats */
5551 [ + + ]: 2049 : if (info->kind != STATS_EXT_EXPRESSIONS)
5552 : 1047 : continue;
5553 : :
5554 : : /* skip stats with mismatching stxdinherit value */
1040 tgl@sss.pgh.pa.us 5555 [ + + ]: 1002 : if (info->inherit != rte->inh)
5556 : 3 : continue;
5557 : :
1625 tomas.vondra@postgre 5558 : 999 : pos = 0;
5559 [ + - + + : 1650 : foreach(expr_item, info->exprs)
+ + ]
5560 : : {
5561 : 1473 : Node *expr = (Node *) lfirst(expr_item);
5562 : :
5563 [ - + ]: 1473 : Assert(expr);
5564 : :
5565 : : /* strip RelabelType before comparing it */
5566 [ + - - + ]: 1473 : if (expr && IsA(expr, RelabelType))
1625 tomas.vondra@postgre 5567 :UBC 0 : expr = (Node *) ((RelabelType *) expr)->arg;
5568 : :
5569 : : /* found a match, see if we can extract pg_statistic row */
1625 tomas.vondra@postgre 5570 [ + + ]:CBC 1473 : if (equal(node, expr))
5571 : : {
5572 : : /*
5573 : : * XXX Not sure if we should cache the tuple somewhere.
5574 : : * Now we just create a new copy every time.
5575 : : */
1329 5576 : 822 : vardata->statsTuple =
5577 : 822 : statext_expressions_load(info->statOid, rte->inh, pos);
5578 : :
5579 : 822 : vardata->freefunc = ReleaseDummy;
5580 : :
5581 : : /*
5582 : : * Test if user has permission to access all rows from the
5583 : : * table.
5584 : : *
5585 : : * For simplicity, we insist on the whole table being
5586 : : * selectable, rather than trying to identify which
5587 : : * column(s) the statistics object depends on.
5588 : : *
5589 : : * Note that for an inheritance child, permissions are
5590 : : * checked on the inheritance root parent, and whole-table
5591 : : * select privilege on the parent doesn't quite guarantee
5592 : : * that the user could read all columns of the child. But
5593 : : * in practice it's unlikely that any interesting security
5594 : : * violation could result from allowing access to the
5595 : : * expression stats, so we allow it anyway. See similar
5596 : : * code in examine_simple_variable() for additional
5597 : : * comments.
5598 : : */
26 dean.a.rasheed@gmail 5599 : 822 : vardata->acl_ok = all_rows_selectable(root,
5600 : : onerel->relid,
5601 : : NULL);
5602 : :
1625 tomas.vondra@postgre 5603 : 822 : break;
5604 : : }
5605 : :
5606 : 651 : pos++;
5607 : : }
5608 : : }
5609 : : }
5610 : :
247 rguo@postgresql.org 5611 : 420134 : bms_free(varnos);
5612 : : }
5613 : :
5614 : : /*
5615 : : * examine_simple_variable
5616 : : * Handle a simple Var for examine_variable
5617 : : *
5618 : : * This is split out as a subroutine so that we can recurse to deal with
5619 : : * Vars referencing subqueries (either sub-SELECT-in-FROM or CTE style).
5620 : : *
5621 : : * We already filled in all the fields of *vardata except for the stats tuple.
5622 : : */
5623 : : static void
5116 tgl@sss.pgh.pa.us 5624 : 1031571 : examine_simple_variable(PlannerInfo *root, Var *var,
5625 : : VariableStatData *vardata)
5626 : : {
5627 : 1031571 : RangeTblEntry *rte = root->simple_rte_array[var->varno];
5628 : :
5629 [ - + ]: 1031571 : Assert(IsA(rte, RangeTblEntry));
5630 : :
5631 [ - + - - ]: 1031571 : if (get_relation_stats_hook &&
5116 tgl@sss.pgh.pa.us 5632 :UBC 0 : (*get_relation_stats_hook) (root, rte, var->varattno, vardata))
5633 : : {
5634 : : /*
5635 : : * The hook took control of acquiring a stats tuple. If it did supply
5636 : : * a tuple, it'd better have supplied a freefunc.
5637 : : */
5638 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
5639 [ # # ]: 0 : !vardata->freefunc)
5640 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
5641 : : }
5116 tgl@sss.pgh.pa.us 5642 [ + + ]:CBC 1031571 : else if (rte->rtekind == RTE_RELATION)
5643 : : {
5644 : : /*
5645 : : * Plain table or parent of an inheritance appendrel, so look up the
5646 : : * column in pg_statistic
5647 : : */
5648 : 974472 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
5649 : : ObjectIdGetDatum(rte->relid),
5650 : 974472 : Int16GetDatum(var->varattno),
5651 : 974472 : BoolGetDatum(rte->inh));
5652 : 974472 : vardata->freefunc = ReleaseSysCache;
5653 : :
3046 peter_e@gmx.net 5654 [ + + ]: 974472 : if (HeapTupleIsValid(vardata->statsTuple))
5655 : : {
5656 : : /*
5657 : : * Test if user has permission to read all rows from this column.
5658 : : *
5659 : : * This requires that the user has the appropriate SELECT
5660 : : * privileges and that there are no securityQuals from security
5661 : : * barrier views or RLS policies. If that's not the case, then we
5662 : : * only permit leakproof functions to be passed pg_statistic data
5663 : : * in vardata, otherwise the functions might reveal data that the
5664 : : * user doesn't have permission to see --- see
5665 : : * statistic_proc_security_check().
5666 : : */
5667 : 753288 : vardata->acl_ok =
26 dean.a.rasheed@gmail 5668 : 753288 : all_rows_selectable(root, var->varno,
5669 : 753288 : bms_make_singleton(var->varattno - FirstLowInvalidHeapAttributeNumber));
5670 : : }
5671 : : else
5672 : : {
5673 : : /* suppress any possible leakproofness checks later */
3046 peter_e@gmx.net 5674 : 221184 : vardata->acl_ok = true;
5675 : : }
5676 : : }
659 tgl@sss.pgh.pa.us 5677 [ + + + + ]: 57099 : else if ((rte->rtekind == RTE_SUBQUERY && !rte->inh) ||
5678 [ + + + + ]: 52174 : (rte->rtekind == RTE_CTE && !rte->self_reference))
5679 : : {
5680 : : /*
5681 : : * Plain subquery (not one that was converted to an appendrel) or
5682 : : * non-recursive CTE. In either case, we can try to find out what the
5683 : : * Var refers to within the subquery. We skip this for appendrel and
5684 : : * recursive-CTE cases because any column stats we did find would
5685 : : * likely not be very relevant.
5686 : : */
5687 : : PlannerInfo *subroot;
5688 : : Query *subquery;
5689 : : List *subtlist;
5690 : : TargetEntry *ste;
5691 : :
5692 : : /*
5693 : : * Punt if it's a whole-row var rather than a plain column reference.
5694 : : */
4317 5695 [ - + ]: 8397 : if (var->varattno == InvalidAttrNumber)
4317 tgl@sss.pgh.pa.us 5696 :UBC 0 : return;
5697 : :
5698 : : /*
5699 : : * Otherwise, find the subquery's planner subroot.
5700 : : */
659 tgl@sss.pgh.pa.us 5701 [ + + ]:CBC 8397 : if (rte->rtekind == RTE_SUBQUERY)
5702 : : {
5703 : : RelOptInfo *rel;
5704 : :
5705 : : /*
5706 : : * Fetch RelOptInfo for subquery. Note that we don't change the
5707 : : * rel returned in vardata, since caller expects it to be a rel of
5708 : : * the caller's query level. Because we might already be
5709 : : * recursing, we can't use that rel pointer either, but have to
5710 : : * look up the Var's rel afresh.
5711 : : */
5712 : 4925 : rel = find_base_rel(root, var->varno);
5713 : :
5714 : 4925 : subroot = rel->subroot;
5715 : : }
5716 : : else
5717 : : {
5718 : : /* CTE case is more difficult */
5719 : : PlannerInfo *cteroot;
5720 : : Index levelsup;
5721 : : int ndx;
5722 : : int plan_id;
5723 : : ListCell *lc;
5724 : :
5725 : : /*
5726 : : * Find the referenced CTE, and locate the subroot previously made
5727 : : * for it.
5728 : : */
5729 : 3472 : levelsup = rte->ctelevelsup;
5730 : 3472 : cteroot = root;
5731 [ + + ]: 6467 : while (levelsup-- > 0)
5732 : : {
5733 : 2995 : cteroot = cteroot->parent_root;
5734 [ - + ]: 2995 : if (!cteroot) /* shouldn't happen */
659 tgl@sss.pgh.pa.us 5735 [ # # ]:UBC 0 : elog(ERROR, "bad levelsup for CTE \"%s\"", rte->ctename);
5736 : : }
5737 : :
5738 : : /*
5739 : : * Note: cte_plan_ids can be shorter than cteList, if we are still
5740 : : * working on planning the CTEs (ie, this is a side-reference from
5741 : : * another CTE). So we mustn't use forboth here.
5742 : : */
659 tgl@sss.pgh.pa.us 5743 :CBC 3472 : ndx = 0;
5744 [ + - + - : 4579 : foreach(lc, cteroot->parse->cteList)
+ - ]
5745 : : {
5746 : 4579 : CommonTableExpr *cte = (CommonTableExpr *) lfirst(lc);
5747 : :
5748 [ + + ]: 4579 : if (strcmp(cte->ctename, rte->ctename) == 0)
5749 : 3472 : break;
5750 : 1107 : ndx++;
5751 : : }
5752 [ - + ]: 3472 : if (lc == NULL) /* shouldn't happen */
659 tgl@sss.pgh.pa.us 5753 [ # # ]:UBC 0 : elog(ERROR, "could not find CTE \"%s\"", rte->ctename);
659 tgl@sss.pgh.pa.us 5754 [ - + ]:CBC 3472 : if (ndx >= list_length(cteroot->cte_plan_ids))
659 tgl@sss.pgh.pa.us 5755 [ # # ]:UBC 0 : elog(ERROR, "could not find plan for CTE \"%s\"", rte->ctename);
659 tgl@sss.pgh.pa.us 5756 :CBC 3472 : plan_id = list_nth_int(cteroot->cte_plan_ids, ndx);
5757 [ - + ]: 3472 : if (plan_id <= 0)
659 tgl@sss.pgh.pa.us 5758 [ # # ]:UBC 0 : elog(ERROR, "no plan was made for CTE \"%s\"", rte->ctename);
659 tgl@sss.pgh.pa.us 5759 :CBC 3472 : subroot = list_nth(root->glob->subroots, plan_id - 1);
5760 : : }
5761 : :
5762 : : /* If the subquery hasn't been planned yet, we have to punt */
5763 [ - + ]: 8397 : if (subroot == NULL)
659 tgl@sss.pgh.pa.us 5764 :UBC 0 : return;
659 tgl@sss.pgh.pa.us 5765 [ - + ]:CBC 8397 : Assert(IsA(subroot, PlannerInfo));
5766 : :
5767 : : /*
5768 : : * We must use the subquery parsetree as mangled by the planner, not
5769 : : * the raw version from the RTE, because we need a Var that will refer
5770 : : * to the subroot's live RelOptInfos. For instance, if any subquery
5771 : : * pullup happened during planning, Vars in the targetlist might have
5772 : : * gotten replaced, and we need to see the replacement expressions.
5773 : : */
5774 : 8397 : subquery = subroot->parse;
5775 [ - + ]: 8397 : Assert(IsA(subquery, Query));
5776 : :
5777 : : /*
5778 : : * Punt if subquery uses set operations or grouping sets, as these
5779 : : * will mash underlying columns' stats beyond recognition. (Set ops
5780 : : * are particularly nasty; if we forged ahead, we would return stats
5781 : : * relevant to only the leftmost subselect...) DISTINCT is also
5782 : : * problematic, but we check that later because there is a possibility
5783 : : * of learning something even with it.
5784 : : */
4951 5785 [ + + ]: 8397 : if (subquery->setOperations ||
1558 5786 [ + + ]: 7457 : subquery->groupingSets)
5007 rhaas@postgresql.org 5787 : 952 : return;
5788 : :
5789 : : /* Get the subquery output expression referenced by the upper Var */
659 tgl@sss.pgh.pa.us 5790 [ + + ]: 7445 : if (subquery->returningList)
5791 : 103 : subtlist = subquery->returningList;
5792 : : else
5793 : 7342 : subtlist = subquery->targetList;
5794 : 7445 : ste = get_tle_by_resno(subtlist, var->varattno);
5116 5795 [ + - - + ]: 7445 : if (ste == NULL || ste->resjunk)
5116 tgl@sss.pgh.pa.us 5796 [ # # ]:UBC 0 : elog(ERROR, "subquery %s does not have attribute %d",
5797 : : rte->eref->aliasname, var->varattno);
5116 tgl@sss.pgh.pa.us 5798 :CBC 7445 : var = (Var *) ste->expr;
5799 : :
5800 : : /*
5801 : : * If subquery uses DISTINCT, we can't make use of any stats for the
5802 : : * variable ... but, if it's the only DISTINCT column, we are entitled
5803 : : * to consider it unique. We do the test this way so that it works
5804 : : * for cases involving DISTINCT ON.
5805 : : */
4951 5806 [ + + ]: 7445 : if (subquery->distinctClause)
5807 : : {
5808 [ + + + + ]: 897 : if (list_length(subquery->distinctClause) == 1 &&
5809 : 297 : targetIsInSortList(ste, InvalidOid, subquery->distinctClause))
5810 : 150 : vardata->isunique = true;
5811 : : /* cannot go further */
5812 : 600 : return;
5813 : : }
5814 : :
5815 : : /* The same idea as with DISTINCT clause works for a GROUP-BY too */
199 akorotkov@postgresql 5816 [ + + ]: 6845 : if (subquery->groupClause)
5817 : : {
5818 [ + + + + ]: 520 : if (list_length(subquery->groupClause) == 1 &&
5819 : 215 : targetIsInSortList(ste, InvalidOid, subquery->groupClause))
5820 : 167 : vardata->isunique = true;
5821 : : /* cannot go further */
5822 : 305 : return;
5823 : : }
5824 : :
5825 : : /*
5826 : : * If the sub-query originated from a view with the security_barrier
5827 : : * attribute, we must not look at the variable's statistics, though it
5828 : : * seems all right to notice the existence of a DISTINCT clause. So
5829 : : * stop here.
5830 : : *
5831 : : * This is probably a harsher restriction than necessary; it's
5832 : : * certainly OK for the selectivity estimator (which is a C function,
5833 : : * and therefore omnipotent anyway) to look at the statistics. But
5834 : : * many selectivity estimators will happily *invoke the operator
5835 : : * function* to try to work out a good estimate - and that's not OK.
5836 : : * So for now, don't dig down for stats.
5837 : : */
4951 tgl@sss.pgh.pa.us 5838 [ + + ]: 6540 : if (rte->security_barrier)
5839 : 711 : return;
5840 : :
5841 : : /* Can only handle a simple Var of subquery's query level */
5116 5842 [ + - + + ]: 5829 : if (var && IsA(var, Var) &&
5843 [ + - ]: 3079 : var->varlevelsup == 0)
5844 : : {
5845 : : /*
5846 : : * OK, recurse into the subquery. Note that the original setting
5847 : : * of vardata->isunique (which will surely be false) is left
5848 : : * unchanged in this situation. That's what we want, since even
5849 : : * if the underlying column is unique, the subquery may have
5850 : : * joined to other tables in a way that creates duplicates.
5851 : : */
659 5852 : 3079 : examine_simple_variable(subroot, var, vardata);
5853 : : }
5854 : : }
5855 : : else
5856 : : {
5857 : : /*
5858 : : * Otherwise, the Var comes from a FUNCTION or VALUES RTE. (We won't
5859 : : * see RTE_JOIN here because join alias Vars have already been
5860 : : * flattened.) There's not much we can do with function outputs, but
5861 : : * maybe someday try to be smarter about VALUES.
5862 : : */
5863 : : }
5864 : : }
5865 : :
5866 : : /*
5867 : : * all_rows_selectable
5868 : : * Test whether the user has permission to select all rows from a given
5869 : : * relation.
5870 : : *
5871 : : * Inputs:
5872 : : * root: the planner info
5873 : : * varno: the index of the relation (assumed to be an RTE_RELATION)
5874 : : * varattnos: the attributes for which permission is required, or NULL if
5875 : : * whole-table access is required
5876 : : *
5877 : : * Returns true if the user has the required select permissions, and there are
5878 : : * no securityQuals from security barrier views or RLS policies.
5879 : : *
5880 : : * Note that if the relation is an inheritance child relation, securityQuals
5881 : : * and access permissions are checked against the inheritance root parent (the
5882 : : * relation actually mentioned in the query) --- see the comments in
5883 : : * expand_single_inheritance_child() for an explanation of why it has to be
5884 : : * done this way.
5885 : : *
5886 : : * If varattnos is non-NULL, its attribute numbers should be offset by
5887 : : * FirstLowInvalidHeapAttributeNumber so that system attributes can be
5888 : : * checked. If varattnos is NULL, only table-level SELECT privileges are
5889 : : * checked, not any column-level privileges.
5890 : : *
5891 : : * Note: if the relation is accessed via a view, this function actually tests
5892 : : * whether the view owner has permission to select from the relation. To
5893 : : * ensure that the current user has permission, it is also necessary to check
5894 : : * that the current user has permission to select from the view, which we do
5895 : : * at planner-startup --- see subquery_planner().
5896 : : *
5897 : : * This is exported so that other estimation functions can use it.
5898 : : */
5899 : : bool
26 dean.a.rasheed@gmail 5900 : 755715 : all_rows_selectable(PlannerInfo *root, Index varno, Bitmapset *varattnos)
5901 : : {
5902 : 755715 : RelOptInfo *rel = find_base_rel_noerr(root, varno);
5903 [ + - ]: 755715 : RangeTblEntry *rte = planner_rt_fetch(varno, root);
5904 : : Oid userid;
5905 : : int varattno;
5906 : :
5907 [ - + ]: 755715 : Assert(rte->rtekind == RTE_RELATION);
5908 : :
5909 : : /*
5910 : : * Determine the user ID to use for privilege checks (either the current
5911 : : * user or the view owner, if we're accessing the table via a view).
5912 : : *
5913 : : * Normally the relation will have an associated RelOptInfo from which we
5914 : : * can find the userid, but it might not if it's a RETURNING Var for an
5915 : : * INSERT target relation. In that case use the RTEPermissionInfo
5916 : : * associated with the RTE.
5917 : : *
5918 : : * If we navigate up to a parent relation, we keep using the same userid,
5919 : : * since it's the same in all relations of a given inheritance tree.
5920 : : */
5921 [ + + ]: 755715 : if (rel)
5922 : 755694 : userid = rel->userid;
5923 : : else
5924 : : {
5925 : : RTEPermissionInfo *perminfo;
5926 : :
5927 : 21 : perminfo = getRTEPermissionInfo(root->parse->rteperminfos, rte);
5928 : 21 : userid = perminfo->checkAsUser;
5929 : : }
5930 [ + + ]: 755715 : if (!OidIsValid(userid))
5931 : 701425 : userid = GetUserId();
5932 : :
5933 : : /*
5934 : : * Permissions and securityQuals must be checked on the table actually
5935 : : * mentioned in the query, so if this is an inheritance child, navigate up
5936 : : * to the inheritance root parent. If the user can read the whole table
5937 : : * or the required columns there, then they can read from the child table
5938 : : * too. For per-column checks, we must find out which of the root
5939 : : * parent's attributes the child relation's attributes correspond to.
5940 : : */
5941 [ + + ]: 755715 : if (root->append_rel_array != NULL)
5942 : : {
5943 : : AppendRelInfo *appinfo;
5944 : :
5945 : 70865 : appinfo = root->append_rel_array[varno];
5946 : :
5947 : : /*
5948 : : * Partitions are mapped to their immediate parent, not the root
5949 : : * parent, so must be ready to walk up multiple AppendRelInfos. But
5950 : : * stop if we hit a parent that is not RTE_RELATION --- that's a
5951 : : * flattened UNION ALL subquery, not an inheritance parent.
5952 : : */
5953 [ + + ]: 110129 : while (appinfo &&
5954 [ + - ]: 39450 : planner_rt_fetch(appinfo->parent_relid,
5955 [ + + ]: 39450 : root)->rtekind == RTE_RELATION)
5956 : : {
5957 : 39264 : Bitmapset *parent_varattnos = NULL;
5958 : :
5959 : : /*
5960 : : * For each child attribute, find the corresponding parent
5961 : : * attribute. In rare cases, the attribute may be local to the
5962 : : * child table, in which case, we've got to live with having no
5963 : : * access to this column.
5964 : : */
5965 : 39264 : varattno = -1;
5966 [ + + ]: 77103 : while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
5967 : : {
5968 : : AttrNumber attno;
5969 : : AttrNumber parent_attno;
5970 : :
5971 : 37839 : attno = varattno + FirstLowInvalidHeapAttributeNumber;
5972 : :
5973 [ + + ]: 37839 : if (attno == InvalidAttrNumber)
5974 : : {
5975 : : /*
5976 : : * Whole-row reference, so must map each column of the
5977 : : * child to the parent table.
5978 : : */
5979 [ + + ]: 18 : for (attno = 1; attno <= appinfo->num_child_cols; attno++)
5980 : : {
5981 : 12 : parent_attno = appinfo->parent_colnos[attno - 1];
5982 [ - + ]: 12 : if (parent_attno == 0)
26 dean.a.rasheed@gmail 5983 :UBC 0 : return false; /* attr is local to child */
5984 : : parent_varattnos =
26 dean.a.rasheed@gmail 5985 :CBC 12 : bms_add_member(parent_varattnos,
5986 : : parent_attno - FirstLowInvalidHeapAttributeNumber);
5987 : : }
5988 : : }
5989 : : else
5990 : : {
5991 [ - + ]: 37833 : if (attno < 0)
5992 : : {
5993 : : /* System attnos are the same in all tables */
26 dean.a.rasheed@gmail 5994 :UBC 0 : parent_attno = attno;
5995 : : }
5996 : : else
5997 : : {
26 dean.a.rasheed@gmail 5998 [ - + ]:CBC 37833 : if (attno > appinfo->num_child_cols)
26 dean.a.rasheed@gmail 5999 :UBC 0 : return false; /* safety check */
26 dean.a.rasheed@gmail 6000 :CBC 37833 : parent_attno = appinfo->parent_colnos[attno - 1];
6001 [ - + ]: 37833 : if (parent_attno == 0)
26 dean.a.rasheed@gmail 6002 :UBC 0 : return false; /* attr is local to child */
6003 : : }
6004 : : parent_varattnos =
26 dean.a.rasheed@gmail 6005 :CBC 37833 : bms_add_member(parent_varattnos,
6006 : : parent_attno - FirstLowInvalidHeapAttributeNumber);
6007 : : }
6008 : : }
6009 : :
6010 : : /* If the parent is itself a child, continue up */
6011 : 39264 : varno = appinfo->parent_relid;
6012 : 39264 : varattnos = parent_varattnos;
6013 : 39264 : appinfo = root->append_rel_array[varno];
6014 : : }
6015 : :
6016 : : /* Perform the access check on this parent rel */
6017 [ + - ]: 70865 : rte = planner_rt_fetch(varno, root);
6018 [ - + ]: 70865 : Assert(rte->rtekind == RTE_RELATION);
6019 : : }
6020 : :
6021 : : /*
6022 : : * For all rows to be accessible, there must be no securityQuals from
6023 : : * security barrier views or RLS policies.
6024 : : */
6025 [ + + ]: 755715 : if (rte->securityQuals != NIL)
6026 : 414 : return false;
6027 : :
6028 : : /*
6029 : : * Test for table-level SELECT privilege.
6030 : : *
6031 : : * If varattnos is non-NULL, this is sufficient to give access to all
6032 : : * requested attributes, even for a child table, since we have verified
6033 : : * that all required child columns have matching parent columns.
6034 : : *
6035 : : * If varattnos is NULL (whole-table access requested), this doesn't
6036 : : * necessarily guarantee that the user can read all columns of a child
6037 : : * table, but we allow it anyway (see comments in examine_variable()) and
6038 : : * don't bother checking any column privileges.
6039 : : */
6040 [ + + ]: 755301 : if (pg_class_aclcheck(rte->relid, userid, ACL_SELECT) == ACLCHECK_OK)
6041 : 755075 : return true;
6042 : :
6043 [ + + ]: 226 : if (varattnos == NULL)
6044 : 6 : return false; /* whole-table access requested */
6045 : :
6046 : : /*
6047 : : * Don't have table-level SELECT privilege, so check per-column
6048 : : * privileges.
6049 : : */
6050 : 220 : varattno = -1;
6051 [ + + ]: 323 : while ((varattno = bms_next_member(varattnos, varattno)) >= 0)
6052 : : {
6053 : 220 : AttrNumber attno = varattno + FirstLowInvalidHeapAttributeNumber;
6054 : :
6055 [ + + ]: 220 : if (attno == InvalidAttrNumber)
6056 : : {
6057 : : /* Whole-row reference, so must have access to all columns */
6058 [ + - ]: 3 : if (pg_attribute_aclcheck_all(rte->relid, userid, ACL_SELECT,
6059 : : ACLMASK_ALL) != ACLCHECK_OK)
6060 : 3 : return false;
6061 : : }
6062 : : else
6063 : : {
6064 [ + + ]: 217 : if (pg_attribute_aclcheck(rte->relid, attno, userid,
6065 : : ACL_SELECT) != ACLCHECK_OK)
6066 : 114 : return false;
6067 : : }
6068 : : }
6069 : :
6070 : : /* If we reach here, have all required column privileges */
6071 : 103 : return true;
6072 : : }
6073 : :
6074 : : /*
6075 : : * examine_indexcol_variable
6076 : : * Try to look up statistical data about an index column/expression.
6077 : : * Fill in a VariableStatData struct to describe the column.
6078 : : *
6079 : : * Inputs:
6080 : : * root: the planner info
6081 : : * index: the index whose column we're interested in
6082 : : * indexcol: 0-based index column number (subscripts index->indexkeys[])
6083 : : *
6084 : : * Outputs: *vardata is filled as follows:
6085 : : * var: the input expression (with any binary relabeling stripped, if
6086 : : * it is or contains a variable; but otherwise the type is preserved)
6087 : : * rel: RelOptInfo for table relation containing variable.
6088 : : * statsTuple: the pg_statistic entry for the variable, if one exists;
6089 : : * otherwise NULL.
6090 : : * freefunc: pointer to a function to release statsTuple with.
6091 : : *
6092 : : * Caller is responsible for doing ReleaseVariableStats() before exiting.
6093 : : */
6094 : : static void
155 pg@bowt.ie 6095 : 373536 : examine_indexcol_variable(PlannerInfo *root, IndexOptInfo *index,
6096 : : int indexcol, VariableStatData *vardata)
6097 : : {
6098 : : AttrNumber colnum;
6099 : : Oid relid;
6100 : :
6101 [ + + ]: 373536 : if (index->indexkeys[indexcol] != 0)
6102 : : {
6103 : : /* Simple variable --- look to stats for the underlying table */
6104 [ + - ]: 372441 : RangeTblEntry *rte = planner_rt_fetch(index->rel->relid, root);
6105 : :
6106 [ - + ]: 372441 : Assert(rte->rtekind == RTE_RELATION);
6107 : 372441 : relid = rte->relid;
6108 [ - + ]: 372441 : Assert(relid != InvalidOid);
6109 : 372441 : colnum = index->indexkeys[indexcol];
6110 : 372441 : vardata->rel = index->rel;
6111 : :
6112 [ - + - - ]: 372441 : if (get_relation_stats_hook &&
155 pg@bowt.ie 6113 :UBC 0 : (*get_relation_stats_hook) (root, rte, colnum, vardata))
6114 : : {
6115 : : /*
6116 : : * The hook took control of acquiring a stats tuple. If it did
6117 : : * supply a tuple, it'd better have supplied a freefunc.
6118 : : */
6119 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6120 [ # # ]: 0 : !vardata->freefunc)
6121 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6122 : : }
6123 : : else
6124 : : {
155 pg@bowt.ie 6125 :CBC 372441 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6126 : : ObjectIdGetDatum(relid),
6127 : : Int16GetDatum(colnum),
6128 : 372441 : BoolGetDatum(rte->inh));
6129 : 372441 : vardata->freefunc = ReleaseSysCache;
6130 : : }
6131 : : }
6132 : : else
6133 : : {
6134 : : /* Expression --- maybe there are stats for the index itself */
6135 : 1095 : relid = index->indexoid;
6136 : 1095 : colnum = indexcol + 1;
6137 : :
6138 [ - + - - ]: 1095 : if (get_index_stats_hook &&
155 pg@bowt.ie 6139 :UBC 0 : (*get_index_stats_hook) (root, relid, colnum, vardata))
6140 : : {
6141 : : /*
6142 : : * The hook took control of acquiring a stats tuple. If it did
6143 : : * supply a tuple, it'd better have supplied a freefunc.
6144 : : */
6145 [ # # ]: 0 : if (HeapTupleIsValid(vardata->statsTuple) &&
6146 [ # # ]: 0 : !vardata->freefunc)
6147 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
6148 : : }
6149 : : else
6150 : : {
155 pg@bowt.ie 6151 :CBC 1095 : vardata->statsTuple = SearchSysCache3(STATRELATTINH,
6152 : : ObjectIdGetDatum(relid),
6153 : : Int16GetDatum(colnum),
6154 : : BoolGetDatum(false));
6155 : 1095 : vardata->freefunc = ReleaseSysCache;
6156 : : }
6157 : : }
6158 : 373536 : }
6159 : :
6160 : : /*
6161 : : * Check whether it is permitted to call func_oid passing some of the
6162 : : * pg_statistic data in vardata. We allow this if either of the following
6163 : : * conditions is met: (1) the user has SELECT privileges on the table or
6164 : : * column underlying the pg_statistic data and there are no securityQuals from
6165 : : * security barrier views or RLS policies, or (2) the function is marked
6166 : : * leakproof.
6167 : : */
6168 : : bool
3046 peter_e@gmx.net 6169 : 489479 : statistic_proc_security_check(VariableStatData *vardata, Oid func_oid)
6170 : : {
6171 [ + + ]: 489479 : if (vardata->acl_ok)
26 dean.a.rasheed@gmail 6172 : 488590 : return true; /* have SELECT privs and no securityQuals */
6173 : :
3046 peter_e@gmx.net 6174 [ - + ]: 889 : if (!OidIsValid(func_oid))
3046 peter_e@gmx.net 6175 :UBC 0 : return false;
6176 : :
3046 peter_e@gmx.net 6177 [ + + ]:CBC 889 : if (get_func_leakproof(func_oid))
6178 : 440 : return true;
6179 : :
6180 [ - + ]: 449 : ereport(DEBUG2,
6181 : : (errmsg_internal("not using statistics because function \"%s\" is not leakproof",
6182 : : get_func_name(func_oid))));
6183 : 449 : return false;
6184 : : }
6185 : :
6186 : : /*
6187 : : * get_variable_numdistinct
6188 : : * Estimate the number of distinct values of a variable.
6189 : : *
6190 : : * vardata: results of examine_variable
6191 : : * *isdefault: set to true if the result is a default rather than based on
6192 : : * anything meaningful.
6193 : : *
6194 : : * NB: be careful to produce a positive integral result, since callers may
6195 : : * compare the result to exact integer counts, or might divide by it.
6196 : : */
6197 : : double
5116 tgl@sss.pgh.pa.us 6198 : 717713 : get_variable_numdistinct(VariableStatData *vardata, bool *isdefault)
6199 : : {
6200 : : double stadistinct;
3317 6201 : 717713 : double stanullfrac = 0.0;
6202 : : double ntuples;
6203 : :
5116 6204 : 717713 : *isdefault = false;
6205 : :
6206 : : /*
6207 : : * Determine the stadistinct value to use. There are cases where we can
6208 : : * get an estimate even without a pg_statistic entry, or can get a better
6209 : : * value than is in pg_statistic. Grab stanullfrac too if we can find it
6210 : : * (otherwise, assume no nulls, for lack of any better idea).
6211 : : */
7872 6212 [ + + ]: 717713 : if (HeapTupleIsValid(vardata->statsTuple))
6213 : : {
6214 : : /* Use the pg_statistic entry */
6215 : : Form_pg_statistic stats;
6216 : :
6217 : 527964 : stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple);
6218 : 527964 : stadistinct = stats->stadistinct;
3317 6219 : 527964 : stanullfrac = stats->stanullfrac;
6220 : : }
7463 6221 [ + + ]: 189749 : else if (vardata->vartype == BOOLOID)
6222 : : {
6223 : : /*
6224 : : * Special-case boolean columns: presumably, two distinct values.
6225 : : *
6226 : : * Are there any other datatypes we should wire in special estimates
6227 : : * for?
6228 : : */
7872 6229 : 296 : stadistinct = 2.0;
6230 : : }
2943 6231 [ + + + + ]: 189453 : else if (vardata->rel && vardata->rel->rtekind == RTE_VALUES)
6232 : : {
6233 : : /*
6234 : : * If the Var represents a column of a VALUES RTE, assume it's unique.
6235 : : * This could of course be very wrong, but it should tend to be true
6236 : : * in well-written queries. We could consider examining the VALUES'
6237 : : * contents to get some real statistics; but that only works if the
6238 : : * entries are all constants, and it would be pretty expensive anyway.
6239 : : */
6240 : 1733 : stadistinct = -1.0; /* unique (and all non null) */
6241 : : }
6242 : : else
6243 : : {
6244 : : /*
6245 : : * We don't keep statistics for system columns, but in some cases we
6246 : : * can infer distinctness anyway.
6247 : : */
7872 6248 [ + + + + ]: 187720 : if (vardata->var && IsA(vardata->var, Var))
6249 : : {
6250 [ + + + ]: 172495 : switch (((Var *) vardata->var)->varattno)
6251 : : {
6252 : 595 : case SelfItemPointerAttributeNumber:
3317 6253 : 595 : stadistinct = -1.0; /* unique (and all non null) */
7872 6254 : 595 : break;
6255 : 5134 : case TableOidAttributeNumber:
7678 bruce@momjian.us 6256 : 5134 : stadistinct = 1.0; /* only 1 value */
7872 tgl@sss.pgh.pa.us 6257 : 5134 : break;
6258 : 166766 : default:
7678 bruce@momjian.us 6259 : 166766 : stadistinct = 0.0; /* means "unknown" */
7872 tgl@sss.pgh.pa.us 6260 : 166766 : break;
6261 : : }
6262 : : }
6263 : : else
7678 bruce@momjian.us 6264 : 15225 : stadistinct = 0.0; /* means "unknown" */
6265 : :
6266 : : /*
6267 : : * XXX consider using estimate_num_groups on expressions?
6268 : : */
6269 : : }
6270 : :
6271 : : /*
6272 : : * If there is a unique index, DISTINCT or GROUP-BY clause for the
6273 : : * variable, assume it is unique no matter what pg_statistic says; the
6274 : : * statistics could be out of date, or we might have found a partial
6275 : : * unique index that proves the var is unique for this query. However,
6276 : : * we'd better still believe the null-fraction statistic.
6277 : : */
6047 tgl@sss.pgh.pa.us 6278 [ + + ]: 717713 : if (vardata->isunique)
3317 6279 : 188403 : stadistinct = -1.0 * (1.0 - stanullfrac);
6280 : :
6281 : : /*
6282 : : * If we had an absolute estimate, use that.
6283 : : */
7872 6284 [ + + ]: 717713 : if (stadistinct > 0.0)
3691 6285 : 163884 : return clamp_row_est(stadistinct);
6286 : :
6287 : : /*
6288 : : * Otherwise we need to get the relation size; punt if not available.
6289 : : */
7872 6290 [ + + ]: 553829 : if (vardata->rel == NULL)
6291 : : {
5116 6292 : 208 : *isdefault = true;
7872 6293 : 208 : return DEFAULT_NUM_DISTINCT;
6294 : : }
6295 : 553621 : ntuples = vardata->rel->tuples;
6296 [ + + ]: 553621 : if (ntuples <= 0.0)
6297 : : {
5116 6298 : 24780 : *isdefault = true;
7872 6299 : 24780 : return DEFAULT_NUM_DISTINCT;
6300 : : }
6301 : :
6302 : : /*
6303 : : * If we had a relative estimate, use that.
6304 : : */
6305 [ + + ]: 528841 : if (stadistinct < 0.0)
3691 6306 : 388052 : return clamp_row_est(-stadistinct * ntuples);
6307 : :
6308 : : /*
6309 : : * With no data, estimate ndistinct = ntuples if the table is small, else
6310 : : * use default. We use DEFAULT_NUM_DISTINCT as the cutoff for "small" so
6311 : : * that the behavior isn't discontinuous.
6312 : : */
7872 6313 [ + + ]: 140789 : if (ntuples < DEFAULT_NUM_DISTINCT)
3691 6314 : 64823 : return clamp_row_est(ntuples);
6315 : :
5116 6316 : 75966 : *isdefault = true;
7872 6317 : 75966 : return DEFAULT_NUM_DISTINCT;
6318 : : }
6319 : :
6320 : : /*
6321 : : * get_variable_range
6322 : : * Estimate the minimum and maximum value of the specified variable.
6323 : : * If successful, store values in *min and *max, and return true.
6324 : : * If no data available, return false.
6325 : : *
6326 : : * sortop is the "<" comparison operator to use. This should generally
6327 : : * be "<" not ">", as only the former is likely to be found in pg_statistic.
6328 : : * The collation must be specified too.
6329 : : */
6330 : : static bool
1919 6331 : 102976 : get_variable_range(PlannerInfo *root, VariableStatData *vardata,
6332 : : Oid sortop, Oid collation,
6333 : : Datum *min, Datum *max)
6334 : : {
6482 6335 : 102976 : Datum tmin = 0;
7872 6336 : 102976 : Datum tmax = 0;
6482 6337 : 102976 : bool have_data = false;
6338 : : int16 typLen;
6339 : : bool typByVal;
6340 : : Oid opfuncoid;
6341 : : FmgrInfo opproc;
6342 : : AttStatsSlot sslot;
6343 : :
6344 : : /*
6345 : : * XXX It's very tempting to try to use the actual column min and max, if
6346 : : * we can get them relatively-cheaply with an index probe. However, since
6347 : : * this function is called many times during join planning, that could
6348 : : * have unpleasant effects on planning speed. Need more investigation
6349 : : * before enabling this.
6350 : : */
6351 : : #ifdef NOT_USED
6352 : : if (get_actual_variable_range(root, vardata, sortop, collation, min, max))
6353 : : return true;
6354 : : #endif
6355 : :
7872 6356 [ + + ]: 102976 : if (!HeapTupleIsValid(vardata->statsTuple))
6357 : : {
6358 : : /* no stats available, so default result */
6359 : 21655 : return false;
6360 : : }
6361 : :
6362 : : /*
6363 : : * If we can't apply the sortop to the stats data, just fail. In
6364 : : * principle, if there's a histogram and no MCVs, we could return the
6365 : : * histogram endpoints without ever applying the sortop ... but it's
6366 : : * probably not worth trying, because whatever the caller wants to do with
6367 : : * the endpoints would likely fail the security check too.
6368 : : */
3046 peter_e@gmx.net 6369 [ - + ]: 81321 : if (!statistic_proc_security_check(vardata,
6370 : 81321 : (opfuncoid = get_opcode(sortop))))
3046 peter_e@gmx.net 6371 :UBC 0 : return false;
6372 : :
1919 tgl@sss.pgh.pa.us 6373 :CBC 81321 : opproc.fn_oid = InvalidOid; /* mark this as not looked up yet */
6374 : :
7872 6375 : 81321 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
6376 : :
6377 : : /*
6378 : : * If there is a histogram with the ordering we want, grab the first and
6379 : : * last values.
6380 : : */
3038 6381 [ + + ]: 81321 : if (get_attstatsslot(&sslot, vardata->statsTuple,
6382 : : STATISTIC_KIND_HISTOGRAM, sortop,
6383 : : ATTSTATSSLOT_VALUES))
6384 : : {
1919 6385 [ + - + - ]: 60706 : if (sslot.stacoll == collation && sslot.nvalues > 0)
6386 : : {
3038 6387 : 60706 : tmin = datumCopy(sslot.values[0], typByVal, typLen);
6388 : 60706 : tmax = datumCopy(sslot.values[sslot.nvalues - 1], typByVal, typLen);
6482 6389 : 60706 : have_data = true;
6390 : : }
3038 6391 : 60706 : free_attstatsslot(&sslot);
6392 : : }
6393 : :
6394 : : /*
6395 : : * Otherwise, if there is a histogram with some other ordering, scan it
6396 : : * and get the min and max values according to the ordering we want. This
6397 : : * of course may not find values that are really extremal according to our
6398 : : * ordering, but it beats ignoring available data.
6399 : : */
1919 6400 [ + + - + ]: 101936 : if (!have_data &&
6401 : 20615 : get_attstatsslot(&sslot, vardata->statsTuple,
6402 : : STATISTIC_KIND_HISTOGRAM, InvalidOid,
6403 : : ATTSTATSSLOT_VALUES))
6404 : : {
1919 tgl@sss.pgh.pa.us 6405 :UBC 0 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
6406 : : collation, typLen, typByVal,
6407 : : &tmin, &tmax, &have_data);
3038 6408 : 0 : free_attstatsslot(&sslot);
6409 : : }
6410 : :
6411 : : /*
6412 : : * If we have most-common-values info, look for extreme MCVs. This is
6413 : : * needed even if we also have a histogram, since the histogram excludes
6414 : : * the MCVs. However, if we *only* have MCVs and no histogram, we should
6415 : : * be pretty wary of deciding that that is a full representation of the
6416 : : * data. Proceed only if the MCVs represent the whole table (to within
6417 : : * roundoff error).
6418 : : */
3038 tgl@sss.pgh.pa.us 6419 [ + + ]:CBC 81321 : if (get_attstatsslot(&sslot, vardata->statsTuple,
6420 : : STATISTIC_KIND_MCV, InvalidOid,
1436 6421 [ + + ]: 81321 : have_data ? ATTSTATSSLOT_VALUES :
6422 : : (ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)))
6423 : : {
6424 : 40048 : bool use_mcvs = have_data;
6425 : :
6426 [ + + ]: 40048 : if (!have_data)
6427 : : {
6428 : 19914 : double sumcommon = 0.0;
6429 : : double nullfrac;
6430 : : int i;
6431 : :
6432 [ + + ]: 146515 : for (i = 0; i < sslot.nnumbers; i++)
6433 : 126601 : sumcommon += sslot.numbers[i];
6434 : 19914 : nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata->statsTuple))->stanullfrac;
6435 [ + + ]: 19914 : if (sumcommon + nullfrac > 0.99999)
6436 : 18561 : use_mcvs = true;
6437 : : }
6438 : :
6439 [ + + ]: 40048 : if (use_mcvs)
6440 : 38695 : get_stats_slot_range(&sslot, opfuncoid, &opproc,
6441 : : collation, typLen, typByVal,
6442 : : &tmin, &tmax, &have_data);
3038 6443 : 40048 : free_attstatsslot(&sslot);
6444 : : }
6445 : :
6482 6446 : 81321 : *min = tmin;
7872 6447 : 81321 : *max = tmax;
6482 6448 : 81321 : return have_data;
6449 : : }
6450 : :
6451 : : /*
6452 : : * get_stats_slot_range: scan sslot for min/max values
6453 : : *
6454 : : * Subroutine for get_variable_range: update min/max/have_data according
6455 : : * to what we find in the statistics array.
6456 : : */
6457 : : static void
1919 6458 : 38695 : get_stats_slot_range(AttStatsSlot *sslot, Oid opfuncoid, FmgrInfo *opproc,
6459 : : Oid collation, int16 typLen, bool typByVal,
6460 : : Datum *min, Datum *max, bool *p_have_data)
6461 : : {
6462 : 38695 : Datum tmin = *min;
6463 : 38695 : Datum tmax = *max;
6464 : 38695 : bool have_data = *p_have_data;
6465 : 38695 : bool found_tmin = false;
6466 : 38695 : bool found_tmax = false;
6467 : :
6468 : : /* Look up the comparison function, if we didn't already do so */
6469 [ + - ]: 38695 : if (opproc->fn_oid != opfuncoid)
6470 : 38695 : fmgr_info(opfuncoid, opproc);
6471 : :
6472 : : /* Scan all the slot's values */
6473 [ + + ]: 1183935 : for (int i = 0; i < sslot->nvalues; i++)
6474 : : {
6475 [ + + ]: 1145240 : if (!have_data)
6476 : : {
6477 : 18561 : tmin = tmax = sslot->values[i];
6478 : 18561 : found_tmin = found_tmax = true;
6479 : 18561 : *p_have_data = have_data = true;
6480 : 18561 : continue;
6481 : : }
6482 [ + + ]: 1126679 : if (DatumGetBool(FunctionCall2Coll(opproc,
6483 : : collation,
6484 : 1126679 : sslot->values[i], tmin)))
6485 : : {
6486 : 28105 : tmin = sslot->values[i];
6487 : 28105 : found_tmin = true;
6488 : : }
6489 [ + + ]: 1126679 : if (DatumGetBool(FunctionCall2Coll(opproc,
6490 : : collation,
6491 : 1126679 : tmax, sslot->values[i])))
6492 : : {
6493 : 53131 : tmax = sslot->values[i];
6494 : 53131 : found_tmax = true;
6495 : : }
6496 : : }
6497 : :
6498 : : /*
6499 : : * Copy the slot's values, if we found new extreme values.
6500 : : */
6501 [ + + ]: 38695 : if (found_tmin)
6502 : 30986 : *min = datumCopy(tmin, typByVal, typLen);
6503 [ + + ]: 38695 : if (found_tmax)
6504 : 21714 : *max = datumCopy(tmax, typByVal, typLen);
6505 : 38695 : }
6506 : :
6507 : :
6508 : : /*
6509 : : * get_actual_variable_range
6510 : : * Attempt to identify the current *actual* minimum and/or maximum
6511 : : * of the specified variable, by looking for a suitable btree index
6512 : : * and fetching its low and/or high values.
6513 : : * If successful, store values in *min and *max, and return true.
6514 : : * (Either pointer can be NULL if that endpoint isn't needed.)
6515 : : * If unsuccessful, return false.
6516 : : *
6517 : : * sortop is the "<" comparison operator to use.
6518 : : * collation is the required collation.
6519 : : */
6520 : : static bool
5724 6521 : 93632 : get_actual_variable_range(PlannerInfo *root, VariableStatData *vardata,
6522 : : Oid sortop, Oid collation,
6523 : : Datum *min, Datum *max)
6524 : : {
6525 : 93632 : bool have_data = false;
6526 : 93632 : RelOptInfo *rel = vardata->rel;
6527 : : RangeTblEntry *rte;
6528 : : ListCell *lc;
6529 : :
6530 : : /* No hope if no relation or it doesn't have indexes */
6531 [ + - + + ]: 93632 : if (rel == NULL || rel->indexlist == NIL)
6532 : 6742 : return false;
6533 : : /* If it has indexes it must be a plain relation */
6534 : 86890 : rte = root->simple_rte_array[rel->relid];
6535 [ - + ]: 86890 : Assert(rte->rtekind == RTE_RELATION);
6536 : :
6537 : : /* ignore partitioned tables. Any indexes here are not real indexes */
971 drowley@postgresql.o 6538 [ + + ]: 86890 : if (rte->relkind == RELKIND_PARTITIONED_TABLE)
6539 : 438 : return false;
6540 : :
6541 : : /* Search through the indexes to see if any match our problem */
5724 tgl@sss.pgh.pa.us 6542 [ + - + + : 170008 : foreach(lc, rel->indexlist)
+ + ]
6543 : : {
6544 : 145521 : IndexOptInfo *index = (IndexOptInfo *) lfirst(lc);
6545 : : ScanDirection indexscandir;
6546 : : StrategyNumber strategy;
6547 : :
6548 : : /* Ignore non-ordering indexes */
155 peter@eisentraut.org 6549 [ - + ]: 145521 : if (index->sortopfamily == NULL)
155 peter@eisentraut.org 6550 :UBC 0 : continue;
6551 : :
6552 : : /*
6553 : : * Ignore partial indexes --- we only want stats that cover the entire
6554 : : * relation.
6555 : : */
5724 tgl@sss.pgh.pa.us 6556 [ + + ]:CBC 145521 : if (index->indpred != NIL)
6557 : 144 : continue;
6558 : :
6559 : : /*
6560 : : * The index list might include hypothetical indexes inserted by a
6561 : : * get_relation_info hook --- don't try to access them.
6562 : : */
5316 6563 [ - + ]: 145377 : if (index->hypothetical)
5724 tgl@sss.pgh.pa.us 6564 :UBC 0 : continue;
6565 : :
6566 : : /*
6567 : : * The first index column must match the desired variable, sortop, and
6568 : : * collation --- but we can use a descending-order index.
6569 : : */
1919 tgl@sss.pgh.pa.us 6570 [ + + ]:CBC 145377 : if (collation != index->indexcollations[0])
6571 : 19226 : continue; /* test first 'cause it's cheapest */
5724 6572 [ + + ]: 126151 : if (!match_index_to_operand(vardata->var, 0, index))
6573 : 64186 : continue;
155 peter@eisentraut.org 6574 : 61965 : strategy = get_op_opfamily_strategy(sortop, index->sortopfamily[0]);
6575 [ + - - ]: 61965 : switch (IndexAmTranslateStrategy(strategy, index->relam, index->sortopfamily[0], true))
6576 : : {
6577 : 61965 : case COMPARE_LT:
5395 tgl@sss.pgh.pa.us 6578 [ - + ]: 61965 : if (index->reverse_sort[0])
5395 tgl@sss.pgh.pa.us 6579 :UBC 0 : indexscandir = BackwardScanDirection;
6580 : : else
5395 tgl@sss.pgh.pa.us 6581 :CBC 61965 : indexscandir = ForwardScanDirection;
6582 : 61965 : break;
155 peter@eisentraut.org 6583 :UBC 0 : case COMPARE_GT:
5395 tgl@sss.pgh.pa.us 6584 [ # # ]: 0 : if (index->reverse_sort[0])
6585 : 0 : indexscandir = ForwardScanDirection;
6586 : : else
6587 : 0 : indexscandir = BackwardScanDirection;
6588 : 0 : break;
6589 : 0 : default:
6590 : : /* index doesn't match the sortop */
6591 : 0 : continue;
6592 : : }
6593 : :
6594 : : /*
6595 : : * Found a suitable index to extract data from. Set up some data that
6596 : : * can be used by both invocations of get_actual_variable_endpoint.
6597 : : */
6598 : : {
6599 : : MemoryContext tmpcontext;
6600 : : MemoryContext oldcontext;
6601 : : Relation heapRel;
6602 : : Relation indexRel;
6603 : : TupleTableSlot *slot;
6604 : : int16 typLen;
6605 : : bool typByVal;
6606 : : ScanKeyData scankeys[1];
6607 : :
6608 : : /* Make sure any cruft gets recycled when we're done */
2248 tgl@sss.pgh.pa.us 6609 :CBC 61965 : tmpcontext = AllocSetContextCreate(CurrentMemoryContext,
6610 : : "get_actual_variable_range workspace",
6611 : : ALLOCSET_DEFAULT_SIZES);
5724 6612 : 61965 : oldcontext = MemoryContextSwitchTo(tmpcontext);
6613 : :
6614 : : /*
6615 : : * Open the table and index so we can read from them. We should
6616 : : * already have some type of lock on each.
6617 : : */
2420 andres@anarazel.de 6618 : 61965 : heapRel = table_open(rte->relid, NoLock);
2347 tgl@sss.pgh.pa.us 6619 : 61965 : indexRel = index_open(index->indexoid, NoLock);
6620 : :
6621 : : /* build some stuff needed for indexscan execution */
2371 andres@anarazel.de 6622 : 61965 : slot = table_slot_create(heapRel, NULL);
5724 tgl@sss.pgh.pa.us 6623 : 61965 : get_typlenbyval(vardata->atttype, &typLen, &typByVal);
6624 : :
6625 : : /* set up an IS NOT NULL scan key so that we ignore nulls */
6626 : 61965 : ScanKeyEntryInitialize(&scankeys[0],
6627 : : SK_ISNULL | SK_SEARCHNOTNULL,
6628 : : 1, /* index col to scan */
6629 : : InvalidStrategy, /* no strategy */
6630 : : InvalidOid, /* no strategy subtype */
6631 : : InvalidOid, /* no collation */
6632 : : InvalidOid, /* no reg proc for this */
6633 : : (Datum) 0); /* constant */
6634 : :
6635 : : /* If min is requested ... */
6636 [ + + ]: 61965 : if (min)
6637 : : {
2248 6638 : 34712 : have_data = get_actual_variable_endpoint(heapRel,
6639 : : indexRel,
6640 : : indexscandir,
6641 : : scankeys,
6642 : : typLen,
6643 : : typByVal,
6644 : : slot,
6645 : : oldcontext,
6646 : : min);
6647 : : }
6648 : : else
6649 : : {
6650 : : /* If min not requested, still want to fetch max */
6651 : 27253 : have_data = true;
6652 : : }
6653 : :
6654 : : /* If max is requested, and we didn't already fail ... */
5724 6655 [ + + + - ]: 61965 : if (max && have_data)
6656 : : {
6657 : : /* scan in the opposite direction; all else is the same */
2248 6658 : 28059 : have_data = get_actual_variable_endpoint(heapRel,
6659 : : indexRel,
6660 : 28059 : -indexscandir,
6661 : : scankeys,
6662 : : typLen,
6663 : : typByVal,
6664 : : slot,
6665 : : oldcontext,
6666 : : max);
6667 : : }
6668 : :
6669 : : /* Clean everything up */
5724 6670 : 61965 : ExecDropSingleTupleTableSlot(slot);
6671 : :
2347 6672 : 61965 : index_close(indexRel, NoLock);
2420 andres@anarazel.de 6673 : 61965 : table_close(heapRel, NoLock);
6674 : :
5724 tgl@sss.pgh.pa.us 6675 : 61965 : MemoryContextSwitchTo(oldcontext);
2248 6676 : 61965 : MemoryContextDelete(tmpcontext);
6677 : :
6678 : : /* And we're done */
5724 6679 : 61965 : break;
6680 : : }
6681 : : }
6682 : :
6683 : 86452 : return have_data;
6684 : : }
6685 : :
6686 : : /*
6687 : : * Get one endpoint datum (min or max depending on indexscandir) from the
6688 : : * specified index. Return true if successful, false if not.
6689 : : * On success, endpoint value is stored to *endpointDatum (and copied into
6690 : : * outercontext).
6691 : : *
6692 : : * scankeys is a 1-element scankey array set up to reject nulls.
6693 : : * typLen/typByVal describe the datatype of the index's first column.
6694 : : * tableslot is a slot suitable to hold table tuples, in case we need
6695 : : * to probe the heap.
6696 : : * (We could compute these values locally, but that would mean computing them
6697 : : * twice when get_actual_variable_range needs both the min and the max.)
6698 : : *
6699 : : * Failure occurs either when the index is empty, or we decide that it's
6700 : : * taking too long to find a suitable tuple.
6701 : : */
6702 : : static bool
2248 6703 : 62771 : get_actual_variable_endpoint(Relation heapRel,
6704 : : Relation indexRel,
6705 : : ScanDirection indexscandir,
6706 : : ScanKey scankeys,
6707 : : int16 typLen,
6708 : : bool typByVal,
6709 : : TupleTableSlot *tableslot,
6710 : : MemoryContext outercontext,
6711 : : Datum *endpointDatum)
6712 : : {
6713 : 62771 : bool have_data = false;
6714 : : SnapshotData SnapshotNonVacuumable;
6715 : : IndexScanDesc index_scan;
6716 : 62771 : Buffer vmbuffer = InvalidBuffer;
1019 6717 : 62771 : BlockNumber last_heap_block = InvalidBlockNumber;
6718 : 62771 : int n_visited_heap_pages = 0;
6719 : : ItemPointer tid;
6720 : : Datum values[INDEX_MAX_KEYS];
6721 : : bool isnull[INDEX_MAX_KEYS];
6722 : : MemoryContext oldcontext;
6723 : :
6724 : : /*
6725 : : * We use the index-only-scan machinery for this. With mostly-static
6726 : : * tables that's a win because it avoids a heap visit. It's also a win
6727 : : * for dynamic data, but the reason is less obvious; read on for details.
6728 : : *
6729 : : * In principle, we should scan the index with our current active
6730 : : * snapshot, which is the best approximation we've got to what the query
6731 : : * will see when executed. But that won't be exact if a new snap is taken
6732 : : * before running the query, and it can be very expensive if a lot of
6733 : : * recently-dead or uncommitted rows exist at the beginning or end of the
6734 : : * index (because we'll laboriously fetch each one and reject it).
6735 : : * Instead, we use SnapshotNonVacuumable. That will accept recently-dead
6736 : : * and uncommitted rows as well as normal visible rows. On the other
6737 : : * hand, it will reject known-dead rows, and thus not give a bogus answer
6738 : : * when the extreme value has been deleted (unless the deletion was quite
6739 : : * recent); that case motivates not using SnapshotAny here.
6740 : : *
6741 : : * A crucial point here is that SnapshotNonVacuumable, with
6742 : : * GlobalVisTestFor(heapRel) as horizon, yields the inverse of the
6743 : : * condition that the indexscan will use to decide that index entries are
6744 : : * killable (see heap_hot_search_buffer()). Therefore, if the snapshot
6745 : : * rejects a tuple (or more precisely, all tuples of a HOT chain) and we
6746 : : * have to continue scanning past it, we know that the indexscan will mark
6747 : : * that index entry killed. That means that the next
6748 : : * get_actual_variable_endpoint() call will not have to re-consider that
6749 : : * index entry. In this way we avoid repetitive work when this function
6750 : : * is used a lot during planning.
6751 : : *
6752 : : * But using SnapshotNonVacuumable creates a hazard of its own. In a
6753 : : * recently-created index, some index entries may point at "broken" HOT
6754 : : * chains in which not all the tuple versions contain data matching the
6755 : : * index entry. The live tuple version(s) certainly do match the index,
6756 : : * but SnapshotNonVacuumable can accept recently-dead tuple versions that
6757 : : * don't match. Hence, if we took data from the selected heap tuple, we
6758 : : * might get a bogus answer that's not close to the index extremal value,
6759 : : * or could even be NULL. We avoid this hazard because we take the data
6760 : : * from the index entry not the heap.
6761 : : *
6762 : : * Despite all this care, there are situations where we might find many
6763 : : * non-visible tuples near the end of the index. We don't want to expend
6764 : : * a huge amount of time here, so we give up once we've read too many heap
6765 : : * pages. When we fail for that reason, the caller will end up using
6766 : : * whatever extremal value is recorded in pg_statistic.
6767 : : */
1851 andres@anarazel.de 6768 : 62771 : InitNonVacuumableSnapshot(SnapshotNonVacuumable,
6769 : : GlobalVisTestFor(heapRel));
6770 : :
2248 tgl@sss.pgh.pa.us 6771 : 62771 : index_scan = index_beginscan(heapRel, indexRel,
6772 : : &SnapshotNonVacuumable, NULL,
6773 : : 1, 0);
6774 : : /* Set it up for index-only scan */
6775 : 62771 : index_scan->xs_want_itup = true;
6776 : 62771 : index_rescan(index_scan, scankeys, 1, NULL, 0);
6777 : :
6778 : : /* Fetch first/next tuple in specified direction */
6779 [ + - ]: 81389 : while ((tid = index_getnext_tid(index_scan, indexscandir)) != NULL)
6780 : : {
1019 6781 : 81389 : BlockNumber block = ItemPointerGetBlockNumber(tid);
6782 : :
2248 6783 [ + + ]: 81389 : if (!VM_ALL_VISIBLE(heapRel,
6784 : : block,
6785 : : &vmbuffer))
6786 : : {
6787 : : /* Rats, we have to visit the heap to check visibility */
6788 [ + + ]: 60434 : if (!index_fetch_heap(index_scan, tableslot))
6789 : : {
6790 : : /*
6791 : : * No visible tuple for this index entry, so we need to
6792 : : * advance to the next entry. Before doing so, count heap
6793 : : * page fetches and give up if we've done too many.
6794 : : *
6795 : : * We don't charge a page fetch if this is the same heap page
6796 : : * as the previous tuple. This is on the conservative side,
6797 : : * since other recently-accessed pages are probably still in
6798 : : * buffers too; but it's good enough for this heuristic.
6799 : : */
6800 : : #define VISITED_PAGES_LIMIT 100
6801 : :
1019 6802 [ + + ]: 18618 : if (block != last_heap_block)
6803 : : {
6804 : 1639 : last_heap_block = block;
6805 : 1639 : n_visited_heap_pages++;
6806 [ - + ]: 1639 : if (n_visited_heap_pages > VISITED_PAGES_LIMIT)
1019 tgl@sss.pgh.pa.us 6807 :UBC 0 : break;
6808 : : }
6809 : :
2248 tgl@sss.pgh.pa.us 6810 :CBC 18618 : continue; /* no visible tuple, try next index entry */
6811 : : }
6812 : :
6813 : : /* We don't actually need the heap tuple for anything */
6814 : 41816 : ExecClearTuple(tableslot);
6815 : :
6816 : : /*
6817 : : * We don't care whether there's more than one visible tuple in
6818 : : * the HOT chain; if any are visible, that's good enough.
6819 : : */
6820 : : }
6821 : :
6822 : : /*
6823 : : * We expect that the index will return data in IndexTuple not
6824 : : * HeapTuple format.
6825 : : */
6826 [ - + ]: 62771 : if (!index_scan->xs_itup)
2248 tgl@sss.pgh.pa.us 6827 [ # # ]:UBC 0 : elog(ERROR, "no data returned for index-only scan");
6828 : :
6829 : : /*
6830 : : * We do not yet support recheck here.
6831 : : */
2248 tgl@sss.pgh.pa.us 6832 [ - + ]:CBC 62771 : if (index_scan->xs_recheck)
155 peter@eisentraut.org 6833 :UBC 0 : break;
6834 : :
6835 : : /* OK to deconstruct the index tuple */
2248 tgl@sss.pgh.pa.us 6836 :CBC 62771 : index_deform_tuple(index_scan->xs_itup,
6837 : : index_scan->xs_itupdesc,
6838 : : values, isnull);
6839 : :
6840 : : /* Shouldn't have got a null, but be careful */
6841 [ - + ]: 62771 : if (isnull[0])
2248 tgl@sss.pgh.pa.us 6842 [ # # ]:UBC 0 : elog(ERROR, "found unexpected null value in index \"%s\"",
6843 : : RelationGetRelationName(indexRel));
6844 : :
6845 : : /* Copy the index column value out to caller's context */
2248 tgl@sss.pgh.pa.us 6846 :CBC 62771 : oldcontext = MemoryContextSwitchTo(outercontext);
6847 : 62771 : *endpointDatum = datumCopy(values[0], typByVal, typLen);
6848 : 62771 : MemoryContextSwitchTo(oldcontext);
6849 : 62771 : have_data = true;
6850 : 62771 : break;
6851 : : }
6852 : :
6853 [ + + ]: 62771 : if (vmbuffer != InvalidBuffer)
6854 : 56270 : ReleaseBuffer(vmbuffer);
6855 : 62771 : index_endscan(index_scan);
6856 : :
6857 : 62771 : return have_data;
6858 : : }
6859 : :
6860 : : /*
6861 : : * find_join_input_rel
6862 : : * Look up the input relation for a join.
6863 : : *
6864 : : * We assume that the input relation's RelOptInfo must have been constructed
6865 : : * already.
6866 : : */
6867 : : static RelOptInfo *
5120 6868 : 5357 : find_join_input_rel(PlannerInfo *root, Relids relids)
6869 : : {
6870 : 5357 : RelOptInfo *rel = NULL;
6871 : :
648 drowley@postgresql.o 6872 [ + - ]: 5357 : if (!bms_is_empty(relids))
6873 : : {
6874 : : int relid;
6875 : :
6876 [ + + ]: 5357 : if (bms_get_singleton_member(relids, &relid))
6877 : 5199 : rel = find_base_rel(root, relid);
6878 : : else
5120 tgl@sss.pgh.pa.us 6879 : 158 : rel = find_join_rel(root, relids);
6880 : : }
6881 : :
6882 [ - + ]: 5357 : if (rel == NULL)
5120 tgl@sss.pgh.pa.us 6883 [ # # ]:UBC 0 : elog(ERROR, "could not find RelOptInfo for given relids");
6884 : :
5120 tgl@sss.pgh.pa.us 6885 :CBC 5357 : return rel;
6886 : : }
6887 : :
6888 : :
6889 : : /*-------------------------------------------------------------------------
6890 : : *
6891 : : * Index cost estimation functions
6892 : : *
6893 : : *-------------------------------------------------------------------------
6894 : : */
6895 : :
6896 : : /*
6897 : : * Extract the actual indexquals (as RestrictInfos) from an IndexClause list
6898 : : */
6899 : : List *
2395 6900 : 382276 : get_quals_from_indexclauses(List *indexclauses)
6901 : : {
2401 6902 : 382276 : List *result = NIL;
6903 : : ListCell *lc;
6904 : :
6905 [ + + + + : 673776 : foreach(lc, indexclauses)
+ + ]
6906 : : {
6907 : 291500 : IndexClause *iclause = lfirst_node(IndexClause, lc);
6908 : : ListCell *lc2;
6909 : :
2396 6910 [ + - + + : 584456 : foreach(lc2, iclause->indexquals)
+ + ]
6911 : : {
6912 : 292956 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
6913 : :
6914 : 292956 : result = lappend(result, rinfo);
6915 : : }
6916 : : }
2401 6917 : 382276 : return result;
6918 : : }
6919 : :
6920 : : /*
6921 : : * Compute the total evaluation cost of the comparison operands in a list
6922 : : * of index qual expressions. Since we know these will be evaluated just
6923 : : * once per scan, there's no need to distinguish startup from per-row cost.
6924 : : *
6925 : : * This can be used either on the result of get_quals_from_indexclauses(),
6926 : : * or directly on an indexorderbys list. In both cases, we expect that the
6927 : : * index key expression is on the left side of binary clauses.
6928 : : */
6929 : : Cost
2395 6930 : 758054 : index_other_operands_eval_cost(PlannerInfo *root, List *indexquals)
6931 : : {
3840 6932 : 758054 : Cost qual_arg_cost = 0;
6933 : : ListCell *lc;
6934 : :
2395 6935 [ + + + + : 1051241 : foreach(lc, indexquals)
+ + ]
6936 : : {
3840 6937 : 293187 : Expr *clause = (Expr *) lfirst(lc);
6938 : : Node *other_operand;
6939 : : QualCost index_qual_cost;
6940 : :
6941 : : /*
6942 : : * Index quals will have RestrictInfos, indexorderbys won't. Look
6943 : : * through RestrictInfo if present.
6944 : : */
2395 6945 [ + + ]: 293187 : if (IsA(clause, RestrictInfo))
6946 : 292950 : clause = ((RestrictInfo *) clause)->clause;
6947 : :
3840 6948 [ + + ]: 293187 : if (IsA(clause, OpExpr))
6949 : : {
2395 6950 : 286086 : OpExpr *op = (OpExpr *) clause;
6951 : :
6952 : 286086 : other_operand = (Node *) lsecond(op->args);
6953 : : }
6954 [ + + ]: 7101 : else if (IsA(clause, RowCompareExpr))
6955 : : {
6956 : 198 : RowCompareExpr *rc = (RowCompareExpr *) clause;
6957 : :
6958 : 198 : other_operand = (Node *) rc->rargs;
6959 : : }
6960 [ + + ]: 6903 : else if (IsA(clause, ScalarArrayOpExpr))
6961 : : {
6962 : 5440 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
6963 : :
6964 : 5440 : other_operand = (Node *) lsecond(saop->args);
6965 : : }
6966 [ + - ]: 1463 : else if (IsA(clause, NullTest))
6967 : : {
6968 : 1463 : other_operand = NULL;
6969 : : }
6970 : : else
6971 : : {
2395 tgl@sss.pgh.pa.us 6972 [ # # ]:UBC 0 : elog(ERROR, "unsupported indexqual type: %d",
6973 : : (int) nodeTag(clause));
6974 : : other_operand = NULL; /* keep compiler quiet */
6975 : : }
6976 : :
3840 tgl@sss.pgh.pa.us 6977 :CBC 293187 : cost_qual_eval_node(&index_qual_cost, other_operand, root);
6978 : 293187 : qual_arg_cost += index_qual_cost.startup + index_qual_cost.per_tuple;
6979 : : }
6980 : 758054 : return qual_arg_cost;
6981 : : }
6982 : :
6983 : : void
7398 6984 : 375784 : genericcostestimate(PlannerInfo *root,
6985 : : IndexPath *path,
6986 : : double loop_count,
6987 : : GenericCosts *costs)
6988 : : {
5005 6989 : 375784 : IndexOptInfo *index = path->indexinfo;
2395 6990 : 375784 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
5005 6991 : 375784 : List *indexOrderBys = path->indexorderbys;
6992 : : Cost indexStartupCost;
6993 : : Cost indexTotalCost;
6994 : : Selectivity indexSelectivity;
6995 : : double indexCorrelation;
6996 : : double numIndexPages;
6997 : : double numIndexTuples;
6998 : : double spc_random_page_cost;
6999 : : double num_sa_scans;
7000 : : double num_outer_scans;
7001 : : double num_scans;
7002 : : double qual_op_cost;
7003 : : double qual_arg_cost;
7004 : : List *selectivityQuals;
7005 : : ListCell *l;
7006 : :
7007 : : /*
7008 : : * If the index is partial, AND the index predicate with the explicitly
7009 : : * given indexquals to produce a more accurate idea of the index
7010 : : * selectivity.
7011 : : */
2395 7012 : 375784 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
7013 : :
7014 : : /*
7015 : : * If caller didn't give us an estimate for ScalarArrayOpExpr index scans,
7016 : : * just assume that the number of index descents is the number of distinct
7017 : : * combinations of array elements from all of the scan's SAOP clauses.
7018 : : */
518 pg@bowt.ie 7019 : 375784 : num_sa_scans = costs->num_sa_scans;
7020 [ + + ]: 375784 : if (num_sa_scans < 1)
7021 : : {
7022 : 3892 : num_sa_scans = 1;
7023 [ + + + + : 8140 : foreach(l, indexQuals)
+ + ]
7024 : : {
7025 : 4248 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
7026 : :
7027 [ + + ]: 4248 : if (IsA(rinfo->clause, ScalarArrayOpExpr))
7028 : : {
7029 : 13 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
7030 : 13 : double alength = estimate_array_length(root, lsecond(saop->args));
7031 : :
7032 [ + - ]: 13 : if (alength > 1)
7033 : 13 : num_sa_scans *= alength;
7034 : : }
7035 : : }
7036 : : }
7037 : :
7038 : : /* Estimate the fraction of main-table tuples that will be visited */
4621 tgl@sss.pgh.pa.us 7039 : 375784 : indexSelectivity = clauselist_selectivity(root, selectivityQuals,
7040 : 375784 : index->rel->relid,
7041 : : JOIN_INNER,
7042 : : NULL);
7043 : :
7044 : : /*
7045 : : * If caller didn't give us an estimate, estimate the number of index
7046 : : * tuples that will be visited. We do it in this rather peculiar-looking
7047 : : * way in order to get the right answer for partial indexes.
7048 : : */
7049 : 375784 : numIndexTuples = costs->numIndexTuples;
7390 7050 [ + + ]: 375784 : if (numIndexTuples <= 0.0)
7051 : : {
4621 7052 : 37515 : numIndexTuples = indexSelectivity * index->rel->tuples;
7053 : :
7054 : : /*
7055 : : * The above calculation counts all the tuples visited across all
7056 : : * scans induced by ScalarArrayOpExpr nodes. We want to consider the
7057 : : * average per-indexscan number, so adjust. This is a handy place to
7058 : : * round to integer, too. (If caller supplied tuple estimate, it's
7059 : : * responsible for handling these considerations.)
7060 : : */
6840 7061 : 37515 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
7062 : : }
7063 : :
7064 : : /*
7065 : : * We can bound the number of tuples by the index size in any case. Also,
7066 : : * always estimate at least one tuple is touched, even when
7067 : : * indexSelectivity estimate is tiny.
7068 : : */
7390 7069 [ + + ]: 375784 : if (numIndexTuples > index->tuples)
7070 : 2700 : numIndexTuples = index->tuples;
9281 7071 [ + + ]: 375784 : if (numIndexTuples < 1.0)
7072 : 37312 : numIndexTuples = 1.0;
7073 : :
7074 : : /*
7075 : : * Estimate the number of index pages that will be retrieved.
7076 : : *
7077 : : * We use the simplistic method of taking a pro-rata fraction of the total
7078 : : * number of index pages. In effect, this counts only leaf pages and not
7079 : : * any overhead such as index metapage or upper tree levels.
7080 : : *
7081 : : * In practice access to upper index levels is often nearly free because
7082 : : * those tend to stay in cache under load; moreover, the cost involved is
7083 : : * highly dependent on index type. We therefore ignore such costs here
7084 : : * and leave it to the caller to add a suitable charge if needed.
7085 : : */
7032 7086 [ + + + + ]: 375784 : if (index->pages > 1 && index->tuples > 1)
7087 : 352879 : numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
7088 : : else
9281 7089 : 22905 : numIndexPages = 1.0;
7090 : :
7091 : : /* fetch estimated page cost for tablespace containing index */
5723 rhaas@postgresql.org 7092 : 375784 : get_tablespace_page_costs(index->reltablespace,
7093 : : &spc_random_page_cost,
7094 : : NULL);
7095 : :
7096 : : /*
7097 : : * Now compute the disk access costs.
7098 : : *
7099 : : * The above calculations are all per-index-scan. However, if we are in a
7100 : : * nestloop inner scan, we can expect the scan to be repeated (with
7101 : : * different search keys) for each row of the outer relation. Likewise,
7102 : : * ScalarArrayOpExpr quals result in multiple index scans. This creates
7103 : : * the potential for cache effects to reduce the number of disk page
7104 : : * fetches needed. We want to estimate the average per-scan I/O cost in
7105 : : * the presence of caching.
7106 : : *
7107 : : * We use the Mackert-Lohman formula (see costsize.c for details) to
7108 : : * estimate the total number of page fetches that occur. While this
7109 : : * wasn't what it was designed for, it seems a reasonable model anyway.
7110 : : * Note that we are counting pages not tuples anymore, so we take N = T =
7111 : : * index size, as if there were one "tuple" per page.
7112 : : */
4971 tgl@sss.pgh.pa.us 7113 : 375784 : num_outer_scans = loop_count;
7114 : 375784 : num_scans = num_sa_scans * num_outer_scans;
7115 : :
7007 7116 [ + + ]: 375784 : if (num_scans > 1)
7117 : : {
7118 : : double pages_fetched;
7119 : :
7120 : : /* total page fetches ignoring cache effects */
7032 7121 : 42196 : pages_fetched = numIndexPages * num_scans;
7122 : :
7123 : : /* use Mackert and Lohman formula to adjust for cache effects */
7124 : 42196 : pages_fetched = index_pages_fetched(pages_fetched,
7125 : : index->pages,
6927 7126 : 42196 : (double) index->pages,
7127 : : root);
7128 : :
7129 : : /*
7130 : : * Now compute the total disk access cost, and then report a pro-rated
7131 : : * share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
7132 : : * since that's internal to the indexscan.)
7133 : : */
4621 7134 : 42196 : indexTotalCost = (pages_fetched * spc_random_page_cost)
7135 : : / num_outer_scans;
7136 : : }
7137 : : else
7138 : : {
7139 : : /*
7140 : : * For a single index scan, we just charge spc_random_page_cost per
7141 : : * page touched.
7142 : : */
7143 : 333588 : indexTotalCost = numIndexPages * spc_random_page_cost;
7144 : : }
7145 : :
7146 : : /*
7147 : : * CPU cost: any complex expressions in the indexquals will need to be
7148 : : * evaluated once at the start of the scan to reduce them to runtime keys
7149 : : * to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
7150 : : * CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
7151 : : * indexqual operator. Because we have numIndexTuples as a per-scan
7152 : : * number, we have to multiply by num_sa_scans to get the correct result
7153 : : * for ScalarArrayOpExpr cases. Similarly add in costs for any index
7154 : : * ORDER BY expressions.
7155 : : *
7156 : : * Note: this neglects the possible costs of rechecking lossy operators.
7157 : : * Detecting that that might be needed seems more expensive than it's
7158 : : * worth, though, considering all the other inaccuracies here ...
7159 : : */
2395 7160 : 375784 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals) +
7161 : 375784 : index_other_operands_eval_cost(root, indexOrderBys);
5392 7162 : 375784 : qual_op_cost = cpu_operator_cost *
7163 : 375784 : (list_length(indexQuals) + list_length(indexOrderBys));
7164 : :
4621 7165 : 375784 : indexStartupCost = qual_arg_cost;
7166 : 375784 : indexTotalCost += qual_arg_cost;
7167 : 375784 : indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
7168 : :
7169 : : /*
7170 : : * Generic assumption about index correlation: there isn't any.
7171 : : */
7172 : 375784 : indexCorrelation = 0.0;
7173 : :
7174 : : /*
7175 : : * Return everything to caller.
7176 : : */
7177 : 375784 : costs->indexStartupCost = indexStartupCost;
7178 : 375784 : costs->indexTotalCost = indexTotalCost;
7179 : 375784 : costs->indexSelectivity = indexSelectivity;
7180 : 375784 : costs->indexCorrelation = indexCorrelation;
7181 : 375784 : costs->numIndexPages = numIndexPages;
7182 : 375784 : costs->numIndexTuples = numIndexTuples;
7183 : 375784 : costs->spc_random_page_cost = spc_random_page_cost;
7184 : 375784 : costs->num_sa_scans = num_sa_scans;
7185 : 375784 : }
7186 : :
7187 : : /*
7188 : : * If the index is partial, add its predicate to the given qual list.
7189 : : *
7190 : : * ANDing the index predicate with the explicitly given indexquals produces
7191 : : * a more accurate idea of the index's selectivity. However, we need to be
7192 : : * careful not to insert redundant clauses, because clauselist_selectivity()
7193 : : * is easily fooled into computing a too-low selectivity estimate. Our
7194 : : * approach is to add only the predicate clause(s) that cannot be proven to
7195 : : * be implied by the given indexquals. This successfully handles cases such
7196 : : * as a qual "x = 42" used with a partial index "WHERE x >= 40 AND x < 50".
7197 : : * There are many other cases where we won't detect redundancy, leading to a
7198 : : * too-low selectivity estimate, which will bias the system in favor of using
7199 : : * partial indexes where possible. That is not necessarily bad though.
7200 : : *
7201 : : * Note that indexQuals contains RestrictInfo nodes while the indpred
7202 : : * does not, so the output list will be mixed. This is OK for both
7203 : : * predicate_implied_by() and clauselist_selectivity(), but might be
7204 : : * problematic if the result were passed to other things.
7205 : : */
7206 : : List *
2395 7207 : 628326 : add_predicate_to_index_quals(IndexOptInfo *index, List *indexQuals)
7208 : : {
4621 7209 : 628326 : List *predExtraQuals = NIL;
7210 : : ListCell *lc;
7211 : :
7212 [ + + ]: 628326 : if (index->indpred == NIL)
7213 : 627313 : return indexQuals;
7214 : :
7215 [ + - + + : 2032 : foreach(lc, index->indpred)
+ + ]
7216 : : {
7217 : 1019 : Node *predQual = (Node *) lfirst(lc);
7218 : 1019 : List *oneQual = list_make1(predQual);
7219 : :
3006 rhaas@postgresql.org 7220 [ + + ]: 1019 : if (!predicate_implied_by(oneQual, indexQuals, false))
4621 tgl@sss.pgh.pa.us 7221 : 918 : predExtraQuals = list_concat(predExtraQuals, oneQual);
7222 : : }
7223 : 1013 : return list_concat(predExtraQuals, indexQuals);
7224 : : }
7225 : :
7226 : : /*
7227 : : * Estimate correlation of btree index's first column.
7228 : : *
7229 : : * If we can get an estimate of the first column's ordering correlation C
7230 : : * from pg_statistic, estimate the index correlation as C for a single-column
7231 : : * index, or C * 0.75 for multiple columns. The idea here is that multiple
7232 : : * columns dilute the importance of the first column's ordering, but don't
7233 : : * negate it entirely.
7234 : : *
7235 : : * We already filled in the stats tuple for *vardata when called.
7236 : : */
7237 : : static double
155 pg@bowt.ie 7238 : 291541 : btcost_correlation(IndexOptInfo *index, VariableStatData *vardata)
7239 : : {
7240 : : Oid sortop;
7241 : : AttStatsSlot sslot;
7242 : 291541 : double indexCorrelation = 0;
7243 : :
7244 [ - + ]: 291541 : Assert(HeapTupleIsValid(vardata->statsTuple));
7245 : :
7246 : 291541 : sortop = get_opfamily_member(index->opfamily[0],
7247 : 291541 : index->opcintype[0],
7248 : 291541 : index->opcintype[0],
7249 : : BTLessStrategyNumber);
7250 [ + - + + ]: 583082 : if (OidIsValid(sortop) &&
7251 : 291541 : get_attstatsslot(&sslot, vardata->statsTuple,
7252 : : STATISTIC_KIND_CORRELATION, sortop,
7253 : : ATTSTATSSLOT_NUMBERS))
7254 : : {
7255 : : double varCorrelation;
7256 : :
7257 [ - + ]: 288179 : Assert(sslot.nnumbers == 1);
7258 : 288179 : varCorrelation = sslot.numbers[0];
7259 : :
7260 [ - + ]: 288179 : if (index->reverse_sort[0])
155 pg@bowt.ie 7261 :UBC 0 : varCorrelation = -varCorrelation;
7262 : :
155 pg@bowt.ie 7263 [ + + ]:CBC 288179 : if (index->nkeycolumns > 1)
7264 : 100448 : indexCorrelation = varCorrelation * 0.75;
7265 : : else
7266 : 187731 : indexCorrelation = varCorrelation;
7267 : :
7268 : 288179 : free_attstatsslot(&sslot);
7269 : : }
7270 : :
7271 : 291541 : return indexCorrelation;
7272 : : }
7273 : :
7274 : : void
3520 tgl@sss.pgh.pa.us 7275 : 371892 : btcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7276 : : Cost *indexStartupCost, Cost *indexTotalCost,
7277 : : Selectivity *indexSelectivity, double *indexCorrelation,
7278 : : double *indexPages)
7279 : : {
5005 7280 : 371892 : IndexOptInfo *index = path->indexinfo;
1148 peter@eisentraut.org 7281 : 371892 : GenericCosts costs = {0};
7282 : 371892 : VariableStatData vardata = {0};
7283 : : double numIndexTuples;
7284 : : Cost descentCost;
7285 : : List *indexBoundQuals;
7286 : : List *indexSkipQuals;
7287 : : int indexcol;
7288 : : bool eqQualHere;
7289 : : bool found_row_compare;
7290 : : bool found_array;
7291 : : bool found_is_null_op;
155 pg@bowt.ie 7292 : 371892 : bool have_correlation = false;
7293 : : double num_sa_scans;
7294 : 371892 : double correlation = 0.0;
7295 : : ListCell *lc;
7296 : :
7297 : : /*
7298 : : * For a btree scan, only leading '=' quals plus inequality quals for the
7299 : : * immediately next attribute contribute to index selectivity (these are
7300 : : * the "boundary quals" that determine the starting and stopping points of
7301 : : * the index scan). Additional quals can suppress visits to the heap, so
7302 : : * it's OK to count them in indexSelectivity, but they should not count
7303 : : * for estimating numIndexTuples. So we must examine the given indexquals
7304 : : * to find out which ones count as boundary quals. We rely on the
7305 : : * knowledge that they are given in index column order. Note that nbtree
7306 : : * preprocessing can add skip arrays that act as leading '=' quals in the
7307 : : * absence of ordinary input '=' quals, so in practice _most_ input quals
7308 : : * are able to act as index bound quals (which we take into account here).
7309 : : *
7310 : : * For a RowCompareExpr, we consider only the first column, just as
7311 : : * rowcomparesel() does.
7312 : : *
7313 : : * If there's a SAOP or skip array in the quals, we'll actually perform up
7314 : : * to N index descents (not just one), but the underlying array key's
7315 : : * operator can be considered to act the same as it normally does.
7316 : : */
7390 tgl@sss.pgh.pa.us 7317 : 371892 : indexBoundQuals = NIL;
155 pg@bowt.ie 7318 : 371892 : indexSkipQuals = NIL;
5005 tgl@sss.pgh.pa.us 7319 : 371892 : indexcol = 0;
7390 7320 : 371892 : eqQualHere = false;
155 pg@bowt.ie 7321 : 371892 : found_row_compare = false;
7322 : 371892 : found_array = false;
5727 tgl@sss.pgh.pa.us 7323 : 371892 : found_is_null_op = false;
6840 7324 : 371892 : num_sa_scans = 1;
2395 7325 [ + + + + : 637088 : foreach(lc, path->indexclauses)
+ + ]
7326 : : {
7327 : 279502 : IndexClause *iclause = lfirst_node(IndexClause, lc);
7328 : : ListCell *lc2;
7329 : :
155 pg@bowt.ie 7330 [ + + ]: 279502 : if (indexcol < iclause->indexcol)
7331 : : {
7332 : 49491 : double num_sa_scans_prev_cols = num_sa_scans;
7333 : :
7334 : : /*
7335 : : * Beginning of a new column's quals.
7336 : : *
7337 : : * Skip scans use skip arrays, which are ScalarArrayOp style
7338 : : * arrays that generate their elements procedurally and on demand.
7339 : : * Given a multi-column index on "(a, b)", and an SQL WHERE clause
7340 : : * "WHERE b = 42", a skip scan will effectively use an indexqual
7341 : : * "WHERE a = ANY('{every col a value}') AND b = 42". (Obviously,
7342 : : * the array on "a" must also return "IS NULL" matches, since our
7343 : : * WHERE clause used no strict operator on "a").
7344 : : *
7345 : : * Here we consider how nbtree will backfill skip arrays for any
7346 : : * index columns that lacked an '=' qual. This maintains our
7347 : : * num_sa_scans estimate, and determines if this new column (the
7348 : : * "iclause->indexcol" column, not the prior "indexcol" column)
7349 : : * can have its RestrictInfos/quals added to indexBoundQuals.
7350 : : *
7351 : : * We'll need to handle columns that have inequality quals, where
7352 : : * the skip array generates values from a range constrained by the
7353 : : * quals (not every possible value). We've been maintaining
7354 : : * indexSkipQuals to help with this; it will now contain all of
7355 : : * the prior column's quals (that is, indexcol's quals) when they
7356 : : * might be used for this.
7357 : : */
7358 [ + + ]: 49491 : if (found_row_compare)
7359 : : {
7360 : : /*
7361 : : * Skip arrays can't be added after a RowCompare input qual
7362 : : * due to limitations in nbtree
7363 : : */
7364 : 12 : break;
7365 : : }
7366 [ + + ]: 49479 : if (eqQualHere)
7367 : : {
7368 : : /*
7369 : : * Don't need to add a skip array for an indexcol that already
7370 : : * has an '=' qual/equality constraint
7371 : : */
7372 : 35280 : indexcol++;
7373 : 35280 : indexSkipQuals = NIL;
7374 : : }
5005 tgl@sss.pgh.pa.us 7375 : 49479 : eqQualHere = false;
7376 : :
155 pg@bowt.ie 7377 [ + + ]: 51022 : while (indexcol < iclause->indexcol)
7378 : : {
7379 : : double ndistinct;
7380 : 15837 : bool isdefault = true;
7381 : :
7382 : 15837 : found_array = true;
7383 : :
7384 : : /*
7385 : : * A skipped attribute's ndistinct forms the basis of our
7386 : : * estimate of the total number of "array elements" used by
7387 : : * its skip array at runtime. Look that up first.
7388 : : */
7389 : 15837 : examine_indexcol_variable(root, index, indexcol, &vardata);
7390 : 15837 : ndistinct = get_variable_numdistinct(&vardata, &isdefault);
7391 : :
7392 [ + + ]: 15837 : if (indexcol == 0)
7393 : : {
7394 : : /*
7395 : : * Get an estimate of the leading column's correlation in
7396 : : * passing (avoids rereading variable stats below)
7397 : : */
7398 [ + + ]: 14193 : if (HeapTupleIsValid(vardata.statsTuple))
7399 : 11043 : correlation = btcost_correlation(index, &vardata);
7400 : 14193 : have_correlation = true;
7401 : : }
7402 : :
7403 [ + + ]: 15837 : ReleaseVariableStats(vardata);
7404 : :
7405 : : /*
7406 : : * If ndistinct is a default estimate, conservatively assume
7407 : : * that no skipping will happen at runtime
7408 : : */
7409 [ + + ]: 15837 : if (isdefault)
7410 : : {
7411 : 2437 : num_sa_scans = num_sa_scans_prev_cols;
7412 : 14294 : break; /* done building indexBoundQuals */
7413 : : }
7414 : :
7415 : : /*
7416 : : * Apply indexcol's indexSkipQuals selectivity to ndistinct
7417 : : */
7418 [ + + ]: 13400 : if (indexSkipQuals != NIL)
7419 : : {
7420 : : List *partialSkipQuals;
7421 : : Selectivity ndistinctfrac;
7422 : :
7423 : : /*
7424 : : * If the index is partial, AND the index predicate with
7425 : : * the index-bound quals to produce a more accurate idea
7426 : : * of the number of distinct values for prior indexcol
7427 : : */
7428 : 332 : partialSkipQuals = add_predicate_to_index_quals(index,
7429 : : indexSkipQuals);
7430 : :
7431 : 332 : ndistinctfrac = clauselist_selectivity(root, partialSkipQuals,
7432 : 332 : index->rel->relid,
7433 : : JOIN_INNER,
7434 : : NULL);
7435 : :
7436 : : /*
7437 : : * If ndistinctfrac is selective (on its own), the scan is
7438 : : * unlikely to benefit from repositioning itself using
7439 : : * later quals. Do not allow iclause->indexcol's quals to
7440 : : * be added to indexBoundQuals (it would increase descent
7441 : : * costs, without lowering numIndexTuples costs by much).
7442 : : */
7443 [ + + ]: 332 : if (ndistinctfrac < DEFAULT_RANGE_INEQ_SEL)
7444 : : {
7445 : 187 : num_sa_scans = num_sa_scans_prev_cols;
7446 : 187 : break; /* done building indexBoundQuals */
7447 : : }
7448 : :
7449 : : /* Adjust ndistinct downward */
7450 : 145 : ndistinct = rint(ndistinct * ndistinctfrac);
7451 [ + - ]: 145 : ndistinct = Max(ndistinct, 1);
7452 : : }
7453 : :
7454 : : /*
7455 : : * When there's no inequality quals, account for the need to
7456 : : * find an initial value by counting -inf/+inf as a value.
7457 : : *
7458 : : * We don't charge anything extra for possible next/prior key
7459 : : * index probes, which are sometimes used to find the next
7460 : : * valid skip array element (ahead of using the located
7461 : : * element value to relocate the scan to the next position
7462 : : * that might contain matching tuples). It seems hard to do
7463 : : * better here. Use of the skip support infrastructure often
7464 : : * avoids most next/prior key probes. But even when it can't,
7465 : : * there's a decent chance that most individual next/prior key
7466 : : * probes will locate a leaf page whose key space overlaps all
7467 : : * of the scan's keys (even the lower-order keys) -- which
7468 : : * also avoids the need for a separate, extra index descent.
7469 : : * Note also that these probes are much cheaper than non-probe
7470 : : * primitive index scans: they're reliably very selective.
7471 : : */
7472 [ + + ]: 13213 : if (indexSkipQuals == NIL)
7473 : 13068 : ndistinct += 1;
7474 : :
7475 : : /*
7476 : : * Update num_sa_scans estimate by multiplying by ndistinct.
7477 : : *
7478 : : * We make the pessimistic assumption that there is no
7479 : : * naturally occurring cross-column correlation. This is
7480 : : * often wrong, but it seems best to err on the side of not
7481 : : * expecting skipping to be helpful...
7482 : : */
7483 : 13213 : num_sa_scans *= ndistinct;
7484 : :
7485 : : /*
7486 : : * ...but back out of adding this latest group of 1 or more
7487 : : * skip arrays when num_sa_scans exceeds the total number of
7488 : : * index pages (revert to num_sa_scans from before indexcol).
7489 : : * This causes a sharp discontinuity in cost (as a function of
7490 : : * the indexcol's ndistinct), but that is representative of
7491 : : * actual runtime costs.
7492 : : *
7493 : : * Note that skipping is helpful when each primitive index
7494 : : * scan only manages to skip over 1 or 2 irrelevant leaf pages
7495 : : * on average. Skip arrays bring savings in CPU costs due to
7496 : : * the scan not needing to evaluate indexquals against every
7497 : : * tuple, which can greatly exceed any savings in I/O costs.
7498 : : * This test is a test of whether num_sa_scans implies that
7499 : : * we're past the point where the ability to skip ceases to
7500 : : * lower the scan's costs (even qual evaluation CPU costs).
7501 : : */
7502 [ + + ]: 13213 : if (index->pages < num_sa_scans)
7503 : : {
7504 : 11670 : num_sa_scans = num_sa_scans_prev_cols;
7505 : 11670 : break; /* done building indexBoundQuals */
7506 : : }
7507 : :
7508 : 1543 : indexcol++;
7509 : 1543 : indexSkipQuals = NIL;
7510 : : }
7511 : :
7512 : : /*
7513 : : * Finished considering the need to add skip arrays to bridge an
7514 : : * initial eqQualHere gap between the old and new index columns
7515 : : * (or there was no initial eqQualHere gap in the first place).
7516 : : *
7517 : : * If an initial gap could not be bridged, then new column's quals
7518 : : * (i.e. iclause->indexcol's quals) won't go into indexBoundQuals,
7519 : : * and so won't affect our final numIndexTuples estimate.
7520 : : */
2395 tgl@sss.pgh.pa.us 7521 [ + + ]: 49479 : if (indexcol != iclause->indexcol)
155 pg@bowt.ie 7522 : 14294 : break; /* done building indexBoundQuals */
7523 : : }
7524 : :
7525 [ - + ]: 265196 : Assert(indexcol == iclause->indexcol);
7526 : :
7527 : : /* Examine each indexqual associated with this index clause */
2395 tgl@sss.pgh.pa.us 7528 [ + - + + : 531762 : foreach(lc2, iclause->indexquals)
+ + ]
7529 : : {
7530 : 266566 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
7531 : 266566 : Expr *clause = rinfo->clause;
7532 : 266566 : Oid clause_op = InvalidOid;
7533 : : int op_strategy;
7534 : :
7535 [ + + ]: 266566 : if (IsA(clause, OpExpr))
7536 : : {
7537 : 260000 : OpExpr *op = (OpExpr *) clause;
7538 : :
7539 : 260000 : clause_op = op->opno;
7540 : : }
7541 [ + + ]: 6566 : else if (IsA(clause, RowCompareExpr))
7542 : : {
7543 : 198 : RowCompareExpr *rc = (RowCompareExpr *) clause;
7544 : :
7545 : 198 : clause_op = linitial_oid(rc->opnos);
155 pg@bowt.ie 7546 : 198 : found_row_compare = true;
7547 : : }
2395 tgl@sss.pgh.pa.us 7548 [ + + ]: 6368 : else if (IsA(clause, ScalarArrayOpExpr))
7549 : : {
7550 : 5226 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
7551 : 5226 : Node *other_operand = (Node *) lsecond(saop->args);
611 7552 : 5226 : double alength = estimate_array_length(root, other_operand);
7553 : :
2395 7554 : 5226 : clause_op = saop->opno;
155 pg@bowt.ie 7555 : 5226 : found_array = true;
7556 : : /* estimate SA descents by indexBoundQuals only */
2395 tgl@sss.pgh.pa.us 7557 [ + + ]: 5226 : if (alength > 1)
7558 : 5126 : num_sa_scans *= alength;
7559 : : }
7560 [ + - ]: 1142 : else if (IsA(clause, NullTest))
7561 : : {
7562 : 1142 : NullTest *nt = (NullTest *) clause;
7563 : :
7564 [ + + ]: 1142 : if (nt->nulltesttype == IS_NULL)
7565 : : {
7566 : 120 : found_is_null_op = true;
7567 : : /* IS NULL is like = for selectivity/skip scan purposes */
7568 : 120 : eqQualHere = true;
7569 : : }
7570 : : }
7571 : : else
2395 tgl@sss.pgh.pa.us 7572 [ # # ]:UBC 0 : elog(ERROR, "unsupported indexqual type: %d",
7573 : : (int) nodeTag(clause));
7574 : :
7575 : : /* check for equality operator */
2395 tgl@sss.pgh.pa.us 7576 [ + + ]:CBC 266566 : if (OidIsValid(clause_op))
7577 : : {
7578 : 265424 : op_strategy = get_op_opfamily_strategy(clause_op,
7579 : 265424 : index->opfamily[indexcol]);
7580 [ - + ]: 265424 : Assert(op_strategy != 0); /* not a member of opfamily?? */
7581 [ + + ]: 265424 : if (op_strategy == BTEqualStrategyNumber)
7582 : 252251 : eqQualHere = true;
7583 : : }
7584 : :
7585 : 266566 : indexBoundQuals = lappend(indexBoundQuals, rinfo);
7586 : :
7587 : : /*
7588 : : * We apply inequality selectivities to estimate index descent
7589 : : * costs with scans that use skip arrays. Save this indexcol's
7590 : : * RestrictInfos if it looks like they'll be needed for that.
7591 : : */
155 pg@bowt.ie 7592 [ + + + + ]: 266566 : if (!eqQualHere && !found_row_compare &&
7593 [ + + ]: 13646 : indexcol < index->nkeycolumns - 1)
7594 : 2852 : indexSkipQuals = lappend(indexSkipQuals, rinfo);
7595 : : }
7596 : : }
7597 : :
7598 : : /*
7599 : : * If index is unique and we found an '=' clause for each column, we can
7600 : : * just assume numIndexTuples = 1 and skip the expensive
7601 : : * clauselist_selectivity calculations. However, an array or NullTest
7602 : : * always invalidates that theory (even when eqQualHere has been set).
7603 : : */
7225 tgl@sss.pgh.pa.us 7604 [ + + ]: 371892 : if (index->unique &&
2709 teodor@sigaev.ru 7605 [ + + + + ]: 299444 : indexcol == index->nkeycolumns - 1 &&
7225 tgl@sss.pgh.pa.us 7606 : 123817 : eqQualHere &&
155 pg@bowt.ie 7607 [ + + ]: 123817 : !found_array &&
5727 tgl@sss.pgh.pa.us 7608 [ + + ]: 120833 : !found_is_null_op)
7390 7609 : 120809 : numIndexTuples = 1.0;
7610 : : else
7611 : : {
7612 : : List *selectivityQuals;
7613 : : Selectivity btreeSelectivity;
7614 : :
7615 : : /*
7616 : : * If the index is partial, AND the index predicate with the
7617 : : * index-bound quals to produce a more accurate idea of the number of
7618 : : * rows covered by the bound conditions.
7619 : : */
2395 7620 : 251083 : selectivityQuals = add_predicate_to_index_quals(index, indexBoundQuals);
7621 : :
4969 7622 : 251083 : btreeSelectivity = clauselist_selectivity(root, selectivityQuals,
7390 7623 : 251083 : index->rel->relid,
7624 : : JOIN_INNER,
7625 : : NULL);
7626 : 251083 : numIndexTuples = btreeSelectivity * index->rel->tuples;
7627 : :
7628 : : /*
7629 : : * btree automatically combines individual array element primitive
7630 : : * index scans whenever the tuples covered by the next set of array
7631 : : * keys are close to tuples covered by the current set. That puts a
7632 : : * natural ceiling on the worst case number of descents -- there
7633 : : * cannot possibly be more than one descent per leaf page scanned.
7634 : : *
7635 : : * Clamp the number of descents to at most 1/3 the number of index
7636 : : * pages. This avoids implausibly high estimates with low selectivity
7637 : : * paths, where scans usually require only one or two descents. This
7638 : : * is most likely to help when there are several SAOP clauses, where
7639 : : * naively accepting the total number of distinct combinations of
7640 : : * array elements as the number of descents would frequently lead to
7641 : : * wild overestimates.
7642 : : *
7643 : : * We somewhat arbitrarily don't just make the cutoff the total number
7644 : : * of leaf pages (we make it 1/3 the total number of pages instead) to
7645 : : * give the btree code credit for its ability to continue on the leaf
7646 : : * level with low selectivity scans.
7647 : : *
7648 : : * Note: num_sa_scans includes both ScalarArrayOp array elements and
7649 : : * skip array elements whose qual affects our numIndexTuples estimate.
7650 : : */
518 pg@bowt.ie 7651 [ + + ]: 251083 : num_sa_scans = Min(num_sa_scans, ceil(index->pages * 0.3333333));
7652 [ + + ]: 251083 : num_sa_scans = Max(num_sa_scans, 1);
7653 : :
7654 : : /*
7655 : : * As in genericcostestimate(), we have to adjust for any array quals
7656 : : * included in indexBoundQuals, and then round to integer.
7657 : : *
7658 : : * It is tempting to make genericcostestimate behave as if array
7659 : : * clauses work in almost the same way as scalar operators during
7660 : : * btree scans, making the top-level scan look like a continuous scan
7661 : : * (as opposed to num_sa_scans-many primitive index scans). After
7662 : : * all, btree scans mostly work like that at runtime. However, such a
7663 : : * scheme would badly bias genericcostestimate's simplistic approach
7664 : : * to calculating numIndexPages through prorating.
7665 : : *
7666 : : * Stick with the approach taken by non-native SAOP scans for now.
7667 : : * genericcostestimate will use the Mackert-Lohman formula to
7668 : : * compensate for repeat page fetches, even though that definitely
7669 : : * won't happen during btree scans (not for leaf pages, at least).
7670 : : * We're usually very pessimistic about the number of primitive index
7671 : : * scans that will be required, but it's not clear how to do better.
7672 : : */
6840 tgl@sss.pgh.pa.us 7673 : 251083 : numIndexTuples = rint(numIndexTuples / num_sa_scans);
7674 : : }
7675 : :
7676 : : /*
7677 : : * Now do generic index cost estimation.
7678 : : */
4621 7679 : 371892 : costs.numIndexTuples = numIndexTuples;
518 pg@bowt.ie 7680 : 371892 : costs.num_sa_scans = num_sa_scans;
7681 : :
2395 tgl@sss.pgh.pa.us 7682 : 371892 : genericcostestimate(root, path, loop_count, &costs);
7683 : :
7684 : : /*
7685 : : * Add a CPU-cost component to represent the costs of initial btree
7686 : : * descent. We don't charge any I/O cost for touching upper btree levels,
7687 : : * since they tend to stay in cache, but we still have to do about log2(N)
7688 : : * comparisons to descend a btree of N leaf tuples. We charge one
7689 : : * cpu_operator_cost per comparison.
7690 : : *
7691 : : * If there are SAOP or skip array keys, charge this once per estimated
7692 : : * index descent. The ones after the first one are not startup cost so
7693 : : * far as the overall plan goes, so just add them to "total" cost.
7694 : : */
4621 7695 [ + + ]: 371892 : if (index->tuples > 1) /* avoid computing log(0) */
7696 : : {
7697 : 353306 : descentCost = ceil(log(index->tuples) / log(2.0)) * cpu_operator_cost;
7698 : 353306 : costs.indexStartupCost += descentCost;
7699 : 353306 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
7700 : : }
7701 : :
7702 : : /*
7703 : : * Even though we're not charging I/O cost for touching upper btree pages,
7704 : : * it's still reasonable to charge some CPU cost per page descended
7705 : : * through. Moreover, if we had no such charge at all, bloated indexes
7706 : : * would appear to have the same search cost as unbloated ones, at least
7707 : : * in cases where only a single leaf page is expected to be visited. This
7708 : : * cost is somewhat arbitrarily set at 50x cpu_operator_cost per page
7709 : : * touched. The number of such pages is btree tree height plus one (ie,
7710 : : * we charge for the leaf page too). As above, charge once per estimated
7711 : : * SAOP/skip array descent.
7712 : : */
972 akorotkov@postgresql 7713 : 371892 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4621 tgl@sss.pgh.pa.us 7714 : 371892 : costs.indexStartupCost += descentCost;
7715 : 371892 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
7716 : :
155 pg@bowt.ie 7717 [ + + ]: 371892 : if (!have_correlation)
7718 : : {
7719 : 357699 : examine_indexcol_variable(root, index, 0, &vardata);
7720 [ + + ]: 357699 : if (HeapTupleIsValid(vardata.statsTuple))
7721 : 280498 : costs.indexCorrelation = btcost_correlation(index, &vardata);
7722 [ + + ]: 357699 : ReleaseVariableStats(vardata);
7723 : : }
7724 : : else
7725 : : {
7726 : : /* btcost_correlation already called earlier on */
7727 : 14193 : costs.indexCorrelation = correlation;
7728 : : }
7729 : :
4621 tgl@sss.pgh.pa.us 7730 : 371892 : *indexStartupCost = costs.indexStartupCost;
7731 : 371892 : *indexTotalCost = costs.indexTotalCost;
7732 : 371892 : *indexSelectivity = costs.indexSelectivity;
7733 : 371892 : *indexCorrelation = costs.indexCorrelation;
3125 rhaas@postgresql.org 7734 : 371892 : *indexPages = costs.numIndexPages;
10651 scrappy@hub.org 7735 : 371892 : }
7736 : :
7737 : : void
3520 tgl@sss.pgh.pa.us 7738 : 209 : hashcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7739 : : Cost *indexStartupCost, Cost *indexTotalCost,
7740 : : Selectivity *indexSelectivity, double *indexCorrelation,
7741 : : double *indexPages)
7742 : : {
1148 peter@eisentraut.org 7743 : 209 : GenericCosts costs = {0};
7744 : :
2395 tgl@sss.pgh.pa.us 7745 : 209 : genericcostestimate(root, path, loop_count, &costs);
7746 : :
7747 : : /*
7748 : : * A hash index has no descent costs as such, since the index AM can go
7749 : : * directly to the target bucket after computing the hash value. There
7750 : : * are a couple of other hash-specific costs that we could conceivably add
7751 : : * here, though:
7752 : : *
7753 : : * Ideally we'd charge spc_random_page_cost for each page in the target
7754 : : * bucket, not just the numIndexPages pages that genericcostestimate
7755 : : * thought we'd visit. However in most cases we don't know which bucket
7756 : : * that will be. There's no point in considering the average bucket size
7757 : : * because the hash AM makes sure that's always one page.
7758 : : *
7759 : : * Likewise, we could consider charging some CPU for each index tuple in
7760 : : * the bucket, if we knew how many there were. But the per-tuple cost is
7761 : : * just a hash value comparison, not a general datatype-dependent
7762 : : * comparison, so any such charge ought to be quite a bit less than
7763 : : * cpu_operator_cost; which makes it probably not worth worrying about.
7764 : : *
7765 : : * A bigger issue is that chance hash-value collisions will result in
7766 : : * wasted probes into the heap. We don't currently attempt to model this
7767 : : * cost on the grounds that it's rare, but maybe it's not rare enough.
7768 : : * (Any fix for this ought to consider the generic lossy-operator problem,
7769 : : * though; it's not entirely hash-specific.)
7770 : : */
7771 : :
4621 7772 : 209 : *indexStartupCost = costs.indexStartupCost;
7773 : 209 : *indexTotalCost = costs.indexTotalCost;
7774 : 209 : *indexSelectivity = costs.indexSelectivity;
7775 : 209 : *indexCorrelation = costs.indexCorrelation;
3125 rhaas@postgresql.org 7776 : 209 : *indexPages = costs.numIndexPages;
10603 scrappy@hub.org 7777 : 209 : }
7778 : :
7779 : : void
3520 tgl@sss.pgh.pa.us 7780 : 2385 : gistcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7781 : : Cost *indexStartupCost, Cost *indexTotalCost,
7782 : : Selectivity *indexSelectivity, double *indexCorrelation,
7783 : : double *indexPages)
7784 : : {
4621 7785 : 2385 : IndexOptInfo *index = path->indexinfo;
1148 peter@eisentraut.org 7786 : 2385 : GenericCosts costs = {0};
7787 : : Cost descentCost;
7788 : :
2395 tgl@sss.pgh.pa.us 7789 : 2385 : genericcostestimate(root, path, loop_count, &costs);
7790 : :
7791 : : /*
7792 : : * We model index descent costs similarly to those for btree, but to do
7793 : : * that we first need an idea of the tree height. We somewhat arbitrarily
7794 : : * assume that the fanout is 100, meaning the tree height is at most
7795 : : * log100(index->pages).
7796 : : *
7797 : : * Although this computation isn't really expensive enough to require
7798 : : * caching, we might as well use index->tree_height to cache it.
7799 : : */
4483 bruce@momjian.us 7800 [ + + ]: 2385 : if (index->tree_height < 0) /* unknown? */
7801 : : {
4621 tgl@sss.pgh.pa.us 7802 [ + + ]: 2378 : if (index->pages > 1) /* avoid computing log(0) */
7803 : 1360 : index->tree_height = (int) (log(index->pages) / log(100.0));
7804 : : else
7805 : 1018 : index->tree_height = 0;
7806 : : }
7807 : :
7808 : : /*
7809 : : * Add a CPU-cost component to represent the costs of initial descent. We
7810 : : * just use log(N) here not log2(N) since the branching factor isn't
7811 : : * necessarily two anyway. As for btree, charge once per SA scan.
7812 : : */
7813 [ + - ]: 2385 : if (index->tuples > 1) /* avoid computing log(0) */
7814 : : {
7815 : 2385 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
7816 : 2385 : costs.indexStartupCost += descentCost;
7817 : 2385 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
7818 : : }
7819 : :
7820 : : /*
7821 : : * Likewise add a per-page charge, calculated the same as for btrees.
7822 : : */
972 akorotkov@postgresql 7823 : 2385 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4621 tgl@sss.pgh.pa.us 7824 : 2385 : costs.indexStartupCost += descentCost;
7825 : 2385 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
7826 : :
7827 : 2385 : *indexStartupCost = costs.indexStartupCost;
7828 : 2385 : *indexTotalCost = costs.indexTotalCost;
7829 : 2385 : *indexSelectivity = costs.indexSelectivity;
7830 : 2385 : *indexCorrelation = costs.indexCorrelation;
3125 rhaas@postgresql.org 7831 : 2385 : *indexPages = costs.numIndexPages;
10603 scrappy@hub.org 7832 : 2385 : }
7833 : :
7834 : : void
3520 tgl@sss.pgh.pa.us 7835 : 892 : spgcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
7836 : : Cost *indexStartupCost, Cost *indexTotalCost,
7837 : : Selectivity *indexSelectivity, double *indexCorrelation,
7838 : : double *indexPages)
7839 : : {
4621 7840 : 892 : IndexOptInfo *index = path->indexinfo;
1148 peter@eisentraut.org 7841 : 892 : GenericCosts costs = {0};
7842 : : Cost descentCost;
7843 : :
2395 tgl@sss.pgh.pa.us 7844 : 892 : genericcostestimate(root, path, loop_count, &costs);
7845 : :
7846 : : /*
7847 : : * We model index descent costs similarly to those for btree, but to do
7848 : : * that we first need an idea of the tree height. We somewhat arbitrarily
7849 : : * assume that the fanout is 100, meaning the tree height is at most
7850 : : * log100(index->pages).
7851 : : *
7852 : : * Although this computation isn't really expensive enough to require
7853 : : * caching, we might as well use index->tree_height to cache it.
7854 : : */
4483 bruce@momjian.us 7855 [ + + ]: 892 : if (index->tree_height < 0) /* unknown? */
7856 : : {
4621 tgl@sss.pgh.pa.us 7857 [ + - ]: 889 : if (index->pages > 1) /* avoid computing log(0) */
7858 : 889 : index->tree_height = (int) (log(index->pages) / log(100.0));
7859 : : else
4621 tgl@sss.pgh.pa.us 7860 :UBC 0 : index->tree_height = 0;
7861 : : }
7862 : :
7863 : : /*
7864 : : * Add a CPU-cost component to represent the costs of initial descent. We
7865 : : * just use log(N) here not log2(N) since the branching factor isn't
7866 : : * necessarily two anyway. As for btree, charge once per SA scan.
7867 : : */
4621 tgl@sss.pgh.pa.us 7868 [ + - ]:CBC 892 : if (index->tuples > 1) /* avoid computing log(0) */
7869 : : {
7870 : 892 : descentCost = ceil(log(index->tuples)) * cpu_operator_cost;
7871 : 892 : costs.indexStartupCost += descentCost;
7872 : 892 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
7873 : : }
7874 : :
7875 : : /*
7876 : : * Likewise add a per-page charge, calculated the same as for btrees.
7877 : : */
972 akorotkov@postgresql 7878 : 892 : descentCost = (index->tree_height + 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
4621 tgl@sss.pgh.pa.us 7879 : 892 : costs.indexStartupCost += descentCost;
7880 : 892 : costs.indexTotalCost += costs.num_sa_scans * descentCost;
7881 : :
7882 : 892 : *indexStartupCost = costs.indexStartupCost;
7883 : 892 : *indexTotalCost = costs.indexTotalCost;
7884 : 892 : *indexSelectivity = costs.indexSelectivity;
7885 : 892 : *indexCorrelation = costs.indexCorrelation;
3125 rhaas@postgresql.org 7886 : 892 : *indexPages = costs.numIndexPages;
5012 tgl@sss.pgh.pa.us 7887 : 892 : }
7888 : :
7889 : :
7890 : : /*
7891 : : * Support routines for gincostestimate
7892 : : */
7893 : :
7894 : : typedef struct
7895 : : {
7896 : : bool attHasFullScan[INDEX_MAX_KEYS];
7897 : : bool attHasNormalScan[INDEX_MAX_KEYS];
7898 : : double partialEntries;
7899 : : double exactEntries;
7900 : : double searchEntries;
7901 : : double arrayScans;
7902 : : } GinQualCounts;
7903 : :
7904 : : /*
7905 : : * Estimate the number of index terms that need to be searched for while
7906 : : * testing the given GIN query, and increment the counts in *counts
7907 : : * appropriately. If the query is unsatisfiable, return false.
7908 : : */
7909 : : static bool
5009 7910 : 1235 : gincost_pattern(IndexOptInfo *index, int indexcol,
7911 : : Oid clause_op, Datum query,
7912 : : GinQualCounts *counts)
7913 : : {
7914 : : FmgrInfo flinfo;
7915 : : Oid extractProcOid;
7916 : : Oid collation;
7917 : : int strategy_op;
7918 : : Oid lefttype,
7919 : : righttype;
7920 : 1235 : int32 nentries = 0;
7921 : 1235 : bool *partial_matches = NULL;
7922 : 1235 : Pointer *extra_data = NULL;
7923 : 1235 : bool *nullFlags = NULL;
7924 : 1235 : int32 searchMode = GIN_SEARCH_MODE_DEFAULT;
7925 : : int32 i;
7926 : :
2704 teodor@sigaev.ru 7927 [ - + ]: 1235 : Assert(indexcol < index->nkeycolumns);
7928 : :
7929 : : /*
7930 : : * Get the operator's strategy number and declared input data types within
7931 : : * the index opfamily. (We don't need the latter, but we use
7932 : : * get_op_opfamily_properties because it will throw error if it fails to
7933 : : * find a matching pg_amop entry.)
7934 : : */
5009 tgl@sss.pgh.pa.us 7935 : 1235 : get_op_opfamily_properties(clause_op, index->opfamily[indexcol], false,
7936 : : &strategy_op, &lefttype, &righttype);
7937 : :
7938 : : /*
7939 : : * GIN always uses the "default" support functions, which are those with
7940 : : * lefttype == righttype == the opclass' opcintype (see
7941 : : * IndexSupportInitialize in relcache.c).
7942 : : */
7943 : 1235 : extractProcOid = get_opfamily_proc(index->opfamily[indexcol],
7944 : 1235 : index->opcintype[indexcol],
7945 : 1235 : index->opcintype[indexcol],
7946 : : GIN_EXTRACTQUERY_PROC);
7947 : :
7948 [ - + ]: 1235 : if (!OidIsValid(extractProcOid))
7949 : : {
7950 : : /* should not happen; throw same error as index_getprocinfo */
5009 tgl@sss.pgh.pa.us 7951 [ # # ]:UBC 0 : elog(ERROR, "missing support function %d for attribute %d of index \"%s\"",
7952 : : GIN_EXTRACTQUERY_PROC, indexcol + 1,
7953 : : get_rel_name(index->indexoid));
7954 : : }
7955 : :
7956 : : /*
7957 : : * Choose collation to pass to extractProc (should match initGinState).
7958 : : */
4533 tgl@sss.pgh.pa.us 7959 [ + + ]:CBC 1235 : if (OidIsValid(index->indexcollations[indexcol]))
7960 : 207 : collation = index->indexcollations[indexcol];
7961 : : else
7962 : 1028 : collation = DEFAULT_COLLATION_OID;
7963 : :
1986 akorotkov@postgresql 7964 : 1235 : fmgr_info(extractProcOid, &flinfo);
7965 : :
7966 : 1235 : set_fn_opclass_options(&flinfo, index->opclassoptions[indexcol]);
7967 : :
7968 : 1235 : FunctionCall7Coll(&flinfo,
7969 : : collation,
7970 : : query,
7971 : : PointerGetDatum(&nentries),
7972 : : UInt16GetDatum(strategy_op),
7973 : : PointerGetDatum(&partial_matches),
7974 : : PointerGetDatum(&extra_data),
7975 : : PointerGetDatum(&nullFlags),
7976 : : PointerGetDatum(&searchMode));
7977 : :
5009 tgl@sss.pgh.pa.us 7978 [ + + + + ]: 1235 : if (nentries <= 0 && searchMode == GIN_SEARCH_MODE_DEFAULT)
7979 : : {
7980 : : /* No match is possible */
7981 : 6 : return false;
7982 : : }
7983 : :
7984 [ + + ]: 3333 : for (i = 0; i < nentries; i++)
7985 : : {
7986 : : /*
7987 : : * For partial match we haven't any information to estimate number of
7988 : : * matched entries in index, so, we just estimate it as 100
7989 : : */
7990 [ + + + + ]: 2104 : if (partial_matches && partial_matches[i])
7991 : 347 : counts->partialEntries += 100;
7992 : : else
7993 : 1757 : counts->exactEntries++;
7994 : :
7995 : 2104 : counts->searchEntries++;
7996 : : }
7997 : :
2058 akorotkov@postgresql 7998 [ + + ]: 1229 : if (searchMode == GIN_SEARCH_MODE_DEFAULT)
7999 : : {
8000 : 987 : counts->attHasNormalScan[indexcol] = true;
8001 : : }
8002 [ + + ]: 242 : else if (searchMode == GIN_SEARCH_MODE_INCLUDE_EMPTY)
8003 : : {
8004 : : /* Treat "include empty" like an exact-match item */
8005 : 22 : counts->attHasNormalScan[indexcol] = true;
5009 tgl@sss.pgh.pa.us 8006 : 22 : counts->exactEntries++;
8007 : 22 : counts->searchEntries++;
8008 : : }
8009 : : else
8010 : : {
8011 : : /* It's GIN_SEARCH_MODE_ALL */
2058 akorotkov@postgresql 8012 : 220 : counts->attHasFullScan[indexcol] = true;
8013 : : }
8014 : :
5009 tgl@sss.pgh.pa.us 8015 : 1229 : return true;
8016 : : }
8017 : :
8018 : : /*
8019 : : * Estimate the number of index terms that need to be searched for while
8020 : : * testing the given GIN index clause, and increment the counts in *counts
8021 : : * appropriately. If the query is unsatisfiable, return false.
8022 : : */
8023 : : static bool
3840 8024 : 1229 : gincost_opexpr(PlannerInfo *root,
8025 : : IndexOptInfo *index,
8026 : : int indexcol,
8027 : : OpExpr *clause,
8028 : : GinQualCounts *counts)
8029 : : {
2395 8030 : 1229 : Oid clause_op = clause->opno;
8031 : 1229 : Node *operand = (Node *) lsecond(clause->args);
8032 : :
8033 : : /* aggressively reduce to a constant, and look through relabeling */
4215 8034 : 1229 : operand = estimate_expression_value(root, operand);
8035 : :
5009 8036 [ - + ]: 1229 : if (IsA(operand, RelabelType))
5009 tgl@sss.pgh.pa.us 8037 :UBC 0 : operand = (Node *) ((RelabelType *) operand)->arg;
8038 : :
8039 : : /*
8040 : : * It's impossible to call extractQuery method for unknown operand. So
8041 : : * unless operand is a Const we can't do much; just assume there will be
8042 : : * one ordinary search entry from the operand at runtime.
8043 : : */
5009 tgl@sss.pgh.pa.us 8044 [ - + ]:CBC 1229 : if (!IsA(operand, Const))
8045 : : {
5009 tgl@sss.pgh.pa.us 8046 :UBC 0 : counts->exactEntries++;
8047 : 0 : counts->searchEntries++;
8048 : 0 : return true;
8049 : : }
8050 : :
8051 : : /* If Const is null, there can be no matches */
5009 tgl@sss.pgh.pa.us 8052 [ - + ]:CBC 1229 : if (((Const *) operand)->constisnull)
5009 tgl@sss.pgh.pa.us 8053 :UBC 0 : return false;
8054 : :
8055 : : /* Otherwise, apply extractQuery and get the actual term counts */
5009 tgl@sss.pgh.pa.us 8056 :CBC 1229 : return gincost_pattern(index, indexcol, clause_op,
8057 : : ((Const *) operand)->constvalue,
8058 : : counts);
8059 : : }
8060 : :
8061 : : /*
8062 : : * Estimate the number of index terms that need to be searched for while
8063 : : * testing the given GIN index clause, and increment the counts in *counts
8064 : : * appropriately. If the query is unsatisfiable, return false.
8065 : : *
8066 : : * A ScalarArrayOpExpr will give rise to N separate indexscans at runtime,
8067 : : * each of which involves one value from the RHS array, plus all the
8068 : : * non-array quals (if any). To model this, we average the counts across
8069 : : * the RHS elements, and add the averages to the counts in *counts (which
8070 : : * correspond to per-indexscan costs). We also multiply counts->arrayScans
8071 : : * by N, causing gincostestimate to scale up its estimates accordingly.
8072 : : */
8073 : : static bool
4215 8074 : 3 : gincost_scalararrayopexpr(PlannerInfo *root,
8075 : : IndexOptInfo *index,
8076 : : int indexcol,
8077 : : ScalarArrayOpExpr *clause,
8078 : : double numIndexEntries,
8079 : : GinQualCounts *counts)
8080 : : {
2395 8081 : 3 : Oid clause_op = clause->opno;
8082 : 3 : Node *rightop = (Node *) lsecond(clause->args);
8083 : : ArrayType *arrayval;
8084 : : int16 elmlen;
8085 : : bool elmbyval;
8086 : : char elmalign;
8087 : : int numElems;
8088 : : Datum *elemValues;
8089 : : bool *elemNulls;
8090 : : GinQualCounts arraycounts;
5009 8091 : 3 : int numPossible = 0;
8092 : : int i;
8093 : :
2395 8094 [ - + ]: 3 : Assert(clause->useOr);
8095 : :
8096 : : /* aggressively reduce to a constant, and look through relabeling */
4215 8097 : 3 : rightop = estimate_expression_value(root, rightop);
8098 : :
5009 8099 [ - + ]: 3 : if (IsA(rightop, RelabelType))
5009 tgl@sss.pgh.pa.us 8100 :UBC 0 : rightop = (Node *) ((RelabelType *) rightop)->arg;
8101 : :
8102 : : /*
8103 : : * It's impossible to call extractQuery method for unknown operand. So
8104 : : * unless operand is a Const we can't do much; just assume there will be
8105 : : * one ordinary search entry from each array entry at runtime, and fall
8106 : : * back on a probably-bad estimate of the number of array entries.
8107 : : */
5009 tgl@sss.pgh.pa.us 8108 [ - + ]:CBC 3 : if (!IsA(rightop, Const))
8109 : : {
5009 tgl@sss.pgh.pa.us 8110 :UBC 0 : counts->exactEntries++;
8111 : 0 : counts->searchEntries++;
611 8112 : 0 : counts->arrayScans *= estimate_array_length(root, rightop);
5009 8113 : 0 : return true;
8114 : : }
8115 : :
8116 : : /* If Const is null, there can be no matches */
5009 tgl@sss.pgh.pa.us 8117 [ - + ]:CBC 3 : if (((Const *) rightop)->constisnull)
5009 tgl@sss.pgh.pa.us 8118 :UBC 0 : return false;
8119 : :
8120 : : /* Otherwise, extract the array elements and iterate over them */
5009 tgl@sss.pgh.pa.us 8121 :CBC 3 : arrayval = DatumGetArrayTypeP(((Const *) rightop)->constvalue);
8122 : 3 : get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
8123 : : &elmlen, &elmbyval, &elmalign);
8124 : 3 : deconstruct_array(arrayval,
8125 : : ARR_ELEMTYPE(arrayval),
8126 : : elmlen, elmbyval, elmalign,
8127 : : &elemValues, &elemNulls, &numElems);
8128 : :
8129 : 3 : memset(&arraycounts, 0, sizeof(arraycounts));
8130 : :
8131 [ + + ]: 9 : for (i = 0; i < numElems; i++)
8132 : : {
8133 : : GinQualCounts elemcounts;
8134 : :
8135 : : /* NULL can't match anything, so ignore, as the executor will */
8136 [ - + ]: 6 : if (elemNulls[i])
5009 tgl@sss.pgh.pa.us 8137 :UBC 0 : continue;
8138 : :
8139 : : /* Otherwise, apply extractQuery and get the actual term counts */
5009 tgl@sss.pgh.pa.us 8140 :CBC 6 : memset(&elemcounts, 0, sizeof(elemcounts));
8141 : :
8142 [ + - ]: 6 : if (gincost_pattern(index, indexcol, clause_op, elemValues[i],
8143 : : &elemcounts))
8144 : : {
8145 : : /* We ignore array elements that are unsatisfiable patterns */
8146 : 6 : numPossible++;
8147 : :
2058 akorotkov@postgresql 8148 [ - + ]: 6 : if (elemcounts.attHasFullScan[indexcol] &&
2058 akorotkov@postgresql 8149 [ # # ]:UBC 0 : !elemcounts.attHasNormalScan[indexcol])
8150 : : {
8151 : : /*
8152 : : * Full index scan will be required. We treat this as if
8153 : : * every key in the index had been listed in the query; is
8154 : : * that reasonable?
8155 : : */
5009 tgl@sss.pgh.pa.us 8156 : 0 : elemcounts.partialEntries = 0;
8157 : 0 : elemcounts.exactEntries = numIndexEntries;
8158 : 0 : elemcounts.searchEntries = numIndexEntries;
8159 : : }
5009 tgl@sss.pgh.pa.us 8160 :CBC 6 : arraycounts.partialEntries += elemcounts.partialEntries;
8161 : 6 : arraycounts.exactEntries += elemcounts.exactEntries;
8162 : 6 : arraycounts.searchEntries += elemcounts.searchEntries;
8163 : : }
8164 : : }
8165 : :
8166 [ - + ]: 3 : if (numPossible == 0)
8167 : : {
8168 : : /* No satisfiable patterns in the array */
5009 tgl@sss.pgh.pa.us 8169 :UBC 0 : return false;
8170 : : }
8171 : :
8172 : : /*
8173 : : * Now add the averages to the global counts. This will give us an
8174 : : * estimate of the average number of terms searched for in each indexscan,
8175 : : * including contributions from both array and non-array quals.
8176 : : */
5009 tgl@sss.pgh.pa.us 8177 :CBC 3 : counts->partialEntries += arraycounts.partialEntries / numPossible;
8178 : 3 : counts->exactEntries += arraycounts.exactEntries / numPossible;
8179 : 3 : counts->searchEntries += arraycounts.searchEntries / numPossible;
8180 : :
8181 : 3 : counts->arrayScans *= numPossible;
8182 : :
8183 : 3 : return true;
8184 : : }
8185 : :
8186 : : /*
8187 : : * GIN has search behavior completely different from other index types
8188 : : */
8189 : : void
3520 8190 : 1127 : gincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8191 : : Cost *indexStartupCost, Cost *indexTotalCost,
8192 : : Selectivity *indexSelectivity, double *indexCorrelation,
8193 : : double *indexPages)
8194 : : {
5005 8195 : 1127 : IndexOptInfo *index = path->indexinfo;
2395 8196 : 1127 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
8197 : : List *selectivityQuals;
5263 bruce@momjian.us 8198 : 1127 : double numPages = index->pages,
8199 : 1127 : numTuples = index->tuples;
8200 : : double numEntryPages,
8201 : : numDataPages,
8202 : : numPendingPages,
8203 : : numEntries;
8204 : : GinQualCounts counts;
8205 : : bool matchPossible;
8206 : : bool fullIndexScan;
8207 : : double partialScale;
8208 : : double entryPagesFetched,
8209 : : dataPagesFetched,
8210 : : dataPagesFetchedBySel;
8211 : : double qual_op_cost,
8212 : : qual_arg_cost,
8213 : : spc_random_page_cost,
8214 : : outer_scans;
8215 : : Cost descentCost;
8216 : : Relation indexRel;
8217 : : GinStatsData ginStats;
8218 : : ListCell *lc;
8219 : : int i;
8220 : :
8221 : : /*
8222 : : * Obtain statistical information from the meta page, if possible. Else
8223 : : * set ginStats to zeroes, and we'll cope below.
8224 : : */
3567 tgl@sss.pgh.pa.us 8225 [ + - ]: 1127 : if (!index->hypothetical)
8226 : : {
8227 : : /* Lock should have already been obtained in plancat.c */
2347 8228 : 1127 : indexRel = index_open(index->indexoid, NoLock);
3567 8229 : 1127 : ginGetStats(indexRel, &ginStats);
2347 8230 : 1127 : index_close(indexRel, NoLock);
8231 : : }
8232 : : else
8233 : : {
3567 tgl@sss.pgh.pa.us 8234 :UBC 0 : memset(&ginStats, 0, sizeof(ginStats));
8235 : : }
8236 : :
8237 : : /*
8238 : : * Assuming we got valid (nonzero) stats at all, nPendingPages can be
8239 : : * trusted, but the other fields are data as of the last VACUUM. We can
8240 : : * scale them up to account for growth since then, but that method only
8241 : : * goes so far; in the worst case, the stats might be for a completely
8242 : : * empty index, and scaling them will produce pretty bogus numbers.
8243 : : * Somewhat arbitrarily, set the cutoff for doing scaling at 4X growth; if
8244 : : * it's grown more than that, fall back to estimating things only from the
8245 : : * assumed-accurate index size. But we'll trust nPendingPages in any case
8246 : : * so long as it's not clearly insane, ie, more than the index size.
8247 : : */
3536 tgl@sss.pgh.pa.us 8248 [ + - ]:CBC 1127 : if (ginStats.nPendingPages < numPages)
8249 : 1127 : numPendingPages = ginStats.nPendingPages;
8250 : : else
3536 tgl@sss.pgh.pa.us 8251 :UBC 0 : numPendingPages = 0;
8252 : :
3536 tgl@sss.pgh.pa.us 8253 [ + - + - ]:CBC 1127 : if (numPages > 0 && ginStats.nTotalPages <= numPages &&
8254 [ + + ]: 1127 : ginStats.nTotalPages > numPages / 4 &&
8255 [ + - + + ]: 1106 : ginStats.nEntryPages > 0 && ginStats.nEntries > 0)
3567 8256 : 974 : {
8257 : : /*
8258 : : * OK, the stats seem close enough to sane to be trusted. But we
8259 : : * still need to scale them by the ratio numPages / nTotalPages to
8260 : : * account for growth since the last VACUUM.
8261 : : */
5263 bruce@momjian.us 8262 : 974 : double scale = numPages / ginStats.nTotalPages;
8263 : :
3536 tgl@sss.pgh.pa.us 8264 : 974 : numEntryPages = ceil(ginStats.nEntryPages * scale);
8265 : 974 : numDataPages = ceil(ginStats.nDataPages * scale);
8266 : 974 : numEntries = ceil(ginStats.nEntries * scale);
8267 : : /* ensure we didn't round up too much */
8268 [ + + ]: 974 : numEntryPages = Min(numEntryPages, numPages - numPendingPages);
8269 [ + + ]: 974 : numDataPages = Min(numDataPages,
8270 : : numPages - numPendingPages - numEntryPages);
8271 : : }
8272 : : else
8273 : : {
8274 : : /*
8275 : : * We might get here because it's a hypothetical index, or an index
8276 : : * created pre-9.1 and never vacuumed since upgrading (in which case
8277 : : * its stats would read as zeroes), or just because it's grown too
8278 : : * much since the last VACUUM for us to put our faith in scaling.
8279 : : *
8280 : : * Invent some plausible internal statistics based on the index page
8281 : : * count (and clamp that to at least 10 pages, just in case). We
8282 : : * estimate that 90% of the index is entry pages, and the rest is data
8283 : : * pages. Estimate 100 entries per entry page; this is rather bogus
8284 : : * since it'll depend on the size of the keys, but it's more robust
8285 : : * than trying to predict the number of entries per heap tuple.
8286 : : */
3567 8287 [ + + ]: 153 : numPages = Max(numPages, 10);
3536 8288 : 153 : numEntryPages = floor((numPages - numPendingPages) * 0.90);
8289 : 153 : numDataPages = numPages - numPendingPages - numEntryPages;
3567 8290 : 153 : numEntries = floor(numEntryPages * 100);
8291 : : }
8292 : :
8293 : : /* In an empty index, numEntries could be zero. Avoid divide-by-zero */
5252 8294 [ - + ]: 1127 : if (numEntries < 1)
5252 tgl@sss.pgh.pa.us 8295 :UBC 0 : numEntries = 1;
8296 : :
8297 : : /*
8298 : : * If the index is partial, AND the index predicate with the index-bound
8299 : : * quals to produce a more accurate idea of the number of rows covered by
8300 : : * the bound conditions.
8301 : : */
2395 tgl@sss.pgh.pa.us 8302 :CBC 1127 : selectivityQuals = add_predicate_to_index_quals(index, indexQuals);
8303 : :
8304 : : /* Estimate the fraction of main-table tuples that will be visited */
5438 8305 : 2254 : *indexSelectivity = clauselist_selectivity(root, selectivityQuals,
5263 bruce@momjian.us 8306 : 1127 : index->rel->relid,
8307 : : JOIN_INNER,
8308 : : NULL);
8309 : :
8310 : : /* fetch estimated page cost for tablespace containing index */
8311 : 1127 : get_tablespace_page_costs(index->reltablespace,
8312 : : &spc_random_page_cost,
8313 : : NULL);
8314 : :
8315 : : /*
8316 : : * Generic assumption about index correlation: there isn't any.
8317 : : */
5438 tgl@sss.pgh.pa.us 8318 : 1127 : *indexCorrelation = 0.0;
8319 : :
8320 : : /*
8321 : : * Examine quals to estimate number of search entries & partial matches
8322 : : */
5009 8323 : 1127 : memset(&counts, 0, sizeof(counts));
8324 : 1127 : counts.arrayScans = 1;
8325 : 1127 : matchPossible = true;
8326 : :
2395 8327 [ + - + + : 2359 : foreach(lc, path->indexclauses)
+ + ]
8328 : : {
8329 : 1232 : IndexClause *iclause = lfirst_node(IndexClause, lc);
8330 : : ListCell *lc2;
8331 : :
8332 [ + - + + : 2458 : foreach(lc2, iclause->indexquals)
+ + ]
8333 : : {
8334 : 1232 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc2);
8335 : 1232 : Expr *clause = rinfo->clause;
8336 : :
8337 [ + + ]: 1232 : if (IsA(clause, OpExpr))
8338 : : {
8339 : 1229 : matchPossible = gincost_opexpr(root,
8340 : : index,
8341 : 1229 : iclause->indexcol,
8342 : : (OpExpr *) clause,
8343 : : &counts);
8344 [ + + ]: 1229 : if (!matchPossible)
8345 : 6 : break;
8346 : : }
8347 [ + - ]: 3 : else if (IsA(clause, ScalarArrayOpExpr))
8348 : : {
8349 : 3 : matchPossible = gincost_scalararrayopexpr(root,
8350 : : index,
8351 : 3 : iclause->indexcol,
8352 : : (ScalarArrayOpExpr *) clause,
8353 : : numEntries,
8354 : : &counts);
8355 [ - + ]: 3 : if (!matchPossible)
2395 tgl@sss.pgh.pa.us 8356 :UBC 0 : break;
8357 : : }
8358 : : else
8359 : : {
8360 : : /* shouldn't be anything else for a GIN index */
8361 [ # # ]: 0 : elog(ERROR, "unsupported GIN indexqual type: %d",
8362 : : (int) nodeTag(clause));
8363 : : }
8364 : : }
8365 : : }
8366 : :
8367 : : /* Fall out if there were any provably-unsatisfiable quals */
5009 tgl@sss.pgh.pa.us 8368 [ + + ]:CBC 1127 : if (!matchPossible)
8369 : : {
8370 : 6 : *indexStartupCost = 0;
8371 : 6 : *indexTotalCost = 0;
8372 : 6 : *indexSelectivity = 0;
3520 8373 : 6 : return;
8374 : : }
8375 : :
8376 : : /*
8377 : : * If attribute has a full scan and at the same time doesn't have normal
8378 : : * scan, then we'll have to scan all non-null entries of that attribute.
8379 : : * Currently, we don't have per-attribute statistics for GIN. Thus, we
8380 : : * must assume the whole GIN index has to be scanned in this case.
8381 : : */
2058 akorotkov@postgresql 8382 : 1121 : fullIndexScan = false;
8383 [ + + ]: 2187 : for (i = 0; i < index->nkeycolumns; i++)
8384 : : {
8385 [ + + + + ]: 1235 : if (counts.attHasFullScan[i] && !counts.attHasNormalScan[i])
8386 : : {
8387 : 169 : fullIndexScan = true;
8388 : 169 : break;
8389 : : }
8390 : : }
8391 : :
8392 [ + + - + ]: 1121 : if (fullIndexScan || indexQuals == NIL)
8393 : : {
8394 : : /*
8395 : : * Full index scan will be required. We treat this as if every key in
8396 : : * the index had been listed in the query; is that reasonable?
8397 : : */
5009 tgl@sss.pgh.pa.us 8398 : 169 : counts.partialEntries = 0;
8399 : 169 : counts.exactEntries = numEntries;
8400 : 169 : counts.searchEntries = numEntries;
8401 : : }
8402 : :
8403 : : /* Will we have more than one iteration of a nestloop scan? */
4971 8404 : 1121 : outer_scans = loop_count;
8405 : :
8406 : : /*
8407 : : * Compute cost to begin scan, first of all, pay attention to pending
8408 : : * list.
8409 : : */
5438 8410 : 1121 : entryPagesFetched = numPendingPages;
8411 : :
8412 : : /*
8413 : : * Estimate number of entry pages read. We need to do
8414 : : * counts.searchEntries searches. Use a power function as it should be,
8415 : : * but tuples on leaf pages usually is much greater. Here we include all
8416 : : * searches in entry tree, including search of first entry in partial
8417 : : * match algorithm
8418 : : */
5009 8419 : 1121 : entryPagesFetched += ceil(counts.searchEntries * rint(pow(numEntryPages, 0.15)));
8420 : :
8421 : : /*
8422 : : * Add an estimate of entry pages read by partial match algorithm. It's a
8423 : : * scan over leaf pages in entry tree. We haven't any useful stats here,
8424 : : * so estimate it as proportion. Because counts.partialEntries is really
8425 : : * pretty bogus (see code above), it's possible that it is more than
8426 : : * numEntries; clamp the proportion to ensure sanity.
8427 : : */
3536 8428 : 1121 : partialScale = counts.partialEntries / numEntries;
8429 [ + + ]: 1121 : partialScale = Min(partialScale, 1.0);
8430 : :
8431 : 1121 : entryPagesFetched += ceil(numEntryPages * partialScale);
8432 : :
8433 : : /*
8434 : : * Partial match algorithm reads all data pages before doing actual scan,
8435 : : * so it's a startup cost. Again, we haven't any useful stats here, so
8436 : : * estimate it as proportion.
8437 : : */
8438 : 1121 : dataPagesFetched = ceil(numDataPages * partialScale);
8439 : :
972 akorotkov@postgresql 8440 : 1121 : *indexStartupCost = 0;
8441 : 1121 : *indexTotalCost = 0;
8442 : :
8443 : : /*
8444 : : * Add a CPU-cost component to represent the costs of initial entry btree
8445 : : * descent. We don't charge any I/O cost for touching upper btree levels,
8446 : : * since they tend to stay in cache, but we still have to do about log2(N)
8447 : : * comparisons to descend a btree of N leaf tuples. We charge one
8448 : : * cpu_operator_cost per comparison.
8449 : : *
8450 : : * If there are ScalarArrayOpExprs, charge this once per SA scan. The
8451 : : * ones after the first one are not startup cost so far as the overall
8452 : : * plan is concerned, so add them only to "total" cost.
8453 : : */
8454 [ + - ]: 1121 : if (numEntries > 1) /* avoid computing log(0) */
8455 : : {
8456 : 1121 : descentCost = ceil(log(numEntries) / log(2.0)) * cpu_operator_cost;
8457 : 1121 : *indexStartupCost += descentCost * counts.searchEntries;
8458 : 1121 : *indexTotalCost += counts.arrayScans * descentCost * counts.searchEntries;
8459 : : }
8460 : :
8461 : : /*
8462 : : * Add a cpu cost per entry-page fetched. This is not amortized over a
8463 : : * loop.
8464 : : */
8465 : 1121 : *indexStartupCost += entryPagesFetched * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8466 : 1121 : *indexTotalCost += entryPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8467 : :
8468 : : /*
8469 : : * Add a cpu cost per data-page fetched. This is also not amortized over a
8470 : : * loop. Since those are the data pages from the partial match algorithm,
8471 : : * charge them as startup cost.
8472 : : */
8473 : 1121 : *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * dataPagesFetched;
8474 : :
8475 : : /*
8476 : : * Since we add the startup cost to the total cost later on, remove the
8477 : : * initial arrayscan from the total.
8478 : : */
8479 : 1121 : *indexTotalCost += dataPagesFetched * (counts.arrayScans - 1) * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8480 : :
8481 : : /*
8482 : : * Calculate cache effects if more than one scan due to nestloops or array
8483 : : * quals. The result is pro-rated per nestloop scan, but the array qual
8484 : : * factor shouldn't be pro-rated (compare genericcostestimate).
8485 : : */
5009 tgl@sss.pgh.pa.us 8486 [ + - + + ]: 1121 : if (outer_scans > 1 || counts.arrayScans > 1)
8487 : : {
8488 : 3 : entryPagesFetched *= outer_scans * counts.arrayScans;
5438 8489 : 3 : entryPagesFetched = index_pages_fetched(entryPagesFetched,
8490 : : (BlockNumber) numEntryPages,
8491 : : numEntryPages, root);
5009 8492 : 3 : entryPagesFetched /= outer_scans;
8493 : 3 : dataPagesFetched *= outer_scans * counts.arrayScans;
5438 8494 : 3 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
8495 : : (BlockNumber) numDataPages,
8496 : : numDataPages, root);
5009 8497 : 3 : dataPagesFetched /= outer_scans;
8498 : : }
8499 : :
8500 : : /*
8501 : : * Here we use random page cost because logically-close pages could be far
8502 : : * apart on disk.
8503 : : */
972 akorotkov@postgresql 8504 : 1121 : *indexStartupCost += (entryPagesFetched + dataPagesFetched) * spc_random_page_cost;
8505 : :
8506 : : /*
8507 : : * Now compute the number of data pages fetched during the scan.
8508 : : *
8509 : : * We assume every entry to have the same number of items, and that there
8510 : : * is no overlap between them. (XXX: tsvector and array opclasses collect
8511 : : * statistics on the frequency of individual keys; it would be nice to use
8512 : : * those here.)
8513 : : */
5009 tgl@sss.pgh.pa.us 8514 : 1121 : dataPagesFetched = ceil(numDataPages * counts.exactEntries / numEntries);
8515 : :
8516 : : /*
8517 : : * If there is a lot of overlap among the entries, in particular if one of
8518 : : * the entries is very frequent, the above calculation can grossly
8519 : : * under-estimate. As a simple cross-check, calculate a lower bound based
8520 : : * on the overall selectivity of the quals. At a minimum, we must read
8521 : : * one item pointer for each matching entry.
8522 : : *
8523 : : * The width of each item pointer varies, based on the level of
8524 : : * compression. We don't have statistics on that, but an average of
8525 : : * around 3 bytes per item is fairly typical.
8526 : : */
5438 8527 : 1121 : dataPagesFetchedBySel = ceil(*indexSelectivity *
4196 heikki.linnakangas@i 8528 : 1121 : (numTuples / (BLCKSZ / 3)));
5438 tgl@sss.pgh.pa.us 8529 [ + + ]: 1121 : if (dataPagesFetchedBySel > dataPagesFetched)
8530 : 931 : dataPagesFetched = dataPagesFetchedBySel;
8531 : :
8532 : : /* Add one page cpu-cost to the startup cost */
972 akorotkov@postgresql 8533 : 1121 : *indexStartupCost += DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost * counts.searchEntries;
8534 : :
8535 : : /*
8536 : : * Add once again a CPU-cost for those data pages, before amortizing for
8537 : : * cache.
8538 : : */
8539 : 1121 : *indexTotalCost += dataPagesFetched * counts.arrayScans * DEFAULT_PAGE_CPU_MULTIPLIER * cpu_operator_cost;
8540 : :
8541 : : /* Account for cache effects, the same as above */
5009 tgl@sss.pgh.pa.us 8542 [ + - + + ]: 1121 : if (outer_scans > 1 || counts.arrayScans > 1)
8543 : : {
8544 : 3 : dataPagesFetched *= outer_scans * counts.arrayScans;
5438 8545 : 3 : dataPagesFetched = index_pages_fetched(dataPagesFetched,
8546 : : (BlockNumber) numDataPages,
8547 : : numDataPages, root);
5009 8548 : 3 : dataPagesFetched /= outer_scans;
8549 : : }
8550 : :
8551 : : /* And apply random_page_cost as the cost per page */
972 akorotkov@postgresql 8552 : 1121 : *indexTotalCost += *indexStartupCost +
5438 tgl@sss.pgh.pa.us 8553 : 1121 : dataPagesFetched * spc_random_page_cost;
8554 : :
8555 : : /*
8556 : : * Add on index qual eval costs, much as in genericcostestimate. We charge
8557 : : * cpu but we can disregard indexorderbys, since GIN doesn't support
8558 : : * those.
8559 : : */
2395 8560 : 1121 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
8561 : 1121 : qual_op_cost = cpu_operator_cost * list_length(indexQuals);
8562 : :
5438 8563 : 1121 : *indexStartupCost += qual_arg_cost;
8564 : 1121 : *indexTotalCost += qual_arg_cost;
8565 : :
8566 : : /*
8567 : : * Add a cpu cost per search entry, corresponding to the actual visited
8568 : : * entries.
8569 : : */
972 akorotkov@postgresql 8570 : 1121 : *indexTotalCost += (counts.searchEntries * counts.arrayScans) * (qual_op_cost);
8571 : : /* Now add a cpu cost per tuple in the posting lists / trees */
8572 : 1121 : *indexTotalCost += (numTuples * *indexSelectivity) * (cpu_index_tuple_cost);
3125 rhaas@postgresql.org 8573 : 1121 : *indexPages = dataPagesFetched;
8574 : : }
8575 : :
8576 : : /*
8577 : : * BRIN has search behavior completely different from other index types
8578 : : */
8579 : : void
3520 tgl@sss.pgh.pa.us 8580 : 5365 : brincostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
8581 : : Cost *indexStartupCost, Cost *indexTotalCost,
8582 : : Selectivity *indexSelectivity, double *indexCorrelation,
8583 : : double *indexPages)
8584 : : {
3956 alvherre@alvh.no-ip. 8585 : 5365 : IndexOptInfo *index = path->indexinfo;
2395 tgl@sss.pgh.pa.us 8586 : 5365 : List *indexQuals = get_quals_from_indexclauses(path->indexclauses);
3956 alvherre@alvh.no-ip. 8587 : 5365 : double numPages = index->pages;
3075 8588 : 5365 : RelOptInfo *baserel = index->rel;
8589 [ + - ]: 5365 : RangeTblEntry *rte = planner_rt_fetch(baserel->relid, root);
8590 : : Cost spc_seq_page_cost;
8591 : : Cost spc_random_page_cost;
8592 : : double qual_arg_cost;
8593 : : double qualSelectivity;
8594 : : BrinStatsData statsData;
8595 : : double indexRanges;
8596 : : double minimalRanges;
8597 : : double estimatedRanges;
8598 : : double selec;
8599 : : Relation indexRel;
8600 : : ListCell *l;
8601 : : VariableStatData vardata;
8602 : :
8603 [ - + ]: 5365 : Assert(rte->rtekind == RTE_RELATION);
8604 : :
8605 : : /* fetch estimated page cost for the tablespace containing the index */
3956 8606 : 5365 : get_tablespace_page_costs(index->reltablespace,
8607 : : &spc_random_page_cost,
8608 : : &spc_seq_page_cost);
8609 : :
8610 : : /*
8611 : : * Obtain some data from the index itself, if possible. Otherwise invent
8612 : : * some plausible internal statistics based on the relation page count.
8613 : : */
2116 michael@paquier.xyz 8614 [ + - ]: 5365 : if (!index->hypothetical)
8615 : : {
8616 : : /*
8617 : : * A lock should have already been obtained on the index in plancat.c.
8618 : : */
8619 : 5365 : indexRel = index_open(index->indexoid, NoLock);
8620 : 5365 : brinGetStats(indexRel, &statsData);
8621 : 5365 : index_close(indexRel, NoLock);
8622 : :
8623 : : /* work out the actual number of ranges in the index */
8624 [ + + ]: 5365 : indexRanges = Max(ceil((double) baserel->pages /
8625 : : statsData.pagesPerRange), 1.0);
8626 : : }
8627 : : else
8628 : : {
8629 : : /*
8630 : : * Assume default number of pages per range, and estimate the number
8631 : : * of ranges based on that.
8632 : : */
2116 michael@paquier.xyz 8633 [ # # ]:UBC 0 : indexRanges = Max(ceil((double) baserel->pages /
8634 : : BRIN_DEFAULT_PAGES_PER_RANGE), 1.0);
8635 : :
8636 : 0 : statsData.pagesPerRange = BRIN_DEFAULT_PAGES_PER_RANGE;
8637 : 0 : statsData.revmapNumPages = (indexRanges / REVMAP_PAGE_MAXITEMS) + 1;
8638 : : }
8639 : :
8640 : : /*
8641 : : * Compute index correlation
8642 : : *
8643 : : * Because we can use all index quals equally when scanning, we can use
8644 : : * the largest correlation (in absolute value) among columns used by the
8645 : : * query. Start at zero, the worst possible case. If we cannot find any
8646 : : * correlation statistics, we will keep it as 0.
8647 : : */
3075 alvherre@alvh.no-ip. 8648 :CBC 5365 : *indexCorrelation = 0;
8649 : :
2395 tgl@sss.pgh.pa.us 8650 [ + - + + : 10731 : foreach(l, path->indexclauses)
+ + ]
8651 : : {
8652 : 5366 : IndexClause *iclause = lfirst_node(IndexClause, l);
8653 : 5366 : AttrNumber attnum = index->indexkeys[iclause->indexcol];
8654 : :
8655 : : /* attempt to lookup stats in relation for this index column */
3075 alvherre@alvh.no-ip. 8656 [ + - ]: 5366 : if (attnum != 0)
8657 : : {
8658 : : /* Simple variable -- look to stats for the underlying table */
8659 [ - + - - ]: 5366 : if (get_relation_stats_hook &&
3075 alvherre@alvh.no-ip. 8660 :UBC 0 : (*get_relation_stats_hook) (root, rte, attnum, &vardata))
8661 : : {
8662 : : /*
8663 : : * The hook took control of acquiring a stats tuple. If it
8664 : : * did supply a tuple, it'd better have supplied a freefunc.
8665 : : */
8666 [ # # # # ]: 0 : if (HeapTupleIsValid(vardata.statsTuple) && !vardata.freefunc)
8667 [ # # ]: 0 : elog(ERROR,
8668 : : "no function provided to release variable stats with");
8669 : : }
8670 : : else
8671 : : {
3075 alvherre@alvh.no-ip. 8672 :CBC 5366 : vardata.statsTuple =
8673 : 5366 : SearchSysCache3(STATRELATTINH,
8674 : : ObjectIdGetDatum(rte->relid),
8675 : : Int16GetDatum(attnum),
8676 : : BoolGetDatum(false));
8677 : 5366 : vardata.freefunc = ReleaseSysCache;
8678 : : }
8679 : : }
8680 : : else
8681 : : {
8682 : : /*
8683 : : * Looks like we've found an expression column in the index. Let's
8684 : : * see if there's any stats for it.
8685 : : */
8686 : :
8687 : : /* get the attnum from the 0-based index. */
2395 tgl@sss.pgh.pa.us 8688 :UBC 0 : attnum = iclause->indexcol + 1;
8689 : :
3075 alvherre@alvh.no-ip. 8690 [ # # # # ]: 0 : if (get_index_stats_hook &&
2999 tgl@sss.pgh.pa.us 8691 : 0 : (*get_index_stats_hook) (root, index->indexoid, attnum, &vardata))
8692 : : {
8693 : : /*
8694 : : * The hook took control of acquiring a stats tuple. If it
8695 : : * did supply a tuple, it'd better have supplied a freefunc.
8696 : : */
3075 alvherre@alvh.no-ip. 8697 [ # # ]: 0 : if (HeapTupleIsValid(vardata.statsTuple) &&
8698 [ # # ]: 0 : !vardata.freefunc)
8699 [ # # ]: 0 : elog(ERROR, "no function provided to release variable stats with");
8700 : : }
8701 : : else
8702 : : {
8703 : 0 : vardata.statsTuple = SearchSysCache3(STATRELATTINH,
8704 : : ObjectIdGetDatum(index->indexoid),
8705 : : Int16GetDatum(attnum),
8706 : : BoolGetDatum(false));
8707 : 0 : vardata.freefunc = ReleaseSysCache;
8708 : : }
8709 : : }
8710 : :
3075 alvherre@alvh.no-ip. 8711 [ + + ]:CBC 5366 : if (HeapTupleIsValid(vardata.statsTuple))
8712 : : {
8713 : : AttStatsSlot sslot;
8714 : :
3038 tgl@sss.pgh.pa.us 8715 [ + - ]: 18 : if (get_attstatsslot(&sslot, vardata.statsTuple,
8716 : : STATISTIC_KIND_CORRELATION, InvalidOid,
8717 : : ATTSTATSSLOT_NUMBERS))
8718 : : {
3075 alvherre@alvh.no-ip. 8719 : 18 : double varCorrelation = 0.0;
8720 : :
3038 tgl@sss.pgh.pa.us 8721 [ + - ]: 18 : if (sslot.nnumbers > 0)
1065 peter@eisentraut.org 8722 : 18 : varCorrelation = fabs(sslot.numbers[0]);
8723 : :
3075 alvherre@alvh.no-ip. 8724 [ + - ]: 18 : if (varCorrelation > *indexCorrelation)
8725 : 18 : *indexCorrelation = varCorrelation;
8726 : :
3038 tgl@sss.pgh.pa.us 8727 : 18 : free_attstatsslot(&sslot);
8728 : : }
8729 : : }
8730 : :
3075 alvherre@alvh.no-ip. 8731 [ + + ]: 5366 : ReleaseVariableStats(vardata);
8732 : : }
8733 : :
8734 : 5365 : qualSelectivity = clauselist_selectivity(root, indexQuals,
8735 : 5365 : baserel->relid,
8736 : : JOIN_INNER, NULL);
8737 : :
8738 : : /*
8739 : : * Now calculate the minimum possible ranges we could match with if all of
8740 : : * the rows were in the perfect order in the table's heap.
8741 : : */
8742 : 5365 : minimalRanges = ceil(indexRanges * qualSelectivity);
8743 : :
8744 : : /*
8745 : : * Now estimate the number of ranges that we'll touch by using the
8746 : : * indexCorrelation from the stats. Careful not to divide by zero (note
8747 : : * we're using the absolute value of the correlation).
8748 : : */
8749 [ + + ]: 5365 : if (*indexCorrelation < 1.0e-10)
8750 : 5347 : estimatedRanges = indexRanges;
8751 : : else
8752 [ + + ]: 18 : estimatedRanges = Min(minimalRanges / *indexCorrelation, indexRanges);
8753 : :
8754 : : /* we expect to visit this portion of the table */
8755 : 5365 : selec = estimatedRanges / indexRanges;
8756 : :
8757 [ - + - + ]: 5365 : CLAMP_PROBABILITY(selec);
8758 : :
8759 : 5365 : *indexSelectivity = selec;
8760 : :
8761 : : /*
8762 : : * Compute the index qual costs, much as in genericcostestimate, to add to
8763 : : * the index costs. We can disregard indexorderbys, since BRIN doesn't
8764 : : * support those.
8765 : : */
2395 tgl@sss.pgh.pa.us 8766 : 5365 : qual_arg_cost = index_other_operands_eval_cost(root, indexQuals);
8767 : :
8768 : : /*
8769 : : * Compute the startup cost as the cost to read the whole revmap
8770 : : * sequentially, including the cost to execute the index quals.
8771 : : */
3075 alvherre@alvh.no-ip. 8772 : 5365 : *indexStartupCost =
8773 : 5365 : spc_seq_page_cost * statsData.revmapNumPages * loop_count;
3956 8774 : 5365 : *indexStartupCost += qual_arg_cost;
8775 : :
8776 : : /*
8777 : : * To read a BRIN index there might be a bit of back and forth over
8778 : : * regular pages, as revmap might point to them out of sequential order;
8779 : : * calculate the total cost as reading the whole index in random order.
8780 : : */
3075 8781 : 5365 : *indexTotalCost = *indexStartupCost +
8782 : 5365 : spc_random_page_cost * (numPages - statsData.revmapNumPages) * loop_count;
8783 : :
8784 : : /*
8785 : : * Charge a small amount per range tuple which we expect to match to. This
8786 : : * is meant to reflect the costs of manipulating the bitmap. The BRIN scan
8787 : : * will set a bit for each page in the range when we find a matching
8788 : : * range, so we must multiply the charge by the number of pages in the
8789 : : * range.
8790 : : */
8791 : 5365 : *indexTotalCost += 0.1 * cpu_operator_cost * estimatedRanges *
8792 : 5365 : statsData.pagesPerRange;
8793 : :
8794 : 5365 : *indexPages = index->pages;
3956 8795 : 5365 : }
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